India’s Trade Data Analysis
Sahil Sharma
2026-03-26
Data analysis is the process of examining, cleaning, transforming, and modeling data to discover useful information and support decision-making.
In this project, we analyze India’s import and export data using R. The dataset contains multiple variables such as product category, country, trade value, and shipment details.
This project helps in understanding real-world trade patterns and applying R programming concepts.
1 Level : Understanding the Dataset
1.1 Question: How to load and view the dataset?
Answer The dataset is loaded using read.csv(). The head() function displays the first few rows to understand the structure.
1.2 Question: What is the structure of the dataset?
str(data)## 'data.frame': 2500 obs. of 21 variables:
## $ Year : num 2024 2023 2024 2020 2022 ...
## $ Month : chr "October" "April" "March" "December" ...
## $ Product_Category: chr "Agriculture" "Textile" "Automobile" "Electronics" ...
## $ Product_Name : chr "Medicine" "Cotton" "Cotton" "Medicine" ...
## $ Import_Export : chr "Export" "Export" "Import" "Export" ...
## $ Quantity : int 5414 5189 415 6108 2951 142 2421 3551 1720 9274 ...
## $ Value_USD : num 72246 24615 7532 9768 72704 ...
## $ Country : chr "China" "USA" "Germany" "Japan" ...
## $ Port : chr "Delhi" "Delhi" "Kandla" "Chennai" ...
## $ Transport_Mode : chr "Land" "Air" "Air" "Land" ...
## $ Tariff : num 8.39 19.93 5.38 10.66 14.46 ...
## $ GST : num 15.63 12.7 5.31 11.14 5.96 ...
## $ Exchange_Rate : num 72.1 82.2 80.9 84.7 75.2 ...
## $ Shipment_Days : num 21 18 29 26 18 14 10 6 17 30 ...
## $ Delivery_Status : chr "Delayed" "On Time" "On Time" "On Time" ...
## $ Trade_Agreement : chr "Non-FTA" "Non-FTA" "FTA" "FTA" ...
## $ Date : chr "24-03-2019" "03-07-2022" "11-03-2018" "05-08-2021" ...
## $ Value_INR : num 5209497 2023491 609044 827328 5467923 ...
## $ Profit : num 692535 393207 94639 90860 588212 ...
## $ Loss : num 131464 147679 31590 10807 229676 ...
## $ Is_Delayed : chr "True" "False" "False" "False" ...Answer
The str() function shows the data types of each column such as numeric, character, logical, and date.
1.3 Question: What are the summary statistics?
summary(data)## Year Month Product_Category Product_Name
## Min. :2018 Length:2500 Length:2500 Length:2500
## 1st Qu.:2019 Class :character Class :character Class :character
## Median :2021 Mode :character Mode :character Mode :character
## Mean :2021
## 3rd Qu.:2023
## Max. :2041
## Import_Export Quantity Value_USD Country
## Length:2500 Min. : 103 Min. : 1018 Length:2500
## Class :character 1st Qu.:2522 1st Qu.: 26370 Class :character
## Mode :character Median :4935 Median : 51512 Mode :character
## Mean :5010 Mean : 50792
## 3rd Qu.:7570 3rd Qu.: 73731
## Max. :9992 Max. :336629
## Port Transport_Mode Tariff GST
## Length:2500 Length:2500 Min. : 5.003 Min. : 5.002
## Class :character Class :character 1st Qu.: 8.829 1st Qu.: 8.360
## Mode :character Mode :character Median :12.506 Median :11.620
## Mean :12.563 Mean :11.565
## 3rd Qu.:16.330 3rd Qu.:14.795
## Max. :56.152 Max. :49.357
## Exchange_Rate Shipment_Days Delivery_Status Trade_Agreement
## Min. :70.01 Min. : 1.00 Length:2500 Length:2500
## 1st Qu.:73.62 1st Qu.: 8.00 Class :character Class :character
## Median :77.26 Median : 16.00 Mode :character Mode :character
## Mean :77.38 Mean : 15.48
## 3rd Qu.:81.14 3rd Qu.: 23.00
## Max. :84.99 Max. :103.20
## Date Value_INR Profit Loss
## Length:2500 Min. : 73727 Min. : 6550 Min. : 1046
## Class :character 1st Qu.: 2020603 1st Qu.: 220607 1st Qu.: 77587
## Mode :character Median : 3990634 Median : 435482 Median : 176331
## Mean : 3922176 Mean : 486719 Mean : 218837
## 3rd Qu.: 5651695 3rd Qu.: 684530 3rd Qu.: 322383
## Max. :25781161 Max. :1598195 Max. :1925322
## Is_Delayed
## Length:2500
## Class :character
## Mode :character
##
##
## Answer The summary() function provides statistical measures like mean, minimum, maximum, and quartiles.
1.4 Question: How many rows and columns are present?
dim(data)## [1] 2500 21Explanation:
The dim() function returns the number of rows and columns in the dataset.
1.5 Question: What are the column names?
colnames(data)## [1] "Year" "Month" "Product_Category" "Product_Name"
## [5] "Import_Export" "Quantity" "Value_USD" "Country"
## [9] "Port" "Transport_Mode" "Tariff" "GST"
## [13] "Exchange_Rate" "Shipment_Days" "Delivery_Status" "Trade_Agreement"
## [17] "Date" "Value_INR" "Profit" "Loss"
## [21] "Is_Delayed"Explanation:
This shows all variable names present in the dataset.
1.6 Question : Check specific column
data$Country## [1] "China" "USA" "Germany" "Japan" "UK" "Japan" "USA"
## [8] "Germany" "Germany" "USA" "UAE" "Japan" "UAE" "UK"
## [15] "USA" "UK" "Germany" "Germany" "USA" "Japan" "UAE"
## [22] "UAE" "UAE" "Germany" "Japan" "UK" "China" "Germany"
## [29] "China" "USA" "UK" "UAE" "Japan" "Japan" "Germany"
## [36] "China" "USA" "UK" "USA" "UAE" "Germany" "Japan"
## [43] "Germany" "UK" "USA" "China" "China" "Japan" "Germany"
## [50] "China" "UAE" "UAE" "Germany" "Japan" "Japan" "Germany"
## [57] "Germany" "Germany" "Germany" "USA" "UK" "UK" "Japan"
## [64] "Germany" "USA" "China" "UAE" "UK" "UAE" "USA"
## [71] "UK" "UAE" "UAE" "UK" "Germany" "China" "Japan"
## [78] "Japan" "Germany" "USA" "Germany" "UAE" "USA" "Japan"
## [85] "Japan" "Germany" "USA" "UK" "Japan" "Germany" "UK"
## [92] "Japan" "China" "UAE" "Japan" "USA" "Germany" "Germany"
## [99] "China" "USA" "Japan" "UK" "UK" "UAE" "UAE"
## [106] "Germany" "Germany" "Japan" "USA" "Japan" "Japan" "UAE"
## [113] "USA" "Germany" "UAE" "UAE" "China" "UAE" "USA"
## [120] "China" "Japan" "UAE" "USA" "China" "UAE" "UAE"
## [127] "China" "Japan" "Germany" "Japan" "Germany" "Germany" "UAE"
## [134] "Japan" "UK" "China" "Germany" "UAE" "UAE" "Germany"
## [141] "Japan" "Japan" "UAE" "UAE" "Germany" "Japan" "USA"
## [148] "UAE" "UK" "Japan" "UAE" "UK" "UAE" "USA"
## [155] "China" "Japan" "UK" "USA" "UAE" "Germany" "USA"
## [162] "UK" "China" "China" "UK" "Germany" "Germany" "Germany"
## [169] "UAE" "UK" "China" "Germany" "Germany" "Japan" "Germany"
## [176] "China" "China" "UK" "UK" "China" "UK" "USA"
## [183] "UAE" "USA" "China" "USA" "UAE" "China" "USA"
## [190] "Japan" "Germany" "China" "Germany" "Japan" "Japan" "UK"
## [197] "Japan" "Germany" "UK" "Germany" "UAE" "USA" "UAE"
## [204] "Japan" "UAE" "USA" "Japan" "UAE" "UK" "UAE"
## [211] "China" "Germany" "UK" "UK" "UK" "USA" "Japan"
## [218] "UAE" "USA" "UK" "USA" "Japan" "Japan" "USA"
## [225] "China" "USA" "Germany" "Germany" "China" "China" "UK"
## [232] "UK" "UK" "Japan" "Japan" "UAE" "Japan" "China"
## [239] "Japan" "China" "China" "UAE" "Japan" "UAE" "USA"
## [246] "UK" "UAE" "China" "China" "UK" "Germany" "UK"
## [253] "UAE" "UK" "UAE" "Japan" "Japan" "USA" "UAE"
## [260] "USA" "UK" "China" "Germany" "Japan" "UK" "UK"
## [267] "USA" "Germany" "Germany" "UAE" "UAE" "UK" "China"
## [274] "Germany" "Japan" "UAE" "Germany" "UAE" "Germany" "Germany"
## [281] "UAE" "Japan" "UK" "Germany" "UAE" "China" "Japan"
## [288] "UAE" "UK" "China" "Japan" "China" "UK" "USA"
## [295] "Germany" "Japan" "UK" "Japan" "UAE" "China" "UAE"
## [302] "Germany" "UAE" "UK" "USA" "USA" "China" "Germany"
## [309] "Japan" "China" "China" "Japan" "Japan" "Japan" "UAE"
## [316] "China" "Germany" "USA" "China" "USA" "China" "UAE"
## [323] "UAE" "China" "UAE" "Germany" "UAE" "Germany" "China"
## [330] "UK" "Germany" "UAE" "China" "USA" "USA" "Germany"
## [337] "Germany" "Germany" "China" "Japan" "UAE" "UK" "Germany"
## [344] "China" "UAE" "Japan" "UK" "Germany" "UK" "UK"
## [351] "Japan" "China" "China" "UAE" "UAE" "UK" "China"
## [358] "UK" "Germany" "China" "UAE" "USA" "Germany" "UK"
## [365] "UAE" "UK" "UAE" "Japan" "China" "Japan" "UK"
## [372] "UK" "UK" "China" "USA" "Japan" "UK" "China"
## [379] "UAE" "USA" "USA" "UAE" "USA" "Japan" "Japan"
## [386] "USA" "USA" "UK" "China" "China" "China" "China"
## [393] "UAE" "China" "China" "USA" "UAE" "Germany" "UAE"
## [400] "China" "USA" "USA" "Germany" "USA" "Japan" "Germany"
## [407] "UAE" "UK" "UK" "USA" "USA" "Germany" "Japan"
## [414] "Japan" "Germany" "USA" "UAE" "Japan" "UK" "Japan"
## [421] "Japan" "Germany" "Germany" "China" "Japan" "China" "Germany"
## [428] "USA" "Japan" "Germany" "Germany" "UK" "Germany" "UAE"
## [435] "China" "USA" "Japan" "China" "Japan" "UK" "UK"
## [442] "Germany" "UAE" "China" "UAE" "Germany" "USA" "UAE"
## [449] "China" "Germany" "UK" "USA" "China" "USA" "Japan"
## [456] "China" "China" "UAE" "UAE" "UK" "USA" "China"
## [463] "China" "USA" "China" "UAE" "Germany" "USA" "Germany"
## [470] "China" "Japan" "UK" "UK" "USA" "Germany" "UK"
## [477] "Germany" "China" "Japan" "USA" "UAE" "UAE" "UAE"
## [484] "USA" "USA" "USA" "Germany" "Japan" "UK" "UAE"
## [491] "Germany" "China" "UAE" "UK" "Germany" "UAE" "Germany"
## [498] "UAE" "China" "UAE" "UAE" "UK" "UAE" "China"
## [505] "USA" "China" "UK" "UK" "China" "Japan" "UAE"
## [512] "USA" "UK" "UAE" "Japan" "Germany" "Japan" "USA"
## [519] "Japan" "Japan" "USA" "Japan" "UAE" "USA" "China"
## [526] "UAE" "Japan" "Japan" "China" "China" "UK" "USA"
## [533] "UAE" "UAE" "UAE" "UK" "UAE" "UAE" "UK"
## [540] "Japan" "USA" "Germany" "UAE" "Germany" "Japan" "Germany"
## [547] "China" "Japan" "UAE" "Japan" "USA" "Germany" "Germany"
## [554] "China" "USA" "Germany" "China" "UK" "Germany" "China"
## [561] "China" "UK" "China" "USA" "China" "USA" "Germany"
## [568] "Germany" "UAE" "China" "Germany" "Germany" "UK" "Japan"
## [575] "USA" "USA" "UAE" "China" "Japan" "UAE" "UK"
## [582] "USA" "USA" "Germany" "Germany" "USA" "China" "UK"
## [589] "UAE" "UK" "UAE" "UK" "UAE" "UAE" "USA"
## [596] "China" "China" "China" "China" "Germany" "UK" "UAE"
## [603] "UAE" "Germany" "Germany" "Germany" "Germany" "Germany" "Japan"
## [610] "UK" "China" "UK" "UAE" "UK" "China" "USA"
## [617] "UAE" "Germany" "Germany" "UK" "USA" "Germany" "UK"
## [624] "USA" "USA" "USA" "UAE" "Japan" "Japan" "UAE"
## [631] "Japan" "UAE" "UAE" "China" "UK" "USA" "China"
## [638] "China" "UK" "USA" "USA" "UK" "China" "UAE"
## [645] "USA" "USA" "Japan" "Japan" "UK" "Germany" "USA"
## [652] "Germany" "Germany" "UK" "UAE" "UK" "Germany" "UK"
## [659] "USA" "Germany" "UAE" "USA" "UAE" "UK" "UAE"
## [666] "UAE" "China" "Germany" "USA" "USA" "UK" "Germany"
## [673] "Japan" "Germany" "Japan" "UAE" "UAE" "China" "UAE"
## [680] "UK" "China" "China" "Japan" "UAE" "UK" "Germany"
## [687] "China" "UK" "China" "China" "UAE" "UAE" "Japan"
## [694] "Germany" "China" "UAE" "UAE" "China" "China" "Germany"
## [701] "UK" "China" "UAE" "Japan" "UAE" "Germany" "USA"
## [708] "Japan" "UK" "Germany" "Germany" "Japan" "UK" "Germany"
## [715] "UAE" "UAE" "UAE" "Japan" "China" "UAE" "China"
## [722] "China" "UAE" "UAE" "UK" "UK" "China" "USA"
## [729] "UK" "Germany" "UK" "UAE" "Germany" "Japan" "UAE"
## [736] "China" "Germany" "Japan" "China" "USA" "Japan" "UAE"
## [743] "USA" "China" "China" "USA" "Germany" "USA" "Japan"
## [750] "UAE" "UAE" "Japan" "USA" "China" "USA" "UAE"
## [757] "Germany" "Japan" "UK" "China" "UAE" "USA" "USA"
## [764] "Japan" "UAE" "USA" "Japan" "China" "UK" "China"
## [771] "Japan" "Japan" "UK" "USA" "UAE" "UK" "USA"
## [778] "Japan" "China" "USA" "UK" "UK" "UK" "China"
## [785] "UAE" "USA" "UAE" "China" "Germany" "Germany" "China"
## [792] "China" "Germany" "Germany" "Japan" "UAE" "UK" "USA"
## [799] "USA" "China" "USA" "UK" "Japan" "Japan" "Germany"
## [806] "Japan" "USA" "USA" "China" "USA" "Germany" "UAE"
## [813] "USA" "UAE" "USA" "Germany" "UK" "UAE" "China"
## [820] "UAE" "USA" "China" "USA" "UK" "USA" "Japan"
## [827] "China" "Japan" "UK" "UAE" "USA" "Germany" "UAE"
## [834] "Germany" "China" "UAE" "UK" "UAE" "Germany" "China"
## [841] "Japan" "China" "UK" "UK" "UK" "USA" "China"
## [848] "China" "Germany" "UK" "Germany" "China" "USA" "UAE"
## [855] "USA" "USA" "USA" "Germany" "UAE" "China" "Germany"
## [862] "UAE" "UAE" "UK" "UAE" "USA" "China" "UK"
## [869] "Germany" "UK" "UK" "Japan" "China" "Germany" "Japan"
## [876] "Germany" "Germany" "China" "Germany" "UAE" "USA" "USA"
## [883] "USA" "Germany" "UAE" "Japan" "UK" "Germany" "UK"
## [890] "UK" "China" "UAE" "UK" "China" "UK" "Germany"
## [897] "China" "UAE" "Japan" "Japan" "UK" "Germany" "Germany"
## [904] "UK" "UAE" "China" "Germany" "USA" "UK" "Germany"
## [911] "Japan" "UK" "UAE" "USA" "Japan" "UAE" "USA"
## [918] "UAE" "China" "China" "Japan" "Japan" "UK" "China"
## [925] "USA" "China" "Japan" "China" "UAE" "China" "Germany"
## [932] "Germany" "Japan" "Germany" "Japan" "Japan" "UK" "China"
## [939] "UAE" "USA" "UK" "USA" "UK" "China" "China"
## [946] "UAE" "Germany" "UK" "UAE" "Japan" "Germany" "Japan"
## [953] "UK" "UAE" "Japan" "UAE" "Germany" "USA" "UK"
## [960] "UK" "USA" "Japan" "UAE" "UK" "Germany" "Germany"
## [967] "Germany" "Germany" "UAE" "China" "USA" "Japan" "Germany"
## [974] "Germany" "Japan" "Japan" "Germany" "USA" "Germany" "USA"
## [981] "Japan" "USA" "UAE" "Japan" "USA" "Germany" "UAE"
## [988] "USA" "UAE" "China" "Japan" "Germany" "Germany" "UAE"
## [995] "Germany" "UAE" "UK" "Germany" "Japan" "UK" "Germany"
## [1002] "Japan" "Germany" "China" "UK" "UAE" "UK" "China"
## [1009] "UAE" "China" "China" "Japan" "China" "USA" "Japan"
## [1016] "UK" "Germany" "Japan" "UAE" "USA" "UK" "USA"
## [1023] "UAE" "Japan" "UAE" "China" "Japan" "Germany" "Japan"
## [1030] "UAE" "USA" "Japan" "UK" "China" "UAE" "UAE"
## [1037] "Japan" "Germany" "UAE" "Germany" "UAE" "Germany" "China"
## [1044] "UAE" "USA" "Germany" "UAE" "China" "China" "China"
## [1051] "China" "Germany" "China" "USA" "China" "Germany" "Japan"
## [1058] "Germany" "Germany" "UK" "Germany" "USA" "Germany" "Germany"
## [1065] "China" "UAE" "UK" "Japan" "UAE" "Germany" "UK"
## [1072] "UAE" "USA" "UAE" "Germany" "Germany" "Japan" "UAE"
## [1079] "UAE" "USA" "UAE" "Germany" "China" "Germany" "UAE"
## [1086] "USA" "Japan" "China" "Germany" "UK" "Germany" "UAE"
## [1093] "UAE" "Germany" "Japan" "UAE" "Japan" "USA" "Japan"
## [1100] "Germany" "Japan" "USA" "USA" "UK" "China" "USA"
## [1107] "Germany" "UAE" "UK" "Japan" "UAE" "Germany" "UAE"
## [1114] "China" "UAE" "UAE" "Japan" "UK" "Japan" "Germany"
## [1121] "UAE" "Japan" "Japan" "USA" "Japan" "USA" "UK"
## [1128] "Germany" "Japan" "UAE" "Japan" "UAE" "UAE" "Germany"
## [1135] "USA" "Japan" "USA" "Germany" "UK" "UAE" "Germany"
## [1142] "China" "USA" "Germany" "UAE" "USA" "UK" "Germany"
## [1149] "China" "UAE" "China" "China" "China" "China" "Germany"
## [1156] "UK" "UAE" "UAE" "UAE" "USA" "Japan" "China"
## [1163] "Japan" "Germany" "Germany" "Germany" "UAE" "UK" "UK"
## [1170] "USA" "Germany" "USA" "USA" "Germany" "USA" "UAE"
## [1177] "UAE" "UAE" "USA" "UAE" "UK" "China" "UK"
## [1184] "Germany" "UAE" "China" "China" "China" "Japan" "UK"
## [1191] "China" "UK" "China" "Japan" "UK" "USA" "China"
## [1198] "UK" "UK" "USA" "UAE" "UK" "China" "UK"
## [1205] "Japan" "Germany" "UAE" "Japan" "Germany" "China" "UAE"
## [1212] "Germany" "Germany" "UAE" "China" "UK" "China" "Germany"
## [1219] "UAE" "USA" "UAE" "USA" "USA" "Japan" "Japan"
## [1226] "China" "Germany" "UK" "USA" "Japan" "USA" "Japan"
## [1233] "UK" "Germany" "UK" "UAE" "China" "Germany" "Japan"
## [1240] "Germany" "Japan" "Germany" "China" "Germany" "Germany" "China"
## [1247] "China" "USA" "UAE" "UK" "USA" "Germany" "USA"
## [1254] "UK" "USA" "USA" "Germany" "UAE" "Japan" "Japan"
## [1261] "Germany" "UK" "China" "USA" "Germany" "China" "Japan"
## [1268] "China" "USA" "USA" "UK" "Japan" "USA" "USA"
## [1275] "Japan" "USA" "UK" "Japan" "UAE" "UK" "Japan"
## [1282] "Germany" "China" "Germany" "UK" "China" "UK" "China"
## [1289] "China" "UAE" "USA" "Japan" "USA" "UAE" "China"
## [1296] "Germany" "Germany" "Japan" "Germany" "USA" "Germany" "Japan"
## [1303] "Japan" "China" "China" "UK" "USA" "Germany" "USA"
## [1310] "Germany" "UK" "Japan" "China" "UAE" "Germany" "China"
## [1317] "Japan" "Japan" "China" "Germany" "UAE" "China" "Germany"
## [1324] "UAE" "UK" "UK" "UAE" "China" "USA" "Germany"
## [1331] "UK" "Japan" "USA" "UK" "UK" "China" "UAE"
## [1338] "Japan" "Germany" "UAE" "Germany" "UAE" "UK" "UAE"
## [1345] "Germany" "USA" "Japan" "UK" "UK" "UAE" "Germany"
## [1352] "UAE" "Japan" "Germany" "USA" "USA" "UAE" "China"
## [1359] "Japan" "USA" "UAE" "China" "USA" "UK" "Japan"
## [1366] "UAE" "Germany" "Germany" "UK" "Germany" "Germany" "USA"
## [1373] "UK" "Japan" "Japan" "Japan" "Germany" "China" "USA"
## [1380] "China" "UAE" "Japan" "Germany" "Germany" "China" "UK"
## [1387] "Germany" "UK" "Japan" "USA" "China" "Germany" "UAE"
## [1394] "Germany" "USA" "USA" "UK" "UK" "China" "Japan"
## [1401] "UAE" "Japan" "Germany" "Germany" "Japan" "UAE" "UK"
## [1408] "UK" "China" "Japan" "Germany" "UK" "USA" "Japan"
## [1415] "Japan" "China" "China" "UAE" "USA" "Japan" "UAE"
## [1422] "China" "China" "USA" "UK" "China" "Germany" "Japan"
## [1429] "China" "Japan" "Germany" "USA" "UK" "UAE" "Japan"
## [1436] "China" "China" "China" "Japan" "Japan" "China" "UAE"
## [1443] "USA" "Japan" "UAE" "UAE" "China" "USA" "Japan"
## [1450] "UAE" "UAE" "Germany" "Japan" "Germany" "China" "USA"
## [1457] "China" "UAE" "USA" "Germany" "UK" "Germany" "China"
## [1464] "Japan" "UAE" "Germany" "China" "Germany" "UK" "USA"
## [1471] "UK" "UAE" "Germany" "China" "UK" "China" "Germany"
## [1478] "UAE" "UK" "Japan" "Germany" "UAE" "USA" "Germany"
## [1485] "USA" "Germany" "Japan" "Japan" "Japan" "Japan" "Japan"
## [1492] "UK" "UAE" "China" "UK" "China" "Germany" "Germany"
## [1499] "Germany" "USA" "USA" "UK" "China" "Germany" "Germany"
## [1506] "UK" "China" "Germany" "USA" "Germany" "UAE" "USA"
## [1513] "UAE" "Japan" "USA" "Japan" "Germany" "China" "Germany"
## [1520] "Japan" "USA" "UAE" "China" "Japan" "UK" "UK"
## [1527] "Germany" "China" "Japan" "UK" "China" "China" "Japan"
## [1534] "Japan" "UAE" "Germany" "Japan" "China" "Germany" "Japan"
## [1541] "USA" "USA" "China" "UK" "Germany" "UK" "UAE"
## [1548] "USA" "Germany" "China" "USA" "USA" "Germany" "UK"
## [1555] "USA" "UAE" "UAE" "Japan" "UAE" "China" "China"
## [1562] "Japan" "China" "UK" "USA" "UK" "Germany" "USA"
## [1569] "UAE" "UAE" "Japan" "USA" "Germany" "UAE" "Germany"
## [1576] "UK" "Germany" "UK" "Japan" "UAE" "Japan" "China"
## [1583] "Japan" "UAE" "Germany" "USA" "Germany" "Germany" "Germany"
## [1590] "UK" "UK" "USA" "Germany" "Germany" "Japan" "USA"
## [1597] "UK" "China" "China" "UK" "UK" "Japan" "Germany"
## [1604] "UAE" "Germany" "China" "UAE" "Germany" "Germany" "UK"
## [1611] "UAE" "Germany" "UK" "China" "UAE" "China" "China"
## [1618] "Japan" "China" "Japan" "China" "UAE" "UK" "China"
## [1625] "Japan" "USA" "Japan" "UAE" "China" "Japan" "Japan"
## [1632] "UK" "UK" "Germany" "USA" "UAE" "Germany" "China"
## [1639] "China" "China" "Germany" "Japan" "Japan" "Japan" "Japan"
## [1646] "Germany" "Japan" "Japan" "USA" "UAE" "UAE" "UAE"
## [1653] "USA" "Germany" "China" "Germany" "Japan" "Japan" "China"
## [1660] "UK" "China" "Germany" "Germany" "Germany" "China" "USA"
## [1667] "Japan" "China" "Germany" "China" "Japan" "Japan" "USA"
## [1674] "Germany" "UAE" "UAE" "USA" "USA" "China" "Japan"
## [1681] "UK" "Germany" "Germany" "China" "Germany" "USA" "China"
## [1688] "UAE" "Germany" "UK" "UAE" "Germany" "UAE" "China"
## [1695] "USA" "Germany" "USA" "UAE" "USA" "USA" "USA"
## [1702] "UK" "Japan" "China" "China" "Germany" "UAE" "UAE"
## [1709] "Germany" "UK" "USA" "UAE" "Germany" "Germany" "China"
## [1716] "USA" "USA" "China" "Japan" "UK" "Japan" "UAE"
## [1723] "China" "Japan" "USA" "Japan" "Germany" "UAE" "USA"
## [1730] "UK" "China" "UAE" "UK" "UAE" "Japan" "Japan"
## [1737] "USA" "China" "USA" "USA" "UK" "Japan" "UK"
## [1744] "USA" "Japan" "USA" "UK" "USA" "Germany" "UAE"
## [1751] "UK" "USA" "USA" "UK" "China" "China" "UAE"
## [1758] "Japan" "Germany" "USA" "UAE" "UAE" "UK" "China"
## [1765] "Japan" "USA" "China" "China" "Germany" "UAE" "China"
## [1772] "UK" "Japan" "UAE" "USA" "China" "China" "UK"
## [1779] "China" "UK" "UAE" "USA" "USA" "Germany" "Germany"
## [1786] "China" "USA" "Germany" "China" "Japan" "USA" "China"
## [1793] "China" "China" "Japan" "USA" "Japan" "Japan" "USA"
## [1800] "USA" "UK" "Germany" "UAE" "Japan" "USA" "Germany"
## [1807] "Germany" "UK" "UAE" "UAE" "China" "Germany" "Germany"
## [1814] "UK" "USA" "Japan" "UK" "China" "UK" "UAE"
## [1821] "UAE" "Japan" "UK" "China" "UK" "UAE" "USA"
## [1828] "Japan" "UAE" "China" "USA" "USA" "UK" "USA"
## [1835] "UK" "Germany" "USA" "UAE" "USA" "UK" "Japan"
## [1842] "UAE" "China" "UAE" "China" "UK" "Germany" "Germany"
## [1849] "UAE" "Japan" "USA" "UK" "China" "USA" "Germany"
## [1856] "Japan" "UK" "UK" "Japan" "China" "Germany" "UK"
## [1863] "UAE" "China" "USA" "UK" "China" "UK" "UK"
## [1870] "China" "Japan" "Japan" "Japan" "China" "China" "Japan"
## [1877] "UAE" "Germany" "UK" "Germany" "China" "USA" "UK"
## [1884] "Japan" "China" "UK" "UAE" "UK" "UAE" "Germany"
## [1891] "China" "Germany" "China" "China" "Germany" "Japan" "USA"
## [1898] "UK" "China" "Japan" "China" "UK" "UK" "UK"
## [1905] "UAE" "Germany" "Japan" "Japan" "UK" "China" "UK"
## [1912] "UAE" "China" "UK" "Japan" "Japan" "USA" "USA"
## [1919] "UK" "China" "Japan" "USA" "UK" "UK" "Japan"
## [1926] "China" "China" "USA" "China" "Japan" "Japan" "UK"
## [1933] "UK" "Germany" "Japan" "UK" "Germany" "China" "USA"
## [1940] "Japan" "Germany" "China" "UK" "Japan" "Japan" "Germany"
## [1947] "USA" "Germany" "China" "Japan" "Japan" "Germany" "China"
## [1954] "Japan" "Germany" "USA" "UAE" "Germany" "Germany" "USA"
## [1961] "UAE" "UK" "Germany" "China" "UK" "UK" "Germany"
## [1968] "USA" "USA" "Japan" "USA" "UK" "USA" "Japan"
## [1975] "UK" "UAE" "USA" "Japan" "Germany" "Germany" "Japan"
## [1982] "China" "USA" "Germany" "Japan" "UAE" "Germany" "Germany"
## [1989] "China" "UK" "USA" "UK" "Japan" "UAE" "USA"
## [1996] "USA" "Japan" "UK" "Germany" "UK" "Germany" "Japan"
## [2003] "China" "China" "Germany" "USA" "UK" "Germany" "Germany"
## [2010] "UK" "UAE" "USA" "UK" "USA" "USA" "China"
## [2017] "UK" "UK" "USA" "China" "Japan" "USA" "Japan"
## [2024] "Japan" "UAE" "UAE" "UAE" "Germany" "UK" "Germany"
## [2031] "USA" "UAE" "USA" "Germany" "Japan" "Germany" "Japan"
## [2038] "USA" "UK" "USA" "Japan" "Japan" "Japan" "Japan"
## [2045] "UAE" "Germany" "UK" "China" "Japan" "UAE" "USA"
## [2052] "USA" "China" "Japan" "UAE" "China" "Germany" "UAE"
## [2059] "USA" "Japan" "Germany" "China" "Germany" "UAE" "UK"
## [2066] "China" "China" "Germany" "Germany" "Japan" "UAE" "China"
## [2073] "Germany" "Germany" "UAE" "USA" "USA" "UAE" "USA"
## [2080] "UAE" "UK" "UAE" "Japan" "UAE" "UAE" "UAE"
## [2087] "USA" "USA" "Germany" "UK" "China" "Germany" "UK"
## [2094] "Germany" "China" "USA" "USA" "UAE" "USA" "Germany"
## [2101] "USA" "UAE" "USA" "Japan" "UK" "Germany" "UAE"
## [2108] "Japan" "Germany" "UAE" "USA" "UAE" "Germany" "UK"
## [2115] "Germany" "UK" "USA" "UAE" "China" "USA" "USA"
## [2122] "Germany" "UK" "USA" "UAE" "UK" "China" "UK"
## [2129] "Germany" "Japan" "Japan" "USA" "Germany" "Germany" "Germany"
## [2136] "UAE" "Germany" "Japan" "UAE" "USA" "UAE" "UAE"
## [2143] "UK" "USA" "Germany" "UK" "UK" "USA" "Germany"
## [2150] "UK" "China" "UAE" "UK" "Japan" "USA" "USA"
## [2157] "Japan" "Germany" "Japan" "China" "Japan" "UK" "Germany"
## [2164] "China" "USA" "UAE" "UAE" "China" "Germany" "China"
## [2171] "Germany" "China" "UAE" "Japan" "USA" "Japan" "Japan"
## [2178] "USA" "Germany" "UK" "UK" "UK" "China" "UAE"
## [2185] "Germany" "Japan" "Japan" "China" "Germany" "UAE" "China"
## [2192] "Japan" "Germany" "UK" "Germany" "UK" "Germany" "Japan"
## [2199] "UK" "USA" "USA" "UAE" "China" "UK" "China"
## [2206] "UAE" "China" "China" "UK" "China" "Japan" "USA"
## [2213] "China" "UK" "Japan" "Germany" "USA" "UK" "UAE"
## [2220] "UAE" "UAE" "China" "UAE" "Germany" "Japan" "UAE"
## [2227] "UAE" "China" "UK" "UK" "China" "Germany" "UAE"
## [2234] "Germany" "China" "China" "UK" "UAE" "China" "UK"
## [2241] "Japan" "Japan" "USA" "Germany" "USA" "Japan" "Germany"
## [2248] "UAE" "UK" "China" "UAE" "Germany" "UK" "USA"
## [2255] "UAE" "Germany" "China" "Germany" "USA" "Japan" "UAE"
## [2262] "USA" "Germany" "Germany" "UAE" "Japan" "UK" "UK"
## [2269] "UAE" "USA" "China" "Germany" "USA" "Japan" "Germany"
## [2276] "China" "UK" "Japan" "Japan" "Germany" "Germany" "Germany"
## [2283] "USA" "UK" "UAE" "Germany" "Japan" "USA" "UK"
## [2290] "Germany" "UK" "UAE" "Germany" "Japan" "UAE" "China"
## [2297] "China" "Germany" "UAE" "Germany" "China" "Germany" "Germany"
## [2304] "Japan" "USA" "Japan" "USA" "China" "USA" "USA"
## [2311] "USA" "China" "UK" "USA" "USA" "UK" "Japan"
## [2318] "UAE" "China" "China" "UAE" "UAE" "UK" "UAE"
## [2325] "USA" "USA" "Germany" "Germany" "USA" "China" "USA"
## [2332] "Germany" "Japan" "USA" "UAE" "UK" "Japan" "UK"
## [2339] "USA" "Germany" "UK" "UAE" "Germany" "Japan" "UAE"
## [2346] "USA" "UAE" "China" "UAE" "Japan" "USA" "UK"
## [2353] "USA" "Japan" "Japan" "USA" "Germany" "China" "UK"
## [2360] "UAE" "Japan" "China" "Japan" "UK" "UAE" "Japan"
## [2367] "China" "USA" "UK" "USA" "UAE" "Germany" "Germany"
## [2374] "USA" "Germany" "Germany" "Japan" "USA" "UAE" "Japan"
## [2381] "UK" "Germany" "China" "UAE" "UAE" "UAE" "Germany"
## [2388] "UAE" "Germany" "UAE" "Japan" "UK" "USA" "USA"
## [2395] "Germany" "UAE" "Japan" "China" "UK" "USA" "China"
## [2402] "USA" "Japan" "China" "UAE" "Germany" "UAE" "USA"
## [2409] "UAE" "UAE" "Japan" "USA" "Japan" "UK" "China"
## [2416] "Germany" "USA" "UAE" "China" "USA" "China" "Japan"
## [2423] "USA" "UK" "Germany" "USA" "UAE" "UAE" "China"
## [2430] "USA" "China" "Germany" "Japan" "Japan" "UAE" "Japan"
## [2437] "UK" "China" "UAE" "USA" "Japan" "Japan" "UK"
## [2444] "USA" "Japan" "UK" "Japan" "Germany" "China" "UK"
## [2451] "UK" "UAE" "UK" "UAE" "Germany" "Germany" "USA"
## [2458] "China" "Germany" "Germany" "Germany" "Japan" "Japan" "UAE"
## [2465] "Germany" "Germany" "Germany" "UAE" "USA" "Japan" "UAE"
## [2472] "China" "UAE" "UK" "Japan" "China" "China" "China"
## [2479] "UK" "UK" "Germany" "UAE" "Japan" "USA" "UK"
## [2486] "Germany" "Germany" "USA" "UAE" "USA" "China" "Germany"
## [2493] "Germany" "USA" "UAE" "UAE" "UK" "China" "China"
## [2500] "UAE"2 Level 2: Data Cleaning and Preparation
2.1 Question: Are there any missing values in the dataset?
colSums(is.na(data))## Year Month Product_Category Product_Name
## 0 0 0 0
## Import_Export Quantity Value_USD Country
## 0 0 0 0
## Port Transport_Mode Tariff GST
## 0 0 0 0
## Exchange_Rate Shipment_Days Delivery_Status Trade_Agreement
## 0 0 0 0
## Date Value_INR Profit Loss
## 0 0 0 0
## Is_Delayed
## 0Explanation: This checks missing values in each column of the dataset. ### Question: How to handle missing values in the dataset?
data$Value_USD[is.na(data$Value_USD)] <- mean(data$Value_USD, na.rm = TRUE)
head(data)## Year Month Product_Category Product_Name Import_Export Quantity Value_USD
## 1 2024 October Agriculture Medicine Export 5414 72245.784
## 2 2023 April Textile Cotton Export 5189 24614.994
## 3 2024 March Automobile Cotton Import 415 7532.450
## 4 2020 December Electronics Medicine Export 6108 9767.587
## 5 2022 April Automobile Mobile Import 2951 72703.563
## 6 2020 March Agriculture Cotton Export 142 71831.337
## Country Port Transport_Mode Tariff GST Exchange_Rate
## 1 China Delhi Land 8.393855 15.633720 72.10798
## 2 USA Delhi Air 19.930770 12.699521 82.20563
## 3 Germany Kandla Air 5.384847 5.309774 80.85602
## 4 Japan Chennai Land 10.659926 11.137832 84.70135
## 5 UK Kandla Land 14.464991 5.959429 75.20845
## 6 Japan Mumbai Air 5.191173 5.838508 70.25781
## Shipment_Days Delivery_Status Trade_Agreement Date Value_INR Profit
## 1 21 Delayed Non-FTA 24-03-2019 5209497.3 692534.55
## 2 18 On Time Non-FTA 03-07-2022 2023491.1 393206.54
## 3 29 On Time FTA 11-03-2018 609043.9 94639.13
## 4 26 On Time FTA 05-08-2021 827327.7 90860.20
## 5 18 Delayed Non-FTA 07-08-2019 5467922.5 588212.45
## 6 14 On Time Non-FTA 09-01-2022 5046712.2 851753.59
## Loss Is_Delayed
## 1 131463.95 True
## 2 147679.02 False
## 3 31589.87 False
## 4 10806.93 False
## 5 229675.60 True
## 6 363622.75 FalseExplanation: Missing values are replaced with the mean, which is called imputation. ### Question: Convert categorical variables into factors
data$Month <- as.factor(data$Month)
data$Product_Category <- as.factor(data$Product_Category)
data$Import_Export <- as.factor(data$Import_Export)
data$Country <- as.factor(data$Country)
head(data)## Year Month Product_Category Product_Name Import_Export Quantity Value_USD
## 1 2024 October Agriculture Medicine Export 5414 72245.784
## 2 2023 April Textile Cotton Export 5189 24614.994
## 3 2024 March Automobile Cotton Import 415 7532.450
## 4 2020 December Electronics Medicine Export 6108 9767.587
## 5 2022 April Automobile Mobile Import 2951 72703.563
## 6 2020 March Agriculture Cotton Export 142 71831.337
## Country Port Transport_Mode Tariff GST Exchange_Rate
## 1 China Delhi Land 8.393855 15.633720 72.10798
## 2 USA Delhi Air 19.930770 12.699521 82.20563
## 3 Germany Kandla Air 5.384847 5.309774 80.85602
## 4 Japan Chennai Land 10.659926 11.137832 84.70135
## 5 UK Kandla Land 14.464991 5.959429 75.20845
## 6 Japan Mumbai Air 5.191173 5.838508 70.25781
## Shipment_Days Delivery_Status Trade_Agreement Date Value_INR Profit
## 1 21 Delayed Non-FTA 24-03-2019 5209497.3 692534.55
## 2 18 On Time Non-FTA 03-07-2022 2023491.1 393206.54
## 3 29 On Time FTA 11-03-2018 609043.9 94639.13
## 4 26 On Time FTA 05-08-2021 827327.7 90860.20
## 5 18 Delayed Non-FTA 07-08-2019 5467922.5 588212.45
## 6 14 On Time Non-FTA 09-01-2022 5046712.2 851753.59
## Loss Is_Delayed
## 1 131463.95 True
## 2 147679.02 False
## 3 31589.87 False
## 4 10806.93 False
## 5 229675.60 True
## 6 363622.75 FalseExplanation: Categorical variables are converted into factors for better analysis.
2.2 Question: Create a new variable (Profit Margin)
data$Profit_Margin <- data$Profit / data$Value_INR
head(data)## Year Month Product_Category Product_Name Import_Export Quantity Value_USD
## 1 2024 October Agriculture Medicine Export 5414 72245.784
## 2 2023 April Textile Cotton Export 5189 24614.994
## 3 2024 March Automobile Cotton Import 415 7532.450
## 4 2020 December Electronics Medicine Export 6108 9767.587
## 5 2022 April Automobile Mobile Import 2951 72703.563
## 6 2020 March Agriculture Cotton Export 142 71831.337
## Country Port Transport_Mode Tariff GST Exchange_Rate
## 1 China Delhi Land 8.393855 15.633720 72.10798
## 2 USA Delhi Air 19.930770 12.699521 82.20563
## 3 Germany Kandla Air 5.384847 5.309774 80.85602
## 4 Japan Chennai Land 10.659926 11.137832 84.70135
## 5 UK Kandla Land 14.464991 5.959429 75.20845
## 6 Japan Mumbai Air 5.191173 5.838508 70.25781
## Shipment_Days Delivery_Status Trade_Agreement Date Value_INR Profit
## 1 21 Delayed Non-FTA 24-03-2019 5209497.3 692534.55
## 2 18 On Time Non-FTA 03-07-2022 2023491.1 393206.54
## 3 29 On Time FTA 11-03-2018 609043.9 94639.13
## 4 26 On Time FTA 05-08-2021 827327.7 90860.20
## 5 18 Delayed Non-FTA 07-08-2019 5467922.5 588212.45
## 6 14 On Time Non-FTA 09-01-2022 5046712.2 851753.59
## Loss Is_Delayed Profit_Margin
## 1 131463.95 True 0.1329369
## 2 147679.02 False 0.1943209
## 3 31589.87 False 0.1553897
## 4 10806.93 False 0.1098237
## 5 229675.60 True 0.1075751
## 6 363622.75 False 0.1687740Explanation: A new variable is created to analyze profitability.
2.3 Question: Replace delayed status with logical values
data$Is_Delayed <- data$Delivery_Status == "Delayed"
head(data)## Year Month Product_Category Product_Name Import_Export Quantity Value_USD
## 1 2024 October Agriculture Medicine Export 5414 72245.784
## 2 2023 April Textile Cotton Export 5189 24614.994
## 3 2024 March Automobile Cotton Import 415 7532.450
## 4 2020 December Electronics Medicine Export 6108 9767.587
## 5 2022 April Automobile Mobile Import 2951 72703.563
## 6 2020 March Agriculture Cotton Export 142 71831.337
## Country Port Transport_Mode Tariff GST Exchange_Rate
## 1 China Delhi Land 8.393855 15.633720 72.10798
## 2 USA Delhi Air 19.930770 12.699521 82.20563
## 3 Germany Kandla Air 5.384847 5.309774 80.85602
## 4 Japan Chennai Land 10.659926 11.137832 84.70135
## 5 UK Kandla Land 14.464991 5.959429 75.20845
## 6 Japan Mumbai Air 5.191173 5.838508 70.25781
## Shipment_Days Delivery_Status Trade_Agreement Date Value_INR Profit
## 1 21 Delayed Non-FTA 24-03-2019 5209497.3 692534.55
## 2 18 On Time Non-FTA 03-07-2022 2023491.1 393206.54
## 3 29 On Time FTA 11-03-2018 609043.9 94639.13
## 4 26 On Time FTA 05-08-2021 827327.7 90860.20
## 5 18 Delayed Non-FTA 07-08-2019 5467922.5 588212.45
## 6 14 On Time Non-FTA 09-01-2022 5046712.2 851753.59
## Loss Is_Delayed Profit_Margin
## 1 131463.95 TRUE 0.1329369
## 2 147679.02 FALSE 0.1943209
## 3 31589.87 FALSE 0.1553897
## 4 10806.93 FALSE 0.1098237
## 5 229675.60 TRUE 0.1075751
## 6 363622.75 FALSE 0.16877403 Level 3: Data Extraction & Filtering
3.1 Question: Which are the top 10 countries with highest total trade value?
data %>%
group_by(Country) %>%
summarise(Total = sum(Value_INR)) %>%
arrange(desc(Total)) %>%
head(10)## # A tibble: 6 × 2
## Country Total
## <fct> <dbl>
## 1 Germany 1767492818.
## 2 UAE 1699138763.
## 3 China 1654206967.
## 4 USA 1603590534.
## 5 Japan 1576420701.
## 6 UK 1504591281.Explanation: This groups the data by country and calculates total trade value, then sorts to find top 10 countries.
3.2 Question: Which product category has the highest trade value?
data %>%
group_by(Product_Category) %>%
summarise(Total = sum(Value_INR)) %>%
arrange(desc(Total))## # A tibble: 5 × 2
## Product_Category Total
## <fct> <dbl>
## 1 Automobile 2052192223.
## 2 Pharma 1978667296.
## 3 Agriculture 1965625404.
## 4 Electronics 1913677902.
## 5 Textile 1895278240.Explanation: This identifies which product category contributes the most to trade. ### Question: What are the top 10 highest value transactions?
data %>%
arrange(desc(Value_INR)) %>%
head(10)## Year Month Product_Category Product_Name Import_Export Quantity
## 1 2018 September Agriculture Car Import 6004
## 2 2018 October Textile Car Export 2028
## 3 2024 January Textile Wheat Import 7686
## 4 2020 March Textile Medicine Import 9953
## 5 2021 October Automobile Car Import 6302
## 6 2019 May Pharma Mobile Import 3772
## 7 2024 August Agriculture Car Export 1568
## 8 2018 June Agriculture Car Import 2747
## 9 2022 March Agriculture Wheat Export 4166
## 10 2023 December Pharma Medicine Export 2109
## Value_USD Country Port Transport_Mode Tariff GST Exchange_Rate
## 1 93954.32 USA Kolkata Air 7.510991 14.160472 80.90321
## 2 98041.47 UAE Delhi Land 11.174415 9.078902 84.89018
## 3 99135.20 Germany Kandla Sea 7.164945 10.042625 83.75195
## 4 99081.53 USA Kandla Air 19.440984 11.026154 83.45255
## 5 99028.62 China Mumbai Sea 17.887226 10.400409 83.30403
## 6 97161.84 Japan Kolkata Air 10.713367 14.963586 84.50883
## 7 97885.58 Japan Delhi Air 16.545236 11.897590 83.72937
## 8 97456.06 China Chennai Land 16.341921 10.067352 83.69293
## 9 96051.96 UAE Kandla Land 16.396022 10.803892 84.78803
## 10 96772.86 China Mumbai Air 11.203306 15.247109 84.09672
## Shipment_Days Delivery_Status Trade_Agreement Date Value_INR Profit
## 1 3 Delayed Non-FTA 14-12-2021 25781161 834894.6
## 2 19 Delayed Non-FTA 04-01-2018 8322759 1566511.2
## 3 19 Delayed Non-FTA 18-07-2019 8302767 1348056.7
## 4 27 Delayed FTA 16-10-2024 8268606 667116.1
## 5 29 On Time FTA 06-12-2023 8249483 1490473.4
## 6 24 On Time Non-FTA 04-01-2024 8211033 1318388.4
## 7 11 On Time FTA 12-07-2023 8195898 693509.6
## 8 29 On Time FTA 22-05-2019 8156384 1000770.7
## 9 21 Delayed Non-FTA 14-02-2023 8144056 470957.2
## 10 29 On Time FTA 12-11-2024 8138280 441052.3
## Loss Is_Delayed Profit_Margin
## 1 631716.6 TRUE 0.03238390
## 2 140446.5 TRUE 0.18822019
## 3 607842.7 TRUE 0.16236235
## 4 668617.4 TRUE 0.08068060
## 5 686439.8 FALSE 0.18067476
## 6 480604.3 FALSE 0.16056303
## 7 800543.0 FALSE 0.08461667
## 8 564227.4 FALSE 0.12269784
## 9 537980.1 TRUE 0.05782834
## 10 453476.4 FALSE 0.05419478Explanation: Sorts dataset by highest trade value and selects top 10 transactions.
3.3 Question: Which ports handle the most trade?
data %>%
group_by(Port) %>%
summarise(Total = sum(Value_INR)) %>%
arrange(desc(Total))## # A tibble: 5 × 2
## Port Total
## <chr> <dbl>
## 1 Kandla 2104047716.
## 2 Kolkata 2080657871.
## 3 Chennai 1930921935.
## 4 Delhi 1920695715.
## 5 Mumbai 1769117827.Explanation: Shows which ports handle the highest trade volume.
3.4 Question: Filter transactions where value is greater than 50,00,000 INR
data %>%
filter(Value_INR > 5000000)## Year Month Product_Category Product_Name Import_Export Quantity
## 1 2024 October Agriculture Medicine Export 5414
## 2 2022 April Automobile Mobile Import 2951
## 3 2020 March Agriculture Cotton Export 142
## 4 2024 August Automobile Wheat Import 2421
## 5 2021 March Agriculture Wheat Export 1720
## 6 2020 June Pharma Cotton Import 9274
## 7 2019 February Electronics Car Import 4787
## 8 2024 September Pharma Wheat Import 2142
## 9 2019 February Pharma Car Export 9576
## 10 2024 March Pharma Medicine Import 1229
## 11 2024 May Electronics Medicine Export 8320
## 12 2023 January Textile Mobile Import 4295
## 13 2018 December Pharma Mobile Export 3395
## 14 2021 July Pharma Cotton Export 8336
## 15 2022 July Pharma Wheat Export 8854
## 16 2022 March Automobile Medicine Import 7070
## 17 2023 May Textile Car Import 3764
## 18 2021 August Pharma Medicine Export 6627
## 19 2022 July Pharma Mobile Export 2510
## 20 2023 December Automobile Wheat Export 2464
## 21 2021 September Textile Medicine Export 2505
## 22 2019 April Textile Cotton Export 1539
## 23 2021 March Agriculture Cotton Import 4319
## 24 2021 September Automobile Medicine Export 9750
## 25 2020 September Electronics Wheat Export 2071
## 26 2020 July Electronics Cotton Import 4558
## 27 2023 January Electronics Mobile Import 7059
## 28 2023 March Automobile Cotton Import 3915
## 29 2021 December Electronics Car Export 1895
## 30 2020 August Agriculture Mobile Import 2685
## 31 2018 July Pharma Car Import 1255
## 32 2024 January Textile Wheat Import 7686
## 33 2021 May Textile Cotton Import 7780
## 34 2020 September Automobile Medicine Export 4997
## 35 2022 August Automobile Cotton Export 9819
## 36 2019 May Electronics Cotton Import 5850
## 37 2019 December Automobile Medicine Import 6950
## 38 2018 May Textile Mobile Export 4123
## 39 2019 August Electronics Cotton Import 9877
## 40 2024 May Pharma Medicine Export 3198
## 41 2021 December Pharma Cotton Import 8923
## 42 2021 March Automobile Cotton Export 813
## 43 2021 March Pharma Medicine Export 1449
## 44 2021 June Agriculture Mobile Export 5848
## 45 2018 April Textile Car Export 5611
## 46 2019 July Pharma Mobile Export 5039
## 47 2024 January Automobile Mobile Import 4007
## 48 2019 December Textile Mobile Import 1900
## 49 2023 July Automobile Medicine Export 3811
## 50 2024 January Agriculture Mobile Import 1207
## 51 2020 November Electronics Cotton Import 9281
## 52 2021 April Textile Wheat Export 8400
## 53 2022 April Textile Medicine Export 4079
## 54 2019 December Pharma Car Import 6692
## 55 2023 May Pharma Car Import 7673
## 56 2018 November Pharma Mobile Export 3365
## 57 2024 October Textile Car Import 8300
## 58 2023 September Automobile Cotton Import 9238
## 59 2021 June Agriculture Cotton Import 7544
## 60 2023 October Pharma Cotton Export 3442
## 61 2024 September Electronics Car Export 5747
## 62 2022 June Textile Mobile Export 2800
## 63 2022 May Pharma Car Import 1784
## 64 2024 June Agriculture Medicine Export 8440
## 65 2022 December Automobile Cotton Import 1111
## 66 2021 March Pharma Wheat Import 2246
## 67 2018 April Textile Cotton Export 5573
## 68 2018 November Textile Cotton Export 3769
## 69 2020 January Pharma Medicine Import 4386
## 70 2022 October Electronics Cotton Import 8073
## 71 2021 November Agriculture Car Export 6499
## 72 2021 August Electronics Mobile Import 5923
## 73 2018 February Agriculture Mobile Import 8550
## 74 2021 January Electronics Medicine Export 9601
## 75 2019 July Automobile Car Export 8361
## 76 2023 May Agriculture Mobile Import 9584
## 77 2023 May Agriculture Car Export 2328
## 78 2019 June Pharma Wheat Export 9674
## 79 2021 July Electronics Car Import 8587
## 80 2019 January Agriculture Medicine Import 9667
## 81 2019 December Electronics Cotton Export 3273
## 82 2024 November Agriculture Car Import 1962
## 83 2021 July Agriculture Cotton Import 1736
## 84 2020 May Automobile Wheat Export 737
## 85 2022 October Textile Wheat Export 7605
## 86 2020 April Pharma Cotton Import 2279
## 87 2023 December Electronics Wheat Import 304
## 88 2020 January Agriculture Mobile Import 8306
## 89 2019 December Agriculture Cotton Import 8872
## 90 2024 September Agriculture Mobile Export 8649
## 91 2018 August Automobile Mobile Export 2277
## 92 2023 July Pharma Cotton Import 535
## 93 2018 October Agriculture Mobile Export 2351
## 94 2018 December Agriculture Cotton Import 6353
## 95 2022 October Textile Wheat Import 9144
## 96 2019 November Agriculture Wheat Export 8163
## 97 2023 December Electronics Medicine Import 9113
## 98 2022 April Automobile Wheat Import 2428
## 99 2020 May Agriculture Mobile Export 947
## 100 2022 June Electronics Cotton Export 5709
## 101 2018 January Pharma Mobile Export 7661
## 102 2020 November Automobile Medicine Import 5008
## 103 2023 March Textile Mobile Import 3316
## 104 2020 October Automobile Mobile Export 652
## 105 2018 February Automobile Cotton Import 2673
## 106 2019 June Agriculture Medicine Export 1322
## 107 2021 March Automobile Medicine Import 3319
## 108 2021 June Automobile Mobile Import 5926
## 109 2023 July Electronics Medicine Export 1021
## 110 2020 April Agriculture Medicine Import 1968
## 111 2019 May Pharma Cotton Export 3549
## 112 2022 February Automobile Cotton Import 7014
## 113 2022 May Automobile Medicine Export 7024
## 114 2023 April Automobile Wheat Import 6452
## 115 2023 February Textile Car Import 6194
## 116 2018 February Automobile Cotton Export 3725
## 117 2024 January Automobile Cotton Export 6900
## 118 2023 May Agriculture Cotton Export 7922
## 119 2020 January Automobile Mobile Export 7637
## 120 2020 February Textile Cotton Export 1884
## 121 2024 November Electronics Wheat Import 242
## 122 2024 July Automobile Car Export 3118
## 123 2023 March Agriculture Car Import 2293
## 124 2019 February Agriculture Cotton Import 3633
## 125 2018 July Agriculture Mobile Export 5551
## 126 2021 November Textile Car Import 1146
## 127 2019 October Electronics Car Export 8715
## 128 2021 July Agriculture Mobile Import 3648
## 129 2019 November Pharma Medicine Import 5325
## 130 2019 July Electronics Mobile Import 8353
## 131 2019 November Agriculture Mobile Import 594
## 132 2023 May Electronics Medicine Export 6283
## 133 2019 February Pharma Mobile Import 627
## 134 2018 September Textile Cotton Export 8646
## 135 2022 October Electronics Medicine Import 3247
## 136 2021 September Electronics Car Export 9655
## 137 2018 May Agriculture Wheat Export 6331
## 138 2020 August Textile Mobile Import 3078
## 139 2024 January Automobile Medicine Import 2582
## 140 2023 December Electronics Wheat Export 8328
## 141 2024 January Automobile Medicine Export 9381
## 142 2019 October Agriculture Wheat Export 2043
## 143 2023 January Textile Wheat Import 6540
## 144 2024 December Agriculture Medicine Export 2454
## 145 2020 February Pharma Cotton Export 7909
## 146 2024 March Textile Mobile Import 720
## 147 2018 October Textile Mobile Export 1709
## 148 2021 December Automobile Car Export 8186
## 149 2021 April Pharma Wheat Import 1826
## 150 2024 September Textile Mobile Import 6536
## 151 2019 March Textile Mobile Import 695
## 152 2018 October Agriculture Cotton Export 304
## 153 2019 April Automobile Medicine Export 5435
## 154 2024 April Electronics Wheat Import 3047
## 155 2021 November Agriculture Wheat Export 2358
## 156 2021 September Pharma Medicine Import 8635
## 157 2023 May Automobile Cotton Import 8138
## 158 2018 March Automobile Car Export 8176
## 159 2023 July Textile Cotton Export 8917
## 160 2018 April Electronics Cotton Export 2250
## 161 2019 July Agriculture Cotton Export 1396
## 162 2019 June Electronics Cotton Export 3115
## 163 2021 December Automobile Mobile Import 802
## 164 2018 August Electronics Medicine Import 9874
## 165 2022 April Automobile Medicine Import 4229
## 166 2018 July Agriculture Mobile Export 2195
## 167 2018 October Pharma Wheat Import 9619
## 168 2023 October Electronics Medicine Import 544
## 169 2018 May Agriculture Wheat Import 2915
## 170 2024 July Textile Medicine Export 3870
## 171 2018 September Automobile Mobile Import 6879
## 172 2019 April Pharma Mobile Import 2966
## 173 2022 September Textile Car Export 7476
## 174 2020 March Pharma Medicine Export 8262
## 175 2021 March Automobile Medicine Import 8066
## 176 2023 December Electronics Medicine Import 244
## 177 2020 September Pharma Wheat Export 2265
## 178 2021 March Textile Cotton Import 8918
## 179 2019 September Automobile Cotton Import 1896
## 180 2018 August Pharma Wheat Export 5242
## 181 2018 December Agriculture Cotton Export 4818
## 182 2021 November Automobile Cotton Export 197
## 183 2020 June Pharma Wheat Import 8376
## 184 2020 November Automobile Mobile Export 4789
## 185 2021 October Pharma Cotton Import 2372
## 186 2023 December Agriculture Medicine Export 8843
## 187 2024 November Automobile Mobile Export 4520
## 188 2018 April Pharma Mobile Export 7672
## 189 2018 September Pharma Wheat Export 8377
## 190 2024 March Automobile Medicine Import 8517
## 191 2022 March Agriculture Wheat Export 4166
## 192 2020 March Electronics Cotton Export 7004
## 193 2021 February Agriculture Car Import 9052
## 194 2019 February Textile Car Import 2946
## 195 2020 July Agriculture Cotton Export 8380
## 196 2022 July Pharma Mobile Import 250
## 197 2021 April Automobile Car Export 897
## 198 2022 June Pharma Mobile Import 6315
## 199 2019 June Automobile Cotton Export 9698
## 200 2024 November Electronics Cotton Export 1801
## 201 2020 November Pharma Car Import 7907
## 202 2022 May Pharma Wheat Import 6850
## 203 2020 November Agriculture Medicine Export 5600
## 204 2022 July Textile Wheat Import 991
## 205 2019 January Automobile Car Export 1347
## 206 2022 April Agriculture Mobile Export 398
## 207 2020 July Pharma Wheat Export 7255
## 208 2022 May Automobile Car Import 5855
## 209 2022 September Electronics Wheat Export 3938
## 210 2022 August Agriculture Car Export 197
## 211 2019 July Textile Mobile Export 257
## 212 2021 February Automobile Wheat Import 8035
## 213 2022 April Automobile Car Import 2212
## 214 2019 September Automobile Cotton Import 5361
## 215 2023 October Pharma Car Export 5476
## 216 2018 March Automobile Mobile Import 7346
## 217 2022 May Pharma Medicine Import 9311
## 218 2023 January Textile Mobile Export 1622
## 219 2019 April Pharma Car Export 7123
## 220 2022 August Electronics Car Export 9849
## 221 2024 July Textile Wheat Export 251
## 222 2021 July Agriculture Medicine Export 9188
## 223 2023 November Textile Cotton Export 7954
## 224 2024 December Agriculture Cotton Import 871
## 225 2020 March Agriculture Cotton Export 397
## 226 2024 September Electronics Cotton Export 3796
## 227 2022 October Textile Mobile Import 6575
## 228 2020 November Electronics Cotton Export 8818
## 229 2020 May Automobile Mobile Export 758
## 230 2022 August Pharma Medicine Export 2348
## 231 2018 September Agriculture Car Import 6004
## 232 2018 April Pharma Wheat Import 9863
## 233 2018 September Automobile Wheat Export 1772
## 234 2023 November Electronics Mobile Import 7728
## 235 2019 March Electronics Car Import 6165
## 236 2018 November Agriculture Wheat Import 726
## 237 2024 August Automobile Car Import 7428
## 238 2023 March Textile Mobile Export 3344
## 239 2023 December Electronics Medicine Export 6000
## 240 2024 December Pharma Mobile Export 1226
## 241 2023 June Electronics Cotton Export 8091
## 242 2020 May Pharma Car Export 7855
## 243 2019 October Agriculture Car Import 9332
## 244 2021 October Pharma Cotton Import 6506
## 245 2020 January Textile Car Export 9836
## 246 2019 July Pharma Cotton Import 7830
## 247 2021 December Pharma Wheat Import 9021
## 248 2021 August Pharma Mobile Import 3583
## 249 2023 November Electronics Mobile Export 1837
## 250 2024 August Electronics Car Export 3707
## 251 2024 September Automobile Wheat Export 4690
## 252 2019 August Automobile Car Import 7513
## 253 2018 April Automobile Medicine Export 1045
## 254 2020 April Textile Mobile Import 4972
## 255 2018 June Electronics Cotton Export 1624
## 256 2023 June Textile Car Import 8254
## 257 2023 July Automobile Cotton Import 4961
## 258 2021 April Automobile Cotton Export 5361
## 259 2019 February Pharma Cotton Export 2653
## 260 2020 October Electronics Cotton Export 2843
## 261 2021 May Agriculture Medicine Export 5929
## 262 2023 December Textile Wheat Export 264
## 263 2024 February Automobile Medicine Export 8964
## 264 2019 May Textile Cotton Import 2813
## 265 2019 October Automobile Car Export 2331
## 266 2022 February Agriculture Medicine Export 2807
## 267 2019 November Agriculture Medicine Export 2803
## 268 2022 July Pharma Cotton Import 9769
## 269 2024 March Automobile Mobile Import 9962
## 270 2020 September Agriculture Medicine Export 3103
## 271 2024 August Agriculture Car Export 1568
## 272 2023 January Electronics Wheat Export 8960
## 273 2022 May Textile Wheat Import 4658
## 274 2020 December Textile Wheat Import 498
## 275 2024 April Textile Medicine Import 2038
## 276 2018 June Automobile Mobile Import 5861
## 277 2020 May Pharma Car Import 2263
## 278 2022 September Pharma Cotton Import 1851
## 279 2020 December Pharma Mobile Export 7521
## 280 2018 October Automobile Car Export 440
## 281 2022 April Textile Mobile Import 6976
## 282 2018 April Electronics Medicine Import 432
## 283 2023 May Pharma Wheat Import 9657
## 284 2019 October Automobile Cotton Import 368
## 285 2024 February Automobile Cotton Export 1962
## 286 2021 September Electronics Wheat Export 6554
## 287 2019 February Pharma Mobile Import 6796
## 288 2022 January Textile Cotton Import 1647
## 289 2024 August Agriculture Wheat Import 8509
## 290 2023 May Textile Wheat Import 5623
## 291 2024 May Electronics Cotton Export 2301
## 292 2023 September Electronics Wheat Import 9428
## 293 2018 July Agriculture Wheat Import 5737
## 294 2023 February Pharma Wheat Export 8851
## 295 2021 February Agriculture Medicine Import 7038
## 296 2024 December Electronics Medicine Import 4589
## 297 2018 October Agriculture Mobile Import 2134
## 298 2019 October Textile Mobile Export 7379
## 299 2022 April Electronics Wheat Import 9028
## 300 2022 July Electronics Cotton Export 3385
## 301 2019 July Pharma Medicine Import 1089
## 302 2019 January Agriculture Medicine Import 6741
## 303 2019 August Automobile Mobile Export 1832
## 304 2023 February Electronics Medicine Import 3803
## 305 2021 June Pharma Wheat Import 1164
## 306 2024 August Textile Cotton Export 1256
## 307 2019 October Electronics Cotton Export 704
## 308 2024 December Agriculture Medicine Import 9834
## 309 2023 July Automobile Mobile Import 3270
## 310 2019 July Agriculture Cotton Export 6816
## 311 2019 August Textile Medicine Import 6938
## 312 2021 July Pharma Cotton Export 1468
## 313 2018 October Electronics Cotton Export 618
## 314 2020 November Electronics Cotton Export 7837
## 315 2024 February Agriculture Wheat Import 8984
## 316 2021 December Agriculture Car Export 3631
## 317 2023 January Agriculture Mobile Import 8056
## 318 2021 May Electronics Mobile Export 7499
## 319 2024 July Agriculture Medicine Import 9496
## 320 2023 September Electronics Cotton Import 8364
## 321 2022 February Pharma Wheat Export 2921
## 322 2018 October Textile Car Export 2028
## 323 2021 August Electronics Car Import 8967
## 324 2021 November Pharma Cotton Import 2997
## 325 2019 December Pharma Wheat Import 3975
## 326 2019 October Electronics Wheat Import 3638
## 327 2020 September Agriculture Car Import 3940
## 328 2020 September Electronics Mobile Import 4506
## 329 2023 January Agriculture Mobile Export 8512
## 330 2021 February Agriculture Mobile Export 6170
## 331 2020 October Agriculture Cotton Import 2481
## 332 2023 May Electronics Wheat Export 6213
## 333 2019 December Automobile Cotton Import 3361
## 334 2018 January Automobile Medicine Import 622
## 335 2018 January Textile Medicine Import 690
## 336 2024 June Textile Wheat Export 1631
## 337 2018 April Pharma Cotton Export 6463
## 338 2019 July Textile Mobile Export 7173
## 339 2024 October Electronics Medicine Import 6482
## 340 2019 July Electronics Car Import 304
## 341 2022 April Automobile Mobile Import 5512
## 342 2018 December Automobile Medicine Import 4273
## 343 2019 September Textile Cotton Export 8900
## 344 2022 July Electronics Medicine Import 4438
## 345 2024 October Agriculture Mobile Import 6945
## 346 2018 August Electronics Wheat Export 2592
## 347 2018 October Automobile Wheat Export 3759
## 348 2020 January Automobile Car Export 974
## 349 2020 November Automobile Medicine Export 4197
## 350 2021 February Agriculture Cotton Export 4969
## 351 2023 April Automobile Car Export 722
## 352 2023 May Textile Wheat Import 4256
## 353 2024 April Pharma Mobile Export 2829
## 354 2020 April Agriculture Medicine Export 5574
## 355 2018 March Electronics Wheat Export 3754
## 356 2022 February Pharma Cotton Import 9071
## 357 2020 March Textile Mobile Import 2082
## 358 2023 March Agriculture Car Import 6313
## 359 2023 August Agriculture Wheat Import 1939
## 360 2021 May Automobile Mobile Import 4831
## 361 2020 January Pharma Cotton Import 6174
## 362 2019 August Pharma Cotton Export 4643
## 363 2019 April Pharma Cotton Import 5556
## 364 2018 October Automobile Medicine Import 5568
## 365 2022 August Agriculture Medicine Export 1322
## 366 2021 March Electronics Wheat Export 2854
## 367 2020 June Pharma Medicine Export 1972
## 368 2021 May Automobile Car Export 4254
## 369 2020 January Automobile Mobile Export 7987
## 370 2023 September Pharma Medicine Export 4584
## 371 2022 October Automobile Car Import 6388
## 372 2023 September Automobile Medicine Export 4088
## 373 2019 July Automobile Medicine Export 172
## 374 2019 September Textile Car Import 1677
## 375 2022 August Textile Car Export 5645
## 376 2020 September Textile Mobile Import 488
## 377 2019 April Textile Medicine Import 2024
## 378 2019 December Pharma Wheat Export 934
## 379 2023 October Electronics Wheat Import 7056
## 380 2018 October Electronics Mobile Import 4193
## 381 2021 March Automobile Cotton Export 5122
## 382 2024 February Automobile Car Import 2027
## 383 2023 December Pharma Medicine Export 9347
## 384 2020 September Automobile Mobile Import 6619
## 385 2024 October Automobile Wheat Export 6750
## 386 2019 September Textile Cotton Export 3575
## 387 2018 September Agriculture Wheat Export 3006
## 388 2019 September Automobile Car Export 1764
## 389 2021 April Pharma Medicine Import 4926
## 390 2022 August Electronics Mobile Export 4494
## 391 2018 December Textile Mobile Import 9279
## 392 2024 March Electronics Cotton Export 2812
## 393 2018 January Agriculture Mobile Import 3092
## 394 2024 February Textile Wheat Export 5177
## 395 2023 June Electronics Medicine Import 8861
## 396 2021 February Agriculture Cotton Export 1086
## 397 2024 April Automobile Medicine Export 629
## 398 2023 April Electronics Wheat Import 3874
## 399 2022 December Electronics Cotton Export 6599
## 400 2020 February Textile Mobile Import 4121
## 401 2018 June Automobile Medicine Export 602
## 402 2022 July Electronics Car Import 5717
## 403 2020 November Textile Mobile Import 3481
## 404 2022 May Textile Wheat Import 2227
## 405 2018 October Electronics Car Export 6055
## 406 2018 March Automobile Mobile Import 3097
## 407 2021 April Textile Mobile Import 1144
## 408 2023 November Agriculture Mobile Import 1913
## 409 2024 February Pharma Car Import 4306
## 410 2018 March Pharma Cotton Import 2082
## 411 2018 June Pharma Mobile Import 1129
## 412 2020 May Agriculture Mobile Import 1204
## 413 2021 April Automobile Mobile Import 4062
## 414 2018 March Electronics Wheat Import 2329
## 415 2019 April Electronics Medicine Export 4878
## 416 2021 September Automobile Car Import 6065
## 417 2022 June Textile Medicine Import 4826
## 418 2020 October Electronics Wheat Import 9771
## 419 2024 September Electronics Medicine Import 1860
## 420 2021 February Textile Mobile Export 7086
## 421 2020 July Textile Cotton Import 8589
## 422 2020 April Electronics Mobile Import 8873
## 423 2021 February Electronics Wheat Export 1857
## 424 2022 June Pharma Medicine Export 139
## 425 2024 June Pharma Wheat Import 4469
## 426 2021 May Pharma Wheat Import 3945
## 427 2018 July Automobile Medicine Export 333
## 428 2023 September Textile Medicine Import 267
## 429 2022 June Electronics Medicine Import 8020
## 430 2021 April Pharma Cotton Import 3017
## 431 2019 May Agriculture Medicine Export 4798
## 432 2022 May Electronics Mobile Import 9436
## 433 2020 September Textile Car Export 4969
## 434 2019 December Agriculture Mobile Import 5663
## 435 2022 April Textile Cotton Export 8003
## 436 2024 August Pharma Cotton Export 4521
## 437 2019 February Automobile Wheat Import 717
## 438 2019 May Pharma Wheat Import 3538
## 439 2020 December Pharma Cotton Export 6066
## 440 2018 June Agriculture Mobile Export 3150
## 441 2023 February Pharma Wheat Import 2548
## 442 2022 May Electronics Car Import 1472
## 443 2024 November Textile Medicine Export 2296
## 444 2024 February Textile Cotton Export 3274
## 445 2019 September Textile Mobile Import 4223
## 446 2024 October Textile Medicine Import 9399
## 447 2022 December Textile Wheat Import 2019
## 448 2018 June Electronics Cotton Import 1582
## 449 2019 February Pharma Cotton Export 4159
## 450 2023 November Textile Car Export 4246
## 451 2021 November Agriculture Cotton Import 3071
## 452 2020 May Textile Wheat Export 3135
## 453 2024 February Pharma Medicine Import 9113
## 454 2021 October Textile Mobile Export 5717
## 455 2018 July Automobile Medicine Export 186
## 456 2023 August Textile Car Export 2640
## 457 2024 September Agriculture Cotton Import 6355
## 458 2022 July Agriculture Wheat Export 9959
## 459 2018 July Agriculture Mobile Import 2561
## 460 2018 November Textile Mobile Export 9793
## 461 2019 August Pharma Mobile Export 9358
## 462 2024 October Pharma Mobile Export 8190
## 463 2021 November Textile Cotton Import 6323
## 464 2022 February Pharma Wheat Import 5748
## 465 2020 March Textile Medicine Import 9953
## 466 2019 May Textile Medicine Import 7807
## 467 2018 March Textile Mobile Export 2707
## 468 2021 August Agriculture Wheat Import 6286
## 469 2023 May Pharma Mobile Import 1567
## 470 2021 January Agriculture Mobile Export 9222
## 471 2023 November Textile Mobile Export 2449
## 472 2019 July Pharma Cotton Import 451
## 473 2022 August Automobile Medicine Import 5432
## 474 2022 September Agriculture Car Export 4115
## 475 2020 October Electronics Car Import 682
## 476 2024 May Electronics Wheat Import 3679
## 477 2022 June Textile Mobile Export 2081
## 478 2018 August Agriculture Wheat Import 4758
## 479 2019 August Pharma Wheat Import 9678
## 480 2020 March Textile Mobile Export 7706
## 481 2022 March Pharma Car Import 9601
## 482 2023 October Automobile Wheat Import 7218
## 483 2019 September Pharma Cotton Export 3426
## 484 2022 January Agriculture Mobile Import 9796
## 485 2018 November Automobile Wheat Export 9432
## 486 2023 December Automobile Medicine Export 9323
## 487 2022 April Agriculture Cotton Import 7622
## 488 2023 April Pharma Cotton Export 2683
## 489 2023 March Agriculture Mobile Import 2670
## 490 2023 June Textile Medicine Import 3107
## 491 2020 April Automobile Car Import 6001
## 492 2021 March Pharma Wheat Import 3689
## 493 2023 January Automobile Wheat Export 7612
## 494 2019 September Electronics Wheat Export 5891
## 495 2020 December Electronics Car Import 6719
## 496 2020 October Automobile Cotton Import 5708
## 497 2024 March Textile Medicine Export 4287
## 498 2022 February Textile Wheat Export 2342
## 499 2024 January Agriculture Medicine Export 2727
## 500 2023 June Pharma Car Import 7022
## 501 2023 June Pharma Medicine Export 7575
## 502 2023 January Electronics Cotton Import 4713
## 503 2023 September Automobile Medicine Export 1109
## 504 2023 July Pharma Wheat Export 4540
## 505 2019 July Textile Medicine Import 1774
## 506 2022 August Electronics Mobile Export 7272
## 507 2020 August Automobile Wheat Import 7042
## 508 2019 January Textile Medicine Export 5830
## 509 2021 November Electronics Wheat Export 9578
## 510 2021 October Pharma Wheat Import 3687
## 511 2024 January Automobile Cotton Export 7396
## 512 2020 April Electronics Car Import 6810
## 513 2022 February Textile Cotton Import 5272
## 514 2021 December Electronics Wheat Export 9464
## 515 2024 March Textile Car Import 3871
## 516 2022 January Agriculture Cotton Export 6873
## 517 2018 December Agriculture Car Export 9899
## 518 2020 June Electronics Cotton Import 1992
## 519 2019 September Automobile Car Import 3906
## 520 2023 April Electronics Car Export 1304
## 521 2021 March Agriculture Cotton Export 6075
## 522 2022 March Textile Medicine Export 3290
## 523 2023 January Textile Medicine Import 2706
## 524 2022 May Textile Cotton Export 8527
## 525 2019 February Automobile Wheat Import 1194
## 526 2020 October Automobile Mobile Export 3512
## 527 2020 February Pharma Mobile Export 770
## 528 2018 January Agriculture Wheat Export 5350
## 529 2023 November Electronics Wheat Export 8313
## 530 2024 September Electronics Medicine Import 7221
## 531 2018 June Agriculture Car Import 2747
## 532 2021 March Automobile Medicine Import 4105
## 533 2018 October Automobile Wheat Import 3508
## 534 2018 March Agriculture Medicine Import 8685
## 535 2023 January Automobile Wheat Import 4664
## 536 2018 March Textile Wheat Export 2728
## 537 2021 September Electronics Medicine Export 8117
## 538 2019 March Agriculture Car Import 6492
## 539 2022 April Textile Mobile Import 6009
## 540 2018 March Pharma Medicine Import 8903
## 541 2024 March Agriculture Wheat Export 7523
## 542 2022 October Textile Medicine Import 2271
## 543 2018 December Pharma Wheat Import 3519
## 544 2022 June Pharma Wheat Export 8797
## 545 2023 September Agriculture Car Export 486
## 546 2019 July Automobile Mobile Import 1482
## 547 2021 September Electronics Cotton Import 3668
## 548 2024 May Pharma Cotton Import 4512
## 549 2019 November Pharma Wheat Export 1496
## 550 2020 December Electronics Wheat Import 6435
## 551 2024 December Automobile Car Export 4410
## 552 2019 July Agriculture Medicine Import 2403
## 553 2022 July Electronics Car Import 5541
## 554 2020 August Textile Car Export 3256
## 555 2022 September Agriculture Medicine Export 3754
## 556 2022 September Agriculture Car Import 8258
## 557 2020 December Automobile Cotton Export 2995
## 558 2021 February Electronics Wheat Import 4680
## 559 2023 July Agriculture Cotton Export 3974
## 560 2019 July Agriculture Wheat Import 6943
## 561 2024 September Pharma Car Export 3179
## 562 2020 December Electronics Wheat Import 5717
## 563 2021 November Electronics Cotton Import 1909
## 564 2020 June Electronics Car Import 1211
## 565 2022 February Textile Medicine Export 9982
## 566 2024 December Agriculture Medicine Export 7417
## 567 2024 March Automobile Car Import 9033
## 568 2019 June Electronics Cotton Import 5720
## 569 2021 May Textile Medicine Export 1475
## 570 2022 March Electronics Mobile Import 7421
## 571 2021 April Automobile Car Export 4544
## 572 2023 March Automobile Cotton Export 3130
## 573 2023 July Pharma Car Import 148
## 574 2024 January Textile Wheat Import 4477
## 575 2019 March Pharma Wheat Import 545
## 576 2018 January Agriculture Medicine Import 301
## 577 2020 November Pharma Cotton Import 1175
## 578 2020 June Agriculture Medicine Export 2502
## 579 2023 August Pharma Mobile Import 3078
## 580 2019 December Textile Car Export 6292
## 581 2022 August Automobile Cotton Export 4246
## 582 2018 October Pharma Car Export 1781
## 583 2019 August Electronics Cotton Export 2927
## 584 2024 September Agriculture Car Import 3927
## 585 2021 October Pharma Medicine Export 473
## 586 2022 April Agriculture Medicine Import 4560
## 587 2020 July Textile Wheat Import 6766
## 588 2018 September Automobile Wheat Import 8859
## 589 2024 March Electronics Car Export 3866
## 590 2023 January Electronics Cotton Export 7123
## 591 2020 December Electronics Cotton Import 5778
## 592 2021 September Automobile Medicine Import 9131
## 593 2022 August Pharma Wheat Import 2505
## 594 2018 December Textile Medicine Export 3555
## 595 2019 October Agriculture Cotton Import 9962
## 596 2023 August Automobile Mobile Import 4261
## 597 2019 November Electronics Wheat Export 5436
## 598 2021 January Agriculture Medicine Import 5544
## 599 2022 May Electronics Mobile Export 6862
## 600 2020 September Pharma Mobile Export 847
## 601 2023 August Agriculture Medicine Export 785
## 602 2022 December Automobile Car Export 2924
## 603 2022 March Textile Car Import 7274
## 604 2018 July Automobile Wheat Export 8159
## 605 2019 July Agriculture Cotton Import 602
## 606 2024 July Textile Cotton Import 7581
## 607 2022 August Textile Wheat Export 6005
## 608 2020 February Pharma Car Import 6806
## 609 2018 December Textile Cotton Export 6876
## 610 2018 September Pharma Mobile Import 7245
## 611 2021 November Electronics Medicine Export 5302
## 612 2024 December Textile Car Import 6075
## 613 2024 August Pharma Mobile Export 3228
## 614 2018 March Electronics Wheat Export 996
## 615 2019 March Agriculture Cotton Export 4719
## 616 2018 September Electronics Wheat Import 3944
## 617 2021 October Automobile Medicine Import 5440
## 618 2023 March Agriculture Mobile Import 9837
## 619 2022 December Automobile Wheat Export 1526
## 620 2021 March Textile Mobile Export 7425
## 621 2021 April Agriculture Mobile Export 4770
## 622 2021 July Pharma Car Export 7690
## 623 2019 June Electronics Mobile Export 8158
## 624 2019 May Pharma Mobile Import 3772
## 625 2018 August Pharma Mobile Export 9131
## 626 2024 February Electronics Mobile Import 9233
## 627 2022 March Electronics Wheat Import 1571
## 628 2021 July Automobile Car Import 7180
## 629 2021 January Electronics Mobile Export 5089
## 630 2019 May Electronics Mobile Export 2406
## 631 2022 December Textile Medicine Import 2317
## 632 2024 September Pharma Medicine Import 5228
## 633 2022 May Pharma Medicine Import 394
## 634 2019 September Electronics Car Import 1517
## 635 2022 August Agriculture Cotton Export 3702
## 636 2019 June Electronics Cotton Export 9063
## 637 2019 July Agriculture Cotton Export 2336
## 638 2024 November Textile Mobile Import 9254
## 639 2019 October Textile Cotton Export 5602
## 640 2019 December Pharma Car Export 7045
## 641 2021 July Automobile Mobile Export 5718
## 642 2024 March Electronics Car Export 5178
## 643 2021 July Agriculture Mobile Import 9767
## 644 2024 December Agriculture Cotton Import 904
## 645 2024 September Textile Wheat Import 7590
## 646 2020 January Textile Car Export 1039
## 647 2022 December Electronics Car Import 9364
## 648 2021 November Electronics Cotton Import 5422
## 649 2022 November Electronics Car Export 7253
## 650 2022 November Automobile Cotton Export 9060
## 651 2023 January Automobile Car Export 1292
## 652 2020 May Agriculture Wheat Export 4849
## 653 2024 June Automobile Mobile Export 9826
## 654 2021 February Automobile Mobile Import 5682
## 655 2019 June Automobile Wheat Import 348
## 656 2018 January Automobile Medicine Import 5131
## 657 2019 December Electronics Medicine Import 3344
## 658 2022 June Automobile Medicine Import 6863
## 659 2020 October Pharma Car Import 9684
## 660 2020 May Automobile Cotton Export 843
## 661 2018 September Electronics Car Export 654
## 662 2023 April Automobile Car Export 2391
## 663 2018 November Electronics Mobile Import 5459
## 664 2021 November Textile Cotton Export 8620
## 665 2021 March Agriculture Mobile Export 4018
## 666 2021 January Agriculture Wheat Export 6844
## 667 2023 March Pharma Wheat Export 6252
## 668 2024 October Pharma Wheat Export 2335
## 669 2023 October Electronics Mobile Export 9446
## 670 2020 February Agriculture Mobile Import 6393
## 671 2018 December Electronics Car Export 5349
## 672 2019 September Pharma Mobile Import 7092
## 673 2020 May Textile Medicine Import 2959
## 674 2024 November Automobile Car Import 6038
## 675 2024 June Pharma Medicine Import 5961
## 676 2018 January Electronics Cotton Import 4855
## 677 2023 July Agriculture Mobile Export 6037
## 678 2022 August Automobile Car Export 9733
## 679 2023 May Electronics Wheat Import 3205
## 680 2023 November Agriculture Wheat Import 387
## 681 2024 September Textile Wheat Export 628
## 682 2022 March Electronics Mobile Export 5665
## 683 2024 November Automobile Mobile Export 5773
## 684 2024 December Automobile Mobile Export 9885
## 685 2019 November Automobile Mobile Export 2079
## 686 2021 November Automobile Cotton Import 6934
## 687 2019 August Textile Mobile Import 9661
## 688 2020 June Textile Medicine Export 175
## 689 2021 January Textile Car Export 1582
## 690 2018 April Agriculture Cotton Export 6796
## 691 2018 March Electronics Mobile Import 5016
## 692 2023 October Automobile Cotton Import 5315
## 693 2022 August Pharma Mobile Import 1507
## 694 2020 December Electronics Cotton Export 9144
## 695 2019 July Pharma Cotton Export 4245
## 696 2018 March Automobile Cotton Import 2969
## 697 2020 March Pharma Cotton Import 1950
## 698 2023 January Agriculture Medicine Export 7493
## 699 2019 July Automobile Medicine Export 4556
## 700 2024 February Pharma Mobile Export 2123
## 701 2024 February Automobile Wheat Import 5097
## 702 2024 July Automobile Medicine Import 6392
## 703 2024 December Pharma Car Import 8983
## 704 2018 September Textile Wheat Export 7929
## 705 2021 January Automobile Medicine Import 8008
## 706 2022 July Automobile Cotton Export 7610
## 707 2020 September Automobile Medicine Export 3145
## 708 2018 July Electronics Medicine Import 4503
## 709 2023 November Textile Car Export 2398
## 710 2018 November Automobile Cotton Export 996
## 711 2019 April Agriculture Mobile Import 4805
## 712 2023 December Automobile Mobile Import 2402
## 713 2020 January Automobile Car Export 7658
## 714 2022 July Textile Cotton Export 7483
## 715 2021 September Agriculture Car Export 9618
## 716 2024 December Agriculture Wheat Import 433
## 717 2019 September Pharma Mobile Import 4636
## 718 2021 January Automobile Wheat Export 631
## 719 2023 October Electronics Medicine Import 8533
## 720 2024 March Pharma Cotton Import 8535
## 721 2022 March Automobile Medicine Export 9647
## 722 2021 December Electronics Car Export 3869
## 723 2020 February Agriculture Wheat Export 738
## 724 2018 January Textile Mobile Export 4214
## 725 2020 October Pharma Cotton Export 1887
## 726 2021 January Electronics Cotton Import 1712
## 727 2024 April Pharma Medicine Import 5233
## 728 2022 July Agriculture Wheat Export 8349
## 729 2021 April Pharma Medicine Export 2251
## 730 2020 May Pharma Car Export 9581
## 731 2023 April Automobile Mobile Import 5048
## 732 2022 November Textile Car Export 103
## 733 2021 May Textile Cotton Export 4386
## 734 2019 October Textile Wheat Import 4315
## 735 2023 February Pharma Car Import 2188
## 736 2023 September Textile Cotton Import 1125
## 737 2021 August Pharma Wheat Import 6410
## 738 2022 December Electronics Wheat Export 7205
## 739 2020 December Pharma Mobile Import 3383
## 740 2023 October Automobile Wheat Export 8310
## 741 2020 March Agriculture Mobile Export 3122
## 742 2023 June Automobile Cotton Export 4383
## 743 2022 September Textile Car Export 3933
## 744 2022 February Textile Mobile Import 2757
## 745 2018 November Pharma Car Import 3703
## 746 2021 July Textile Cotton Import 4972
## 747 2020 September Agriculture Medicine Export 5906
## 748 2020 September Agriculture Medicine Import 7078
## 749 2019 August Pharma Mobile Import 3580
## 750 2021 May Agriculture Cotton Import 9811
## 751 2021 December Pharma Cotton Import 1693
## 752 2023 October Automobile Car Import 6454
## 753 2018 December Textile Car Import 3287
## 754 2024 May Automobile Mobile Import 5077
## 755 2020 December Automobile Mobile Import 753
## 756 2019 April Automobile Car Import 9836
## 757 2023 May Electronics Medicine Import 6490
## 758 2023 May Electronics Car Export 4981
## 759 2020 February Electronics Mobile Import 4337
## 760 2021 October Pharma Medicine Import 8241
## 761 2021 June Electronics Wheat Export 2652
## 762 2018 May Automobile Cotton Import 7431
## 763 2024 December Automobile Medicine Export 2471
## 764 2024 June Electronics Wheat Import 7992
## 765 2021 October Electronics Medicine Import 1296
## 766 2020 December Textile Mobile Export 2640
## 767 2020 November Automobile Car Import 7902
## 768 2020 March Electronics Medicine Import 4237
## 769 2021 October Automobile Wheat Export 7767
## 770 2024 December Textile Car Export 1084
## 771 2023 February Pharma Cotton Import 7367
## 772 2019 July Automobile Medicine Export 6694
## 773 2023 May Textile Car Export 5820
## 774 2024 October Pharma Cotton Export 1715
## 775 2023 April Pharma Wheat Export 6579
## 776 2023 April Automobile Medicine Export 1320
## 777 2020 October Automobile Mobile Export 8060
## 778 2021 January Pharma Cotton Import 8634
## 779 2021 August Electronics Cotton Export 9543
## 780 2024 January Agriculture Mobile Export 8332
## 781 2023 August Pharma Car Export 7586
## 782 2020 July Automobile Car Export 3551
## 783 2024 April Agriculture Wheat Export 1945
## 784 2020 October Agriculture Car Import 4115
## 785 2023 January Agriculture Cotton Export 5692
## 786 2021 February Agriculture Car Import 2927
## 787 2022 May Textile Medicine Import 4366
## 788 2024 July Automobile Cotton Export 2450
## 789 2023 April Textile Cotton Import 6176
## 790 2023 March Automobile Wheat Import 3560
## 791 2018 February Pharma Cotton Import 7955
## 792 2024 May Pharma Wheat Import 4257
## 793 2020 December Automobile Medicine Export 4407
## 794 2020 May Pharma Cotton Import 7074
## 795 2024 September Textile Cotton Export 2074
## 796 2019 December Pharma Mobile Import 8945
## 797 2024 February Agriculture Car Import 2351
## 798 2023 October Agriculture Medicine Import 5841
## 799 2020 September Agriculture Cotton Export 7749
## 800 2022 November Automobile Mobile Export 8664
## 801 2021 August Electronics Medicine Export 7774
## 802 2019 February Textile Car Import 4060
## 803 2018 May Electronics Medicine Import 1526
## 804 2021 October Textile Medicine Import 8411
## 805 2020 March Electronics Cotton Export 982
## 806 2020 October Electronics Mobile Export 3953
## 807 2018 April Electronics Wheat Import 4795
## 808 2018 February Textile Medicine Import 2787
## 809 2022 March Pharma Medicine Import 4301
## 810 2019 August Agriculture Cotton Export 9952
## 811 2019 June Agriculture Medicine Export 5232
## 812 2021 May Electronics Medicine Import 9102
## 813 2020 July Agriculture Cotton Import 4915
## 814 2020 November Automobile Wheat Export 7412
## 815 2018 December Electronics Mobile Import 2717
## 816 2021 May Pharma Car Export 1645
## 817 2024 September Electronics Cotton Export 1951
## 818 2018 June Pharma Mobile Export 8239
## 819 2021 March Pharma Wheat Export 8185
## 820 2019 March Pharma Cotton Import 5341
## 821 2023 October Textile Medicine Import 9357
## 822 2018 June Automobile Wheat Import 9095
## 823 2020 May Textile Medicine Import 2042
## 824 2020 June Pharma Car Import 7583
## 825 2023 July Pharma Car Import 499
## 826 2019 August Electronics Car Import 5141
## 827 2019 August Agriculture Cotton Export 9660
## 828 2018 May Pharma Mobile Import 6551
## 829 2023 January Pharma Cotton Export 2750
## 830 2024 October Agriculture Medicine Export 9709
## 831 2018 July Electronics Car Import 5287
## 832 2022 May Pharma Cotton Export 8659
## 833 2022 March Textile Mobile Export 451
## 834 2024 January Agriculture Car Export 5181
## 835 2019 July Agriculture Car Import 1494
## 836 2023 December Electronics Medicine Import 8529
## 837 2024 November Electronics Car Export 2383
## 838 2021 September Automobile Wheat Export 5966
## 839 2024 September Agriculture Mobile Import 873
## 840 2024 May Agriculture Wheat Import 2736
## 841 2022 September Electronics Medicine Export 4971
## 842 2020 April Automobile Cotton Export 5489
## 843 2021 December Pharma Car Import 8588
## 844 2020 September Textile Cotton Import 5224
## 845 2022 June Pharma Car Export 3153
## 846 2019 June Textile Mobile Import 4753
## 847 2021 January Electronics Medicine Import 6963
## 848 2023 June Agriculture Medicine Import 1402
## 849 2022 July Automobile Medicine Import 3430
## 850 2019 May Automobile Mobile Import 4112
## 851 2020 December Agriculture Wheat Import 4094
## 852 2019 November Automobile Mobile Export 8556
## 853 2024 December Pharma Car Export 261
## 854 2023 May Agriculture Wheat Import 1704
## 855 2020 July Agriculture Medicine Import 748
## 856 2019 August Pharma Mobile Import 399
## 857 2024 June Agriculture Wheat Export 3902
## 858 2022 May Electronics Wheat Import 6189
## 859 2023 May Automobile Car Import 6244
## 860 2018 June Textile Wheat Export 9543
## 861 2021 June Pharma Car Import 2732
## 862 2020 September Agriculture Cotton Export 8089
## 863 2024 March Automobile Wheat Import 5911
## 864 2021 May Agriculture Cotton Export 5369
## 865 2018 August Pharma Wheat Export 6769
## 866 2024 December Electronics Medicine Export 9006
## 867 2024 August Pharma Medicine Export 8406
## 868 2021 April Pharma Wheat Import 4005
## 869 2020 March Automobile Car Import 2896
## 870 2018 November Textile Mobile Export 8341
## 871 2020 May Automobile Wheat Import 7165
## 872 2022 January Electronics Wheat Import 7365
## 873 2019 January Automobile Wheat Export 2045
## 874 2024 October Automobile Mobile Import 1203
## 875 2021 March Agriculture Car Import 4517
## 876 2018 June Textile Medicine Import 1499
## 877 2019 May Pharma Medicine Export 3786
## 878 2022 May Electronics Car Import 3651
## 879 2021 October Automobile Car Import 6302
## 880 2019 July Electronics Mobile Import 1815
## 881 2019 July Agriculture Car Import 1469
## 882 2024 February Automobile Wheat Export 839
## 883 2018 March Automobile Cotton Import 7618
## 884 2023 May Pharma Car Import 9197
## 885 2023 February Textile Cotton Import 1823
## 886 2023 April Automobile Wheat Export 9007
## 887 2023 December Pharma Medicine Export 2109
## Value_USD Country Port Transport_Mode Tariff GST Exchange_Rate
## 1 72245.78 China Delhi Land 8.393855 15.633720 72.10798
## 2 72703.56 UK Kandla Land 14.464991 5.959429 75.20845
## 3 71831.34 Japan Mumbai Air 5.191173 5.838508 70.25781
## 4 79679.16 USA Kandla Air 15.618106 9.197555 83.33391
## 5 98653.15 Germany Chennai Land 14.661780 5.718116 75.52313
## 6 73066.92 USA Mumbai Land 18.683610 13.109409 73.41378
## 7 99474.30 Japan Kandla Land 15.012159 13.857362 73.90917
## 8 85738.58 UAE Kolkata Air 17.089705 14.458680 71.51720
## 9 94272.21 Germany Kolkata Air 13.589188 14.911746 71.43499
## 10 90548.87 Japan Mumbai Air 19.640232 16.625739 82.07305
## 11 67594.43 Germany Delhi Air 6.009317 10.032519 81.39947
## 12 90083.97 Japan Kolkata Land 14.680245 11.153261 74.97772
## 13 79725.53 China Chennai Land 7.361256 5.811229 77.72281
## 14 76472.60 UK Delhi Air 12.275340 9.197278 81.53872
## 15 64699.75 Japan Delhi Sea 17.737193 14.241916 81.71390
## 16 63674.56 Germany Mumbai Air 5.566796 15.443878 80.73125
## 17 80995.94 USA Mumbai Sea 5.916083 5.002376 81.14798
## 18 99039.52 UAE Mumbai Land 13.067879 17.803797 75.05032
## 19 70212.48 Japan Chennai Land 19.175892 9.381768 74.75773
## 20 79831.74 China Delhi Land 11.143709 5.269992 73.56093
## 21 74815.05 Germany Kandla Air 16.603410 16.568804 71.74239
## 22 83824.56 UAE Chennai Air 12.088199 6.696970 80.99288
## 23 61875.68 Japan Mumbai Land 10.330912 9.897248 82.33828
## 24 99027.10 Germany Kandla Air 5.338229 16.074536 75.04471
## 25 67431.34 USA Mumbai Land 13.698281 8.388184 75.43178
## 26 67073.49 UK Chennai Sea 11.344290 5.893414 74.67987
## 27 74478.97 Japan Kolkata Air 9.708591 7.747727 80.68305
## 28 64850.05 UK Kolkata Air 11.511512 11.215966 81.23289
## 29 92244.14 UK Kandla Sea 16.329582 16.695337 82.54138
## 30 68771.42 Germany Delhi Sea 5.772362 14.424469 77.76794
## 31 73151.36 China Chennai Sea 8.286157 9.522497 83.80542
## 32 99135.20 Germany Kandla Sea 7.164945 10.042625 83.75195
## 33 92168.64 USA Mumbai Land 17.852905 8.394208 81.68364
## 34 81780.28 Japan Kolkata Land 16.442747 14.047151 82.62163
## 35 93948.95 UK Delhi Land 13.274821 10.135523 83.90405
## 36 84095.67 China Chennai Land 18.457424 9.375465 81.68054
## 37 93081.55 UAE Delhi Sea 18.704946 12.025304 74.43500
## 38 99312.89 China Delhi Air 14.346460 9.176489 75.83731
## 39 85577.89 UK Mumbai Land 17.060113 16.668646 83.02403
## 40 97282.66 UK Kolkata Sea 9.010893 14.676784 70.69809
## 41 90744.14 UAE Mumbai Air 17.213498 7.052937 71.10222
## 42 83802.34 UAE Chennai Sea 9.994423 14.762540 70.46183
## 43 61425.03 Germany Kandla Sea 12.718671 6.874221 82.87687
## 44 84389.82 Germany Delhi Land 19.136105 6.369320 80.54406
## 45 64632.19 UAE Chennai Air 12.741985 5.642846 77.54498
## 46 72136.18 UAE Kandla Air 18.636312 7.886325 74.25288
## 47 95237.76 UAE Chennai Air 6.450727 7.265228 73.26792
## 48 77280.99 Japan Kolkata Air 6.355154 15.039755 80.20316
## 49 96712.13 Germany Kolkata Air 13.948678 11.869672 77.19546
## 50 96093.28 Japan Chennai Land 14.259520 12.146910 79.82735
## 51 81478.39 Germany Chennai Sea 11.719113 5.345599 72.37191
## 52 70253.25 Germany Kolkata Air 16.239660 5.596053 76.18308
## 53 76305.67 UAE Mumbai Sea 7.012626 8.667712 76.88003
## 54 67172.99 UK Kandla Sea 7.087333 9.325871 79.47696
## 55 79314.63 UAE Kolkata Air 12.717703 6.743057 71.08928
## 56 80001.81 UAE Chennai Sea 18.095383 7.861152 81.84817
## 57 84791.37 Japan Kandla Air 11.618251 12.873997 70.11756
## 58 74555.49 Japan Delhi Air 6.882060 6.122711 71.19282
## 59 67747.95 UK Chennai Sea 9.898426 5.879766 81.31202
## 60 82396.38 China Delhi Air 12.833879 8.480674 84.34457
## 61 93853.34 Japan Chennai Air 7.374775 16.132058 84.98696
## 62 65070.42 UK Mumbai Land 11.260099 11.600947 77.49064
## 63 87930.28 UAE Kandla Land 9.669408 5.023003 76.75423
## 64 68785.87 Germany Delhi Air 6.761183 11.398116 82.55990
## 65 89008.74 USA Mumbai Air 9.220930 9.130813 75.74102
## 66 63282.23 UK Chennai Land 8.610877 5.837143 80.86054
## 67 87048.91 Germany Kolkata Air 5.610429 6.662419 78.28068
## 68 99790.81 China Chennai Air 14.062621 15.027605 71.04652
## 69 85218.35 China Kandla Sea 8.170439 14.757199 80.15858
## 70 85194.81 China Kandla Land 11.440255 9.528733 81.88210
## 71 76926.72 Japan Delhi Sea 14.850664 11.469933 84.37926
## 72 65624.30 UK Kolkata Land 7.081911 14.576194 80.78279
## 73 74587.58 UAE Chennai Sea 14.854110 11.908029 84.38110
## 74 85678.69 UAE Delhi Land 10.885360 5.262399 73.31715
## 75 83679.21 UAE Kandla Sea 5.741502 16.702050 72.71218
## 76 60672.96 Germany Delhi Sea 11.626827 5.533146 82.68370
## 77 83842.63 USA Kandla Land 13.892083 17.260287 70.37011
## 78 88734.01 Japan Kolkata Sea 10.040614 17.309286 72.36732
## 79 62300.00 UAE Delhi Sea 8.906994 11.157567 83.58600
## 80 89671.81 Japan Chennai Sea 13.925421 6.906465 78.59396
## 81 82980.23 USA Chennai Land 10.983475 14.220044 80.43399
## 82 69605.30 China Kolkata Land 17.432781 15.873124 76.91124
## 83 88478.25 USA Mumbai Land 13.669726 16.121197 72.08120
## 84 88773.41 Germany Mumbai Sea 19.952136 7.833147 81.72460
## 85 70692.37 China Mumbai Land 11.574202 10.604442 84.14880
## 86 74496.42 UK Kolkata Air 18.080132 8.836475 75.48632
## 87 75911.35 Japan Delhi Sea 16.493379 13.105475 78.02267
## 88 72660.72 Japan Delhi Air 7.808854 14.690234 71.21858
## 89 98006.84 China Kandla Land 12.748650 15.750809 76.61378
## 90 85614.98 USA Mumbai Sea 16.468944 12.722450 81.21402
## 91 84699.54 China Delhi Air 19.826728 5.996621 82.29886
## 92 69335.65 UAE Mumbai Land 12.160196 10.652279 78.48228
## 93 94073.69 UK Kolkata Sea 12.682994 12.032697 72.16284
## 94 73757.98 UAE Kolkata Air 5.861092 16.699527 78.46508
## 95 97484.80 Japan Kolkata Sea 13.480837 15.859965 79.91995
## 96 94261.12 Japan Kandla Air 18.292426 5.992187 72.55018
## 97 87830.61 UAE Delhi Sea 10.134300 13.402568 71.63719
## 98 60718.41 UK Kandla Land 16.159291 13.191893 82.70713
## 99 61666.26 UK Chennai Air 19.138385 12.779666 83.63168
## 100 67399.02 Germany Kandla Air 14.115674 16.952316 84.89017
## 101 76937.61 UAE Kandla Sea 18.244068 17.661248 84.21656
## 102 72285.07 China Delhi Air 12.915191 12.339117 72.06375
## 103 77421.75 UAE Kolkata Land 16.667682 17.090163 71.08613
## 104 66261.97 Germany Mumbai Sea 16.616299 9.473117 83.42888
## 105 90388.78 UK Kolkata Air 6.211881 17.234730 78.25098
## 106 83520.37 China Kolkata Sea 8.618705 15.023285 81.13934
## 107 97502.57 Japan Delhi Sea 12.274949 6.647373 77.05265
## 108 82823.60 UAE Kandla Air 5.599582 14.523172 78.63594
## 109 68631.64 UK Kolkata Land 6.552661 17.538310 83.80106
## 110 75982.99 Germany Delhi Sea 17.649920 6.892184 72.52544
## 111 71312.47 USA Chennai Air 15.416132 7.858856 72.91240
## 112 98719.12 China Delhi Sea 15.548335 9.883440 81.37426
## 113 60976.64 Germany Kolkata Land 19.924288 8.886373 84.47658
## 114 71307.36 China Kolkata Sea 9.275415 9.110204 84.71963
## 115 69964.01 China Mumbai Sea 10.603574 7.199857 79.50859
## 116 89314.49 UAE Kandla Land 17.910811 7.024053 77.94582
## 117 86625.94 China Kolkata Air 14.298031 12.582980 78.51926
## 118 68775.65 USA Kandla Land 15.382754 8.098227 79.00946
## 119 79136.25 China Delhi Sea 12.790286 8.955479 78.39525
## 120 94070.29 UAE Kandla Land 11.575894 12.102934 76.41050
## 121 91844.20 Japan Kolkata Air 8.210763 16.274653 70.80975
## 122 75160.52 China Mumbai Sea 15.013352 16.268638 75.96541
## 123 92112.45 China Kolkata Sea 7.958585 11.313110 72.96994
## 124 82324.13 UAE Kandla Air 5.242513 16.870784 74.05696
## 125 99405.94 UK Kolkata Sea 9.750099 15.951415 70.29639
## 126 85686.20 China Chennai Air 17.113785 12.903887 78.44221
## 127 64266.16 UK Kolkata Air 7.481955 17.196035 84.96098
## 128 84059.23 USA Chennai Land 7.334631 5.967475 74.77487
## 129 78477.78 Japan Chennai Air 19.102709 17.327776 81.06166
## 130 70534.82 UK Mumbai Land 15.454126 8.794829 73.56040
## 131 73891.10 USA Kandla Land 18.029575 11.284495 72.37742
## 132 74922.00 USA Kolkata Sea 17.421342 5.312607 84.47438
## 133 81614.79 USA Mumbai Sea 10.309282 13.649805 83.25263
## 134 65249.92 USA Delhi Land 15.268245 16.553306 76.95100
## 135 96650.37 USA Kolkata Sea 9.325423 5.358993 83.83179
## 136 63009.60 China Chennai Land 11.654283 5.246777 79.44546
## 137 81379.38 China Kolkata Sea 6.238814 13.347788 75.11489
## 138 65964.62 UAE Kandla Air 8.160691 14.572651 78.34187
## 139 84022.91 USA Kolkata Land 6.631918 10.848225 75.61392
## 140 92722.51 USA Kandla Air 8.598340 17.958619 75.76261
## 141 69864.02 Japan Mumbai Sea 15.759420 14.551508 76.77498
## 142 86092.44 UK Kandla Air 10.810466 7.053236 80.62846
## 143 98718.44 USA Chennai Air 17.549854 9.375275 72.05400
## 144 88256.05 USA Mumbai Land 7.393374 10.606942 71.55385
## 145 66710.71 Japan Chennai Air 11.353419 12.184462 81.89965
## 146 64419.94 UK Chennai Land 8.081555 15.511590 83.56618
## 147 71104.51 Japan Delhi Air 7.248441 9.529530 83.40208
## 148 77483.28 Germany Kolkata Sea 10.598196 12.269725 76.97175
## 149 73180.27 China Kolkata Air 7.737686 12.282061 77.21774
## 150 92504.55 Germany Kandla Land 8.076186 5.563652 70.32687
## 151 89766.75 UK Mumbai Air 19.245586 8.369656 84.10305
## 152 91035.14 China Chennai Land 16.159264 13.060062 76.65378
## 153 61384.11 USA Delhi Sea 8.190114 16.957794 83.08373
## 154 94043.65 China Kandla Sea 8.162227 17.746561 75.13894
## 155 89459.23 Germany Chennai Sea 10.056341 16.639886 77.39984
## 156 66797.09 USA Chennai Air 16.892036 10.364445 80.88144
## 157 64778.44 China Mumbai Air 17.513061 10.927335 84.21443
## 158 67769.20 China Chennai Air 17.253474 9.510664 81.41677
## 159 73027.24 China Delhi Air 9.685349 14.374097 75.52041
## 160 85065.55 China Kolkata Air 8.424605 12.179518 72.23587
## 161 75951.01 USA Chennai Land 18.223258 13.937495 75.36728
## 162 75529.54 Japan Kandla Land 12.188721 13.420048 72.05103
## 163 88834.03 UK Delhi Air 10.632603 8.098933 76.07666
## 164 91103.66 USA Kolkata Air 11.681779 7.553818 73.86609
## 165 91980.09 Germany Mumbai Air 10.251669 12.878060 72.51387
## 166 99393.53 UK Kandla Air 8.749076 17.564844 75.04375
## 167 71570.24 USA Kolkata Sea 15.572126 15.962173 80.50063
## 168 71471.10 UK Chennai Land 9.930756 11.282718 81.99843
## 169 80178.69 UAE Kandla Land 12.525335 15.340937 83.22336
## 170 89674.37 UAE Kolkata Land 11.829388 14.515329 75.89992
## 171 90780.74 USA Kolkata Sea 8.795193 13.780975 73.01450
## 172 95130.73 UK Kandla Air 11.434879 8.062353 81.83824
## 173 69522.22 UK Delhi Sea 15.027848 7.738400 76.58375
## 174 65147.22 China Kolkata Sea 13.533418 15.157813 82.33669
## 175 96917.39 UAE Delhi Sea 8.664699 8.542504 82.42826
## 176 73031.86 UK Kolkata Air 15.923805 7.013580 72.92112
## 177 73733.48 China Kandla Sea 17.418974 8.237292 73.40481
## 178 93595.98 UAE Kandla Land 13.633932 10.231136 81.07887
## 179 71401.13 Japan Delhi Sea 11.760979 13.457065 71.72918
## 180 89271.02 China Kandla Sea 11.242228 6.432890 78.79363
## 181 73957.50 UK Kandla Air 9.095175 12.930655 72.29790
## 182 85380.28 UAE Mumbai Air 8.012609 17.340677 75.67174
## 183 80124.97 UAE Mumbai Sea 9.644395 17.123458 74.91083
## 184 63430.17 USA Mumbai Sea 16.537273 6.856126 83.28652
## 185 74396.53 China Mumbai Air 6.514057 6.302999 78.02258
## 186 94144.60 Japan Delhi Air 18.077302 15.493494 83.54324
## 187 96541.81 Japan Chennai Sea 17.210299 14.563147 79.54597
## 188 86682.95 USA Mumbai Air 13.737623 8.758310 76.62075
## 189 98231.48 Germany Kandla Air 11.033290 11.101816 81.20015
## 190 87596.16 USA Kolkata Sea 17.719871 12.124697 76.16274
## 191 96051.96 UAE Kandla Land 16.396022 10.803892 84.78803
## 192 85064.02 Germany Mumbai Sea 10.908448 8.550896 84.82885
## 193 99095.88 Japan Kolkata Land 18.520512 8.829497 74.06608
## 194 66050.70 USA Kandla Land 19.466083 9.023474 84.19013
## 195 83628.11 USA Kandla Air 16.155569 9.537291 71.40500
## 196 83140.79 Germany Kolkata Sea 6.375306 12.703743 82.72963
## 197 92814.45 UAE Kolkata Sea 8.218962 11.044221 72.25167
## 198 70349.72 UK Kolkata Air 16.571229 10.183173 74.71959
## 199 87234.29 UAE Delhi Sea 11.485205 5.321457 77.16735
## 200 75131.27 UK Kolkata Land 9.615866 8.805556 73.13154
## 201 94477.90 China Delhi Land 19.151631 12.680765 78.33078
## 202 97042.17 China Mumbai Air 12.025256 11.625952 79.71828
## 203 91480.56 Germany Mumbai Air 12.710487 13.166561 84.38179
## 204 80182.55 Germany Kolkata Land 6.180349 6.479555 76.33509
## 205 62868.11 UAE Kolkata Land 18.973765 5.909167 80.70426
## 206 65930.75 Germany Kandla Land 13.554256 6.414153 78.74101
## 207 63322.76 UK Kolkata Air 19.600573 11.380423 82.41872
## 208 85226.30 USA Kandla Land 15.786237 12.482540 70.10729
## 209 83161.61 UK Mumbai Air 5.683699 17.694359 74.03462
## 210 89755.75 USA Chennai Land 5.924272 7.762670 80.20284
## 211 67580.01 USA Delhi Land 19.982073 10.738860 75.21521
## 212 75683.12 USA Kolkata Air 13.053493 14.471080 80.67251
## 213 67584.43 Japan Kandla Land 9.835599 10.077360 80.90865
## 214 78029.09 UAE Delhi Land 9.151740 14.134191 73.08575
## 215 90443.22 UAE Delhi Land 6.984544 14.839930 76.24662
## 216 97576.51 UAE Chennai Air 17.398321 5.980780 78.56085
## 217 84751.79 UK Chennai Sea 14.746382 11.850948 84.08990
## 218 73130.88 USA Mumbai Land 13.115186 11.051314 74.76977
## 219 78349.70 China Mumbai Air 10.954021 5.550928 78.32034
## 220 71427.29 UAE Delhi Air 19.748187 11.548659 80.61125
## 221 76115.70 USA Kolkata Air 12.721097 13.074558 77.21468
## 222 97394.57 USA Kolkata Air 8.881817 7.996267 78.52306
## 223 81277.89 Germany Kandla Land 6.499597 6.514096 72.17471
## 224 88904.10 UK Delhi Air 16.016284 12.604906 82.86400
## 225 66071.70 UK Kolkata Air 8.561571 11.261784 80.10940
## 226 99426.32 USA Delhi Sea 6.325702 8.925800 79.06877
## 227 72038.85 UAE Mumbai Air 5.809393 17.071947 70.23260
## 228 81294.94 UAE Chennai Air 13.895568 17.952960 74.81434
## 229 78242.29 UAE Delhi Land 15.381953 15.420216 71.73875
## 230 83714.90 China Chennai Land 14.019929 8.790932 75.45881
## 231 93954.32 USA Kolkata Air 7.510991 14.160472 80.90321
## 232 73987.99 Germany Kandla Sea 18.838542 14.857819 79.00180
## 233 81573.09 Germany Chennai Air 8.618148 15.398216 73.96513
## 234 81815.09 UAE Mumbai Land 15.769812 8.209807 77.88617
## 235 76842.39 UAE Kandla Land 14.359439 11.557217 77.69367
## 236 69608.30 China Kolkata Land 7.530625 10.132328 80.43331
## 237 89431.06 Germany Kandla Sea 10.340920 5.174202 71.25600
## 238 87652.08 UK Chennai Sea 17.587961 9.919649 71.61977
## 239 72289.39 UK Kandla Sea 10.683040 17.020548 71.65580
## 240 69002.14 Germany Kandla Air 11.309273 11.594204 72.82719
## 241 71444.99 Japan Delhi Air 14.300952 6.733386 76.07867
## 242 67111.40 UAE Delhi Land 17.631057 16.998607 84.12119
## 243 90886.29 UAE Kolkata Sea 16.127328 9.282639 83.11790
## 244 73401.91 USA Kolkata Air 7.403708 6.664432 84.69839
## 245 89782.74 Germany Kandla Land 16.838203 13.803027 82.51818
## 246 84610.31 Germany Delhi Sea 19.535002 15.270912 71.91752
## 247 87809.87 Japan Kandla Sea 14.750545 8.075891 74.83600
## 248 90107.56 China Kandla Land 5.262929 13.497149 74.58736
## 249 92812.04 Germany Kolkata Sea 10.743166 7.234099 80.05454
## 250 73596.09 UAE Kandla Sea 15.422434 5.123949 83.25291
## 251 91969.95 USA Kolkata Air 13.074997 10.126297 75.75370
## 252 83113.55 China Kandla Sea 7.876056 6.530486 78.28350
## 253 95288.45 USA Chennai Sea 13.219643 7.136594 72.04259
## 254 82857.34 China Mumbai Air 6.670245 17.959209 70.79832
## 255 87462.16 Japan Chennai Land 16.681386 14.528668 82.62754
## 256 78637.64 UK Delhi Land 11.153640 14.286602 81.91830
## 257 62025.33 UAE Delhi Land 16.011852 16.405525 82.53729
## 258 98452.98 USA Mumbai Air 10.967606 5.516012 81.82893
## 259 98658.70 UAE Mumbai Sea 19.429649 7.343773 78.64739
## 260 63715.64 USA Chennai Land 6.775197 9.349787 79.01651
## 261 97614.53 Japan Kolkata Land 8.243453 5.153526 77.32182
## 262 64484.44 China Kolkata Air 5.532927 12.952229 80.99234
## 263 69002.57 UAE Kandla Land 13.938044 15.795806 79.25624
## 264 99906.04 China Kolkata Air 17.238732 16.131899 78.24691
## 265 88201.43 USA Chennai Land 13.635777 14.632998 70.22596
## 266 64048.85 UK Chennai Sea 6.152648 17.364148 78.90886
## 267 61410.10 China Chennai Air 6.781034 12.744259 82.27730
## 268 76107.71 China Kandla Land 16.980220 9.489401 81.97845
## 269 80659.74 Germany Kolkata Sea 6.182913 15.291009 77.49566
## 270 92642.01 Germany Kolkata Sea 12.836374 7.895750 78.37952
## 271 97885.58 Japan Delhi Air 16.545236 11.897590 83.72937
## 272 83388.92 UAE Kandla Land 13.229253 11.615548 78.21165
## 273 91843.77 UK Kandla Air 6.278554 5.837309 72.14375
## 274 83280.48 USA Kandla Sea 19.674952 7.148078 72.97843
## 275 77282.33 USA Mumbai Sea 11.138545 8.632183 84.13795
## 276 99756.63 China Delhi Air 11.780534 6.465407 78.44822
## 277 89272.69 Japan Delhi Sea 11.618889 10.696547 79.03867
## 278 77262.21 USA Delhi Air 7.682699 5.589127 82.25666
## 279 95394.16 USA Kandla Land 18.568571 15.610595 76.03540
## 280 86459.80 Germany Chennai Sea 16.871978 6.476451 71.78603
## 281 87587.41 USA Mumbai Sea 17.766164 16.661723 75.92473
## 282 70635.62 UAE Kandla Sea 5.862967 10.883267 79.40400
## 283 71695.56 USA Delhi Land 12.566766 6.678580 79.83633
## 284 85965.46 UK Chennai Land 19.366667 11.166927 80.91446
## 285 84575.62 China Kolkata Air 14.338652 13.840531 84.99039
## 286 71652.79 UAE Chennai Air 15.297552 17.307170 82.78791
## 287 90916.89 USA Mumbai Land 11.304184 6.436139 80.69554
## 288 96261.13 UK Delhi Air 13.878165 10.601553 82.81424
## 289 74788.49 China Delhi Sea 13.392822 12.782830 83.00632
## 290 87147.80 UK Kolkata Air 16.453628 11.893749 83.42638
## 291 79470.60 UAE Kandla Sea 15.887261 12.633729 82.27611
## 292 70092.61 USA Kandla Land 10.047797 8.229543 75.29496
## 293 96772.46 Germany Delhi Air 13.117619 6.374785 71.81609
## 294 72255.46 China Chennai Sea 15.226596 13.715595 83.85713
## 295 65943.37 UAE Chennai Air 18.790495 15.668302 84.69967
## 296 73349.14 Japan Kandla Sea 7.903952 5.020721 76.00380
## 297 89070.03 China Delhi Land 6.696959 9.842905 72.01009
## 298 81585.42 UK Chennai Land 15.528228 17.924722 81.47119
## 299 83933.59 USA Kolkata Land 17.157724 7.004229 72.12997
## 300 90854.23 Germany Delhi Sea 14.488325 15.424776 74.32318
## 301 99486.19 Germany Delhi Air 9.546295 8.702506 70.55857
## 302 68032.15 USA Mumbai Land 11.018013 11.161795 82.07390
## 303 79588.14 USA Kandla Land 19.750271 10.722005 71.10055
## 304 68238.93 USA Chennai Air 14.961306 17.761254 82.13498
## 305 80007.06 Germany Kolkata Land 19.215625 13.123592 74.68865
## 306 90533.45 UAE Mumbai Sea 18.402934 11.542577 77.34390
## 307 96793.82 UK Kandla Air 12.710870 17.004133 83.63302
## 308 96427.27 UK Delhi Land 15.240230 7.093838 82.32833
## 309 86829.29 UK Delhi Sea 6.392954 15.973538 75.34939
## 310 67434.90 UK Kolkata Land 7.194825 10.924398 82.04923
## 311 81265.03 Japan Kandla Land 9.587237 14.944736 76.79596
## 312 86905.62 China Kolkata Sea 12.851274 14.399628 84.85070
## 313 74006.73 Germany Kandla Land 12.948392 10.140822 70.39647
## 314 75716.46 Germany Delhi Sea 12.534114 15.753373 81.40864
## 315 99267.82 Germany Kolkata Land 5.785480 10.303386 71.91176
## 316 86103.83 Japan Kolkata Land 15.697856 17.092907 81.19321
## 317 84412.41 UK Mumbai Air 9.361443 13.851210 75.55762
## 318 81581.20 China Mumbai Sea 17.156848 13.769120 78.00710
## 319 97014.06 UK Chennai Air 8.244027 9.622055 70.33042
## 320 75363.65 Japan Mumbai Air 17.654745 14.205613 70.42401
## 321 70751.95 UK Chennai Land 7.015047 11.067943 73.79247
## 322 98041.47 UAE Delhi Land 11.174415 9.078902 84.89018
## 323 86659.32 USA Mumbai Land 14.722476 10.574005 84.92990
## 324 83274.96 UK Kandla Air 13.688330 6.239680 77.62514
## 325 74163.70 Germany Kandla Sea 14.317955 10.512502 84.24283
## 326 88936.10 UAE Mumbai Sea 5.554855 6.580908 81.34136
## 327 74019.16 China Kolkata Sea 10.029453 5.965297 81.77565
## 328 66024.63 Japan Chennai Sea 11.002236 8.761260 79.72326
## 329 75716.54 China Delhi Land 8.155050 11.497364 80.68161
## 330 65196.33 UAE Kolkata Land 8.889398 11.089200 80.41863
## 331 72756.79 Germany Delhi Air 10.715387 15.510457 77.18669
## 332 98442.34 Japan Mumbai Sea 11.275508 7.365131 75.44987
## 333 86711.08 Germany Chennai Sea 8.032967 12.181768 80.06379
## 334 66671.18 Japan Delhi Air 10.996910 16.432605 84.87904
## 335 77411.71 UK Kandla Sea 18.616003 17.541855 77.61691
## 336 83459.02 USA Mumbai Sea 19.168776 6.455223 77.06223
## 337 74970.78 UK Chennai Land 8.513158 7.849077 78.92824
## 338 84232.37 UK Delhi Sea 13.435625 15.106220 77.23546
## 339 93442.32 Japan Mumbai Land 12.988763 11.882407 82.43443
## 340 89260.18 UAE Kandla Land 16.774083 7.911924 73.81031
## 341 74423.72 Japan Kolkata Air 15.142335 17.711387 74.45341
## 342 63659.01 Germany Delhi Air 13.244154 13.961947 81.95054
## 343 88656.69 USA Delhi Sea 7.090831 15.557703 79.15820
## 344 90071.26 UK Mumbai Air 5.516870 10.183125 75.34765
## 345 74123.80 USA Mumbai Land 11.348234 13.636266 70.73176
## 346 74211.16 Japan Kolkata Land 5.407756 12.569355 78.90757
## 347 70223.83 UAE Mumbai Air 6.234256 6.345442 79.90268
## 348 99008.66 Germany Delhi Sea 13.045706 14.986083 79.33690
## 349 92511.25 UAE Delhi Land 5.236580 15.485182 84.45769
## 350 68566.11 China Chennai Sea 5.897668 17.570787 74.73868
## 351 86412.23 Germany Delhi Air 13.517507 12.207417 78.89609
## 352 73426.40 Germany Delhi Air 17.844101 10.929957 84.62710
## 353 74920.97 USA Chennai Air 9.368986 11.152686 70.50698
## 354 69874.75 USA Kandla Sea 11.388089 11.089941 81.62209
## 355 68028.37 UAE Kandla Air 8.007664 10.143456 78.88341
## 356 70991.84 Japan Chennai Land 14.637500 7.088977 75.11629
## 357 65502.39 UAE Chennai Sea 19.989631 14.852233 84.62788
## 358 83123.45 Germany Delhi Sea 13.461009 13.099524 84.71910
## 359 73190.88 Germany Chennai Land 10.129947 5.458495 74.45330
## 360 70061.74 UAE Kolkata Land 13.002116 6.919883 78.32879
## 361 63417.19 UK Delhi Air 11.370291 8.607053 83.63000
## 362 88033.83 Japan Mumbai Land 17.462242 12.543750 84.59067
## 363 77144.74 Germany Kolkata Sea 10.376854 12.884258 78.40010
## 364 82892.35 UK Kolkata Air 16.485607 14.288293 70.32405
## 365 74275.45 China Kandla Air 9.909726 9.289812 75.97133
## 366 74166.55 UAE Chennai Sea 10.367349 10.083268 74.90112
## 367 86998.13 China Kandla Land 19.501836 16.875125 77.33513
## 368 84617.73 China Kandla Sea 18.512982 8.329821 79.73668
## 369 76916.64 USA Kolkata Land 7.206877 10.102705 73.25491
## 370 76587.75 Germany Kandla Land 7.140462 8.447163 76.26831
## 371 90318.74 USA Mumbai Land 5.477701 14.720688 70.75443
## 372 97303.39 Japan Kandla Land 11.373829 11.572925 78.96089
## 373 76458.35 USA Chennai Land 13.758473 12.074109 72.23262
## 374 68705.13 Japan Kolkata Land 9.871537 15.763828 76.14796
## 375 96668.96 China Mumbai Air 11.478461 6.339457 83.33948
## 376 82626.63 UAE Mumbai Sea 5.598091 12.820902 70.06895
## 377 68482.83 UAE Mumbai Land 18.618561 16.838906 80.52734
## 378 94853.16 China Delhi Air 12.140580 11.715640 83.99230
## 379 74594.49 UAE Mumbai Land 6.971683 17.372784 77.70793
## 380 60731.22 Germany Kandla Land 13.824320 10.449886 82.96770
## 381 74076.67 UAE Kolkata Land 8.184895 7.971800 84.89949
## 382 85328.70 China Kolkata Sea 7.622404 16.481125 75.17024
## 383 88558.17 China Mumbai Land 10.065528 11.274713 74.69974
## 384 73834.78 China Kandla Sea 8.104058 12.094940 77.11799
## 385 74985.96 Japan Delhi Air 18.684575 15.991848 72.56031
## 386 74665.64 Germany Chennai Land 12.355163 9.143247 82.92740
## 387 90022.56 Germany Kolkata Sea 10.128880 16.811460 74.10609
## 388 80270.82 UK Kandla Air 9.596981 8.539951 84.14322
## 389 72353.66 UAE Chennai Air 14.195867 15.865583 76.39170
## 390 75265.45 Germany Delhi Sea 19.334418 11.857941 71.95272
## 391 82048.91 UAE Kandla Land 19.234878 16.242599 72.25016
## 392 63405.04 UAE Kandla Sea 7.505193 15.250034 81.61449
## 393 68710.89 Germany Delhi Air 11.253447 16.056127 73.65239
## 394 73292.78 Germany Kandla Land 19.428371 10.470231 80.96222
## 395 73711.50 UAE Kolkata Sea 6.650755 16.081766 72.08364
## 396 83175.83 UAE Kandla Land 5.668233 16.639496 77.22367
## 397 78700.83 Japan Chennai Land 13.844822 15.105359 78.06002
## 398 70228.96 USA Kolkata Land 11.350433 14.366745 74.13351
## 399 88824.31 Germany Mumbai Air 10.971580 14.408183 81.38521
## 400 78970.11 Germany Kolkata Sea 19.022490 16.179447 77.42696
## 401 63287.00 UAE Kolkata Land 18.445712 11.339015 80.71352
## 402 65361.55 UAE Delhi Air 7.823559 11.681998 80.08957
## 403 97982.60 UK Mumbai Air 11.121432 15.152919 70.64818
## 404 63482.53 Germany Delhi Land 9.304862 8.655465 80.96483
## 405 68880.74 Japan Kolkata Air 17.860881 9.071980 81.35710
## 406 70155.92 USA Chennai Land 19.435568 7.977972 81.88737
## 407 61446.07 UK Kandla Air 6.158403 10.887092 83.13670
## 408 97169.25 UAE Mumbai Land 7.913752 10.412358 75.30857
## 409 92324.90 Japan Delhi Air 5.042553 12.650922 72.40579
## 410 84185.48 Germany Kolkata Air 14.050948 5.495640 71.79233
## 411 79619.03 China Kandla Air 15.747161 9.889407 74.87805
## 412 86853.34 USA Kandla Sea 8.178456 9.486857 71.37041
## 413 78090.18 China Kandla Sea 19.349099 8.399296 83.35048
## 414 93602.13 Germany Mumbai Air 17.274341 14.737869 72.44585
## 415 80019.74 Japan Mumbai Land 10.607971 7.778999 76.76470
## 416 77191.43 Japan Chennai Land 8.281843 10.297545 79.67171
## 417 96028.96 Germany Chennai Air 11.502172 12.933501 76.07879
## 418 91517.98 Germany Kolkata Sea 10.489294 14.853755 77.83732
## 419 65558.13 UAE Delhi Land 8.988371 9.483749 84.96220
## 420 86894.88 USA Kolkata Air 15.569966 10.964082 81.74553
## 421 82762.05 USA Kandla Air 19.914222 11.373628 84.13403
## 422 70472.90 UAE Kolkata Sea 6.191210 7.761686 82.18268
## 423 97230.36 UAE Kolkata Sea 14.359709 13.800492 82.62072
## 424 84622.82 UK Kandla Air 7.093445 7.329233 76.40287
## 425 66691.99 UAE Mumbai Sea 18.868648 5.086412 81.97552
## 426 77920.73 China Chennai Sea 11.115347 15.836792 79.54828
## 427 77329.30 China Chennai Air 12.663992 7.738985 78.25138
## 428 69173.17 UK Mumbai Sea 8.637825 17.370109 78.24729
## 429 75893.57 China Kandla Air 15.772049 6.309709 74.04593
## 430 90929.48 Japan Mumbai Land 18.447751 8.383473 81.15249
## 431 86116.30 USA Chennai Sea 11.202291 10.135333 81.12678
## 432 71854.72 China Delhi Sea 6.482579 12.422034 77.51415
## 433 78864.17 UK Chennai Land 6.684252 17.225810 72.82319
## 434 93213.33 USA Delhi Land 16.497666 14.214194 74.83490
## 435 92599.10 UAE Delhi Air 5.317912 5.342591 77.59955
## 436 82128.17 UK Delhi Air 17.245696 7.739407 80.33534
## 437 81308.49 UAE Kolkata Land 19.259777 16.063843 76.73730
## 438 64817.65 Japan Kolkata Land 7.025765 15.900813 83.94738
## 439 98054.72 Germany Chennai Land 17.496035 8.642219 71.26165
## 440 88640.22 Germany Kolkata Land 12.044438 11.473327 84.46530
## 441 74473.57 China Chennai Air 7.453172 17.824831 81.53995
## 442 72391.49 UAE Kolkata Sea 8.666701 6.160669 72.01048
## 443 92170.94 USA Kolkata Land 7.746513 13.929896 79.39699
## 444 86786.76 USA Kolkata Land 14.300182 11.735211 84.15790
## 445 72994.34 Japan Mumbai Air 6.550745 16.036469 74.08811
## 446 85710.27 Japan Kolkata Air 16.060511 16.788727 73.41846
## 447 90430.37 Germany Chennai Air 5.159840 8.916551 78.24041
## 448 83281.68 UK Mumbai Air 15.656144 7.700371 82.07686
## 449 81554.58 UK Kolkata Air 14.239887 16.300897 74.08895
## 450 69565.87 China Kolkata Land 12.220227 6.430161 81.13351
## 451 78128.59 Germany Kandla Land 5.115923 9.706925 72.64784
## 452 71666.07 Germany Kandla Air 14.685297 6.307760 79.71535
## 453 86188.42 China Mumbai Land 16.792508 17.686959 76.21927
## 454 69001.73 China Kandla Land 15.371850 12.463549 79.56927
## 455 72556.57 UAE Delhi Land 8.582640 7.175637 78.14194
## 456 66319.44 UK Kolkata Land 18.671417 15.627299 84.52577
## 457 71288.95 Germany Delhi Land 8.616791 10.745896 74.63967
## 458 81584.07 USA Delhi Land 6.016198 16.351004 76.37574
## 459 80901.40 Germany Delhi Sea 15.489450 8.057870 80.94565
## 460 66165.11 UK Mumbai Land 10.237052 15.634782 78.14144
## 461 61710.98 China Kolkata Land 18.071193 6.396768 84.09172
## 462 80360.00 USA Kolkata Land 14.455431 7.276415 76.91013
## 463 87583.12 USA Chennai Air 8.444184 14.479526 84.85721
## 464 72116.20 Japan Kandla Air 19.228892 7.521038 79.72305
## 465 99081.53 USA Kandla Air 19.440984 11.026154 83.45255
## 466 67721.93 USA Kolkata Air 13.822314 14.319507 78.69914
## 467 98552.67 Japan Delhi Land 7.505938 8.589256 74.99520
## 468 81138.41 Japan Kandla Land 17.575890 10.313167 73.10436
## 469 71910.86 Japan Kandla Air 8.784413 5.071801 74.60387
## 470 93390.78 Germany Delhi Sea 16.388201 13.226811 82.34015
## 471 77939.17 UK Mumbai Land 8.790345 15.630920 81.40074
## 472 77112.48 China Kolkata Sea 12.702690 15.439858 73.06817
## 473 72766.54 UK Delhi Air 16.095739 16.598577 78.15360
## 474 70814.35 USA Kolkata Land 18.092883 9.282390 80.22641
## 475 82262.64 Japan Chennai Air 18.926139 13.181300 70.64931
## 476 95925.40 Germany Delhi Sea 18.269599 12.066727 71.55489
## 477 74669.10 USA Chennai Land 19.178688 9.284621 70.68038
## 478 92114.36 China Chennai Sea 10.479029 9.678229 79.96145
## 479 92523.24 UK Delhi Land 10.758488 6.854242 74.27868
## 480 70776.15 UK Mumbai Air 13.905296 12.363506 76.97007
## 481 94244.44 Germany Kandla Air 10.492427 17.791738 70.99352
## 482 70682.12 Germany Kandla Land 10.027720 16.074953 77.26405
## 483 97153.12 China Delhi Land 14.476069 11.326929 70.71247
## 484 71272.70 USA Mumbai Land 5.236901 5.987414 75.93575
## 485 69651.30 China Kandla Land 5.605101 11.227160 78.98167
## 486 88809.79 Japan Kolkata Air 5.210334 14.691791 73.56384
## 487 76668.01 UAE Kandla Sea 14.019292 16.246329 82.69212
## 488 69535.74 Germany Delhi Land 17.227007 6.564482 80.34655
## 489 77157.77 Germany Mumbai Air 12.169101 8.438941 83.30754
## 490 73922.15 USA Mumbai Air 19.800227 10.362682 71.59637
## 491 82567.07 Japan Delhi Sea 18.597083 6.161571 70.32097
## 492 74199.36 UK Kandla Land 10.104819 16.691587 72.86911
## 493 90945.68 UK Kandla Land 12.695142 11.138150 76.85001
## 494 84588.47 USA Kandla Sea 8.250467 14.131878 76.42641
## 495 88033.29 USA Kandla Sea 13.987156 8.707966 71.78309
## 496 86591.77 UAE Delhi Land 9.474082 7.426498 84.70513
## 497 89222.93 China Delhi Land 15.951522 13.485147 82.26035
## 498 97537.54 Japan Delhi Land 6.678358 12.126509 82.02994
## 499 81489.15 UAE Kandla Sea 16.296382 16.582946 78.38192
## 500 90588.51 Germany Mumbai Air 6.787369 16.506340 75.60942
## 501 67733.36 Japan Chennai Air 8.165264 13.952435 79.41795
## 502 89077.69 Japan Chennai Land 8.246756 8.796602 81.02247
## 503 75155.32 Japan Delhi Air 17.315926 12.286322 72.27651
## 504 72633.41 Germany Delhi Land 19.541358 6.699497 84.13107
## 505 82355.99 China Mumbai Land 7.297119 10.764627 84.03611
## 506 93510.18 Japan Mumbai Land 15.252442 15.288693 73.97229
## 507 75893.32 Germany Kandla Air 11.091927 12.208838 79.20744
## 508 88388.49 Japan Mumbai Sea 5.181776 8.055113 71.72211
## 509 69994.41 Germany Chennai Land 15.195366 10.246981 82.08742
## 510 74803.79 UK Delhi Sea 15.371029 16.281263 72.03908
## 511 71104.34 Japan Kolkata Sea 17.106562 10.306601 75.90842
## 512 86354.31 Germany Kandla Sea 17.811822 5.200348 78.41206
## 513 90358.88 Japan Kolkata Land 12.758659 9.159873 83.78513
## 514 70789.01 China Kolkata Sea 8.797252 12.703359 79.80498
## 515 89743.80 Japan Kandla Land 19.841005 7.266936 79.39518
## 516 89179.80 China Mumbai Sea 6.035419 14.620512 71.04986
## 517 79166.13 Japan Delhi Sea 11.317118 17.210605 72.91931
## 518 71919.19 China Delhi Sea 8.573122 7.249583 83.02682
## 519 75187.99 China Kandla Land 8.701451 12.081055 76.48103
## 520 70827.97 Japan Kandla Sea 10.855208 8.946342 75.80767
## 521 75550.20 Germany Kolkata Air 19.205697 17.177366 76.58877
## 522 84083.93 China Kandla Sea 13.898465 7.123457 84.24868
## 523 90532.20 China Mumbai Sea 16.480265 8.192275 75.35573
## 524 84355.14 Japan Kandla Land 8.727074 16.435504 70.96080
## 525 95171.48 Japan Kandla Air 17.064892 12.565963 82.58400
## 526 96484.52 Japan Mumbai Land 18.850484 5.219995 84.23356
## 527 73782.39 China Delhi Sea 15.775715 11.978222 76.66927
## 528 74692.13 China Delhi Sea 8.433598 7.788989 72.09516
## 529 93069.88 USA Kandla Air 18.647031 10.434917 73.34780
## 530 99480.51 UAE Mumbai Sea 16.822877 13.035303 77.91088
## 531 97456.06 China Chennai Land 16.341921 10.067352 83.69293
## 532 91634.61 USA Chennai Air 11.711556 8.728797 82.34489
## 533 92953.21 Germany Chennai Land 6.424502 6.561940 84.73613
## 534 89459.17 China Chennai Air 19.713973 7.846282 78.01551
## 535 63694.49 Germany Kolkata Land 14.760760 10.892836 83.00435
## 536 62791.74 Japan Kandla Air 13.219690 5.830581 81.05379
## 537 98696.74 Germany Delhi Sea 18.808700 5.914947 82.33195
## 538 87812.04 Germany Chennai Sea 12.193134 13.660611 83.25441
## 539 89932.29 Japan Mumbai Land 17.431818 6.403131 82.02269
## 540 95822.90 China Kandla Land 14.511454 12.147910 77.53441
## 541 88459.53 Germany Kandla Air 14.035032 16.556038 75.95857
## 542 74852.91 Germany Chennai Sea 13.387872 6.651377 72.46561
## 543 61620.72 China Mumbai Air 19.236350 6.804946 84.16519
## 544 95531.92 Germany Kolkata Sea 5.054521 15.807682 71.93680
## 545 99862.45 Germany Mumbai Land 15.216886 5.307106 76.55861
## 546 94126.39 UAE Delhi Land 11.999887 9.393144 74.20800
## 547 85407.92 USA Chennai Sea 11.147066 5.576894 77.30367
## 548 79926.99 China Kandla Air 11.517257 8.123431 81.84513
## 549 68824.28 Japan Kolkata Sea 17.052011 11.422864 74.40467
## 550 73989.42 Japan Kolkata Sea 7.086385 11.937833 73.48565
## 551 90730.50 Japan Kolkata Sea 10.177739 6.392991 73.46852
## 552 78089.99 China Kolkata Land 18.374699 8.331861 74.04503
## 553 63677.06 Germany Chennai Air 13.464257 15.796952 82.08577
## 554 88677.69 UAE Delhi Sea 10.458107 11.938055 84.47185
## 555 86709.22 Germany Chennai Sea 15.471395 17.305568 83.61556
## 556 90865.55 UK Kandla Land 19.986461 12.703256 82.98081
## 557 68832.28 Japan Kandla Sea 13.509754 6.671765 81.91026
## 558 76913.38 UAE Kandla Air 10.792137 11.530900 76.16434
## 559 66604.42 China Chennai Sea 14.457031 6.788028 81.70077
## 560 73047.97 Japan Delhi Air 11.754882 9.822051 76.21189
## 561 66682.40 Germany Chennai Sea 12.719463 6.059343 81.18533
## 562 76653.46 Germany Mumbai Air 14.275371 7.894678 75.55942
## 563 84502.46 UK Mumbai Sea 17.094565 9.940235 83.13552
## 564 72904.76 Germany Chennai Air 15.604073 5.497824 70.32729
## 565 65464.51 UK Kandla Land 14.946908 16.425114 84.21669
## 566 90974.73 UAE Kandla Air 14.435155 8.164074 70.08971
## 567 74687.59 Japan Mumbai Sea 8.228294 5.364246 76.22759
## 568 77756.61 Germany Chennai Land 17.441227 8.516128 77.20961
## 569 84747.74 Germany Mumbai Air 17.934753 13.434509 70.58332
## 570 97995.36 Germany Kolkata Air 18.479315 11.759852 72.65940
## 571 92242.10 UK Kandla Land 19.026012 11.138050 83.49543
## 572 99910.76 USA Mumbai Sea 9.906183 11.310586 73.41264
## 573 90155.32 Germany Kolkata Air 18.675814 10.678434 74.59777
## 574 82263.87 Japan Mumbai Sea 9.008666 5.193740 80.39982
## 575 69697.34 UK Chennai Air 9.104842 7.579274 77.20585
## 576 74782.47 China Kandla Air 15.833532 14.641095 73.20675
## 577 73315.38 Japan Delhi Sea 8.421849 14.004657 74.67911
## 578 93447.44 Germany Kolkata Sea 12.477701 8.808112 78.34160
## 579 91453.42 UK Kandla Sea 15.858400 14.815303 81.01442
## 580 70203.24 China Mumbai Air 15.522747 12.893720 72.80873
## 581 82054.05 China Kandla Air 12.206467 17.278212 73.37082
## 582 72113.53 UK Kandla Sea 13.623217 8.636901 82.39807
## 583 89866.42 Japan Mumbai Land 9.518024 5.438889 76.92013
## 584 65634.30 Japan Chennai Sea 12.593462 16.007921 78.12433
## 585 93892.90 Japan Chennai Sea 5.101547 5.880812 72.61102
## 586 87775.94 UK Chennai Sea 10.016160 8.820738 71.20147
## 587 82816.17 UAE Kolkata Land 19.280130 16.839552 84.72710
## 588 73322.68 Germany Delhi Land 17.785670 15.477380 82.50659
## 589 86930.84 China Chennai Sea 13.909049 14.379792 70.82292
## 590 87748.08 Japan Delhi Land 10.483556 17.238029 74.33876
## 591 77745.29 Japan Delhi Sea 19.950020 16.456190 78.14651
## 592 73441.05 Japan Mumbai Land 18.413597 14.144409 82.41997
## 593 83950.40 USA Kandla Land 19.897795 12.142509 78.31938
## 594 83229.98 China Chennai Air 7.368144 14.334217 78.96108
## 595 99668.27 UK Kolkata Land 9.989177 15.241326 81.13960
## 596 83123.13 China Kolkata Land 19.666170 14.153756 74.53993
## 597 95251.03 Japan Mumbai Sea 14.920776 8.549376 70.68675
## 598 92038.29 USA Kandla Air 10.370347 11.919831 76.15878
## 599 90858.96 Germany Kandla Sea 8.971614 13.588320 79.58308
## 600 92515.37 China Kandla Air 11.375035 12.635699 74.76715
## 601 86782.34 China Kolkata Air 9.837476 17.676624 76.02928
## 602 87284.57 USA Delhi Sea 5.382448 8.506041 73.31261
## 603 97622.31 Germany Delhi Sea 12.826612 13.287348 80.28039
## 604 89190.43 UK Kandla Land 18.569140 8.630914 74.72211
## 605 91075.17 UAE Delhi Air 10.793615 13.447073 76.05539
## 606 93215.98 USA Mumbai Land 9.004424 17.848769 84.95263
## 607 80454.32 UAE Chennai Land 6.682121 14.558148 82.72606
## 608 76840.78 USA Delhi Land 11.206803 14.194731 72.64402
## 609 65899.99 UK Chennai Land 15.109523 8.101973 84.94083
## 610 87848.84 USA Kandla Sea 11.063854 5.364732 77.52077
## 611 64126.78 China Kolkata Air 14.696297 6.017993 79.91055
## 612 75422.19 USA Kandla Air 18.859695 5.670575 79.29659
## 613 94519.47 UK Chennai Sea 13.180696 11.879361 76.85680
## 614 96450.89 UAE Kolkata Sea 16.004833 17.889335 77.09869
## 615 74725.75 China Mumbai Air 19.393090 13.139879 82.31829
## 616 70972.16 Japan Mumbai Sea 15.800132 11.883330 73.55942
## 617 89624.60 USA Kandla Air 10.125531 7.839686 72.87908
## 618 81618.43 Japan Kolkata Air 10.218336 9.918743 82.82753
## 619 69255.30 UK Mumbai Air 13.894287 14.542573 74.89464
## 620 90033.31 China Mumbai Air 8.694574 6.716814 79.58202
## 621 70377.22 China Chennai Land 13.692879 10.622879 72.60358
## 622 67503.61 Japan Delhi Sea 14.636066 7.618705 81.71248
## 623 75837.41 USA Kolkata Air 5.730224 11.363439 74.61232
## 624 97161.84 Japan Kolkata Air 10.713367 14.963586 84.50883
## 625 59304.38 UK Mumbai Land 6.468236 12.685955 84.78118
## 626 71090.71 Germany Kandla Sea 15.839831 10.696446 82.09337
## 627 70485.06 USA Kolkata Land 18.330775 5.357802 84.56838
## 628 60984.78 USA Kandla Land 9.840931 13.509563 84.58524
## 629 95709.42 China Chennai Land 9.992458 8.119680 72.17850
## 630 75613.50 Japan Kolkata Sea 9.922898 14.017311 72.35688
## 631 68611.90 Germany Kandla Sea 13.274480 14.794955 78.70199
## 632 88467.10 China Kolkata Land 7.367562 13.860229 83.03551
## 633 94849.26 China Mumbai Sea 13.894075 16.722937 70.14033
## 634 82995.88 UK Delhi Land 19.329550 5.751324 82.48465
## 635 65826.88 USA Kolkata Land 16.561762 9.124913 77.96169
## 636 72507.15 China Kandla Land 18.006810 10.941635 80.19205
## 637 75528.90 China Kolkata Air 18.941486 8.959412 79.57639
## 638 74376.76 China Kandla Sea 9.124262 5.400333 83.04800
## 639 63882.04 China Chennai Sea 18.082222 12.955880 81.00567
## 640 75674.51 Germany Mumbai Air 16.590155 14.312065 70.07224
## 641 91482.46 UK Chennai Air 18.021524 8.268955 72.87399
## 642 63415.25 UAE Kandla Land 5.955180 11.255662 80.38996
## 643 72474.48 Japan Kolkata Sea 8.274805 5.678499 74.50358
## 644 87010.80 China Kandla Sea 9.104468 9.332265 71.49103
## 645 71584.44 UAE Delhi Sea 17.987895 5.121002 70.08228
## 646 71397.43 UK Mumbai Air 6.676872 12.493489 80.69567
## 647 66890.92 UK Chennai Air 18.611846 13.738607 83.19195
## 648 80496.72 Japan Mumbai Sea 14.046737 5.242876 76.54000
## 649 91870.99 UAE Kolkata Sea 16.333429 12.109097 75.26821
## 650 74371.49 China Chennai Air 8.281928 15.086982 72.09278
## 651 93245.18 China Kolkata Air 17.653243 6.163623 74.99516
## 652 80583.37 UK Kandla Air 8.105019 5.439553 80.93548
## 653 73321.94 Japan Mumbai Sea 14.877728 6.562246 71.53869
## 654 78313.13 USA Kandla Land 16.282506 12.364311 75.64720
## 655 81333.02 UK Kandla Sea 5.660763 17.497136 71.22905
## 656 92011.31 USA Kandla Sea 8.746673 9.414542 71.05687
## 657 92468.10 USA Kolkata Sea 6.297163 13.964222 78.61765
## 658 82157.61 UK Kandla Land 13.762466 16.403437 74.94605
## 659 81019.14 Japan Mumbai Land 12.517390 10.861024 79.07299
## 660 85628.99 Japan Kandla Sea 11.359519 8.573557 75.39923
## 661 72624.29 China Chennai Sea 8.920581 13.801798 72.64241
## 662 86003.13 Japan Kandla Air 5.882946 15.776448 78.34618
## 663 94975.71 UK Delhi Land 16.756023 11.973375 72.82070
## 664 81600.49 UAE Kandla Air 12.703307 15.463183 70.57775
## 665 59402.80 UK Kandla Sea 13.233395 12.686714 84.43940
## 666 87602.07 China Kandla Land 13.939435 16.080762 81.06193
## 667 69720.13 China Mumbai Land 6.515396 16.593371 72.55376
## 668 80052.66 China Kandla Land 9.610713 5.680373 79.42431
## 669 91786.06 Germany Mumbai Air 18.037350 9.531677 80.62991
## 670 88176.72 USA Chennai Sea 17.452585 11.442652 74.55313
## 671 67941.08 UK Mumbai Sea 15.736868 11.099990 79.31984
## 672 71115.91 China Kandla Land 7.024354 14.173927 80.60586
## 673 81469.81 Japan Mumbai Sea 18.528657 9.277424 82.35493
## 674 77206.52 UK Kolkata Air 7.163424 12.917990 77.26586
## 675 78976.09 Japan Chennai Air 19.345338 17.222839 76.66170
## 676 81806.10 UK Kandla Air 7.732170 15.851845 73.23950
## 677 97079.56 Japan Delhi Air 16.569565 9.499664 83.49094
## 678 93220.24 USA Chennai Sea 15.176941 5.133546 78.73339
## 679 61785.70 USA Chennai Sea 7.393512 6.171323 82.72524
## 680 75886.63 China Kandla Air 10.335593 17.082334 71.34393
## 681 89839.95 Japan Kolkata Land 18.639658 17.703846 77.95119
## 682 65876.28 China Chennai Air 11.345094 12.775812 82.36984
## 683 78912.36 USA Mumbai Land 10.342951 16.877362 71.19974
## 684 88130.32 Japan Chennai Land 12.154928 12.688131 82.28567
## 685 87720.32 Japan Mumbai Sea 5.859613 6.615607 73.47458
## 686 93245.51 Germany Kandla Land 16.392112 6.884587 75.64344
## 687 75031.62 UK Kolkata Air 9.322439 12.890249 72.59064
## 688 74330.42 China Kandla Land 14.454295 17.523096 72.63268
## 689 95865.60 Japan Kandla Air 13.926322 12.841901 73.95535
## 690 86273.75 Germany Chennai Land 18.907103 15.413830 76.27645
## 691 95311.09 China Chennai Sea 10.009395 12.736114 78.41345
## 692 91631.19 China Delhi Land 16.970796 14.188187 73.40728
## 693 67288.58 China Mumbai Land 8.371193 15.915362 78.94651
## 694 72508.51 Japan Chennai Land 6.929015 5.915673 74.22543
## 695 78771.15 Germany Mumbai Sea 8.748689 14.416660 81.64337
## 696 79599.03 USA Chennai Air 8.740460 10.142629 74.98699
## 697 71660.20 UAE Kandla Sea 19.347349 16.376996 74.77226
## 698 90432.49 USA Kandla Land 6.874996 11.537315 71.75875
## 699 89659.82 UK Kandla Sea 12.106206 8.000203 72.98231
## 700 91864.14 UK Delhi Sea 5.095003 5.065499 71.86764
## 701 91100.10 USA Kolkata Land 11.955763 14.428376 83.50003
## 702 83292.92 Germany Mumbai Air 10.666575 10.947832 74.00722
## 703 78414.68 Japan Delhi Land 14.038902 15.879255 73.76738
## 704 74988.42 USA Kandla Land 18.707538 5.812251 84.01231
## 705 69653.81 China Kandla Sea 8.761004 12.633334 82.92628
## 706 72729.17 USA Kandla Air 11.276508 13.493145 72.40767
## 707 98047.27 UK Delhi Sea 13.361720 16.474861 73.98574
## 708 72084.41 Germany Kolkata Sea 9.454281 12.349116 78.80085
## 709 94967.55 UK Chennai Land 18.798948 17.326331 80.76006
## 710 63851.72 UK Kandla Air 14.492769 6.305784 82.65794
## 711 62854.76 UAE Mumbai Land 6.311950 17.639371 82.97632
## 712 87084.19 USA Kandla Land 9.478715 17.796422 76.40671
## 713 90538.12 China Kandla Sea 15.291942 8.503945 80.88199
## 714 73335.79 UK Kandla Land 15.118935 9.427551 77.82466
## 715 76246.93 USA Kolkata Land 18.312587 7.430374 72.86169
## 716 70748.60 USA Mumbai Air 6.304493 10.333182 81.30729
## 717 96422.83 Japan Kolkata Land 19.786034 13.133442 72.56092
## 718 95381.95 UAE Kolkata Sea 6.068624 15.590771 79.58772
## 719 71609.65 UAE Chennai Land 17.422703 6.817937 74.94863
## 720 82977.69 Germany Kandla Sea 10.415819 12.326774 80.54187
## 721 67706.64 Japan Chennai Sea 13.806052 17.539882 84.30248
## 722 94084.49 Japan Kolkata Sea 9.949221 13.722177 81.09731
## 723 90679.24 Japan Kolkata Land 19.071701 9.183149 74.57313
## 724 64820.32 Japan Kolkata Air 11.529026 17.039190 79.74600
## 725 68472.88 Japan Mumbai Air 6.330996 8.177011 74.89403
## 726 92887.26 UAE Mumbai Land 7.409474 16.096752 76.41204
## 727 88687.62 USA Kandla Land 10.521502 16.032650 83.91134
## 728 85469.09 Japan Delhi Air 17.162476 9.654858 80.29942
## 729 71365.50 UK Mumbai Air 14.012642 9.108343 83.61102
## 730 71645.20 Japan Kandla Land 6.829951 6.182533 74.41198
## 731 93566.51 Germany Kandla Air 9.469566 10.412845 79.22675
## 732 97665.50 Germany Mumbai Land 6.689571 5.095868 78.88478
## 733 73126.36 UAE Delhi Air 9.920404 8.218113 84.24300
## 734 92176.74 UK Mumbai Air 19.319137 17.751078 81.35901
## 735 91411.98 UAE Kolkata Sea 13.537866 5.835990 73.09497
## 736 76826.06 UAE Kolkata Land 12.406237 14.996162 73.95284
## 737 89123.43 UAE Mumbai Land 7.717665 16.230989 81.30001
## 738 89220.21 USA Kolkata Land 9.152984 5.459312 76.86454
## 739 91588.28 Germany Mumbai Air 16.537203 16.278763 79.20774
## 740 67508.30 UK Chennai Sea 12.725867 9.192550 81.52291
## 741 80635.96 China Chennai Sea 19.869417 8.281017 75.04412
## 742 65479.91 USA Delhi Land 19.912562 5.069935 77.46793
## 743 65153.24 UAE Chennai Sea 11.913483 17.929917 82.52084
## 744 67971.15 Japan Mumbai Sea 7.099180 10.105550 76.89972
## 745 63788.35 Germany Delhi Land 8.616462 14.565032 83.85626
## 746 95492.70 UAE Delhi Land 19.747155 13.679342 76.99584
## 747 75587.34 Japan Delhi Sea 17.966113 6.660305 76.41400
## 748 98466.82 UAE Delhi Land 19.947013 7.143668 76.52711
## 749 82452.37 USA Kolkata Land 10.370888 6.307676 82.49825
## 750 89730.68 UK Chennai Sea 19.266682 9.699210 82.63706
## 751 91721.38 USA Kandla Air 12.508283 16.664912 84.46890
## 752 89888.23 USA Kandla Sea 9.379352 11.862401 79.28948
## 753 79196.69 UAE Kolkata Air 6.891797 11.792202 79.05947
## 754 64851.98 UK Kandla Sea 8.980834 12.390932 80.68081
## 755 86439.24 Germany Mumbai Land 7.921586 13.794077 70.25510
## 756 91465.26 Japan Delhi Air 13.332935 15.762118 83.76515
## 757 95836.37 Germany Kandla Sea 9.074466 16.371773 83.49077
## 758 92427.90 UAE Mumbai Land 8.618947 11.028316 79.95610
## 759 70018.20 UAE Chennai Land 8.609812 14.282272 82.86498
## 760 84277.21 UAE Delhi Sea 8.132465 15.224453 83.30140
## 761 90389.79 UK Mumbai Land 19.448937 5.291399 71.84376
## 762 70548.39 USA Chennai Land 10.451619 6.997860 82.77316
## 763 73135.85 China Chennai Air 10.610415 15.246622 83.70865
## 764 91661.58 USA Chennai Sea 17.477346 6.085217 77.66591
## 765 90472.52 UAE Mumbai Land 10.072824 7.543639 79.74653
## 766 94494.79 China Chennai Land 10.206572 8.236826 83.07845
## 767 78288.25 China Kolkata Sea 13.292479 7.677800 84.77871
## 768 62628.54 Japan Delhi Sea 13.316955 15.191506 84.90139
## 769 81917.49 USA Kolkata Air 6.766048 17.335145 76.68132
## 770 75846.18 UK Chennai Sea 11.126654 7.005420 71.25451
## 771 72678.77 UAE Delhi Air 13.126227 13.615653 71.20839
## 772 86555.20 Germany Delhi Land 11.144853 16.558727 79.47014
## 773 65905.17 UAE Kandla Air 16.423484 8.161574 77.24028
## 774 73888.54 Germany Kolkata Sea 17.381441 12.845650 73.73082
## 775 94271.14 UK Kolkata Sea 18.972033 16.091827 73.06520
## 776 90724.06 USA Delhi Land 11.615661 12.940982 79.33930
## 777 64007.71 UAE Kolkata Air 11.402280 8.142271 82.70260
## 778 70799.77 UK Kolkata Land 19.675538 12.284435 77.04744
## 779 68024.08 China Kolkata Air 9.112479 15.130533 81.75756
## 780 91371.43 USA Chennai Air 15.572814 6.999712 83.53969
## 781 73010.94 China Delhi Sea 14.964792 11.602372 74.80824
## 782 77365.01 UK Chennai Land 13.927796 11.798233 72.11025
## 783 99433.16 USA Kandla Air 17.496151 13.611212 71.45905
## 784 80272.21 UAE Mumbai Land 15.111014 9.043578 77.79127
## 785 80344.31 China Chennai Sea 19.870026 11.575406 78.98181
## 786 89444.13 UK Kolkata Air 12.708179 7.901696 78.59605
## 787 69611.02 UAE Chennai Sea 12.298924 17.944887 75.02642
## 788 91735.55 Japan Kolkata Air 9.075429 9.490626 75.60278
## 789 70770.94 USA Delhi Air 8.461959 6.666100 73.99036
## 790 69895.09 Germany Kandla Land 16.742159 16.274225 74.14201
## 791 91259.29 China Mumbai Air 13.880350 7.880013 70.36570
## 792 93094.40 Germany Delhi Air 10.315705 14.935684 83.44050
## 793 72475.85 UK Delhi Air 11.783351 15.818457 80.83574
## 794 86296.77 UAE Kolkata Land 5.614575 15.263739 75.09381
## 795 70532.37 Germany Kolkata Land 15.611261 13.412312 72.01036
## 796 66485.24 Germany Chennai Sea 17.611020 12.991013 82.72099
## 797 75590.85 USA Kolkata Sea 10.414168 7.507487 80.14985
## 798 90293.73 USA Kolkata Land 9.749025 13.592563 70.73429
## 799 64271.35 UAE Kandla Sea 18.700019 15.267288 83.59286
## 800 77965.18 Japan Mumbai Land 9.091388 10.105835 83.89432
## 801 96767.64 China Delhi Air 11.843042 13.199000 82.75006
## 802 73729.86 China Delhi Land 17.433652 12.104126 74.42373
## 803 83490.02 Germany Kandla Air 14.467467 5.036681 77.53012
## 804 88826.79 Germany Mumbai Air 7.993222 10.370510 81.61318
## 805 80788.69 UAE Kandla Sea 16.664805 15.263605 81.43094
## 806 98661.70 Japan Kolkata Sea 6.394556 10.522463 77.60639
## 807 331120.37 USA Mumbai Sea 5.364560 8.721179 79.95654
## 808 81452.80 Germany Kolkata Sea 12.913177 8.641932 83.63777
## 809 62589.54 Germany Delhi Air 7.798067 9.451667 81.76012
## 810 75367.39 UAE Delhi Air 14.097790 14.131693 79.64174
## 811 72017.50 China Mumbai Air 15.181881 13.441521 72.31698
## 812 74914.79 UAE Delhi Land 18.094656 17.723895 82.47172
## 813 96694.07 China Kolkata Sea 17.052147 6.970994 79.43122
## 814 95973.60 Japan Kandla Land 5.834184 6.715754 82.72342
## 815 97882.86 China Delhi Air 8.013042 7.954879 71.04990
## 816 88295.25 USA Kolkata Land 14.385972 12.945961 70.91770
## 817 88317.48 Japan Kandla Air 9.248898 12.956313 72.28655
## 818 86212.68 UAE Mumbai Land 8.290819 6.602256 72.21159
## 819 86414.57 UAE Mumbai Land 7.999757 11.626515 83.03960
## 820 69488.75 UAE Mumbai Land 15.769123 6.947785 75.69071
## 821 75640.69 Germany Delhi Land 19.077025 16.444453 74.75256
## 822 74829.25 Germany Kolkata Land 11.948299 12.161460 77.38790
## 823 90968.05 USA Mumbai Air 7.438338 12.100332 72.31462
## 824 77200.57 UAE Kolkata Sea 11.049100 15.585021 78.87157
## 825 73921.30 Japan Kolkata Sea 16.902714 12.608899 72.77787
## 826 92747.73 UK Chennai Land 15.444580 11.599794 82.68901
## 827 83953.99 Germany Kandla Land 7.222914 12.439757 75.22638
## 828 68276.30 USA Chennai Sea 12.871082 17.107440 76.26125
## 829 74764.44 Germany Chennai Land 14.694990 7.697635 70.87650
## 830 70684.23 UK Delhi Sea 15.436621 9.909564 76.85477
## 831 78643.01 Japan Delhi Sea 11.409649 15.262737 84.87252
## 832 76724.67 UK Kandla Air 18.582470 6.776471 77.96207
## 833 69960.58 UAE Kolkata Air 7.280782 13.306536 79.05290
## 834 75386.50 Japan Kolkata Sea 9.114512 7.973138 72.04134
## 835 99901.77 USA Delhi Land 16.121076 8.535804 70.84917
## 836 76954.25 UK Delhi Land 6.312876 15.651132 77.14993
## 837 77914.48 USA Chennai Air 10.855270 13.476147 79.48828
## 838 93916.61 UAE Chennai Land 9.875403 6.946210 75.85431
## 839 86020.48 USA Kolkata Land 7.844096 6.757326 79.41388
## 840 64283.51 Germany Delhi Land 18.834445 6.105076 80.16688
## 841 68660.64 UK Chennai Air 17.678423 10.024563 77.22230
## 842 92645.40 Germany Mumbai Air 6.790493 5.764260 83.70592
## 843 70115.00 UAE Chennai Land 12.520354 13.186264 74.78748
## 844 84331.40 UAE Delhi Land 18.220544 9.965365 73.75428
## 845 97087.08 UAE Mumbai Land 10.864339 11.484561 81.64250
## 846 91478.76 UAE Delhi Land 17.816602 7.733427 80.85907
## 847 94912.79 USA Kolkata Land 10.485186 9.676441 72.44885
## 848 89208.22 Germany Delhi Sea 17.650933 9.715322 83.17597
## 849 76297.16 UAE Delhi Sea 18.942506 17.751796 84.82159
## 850 96071.10 USA Mumbai Sea 9.142637 16.091734 71.50063
## 851 73458.88 Japan Delhi Sea 7.855585 7.694600 75.94753
## 852 95925.34 USA Delhi Air 13.748320 11.912171 81.77726
## 853 69251.88 UK Chennai Air 7.497215 10.500464 79.82821
## 854 98964.44 China Chennai Land 14.377294 12.654776 76.40062
## 855 78418.20 Germany Chennai Sea 14.075352 5.883681 81.54090
## 856 83501.35 UAE Kandla Land 15.740463 13.706814 83.69765
## 857 71735.12 USA Chennai Air 12.676584 9.816003 78.17592
## 858 97452.56 Japan Mumbai Land 5.801528 13.251758 74.21170
## 859 90267.15 USA Kolkata Sea 13.703564 13.576433 81.57934
## 860 79620.18 USA Kolkata Land 6.611278 8.902416 76.79772
## 861 89064.39 China Chennai Sea 14.637613 12.303518 70.86916
## 862 90647.31 Germany Chennai Air 10.082308 16.232791 77.47949
## 863 81579.41 Japan Kandla Land 11.675973 17.954012 79.22219
## 864 96253.66 Japan Mumbai Air 8.120801 10.832611 81.50678
## 865 63414.89 China Kandla Land 11.915742 17.736791 80.20319
## 866 74983.59 USA Chennai Sea 15.754620 15.326490 75.92385
## 867 84004.64 UK Mumbai Air 9.722816 10.148613 80.44148
## 868 84471.66 UK Delhi Sea 17.771422 6.085324 74.33211
## 869 90062.63 China Kandla Sea 7.301488 8.256858 75.73868
## 870 92365.13 UK Kandla Land 6.607073 6.330015 77.50160
## 871 79827.78 UK Kandla Land 9.592917 11.841844 84.59273
## 872 91857.48 China Mumbai Land 10.828304 14.745154 78.48045
## 873 91940.68 Germany Chennai Air 6.441337 9.008214 77.89322
## 874 71539.99 Japan Delhi Land 12.015936 7.077567 80.92841
## 875 95105.02 Germany Kolkata Sea 9.043830 8.872117 72.77274
## 876 81534.56 USA Kandla Air 17.179720 7.299478 74.62876
## 877 67859.12 UAE Mumbai Land 17.868766 11.937550 84.04671
## 878 90238.40 UK Kandla Sea 19.811028 9.697966 78.48161
## 879 99028.62 China Mumbai Sea 17.887226 10.400409 83.30403
## 880 80437.65 China Mumbai Land 13.848400 16.404920 83.50243
## 881 87785.96 UK Delhi Sea 11.276468 13.314129 73.56808
## 882 84274.75 UK Kandla Land 7.599030 12.019682 80.72011
## 883 64827.56 UAE Delhi Land 9.268341 8.975295 82.23102
## 884 97622.74 Germany Delhi Air 5.225811 6.146198 82.95131
## 885 85345.97 USA Kandla Air 12.959003 6.236186 72.61341
## 886 89750.44 China Kandla Sea 14.351635 7.060679 75.15440
## 887 96772.86 China Mumbai Air 11.203306 15.247109 84.09672
## Shipment_Days Delivery_Status Trade_Agreement Date Value_INR
## 1 21 Delayed Non-FTA 24-03-2019 5209497
## 2 18 Delayed Non-FTA 07-08-2019 5467923
## 3 14 On Time Non-FTA 09-01-2022 5046712
## 4 10 On Time Non-FTA 16-11-2021 6639976
## 5 17 Delayed FTA 03-05-2020 7450595
## 6 30 Delayed Non-FTA 13-02-2024 5364118
## 7 14 Delayed FTA 02-06-2020 7352062
## 8 25 On Time Non-FTA 07-12-2022 6131783
## 9 9 Delayed Non-FTA 21-04-2024 6734334
## 10 11 Delayed Non-FTA 19-07-2019 7431622
## 11 26 On Time FTA 08-06-2023 5502150
## 12 7 Delayed Non-FTA 22-04-2018 6754290
## 13 25 Delayed FTA 06-04-2021 6196493
## 14 6 Delayed Non-FTA 01-02-2020 6235478
## 15 16 Delayed FTA 28-04-2020 5286869
## 16 19 On Time Non-FTA 26-08-2019 5140527
## 17 4 Delayed Non-FTA 03-07-2019 6572657
## 18 28 On Time Non-FTA 27-08-2022 7432948
## 19 13 On Time Non-FTA 15-11-2020 5248926
## 20 10 Delayed Non-FTA 02-12-2023 5872498
## 21 16 On Time Non-FTA 17-10-2022 5367410
## 22 28 On Time Non-FTA 13-03-2021 6789193
## 23 26 On Time Non-FTA 27-06-2023 5094738
## 24 24 On Time FTA 07-05-2024 7431460
## 25 3 Delayed Non-FTA 23-06-2024 5086466
## 26 18 On Time Non-FTA 16-05-2024 5009040
## 27 6 On Time Non-FTA 09-02-2022 6009190
## 28 17 Delayed FTA 25-06-2022 5267957
## 29 11 Delayed FTA 27-06-2019 7613959
## 30 26 On Time FTA 22-05-2021 5348212
## 31 21 On Time Non-FTA 30-04-2018 6130481
## 32 19 Delayed Non-FTA 18-07-2019 8302767
## 33 7 Delayed FTA 10-10-2024 7528670
## 34 22 Delayed FTA 14-09-2021 6756820
## 35 28 On Time Non-FTA 30-06-2020 7882698
## 36 7 On Time Non-FTA 01-04-2019 6868979
## 37 25 Delayed FTA 18-09-2019 6928525
## 38 4 On Time FTA 12-06-2024 7531622
## 39 1 On Time FTA 13-07-2019 7105022
## 40 4 On Time FTA 23-11-2024 6877698
## 41 29 Delayed FTA 11-12-2023 6452110
## 42 3 On Time FTA 07-07-2019 5904866
## 43 25 On Time FTA 11-12-2018 5090714
## 44 7 Delayed Non-FTA 28-07-2018 6797099
## 45 17 On Time Non-FTA 24-09-2018 5011902
## 46 6 Delayed FTA 15-06-2023 5356320
## 47 5 Delayed Non-FTA 01-04-2020 6977873
## 48 14 On Time FTA 04-04-2022 6198179
## 49 12 Delayed Non-FTA 02-07-2018 7465737
## 50 24 On Time Non-FTA 01-04-2019 7670872
## 51 1 Delayed Non-FTA 03-12-2021 5896746
## 52 21 On Time FTA 27-03-2023 5352109
## 53 10 On Time FTA 06-03-2018 5866382
## 54 4 Delayed Non-FTA 22-08-2024 5338704
## 55 1 Delayed FTA 07-05-2021 5638420
## 56 10 On Time FTA 04-05-2021 6548002
## 57 23 Delayed Non-FTA 07-03-2024 5945364
## 58 13 Delayed FTA 25-03-2022 5307816
## 59 20 Delayed FTA 16-06-2018 5508723
## 60 13 Delayed Non-FTA 14-09-2022 6949687
## 61 22 On Time FTA 13-05-2021 7976310
## 62 22 On Time FTA 27-04-2019 5042348
## 63 8 Delayed FTA 27-02-2021 6749021
## 64 17 Delayed Non-FTA 30-06-2024 5678954
## 65 27 On Time FTA 22-05-2018 6741613
## 66 9 On Time Non-FTA 02-04-2024 5117035
## 67 23 On Time Non-FTA 27-03-2019 6814248
## 68 3 Delayed FTA 25-01-2020 7089790
## 69 19 Delayed Non-FTA 12-10-2024 6830982
## 70 1 Delayed FTA 03-09-2019 6975930
## 71 9 On Time Non-FTA 07-03-2023 6491020
## 72 25 On Time FTA 10-06-2024 5301314
## 73 24 Delayed FTA 19-10-2022 6293782
## 74 16 On Time Non-FTA 08-12-2022 6281717
## 75 21 Delayed Non-FTA 09-07-2019 6084498
## 76 13 On Time FTA 25-01-2022 5016665
## 77 29 On Time Non-FTA 02-10-2022 5900015
## 78 26 On Time Non-FTA 03-12-2022 6421442
## 79 1 Delayed Non-FTA 19-12-2021 5207408
## 80 9 On Time Non-FTA 21-07-2022 7047662
## 81 26 On Time Non-FTA 14-05-2018 6674431
## 82 21 Delayed FTA 05-10-2024 5353430
## 83 24 On Time FTA 13-03-2022 6377618
## 84 1 Delayed FTA 11-10-2022 7254971
## 85 14 On Time Non-FTA 13-01-2021 5948678
## 86 1 Delayed Non-FTA 06-01-2018 5623460
## 87 22 On Time FTA 20-08-2019 5922806
## 88 23 Delayed FTA 16-11-2024 5174794
## 89 30 On Time FTA 25-11-2019 7508674
## 90 6 On Time Non-FTA 08-05-2023 6953137
## 91 19 Delayed FTA 17-05-2024 6970675
## 92 6 Delayed Non-FTA 08-01-2020 5441620
## 93 26 On Time FTA 27-04-2023 6788625
## 94 9 Delayed Non-FTA 28-11-2024 5787426
## 95 21 On Time FTA 16-12-2023 7790980
## 96 24 Delayed FTA 20-09-2023 6838661
## 97 3 Delayed Non-FTA 06-08-2022 6291938
## 98 10 Delayed Non-FTA 27-05-2018 5021846
## 99 25 On Time Non-FTA 27-04-2018 5157253
## 100 18 Delayed FTA 14-11-2020 5721514
## 101 26 On Time FTA 22-07-2023 6479421
## 102 18 Delayed Non-FTA 02-02-2022 5209133
## 103 19 On Time FTA 04-09-2020 5503613
## 104 22 On Time Non-FTA 26-07-2020 5528162
## 105 15 On Time FTA 24-11-2022 7073011
## 106 27 Delayed Non-FTA 12-01-2023 6776788
## 107 4 Delayed FTA 05-09-2020 7512832
## 108 26 Delayed Non-FTA 03-05-2022 6512912
## 109 25 On Time Non-FTA 13-12-2018 5751404
## 110 3 On Time Non-FTA 22-04-2022 5510699
## 111 22 Delayed FTA 30-06-2018 5199563
## 112 5 Delayed FTA 15-10-2024 8033195
## 113 12 On Time FTA 03-12-2022 5151098
## 114 5 Delayed Non-FTA 30-09-2023 6041133
## 115 19 Delayed FTA 10-07-2020 5562740
## 116 6 Delayed FTA 04-02-2019 6961691
## 117 10 Delayed FTA 01-06-2020 6801805
## 118 5 Delayed Non-FTA 29-10-2022 5433927
## 119 27 Delayed FTA 02-01-2022 6203906
## 120 13 On Time FTA 16-09-2019 7187959
## 121 25 On Time Non-FTA 05-06-2023 6503465
## 122 30 Delayed Non-FTA 26-06-2020 5709600
## 123 3 Delayed Non-FTA 06-07-2018 6721440
## 124 29 Delayed FTA 29-12-2022 6096675
## 125 20 Delayed Non-FTA 07-11-2019 6987878
## 126 28 Delayed FTA 15-08-2021 6721414
## 127 23 On Time FTA 22-11-2023 5460116
## 128 20 On Time Non-FTA 06-05-2019 6285518
## 129 14 Delayed Non-FTA 25-09-2021 6361539
## 130 2 Delayed FTA 23-10-2023 5188570
## 131 3 Delayed Non-FTA 14-10-2019 5348047
## 132 7 Delayed FTA 19-07-2020 6328989
## 133 15 Delayed FTA 20-05-2024 6794646
## 134 14 Delayed FTA 16-08-2018 5021047
## 135 21 On Time Non-FTA 20-01-2019 8102374
## 136 20 Delayed FTA 25-03-2020 5005827
## 137 5 On Time Non-FTA 06-04-2023 6112803
## 138 8 Delayed Non-FTA 24-11-2019 5167792
## 139 14 Delayed FTA 05-05-2024 6353301
## 140 27 Delayed FTA 12-05-2021 7024899
## 141 17 On Time FTA 23-05-2024 5363809
## 142 20 On Time Non-FTA 12-01-2019 6941500
## 143 14 On Time FTA 07-01-2019 7113059
## 144 16 On Time FTA 15-01-2023 6315060
## 145 14 Delayed Non-FTA 09-06-2021 5463583
## 146 24 Delayed Non-FTA 25-10-2024 5383328
## 147 15 On Time Non-FTA 13-04-2019 5930263
## 148 14 Delayed FTA 19-12-2018 5964024
## 149 17 Delayed FTA 06-06-2018 5650815
## 150 27 On Time Non-FTA 16-06-2024 6505555
## 151 10 On Time FTA 18-01-2022 7549657
## 152 24 On Time FTA 04-09-2020 6978187
## 153 24 On Time FTA 02-12-2019 5100021
## 154 27 Delayed FTA 03-11-2021 7066340
## 155 19 On Time FTA 01-03-2020 6924130
## 156 4 On Time FTA 03-04-2023 5402645
## 157 28 Delayed FTA 01-02-2020 5455279
## 158 2 On Time FTA 12-12-2022 5517549
## 159 25 On Time Non-FTA 28-12-2024 5515047
## 160 29 Delayed Non-FTA 03-02-2022 6144784
## 161 27 Delayed Non-FTA 23-04-2022 5724222
## 162 11 Delayed Non-FTA 03-05-2019 5441981
## 163 4 On Time Non-FTA 13-07-2018 6758196
## 164 14 On Time FTA 26-07-2023 6729471
## 165 3 On Time Non-FTA 20-12-2020 6669833
## 166 26 Delayed Non-FTA 19-07-2023 7458863
## 167 15 On Time FTA 12-10-2021 5761450
## 168 2 On Time Non-FTA 26-05-2018 5860518
## 169 23 On Time Non-FTA 18-08-2020 6672740
## 170 3 On Time FTA 24-04-2021 6806277
## 171 10 On Time Non-FTA 24-09-2019 6628311
## 172 23 On Time FTA 03-08-2022 7785332
## 173 4 Delayed Non-FTA 05-03-2022 5324273
## 174 26 On Time FTA 11-09-2023 5364006
## 175 14 On Time FTA 13-04-2021 7988732
## 176 24 On Time FTA 01-11-2018 5325566
## 177 26 On Time FTA 15-07-2019 5412392
## 178 10 Delayed FTA 21-08-2018 7588657
## 179 9 On Time FTA 18-03-2022 5121545
## 180 14 Delayed FTA 09-01-2024 7033988
## 181 20 On Time Non-FTA 09-02-2024 5346972
## 182 21 On Time FTA 22-07-2019 6460874
## 183 26 Delayed FTA 20-10-2019 6002228
## 184 26 On Time FTA 29-11-2018 5282878
## 185 24 Delayed FTA 29-12-2020 5804609
## 186 10 On Time Non-FTA 02-04-2018 7865145
## 187 13 Delayed FTA 25-08-2019 7679511
## 188 26 Delayed Non-FTA 05-05-2024 6641712
## 189 25 Delayed FTA 07-02-2021 7976411
## 190 20 Delayed Non-FTA 21-10-2023 6671564
## 191 21 Delayed Non-FTA 14-02-2023 8144056
## 192 30 On Time Non-FTA 10-08-2024 7215883
## 193 3 Delayed FTA 14-02-2020 7339643
## 194 16 Delayed Non-FTA 05-05-2018 5560817
## 195 24 Delayed FTA 21-05-2019 5971465
## 196 9 On Time FTA 28-08-2022 6878206
## 197 26 On Time Non-FTA 17-01-2018 6705999
## 198 19 On Time FTA 13-11-2024 5256502
## 199 11 Delayed Non-FTA 30-05-2023 6731639
## 200 5 On Time FTA 16-06-2023 5494465
## 201 9 On Time Non-FTA 28-04-2020 7400528
## 202 24 On Time FTA 13-07-2020 7736035
## 203 3 On Time Non-FTA 26-07-2019 7719293
## 204 3 Delayed FTA 15-07-2022 6120742
## 205 13 On Time Non-FTA 25-07-2019 5073724
## 206 29 Delayed Non-FTA 03-08-2024 5191454
## 207 2 On Time Non-FTA 30-07-2018 5218980
## 208 5 On Time Non-FTA 09-04-2019 5974985
## 209 7 Delayed FTA 04-01-2023 6156838
## 210 21 On Time Non-FTA 15-07-2021 7198666
## 211 18 Delayed Non-FTA 09-11-2019 5083044
## 212 10 Delayed Non-FTA 15-08-2022 6105548
## 213 13 Delayed Non-FTA 24-07-2023 5468165
## 214 24 Delayed FTA 13-04-2018 5702815
## 215 25 On Time Non-FTA 11-03-2020 6895990
## 216 27 Delayed Non-FTA 19-09-2022 7665694
## 217 4 Delayed Non-FTA 07-03-2024 7126769
## 218 12 On Time Non-FTA 11-01-2020 5467979
## 219 28 On Time Non-FTA 31-08-2022 6136375
## 220 3 Delayed Non-FTA 21-01-2024 5757843
## 221 19 On Time Non-FTA 11-04-2020 5877249
## 222 21 Delayed FTA 07-09-2022 7647720
## 223 11 On Time Non-FTA 13-10-2021 5866208
## 224 27 Delayed FTA 05-04-2021 7366949
## 225 5 Delayed FTA 09-08-2021 5292964
## 226 15 On Time Non-FTA 23-10-2018 7861517
## 227 29 Delayed FTA 07-01-2022 5059475
## 228 24 On Time Non-FTA 02-01-2019 6082027
## 229 9 On Time Non-FTA 04-06-2023 5613004
## 230 27 On Time Non-FTA 09-06-2021 6317026
## 231 3 Delayed Non-FTA 14-12-2021 25781161
## 232 16 On Time FTA 17-06-2022 5845184
## 233 1 Delayed FTA 04-10-2019 6033564
## 234 28 Delayed FTA 23-01-2024 6372264
## 235 29 Delayed FTA 13-04-2018 5970167
## 236 22 On Time FTA 11-04-2018 5598826
## 237 26 Delayed FTA 14-10-2022 6372500
## 238 21 On Time FTA 06-04-2021 6277622
## 239 22 Delayed FTA 09-02-2018 5179954
## 240 4 On Time Non-FTA 14-07-2021 5025232
## 241 29 Delayed FTA 02-04-2019 5435440
## 242 28 On Time Non-FTA 29-08-2024 5645491
## 243 27 On Time Non-FTA 11-02-2022 7554278
## 244 20 On Time FTA 29-02-2020 6217024
## 245 4 Delayed Non-FTA 14-11-2020 7408709
## 246 7 Delayed Non-FTA 04-02-2022 6084964
## 247 24 On Time FTA 04-07-2018 6571340
## 248 28 On Time Non-FTA 19-01-2018 6720885
## 249 10 On Time FTA 04-07-2022 7430025
## 250 17 On Time Non-FTA 13-06-2018 6127088
## 251 18 On Time Non-FTA 08-06-2022 6967064
## 252 9 Delayed FTA 26-10-2019 6506419
## 253 5 On Time Non-FTA 29-12-2020 6864827
## 254 2 On Time FTA 14-02-2022 5866160
## 255 9 On Time Non-FTA 30-10-2019 7226783
## 256 16 On Time FTA 15-02-2022 6441862
## 257 10 On Time FTA 10-12-2024 5119403
## 258 11 On Time FTA 04-10-2019 8056302
## 259 11 Delayed FTA 11-10-2019 7759249
## 260 4 Delayed Non-FTA 17-01-2022 5034587
## 261 15 On Time Non-FTA 06-06-2024 7547733
## 262 4 Delayed FTA 26-12-2023 5222746
## 263 2 On Time FTA 30-01-2023 5468884
## 264 26 On Time Non-FTA 17-07-2019 7817339
## 265 19 On Time Non-FTA 12-09-2021 6194030
## 266 10 On Time Non-FTA 08-10-2019 5054021
## 267 2 Delayed FTA 22-03-2018 5052658
## 268 7 Delayed FTA 21-12-2018 6239192
## 269 11 On Time Non-FTA 10-07-2021 6250780
## 270 23 Delayed FTA 28-08-2020 7261236
## 271 11 On Time FTA 12-07-2023 8195898
## 272 9 On Time Non-FTA 23-06-2022 6521985
## 273 27 Delayed FTA 16-12-2024 6625953
## 274 5 On Time FTA 05-07-2024 6077679
## 275 20 Delayed FTA 13-06-2024 6502377
## 276 29 On Time Non-FTA 22-09-2019 7825730
## 277 9 On Time Non-FTA 21-10-2018 7055994
## 278 8 Delayed FTA 18-09-2021 6355331
## 279 15 On Time Non-FTA 06-11-2021 7253334
## 280 30 On Time Non-FTA 05-03-2024 6206606
## 281 24 On Time Non-FTA 25-04-2020 6650051
## 282 7 Delayed Non-FTA 01-07-2021 5608751
## 283 23 Delayed Non-FTA 13-02-2020 5723910
## 284 13 On Time FTA 19-06-2018 6955849
## 285 29 On Time Non-FTA 09-07-2021 7188115
## 286 7 Delayed Non-FTA 22-08-2019 5931984
## 287 23 On Time Non-FTA 29-11-2023 7336587
## 288 16 On Time Non-FTA 23-03-2019 7971793
## 289 3 On Time FTA 20-05-2020 6207917
## 290 18 On Time FTA 24-09-2019 7270426
## 291 5 On Time FTA 06-02-2019 6538532
## 292 7 Delayed FTA 23-04-2018 5277620
## 293 29 Delayed Non-FTA 14-02-2022 6949820
## 294 8 On Time FTA 13-03-2020 6059136
## 295 26 Delayed Non-FTA 08-08-2019 5585382
## 296 14 On Time Non-FTA 08-06-2019 5574813
## 297 23 Delayed Non-FTA 07-05-2022 6413941
## 298 11 On Time Non-FTA 26-08-2021 6646862
## 299 29 Delayed FTA 15-06-2018 6054127
## 300 18 On Time Non-FTA 07-07-2023 6752575
## 301 19 On Time Non-FTA 30-03-2020 7019603
## 302 23 Delayed FTA 25-06-2019 5583663
## 303 11 Delayed Non-FTA 17-05-2019 5658761
## 304 10 On Time FTA 19-05-2020 5604804
## 305 3 Delayed FTA 23-01-2019 5975620
## 306 2 On Time FTA 02-02-2023 7002210
## 307 24 On Time Non-FTA 20-07-2022 8095160
## 308 7 Delayed Non-FTA 23-05-2019 7938696
## 309 30 On Time Non-FTA 19-07-2024 6542535
## 310 9 On Time FTA 26-12-2018 5532982
## 311 12 Delayed FTA 27-02-2021 6240826
## 312 14 On Time Non-FTA 22-05-2020 7374003
## 313 29 On Time FTA 01-03-2022 5209812
## 314 8 Delayed Non-FTA 02-07-2022 6163974
## 315 15 Delayed FTA 11-10-2020 7138524
## 316 29 Delayed FTA 15-08-2024 6991046
## 317 14 On Time FTA 17-12-2018 6378000
## 318 18 Delayed Non-FTA 10-08-2022 6363913
## 319 10 Delayed Non-FTA 01-02-2020 6823040
## 320 28 On Time Non-FTA 14-12-2019 5307410
## 321 25 On Time Non-FTA 27-05-2022 5220962
## 322 19 Delayed Non-FTA 04-01-2018 8322759
## 323 27 On Time Non-FTA 18-06-2024 7359967
## 324 21 Delayed FTA 08-03-2020 6464231
## 325 29 On Time Non-FTA 12-06-2020 6247759
## 326 17 On Time Non-FTA 28-08-2023 7234183
## 327 14 On Time FTA 03-10-2021 6052965
## 328 24 Delayed FTA 12-07-2018 5263699
## 329 30 Delayed Non-FTA 29-09-2023 6108933
## 330 8 On Time FTA 04-05-2020 5242999
## 331 25 On Time Non-FTA 10-08-2023 5615855
## 332 3 On Time FTA 05-09-2022 7427462
## 333 15 On Time FTA 26-08-2020 6942417
## 334 22 Delayed Non-FTA 07-04-2024 5658985
## 335 29 On Time Non-FTA 08-01-2018 6008458
## 336 28 Delayed Non-FTA 24-11-2019 6431538
## 337 1 Delayed FTA 02-12-2019 5917311
## 338 14 On Time FTA 16-10-2021 6505726
## 339 27 Delayed Non-FTA 04-11-2018 7702865
## 340 26 Delayed Non-FTA 03-06-2022 6588321
## 341 17 On Time Non-FTA 10-02-2022 5541099
## 342 8 On Time Non-FTA 12-01-2020 5216890
## 343 1 On Time Non-FTA 07-03-2023 7017904
## 344 20 Delayed Non-FTA 20-11-2022 6786658
## 345 8 On Time FTA 02-05-2018 5242907
## 346 11 On Time FTA 23-03-2023 5855822
## 347 4 On Time Non-FTA 10-02-2022 5611072
## 348 10 Delayed FTA 13-08-2021 7855040
## 349 17 Delayed FTA 07-02-2021 7813287
## 350 14 On Time Non-FTA 15-02-2023 5124541
## 351 14 On Time Non-FTA 29-03-2021 6817587
## 352 12 On Time Non-FTA 17-01-2023 6213863
## 353 26 Delayed FTA 23-02-2018 5282451
## 354 25 Delayed Non-FTA 02-01-2019 5703323
## 355 1 Delayed Non-FTA 10-05-2024 5366310
## 356 19 Delayed Non-FTA 18-06-2023 5332644
## 357 9 Delayed Non-FTA 10-03-2023 5543329
## 358 10 Delayed Non-FTA 04-11-2019 7042144
## 359 26 Delayed Non-FTA 29-10-2024 5449302
## 360 8 Delayed FTA 04-10-2019 5487851
## 361 25 Delayed Non-FTA 26-01-2020 5303579
## 362 8 On Time FTA 14-04-2024 7446841
## 363 25 On Time Non-FTA 17-11-2021 6048156
## 364 4 Delayed FTA 30-04-2018 5829326
## 365 9 On Time FTA 25-02-2024 5642805
## 366 15 On Time Non-FTA 12-11-2020 5555157
## 367 18 On Time Non-FTA 23-11-2020 6728012
## 368 5 On Time Non-FTA 18-04-2024 6747137
## 369 1 Delayed Non-FTA 20-02-2020 5634521
## 370 27 Delayed Non-FTA 30-07-2020 5841218
## 371 3 On Time FTA 20-03-2022 6390452
## 372 7 On Time FTA 06-12-2023 7683162
## 373 27 Delayed Non-FTA 23-12-2023 5522787
## 374 1 On Time FTA 28-06-2018 5231755
## 375 21 Delayed FTA 30-03-2018 8056341
## 376 21 Delayed FTA 01-09-2019 5789561
## 377 9 On Time FTA 15-10-2024 5514740
## 378 24 On Time Non-FTA 19-09-2019 7966935
## 379 15 Delayed FTA 07-12-2022 5796583
## 380 14 On Time Non-FTA 29-02-2024 5038729
## 381 22 Delayed FTA 03-12-2018 6289071
## 382 17 On Time Non-FTA 26-05-2023 6414178
## 383 21 On Time FTA 02-04-2020 6615272
## 384 10 Delayed FTA 31-01-2021 5693990
## 385 20 On Time FTA 01-10-2021 5441005
## 386 20 Delayed Non-FTA 02-04-2020 6191827
## 387 6 Delayed Non-FTA 07-08-2023 6671219
## 388 3 On Time Non-FTA 04-01-2022 6754246
## 389 15 Delayed Non-FTA 07-11-2018 5527220
## 390 10 On Time FTA 16-06-2018 5415554
## 391 5 On Time Non-FTA 03-10-2021 5928047
## 392 25 Delayed Non-FTA 23-02-2020 5174770
## 393 7 Delayed Non-FTA 07-08-2024 5060721
## 394 14 Delayed Non-FTA 27-07-2021 5933946
## 395 8 On Time FTA 28-07-2022 5313393
## 396 29 Delayed Non-FTA 28-11-2019 6423143
## 397 3 On Time Non-FTA 29-08-2021 6143388
## 398 11 Delayed Non-FTA 02-12-2021 5206320
## 399 8 On Time Non-FTA 13-03-2018 7228985
## 400 29 Delayed Non-FTA 13-11-2023 6114415
## 401 8 On Time Non-FTA 13-05-2023 5108117
## 402 28 Delayed Non-FTA 02-06-2021 5234779
## 403 11 On Time Non-FTA 24-02-2019 6922293
## 404 2 On Time Non-FTA 29-01-2018 5139852
## 405 30 On Time FTA 21-03-2019 5603937
## 406 30 Delayed Non-FTA 18-06-2024 5744884
## 407 15 On Time Non-FTA 12-10-2023 5108423
## 408 23 Delayed FTA 11-01-2023 7317678
## 409 6 Delayed Non-FTA 18-08-2021 6684857
## 410 27 On Time FTA 03-12-2021 6043871
## 411 23 On Time Non-FTA 20-10-2023 5961718
## 412 7 On Time Non-FTA 19-12-2023 6198758
## 413 7 On Time Non-FTA 12-05-2023 6508854
## 414 24 On Time Non-FTA 03-05-2022 6781086
## 415 17 On Time FTA 01-10-2021 6142691
## 416 28 On Time FTA 25-04-2024 6149973
## 417 18 Delayed FTA 21-06-2019 7305768
## 418 17 Delayed Non-FTA 12-09-2022 7123514
## 419 18 Delayed FTA 05-12-2020 5569963
## 420 8 Delayed FTA 22-11-2020 7103268
## 421 27 Delayed Non-FTA 30-04-2019 6963105
## 422 24 Delayed Non-FTA 29-03-2019 5791652
## 423 24 On Time Non-FTA 28-06-2024 8033242
## 424 5 On Time FTA 05-03-2022 6465426
## 425 24 On Time Non-FTA 28-01-2023 5467110
## 426 2 Delayed FTA 17-12-2024 6198460
## 427 25 On Time FTA 15-03-2019 6051125
## 428 28 Delayed FTA 11-07-2020 5412613
## 429 29 Delayed FTA 18-12-2024 5619610
## 430 8 On Time FTA 05-12-2019 7379154
## 431 10 On Time FTA 31-08-2021 6986338
## 432 6 Delayed FTA 04-07-2021 5569758
## 433 8 Delayed Non-FTA 24-08-2019 5743140
## 434 18 On Time FTA 07-11-2019 6975610
## 435 23 On Time Non-FTA 22-10-2022 7185648
## 436 5 Delayed Non-FTA 10-03-2019 6597795
## 437 6 Delayed Non-FTA 03-05-2022 6239395
## 438 23 Delayed Non-FTA 09-09-2021 5441272
## 439 14 Delayed Non-FTA 09-12-2019 6987541
## 440 7 Delayed FTA 04-02-2020 7487023
## 441 11 On Time FTA 06-05-2019 6072571
## 442 1 Delayed Non-FTA 30-11-2019 5212946
## 443 5 Delayed FTA 27-05-2021 7318096
## 444 25 On Time FTA 23-02-2018 7303792
## 445 18 Delayed Non-FTA 02-11-2019 5408013
## 446 20 On Time FTA 20-04-2024 6292716
## 447 6 Delayed FTA 24-03-2019 7075309
## 448 5 On Time FTA 19-12-2022 6835498
## 449 17 Delayed FTA 10-06-2022 6042293
## 450 30 Delayed FTA 06-09-2018 5644124
## 451 11 On Time Non-FTA 14-09-2021 5675873
## 452 20 Delayed FTA 03-12-2019 5712886
## 453 26 On Time FTA 23-02-2023 6569219
## 454 20 On Time FTA 26-06-2021 5490417
## 455 29 Delayed FTA 12-05-2020 5669711
## 456 14 Delayed FTA 06-12-2020 5605702
## 457 30 On Time Non-FTA 12-12-2023 5320983
## 458 24 Delayed FTA 05-03-2023 6231044
## 459 7 On Time FTA 05-09-2021 6548616
## 460 7 On Time FTA 22-05-2021 5170237
## 461 7 Delayed Non-FTA 25-10-2021 5189383
## 462 20 On Time Non-FTA 14-03-2023 6180498
## 463 3 On Time FTA 25-04-2020 7432060
## 464 20 On Time FTA 22-08-2022 5749324
## 465 27 Delayed FTA 16-10-2024 8268606
## 466 17 On Time FTA 21-09-2018 5329657
## 467 6 On Time Non-FTA 14-05-2021 7390977
## 468 23 On Time FTA 13-01-2024 5931571
## 469 11 On Time Non-FTA 29-03-2023 5364829
## 470 22 Delayed Non-FTA 19-12-2024 7689812
## 471 22 Delayed Non-FTA 10-12-2023 6344307
## 472 17 On Time FTA 27-02-2021 5634468
## 473 29 Delayed Non-FTA 20-02-2024 5686967
## 474 30 Delayed FTA 04-05-2022 5681181
## 475 2 On Time Non-FTA 17-04-2019 5811799
## 476 14 Delayed FTA 10-05-2023 6863932
## 477 14 On Time Non-FTA 15-01-2020 5277641
## 478 7 On Time FTA 02-06-2018 7365598
## 479 17 On Time Non-FTA 01-07-2024 6872504
## 480 9 On Time FTA 04-05-2022 5447645
## 481 23 Delayed Non-FTA 26-04-2019 6690745
## 482 23 On Time Non-FTA 05-02-2020 5461187
## 483 27 Delayed Non-FTA 17-01-2020 6869937
## 484 18 On Time Non-FTA 26-06-2022 5412146
## 485 24 Delayed Non-FTA 20-09-2023 5501176
## 486 28 On Time Non-FTA 25-02-2021 6533189
## 487 5 On Time FTA 22-03-2019 6339840
## 488 10 On Time FTA 23-10-2019 5586957
## 489 21 Delayed Non-FTA 20-03-2019 6427824
## 490 22 On Time FTA 08-12-2020 5292557
## 491 14 On Time FTA 25-05-2023 5806197
## 492 26 On Time Non-FTA 11-11-2018 5406842
## 493 8 On Time Non-FTA 18-11-2020 6989176
## 494 20 On Time Non-FTA 15-06-2021 6464793
## 495 23 Delayed FTA 29-11-2021 6319301
## 496 15 On Time FTA 10-01-2019 7334767
## 497 4 Delayed FTA 18-11-2021 7339510
## 498 24 On Time Non-FTA 03-10-2024 8000999
## 499 23 Delayed FTA 28-05-2022 6387276
## 500 1 On Time Non-FTA 17-01-2018 6849344
## 501 10 On Time FTA 02-12-2023 5379244
## 502 6 On Time FTA 17-07-2020 7217295
## 503 12 On Time FTA 18-05-2020 5431964
## 504 29 On Time FTA 05-01-2023 6110727
## 505 25 Delayed FTA 14-11-2024 6920877
## 506 3 Delayed Non-FTA 05-05-2024 6917163
## 507 16 On Time FTA 28-05-2024 6011316
## 508 24 Delayed FTA 21-12-2020 6339409
## 509 1 On Time FTA 26-03-2022 5745660
## 510 29 Delayed FTA 18-01-2024 5388796
## 511 23 On Time Non-FTA 19-02-2024 5397418
## 512 8 Delayed FTA 20-05-2023 6771219
## 513 13 Delayed FTA 22-09-2024 7570731
## 514 10 On Time FTA 09-07-2021 5649316
## 515 15 On Time FTA 09-09-2018 7125225
## 516 12 On Time Non-FTA 10-12-2021 6336213
## 517 9 On Time FTA 27-08-2020 5772740
## 518 1 On Time FTA 01-02-2023 5971222
## 519 7 Delayed Non-FTA 16-06-2020 5750455
## 520 26 On Time FTA 30-01-2020 5369304
## 521 2 Delayed Non-FTA 06-04-2024 5786297
## 522 24 Delayed Non-FTA 22-12-2023 7083960
## 523 18 Delayed Non-FTA 16-12-2021 6822120
## 524 16 On Time FTA 12-05-2022 5985908
## 525 12 On Time FTA 24-07-2019 7859641
## 526 4 On Time FTA 12-02-2021 8127235
## 527 12 On Time FTA 27-01-2023 5656842
## 528 13 Delayed Non-FTA 14-06-2024 5384941
## 529 16 On Time FTA 05-01-2018 6826471
## 530 11 On Time FTA 12-09-2019 7750614
## 531 29 On Time FTA 22-05-2019 8156384
## 532 19 Delayed FTA 30-05-2023 7545642
## 533 24 On Time Non-FTA 01-11-2020 7876495
## 534 16 On Time FTA 13-08-2022 6979203
## 535 17 Delayed FTA 10-04-2024 5286920
## 536 21 Delayed Non-FTA 14-01-2023 5089508
## 537 4 Delayed Non-FTA 01-08-2018 8125894
## 538 15 Delayed Non-FTA 07-01-2018 7310740
## 539 6 Delayed FTA 04-02-2022 7376488
## 540 28 On Time FTA 15-09-2024 7429572
## 541 6 On Time FTA 14-04-2021 6719259
## 542 21 Delayed FTA 27-11-2019 5424262
## 543 7 On Time Non-FTA 09-03-2019 5186319
## 544 30 On Time Non-FTA 18-11-2018 6872260
## 545 11 Delayed Non-FTA 03-12-2019 7645330
## 546 22 Delayed FTA 15-06-2019 6984932
## 547 29 On Time Non-FTA 02-09-2019 6602345
## 548 3 Delayed Non-FTA 20-01-2022 6541635
## 549 21 Delayed FTA 23-11-2023 5120848
## 550 17 Delayed FTA 12-08-2018 5437160
## 551 9 Delayed Non-FTA 18-02-2019 6665836
## 552 24 Delayed Non-FTA 26-05-2019 5782176
## 553 2 Delayed Non-FTA 01-06-2020 5226981
## 554 18 On Time Non-FTA 17-06-2024 7490769
## 555 15 On Time Non-FTA 02-10-2021 7250240
## 556 23 Delayed FTA 19-05-2023 7540097
## 557 5 Delayed Non-FTA 16-11-2018 5638070
## 558 11 On Time FTA 08-05-2022 5858057
## 559 2 On Time Non-FTA 12-02-2024 5441632
## 560 10 On Time Non-FTA 28-03-2020 5567124
## 561 21 Delayed Non-FTA 10-01-2020 5413633
## 562 17 On Time Non-FTA 26-08-2024 5791891
## 563 14 On Time FTA 27-09-2023 7025156
## 564 6 On Time Non-FTA 18-07-2023 5127194
## 565 28 On Time FTA 27-01-2018 5513205
## 566 29 On Time FTA 15-11-2024 6376393
## 567 4 Delayed FTA 16-06-2021 5693255
## 568 26 Delayed FTA 27-11-2021 6003557
## 569 27 Delayed FTA 16-09-2018 5981777
## 570 1 On Time Non-FTA 11-02-2021 7120284
## 571 26 On Time FTA 07-12-2024 7701794
## 572 7 On Time Non-FTA 06-08-2022 7334712
## 573 29 On Time FTA 18-02-2023 6725386
## 574 20 On Time Non-FTA 17-08-2020 6614000
## 575 15 On Time FTA 07-08-2020 5381042
## 576 11 On Time FTA 21-08-2018 5474582
## 577 26 On Time FTA 26-09-2024 5475127
## 578 2 Delayed FTA 02-06-2020 7320821
## 579 7 Delayed FTA 05-02-2019 7409045
## 580 11 Delayed FTA 16-09-2018 5111409
## 581 23 On Time FTA 31-10-2020 6020373
## 582 27 Delayed FTA 19-08-2021 5942016
## 583 10 On Time FTA 28-08-2020 6912537
## 584 6 On Time FTA 17-09-2021 5127636
## 585 14 Delayed FTA 14-06-2023 6817659
## 586 30 Delayed Non-FTA 30-04-2021 6249776
## 587 15 Delayed Non-FTA 14-07-2019 7016774
## 588 7 Delayed Non-FTA 16-09-2018 6049604
## 589 29 On Time Non-FTA 03-05-2020 6156696
## 590 4 On Time Non-FTA 18-01-2022 6523084
## 591 9 Delayed Non-FTA 18-01-2023 6075523
## 592 23 Delayed FTA 18-12-2021 6053010
## 593 21 Delayed Non-FTA 13-04-2020 6574944
## 594 20 Delayed Non-FTA 10-10-2020 6571929
## 595 1 Delayed FTA 13-10-2022 8087044
## 596 6 On Time FTA 16-01-2018 6195993
## 597 2 Delayed Non-FTA 06-12-2023 6732985
## 598 8 On Time FTA 10-08-2024 7009524
## 599 29 Delayed FTA 21-07-2019 7230836
## 600 18 Delayed FTA 08-10-2020 6917111
## 601 25 Delayed Non-FTA 02-05-2022 6597998
## 602 24 Delayed Non-FTA 26-11-2023 6399060
## 603 6 On Time FTA 13-01-2021 7837157
## 604 11 On Time Non-FTA 30-07-2019 6664497
## 605 30 Delayed FTA 10-07-2020 6926758
## 606 27 On Time Non-FTA 03-11-2019 7918942
## 607 21 On Time Non-FTA 23-08-2023 6655669
## 608 9 Delayed FTA 07-10-2020 5582023
## 609 4 Delayed FTA 22-12-2019 5597600
## 610 20 Delayed FTA 24-09-2023 6810110
## 611 10 Delayed FTA 19-11-2022 5124406
## 612 3 Delayed Non-FTA 22-10-2019 5980723
## 613 8 On Time Non-FTA 29-12-2019 7264464
## 614 23 On Time Non-FTA 10-02-2023 7436237
## 615 8 On Time FTA 04-12-2024 6151295
## 616 19 On Time Non-FTA 31-01-2019 5220671
## 617 28 On Time Non-FTA 20-10-2018 6531758
## 618 4 On Time Non-FTA 18-05-2020 6760253
## 619 19 On Time FTA 29-04-2020 5186851
## 620 21 On Time FTA 27-03-2018 7165033
## 621 24 On Time Non-FTA 12-03-2024 5109638
## 622 29 On Time Non-FTA 29-05-2021 5515887
## 623 17 Delayed Non-FTA 02-01-2024 5658405
## 624 24 On Time Non-FTA 04-01-2024 8211033
## 625 14 Delayed Non-FTA 29-12-2021 5027895
## 626 4 On Time Non-FTA 11-01-2020 5836076
## 627 5 On Time FTA 08-03-2022 5960807
## 628 27 On Time Non-FTA 18-05-2024 5158412
## 629 12 Delayed Non-FTA 03-08-2022 6908162
## 630 26 On Time Non-FTA 07-07-2022 5471157
## 631 24 On Time Non-FTA 06-05-2023 5399893
## 632 7 Delayed Non-FTA 27-09-2022 7345911
## 633 12 On Time FTA 04-05-2023 6652758
## 634 3 On Time FTA 11-02-2024 6845887
## 635 1 Delayed Non-FTA 06-09-2020 5131975
## 636 7 On Time FTA 11-07-2020 5814497
## 637 18 On Time FTA 13-08-2021 6010317
## 638 22 On Time Non-FTA 28-01-2024 6176841
## 639 19 On Time Non-FTA 31-12-2020 5174807
## 640 16 On Time FTA 03-08-2018 5302682
## 641 22 Delayed Non-FTA 12-11-2024 6666692
## 642 26 On Time FTA 30-10-2024 5097949
## 643 23 Delayed FTA 11-05-2021 5399608
## 644 14 Delayed Non-FTA 03-11-2024 6220492
## 645 18 On Time FTA 02-10-2022 5016800
## 646 10 Delayed Non-FTA 01-03-2021 5761463
## 647 14 Delayed FTA 28-05-2024 5564786
## 648 26 On Time Non-FTA 15-08-2018 6161218
## 649 22 Delayed FTA 10-07-2020 6914965
## 650 29 On Time FTA 26-09-2019 5361648
## 651 10 Delayed Non-FTA 23-08-2024 6992937
## 652 1 Delayed FTA 03-07-2020 6522054
## 653 10 On Time Non-FTA 27-07-2023 5245356
## 654 4 On Time FTA 01-06-2022 5924169
## 655 16 Delayed FTA 04-12-2019 5793274
## 656 20 Delayed FTA 08-11-2019 6538036
## 657 27 On Time FTA 03-10-2021 7269625
## 658 25 On Time FTA 06-03-2019 6157389
## 659 15 Delayed Non-FTA 07-03-2018 6406425
## 660 22 Delayed Non-FTA 27-07-2019 6456359
## 661 4 Delayed Non-FTA 07-01-2019 5275603
## 662 25 Delayed Non-FTA 05-01-2023 6738017
## 663 2 Delayed Non-FTA 09-11-2021 6916198
## 664 1 Delayed FTA 02-03-2020 5759179
## 665 26 On Time Non-FTA 08-12-2022 5015937
## 666 25 On Time Non-FTA 12-08-2018 7101193
## 667 12 Delayed Non-FTA 03-05-2022 5058457
## 668 26 Delayed Non-FTA 09-12-2018 6358127
## 669 7 On Time FTA 05-02-2023 7400702
## 670 17 Delayed FTA 07-03-2020 6573851
## 671 13 Delayed FTA 15-07-2020 5389076
## 672 2 On Time Non-FTA 16-04-2023 5732359
## 673 19 Delayed FTA 08-11-2024 6709440
## 674 30 Delayed FTA 24-12-2019 5965428
## 675 21 Delayed FTA 12-06-2021 6054441
## 676 2 On Time FTA 06-04-2021 5991438
## 677 4 Delayed FTA 05-03-2022 8105264
## 678 17 On Time Non-FTA 25-07-2023 7339546
## 679 8 Delayed Non-FTA 07-06-2024 5111237
## 680 25 On Time Non-FTA 10-04-2018 5414051
## 681 7 Delayed FTA 18-08-2024 7003131
## 682 30 On Time Non-FTA 12-11-2018 5426218
## 683 3 Delayed Non-FTA 16-04-2021 5618540
## 684 21 Delayed Non-FTA 11-04-2024 7251862
## 685 14 On Time Non-FTA 10-01-2023 6445214
## 686 15 Delayed FTA 10-03-2019 7053411
## 687 30 Delayed FTA 31-03-2018 5446593
## 688 8 Delayed Non-FTA 31-12-2024 5398817
## 689 18 On Time Non-FTA 10-08-2018 7089774
## 690 29 On Time Non-FTA 05-08-2022 6580655
## 691 23 On Time FTA 28-08-2023 7473671
## 692 18 On Time Non-FTA 26-03-2022 6726397
## 693 27 Delayed Non-FTA 16-10-2021 5312198
## 694 19 Delayed FTA 20-08-2024 5381976
## 695 16 Delayed FTA 01-10-2023 6431142
## 696 18 Delayed FTA 19-09-2022 5968891
## 697 28 On Time Non-FTA 29-03-2024 5358195
## 698 5 On Time Non-FTA 18-08-2020 6489323
## 699 13 On Time FTA 28-12-2021 6543581
## 700 11 Delayed FTA 27-09-2021 6602060
## 701 26 On Time FTA 15-04-2021 7606861
## 702 29 Delayed Non-FTA 21-01-2022 6164278
## 703 21 Delayed FTA 11-10-2022 5784445
## 704 12 On Time Non-FTA 10-03-2018 6299950
## 705 28 Delayed Non-FTA 23-04-2023 5776132
## 706 30 On Time Non-FTA 21-07-2024 5266149
## 707 26 Delayed FTA 03-05-2022 7254100
## 708 7 On Time FTA 01-07-2024 5680312
## 709 7 On Time FTA 27-02-2018 7669586
## 710 24 On Time FTA 14-05-2021 5277852
## 711 1 On Time FTA 24-09-2019 5215457
## 712 7 Delayed Non-FTA 26-01-2023 6653816
## 713 30 Delayed FTA 07-04-2023 7322904
## 714 30 Delayed Non-FTA 09-03-2022 5707333
## 715 5 On Time FTA 28-12-2018 5555480
## 716 3 On Time Non-FTA 22-05-2020 5752376
## 717 19 On Time FTA 25-05-2023 6996529
## 718 20 Delayed Non-FTA 06-11-2019 7591232
## 719 11 Delayed Non-FTA 20-08-2024 5367045
## 720 4 On Time Non-FTA 02-05-2024 6683179
## 721 30 Delayed Non-FTA 07-02-2019 5707838
## 722 2 On Time FTA 04-05-2024 7629999
## 723 10 Delayed FTA 30-07-2018 6762235
## 724 2 Delayed FTA 10-10-2019 5169161
## 725 17 Delayed Non-FTA 15-06-2019 5128210
## 726 13 Delayed Non-FTA 31-08-2019 7097705
## 727 3 Delayed FTA 22-06-2021 7441897
## 728 14 On Time Non-FTA 01-01-2022 6863119
## 729 5 Delayed Non-FTA 22-02-2019 5966942
## 730 30 Delayed Non-FTA 14-06-2023 5331261
## 731 3 On Time Non-FTA 28-08-2024 7412970
## 732 27 Delayed Non-FTA 03-10-2024 7704321
## 733 4 On Time FTA 06-05-2020 6160384
## 734 30 On Time FTA 16-09-2020 7499408
## 735 29 On Time Non-FTA 03-04-2020 6681756
## 736 19 On Time Non-FTA 11-07-2024 5681506
## 737 18 On Time FTA 11-05-2018 7245735
## 738 6 On Time FTA 19-12-2023 6857871
## 739 23 Delayed FTA 07-04-2018 7254501
## 740 6 Delayed FTA 23-07-2018 5503473
## 741 17 Delayed Non-FTA 22-03-2019 6051255
## 742 23 Delayed Non-FTA 05-04-2021 5072593
## 743 11 Delayed Non-FTA 21-01-2024 5376500
## 744 2 Delayed Non-FTA 27-07-2023 5226962
## 745 17 Delayed Non-FTA 12-09-2024 5349052
## 746 20 On Time FTA 06-05-2024 7352540
## 747 13 Delayed FTA 22-05-2022 5775931
## 748 18 On Time Non-FTA 29-12-2019 7535381
## 749 1 On Time FTA 23-05-2018 6802176
## 750 24 Delayed Non-FTA 22-02-2021 7415079
## 751 4 Delayed Non-FTA 04-08-2018 7747604
## 752 9 On Time FTA 11-01-2021 7127191
## 753 9 Delayed Non-FTA 06-03-2023 6261248
## 754 25 On Time Non-FTA 15-02-2024 5232310
## 755 14 On Time FTA 23-07-2022 6072797
## 756 20 Delayed Non-FTA 11-11-2021 7661600
## 757 7 Delayed Non-FTA 09-12-2023 8001452
## 758 26 On Time FTA 27-09-2023 7390175
## 759 21 Delayed Non-FTA 22-08-2023 5802057
## 760 11 On Time FTA 31-10-2023 7020409
## 761 15 On Time FTA 23-02-2020 6493942
## 762 19 On Time Non-FTA 06-01-2023 5839513
## 763 27 Delayed FTA 09-09-2018 6122103
## 764 29 Delayed FTA 13-01-2022 7118980
## 765 9 On Time Non-FTA 01-09-2022 7214870
## 766 29 On Time FTA 29-05-2023 7850481
## 767 16 On Time FTA 08-05-2022 6637177
## 768 19 Delayed Non-FTA 07-08-2019 5317250
## 769 22 Delayed Non-FTA 19-05-2023 6281541
## 770 11 Delayed Non-FTA 08-04-2022 5404383
## 771 7 Delayed Non-FTA 09-06-2022 5175338
## 772 15 On Time FTA 29-06-2022 6878554
## 773 23 Delayed Non-FTA 29-01-2018 5090534
## 774 23 Delayed Non-FTA 30-03-2022 5447862
## 775 16 Delayed FTA 03-05-2019 6887939
## 776 8 Delayed FTA 29-09-2023 7197984
## 777 2 On Time Non-FTA 03-12-2020 5293604
## 778 27 Delayed FTA 19-05-2024 5454941
## 779 7 On Time Non-FTA 05-01-2020 5561482
## 780 13 Delayed FTA 06-07-2021 7633141
## 781 10 On Time FTA 14-05-2019 5461820
## 782 4 On Time FTA 13-10-2022 5578810
## 783 12 On Time FTA 10-06-2024 7105400
## 784 21 On Time Non-FTA 22-11-2021 6244477
## 785 5 On Time FTA 14-11-2021 6345740
## 786 4 Delayed FTA 17-05-2021 7029955
## 787 27 On Time Non-FTA 03-02-2023 5222665
## 788 16 Delayed Non-FTA 07-11-2019 6935462
## 789 13 On Time Non-FTA 24-02-2023 5236367
## 790 19 On Time Non-FTA 20-12-2018 5182163
## 791 25 Delayed Non-FTA 03-02-2023 6421523
## 792 10 Delayed Non-FTA 12-03-2018 7767844
## 793 26 On Time Non-FTA 12-03-2022 5858638
## 794 7 Delayed Non-FTA 14-09-2019 6480354
## 795 28 On Time FTA 14-04-2020 5079061
## 796 23 On Time Non-FTA 15-04-2024 5499725
## 797 1 Delayed Non-FTA 20-02-2021 6058595
## 798 22 On Time FTA 08-07-2024 6386863
## 799 4 Delayed FTA 15-04-2022 5372627
## 800 5 On Time Non-FTA 26-08-2023 6540836
## 801 29 Delayed Non-FTA 09-10-2020 8007527
## 802 23 On Time Non-FTA 26-09-2022 5487251
## 803 29 On Time Non-FTA 25-10-2020 6472991
## 804 2 On Time FTA 30-01-2020 7249436
## 805 25 On Time FTA 27-01-2021 6578699
## 806 3 On Time Non-FTA 29-10-2024 7656778
## 807 20 On Time Non-FTA 08-12-2019 7641969
## 808 21 On Time FTA 26-12-2020 6812531
## 809 12 On Time FTA 31-08-2019 5117328
## 810 12 On Time FTA 06-07-2020 6002390
## 811 1 Delayed FTA 27-05-2024 5208088
## 812 25 Delayed FTA 29-09-2023 6178351
## 813 27 Delayed FTA 02-04-2020 7680528
## 814 28 On Time Non-FTA 01-07-2024 7939264
## 815 21 On Time Non-FTA 07-07-2023 6954567
## 816 4 On Time FTA 18-09-2023 6261696
## 817 21 Delayed FTA 20-02-2021 6384166
## 818 18 On Time FTA 10-08-2022 6225555
## 819 8 Delayed Non-FTA 21-01-2024 7175831
## 820 4 Delayed Non-FTA 08-07-2021 5259652
## 821 29 On Time FTA 30-06-2022 5654335
## 822 25 On Time FTA 11-05-2024 5790879
## 823 12 On Time FTA 02-01-2020 6578320
## 824 25 Delayed FTA 26-03-2024 6088930
## 825 30 Delayed Non-FTA 08-12-2021 5379834
## 826 20 Delayed Non-FTA 13-08-2018 7669218
## 827 1 On Time FTA 18-03-2020 6315555
## 828 29 Delayed FTA 13-11-2019 5206836
## 829 6 Delayed Non-FTA 12-12-2024 5299042
## 830 27 On Time Non-FTA 06-01-2021 5432420
## 831 10 On Time Non-FTA 01-09-2018 6674631
## 832 10 Delayed Non-FTA 28-12-2024 5981615
## 833 28 Delayed Non-FTA 09-05-2023 5530587
## 834 21 On Time FTA 05-08-2024 5430944
## 835 9 On Time Non-FTA 10-04-2023 7077958
## 836 13 On Time FTA 14-12-2019 5937015
## 837 29 Delayed FTA 11-07-2020 6193288
## 838 27 Delayed Non-FTA 10-10-2019 7123980
## 839 12 On Time FTA 30-12-2019 6831220
## 840 16 On Time Non-FTA 10-07-2021 5153408
## 841 15 Delayed Non-FTA 03-04-2020 5302132
## 842 28 Delayed Non-FTA 01-05-2020 7754969
## 843 1 On Time FTA 23-11-2023 5243724
## 844 12 On Time FTA 10-07-2024 6219802
## 845 15 On Time Non-FTA 21-03-2024 7926432
## 846 27 Delayed FTA 19-10-2018 7396888
## 847 30 Delayed Non-FTA 08-12-2023 6876323
## 848 24 Delayed FTA 27-12-2022 7419979
## 849 20 On Time FTA 15-12-2019 6471646
## 850 22 Delayed FTA 05-07-2023 6869145
## 851 21 On Time Non-FTA 04-10-2023 5579021
## 852 10 Delayed Non-FTA 15-10-2023 7844511
## 853 1 On Time FTA 25-11-2018 5528254
## 854 10 On Time Non-FTA 03-06-2020 7560945
## 855 25 Delayed Non-FTA 07-07-2024 6394291
## 856 20 Delayed Non-FTA 25-09-2021 6988867
## 857 18 Delayed Non-FTA 28-01-2019 5607959
## 858 25 Delayed FTA 19-11-2019 7232120
## 859 7 Delayed Non-FTA 31-07-2022 7363935
## 860 29 Delayed FTA 15-06-2024 6114648
## 861 13 On Time Non-FTA 06-05-2023 6311919
## 862 5 On Time FTA 02-01-2022 7023307
## 863 11 Delayed Non-FTA 04-07-2020 6462899
## 864 14 Delayed Non-FTA 16-04-2021 7845326
## 865 29 Delayed FTA 19-07-2018 5086077
## 866 21 On Time Non-FTA 25-09-2024 5693043
## 867 13 On Time FTA 23-01-2022 6757458
## 868 11 Delayed FTA 24-03-2021 6278957
## 869 24 On Time Non-FTA 18-08-2023 6821225
## 870 7 On Time FTA 23-11-2020 7158445
## 871 29 Delayed Non-FTA 27-09-2024 6752850
## 872 13 Delayed FTA 17-06-2024 7209017
## 873 29 On Time Non-FTA 19-09-2022 7161556
## 874 7 On Time Non-FTA 01-05-2018 5789617
## 875 22 On Time Non-FTA 14-06-2020 6921053
## 876 8 On Time FTA 13-08-2022 6084823
## 877 24 On Time FTA 18-03-2021 5703336
## 878 12 On Time Non-FTA 09-09-2023 7082055
## 879 29 On Time FTA 06-12-2023 8249483
## 880 27 On Time FTA 30-04-2023 6716739
## 881 7 On Time FTA 05-01-2021 6458244
## 882 9 Delayed Non-FTA 12-02-2020 6802667
## 883 24 Delayed FTA 12-07-2019 5330837
## 884 9 Delayed Non-FTA 25-12-2021 8097934
## 885 9 On Time FTA 23-02-2024 6197262
## 886 16 Delayed FTA 14-04-2023 6745140
## 887 29 On Time FTA 12-11-2024 8138280
## Profit Loss Is_Delayed Profit_Margin
## 1 692534.6 131463.95 TRUE 0.13293692
## 2 588212.5 229675.60 TRUE 0.10757513
## 3 851753.6 363622.75 FALSE 0.16877396
## 4 1222095.3 520933.47 FALSE 0.18405116
## 5 1191564.4 283072.84 TRUE 0.15992875
## 6 884828.6 266392.67 TRUE 0.16495322
## 7 887323.4 114710.66 TRUE 0.12069041
## 8 1049436.9 243446.83 FALSE 0.17114709
## 9 500768.9 479985.09 TRUE 0.07436057
## 10 1383666.3 138689.56 TRUE 0.18618631
## 11 801441.9 233300.52 FALSE 0.14565977
## 12 1228378.3 302236.85 TRUE 0.18186638
## 13 927774.1 461792.44 TRUE 0.14972569
## 14 336337.2 201247.73 TRUE 0.05393928
## 15 441157.1 199421.11 TRUE 0.08344392
## 16 637097.3 487478.59 FALSE 0.12393619
## 17 526574.2 654081.81 TRUE 0.08011587
## 18 1181633.9 642167.48 FALSE 0.15897244
## 19 1039805.2 73048.26 FALSE 0.19809866
## 20 665045.6 361103.99 TRUE 0.11324749
## 21 339578.1 479236.14 FALSE 0.06326665
## 22 1296193.8 641295.31 FALSE 0.19092016
## 23 723798.4 196861.96 FALSE 0.14206784
## 24 1081729.2 180894.38 FALSE 0.14556079
## 25 710656.3 263631.24 TRUE 0.13971512
## 26 605696.4 313843.87 FALSE 0.12092065
## 27 508126.2 510227.56 FALSE 0.08455819
## 28 551267.6 267564.71 TRUE 0.10464542
## 29 411397.3 224648.37 TRUE 0.05403199
## 30 971897.2 149647.78 FALSE 0.18172377
## 31 1104663.1 211496.98 FALSE 0.18019191
## 32 1348056.7 607842.67 TRUE 0.16236235
## 33 490970.5 492845.71 TRUE 0.06521345
## 34 718217.8 131990.26 TRUE 0.10629524
## 35 643918.0 557535.41 FALSE 0.08168752
## 36 894278.1 618076.26 FALSE 0.13019083
## 37 919151.9 380209.53 TRUE 0.13266198
## 38 533721.2 461064.94 FALSE 0.07086404
## 39 1117475.0 515452.56 FALSE 0.15727960
## 40 480025.3 76789.72 FALSE 0.06979447
## 41 489115.1 467643.14 TRUE 0.07580701
## 42 795832.8 506525.69 FALSE 0.13477575
## 43 724431.0 193600.08 FALSE 0.14230440
## 44 692311.4 653355.33 TRUE 0.10185395
## 45 885786.3 473540.63 FALSE 0.17673656
## 46 273694.8 156686.99 TRUE 0.05109754
## 47 562949.6 395905.52 TRUE 0.08067640
## 48 1032583.0 120772.77 FALSE 0.16659458
## 49 820081.7 541867.05 TRUE 0.10984604
## 50 1214897.4 352931.62 FALSE 0.15837801
## 51 1139178.5 253968.76 TRUE 0.19318764
## 52 654608.6 157924.40 FALSE 0.12230854
## 53 478441.6 330626.09 FALSE 0.08155650
## 54 1059137.7 297531.87 TRUE 0.19838852
## 55 347625.8 317637.60 TRUE 0.06165305
## 56 1202858.7 606207.39 FALSE 0.18369859
## 57 622495.7 114963.38 TRUE 0.10470271
## 58 1028909.3 282980.81 TRUE 0.19384797
## 59 725976.9 402789.43 TRUE 0.13178679
## 60 920553.2 178114.69 TRUE 0.13245967
## 61 1416268.7 363631.70 FALSE 0.17755940
## 62 270755.1 232025.37 FALSE 0.05369624
## 63 676273.9 580713.76 TRUE 0.10020326
## 64 803068.0 461864.72 TRUE 0.14141125
## 65 957824.7 390272.69 FALSE 0.14207650
## 66 607515.0 337721.94 FALSE 0.11872402
## 67 773176.0 203086.82 FALSE 0.11346461
## 68 1304432.3 407881.99 TRUE 0.18398744
## 69 1104798.8 558510.00 TRUE 0.16173351
## 70 1281387.1 280263.85 TRUE 0.18368693
## 71 981676.7 242544.44 FALSE 0.15123612
## 72 666729.0 195359.61 FALSE 0.12576675
## 73 512116.3 386458.28 TRUE 0.08136862
## 74 1222475.5 397315.34 FALSE 0.19460848
## 75 613380.6 359685.23 TRUE 0.10081038
## 76 714924.7 187263.13 FALSE 0.14250998
## 77 988649.7 427121.94 FALSE 0.16756731
## 78 928235.4 531534.30 FALSE 0.14455249
## 79 631794.5 251432.67 TRUE 0.12132611
## 80 1395669.2 683125.92 FALSE 0.19803294
## 81 651773.1 524633.72 FALSE 0.09765224
## 82 715454.3 527434.54 TRUE 0.13364408
## 83 943282.9 316372.88 FALSE 0.14790520
## 84 1306134.2 426569.09 TRUE 0.18003299
## 85 774809.8 395592.87 FALSE 0.13024906
## 86 314502.9 437146.55 TRUE 0.05592694
## 87 549324.4 423077.65 FALSE 0.09274732
## 88 1010535.5 109734.20 TRUE 0.19528036
## 89 1349816.8 177720.24 FALSE 0.17976767
## 90 635204.3 178668.22 FALSE 0.09135507
## 91 446740.2 166880.62 TRUE 0.06408851
## 92 1009764.8 254592.39 TRUE 0.18556326
## 93 1244944.6 367741.53 FALSE 0.18338687
## 94 907941.4 212674.90 TRUE 0.15688173
## 95 876319.2 450939.91 FALSE 0.11247869
## 96 668753.8 243440.20 TRUE 0.09779017
## 97 974109.5 314100.55 TRUE 0.15481867
## 98 1002115.2 222097.97 TRUE 0.19955117
## 99 668127.0 453685.73 FALSE 0.12955094
## 100 1143755.0 400221.54 TRUE 0.19990427
## 101 1240976.4 569641.03 FALSE 0.19152582
## 102 913886.3 491607.40 TRUE 0.17543923
## 103 1014870.6 300824.96 FALSE 0.18440081
## 104 821852.0 93292.80 FALSE 0.14866642
## 105 1031348.7 88330.50 FALSE 0.14581467
## 106 628482.8 654718.72 TRUE 0.09274052
## 107 659230.0 79234.53 TRUE 0.08774721
## 108 622314.0 166418.24 TRUE 0.09555081
## 109 596168.1 558314.65 FALSE 0.10365610
## 110 600461.2 350136.99 FALSE 0.10896280
## 111 1035962.8 125074.61 TRUE 0.19924036
## 112 845637.6 613593.75 TRUE 0.10526790
## 113 436999.8 264966.01 FALSE 0.08483623
## 114 897170.6 574843.86 TRUE 0.14851032
## 115 867958.3 257445.50 TRUE 0.15603071
## 116 1000977.4 663312.70 TRUE 0.14378366
## 117 1096856.3 429887.34 TRUE 0.16125960
## 118 593665.4 224605.31 TRUE 0.10925162
## 119 1003319.9 580144.80 TRUE 0.16172389
## 120 594469.1 277631.99 FALSE 0.08270347
## 121 344096.2 475903.07 FALSE 0.05290967
## 122 913157.6 158149.04 TRUE 0.15993373
## 123 472176.9 370165.83 TRUE 0.07024937
## 124 865359.1 565624.94 TRUE 0.14193952
## 125 671432.5 358148.94 TRUE 0.09608531
## 126 527137.5 549813.93 TRUE 0.07842657
## 127 552776.8 249750.83 FALSE 0.10123902
## 128 683181.0 78088.53 FALSE 0.10869127
## 129 511802.8 302890.32 TRUE 0.08045266
## 130 706485.5 206687.26 TRUE 0.13616189
## 131 937798.2 167745.07 TRUE 0.17535340
## 132 325105.0 185214.10 TRUE 0.05136760
## 133 578638.2 543361.06 TRUE 0.08516090
## 134 573921.2 78252.03 TRUE 0.11430310
## 135 990205.0 549528.82 FALSE 0.12221171
## 136 267905.3 473508.46 TRUE 0.05351868
## 137 747113.6 389131.12 FALSE 0.12222111
## 138 475640.4 93275.85 TRUE 0.09203940
## 139 1180273.2 288572.67 TRUE 0.18577321
## 140 1122819.4 364182.25 TRUE 0.15983423
## 141 1035816.6 133620.06 FALSE 0.19311212
## 142 660750.9 257856.49 FALSE 0.09518848
## 143 665973.4 490149.77 FALSE 0.09362686
## 144 316909.7 296376.14 FALSE 0.05018316
## 145 631037.5 513019.58 TRUE 0.11549883
## 146 711721.8 228851.21 TRUE 0.13220852
## 147 900083.6 399520.66 FALSE 0.15177802
## 148 586449.6 213378.91 TRUE 0.09833119
## 149 485348.1 466760.60 TRUE 0.08588994
## 150 952249.0 193803.26 FALSE 0.14637475
## 151 1302551.1 126598.37 FALSE 0.17253116
## 152 419798.5 275238.11 FALSE 0.06015868
## 153 584728.3 277428.54 FALSE 0.11465213
## 154 408292.6 606849.69 TRUE 0.05777993
## 155 365442.8 342872.19 FALSE 0.05277815
## 156 605706.0 162610.59 FALSE 0.11211287
## 157 843050.8 509141.28 TRUE 0.15453852
## 158 974103.0 512659.07 FALSE 0.17654632
## 159 652439.3 267061.70 FALSE 0.11830167
## 160 1216060.0 215705.25 TRUE 0.19790118
## 161 426556.2 481050.84 TRUE 0.07451777
## 162 479229.6 465539.63 TRUE 0.08806160
## 163 481229.3 531522.71 FALSE 0.07120677
## 164 1185866.2 151581.29 FALSE 0.17621981
## 165 1190673.7 154295.35 FALSE 0.17851628
## 166 378158.1 124007.78 TRUE 0.05069916
## 167 1035732.8 97050.79 FALSE 0.17976948
## 168 471332.5 574882.07 FALSE 0.08042506
## 169 1035325.5 98413.73 FALSE 0.15515749
## 170 898437.5 1925322.40 FALSE 0.13200131
## 171 1273696.5 644944.56 FALSE 0.19216006
## 172 873120.1 318716.02 FALSE 0.11214938
## 173 499729.0 518840.41 TRUE 0.09385864
## 174 681193.4 158212.78 FALSE 0.12699339
## 175 427389.3 82097.06 FALSE 0.05349901
## 176 627448.3 516101.30 FALSE 0.11781816
## 177 394091.8 417927.99 FALSE 0.07281286
## 178 746418.3 316229.44 TRUE 0.09835975
## 179 470200.8 497161.14 FALSE 0.09180839
## 180 449960.5 280003.81 TRUE 0.06396947
## 181 418621.9 315543.09 FALSE 0.07829140
## 182 1212240.6 97893.05 FALSE 0.18762794
## 183 734018.9 376594.88 TRUE 0.12229108
## 184 808369.3 151954.79 FALSE 0.15301683
## 185 932623.9 432873.81 TRUE 0.16066954
## 186 679681.3 542870.25 FALSE 0.08641688
## 187 1489036.8 246837.21 TRUE 0.19389733
## 188 499000.9 205905.13 TRUE 0.07513137
## 189 917007.0 234977.39 TRUE 0.11496486
## 190 779038.9 649928.66 TRUE 0.11677006
## 191 470957.2 537980.06 TRUE 0.05782834
## 192 1099900.7 681980.98 FALSE 0.15242773
## 193 1396717.8 565416.95 TRUE 0.19029778
## 194 997023.6 59847.53 TRUE 0.17929444
## 195 447988.2 204113.15 TRUE 0.07502149
## 196 1271353.0 629549.04 FALSE 0.18483787
## 197 1091444.6 592938.76 FALSE 0.16275646
## 198 293196.3 91311.84 FALSE 0.05577783
## 199 733639.5 274183.49 TRUE 0.10898378
## 200 714038.1 83143.64 FALSE 0.12995588
## 201 404450.8 670434.97 FALSE 0.05465161
## 202 1002036.1 475053.84 FALSE 0.12952838
## 203 653098.9 696182.06 FALSE 0.08460604
## 204 601177.3 262133.67 TRUE 0.09821967
## 205 793653.1 413295.44 FALSE 0.15642417
## 206 557018.6 473319.66 TRUE 0.10729529
## 207 525481.1 419458.01 FALSE 0.10068655
## 208 324881.9 131198.72 FALSE 0.05437367
## 209 589554.5 129796.26 TRUE 0.09575604
## 210 1079912.0 485558.11 FALSE 0.15001558
## 211 694868.3 392413.40 TRUE 0.13670318
## 212 473134.9 197757.35 TRUE 0.07749262
## 213 488683.6 327847.31 TRUE 0.08936884
## 214 602428.1 123034.27 TRUE 0.10563697
## 215 375161.6 623506.60 FALSE 0.05440287
## 216 584206.3 104723.83 TRUE 0.07621049
## 217 408335.5 290856.14 TRUE 0.05729601
## 218 571606.6 91246.32 FALSE 0.10453709
## 219 443783.8 287691.37 FALSE 0.07232019
## 220 782476.5 322638.98 TRUE 0.13589750
## 221 540939.8 382758.45 FALSE 0.09203961
## 222 890158.7 539685.96 TRUE 0.11639530
## 223 350351.5 180847.79 FALSE 0.05972367
## 224 953020.8 654569.38 TRUE 0.12936438
## 225 405761.7 505750.16 TRUE 0.07666057
## 226 1505836.7 618615.31 FALSE 0.19154531
## 227 834893.0 121327.76 TRUE 0.16501574
## 228 700930.4 365593.11 FALSE 0.11524618
## 229 675491.1 160764.97 FALSE 0.12034395
## 230 682014.1 510265.36 FALSE 0.10796442
## 231 834894.6 631716.58 TRUE 0.03238390
## 232 768494.6 452329.74 FALSE 0.13147484
## 233 515140.1 125292.18 TRUE 0.08537906
## 234 1142498.8 326737.05 TRUE 0.17929243
## 235 615959.1 127429.66 TRUE 0.10317285
## 236 728364.2 559391.94 FALSE 0.13009232
## 237 920857.2 543619.55 TRUE 0.14450485
## 238 815703.2 129188.66 FALSE 0.12993825
## 239 565247.3 149473.74 TRUE 0.10912206
## 240 540923.5 174367.24 FALSE 0.10764151
## 241 405918.7 175972.09 TRUE 0.07468001
## 242 809326.0 231137.98 FALSE 0.14335794
## 243 1375031.0 210117.83 FALSE 0.18202017
## 244 367359.0 496875.97 FALSE 0.05908920
## 245 1284605.6 304180.86 TRUE 0.17339129
## 246 1216698.1 183343.25 TRUE 0.19995158
## 247 456415.6 444841.63 FALSE 0.06945549
## 248 1146143.9 429371.34 FALSE 0.17053467
## 249 784266.9 147377.56 FALSE 0.10555374
## 250 345547.8 122927.39 FALSE 0.05639674
## 251 1000402.5 229399.18 FALSE 0.14359025
## 252 831455.6 366936.91 TRUE 0.12779004
## 253 488347.6 554472.21 FALSE 0.07113763
## 254 790622.9 223534.46 FALSE 0.13477690
## 255 1323441.1 700340.14 FALSE 0.18313004
## 256 1273226.2 351431.51 FALSE 0.19764880
## 257 376859.2 422973.83 FALSE 0.07361390
## 258 1288153.9 704564.90 FALSE 0.15989394
## 259 1516761.4 150278.15 TRUE 0.19547786
## 260 328735.2 441896.16 TRUE 0.06529537
## 261 1333857.2 707926.78 FALSE 0.17672288
## 262 593797.4 142216.28 TRUE 0.11369450
## 263 734829.4 373842.77 FALSE 0.13436552
## 264 1385766.1 660945.73 FALSE 0.17726827
## 265 1125873.9 464099.63 FALSE 0.18176761
## 266 898969.0 438628.48 FALSE 0.17787203
## 267 520482.4 361255.45 TRUE 0.10301161
## 268 437837.8 189698.12 TRUE 0.07017540
## 269 969250.8 527765.59 FALSE 0.15506078
## 270 1312217.0 428431.58 TRUE 0.18071538
## 271 693509.6 800542.96 FALSE 0.08461667
## 272 402625.4 434517.45 FALSE 0.06173357
## 273 783647.7 320515.48 TRUE 0.11826942
## 274 963032.6 515187.35 FALSE 0.15845401
## 275 1033739.4 311045.41 TRUE 0.15897869
## 276 699544.6 224309.72 FALSE 0.08939033
## 277 1128349.9 521531.35 FALSE 0.15991366
## 278 839261.9 169416.98 TRUE 0.13205636
## 279 660919.9 119420.13 FALSE 0.09111947
## 280 494700.4 381826.92 FALSE 0.07970547
## 281 361882.1 649903.56 FALSE 0.05441795
## 282 584592.5 390839.49 TRUE 0.10422864
## 283 1142306.1 378293.78 TRUE 0.19956744
## 284 591834.2 504512.32 FALSE 0.08508439
## 285 1160372.9 684357.65 FALSE 0.16142937
## 286 659738.0 424202.22 TRUE 0.11121709
## 287 1113908.2 116385.13 FALSE 0.15182921
## 288 1565476.6 254654.01 FALSE 0.19637699
## 289 797778.5 83622.97 FALSE 0.12850985
## 290 453463.6 685459.54 FALSE 0.06237098
## 291 749832.9 420970.65 FALSE 0.11467909
## 292 833353.1 405102.85 TRUE 0.15790321
## 293 1221383.4 391214.16 TRUE 0.17574318
## 294 989196.7 463071.65 FALSE 0.16325706
## 295 642824.4 142057.86 TRUE 0.11509050
## 296 680036.8 150973.51 FALSE 0.12198378
## 297 623361.1 460549.46 TRUE 0.09718847
## 298 1000419.5 569591.35 FALSE 0.15051005
## 299 758335.8 285888.74 TRUE 0.12525930
## 300 959383.6 172582.57 FALSE 0.14207670
## 301 1272459.0 511323.79 FALSE 0.18127222
## 302 949802.4 248539.59 TRUE 0.17010380
## 303 485633.5 342991.29 TRUE 0.08581976
## 304 365212.8 414839.34 FALSE 0.06516067
## 305 502874.0 274688.46 TRUE 0.08415428
## 306 781723.0 127711.55 FALSE 0.11163946
## 307 971452.8 559555.91 FALSE 0.12000416
## 308 1368635.4 183440.31 TRUE 0.17240053
## 309 384318.4 573164.03 FALSE 0.05874151
## 310 1088184.9 366553.76 FALSE 0.19667242
## 311 792234.5 161578.70 TRUE 0.12694387
## 312 612176.7 648881.64 FALSE 0.08301823
## 313 265256.8 226282.86 FALSE 0.05091486
## 314 789927.7 375488.11 TRUE 0.12815234
## 315 1426029.5 187795.52 TRUE 0.19976531
## 316 1391521.1 584793.31 TRUE 0.19904334
## 317 789311.6 187149.69 FALSE 0.12375534
## 318 538964.1 586606.91 TRUE 0.08469069
## 319 1320634.3 289128.86 TRUE 0.19355511
## 320 875046.1 278342.90 FALSE 0.16487252
## 321 364896.6 180085.12 FALSE 0.06989068
## 322 1566511.2 140446.55 TRUE 0.18822019
## 323 397354.6 269537.59 FALSE 0.05398864
## 324 528597.5 397437.14 TRUE 0.08177268
## 325 1024451.2 475898.94 FALSE 0.16397097
## 326 764134.1 689204.99 FALSE 0.10562825
## 327 854980.2 382786.37 FALSE 0.14124982
## 328 774276.9 201908.70 TRUE 0.14709750
## 329 627507.8 320293.78 TRUE 0.10271971
## 330 718660.6 232895.78 FALSE 0.13707052
## 331 709532.0 122277.89 FALSE 0.12634443
## 332 766942.5 148724.30 FALSE 0.10325768
## 333 1161281.6 607653.69 FALSE 0.16727339
## 334 909475.6 255107.46 TRUE 0.16071354
## 335 672190.9 554743.46 FALSE 0.11187411
## 336 515132.4 440491.02 TRUE 0.08009473
## 337 456387.0 92982.24 TRUE 0.07712743
## 338 1295200.4 404115.40 FALSE 0.19908622
## 339 1167587.2 709986.43 TRUE 0.15157830
## 340 623894.8 569071.13 TRUE 0.09469709
## 341 581829.4 183723.52 FALSE 0.10500252
## 342 475759.9 64120.99 FALSE 0.09119606
## 343 401124.7 330946.45 FALSE 0.05715734
## 344 564932.5 289224.90 TRUE 0.08324163
## 345 318028.3 243642.79 FALSE 0.06065878
## 346 1102963.0 577325.88 FALSE 0.18835323
## 347 1080507.8 170962.78 FALSE 0.19256708
## 348 1441453.6 625823.84 TRUE 0.18350684
## 349 802544.7 656759.07 TRUE 0.10271537
## 350 651569.3 104857.58 FALSE 0.12714687
## 351 973198.6 458032.37 FALSE 0.14274826
## 352 430143.7 501919.00 FALSE 0.06922323
## 353 747876.9 176308.36 TRUE 0.14157764
## 354 317217.9 424609.78 TRUE 0.05561983
## 355 976160.6 213265.74 TRUE 0.18190538
## 356 1026266.0 168078.07 TRUE 0.19244976
## 357 748406.1 136593.55 TRUE 0.13501023
## 358 667712.1 236109.65 TRUE 0.09481660
## 359 1009063.2 532038.14 TRUE 0.18517292
## 360 462524.8 213865.79 TRUE 0.08428159
## 361 700205.7 234337.81 TRUE 0.13202511
## 362 648646.5 527585.08 FALSE 0.08710359
## 363 814353.2 451654.68 FALSE 0.13464488
## 364 708217.8 161972.84 TRUE 0.12149223
## 365 1087504.3 104890.69 FALSE 0.19272405
## 366 366117.6 451785.72 FALSE 0.06590589
## 367 623142.1 309700.08 FALSE 0.09261906
## 368 911360.1 228585.34 FALSE 0.13507360
## 369 903026.9 198377.52 TRUE 0.16026684
## 370 847901.8 184605.73 TRUE 0.14515839
## 371 874932.5 134099.96 FALSE 0.13691246
## 372 1319566.6 158074.28 FALSE 0.17174785
## 373 716464.7 93657.46 TRUE 0.12972883
## 374 581073.7 187449.37 FALSE 0.11106668
## 375 1432012.4 140470.15 TRUE 0.17774973
## 376 511166.0 199660.13 TRUE 0.08829098
## 377 976883.1 176119.14 FALSE 0.17714037
## 378 818075.1 600238.10 FALSE 0.10268379
## 379 832653.7 429638.23 TRUE 0.14364561
## 380 770795.4 176882.21 FALSE 0.15297417
## 381 849595.6 596407.10 TRUE 0.13509079
## 382 747175.1 279098.95 FALSE 0.11648804
## 383 642028.1 454158.80 FALSE 0.09705240
## 384 833215.8 62240.76 TRUE 0.14633251
## 385 1055928.9 342689.45 FALSE 0.19406872
## 386 618063.0 360110.93 TRUE 0.09981917
## 387 895056.2 535228.93 TRUE 0.13416680
## 388 637891.3 426898.89 FALSE 0.09444301
## 389 465327.3 158853.97 TRUE 0.08418832
## 390 507216.3 243683.56 FALSE 0.09365917
## 391 588192.7 424476.97 FALSE 0.09922201
## 392 883453.4 325189.88 TRUE 0.17072321
## 393 336928.5 490358.54 TRUE 0.06657717
## 394 1014276.1 225345.09 TRUE 0.17092775
## 395 303690.8 111303.98 FALSE 0.05715571
## 396 832927.1 571255.08 TRUE 0.12967594
## 397 903282.9 167450.36 FALSE 0.14703334
## 398 647675.5 188245.73 TRUE 0.12440179
## 399 388053.2 403236.91 FALSE 0.05368018
## 400 687762.9 266415.37 TRUE 0.11248220
## 401 610661.1 298829.39 FALSE 0.11954721
## 402 597546.7 120971.93 TRUE 0.11414937
## 403 539854.7 407087.28 FALSE 0.07798784
## 404 659392.4 334888.06 FALSE 0.12829016
## 405 687003.6 515215.62 FALSE 0.12259303
## 406 333325.8 399948.99 TRUE 0.05802133
## 407 497678.1 364160.50 FALSE 0.09742305
## 408 704509.3 687676.75 TRUE 0.09627499
## 409 921343.7 89451.41 TRUE 0.13782550
## 410 803370.8 281100.07 FALSE 0.13292321
## 411 685505.6 425747.41 FALSE 0.11498457
## 412 1031187.4 538216.61 FALSE 0.16635388
## 413 812202.8 83079.45 FALSE 0.12478431
## 414 1155545.5 228763.39 FALSE 0.17040715
## 415 591393.5 94919.64 FALSE 0.09627597
## 416 991905.8 209491.92 FALSE 0.16128619
## 417 670900.0 618299.70 TRUE 0.09183155
## 418 889959.0 646537.61 TRUE 0.12493257
## 419 297074.5 276080.96 TRUE 0.05333510
## 420 763905.7 77607.14 TRUE 0.10754286
## 421 627901.2 514661.35 TRUE 0.09017547
## 422 1135059.9 314447.29 TRUE 0.19598205
## 423 1307505.2 722117.61 FALSE 0.16276183
## 424 645414.7 516355.73 FALSE 0.09982555
## 425 811182.6 448975.08 FALSE 0.14837502
## 426 1182680.9 224020.82 TRUE 0.19080237
## 427 305214.3 449699.29 FALSE 0.05043926
## 428 940178.9 223443.66 TRUE 0.17370149
## 429 790744.2 119443.54 TRUE 0.14071158
## 430 948041.0 472438.48 FALSE 0.12847557
## 431 718636.5 626616.60 FALSE 0.10286312
## 432 611589.1 223926.09 TRUE 0.10980532
## 433 1071989.9 285636.84 TRUE 0.18665572
## 434 1204547.4 290985.01 FALSE 0.17267986
## 435 385364.9 292721.62 FALSE 0.05362980
## 436 1030489.0 326140.49 TRUE 0.15618689
## 437 580745.6 532471.31 TRUE 0.09307723
## 438 324103.4 376009.94 TRUE 0.05956390
## 439 846871.0 165277.42 TRUE 0.12119729
## 440 1288589.8 380954.32 TRUE 0.17210977
## 441 1194326.3 260763.45 FALSE 0.19667555
## 442 989723.1 251599.05 TRUE 0.18985870
## 443 1138486.0 357277.37 TRUE 0.15557135
## 444 379509.7 159767.47 FALSE 0.05196065
## 445 887553.9 491064.31 TRUE 0.16411832
## 446 1170283.9 180204.97 FALSE 0.18597438
## 447 881563.5 702769.53 TRUE 0.12459718
## 448 792444.1 388378.05 FALSE 0.11593070
## 449 765638.7 574091.21 TRUE 0.12671326
## 450 1021283.9 443469.62 TRUE 0.18094641
## 451 332699.3 384918.97 FALSE 0.05861640
## 452 1061066.8 162651.56 TRUE 0.18573217
## 453 830309.0 187072.12 FALSE 0.12639387
## 454 472304.2 434119.66 FALSE 0.08602337
## 455 1059890.9 88426.81 TRUE 0.18693914
## 456 667058.8 249698.26 TRUE 0.11899649
## 457 340419.0 315409.64 FALSE 0.06397672
## 458 887034.7 505015.20 TRUE 0.14235731
## 459 717420.1 390643.10 FALSE 0.10955293
## 460 538525.5 378578.83 FALSE 0.10415878
## 461 739525.4 86983.30 TRUE 0.14250739
## 462 835280.0 473604.86 FALSE 0.13514769
## 463 624355.0 662293.75 FALSE 0.08400833
## 464 869130.8 475363.17 FALSE 0.15117097
## 465 667116.1 668617.40 TRUE 0.08068060
## 466 944079.7 238527.05 FALSE 0.17713702
## 467 1247874.7 545834.22 FALSE 0.16883759
## 468 705092.8 179824.35 FALSE 0.11887118
## 469 894823.6 239409.30 FALSE 0.16679445
## 470 896897.4 608638.15 TRUE 0.11663451
## 471 611221.0 502219.98 TRUE 0.09634165
## 472 979234.1 297276.96 FALSE 0.17379353
## 473 338733.9 203926.45 TRUE 0.05956318
## 474 631332.6 105629.55 TRUE 0.11112699
## 475 304768.0 319986.84 FALSE 0.05243954
## 476 599378.7 631496.97 TRUE 0.08732294
## 477 646606.9 331227.01 FALSE 0.12251817
## 478 693194.3 327867.94 FALSE 0.09411242
## 479 360379.6 517639.65 FALSE 0.05243790
## 480 747303.4 386817.80 FALSE 0.13717916
## 481 558451.3 493278.52 TRUE 0.08346624
## 482 662625.2 274350.22 FALSE 0.12133354
## 483 1203358.5 295371.48 TRUE 0.17516296
## 484 769291.6 322978.54 FALSE 0.14214169
## 485 518512.5 248224.51 TRUE 0.09425485
## 486 583910.6 419444.52 FALSE 0.08937603
## 487 885296.5 573770.13 FALSE 0.13964018
## 488 643217.0 366055.41 FALSE 0.11512833
## 489 533977.0 594607.14 TRUE 0.08307274
## 490 423707.9 293993.22 FALSE 0.08005730
## 491 991943.1 298726.57 FALSE 0.17084216
## 492 946162.1 236662.78 FALSE 0.17499350
## 493 930258.2 433682.25 FALSE 0.13309984
## 494 755551.8 526018.44 FALSE 0.11687176
## 495 665235.8 369166.00 TRUE 0.10527047
## 496 411158.2 489795.75 FALSE 0.05605607
## 497 447185.5 721916.67 TRUE 0.06092852
## 498 883486.7 517486.63 FALSE 0.11042206
## 499 877375.2 539080.61 TRUE 0.13736297
## 500 980880.9 212799.77 FALSE 0.14320801
## 501 962931.1 432569.80 FALSE 0.17900863
## 502 1345431.3 568565.40 FALSE 0.18641768
## 503 468407.3 83395.00 FALSE 0.08623166
## 504 634651.6 249462.90 FALSE 0.10385862
## 505 766040.7 648500.25 TRUE 0.11068549
## 506 502709.3 679688.06 TRUE 0.07267566
## 507 453690.5 99810.49 FALSE 0.07547275
## 508 938379.4 141569.12 TRUE 0.14802318
## 509 501162.7 547814.49 FALSE 0.08722456
## 510 898183.0 505516.46 TRUE 0.16667601
## 511 528680.3 155066.41 FALSE 0.09795060
## 512 1325319.0 252861.58 TRUE 0.19572825
## 513 883561.2 612381.54 TRUE 0.11670751
## 514 541657.0 175853.70 FALSE 0.09588010
## 515 976595.1 468330.63 FALSE 0.13706164
## 516 569954.5 170262.85 FALSE 0.08995193
## 517 1013223.2 296630.80 FALSE 0.17551858
## 518 507473.2 318318.39 FALSE 0.08498649
## 519 683919.4 263123.44 TRUE 0.11893310
## 520 406085.0 435378.75 FALSE 0.07563085
## 521 897503.8 63243.05 TRUE 0.15510849
## 522 659093.3 345191.73 TRUE 0.09304024
## 523 730111.2 523590.45 TRUE 0.10702116
## 524 693653.3 370134.33 FALSE 0.11588106
## 525 1279422.4 555391.54 FALSE 0.16278381
## 526 1548634.1 719448.92 FALSE 0.19054871
## 527 703892.5 343281.98 FALSE 0.12443207
## 528 278765.3 134423.57 TRUE 0.05176756
## 529 1231626.3 284328.55 FALSE 0.18041917
## 530 920507.7 283667.38 FALSE 0.11876577
## 531 1000770.7 564227.43 FALSE 0.12269784
## 532 1276654.4 379187.57 TRUE 0.16919096
## 533 1320899.7 536925.97 FALSE 0.16770146
## 534 1005561.0 652089.30 FALSE 0.14407963
## 535 521013.6 93081.96 TRUE 0.09854766
## 536 315598.8 477585.51 TRUE 0.06200968
## 537 830288.8 563088.98 TRUE 0.10217814
## 538 999427.0 457105.63 TRUE 0.13670668
## 539 1360232.0 504352.14 TRUE 0.18440102
## 540 1419409.0 296843.21 FALSE 0.19104856
## 541 978448.1 205380.25 FALSE 0.14561845
## 542 1029079.8 78492.94 TRUE 0.18971795
## 543 499995.4 364348.78 FALSE 0.09640659
## 544 1089829.8 517627.45 FALSE 0.15858390
## 545 838247.0 557185.28 TRUE 0.10964170
## 546 546389.4 602463.55 TRUE 0.07822401
## 547 366618.8 614987.00 FALSE 0.05552857
## 548 419730.0 374594.69 TRUE 0.06416286
## 549 574132.9 141380.32 TRUE 0.11211677
## 550 1085905.8 527145.24 TRUE 0.19971929
## 551 1094562.5 619345.91 TRUE 0.16420484
## 552 678305.5 410476.13 TRUE 0.11730973
## 553 873693.0 357183.51 TRUE 0.16715061
## 554 1199554.1 144234.76 FALSE 0.16013764
## 555 898517.4 443245.50 FALSE 0.12392934
## 556 661148.3 280876.87 TRUE 0.08768433
## 557 286971.7 335519.79 TRUE 0.05089893
## 558 1139580.5 454314.58 FALSE 0.19453217
## 559 797051.5 259872.37 FALSE 0.14647289
## 560 466526.3 288425.78 FALSE 0.08380023
## 561 601727.3 232862.97 TRUE 0.11115038
## 562 1008634.2 574206.15 FALSE 0.17414592
## 563 568830.2 433492.29 FALSE 0.08097048
## 564 910747.0 108633.41 FALSE 0.17763070
## 565 619509.5 246788.11 FALSE 0.11236831
## 566 1248260.3 365111.27 FALSE 0.19576277
## 567 705272.6 407463.09 TRUE 0.12387863
## 568 636323.9 121339.76 TRUE 0.10599113
## 569 924911.8 341659.35 TRUE 0.15462160
## 570 597070.2 555651.51 FALSE 0.08385483
## 571 1429919.9 466381.23 FALSE 0.18566064
## 572 1099677.8 577722.05 FALSE 0.14992787
## 573 419544.8 485009.01 FALSE 0.06238226
## 574 913584.4 337412.47 FALSE 0.13812887
## 575 285823.8 453673.10 FALSE 0.05311680
## 576 816409.8 453249.08 FALSE 0.14912734
## 577 763289.8 389871.92 FALSE 0.13941043
## 578 1109232.1 643505.67 TRUE 0.15151744
## 579 1016769.4 584272.68 TRUE 0.13723353
## 580 335242.6 81701.64 TRUE 0.06558713
## 581 966498.4 329142.71 FALSE 0.16053796
## 582 774618.7 367297.99 TRUE 0.13036295
## 583 1037662.9 218911.99 FALSE 0.15011319
## 584 883400.6 102168.10 FALSE 0.17228224
## 585 909880.0 362154.21 TRUE 0.13345929
## 586 1144650.4 504648.44 TRUE 0.18315062
## 587 1018881.7 441241.23 TRUE 0.14520658
## 588 1039213.1 490316.77 TRUE 0.17178200
## 589 781113.8 267713.72 FALSE 0.12687224
## 590 643654.3 507387.91 FALSE 0.09867332
## 591 1093740.3 366306.45 TRUE 0.18002406
## 592 498533.5 356144.24 TRUE 0.08236127
## 593 935655.1 571162.21 TRUE 0.14230618
## 594 1074365.2 250789.89 TRUE 0.16347790
## 595 1598195.4 440334.63 TRUE 0.19762418
## 596 801962.4 161318.78 FALSE 0.12943243
## 597 821151.1 305643.72 TRUE 0.12195943
## 598 1229369.5 363033.27 FALSE 0.17538559
## 599 934936.1 236506.49 TRUE 0.12929849
## 600 751580.0 610592.16 TRUE 0.10865519
## 601 441415.0 512452.55 TRUE 0.06690134
## 602 739291.9 570827.88 TRUE 0.11553133
## 603 432548.0 187964.06 FALSE 0.05519195
## 604 458412.1 503670.06 FALSE 0.06878419
## 605 1374933.9 483799.25 TRUE 0.19849603
## 606 1380155.7 737610.41 FALSE 0.17428536
## 607 1230143.0 141682.62 FALSE 0.18482634
## 608 692869.0 488105.88 TRUE 0.12412506
## 609 1102395.4 147640.69 TRUE 0.19694074
## 610 648375.2 269072.87 TRUE 0.09520774
## 611 979605.2 427467.35 TRUE 0.19116465
## 612 315201.1 420364.28 TRUE 0.05270285
## 613 1040527.9 711275.94 FALSE 0.14323534
## 614 982239.2 333672.76 FALSE 0.13208820
## 615 769056.8 77452.84 FALSE 0.12502355
## 616 814639.7 281613.79 FALSE 0.15604120
## 617 908035.6 577663.27 FALSE 0.13901856
## 618 712034.6 165498.96 FALSE 0.10532663
## 619 922936.9 54262.11 FALSE 0.17793782
## 620 614175.3 373348.73 FALSE 0.08571843
## 621 982888.7 497798.95 FALSE 0.19235975
## 622 664852.3 531925.71 FALSE 0.12053406
## 623 1035939.3 369269.84 TRUE 0.18307975
## 624 1318388.4 480604.27 FALSE 0.16056303
## 625 272172.4 466868.69 TRUE 0.05413248
## 626 353145.4 323767.63 FALSE 0.06051076
## 627 715751.7 218008.05 FALSE 0.12007630
## 628 855671.0 277214.34 FALSE 0.16587877
## 629 1328036.4 389541.25 TRUE 0.19224163
## 630 299176.5 220125.15 FALSE 0.05468249
## 631 836910.8 501779.27 FALSE 0.15498654
## 632 1035244.1 623648.64 TRUE 0.14092794
## 633 964050.8 279835.03 FALSE 0.14490994
## 634 961675.3 646307.83 FALSE 0.14047491
## 635 877337.2 358662.56 TRUE 0.17095508
## 636 391672.3 321483.64 FALSE 0.06736134
## 637 1199755.2 230012.36 FALSE 0.19961594
## 638 735199.6 470328.55 FALSE 0.11902518
## 639 276756.3 55058.46 FALSE 0.05348147
## 640 338208.9 479259.20 FALSE 0.06378072
## 641 1307106.4 100587.61 TRUE 0.19606522
## 642 650049.7 66937.04 FALSE 0.12751200
## 643 540708.5 91142.00 TRUE 0.10013848
## 644 823290.3 289318.06 TRUE 0.13235132
## 645 702380.8 151298.82 FALSE 0.14000573
## 646 663550.5 388762.52 TRUE 0.11517047
## 647 508032.1 198395.49 TRUE 0.09129410
## 648 1153227.8 338275.45 FALSE 0.18717529
## 649 1251113.7 494157.80 TRUE 0.18092843
## 650 1010200.6 69240.78 FALSE 0.18841234
## 651 717542.8 691263.35 TRUE 0.10260964
## 652 505618.9 316772.30 TRUE 0.07752449
## 653 369853.2 78141.25 FALSE 0.07051061
## 654 825577.1 489744.66 FALSE 0.13935744
## 655 679653.8 238800.73 TRUE 0.11731774
## 656 1037026.5 586270.81 TRUE 0.15861438
## 657 608421.4 136811.24 FALSE 0.08369365
## 658 752215.8 514843.86 FALSE 0.12216474
## 659 423741.7 318898.95 TRUE 0.06614324
## 660 1043041.4 341757.30 TRUE 0.16155257
## 661 280284.8 172089.92 TRUE 0.05312849
## 662 1341577.6 615568.54 TRUE 0.19910570
## 663 761418.9 192848.06 TRUE 0.11009212
## 664 782503.4 66456.14 TRUE 0.13587066
## 665 472583.3 221822.88 FALSE 0.09421637
## 666 814093.2 197421.97 FALSE 0.11464176
## 667 785977.7 270745.98 TRUE 0.15537894
## 668 984188.3 455204.99 TRUE 0.15479216
## 669 626917.0 329810.89 FALSE 0.08471047
## 670 865942.9 83670.47 TRUE 0.13172536
## 671 561384.6 276482.88 TRUE 0.10417084
## 672 1107365.1 472181.36 FALSE 0.19317790
## 673 673687.1 471204.16 TRUE 0.10040883
## 674 579024.7 409709.27 TRUE 0.09706339
## 675 853517.4 598483.64 TRUE 0.14097376
## 676 747707.1 272808.19 FALSE 0.12479593
## 677 789272.1 292288.80 TRUE 0.09737772
## 678 786341.9 465111.42 FALSE 0.10713767
## 679 906277.1 150954.29 TRUE 0.17731070
## 680 627868.8 206844.63 FALSE 0.11597024
## 681 541635.1 432395.13 TRUE 0.07734185
## 682 786609.8 500165.22 FALSE 0.14496465
## 683 612846.9 288327.10 TRUE 0.10907584
## 684 798028.9 642140.37 TRUE 0.11004468
## 685 674073.3 509803.38 FALSE 0.10458510
## 686 949215.2 455719.50 TRUE 0.13457534
## 687 927302.6 162970.03 TRUE 0.17025369
## 688 981400.8 148203.47 TRUE 0.18178069
## 689 1001168.8 534296.85 FALSE 0.14121307
## 690 505213.9 125888.98 FALSE 0.07677258
## 691 840097.0 206246.19 FALSE 0.11240755
## 692 593625.7 180030.70 FALSE 0.08825315
## 693 319170.5 382575.16 TRUE 0.06008257
## 694 623498.1 66259.79 TRUE 0.11584930
## 695 367783.9 535816.79 TRUE 0.05718796
## 696 665506.5 497556.19 TRUE 0.11149583
## 697 655001.3 432698.51 FALSE 0.12224289
## 698 718159.2 389588.44 FALSE 0.11066783
## 699 726788.5 609354.61 FALSE 0.11106892
## 700 561129.6 451282.89 TRUE 0.08499311
## 701 989163.3 272442.75 FALSE 0.13003568
## 702 607716.4 120380.42 TRUE 0.09858680
## 703 1042857.6 94351.57 TRUE 0.18028653
## 704 836582.9 156209.19 FALSE 0.13279199
## 705 629106.1 558930.00 TRUE 0.10891478
## 706 496507.0 506917.47 FALSE 0.09428275
## 707 632882.0 329450.10 TRUE 0.08724473
## 708 857256.8 86441.61 FALSE 0.15091719
## 709 1363037.7 474857.99 FALSE 0.17771986
## 710 780383.7 284633.16 FALSE 0.14786011
## 711 761538.5 452086.79 FALSE 0.14601568
## 712 489815.9 650361.52 TRUE 0.07361428
## 713 1359988.0 91964.23 TRUE 0.18571704
## 714 1006399.0 231081.66 TRUE 0.17633438
## 715 344058.5 163795.28 FALSE 0.06193137
## 716 861921.2 256183.93 FALSE 0.14983741
## 717 817365.6 135724.48 FALSE 0.11682445
## 718 501460.8 252475.71 TRUE 0.06605790
## 719 616242.9 133652.45 TRUE 0.11481977
## 720 1323900.6 261675.32 FALSE 0.19809446
## 721 452503.8 58741.27 TRUE 0.07927761
## 722 1074107.5 679978.79 FALSE 0.14077427
## 723 870770.1 668703.39 TRUE 0.12876957
## 724 764998.3 98481.84 TRUE 0.14799274
## 725 272773.3 428891.68 TRUE 0.05319075
## 726 509036.7 318230.48 TRUE 0.07171849
## 727 476920.1 629409.59 TRUE 0.06408583
## 728 1082304.0 498777.01 FALSE 0.15769857
## 729 747365.0 551458.96 TRUE 0.12525092
## 730 597681.9 496268.60 TRUE 0.11210891
## 731 766902.4 325674.20 FALSE 0.10345413
## 732 1195080.0 123701.61 TRUE 0.15511815
## 733 849182.6 116727.92 FALSE 0.13784574
## 734 1112337.6 573684.74 FALSE 0.14832338
## 735 521097.1 647767.41 FALSE 0.07798805
## 736 557322.1 337799.66 FALSE 0.09809409
## 737 1422620.0 441498.10 FALSE 0.19633893
## 738 928228.3 467876.11 FALSE 0.13535226
## 739 383152.2 508347.20 TRUE 0.05281580
## 740 468665.5 198755.67 TRUE 0.08515813
## 741 410262.8 414643.59 TRUE 0.06779796
## 742 317142.2 270898.47 TRUE 0.06252071
## 743 504735.6 348355.40 TRUE 0.09387809
## 744 414344.9 237307.55 TRUE 0.07927070
## 745 662085.6 382253.91 TRUE 0.12377624
## 746 448180.3 369681.23 FALSE 0.06095584
## 747 1001527.3 574834.81 TRUE 0.17339669
## 748 619851.9 347129.61 FALSE 0.08225886
## 749 494518.1 273527.68 FALSE 0.07269998
## 750 1284100.2 350620.61 TRUE 0.17317418
## 751 693517.4 553853.70 TRUE 0.08951379
## 752 1152428.3 250858.17 FALSE 0.16169460
## 753 580579.1 480857.81 TRUE 0.09272578
## 754 373664.7 282196.75 FALSE 0.07141487
## 755 609493.1 451879.58 FALSE 0.10036448
## 756 1198694.1 550282.79 TRUE 0.15645479
## 757 1553276.1 322988.01 TRUE 0.19412427
## 758 1471668.0 713543.26 FALSE 0.19913847
## 759 797480.4 157682.82 TRUE 0.13744787
## 760 511356.4 250150.26 FALSE 0.07283855
## 761 505597.1 148520.38 FALSE 0.07785673
## 762 399169.3 450505.04 FALSE 0.06835660
## 763 1210834.2 272721.27 TRUE 0.19778076
## 764 370113.2 479294.51 TRUE 0.05198964
## 765 707016.4 573552.51 FALSE 0.09799434
## 766 670748.9 509849.03 FALSE 0.08544048
## 767 857023.4 574290.38 FALSE 0.12912470
## 768 305089.0 135287.07 TRUE 0.05737721
## 769 1013653.7 445885.49 TRUE 0.16137023
## 770 373603.7 339074.67 TRUE 0.06912977
## 771 359397.9 73339.80 TRUE 0.06944434
## 772 1248721.2 665877.26 FALSE 0.18153832
## 773 526282.0 473724.06 TRUE 0.10338444
## 774 699460.2 246342.22 TRUE 0.12839168
## 775 1369095.6 87827.05 TRUE 0.19876709
## 776 1306014.9 544967.37 TRUE 0.18144177
## 777 858303.9 325940.29 FALSE 0.16213980
## 778 379024.2 391143.18 TRUE 0.06948273
## 779 748979.0 276594.33 FALSE 0.13467254
## 780 1337988.8 585222.21 TRUE 0.17528679
## 781 281871.0 174022.21 FALSE 0.05160752
## 782 516415.7 447896.27 FALSE 0.09256735
## 783 1181906.5 708237.83 FALSE 0.16633920
## 784 746804.9 406167.72 FALSE 0.11959447
## 785 523860.4 72389.79 FALSE 0.08255309
## 786 452870.9 688309.12 TRUE 0.06442017
## 787 450330.9 187234.54 FALSE 0.08622626
## 788 424274.1 548839.00 TRUE 0.06117460
## 789 460322.1 327100.91 FALSE 0.08790867
## 790 446925.4 393626.10 FALSE 0.08624303
## 791 1209591.8 308706.51 TRUE 0.18836524
## 792 1016049.6 408311.51 TRUE 0.13080202
## 793 388209.5 83371.41 FALSE 0.06626275
## 794 533884.4 202776.78 TRUE 0.08238507
## 795 460611.0 318335.08 FALSE 0.09068822
## 796 587037.8 117750.44 FALSE 0.10673948
## 797 1144349.7 295612.38 TRUE 0.18888038
## 798 1046729.2 537018.84 FALSE 0.16388785
## 799 464393.1 313026.22 TRUE 0.08643688
## 800 627657.3 613052.84 FALSE 0.09595979
## 801 411765.5 629571.07 TRUE 0.05142230
## 802 631147.4 130119.25 FALSE 0.11502070
## 803 357678.5 295703.03 FALSE 0.05525707
## 804 954841.7 168973.00 FALSE 0.13171255
## 805 517527.3 648778.11 FALSE 0.07866712
## 806 1042562.4 433440.65 FALSE 0.13616204
## 807 599645.7 462658.46 FALSE 0.07846744
## 808 628895.1 339232.95 FALSE 0.09231446
## 809 396129.2 431511.05 FALSE 0.07740937
## 810 358689.9 269207.18 FALSE 0.05975784
## 811 644461.0 186115.23 TRUE 0.12374234
## 812 813819.0 364610.27 TRUE 0.13172106
## 813 1314500.5 156503.69 TRUE 0.17114715
## 814 1299861.3 113303.35 FALSE 0.16372567
## 815 804912.4 668138.40 FALSE 0.11573867
## 816 1035102.9 107420.53 FALSE 0.16530712
## 817 336050.4 396586.64 TRUE 0.05263810
## 818 594345.2 436450.80 FALSE 0.09546864
## 819 531499.2 391438.77 TRUE 0.07406797
## 820 351860.4 57934.04 TRUE 0.06689803
## 821 793438.5 213839.24 FALSE 0.14032393
## 822 1042505.6 248961.38 FALSE 0.18002545
## 823 421058.9 222682.65 FALSE 0.06400706
## 824 618916.3 479903.98 TRUE 0.10164615
## 825 469325.5 287536.51 TRUE 0.08723791
## 826 1390098.7 279143.14 TRUE 0.18125692
## 827 1177714.2 380262.24 FALSE 0.18647832
## 828 797416.1 96170.26 TRUE 0.15314794
## 829 919352.2 211092.74 TRUE 0.17349405
## 830 582585.5 495587.40 FALSE 0.10724235
## 831 1173660.0 458279.62 FALSE 0.17583895
## 832 1166590.6 111749.48 TRUE 0.19502938
## 833 597398.5 499366.82 TRUE 0.10801721
## 834 607990.4 273363.76 FALSE 0.11194931
## 835 1111902.9 226140.91 FALSE 0.15709374
## 836 853526.0 199141.98 FALSE 0.14376348
## 837 1027102.4 502917.57 TRUE 0.16584122
## 838 985143.4 100597.11 TRUE 0.13828554
## 839 554940.7 545594.10 FALSE 0.08123596
## 840 914855.9 184379.58 FALSE 0.17752444
## 841 711897.7 299368.81 TRUE 0.13426630
## 842 1267918.2 519006.51 TRUE 0.16349752
## 843 515898.9 77349.82 FALSE 0.09838407
## 844 335983.0 407568.91 FALSE 0.05401829
## 845 1220127.5 685535.43 FALSE 0.15393149
## 846 397032.1 89803.32 TRUE 0.05367556
## 847 423368.4 399678.89 TRUE 0.06156902
## 848 1076260.3 511557.92 TRUE 0.14504896
## 849 1104062.9 466829.22 FALSE 0.17060001
## 850 390574.6 125872.72 TRUE 0.05685928
## 851 684771.3 199206.12 FALSE 0.12274041
## 852 916355.9 99165.33 TRUE 0.11681493
## 853 1012008.2 374336.64 FALSE 0.18306109
## 854 780152.3 463542.41 FALSE 0.10318186
## 855 810948.8 447632.17 TRUE 0.12682387
## 856 1154555.3 102431.79 TRUE 0.16519921
## 857 967107.5 203803.13 TRUE 0.17245267
## 858 666844.3 222009.20 TRUE 0.09220593
## 859 882624.4 335314.55 TRUE 0.11985772
## 860 879223.5 445533.35 TRUE 0.14378973
## 861 1070032.0 447213.78 FALSE 0.16952564
## 862 779129.7 294326.75 FALSE 0.11093487
## 863 813355.3 475295.59 TRUE 0.12584991
## 864 411144.0 204150.17 TRUE 0.05240623
## 865 415584.0 142408.54 TRUE 0.08171012
## 866 457316.3 394734.99 FALSE 0.08032897
## 867 726501.9 587873.06 FALSE 0.10751112
## 868 504199.5 576030.69 TRUE 0.08029988
## 869 529340.6 620681.36 FALSE 0.07760199
## 870 1309060.9 548117.16 FALSE 0.18286943
## 871 510588.7 204591.01 TRUE 0.07561084
## 872 466708.1 236105.50 TRUE 0.06473949
## 873 1174736.6 347282.42 FALSE 0.16403371
## 874 949689.9 210482.82 FALSE 0.16403329
## 875 991728.5 665384.23 FALSE 0.14329156
## 876 1053799.6 91096.61 FALSE 0.17318492
## 877 295061.2 170668.10 FALSE 0.05173484
## 878 409444.9 595551.54 FALSE 0.05781442
## 879 1490473.4 686439.82 FALSE 0.18067476
## 880 889088.0 247602.96 FALSE 0.13236900
## 881 1259099.9 510589.09 FALSE 0.19496009
## 882 917801.3 373085.67 TRUE 0.13491786
## 883 351105.1 426481.11 TRUE 0.06586303
## 884 1259032.0 273662.81 TRUE 0.15547570
## 885 349061.9 615406.14 FALSE 0.05632518
## 886 1193063.6 634467.31 TRUE 0.17687750
## 887 441052.3 453476.38 FALSE 0.05419478Explanation: Filters transactions where trade value is greater than 50 lakh INR. ### Question: Sort data based on shipment days
data %>%
arrange(Shipment_Days)## Year Month Product_Category Product_Name Import_Export Quantity
## 1 2022.000 September Agriculture Mobile Export 4499
## 2 2021.000 October Automobile Mobile Import 4615
## 3 2019.000 August Electronics Cotton Import 9877
## 4 2019.000 February Automobile Mobile Import 5832
## 5 2020.000 November Electronics Cotton Import 9281
## 6 2023.000 May Pharma Car Import 7673
## 7 2021.000 October Pharma Wheat Import 3258
## 8 2021.000 May Agriculture Car Export 9652
## 9 2022.000 October Electronics Cotton Import 8073
## 10 2021.000 July Electronics Car Import 8587
## 11 2020.000 May Automobile Wheat Export 737
## 12 2020.000 April Pharma Cotton Import 2279
## 13 2019.000 January Automobile Mobile Export 3163
## 14 2018.000 November Automobile Mobile Export 8352
## 15 2020.000 January Automobile Wheat Export 6447
## 16 2020.000 March Electronics Medicine Import 8780
## 17 2020.000 May Agriculture Car Import 3784
## 18 2019.000 February Automobile Wheat Import 2561
## 19 2018.000 November Agriculture Car Import 8195
## 20 2021.000 July Electronics Cotton Import 622
## 21 2023.000 April Textile Mobile Import 3855
## 22 2021.000 March Pharma Car Export 4573
## 23 2022.000 February Pharma Medicine Export 8628
## 24 2022.000 June Automobile Car Import 9546
## 25 2024.000 September Textile Mobile Import 8838
## 26 2018.000 September Automobile Wheat Export 1772
## 27 2022.000 December Electronics Cotton Export 1810
## 28 2019.000 September Agriculture Cotton Import 348
## 29 2024.000 August Textile Wheat Export 2861
## 30 2019.000 April Agriculture Medicine Import 4569
## 31 2021.000 May Pharma Medicine Export 7839
## 32 2023.000 October Electronics Wheat Import 8619
## 33 2018.000 July Automobile Medicine Export 8843
## 34 2024.000 May Automobile Mobile Import 1446
## 35 2023.000 December Electronics Medicine Import 7892
## 36 2022.000 January Textile Mobile Export 3147
## 37 2018.000 April Pharma Cotton Export 6463
## 38 2019.000 November Agriculture Medicine Export 8245
## 39 2019.000 September Textile Cotton Export 8900
## 40 2018.000 March Pharma Mobile Import 5718
## 41 2018.000 March Electronics Wheat Export 3754
## 42 2019.000 July Pharma Wheat Export 2970
## 43 2020.000 January Automobile Mobile Export 7987
## 44 2019.000 September Textile Car Import 1677
## 45 2019.000 April Textile Wheat Export 8021
## 46 2021.000 September Textile Mobile Export 4830
## 47 2022.000 July Pharma Wheat Export 3522
## 48 2022.000 May Electronics Car Import 1472
## 49 2022.000 November Textile Wheat Import 3499
## 50 2024.000 January Agriculture Cotton Import 1331
## 51 2023.000 June Pharma Car Import 7022
## 52 2021.000 November Electronics Wheat Export 9578
## 53 2023.000 June Textile Medicine Export 3082
## 54 2020.000 June Electronics Cotton Import 1992
## 55 2021.000 May Automobile Wheat Export 7112
## 56 2022.000 August Automobile Mobile Export 5946
## 57 2022.000 September Automobile Cotton Import 4881
## 58 2022.000 March Electronics Mobile Import 7421
## 59 2020.000 December Textile Cotton Export 9537
## 60 2019.000 August Textile Cotton Import 1599
## 61 2019.000 October Agriculture Cotton Import 9962
## 62 2023.000 July Electronics Car Export 240
## 63 2022.000 August Agriculture Cotton Export 3702
## 64 2021.000 October Automobile Wheat Import 8517
## 65 2020.000 May Agriculture Wheat Export 4849
## 66 2022.000 February Electronics Mobile Export 7526
## 67 2021.000 November Textile Cotton Export 8620
## 68 2019.000 September Pharma Medicine Import 5921
## 69 2021.000 August Electronics Car Export 9640
## 70 2018.000 October Textile Wheat Export 2750
## 71 2020.000 January Textile Wheat Export 9649
## 72 2020.000 July Electronics Wheat Export 4719
## 73 2020.000 July Electronics Car Export 6179
## 74 2022.000 November Pharma Wheat Import 1471
## 75 2019.000 April Agriculture Mobile Import 4805
## 76 2018.000 December Pharma Car Import 3403
## 77 2022.000 January Pharma Mobile Import 6077
## 78 2019.000 August Pharma Mobile Import 3580
## 79 2020.000 February Agriculture Car Export 8837
## 80 2022.000 July Automobile Wheat Import 5675
## 81 2019.000 February Pharma Car Import 9979
## 82 2020.000 July Electronics Wheat Export 5397
## 83 2019.000 April Pharma Medicine Export 9812
## 84 2024.000 February Agriculture Car Import 2351
## 85 2019.000 June Agriculture Medicine Export 5232
## 86 2019.000 August Agriculture Cotton Export 9660
## 87 2023.000 May Pharma Car Import 5545
## 88 2021.000 December Pharma Car Import 8588
## 89 2018.000 August Agriculture Car Import 1571
## 90 2024.000 December Pharma Car Export 261
## 91 2022.000 September Automobile Cotton Import 841
## 92 2023.000 October Textile Mobile Export 9001
## 93 2020.000 April Pharma Mobile Import 4959
## 94 2021.000 February Agriculture Mobile Export 140
## 95 2024.000 April Electronics Car Import 5628
## 96 2024.000 March Automobile Car Export 156
## 97 2019.000 April Textile Mobile Export 5563
## 98 2019.000 February Textile Mobile Import 2529
## 99 2019.000 July Electronics Mobile Import 8353
## 100 2024.000 November Electronics Medicine Import 3016
## 101 2023.000 July Automobile Wheat Export 861
## 102 2018.000 March Automobile Car Export 8176
## 103 2023.000 October Electronics Medicine Import 544
## 104 2023.000 March Agriculture Wheat Import 8097
## 105 2018.000 November Agriculture Mobile Export 3221
## 106 2019.000 June Pharma Car Export 4312
## 107 2020.000 July Pharma Wheat Export 7255
## 108 2023.000 May Agriculture Car Import 2975
## 109 2018.000 June Textile Cotton Import 3776
## 110 2023.000 May Automobile Car Export 6946
## 111 2023.000 December Agriculture Mobile Import 4157
## 112 2020.000 April Textile Mobile Import 4972
## 113 2024.000 February Automobile Medicine Export 8964
## 114 2019.000 November Agriculture Medicine Export 2803
## 115 2024.000 December Agriculture Car Import 2206
## 116 2024.000 August Textile Cotton Export 1256
## 117 2018.000 March Electronics Medicine Export 6822
## 118 2019.000 November Textile Cotton Export 6323
## 119 2018.000 February Pharma Medicine Export 2808
## 120 2024.000 April Agriculture Car Import 4591
## 121 2022.000 August Electronics Cotton Import 1089
## 122 2021.000 January Automobile Wheat Import 5972
## 123 2023.000 May Textile Car Export 5813
## 124 2018.000 February Automobile Cotton Export 4025
## 125 2022.000 May Textile Wheat Import 2227
## 126 2024.000 March Textile Wheat Import 5422
## 127 2021.000 May Pharma Wheat Import 3945
## 128 2021.000 April Electronics Mobile Export 2117
## 129 2019.000 August Agriculture Cotton Export 5114
## 130 2022.000 March Automobile Medicine Import 2662
## 131 2021.000 October Textile Wheat Export 2605
## 132 2024.000 March Automobile Cotton Export 810
## 133 2020.000 October Electronics Car Import 682
## 134 2020.000 May Electronics Mobile Export 6638
## 135 2020.000 July Textile Wheat Export 4719
## 136 2021.000 March Agriculture Cotton Export 6075
## 137 2018.000 July Textile Cotton Import 3774
## 138 2020.000 September Pharma Mobile Import 4938
## 139 2022.000 March Pharma Cotton Import 5847
## 140 2018.000 April Pharma Cotton Import 6320
## 141 2021.000 January Agriculture Car Import 3670
## 142 2021.000 September Textile Car Import 726
## 143 2022.000 July Electronics Car Import 5541
## 144 2023.000 October Electronics Medicine Import 287
## 145 2022.000 September Agriculture Car Import 8680
## 146 2023.000 July Agriculture Cotton Export 3974
## 147 2020.000 June Agriculture Medicine Export 2502
## 148 2022.000 November Automobile Mobile Import 5758
## 149 2019.000 November Electronics Wheat Export 5436
## 150 2024.000 May Textile Wheat Export 6802
## 151 2021.000 January Pharma Car Export 419
## 152 2022.000 September Textile Wheat Import 4130
## 153 2021.000 June Pharma Medicine Import 9665
## 154 2018.000 November Electronics Mobile Import 5459
## 155 2019.000 September Pharma Mobile Import 7092
## 156 2018.000 January Electronics Cotton Import 4855
## 157 2019.000 November Textile Cotton Export 9632
## 158 2024.000 November Automobile Car Export 9589
## 159 2018.000 April Automobile Wheat Import 6297
## 160 2020.000 November Pharma Cotton Export 8175
## 161 2023.000 December Pharma Car Export 5479
## 162 2021.000 December Electronics Car Export 3869
## 163 2018.000 January Textile Mobile Export 4214
## 164 2018.000 March Textile Mobile Import 6444
## 165 2022.000 April Automobile Mobile Import 7723
## 166 2024.000 February Automobile Mobile Import 4839
## 167 2022.000 February Textile Mobile Import 2757
## 168 2022.000 December Automobile Wheat Import 3955
## 169 2019.000 April Automobile Cotton Export 1375
## 170 2019.000 September Electronics Mobile Import 7788
## 171 2020.000 October Automobile Mobile Export 8060
## 172 2018.000 November Textile Cotton Export 8903
## 173 2024.000 January Electronics Medicine Export 5303
## 174 2021.000 July Electronics Mobile Export 864
## 175 2021.000 October Textile Medicine Import 8411
## 176 2019.000 March Textile Cotton Export 141
## 177 2020.000 June Pharma Cotton Export 2869
## 178 2019.000 October Agriculture Mobile Export 1126
## 179 2023.000 July Automobile Mobile Import 9500
## 180 2018.000 November Electronics Mobile Export 6654
## 181 2023.000 November Agriculture Mobile Import 2576
## 182 2020.000 September Electronics Wheat Export 2071
## 183 2021.000 March Automobile Cotton Export 813
## 184 2021.000 February Agriculture Medicine Import 5742
## 185 2024.000 May Automobile Car Import 6848
## 186 2018.000 November Textile Cotton Export 3769
## 187 2023.000 December Electronics Medicine Import 9113
## 188 2018.000 November Textile Cotton Import 8221
## 189 2021.000 December Pharma Mobile Export 9984
## 190 2020.000 August Automobile Wheat Import 7347
## 191 2020.000 April Agriculture Medicine Import 1968
## 192 2020.000 October Automobile Mobile Export 9535
## 193 2023.000 March Agriculture Car Import 2293
## 194 2019.000 November Agriculture Mobile Import 594
## 195 2022.000 June Agriculture Medicine Export 8230
## 196 2022.000 April Automobile Medicine Import 4229
## 197 2024.000 July Textile Medicine Export 3870
## 198 2024.000 January Electronics Wheat Import 5489
## 199 2021.000 February Agriculture Car Import 9052
## 200 2024.000 September Electronics Medicine Import 9686
## 201 2020.000 November Agriculture Medicine Export 5600
## 202 2022.000 July Textile Wheat Import 991
## 203 2023.000 November Textile Cotton Import 1961
## 204 2019.000 October Agriculture Car Import 8822
## 205 2018.000 October Textile Medicine Import 9420
## 206 2022.000 August Electronics Car Export 9849
## 207 2018.000 September Agriculture Car Import 6004
## 208 2023.000 November Textile Car Import 5784
## 209 2018.000 April Textile Car Import 5420
## 210 2023.000 January Pharma Car Import 6300
## 211 2019.000 March Agriculture Mobile Import 2409
## 212 2024.000 August Agriculture Wheat Import 8509
## 213 2021.000 June Pharma Wheat Import 1164
## 214 2021.000 February Electronics Cotton Export 1351
## 215 2023.000 September Electronics Cotton Export 1240
## 216 2023.000 May Electronics Wheat Export 6213
## 217 2018.000 February Textile Wheat Export 7204
## 218 2021.000 November Electronics Cotton Export 4357
## 219 2023.000 October Pharma Cotton Export 8817
## 220 2019.000 March Automobile Mobile Export 5113
## 221 2022.000 October Automobile Car Import 6388
## 222 2019.000 September Automobile Car Export 1764
## 223 2019.000 May Electronics Wheat Export 6703
## 224 2024.000 April Automobile Medicine Export 629
## 225 2018.000 June Textile Cotton Export 3322
## 226 2022.000 November Electronics Wheat Import 5270
## 227 2021.000 March Electronics Car Export 9611
## 228 2018.000 April Agriculture Wheat Export 7398
## 229 2021.000 June Pharma Medicine Export 2141
## 230 2024.000 June Pharma Wheat Import 4539
## 231 2021.000 November Textile Cotton Import 6323
## 232 2019.000 March Pharma Car Export 978
## 233 2020.000 March Automobile Car Export 9393
## 234 2022.000 August Electronics Mobile Export 7272
## 235 2023.000 February Agriculture Mobile Export 6639
## 236 2021.000 November Textile Car Import 8967
## 237 2023.000 February Pharma Mobile Import 4771
## 238 2024.000 August Electronics Car Export 7161
## 239 2024.000 May Pharma Cotton Import 4512
## 240 2018.000 May Electronics Mobile Import 1357
## 241 2022.000 January Automobile Wheat Export 2185
## 242 2019.000 January Electronics Medicine Import 4512
## 243 2021.000 June Textile Wheat Export 3902
## 244 2019.000 December Automobile Medicine Import 5869
## 245 2024.000 December Textile Car Import 6075
## 246 2023.000 July Agriculture Wheat Export 1216
## 247 2019.000 September Electronics Car Import 1517
## 248 2022.000 March Automobile Wheat Import 3479
## 249 2024.000 June Textile Car Export 9366
## 250 2021.000 July Electronics Mobile Import 461
## 251 2024.000 November Automobile Mobile Export 5773
## 252 2024.000 August Automobile Medicine Export 5201
## 253 2022.000 September Agriculture Medicine Export 4961
## 254 2024.000 December Agriculture Wheat Import 433
## 255 2024.000 December Automobile Wheat Import 5869
## 256 2024.000 April Pharma Medicine Import 5233
## 257 2021.000 November Textile Wheat Export 6967
## 258 2023.000 April Automobile Mobile Import 5048
## 259 2024.000 November Textile Medicine Export 7615
## 260 2023.000 May Pharma Cotton Import 6307
## 261 2023.000 August Pharma Car Import 3559
## 262 2020.000 October Electronics Mobile Export 3953
## 263 2023.000 April Pharma Mobile Export 9919
## 264 2024.000 April Agriculture Medicine Export 9313
## 265 2024.000 November Electronics Medicine Export 4616
## 266 2023.000 March Automobile Mobile Export 6763
## 267 2019.000 July Textile Mobile Export 2174
## 268 2020.000 July Automobile Wheat Export 8078
## 269 2022.000 April Electronics Car Import 9404
## 270 2021.000 November Pharma Mobile Export 3139
## 271 2023.000 May Textile Car Import 3764
## 272 2022.000 July Textile Medicine Import 8125
## 273 2018.000 May Textile Mobile Export 4123
## 274 2024.000 May Pharma Medicine Export 3198
## 275 2021.000 June Automobile Wheat Export 6659
## 276 2021.000 November Textile Cotton Export 5990
## 277 2019.000 December Pharma Car Import 6692
## 278 2024.000 April Textile Medicine Export 1484
## 279 2021.000 January Electronics Wheat Import 3661
## 280 2019.000 July Automobile Cotton Import 2327
## 281 2021.000 March Automobile Medicine Import 3319
## 282 2021.000 December Agriculture Mobile Export 6508
## 283 2018.000 December Electronics Medicine Import 1835
## 284 2024.000 March Automobile Car Export 9009
## 285 2024.000 August Agriculture Mobile Import 229
## 286 2024.000 May Textile Wheat Import 1245
## 287 2021.000 September Pharma Medicine Import 8635
## 288 2024.000 December Agriculture Car Export 3993
## 289 2021.000 December Automobile Mobile Import 802
## 290 2023.000 February Automobile Mobile Import 1686
## 291 2022.000 September Textile Car Export 7476
## 292 2023.000 February Textile Medicine Import 8690
## 293 2020.000 June Textile Wheat Export 1182
## 294 2020.000 March Pharma Mobile Export 8558
## 295 2021.000 March Electronics Medicine Export 5963
## 296 2023.000 December Textile Medicine Import 3023
## 297 2024.000 August Electronics Mobile Import 2819
## 298 2024.000 May Electronics Cotton Import 2602
## 299 2020.000 July Pharma Car Import 7054
## 300 2022.000 May Pharma Medicine Import 9311
## 301 2021.000 January Textile Car Import 3883
## 302 2022.000 March Textile Wheat Export 4428
## 303 2018.000 May Textile Car Import 9759
## 304 2020.000 May Electronics Cotton Export 5331
## 305 2024.000 December Pharma Mobile Export 1226
## 306 2020.000 January Textile Car Export 9836
## 307 2020.000 October Electronics Cotton Export 2843
## 308 2023.000 December Textile Wheat Export 264
## 309 2023.000 March Automobile Mobile Import 5982
## 310 2019.000 May Automobile Mobile Export 4728
## 311 2020.000 July Textile Mobile Import 1800
## 312 2021.000 March Electronics Medicine Import 4964
## 313 2018.000 October Automobile Wheat Export 3759
## 314 2018.000 October Automobile Medicine Import 5568
## 315 2021.000 October Textile Cotton Import 7026
## 316 2020.000 December Textile Car Import 9478
## 317 2019.000 November Pharma Car Export 9633
## 318 2018.000 October Textile Medicine Export 6417
## 319 2021.000 February Electronics Cotton Export 1600
## 320 2020.000 August Agriculture Cotton Import 8840
## 321 2024.000 March Textile Medicine Export 4287
## 322 2021.000 November Textile Car Import 8254
## 323 2022.000 July Electronics Medicine Export 9992
## 324 2023.000 August Pharma Cotton Import 9032
## 325 2020.000 October Automobile Mobile Export 3512
## 326 2021.000 September Electronics Medicine Export 8117
## 327 2019.000 October Automobile Mobile Import 3266
## 328 2024.000 August Textile Mobile Import 3861
## 329 2024.000 December Agriculture Cotton Import 9814
## 330 2024.000 March Automobile Car Import 9033
## 331 2024.000 November Automobile Medicine Import 9879
## 332 2018.000 October Pharma Medicine Export 6426
## 333 2021.000 March Textile Car Export 4205
## 334 2023.000 January Electronics Cotton Export 7123
## 335 2024.000 September Automobile Medicine Export 1622
## 336 2019.000 January Pharma Mobile Import 3939
## 337 2018.000 December Textile Cotton Export 6876
## 338 2023.000 March Agriculture Mobile Import 9837
## 339 2023.000 July Textile Cotton Import 1719
## 340 2021.000 March Textile Mobile Import 5158
## 341 2024.000 February Electronics Mobile Import 9233
## 342 2020.000 March Pharma Mobile Import 6435
## 343 2023.000 May Pharma Wheat Export 2276
## 344 2019.000 February Textile Car Import 2235
## 345 2021.000 February Automobile Mobile Import 5682
## 346 2020.000 March Agriculture Cotton Export 6583
## 347 2019.000 June Textile Cotton Import 8478
## 348 2018.000 September Electronics Car Export 654
## 349 2024.000 July Pharma Medicine Import 5274
## 350 2023.000 July Agriculture Mobile Export 6037
## 351 2019.000 August Electronics Medicine Export 7751
## 352 2022.000 September Automobile Car Import 8113
## 353 2020.000 August Automobile Car Import 9395
## 354 2024.000 March Pharma Cotton Import 8535
## 355 2021.000 April Automobile Car Import 4167
## 356 2018.000 October Pharma Mobile Export 3525
## 357 2023.000 May Textile Medicine Export 8413
## 358 2021.000 May Textile Cotton Export 4386
## 359 2021.000 December Pharma Cotton Import 1693
## 360 2018.000 March Pharma Cotton Import 3190
## 361 2023.000 December Textile Cotton Import 6726
## 362 2020.000 July Automobile Car Export 3551
## 363 2024.000 November Electronics Medicine Export 7379
## 364 2021.000 February Agriculture Car Import 2927
## 365 2020.000 September Agriculture Cotton Export 7749
## 366 2022.000 February Agriculture Mobile Import 8926
## 367 2021.000 May Pharma Car Export 1645
## 368 2018.000 July Automobile Mobile Import 9376
## 369 2019.000 March Pharma Cotton Import 5341
## 370 2024.000 October Textile Cotton Import 9923
## 371 2023.000 August Textile Wheat Import 3020
## 372 2021.000 October Electronics Medicine Import 4477
## 373 2019.000 April Agriculture Car Export 2220
## 374 2021.000 February Agriculture Cotton Export 7633
## 375 2024.000 February Automobile Wheat Export 2601
## 376 2020.000 July Agriculture Mobile Export 3852
## 377 2024.000 November Textile Wheat Import 5568
## 378 2024.000 January Automobile Mobile Import 4007
## 379 2019.000 January Textile Wheat Import 7772
## 380 2022.000 December Textile Cotton Export 4324
## 381 2020.000 March Agriculture Mobile Export 8315
## 382 2022.000 February Automobile Cotton Import 7014
## 383 2023.000 April Automobile Wheat Import 6452
## 384 2023.000 May Agriculture Cotton Export 7922
## 385 2018.000 May Agriculture Wheat Export 6331
## 386 2024.000 November Electronics Cotton Export 1801
## 387 2020.000 November Electronics Medicine Import 3853
## 388 2022.000 May Automobile Car Import 5855
## 389 2020.000 March Agriculture Cotton Export 397
## 390 2024.000 September Pharma Car Import 2909
## 391 2018.000 April Automobile Medicine Export 1045
## 392 2024.000 March Textile Car Export 7646
## 393 2020.000 December Textile Wheat Import 498
## 394 2024.000 May Electronics Cotton Export 2301
## 395 2022.000 January Electronics Mobile Export 3122
## 396 2019.000 June Textile Car Import 5368
## 397 2020.000 March Textile Car Import 4747
## 398 2023.000 December Textile Cotton Export 4599
## 399 2018.000 March Pharma Medicine Import 7653
## 400 2024.000 October Electronics Car Export 5188
## 401 2022.000 November Agriculture Medicine Import 397
## 402 2021.000 May Automobile Car Export 4254
## 403 2022.000 November Electronics Car Import 6696
## 404 2018.000 December Textile Mobile Import 9279
## 405 2024.000 December Automobile Car Import 943
## 406 2022.000 June Pharma Medicine Export 139
## 407 2024.000 August Pharma Cotton Export 4521
## 408 2024.000 November Textile Medicine Export 2296
## 409 2018.000 June Electronics Cotton Import 1582
## 410 2019.000 February Electronics Mobile Export 1940
## 411 2021.000 January Textile Wheat Export 7082
## 412 2022.000 April Agriculture Cotton Import 7622
## 413 2018.000 February Pharma Cotton Import 1335
## 414 2021.000 September Agriculture Cotton Import 9591
## 415 2020.000 July Agriculture Cotton Import 6878
## 416 2020.000 December Automobile Cotton Export 2995
## 417 2020.000 April Automobile Medicine Export 4435
## 418 2019.000 January Textile Car Import 6310
## 419 2022.000 March Electronics Wheat Import 1571
## 420 2018.000 July Automobile Cotton Import 5891
## 421 2021.000 August Textile Mobile Export 5062
## 422 2022.000 November Automobile Car Export 2017
## 423 2023.000 October Electronics Car Export 7453
## 424 2023.000 January Agriculture Medicine Export 7493
## 425 2021.000 September Agriculture Car Export 9618
## 426 2021.000 April Pharma Medicine Export 2251
## 427 2019.000 October Pharma Medicine Import 3679
## 428 2020.000 October Electronics Medicine Import 5029
## 429 2020.000 May Pharma Car Import 5782
## 430 2018.000 October Automobile Medicine Export 3510
## 431 2023.000 January Agriculture Cotton Export 5692
## 432 2021.000 March Agriculture Car Import 9904
## 433 2022.000 November Automobile Mobile Export 8664
## 434 2018.000 July Pharma Medicine Import 3451
## 435 2024.000 October Pharma Medicine Export 5794
## 436 2018.000 April Agriculture Mobile Export 5360
## 437 2021.000 September Electronics Mobile Import 7446
## 438 2021.000 December Agriculture Cotton Export 1125
## 439 2020.000 September Agriculture Cotton Export 8089
## 440 2024.000 August Agriculture Car Export 149
## 441 2021.000 June Automobile Mobile Export 9632
## 442 2018.000 April Electronics Medicine Export 6126
## 443 2019.000 April Textile Medicine Export 3551
## 444 2021.000 July Pharma Cotton Export 8336
## 445 2023.000 January Electronics Mobile Import 7059
## 446 2021.000 September Textile Wheat Export 4225
## 447 2019.000 July Pharma Mobile Export 5039
## 448 2024.000 September Agriculture Mobile Export 8649
## 449 2023.000 July Pharma Cotton Import 535
## 450 2022.000 January Pharma Cotton Import 5545
## 451 2018.000 February Automobile Cotton Export 3725
## 452 2020.000 November Textile Car Import 5411
## 453 2022.000 May Pharma Medicine Import 8813
## 454 2024.000 November Textile Car Import 368
## 455 2021.000 July Pharma Cotton Export 659
## 456 2020.000 March Agriculture Cotton Import 4006
## 457 2024.000 March Textile Cotton Import 870
## 458 2020.000 August Electronics Car Import 6771
## 459 2020.000 February Textile Medicine Import 4995
## 460 2020.000 September Agriculture Wheat Import 3377
## 461 2023.000 September Automobile Cotton Export 7997
## 462 2018.000 May Electronics Mobile Export 8836
## 463 2018.000 August Textile Cotton Import 3102
## 464 2023.000 May Electronics Mobile Export 9764
## 465 2021.000 August Pharma Mobile Export 8599
## 466 2018.000 October Pharma Mobile Export 8670
## 467 2023.000 August Textile Car Export 5040
## 468 2022.000 June Automobile Medicine Import 4854
## 469 2024.000 March Electronics Wheat Import 7451
## 470 2018.000 September Agriculture Wheat Export 3006
## 471 2018.000 September Agriculture Mobile Import 527
## 472 2024.000 February Pharma Car Import 4306
## 473 2022.000 May Electronics Mobile Import 9436
## 474 2019.000 February Automobile Wheat Import 717
## 475 2022.000 December Textile Wheat Import 2019
## 476 2023.000 October Automobile Mobile Export 4680
## 477 2018.000 March Textile Mobile Export 2707
## 478 2023.000 December Automobile Mobile Export 2534
## 479 2021.000 March Textile Mobile Import 4575
## 480 2023.000 January Electronics Cotton Import 4713
## 481 2023.000 September Textile Car Import 3059
## 482 2018.000 December Pharma Medicine Import 604
## 483 2018.000 January Textile Mobile Import 1582
## 484 2022.000 April Textile Mobile Import 6009
## 485 2021.000 March Automobile Mobile Import 9221
## 486 2024.000 March Agriculture Wheat Export 7523
## 487 2021.000 January Textile Wheat Import 942
## 488 2020.000 June Electronics Car Import 1211
## 489 2024.000 September Agriculture Car Import 3927
## 490 2018.000 June Automobile Cotton Export 221
## 491 2023.000 August Automobile Mobile Import 4261
## 492 2022.000 April Automobile Mobile Import 4302
## 493 2023.000 February Agriculture Wheat Import 8569
## 494 2022.000 March Textile Car Import 7274
## 495 2022.000 January Agriculture Car Export 3984
## 496 2021.000 February Pharma Medicine Import 8324
## 497 2023.000 March Automobile Wheat Import 5569
## 498 2021.000 November Pharma Cotton Import 932
## 499 2019.000 February Pharma Mobile Import 7078
## 500 2021.000 April Pharma Car Export 1427
## 501 2024.000 September Automobile Medicine Import 2785
## 502 2021.000 August Automobile Medicine Import 2830
## 503 2020.000 September Textile Medicine Import 2216
## 504 2022.000 December Electronics Wheat Export 7205
## 505 2023.000 October Automobile Wheat Export 8310
## 506 2020.000 April Pharma Medicine Import 9286
## 507 2018.000 August Electronics Mobile Export 3974
## 508 2020.000 February Electronics Mobile Import 6232
## 509 2023.000 October Automobile Wheat Import 589
## 510 2021.000 August Textile Mobile Import 529
## 511 2023.000 January Pharma Cotton Export 2750
## 512 2020.000 January Agriculture Cotton Import 3738
## 513 2023.000 January Pharma Car Export 3551
## 514 2023.000 January Textile Mobile Import 4295
## 515 2021.000 May Textile Cotton Import 7780
## 516 2019.000 July Pharma Car Import 5832
## 517 2019.000 May Electronics Cotton Import 5850
## 518 2021.000 June Agriculture Mobile Export 5848
## 519 2021.000 November Textile Wheat Export 3577
## 520 2021.000 September Textile Mobile Import 9682
## 521 2019.000 April Pharma Cotton Export 9572
## 522 2023.000 May Electronics Medicine Export 6283
## 523 2023.000 December Automobile Cotton Export 5672
## 524 2018.000 September Textile Car Import 1051
## 525 2024.000 February Textile Cotton Export 8608
## 526 2022.000 September Electronics Wheat Export 3938
## 527 2024.000 June Automobile Mobile Import 6061
## 528 2023.000 December Pharma Cotton Import 626
## 529 2019.000 August Agriculture Medicine Export 5872
## 530 2019.000 July Pharma Cotton Import 7830
## 531 2024.000 November Automobile Cotton Import 685
## 532 2024.000 December Automobile Cotton Import 6601
## 533 2022.000 July Pharma Cotton Import 9769
## 534 2018.000 April Electronics Medicine Import 432
## 535 2021.000 September Electronics Wheat Export 6554
## 536 2023.000 September Electronics Wheat Import 9428
## 537 2024.000 December Agriculture Medicine Import 9834
## 538 2019.000 May Agriculture Wheat Export 3088
## 539 2022.000 April Electronics Medicine Import 7245
## 540 2021.000 June Electronics Car Import 9961
## 541 2023.000 September Automobile Medicine Export 4088
## 542 2023.000 June Automobile Medicine Export 9560
## 543 2018.000 January Agriculture Mobile Import 3092
## 544 2024.000 April Pharma Medicine Export 872
## 545 2019.000 February Textile Mobile Export 8486
## 546 2021.000 July Automobile Medicine Export 7035
## 547 2020.000 May Agriculture Mobile Import 1204
## 548 2021.000 April Automobile Mobile Import 4062
## 549 2018.000 June Agriculture Mobile Export 3150
## 550 2022.000 October Textile Medicine Import 2657
## 551 2018.000 July Agriculture Mobile Import 2561
## 552 2018.000 November Textile Mobile Export 9793
## 553 2019.000 August Pharma Mobile Export 9358
## 554 2024.000 December Pharma Cotton Export 5427
## 555 2020.000 March Agriculture Car Import 4478
## 556 2020.000 January Textile Mobile Export 6534
## 557 2018.000 August Agriculture Wheat Import 4758
## 558 2018.000 October Pharma Mobile Export 8018
## 559 2018.000 November Pharma Car Export 786
## 560 2019.000 September Automobile Car Import 3906
## 561 2018.000 August Electronics Cotton Export 1267
## 562 2020.000 November Agriculture Mobile Export 8911
## 563 2018.000 December Pharma Wheat Import 3519
## 564 2023.000 March Agriculture Cotton Import 1928
## 565 2023.000 March Automobile Cotton Export 3130
## 566 2021.000 August Textile Medicine Export 9758
## 567 2023.000 August Pharma Mobile Import 3078
## 568 2019.000 July Textile Wheat Export 6223
## 569 2022.000 June Electronics Wheat Import 3596
## 570 2020.000 February Textile Mobile Import 8071
## 571 2018.000 September Automobile Wheat Import 8859
## 572 2018.000 September Electronics Mobile Export 4919
## 573 2020.000 October Automobile Wheat Import 5455
## 574 2020.000 August Automobile Cotton Export 1593
## 575 2024.000 February Textile Cotton Import 7141
## 576 2018.000 August Pharma Mobile Import 2634
## 577 2019.000 June Pharma Cotton Export 1939
## 578 2021.000 November Textile Car Export 3366
## 579 2024.000 September Pharma Medicine Import 5228
## 580 2019.000 June Electronics Cotton Export 9063
## 581 2020.000 January Automobile Cotton Export 5934
## 582 2020.000 May Textile Medicine Import 8298
## 583 2023.000 October Electronics Mobile Export 9446
## 584 2022.000 August Agriculture Mobile Export 1399
## 585 2024.000 September Textile Wheat Export 628
## 586 2022.000 June Agriculture Medicine Import 5430
## 587 2018.000 July Electronics Medicine Import 4503
## 588 2023.000 November Textile Car Export 2398
## 589 2023.000 December Automobile Mobile Import 2402
## 590 2018.000 March Agriculture Cotton Import 1686
## 591 2023.000 May Electronics Medicine Import 6490
## 592 2020.000 March Pharma Medicine Export 4328
## 593 2019.000 December Agriculture Medicine Export 1728
## 594 2023.000 February Pharma Cotton Import 7367
## 595 2024.000 June Textile Cotton Import 2694
## 596 2021.000 August Electronics Cotton Export 9543
## 597 2020.000 May Pharma Cotton Import 7074
## 598 2020.000 April Automobile Wheat Import 8375
## 599 2022.000 November Automobile Medicine Import 4838
## 600 2019.000 October Pharma Medicine Import 8889
## 601 2022.000 December Textile Car Import 5812
## 602 2020.000 October Agriculture Medicine Export 5299
## 603 2023.000 May Automobile Car Import 6244
## 604 2023.000 August Electronics Wheat Import 2554
## 605 2018.000 November Textile Mobile Export 8341
## 606 2023.000 December Electronics Medicine Export 8803
## 607 2021.000 December Agriculture Mobile Export 5589
## 608 2024.000 October Automobile Mobile Import 1203
## 609 2019.000 July Agriculture Car Import 1469
## 610 2018.000 December Pharma Car Import 4707
## 611 2020.000 November Textile Wheat Export 9754
## 612 2022.000 December Pharma Wheat Import 9229
## 613 2024.000 September Textile Cotton Export 3805
## 614 2023.000 December Agriculture Mobile Export 9175
## 615 2019.000 November Electronics Medicine Export 9521
## 616 2022.000 May Pharma Car Import 1784
## 617 2020.000 June Automobile Medicine Export 2415
## 618 2021.000 August Textile Mobile Export 8099
## 619 2022.000 April Pharma Wheat Import 9886
## 620 2024.000 August Automobile Wheat Export 7561
## 621 2023.000 August Automobile Cotton Import 7773
## 622 2023.000 December Pharma Wheat Export 1380
## 623 2020.000 August Textile Mobile Import 3078
## 624 2023.000 June Automobile Car Import 5582
## 625 2019.000 March Agriculture Cotton Export 1598
## 626 2020.000 September Agriculture Medicine Import 4165
## 627 2018.000 April Electronics Medicine Import 8080
## 628 2018.000 May Pharma Medicine Import 9148
## 629 2020.000 July Textile Car Import 5020
## 630 2022.000 September Pharma Cotton Import 1851
## 631 2023.000 February Pharma Wheat Export 8851
## 632 2020.000 November Automobile Cotton Export 6334
## 633 2020.000 November Electronics Cotton Export 7837
## 634 2018.000 October Textile Medicine Export 8873
## 635 2021.000 February Agriculture Mobile Export 6170
## 636 2018.000 December Automobile Medicine Import 4273
## 637 2024.000 October Agriculture Mobile Import 6945
## 638 2023.000 November Agriculture Car Export 6198
## 639 2021.000 May Automobile Mobile Import 4831
## 640 2019.000 August Pharma Cotton Export 4643
## 641 2022.000 December Agriculture Medicine Export 8382
## 642 2023.000 September Electronics Mobile Export 3600
## 643 2023.000 June Electronics Medicine Import 8861
## 644 2022.000 December Electronics Cotton Export 6599
## 645 2018.000 June Automobile Medicine Export 602
## 646 2022.000 November Agriculture Mobile Import 2489
## 647 2023.000 March Electronics Cotton Export 5443
## 648 2021.000 February Textile Mobile Export 7086
## 649 2021.000 December Agriculture Mobile Export 2793
## 650 2021.000 April Pharma Cotton Import 3017
## 651 2020.000 September Textile Car Export 4969
## 652 2018.000 November Agriculture Medicine Import 6079
## 653 2018.000 July Electronics Medicine Export 3722
## 654 2023.000 January Automobile Wheat Export 7612
## 655 2021.000 January Agriculture Wheat Export 4247
## 656 2018.000 June Electronics Wheat Import 1084
## 657 2020.000 April Electronics Car Import 6810
## 658 2022.000 June Pharma Car Export 7744
## 659 2024.000 August Textile Medicine Export 5797
## 660 2019.000 April Textile Mobile Import 1747
## 661 2018.000 February Automobile Cotton Import 4307
## 662 2021.000 January Agriculture Medicine Import 5544
## 663 2021.000 January Automobile Car Export 4628
## 664 2024.000 August Pharma Mobile Export 3228
## 665 2019.000 March Agriculture Cotton Export 4719
## 666 2019.000 February Automobile Medicine Import 9061
## 667 2024.000 December Automobile Cotton Export 1927
## 668 2019.000 October Automobile Car Import 1630
## 669 2022.000 August Agriculture Cotton Import 5814
## 670 2023.000 May Textile Mobile Export 8689
## 671 2023.000 May Electronics Wheat Import 3205
## 672 2020.000 June Textile Medicine Export 175
## 673 2019.000 May Pharma Car Import 6127
## 674 2023.000 April Automobile Medicine Export 1320
## 675 2024.000 June Pharma Wheat Export 8633
## 676 2024.000 January Pharma Cotton Import 9438
## 677 2021.000 February Textile Wheat Export 6224
## 678 2021.000 March Pharma Wheat Export 8185
## 679 2020.000 July Automobile Wheat Export 321
## 680 2018.000 June Textile Medicine Import 1499
## 681 2019.000 September Electronics Medicine Export 8575
## 682 2019.000 February Pharma Car Export 9576
## 683 2023.000 January Automobile Car Import 9023
## 684 2021.000 March Pharma Wheat Import 2246
## 685 2021.000 November Agriculture Car Export 6499
## 686 2018.000 February Textile Medicine Export 6374
## 687 2020.000 September Pharma Cotton Export 3170
## 688 2019.000 January Agriculture Medicine Import 9667
## 689 2018.000 December Agriculture Cotton Import 6353
## 690 2019.000 June Pharma Medicine Export 8660
## 691 2023.000 November Pharma Cotton Export 4825
## 692 2023.000 January Pharma Car Export 2244
## 693 2022.000 January Textile Mobile Export 1178
## 694 2018.000 November Pharma Car Export 1823
## 695 2019.000 September Automobile Cotton Import 1896
## 696 2022.000 July Pharma Mobile Import 250
## 697 2020.000 November Pharma Car Import 7907
## 698 2020.000 May Automobile Mobile Export 758
## 699 2021.000 June Agriculture Medicine Export 8786
## 700 2019.000 August Automobile Car Import 7513
## 701 2018.000 June Electronics Cotton Export 1624
## 702 2021.000 May Pharma Medicine Import 5565
## 703 2023.000 September Electronics Wheat Import 3267
## 704 2023.000 November Automobile Wheat Import 1741
## 705 2023.000 January Electronics Wheat Export 8960
## 706 2020.000 May Pharma Car Import 2263
## 707 2021.000 January Automobile Car Import 5104
## 708 2019.000 July Agriculture Cotton Export 6816
## 709 2018.000 January Agriculture Car Import 7880
## 710 2023.000 May Agriculture Mobile Import 5112
## 711 2024.000 February Automobile Wheat Import 4982
## 712 2020.000 March Textile Mobile Import 2082
## 713 2022.000 August Agriculture Medicine Export 1322
## 714 2019.000 April Textile Medicine Import 2024
## 715 2021.000 September Textile Wheat Import 2267
## 716 2020.000 November Agriculture Car Import 5572
## 717 2018.000 July Pharma Cotton Import 9700
## 718 2018.000 December Pharma Car Export 6282
## 719 2022.000 September Electronics Car Import 5530
## 720 2018.000 April Electronics Medicine Export 1636
## 721 2020.000 March Textile Mobile Export 7706
## 722 2024.000 August Electronics Car Import 6840
## 723 2021.000 August Automobile Car Import 7183
## 724 2021.000 July Automobile Car Import 4568
## 725 2018.000 December Agriculture Car Export 9899
## 726 2024.000 July Automobile Wheat Export 748
## 727 2041.092 December Agriculture Medicine Export 7217
## 728 2024.000 December Automobile Car Export 4410
## 729 2023.000 September Pharma Medicine Import 5245
## 730 2023.000 October Electronics Car Export 1630
## 731 2020.000 December Electronics Cotton Import 5778
## 732 2022.000 January Automobile Wheat Export 6140
## 733 2022.000 December Textile Car Import 4110
## 734 2021.000 February Automobile Car Export 441
## 735 2020.000 February Pharma Car Import 6806
## 736 2024.000 December Pharma Cotton Export 9209
## 737 2020.000 September Textile Medicine Import 5662
## 738 2022.000 June Textile Car Import 7447
## 739 2020.000 March Electronics Cotton Export 9814
## 740 2018.000 October Electronics Car Import 5612
## 741 2020.000 January Electronics Car Export 282
## 742 2018.000 August Automobile Wheat Export 1873
## 743 2023.000 October Automobile Car Import 6454
## 744 2018.000 December Textile Car Import 3287
## 745 2023.000 April Textile Medicine Export 7048
## 746 2021.000 October Electronics Medicine Import 1296
## 747 2020.000 August Automobile Mobile Export 1348
## 748 2023.000 February Electronics Mobile Import 7047
## 749 2023.000 January Pharma Medicine Import 8165
## 750 2020.000 December Agriculture Car Import 5904
## 751 2024.000 June Agriculture Cotton Import 9666
## 752 2021.000 June Textile Wheat Import 6223
## 753 2019.000 July Agriculture Car Import 1494
## 754 2019.000 June Textile Wheat Export 9133
## 755 2023.000 April Textile Wheat Export 3039
## 756 2023.000 September Automobile Wheat Import 2196
## 757 2019.000 August Automobile Medicine Export 481
## 758 2024.000 February Automobile Wheat Export 839
## 759 2023.000 May Pharma Car Import 9197
## 760 2023.000 February Textile Cotton Import 1823
## 761 2024.000 August Automobile Wheat Import 2421
## 762 2023.000 December Automobile Wheat Export 2464
## 763 2022.000 April Textile Medicine Export 4079
## 764 2018.000 January Automobile Cotton Import 8210
## 765 2018.000 November Pharma Mobile Export 3365
## 766 2024.000 February Agriculture Mobile Import 2503
## 767 2022.000 February Electronics Mobile Export 1267
## 768 2018.000 September Textile Wheat Export 4167
## 769 2018.000 February Agriculture Mobile Import 1946
## 770 2022.000 April Automobile Wheat Import 2428
## 771 2019.000 March Agriculture Mobile Import 9435
## 772 2022.000 April Textile Mobile Import 6779
## 773 2024.000 January Automobile Cotton Export 6900
## 774 2019.000 March Electronics Wheat Export 846
## 775 2019.000 March Textile Mobile Import 695
## 776 2021.000 July Electronics Medicine Import 8580
## 777 2019.000 November Textile Mobile Import 7875
## 778 2018.000 September Automobile Mobile Import 6879
## 779 2021.000 March Textile Cotton Import 8918
## 780 2023.000 December Agriculture Medicine Export 8843
## 781 2018.000 January Pharma Medicine Export 7298
## 782 2021.000 February Automobile Wheat Import 8035
## 783 2023.000 November Electronics Mobile Export 1837
## 784 2023.000 July Automobile Cotton Import 4961
## 785 2018.000 October Electronics Wheat Export 8077
## 786 2018.000 December Electronics Medicine Export 7435
## 787 2024.000 February Electronics Car Import 5073
## 788 2022.000 February Agriculture Medicine Export 2807
## 789 2021.000 November Automobile Mobile Export 7833
## 790 2020.000 January Electronics Car Export 2577
## 791 2023.000 February Electronics Medicine Import 3803
## 792 2021.000 June Agriculture Medicine Export 2560
## 793 2024.000 July Agriculture Medicine Import 9496
## 794 2024.000 April Pharma Medicine Export 5075
## 795 2020.000 January Automobile Car Export 974
## 796 2023.000 March Agriculture Car Import 6313
## 797 2019.000 December Electronics Cotton Export 8879
## 798 2018.000 April Agriculture Wheat Import 8968
## 799 2020.000 September Automobile Mobile Import 6619
## 800 2022.000 August Electronics Mobile Export 4494
## 801 2023.000 July Pharma Wheat Import 1192
## 802 2020.000 January Textile Car Import 2050
## 803 2021.000 August Textile Mobile Import 4849
## 804 2024.000 July Agriculture Mobile Export 2430
## 805 2023.000 April Automobile Car Export 4430
## 806 2019.000 May Agriculture Medicine Export 4798
## 807 2022.000 August Automobile Car Export 4285
## 808 2023.000 November Textile Cotton Import 5380
## 809 2021.000 August Electronics Wheat Import 7859
## 810 2022.000 July Textile Wheat Export 9842
## 811 2019.000 October Textile Car Export 2354
## 812 2021.000 July Agriculture Cotton Export 2782
## 813 2022.000 January Electronics Medicine Import 3096
## 814 2023.000 April Pharma Cotton Export 2683
## 815 2021.000 April Pharma Cotton Export 9006
## 816 2023.000 June Pharma Medicine Export 7575
## 817 2019.000 November Electronics Mobile Import 1546
## 818 2022.000 June Pharma Medicine Import 7856
## 819 2021.000 December Electronics Wheat Export 9464
## 820 2019.000 September Agriculture Mobile Export 9227
## 821 2020.000 October Electronics Mobile Export 4456
## 822 2018.000 November Agriculture Cotton Import 7326
## 823 2019.000 July Agriculture Wheat Import 6943
## 824 2021.000 September Textile Car Import 3004
## 825 2019.000 August Electronics Cotton Export 2927
## 826 2023.000 October Agriculture Mobile Import 5286
## 827 2021.000 November Electronics Medicine Export 5302
## 828 2024.000 February Agriculture Mobile Export 1787
## 829 2024.000 November Electronics Cotton Import 1606
## 830 2023.000 February Automobile Cotton Export 1751
## 831 2020.000 January Textile Car Export 1039
## 832 2023.000 January Automobile Car Export 1292
## 833 2024.000 June Automobile Mobile Export 9826
## 834 2022.000 April Textile Mobile Import 2864
## 835 2022.000 August Pharma Wheat Export 6591
## 836 2019.000 December Automobile Mobile Export 9983
## 837 2019.000 December Pharma Mobile Export 7133
## 838 2018.000 December Pharma Car Export 1981
## 839 2021.000 February Automobile Wheat Export 4263
## 840 2022.000 August Textile Mobile Export 5205
## 841 2021.000 November Pharma Medicine Import 3699
## 842 2018.000 March Electronics Cotton Export 3500
## 843 2020.000 February Agriculture Wheat Export 738
## 844 2018.000 April Agriculture Wheat Import 6171
## 845 2020.000 January Textile Cotton Import 516
## 846 2024.000 October Automobile Mobile Import 1607
## 847 2023.000 August Pharma Car Export 7586
## 848 2018.000 June Automobile Medicine Import 1006
## 849 2024.000 August Pharma Mobile Import 4396
## 850 2024.000 May Pharma Wheat Import 4257
## 851 2020.000 May Automobile Wheat Import 3675
## 852 2021.000 December Automobile Car Import 3978
## 853 2022.000 September Textile Medicine Import 8065
## 854 2018.000 July Electronics Car Import 5287
## 855 2022.000 May Pharma Cotton Export 8659
## 856 2020.000 June Automobile Wheat Import 7015
## 857 2019.000 November Automobile Mobile Export 8556
## 858 2023.000 May Agriculture Wheat Import 1704
## 859 2021.000 June Automobile Cotton Export 5079
## 860 2024.000 December Pharma Mobile Export 4316
## 861 2024.000 March Pharma Medicine Import 1229
## 862 2022.000 April Textile Wheat Export 2906
## 863 2021.000 December Electronics Car Export 1895
## 864 2018.000 December Textile Cotton Import 438
## 865 2018.000 September Agriculture Medicine Export 8174
## 866 2021.000 May Pharma Car Import 9554
## 867 2018.000 September Automobile Mobile Import 8112
## 868 2019.000 June Electronics Wheat Export 8573
## 869 2024.000 November Pharma Wheat Import 9320
## 870 2022.000 February Textile Medicine Import 662
## 871 2020.000 February Agriculture Mobile Import 4378
## 872 2019.000 June Electronics Cotton Export 3115
## 873 2018.000 September Textile Car Import 2598
## 874 2022.000 June Agriculture Cotton Import 4825
## 875 2021.000 November Electronics Cotton Import 2334
## 876 2019.000 July Agriculture Car Export 3322
## 877 2018.000 September Electronics Mobile Import 8158
## 878 2019.000 June Automobile Cotton Export 9698
## 879 2023.000 November Textile Cotton Export 7954
## 880 2019.000 April Textile Wheat Import 1445
## 881 2021.000 April Automobile Cotton Export 5361
## 882 2019.000 February Pharma Cotton Export 2653
## 883 2024.000 March Automobile Mobile Import 9962
## 884 2024.000 August Agriculture Car Export 1568
## 885 2019.000 October Textile Mobile Export 7379
## 886 2019.000 August Automobile Mobile Export 1832
## 887 2018.000 August Electronics Wheat Export 2592
## 888 2023.000 April Electronics Wheat Import 3874
## 889 2020.000 November Textile Mobile Import 3481
## 890 2023.000 July Textile Mobile Import 7719
## 891 2023.000 October Agriculture Medicine Import 7923
## 892 2018.000 November Pharma Mobile Export 1790
## 893 2023.000 February Pharma Wheat Import 2548
## 894 2024.000 March Textile Car Export 570
## 895 2021.000 November Agriculture Cotton Import 3071
## 896 2021.000 May Automobile Medicine Export 4576
## 897 2023.000 May Pharma Mobile Import 1567
## 898 2022.000 June Textile Wheat Import 1919
## 899 2022.000 July Pharma Mobile Export 5288
## 900 2019.000 October Agriculture Mobile Export 2264
## 901 2023.000 August Electronics Car Import 9711
## 902 2020.000 April Electronics Wheat Import 9619
## 903 2024.000 September Electronics Medicine Import 7221
## 904 2020.000 July Pharma Wheat Export 2862
## 905 2023.000 September Agriculture Car Export 486
## 906 2021.000 February Electronics Wheat Import 4680
## 907 2018.000 January Agriculture Medicine Import 301
## 908 2019.000 December Textile Car Export 6292
## 909 2022.000 August Agriculture Wheat Export 9367
## 910 2018.000 December Textile Mobile Export 8834
## 911 2021.000 April Pharma Mobile Import 7090
## 912 2020.000 February Textile Car Export 692
## 913 2018.000 July Automobile Wheat Export 8159
## 914 2020.000 June Textile Cotton Export 9837
## 915 2018.000 September Agriculture Wheat Export 2036
## 916 2018.000 July Agriculture Wheat Import 7759
## 917 2021.000 April Electronics Wheat Import 6755
## 918 2019.000 March Electronics Wheat Import 4641
## 919 2019.000 July Electronics Medicine Import 6970
## 920 2022.000 October Pharma Car Export 248
## 921 2024.000 January Automobile Medicine Export 7762
## 922 2024.000 February Pharma Mobile Export 2123
## 923 2018.000 January Pharma Wheat Import 6983
## 924 2023.000 October Electronics Medicine Import 8533
## 925 2024.000 November Automobile Medicine Import 6846
## 926 2022.000 September Textile Car Export 3933
## 927 2021.000 October Pharma Medicine Import 8241
## 928 2020.000 September Textile Medicine Export 4791
## 929 2020.000 February Textile Mobile Export 2171
## 930 2024.000 December Textile Car Export 1084
## 931 2024.000 January Textile Cotton Export 5550
## 932 2019.000 December Agriculture Mobile Import 3824
## 933 2024.000 March Automobile Wheat Import 5911
## 934 2021.000 April Pharma Wheat Import 4005
## 935 2020.000 August Agriculture Wheat Export 1279
## 936 2020.000 June Electronics Medicine Export 2178
## 937 2018.000 August Pharma Wheat Import 8798
## 938 2023.000 July Electronics Cotton Export 189
## 939 2020.000 April Agriculture Medicine Import 9419
## 940 2021.000 November Pharma Wheat Import 9249
## 941 2023.000 July Automobile Medicine Export 3811
## 942 2018.000 August Textile Wheat Import 4728
## 943 2022.000 January Pharma Wheat Export 3743
## 944 2018.000 December Electronics Medicine Export 1779
## 945 2020.000 March Automobile Wheat Import 4038
## 946 2019.000 July Automobile Cotton Import 6026
## 947 2020.000 November Agriculture Wheat Import 8806
## 948 2024.000 September Automobile Mobile Import 2114
## 949 2018.000 November Automobile Mobile Export 1136
## 950 2020.000 December Automobile Car Import 1773
## 951 2022.000 May Automobile Medicine Export 7024
## 952 2020.000 December Automobile Mobile Export 223
## 953 2019.000 May Agriculture Wheat Export 6167
## 954 2018.000 November Electronics Mobile Export 6199
## 955 2024.000 March Automobile Car Import 1814
## 956 2023.000 October Pharma Car Export 9771
## 957 2020.000 January Electronics Cotton Export 7262
## 958 2021.000 July Automobile Wheat Export 3186
## 959 2021.000 June Agriculture Medicine Import 8874
## 960 2021.000 February Electronics Wheat Import 873
## 961 2023.000 January Textile Mobile Export 1622
## 962 2022.000 July Agriculture Cotton Export 8801
## 963 2023.000 April Pharma Wheat Import 1901
## 964 2022.000 March Agriculture Wheat Import 3590
## 965 2018.000 December Electronics Wheat Import 2311
## 966 2021.000 November Electronics Medicine Export 4948
## 967 2020.000 April Automobile Medicine Export 4611
## 968 2019.000 August Textile Medicine Import 6938
## 969 2019.000 August Textile Medicine Import 4932
## 970 2023.000 May Textile Wheat Import 4256
## 971 2021.000 March Electronics Car Export 3208
## 972 2022.000 September Automobile Mobile Import 6412
## 973 2021.000 July Electronics Wheat Import 4002
## 974 2018.000 June Textile Wheat Import 4856
## 975 2021.000 May Pharma Medicine Export 5170
## 976 2022.000 June Agriculture Mobile Import 5073
## 977 2023.000 September Automobile Medicine Export 1109
## 978 2021.000 June Automobile Cotton Import 144
## 979 2022.000 January Agriculture Cotton Export 6873
## 980 2019.000 February Automobile Wheat Import 1194
## 981 2020.000 February Pharma Mobile Export 770
## 982 2024.000 November Pharma Mobile Export 3094
## 983 2018.000 July Textile Cotton Export 4617
## 984 2023.000 June Automobile Car Export 5994
## 985 2018.000 November Agriculture Cotton Export 8248
## 986 2019.000 February Electronics Car Export 2884
## 987 2021.000 January Electronics Mobile Export 5089
## 988 2019.000 February Pharma Mobile Export 2419
## 989 2022.000 May Pharma Medicine Import 394
## 990 2021.000 October Electronics Cotton Import 6056
## 991 2020.000 February Electronics Car Export 4094
## 992 2021.000 October Agriculture Wheat Import 6753
## 993 2023.000 March Pharma Wheat Export 6252
## 994 2019.000 November Automobile Mobile Export 5298
## 995 2018.000 September Textile Wheat Export 7929
## 996 2021.000 July Electronics Medicine Export 4215
## 997 2024.000 April Agriculture Cotton Export 1002
## 998 2023.000 March Automobile Mobile Export 2706
## 999 2023.000 September Automobile Mobile Export 2253
## 1000 2023.000 March Textile Cotton Export 4661
## 1001 2022.000 July Electronics Wheat Import 2599
## 1002 2023.000 April Pharma Car Import 8300
## 1003 2019.000 September Agriculture Medicine Export 2027
## 1004 2024.000 April Agriculture Wheat Export 1945
## 1005 2018.000 August Textile Mobile Export 9131
## 1006 2022.000 March Pharma Medicine Import 4301
## 1007 2019.000 August Agriculture Cotton Export 9952
## 1008 2018.000 April Automobile Mobile Import 4314
## 1009 2020.000 May Textile Medicine Import 2042
## 1010 2018.000 February Automobile Medicine Export 2571
## 1011 2020.000 December Agriculture Medicine Export 4887
## 1012 2019.000 May Automobile Car Export 7748
## 1013 2024.000 September Agriculture Mobile Import 873
## 1014 2021.000 August Pharma Wheat Export 4090
## 1015 2020.000 September Textile Cotton Import 5224
## 1016 2023.000 May Agriculture Cotton Export 3908
## 1017 2023.000 January Automobile Cotton Export 6751
## 1018 2022.000 May Electronics Car Import 3651
## 1019 2024.000 February Textile Cotton Export 5788
## 1020 2018.000 July Agriculture Mobile Export 5072
## 1021 2022.000 July Pharma Mobile Export 2510
## 1022 2024.000 October Electronics Car Export 4361
## 1023 2023.000 May Electronics Car Import 2517
## 1024 2019.000 October Electronics Mobile Import 6176
## 1025 2024.000 September Agriculture Medicine Export 8206
## 1026 2023.000 September Automobile Cotton Import 9238
## 1027 2023.000 October Pharma Cotton Export 3442
## 1028 2024.000 February Textile Cotton Import 7682
## 1029 2018.000 May Pharma Car Import 4087
## 1030 2023.000 May Agriculture Mobile Import 9584
## 1031 2024.000 July Automobile Car Import 8976
## 1032 2021.000 May Automobile Mobile Import 4776
## 1033 2020.000 March Automobile Medicine Export 1438
## 1034 2022.000 October Electronics Wheat Import 8380
## 1035 2018.000 January Electronics Wheat Import 7427
## 1036 2019.000 September Agriculture Car Import 4307
## 1037 2020.000 February Textile Cotton Export 1884
## 1038 2019.000 November Electronics Car Import 5652
## 1039 2024.000 December Textile Wheat Export 932
## 1040 2019.000 September Agriculture Car Export 8474
## 1041 2024.000 November Automobile Mobile Export 4520
## 1042 2024.000 November Automobile Mobile Export 5042
## 1043 2022.000 November Pharma Cotton Import 8409
## 1044 2019.000 January Automobile Car Export 1347
## 1045 2022.000 April Automobile Car Import 2212
## 1046 2021.000 February Textile Medicine Export 5547
## 1047 2019.000 October Automobile Cotton Import 368
## 1048 2023.000 August Automobile Mobile Export 4340
## 1049 2024.000 February Agriculture Cotton Export 9984
## 1050 2024.000 January Agriculture Mobile Export 4440
## 1051 2019.000 November Textile Cotton Export 3182
## 1052 2019.000 September Textile Cotton Export 4313
## 1053 2024.000 December Textile Mobile Export 641
## 1054 2022.000 February Agriculture Wheat Export 4809
## 1055 2023.000 June Automobile Car Import 1861
## 1056 2019.000 February Agriculture Wheat Import 382
## 1057 2019.000 June Textile Wheat Export 3921
## 1058 2018.000 November Electronics Car Export 2118
## 1059 2022.000 February Textile Car Export 8327
## 1060 2019.000 June Electronics Cotton Import 219
## 1061 2018.000 February Agriculture Mobile Import 2779
## 1062 2022.000 February Textile Cotton Import 5272
## 1063 2020.000 May Electronics Cotton Import 6712
## 1064 2018.000 January Agriculture Wheat Export 5350
## 1065 2023.000 November Pharma Medicine Export 5447
## 1066 2024.000 August Agriculture Cotton Export 3413
## 1067 2018.000 July Agriculture Wheat Export 8563
## 1068 2018.000 April Automobile Mobile Export 9693
## 1069 2021.000 October Automobile Mobile Export 1132
## 1070 2021.000 March Textile Medicine Import 5518
## 1071 2021.000 September Electronics Cotton Export 5467
## 1072 2018.000 May Automobile Medicine Import 5509
## 1073 2021.000 June Agriculture Mobile Export 4746
## 1074 2019.000 November Automobile Medicine Import 6829
## 1075 2018.000 December Electronics Car Export 5349
## 1076 2019.000 July Automobile Medicine Export 4556
## 1077 2022.000 August Textile Mobile Import 677
## 1078 2018.000 June Pharma Car Import 7473
## 1079 2021.000 January Electronics Cotton Import 1712
## 1080 2021.000 December Automobile Mobile Import 526
## 1081 2020.000 September Agriculture Medicine Export 5906
## 1082 2020.000 November Automobile Wheat Export 9863
## 1083 2024.000 January Agriculture Mobile Export 8332
## 1084 2020.000 June Pharma Car Import 7463
## 1085 2023.000 April Textile Cotton Import 6176
## 1086 2020.000 June Automobile Cotton Export 3809
## 1087 2023.000 September Textile Medicine Export 9807
## 1088 2020.000 November Agriculture Car Import 2912
## 1089 2023.000 December Electronics Medicine Import 8529
## 1090 2022.000 May Pharma Medicine Import 8209
## 1091 2021.000 June Pharma Car Import 2732
## 1092 2024.000 August Pharma Medicine Export 8406
## 1093 2022.000 January Electronics Wheat Import 7365
## 1094 2020.000 March Agriculture Cotton Export 142
## 1095 2019.000 February Electronics Car Import 4787
## 1096 2019.000 February Pharma Mobile Import 1250
## 1097 2020.000 January Textile Cotton Export 9332
## 1098 2024.000 May Agriculture Medicine Export 1603
## 1099 2019.000 December Textile Mobile Import 1900
## 1100 2022.000 June Textile Wheat Import 333
## 1101 2022.000 October Textile Wheat Export 7605
## 1102 2021.000 June Agriculture Mobile Export 5757
## 1103 2021.000 June Automobile Medicine Import 8504
## 1104 2019.000 November Pharma Medicine Import 5325
## 1105 2018.000 September Textile Cotton Export 8646
## 1106 2024.000 January Automobile Medicine Import 2582
## 1107 2023.000 January Textile Wheat Import 6540
## 1108 2020.000 February Pharma Cotton Export 7909
## 1109 2021.000 December Automobile Car Export 8186
## 1110 2019.000 August Pharma Wheat Export 1176
## 1111 2020.000 February Pharma Car Import 7545
## 1112 2021.000 September Automobile Wheat Export 9956
## 1113 2018.000 August Electronics Medicine Import 9874
## 1114 2021.000 March Automobile Medicine Import 8066
## 1115 2018.000 August Pharma Wheat Export 5242
## 1116 2020.000 March Agriculture Medicine Import 6142
## 1117 2018.000 November Pharma Medicine Export 8661
## 1118 2018.000 February Electronics Medicine Export 6797
## 1119 2024.000 July Agriculture Medicine Export 8751
## 1120 2023.000 June Automobile Wheat Export 6933
## 1121 2024.000 February Electronics Medicine Import 5007
## 1122 2024.000 October Pharma Car Export 5570
## 1123 2024.000 December Electronics Medicine Import 4589
## 1124 2021.000 July Pharma Cotton Export 1468
## 1125 2023.000 January Agriculture Mobile Import 8056
## 1126 2021.000 January Agriculture Car Export 9304
## 1127 2020.000 September Agriculture Car Import 3940
## 1128 2019.000 July Textile Mobile Export 7173
## 1129 2021.000 February Agriculture Cotton Export 4969
## 1130 2023.000 April Automobile Car Export 722
## 1131 2021.000 July Agriculture Cotton Export 1070
## 1132 2018.000 October Electronics Mobile Import 4193
## 1133 2024.000 February Automobile Wheat Export 8522
## 1134 2024.000 February Textile Wheat Export 5177
## 1135 2018.000 August Electronics Wheat Export 6801
## 1136 2018.000 September Automobile Medicine Export 3615
## 1137 2020.000 July Pharma Cotton Import 5642
## 1138 2020.000 December Pharma Cotton Export 6066
## 1139 2023.000 August Textile Car Export 2640
## 1140 2024.000 May Electronics Wheat Import 3679
## 1141 2022.000 June Textile Mobile Export 2081
## 1142 2022.000 January Agriculture Cotton Import 231
## 1143 2020.000 April Automobile Car Import 6001
## 1144 2024.000 November Automobile Medicine Import 9793
## 1145 2021.000 November Agriculture Mobile Export 3813
## 1146 2024.000 April Electronics Mobile Import 9988
## 1147 2020.000 April Textile Medicine Import 8585
## 1148 2021.000 May Automobile Car Export 3241
## 1149 2018.000 December Textile Mobile Export 8095
## 1150 2021.000 November Electronics Cotton Import 1909
## 1151 2020.000 February Automobile Car Export 6281
## 1152 2021.000 October Pharma Medicine Export 473
## 1153 2018.000 June Agriculture Car Import 6377
## 1154 2023.000 October Pharma Mobile Export 3435
## 1155 2018.000 August Pharma Mobile Export 9131
## 1156 2024.000 February Pharma Wheat Export 2206
## 1157 2024.000 December Agriculture Cotton Import 904
## 1158 2022.000 December Electronics Car Import 9364
## 1159 2022.000 February Agriculture Wheat Export 5443
## 1160 2019.000 December Textile Medicine Import 4761
## 1161 2018.000 October Pharma Medicine Import 265
## 1162 2019.000 November Automobile Mobile Export 2079
## 1163 2024.000 July Electronics Medicine Import 2726
## 1164 2022.000 July Pharma Wheat Import 9883
## 1165 2020.000 December Agriculture Car Export 8112
## 1166 2022.000 July Agriculture Wheat Export 8349
## 1167 2021.000 March Textile Car Export 3689
## 1168 2020.000 November Electronics Cotton Export 2914
## 1169 2020.000 December Automobile Mobile Import 753
## 1170 2021.000 December Pharma Mobile Export 610
## 1171 2021.000 December Automobile Mobile Export 1948
## 1172 2022.000 March Agriculture Mobile Import 5128
## 1173 2022.000 May Electronics Cotton Import 8611
## 1174 2021.000 May Agriculture Cotton Export 5369
## 1175 2021.000 February Pharma Car Import 7717
## 1176 2020.000 February Automobile Car Import 1613
## 1177 2020.000 August Automobile Car Import 2121
## 1178 2024.000 October Agriculture Cotton Import 503
## 1179 2022.000 September Automobile Medicine Import 9303
## 1180 2021.000 September Electronics Cotton Export 5030
## 1181 2018.000 February Automobile Cotton Import 2673
## 1182 2023.000 November Automobile Cotton Export 4952
## 1183 2024.000 January Electronics Car Export 8987
## 1184 2020.000 September Pharma Car Export 6231
## 1185 2019.000 February Pharma Mobile Import 627
## 1186 2018.000 October Textile Mobile Export 1709
## 1187 2024.000 April Automobile Car Export 2456
## 1188 2024.000 October Textile Wheat Export 8111
## 1189 2024.000 July Pharma Car Export 5504
## 1190 2018.000 October Pharma Wheat Import 9619
## 1191 2022.000 July Textile Wheat Export 3095
## 1192 2022.000 December Electronics Medicine Export 3695
## 1193 2020.000 September Agriculture Medicine Import 6131
## 1194 2022.000 June Agriculture Medicine Export 4423
## 1195 2024.000 September Electronics Cotton Export 3796
## 1196 2024.000 October Pharma Wheat Export 5285
## 1197 2021.000 May Agriculture Medicine Export 5929
## 1198 2019.000 April Pharma Cotton Import 4080
## 1199 2020.000 December Pharma Mobile Export 7521
## 1200 2019.000 August Automobile Medicine Export 1334
## 1201 2021.000 September Agriculture Medicine Export 8363
## 1202 2024.000 February Agriculture Wheat Import 8984
## 1203 2018.000 February Agriculture Medicine Export 7736
## 1204 2019.000 December Automobile Cotton Import 3361
## 1205 2021.000 March Electronics Wheat Export 2854
## 1206 2021.000 January Agriculture Wheat Import 5526
## 1207 2023.000 October Electronics Wheat Import 7056
## 1208 2024.000 May Agriculture Medicine Import 5349
## 1209 2021.000 April Pharma Medicine Import 4926
## 1210 2021.000 April Textile Mobile Import 1144
## 1211 2024.000 July Electronics Car Import 4898
## 1212 2020.000 October Automobile Cotton Import 5708
## 1213 2023.000 December Pharma Medicine Import 626
## 1214 2024.000 March Textile Car Import 3871
## 1215 2018.000 October Agriculture Car Import 3837
## 1216 2019.000 March Agriculture Car Import 6492
## 1217 2024.000 December Electronics Cotton Export 4044
## 1218 2022.000 September Agriculture Medicine Export 3754
## 1219 2019.000 March Pharma Wheat Import 545
## 1220 2023.000 November Agriculture Medicine Import 8387
## 1221 2023.000 December Electronics Cotton Export 9024
## 1222 2021.000 August Agriculture Car Import 1194
## 1223 2020.000 July Textile Wheat Import 6766
## 1224 2022.000 March Agriculture Car Import 3534
## 1225 2019.000 June Agriculture Car Export 9765
## 1226 2024.000 October Automobile Wheat Import 2162
## 1227 2018.000 January Textile Car Import 3123
## 1228 2021.000 May Pharma Wheat Export 1901
## 1229 2020.000 October Pharma Car Import 9684
## 1230 2024.000 August Electronics Wheat Import 661
## 1231 2024.000 December Agriculture Medicine Export 4924
## 1232 2023.000 April Automobile Wheat Export 3788
## 1233 2021.000 November Automobile Cotton Import 6934
## 1234 2023.000 January Agriculture Car Import 2880
## 1235 2019.000 January Electronics Cotton Import 8484
## 1236 2020.000 August Textile Medicine Export 3299
## 1237 2024.000 December Pharma Mobile Import 4413
## 1238 2021.000 June Electronics Wheat Export 2652
## 1239 2024.000 May Automobile Cotton Export 1905
## 1240 2019.000 July Automobile Medicine Export 6694
## 1241 2020.000 December Agriculture Wheat Import 1577
## 1242 2023.000 April Textile Wheat Export 2926
## 1243 2023.000 September Automobile Cotton Import 5867
## 1244 2022.000 September Electronics Medicine Export 4971
## 1245 2022.000 June Pharma Car Export 3153
## 1246 2022.000 November Agriculture Cotton Import 1392
## 1247 2022.000 July Electronics Cotton Import 7362
## 1248 2020.000 March Electronics Cotton Import 626
## 1249 2019.000 January Electronics Cotton Export 1539
## 1250 2019.000 February Electronics Cotton Import 2339
## 1251 2022.000 July Pharma Wheat Export 8854
## 1252 2021.000 September Textile Medicine Export 2505
## 1253 2024.000 December Electronics Medicine Export 478
## 1254 2023.000 March Electronics Medicine Export 7357
## 1255 2021.000 January Electronics Medicine Export 9601
## 1256 2024.000 November Electronics Mobile Import 235
## 1257 2022.000 February Electronics Mobile Export 3076
## 1258 2023.000 May Automobile Mobile Import 652
## 1259 2019.000 August Automobile Car Import 1382
## 1260 2024.000 November Electronics Cotton Export 9076
## 1261 2023.000 April Textile Car Import 2524
## 1262 2024.000 December Agriculture Medicine Export 2454
## 1263 2021.000 April Electronics Wheat Import 5879
## 1264 2023.000 March Agriculture Medicine Import 2699
## 1265 2022.000 March Textile Wheat Export 1838
## 1266 2018.000 November Textile Mobile Export 801
## 1267 2022.000 March Automobile Car Import 6432
## 1268 2019.000 February Textile Car Import 2946
## 1269 2018.000 April Pharma Wheat Import 9863
## 1270 2024.000 May Agriculture Wheat Export 9028
## 1271 2024.000 February Agriculture Medicine Import 7819
## 1272 2023.000 May Pharma Medicine Export 6242
## 1273 2023.000 October Pharma Wheat Import 9052
## 1274 2023.000 June Textile Car Import 8254
## 1275 2018.000 March Pharma Cotton Export 6030
## 1276 2023.000 June Electronics Cotton Import 9674
## 1277 2022.000 January Textile Cotton Import 1647
## 1278 2018.000 January Automobile Medicine Import 2217
## 1279 2023.000 September Textile Car Export 6693
## 1280 2021.000 January Automobile Cotton Import 8032
## 1281 2020.000 October Pharma Wheat Import 905
## 1282 2020.000 January Agriculture Medicine Export 6857
## 1283 2022.000 December Automobile Medicine Export 6157
## 1284 2023.000 December Agriculture Car Export 5068
## 1285 2018.000 April Textile Medicine Export 377
## 1286 2024.000 January Pharma Cotton Export 9891
## 1287 2021.000 October Pharma Car Export 3555
## 1288 2018.000 June Pharma Medicine Import 8885
## 1289 2024.000 March Agriculture Car Import 9682
## 1290 2020.000 June Textile Cotton Import 6754
## 1291 2019.000 March Pharma Medicine Import 8588
## 1292 2021.000 July Automobile Mobile Import 6097
## 1293 2019.000 May Agriculture Wheat Export 1823
## 1294 2018.000 September Automobile Mobile Export 3321
## 1295 2020.000 August Automobile Wheat Import 7042
## 1296 2022.000 May Textile Cotton Export 8527
## 1297 2018.000 November Electronics Medicine Export 1598
## 1298 2023.000 November Electronics Wheat Export 8313
## 1299 2018.000 March Agriculture Medicine Import 8685
## 1300 2023.000 March Agriculture Medicine Export 5832
## 1301 2022.000 August Pharma Mobile Import 1821
## 1302 2019.000 December Automobile Mobile Import 6525
## 1303 2021.000 October Automobile Car Import 8840
## 1304 2019.000 April Textile Car Export 412
## 1305 2020.000 May Pharma Medicine Export 2391
## 1306 2019.000 December Pharma Car Export 7045
## 1307 2019.000 June Automobile Wheat Import 348
## 1308 2020.000 September Textile Mobile Import 2138
## 1309 2023.000 July Electronics Car Export 7836
## 1310 2019.000 July Pharma Cotton Export 4245
## 1311 2020.000 March Textile Cotton Import 3755
## 1312 2018.000 September Automobile Car Export 3524
## 1313 2020.000 September Pharma Mobile Export 5388
## 1314 2020.000 November Automobile Car Import 7902
## 1315 2024.000 September Automobile Wheat Import 213
## 1316 2023.000 April Pharma Wheat Export 6579
## 1317 2022.000 May Agriculture Medicine Export 449
## 1318 2024.000 July Automobile Cotton Export 2450
## 1319 2023.000 August Electronics Wheat Export 4294
## 1320 2022.000 May Pharma Wheat Import 6583
## 1321 2024.000 May Agriculture Wheat Import 2736
## 1322 2023.000 December Agriculture Medicine Import 8211
## 1323 2019.000 June Automobile Medicine Export 918
## 1324 2023.000 April Automobile Wheat Export 9007
## 1325 2021.000 March Agriculture Wheat Export 1720
## 1326 2023.000 March Automobile Cotton Import 3915
## 1327 2018.000 April Textile Car Export 5611
## 1328 2024.000 June Agriculture Medicine Export 8440
## 1329 2019.000 September Pharma Car Export 9148
## 1330 2024.000 December Pharma Medicine Import 1981
## 1331 2018.000 August Automobile Medicine Export 3270
## 1332 2024.000 January Automobile Medicine Export 9381
## 1333 2021.000 April Pharma Wheat Import 1826
## 1334 2018.000 September Agriculture Medicine Import 6402
## 1335 2020.000 October Agriculture Car Import 5634
## 1336 2021.000 April Textile Car Export 4165
## 1337 2024.000 May Textile Medicine Export 7131
## 1338 2020.000 September Agriculture Medicine Export 7825
## 1339 2021.000 March Pharma Cotton Export 9712
## 1340 2020.000 August Agriculture Mobile Export 3799
## 1341 2023.000 September Automobile Wheat Import 4997
## 1342 2021.000 December Automobile Car Export 8920
## 1343 2018.000 September Textile Cotton Import 3597
## 1344 2024.000 August Electronics Car Export 3707
## 1345 2021.000 July Agriculture Cotton Import 9582
## 1346 2019.000 February Textile Cotton Import 6143
## 1347 2019.000 October Electronics Wheat Import 3638
## 1348 2022.000 April Automobile Mobile Import 5512
## 1349 2020.000 November Automobile Medicine Export 4197
## 1350 2021.000 December Agriculture Medicine Import 3882
## 1351 2024.000 March Automobile Mobile Export 5894
## 1352 2024.000 February Automobile Car Import 2027
## 1353 2022.000 August Textile Medicine Import 6983
## 1354 2024.000 September Agriculture Mobile Import 8851
## 1355 2019.000 April Electronics Medicine Export 4878
## 1356 2020.000 October Electronics Wheat Import 9771
## 1357 2021.000 December Textile Cotton Export 1731
## 1358 2019.000 February Pharma Cotton Export 4159
## 1359 2019.000 May Textile Medicine Import 7807
## 1360 2019.000 July Pharma Cotton Import 451
## 1361 2018.000 February Textile Cotton Export 6140
## 1362 2019.000 August Pharma Wheat Import 9678
## 1363 2024.000 June Automobile Car Export 500
## 1364 2022.000 February Electronics Mobile Export 8083
## 1365 2023.000 May Pharma Car Import 6223
## 1366 2018.000 February Pharma Car Export 2783
## 1367 2023.000 January Automobile Wheat Import 4664
## 1368 2020.000 December Electronics Wheat Import 6435
## 1369 2019.000 June Textile Car Export 1529
## 1370 2020.000 December Electronics Wheat Import 5717
## 1371 2022.000 March Textile Wheat Import 211
## 1372 2018.000 November Automobile Medicine Export 8744
## 1373 2018.000 February Pharma Mobile Import 5106
## 1374 2022.000 October Agriculture Cotton Export 1790
## 1375 2023.000 April Electronics Cotton Import 6279
## 1376 2019.000 June Electronics Mobile Export 8158
## 1377 2020.000 July Electronics Car Export 496
## 1378 2022.000 April Agriculture Mobile Import 264
## 1379 2021.000 May Electronics Car Import 1505
## 1380 2024.000 July Automobile Medicine Export 4897
## 1381 2019.000 August Textile Wheat Import 4565
## 1382 2020.000 February Agriculture Mobile Import 6393
## 1383 2022.000 August Automobile Car Export 9733
## 1384 2022.000 October Pharma Cotton Export 9964
## 1385 2020.000 August Electronics Wheat Export 3549
## 1386 2018.000 August Electronics Wheat Export 3868
## 1387 2024.000 November Pharma Cotton Export 4868
## 1388 2020.000 October Pharma Cotton Export 1887
## 1389 2019.000 December Agriculture Car Import 4088
## 1390 2020.000 March Agriculture Mobile Export 3122
## 1391 2018.000 November Pharma Car Import 3703
## 1392 2020.000 March Automobile Wheat Import 4342
## 1393 2024.000 April Automobile Medicine Import 4626
## 1394 2023.000 October Pharma Cotton Import 3589
## 1395 2018.000 November Textile Car Import 5586
## 1396 2020.000 September Automobile Cotton Import 8330
## 1397 2024.000 May Automobile Wheat Import 9648
## 1398 2021.000 May Textile Cotton Import 3885
## 1399 2021.000 November Automobile Mobile Export 3190
## 1400 2019.000 December Textile Wheat Import 5084
## 1401 2021.000 January Pharma Car Import 1634
## 1402 2022.000 April Agriculture Cotton Export 8960
## 1403 2023.000 December Electronics Cotton Import 5738
## 1404 2019.000 October Agriculture Medicine Import 6538
## 1405 2023.000 April Textile Cotton Export 5189
## 1406 2022.000 April Automobile Mobile Import 2951
## 1407 2024.000 June Electronics Wheat Export 3519
## 1408 2020.000 July Electronics Cotton Import 4558
## 1409 2019.000 November Automobile Cotton Import 9845
## 1410 2019.000 May Electronics Mobile Import 9986
## 1411 2024.000 January Textile Mobile Export 3406
## 1412 2024.000 May Electronics Car Export 7261
## 1413 2022.000 June Electronics Cotton Export 5709
## 1414 2020.000 November Automobile Medicine Import 5008
## 1415 2024.000 November Textile Car Export 4066
## 1416 2022.000 August Textile Car Export 8516
## 1417 2021.000 December Pharma Mobile Export 250
## 1418 2019.000 October Electronics Mobile Import 3960
## 1419 2023.000 March Automobile Mobile Import 9389
## 1420 2020.000 February Pharma Cotton Export 7795
## 1421 2024.000 September Electronics Wheat Export 9959
## 1422 2023.000 July Electronics Mobile Import 2148
## 1423 2019.000 July Textile Mobile Export 257
## 1424 2021.000 April Pharma Medicine Import 1128
## 1425 2022.000 January Automobile Medicine Import 1375
## 1426 2024.000 September Automobile Wheat Export 4690
## 1427 2020.000 January Pharma Wheat Export 1446
## 1428 2023.000 July Automobile Car Import 3800
## 1429 2023.000 May Textile Wheat Import 5623
## 1430 2022.000 July Electronics Cotton Export 3385
## 1431 2020.000 May Automobile Mobile Export 5746
## 1432 2021.000 May Electronics Mobile Export 7499
## 1433 2021.000 November Electronics Wheat Export 5133
## 1434 2019.000 July Textile Mobile Import 1761
## 1435 2022.000 December Electronics Wheat Import 9176
## 1436 2020.000 June Pharma Medicine Export 1972
## 1437 2020.000 September Electronics Mobile Export 2074
## 1438 2023.000 April Pharma Wheat Export 2583
## 1439 2021.000 September Automobile Medicine Export 6138
## 1440 2022.000 June Textile Medicine Import 4826
## 1441 2024.000 September Electronics Medicine Import 1860
## 1442 2019.000 December Agriculture Mobile Import 5663
## 1443 2019.000 December Agriculture Car Export 9052
## 1444 2019.000 September Textile Mobile Import 4223
## 1445 2024.000 October Electronics Mobile Import 3188
## 1446 2022.000 January Agriculture Mobile Import 9796
## 1447 2019.000 June Pharma Wheat Import 3349
## 1448 2023.000 March Electronics Medicine Import 8456
## 1449 2019.000 March Agriculture Medicine Import 5467
## 1450 2023.000 January Textile Medicine Import 2706
## 1451 2022.000 May Automobile Cotton Import 1270
## 1452 2024.000 February Textile Mobile Export 4510
## 1453 2024.000 September Pharma Medicine Import 4975
## 1454 2020.000 August Textile Car Export 3256
## 1455 2023.000 January Electronics Cotton Export 8817
## 1456 2022.000 December Electronics Medicine Import 1783
## 1457 2020.000 September Pharma Mobile Export 5457
## 1458 2022.000 November Pharma Car Import 1058
## 1459 2020.000 September Pharma Mobile Export 847
## 1460 2021.000 October Electronics Wheat Import 2947
## 1461 2020.000 June Automobile Cotton Export 1412
## 1462 2019.000 May Electronics Mobile Export 4152
## 1463 2019.000 October Automobile Wheat Export 4510
## 1464 2019.000 July Agriculture Cotton Export 2336
## 1465 2023.000 August Electronics Wheat Export 1579
## 1466 2020.000 June Automobile Medicine Import 5300
## 1467 2024.000 September Textile Wheat Import 7590
## 1468 2024.000 August Textile Car Export 7663
## 1469 2022.000 March Textile Wheat Export 992
## 1470 2021.000 April Electronics Mobile Export 3409
## 1471 2021.000 January Textile Car Export 1582
## 1472 2023.000 October Automobile Cotton Import 5315
## 1473 2018.000 March Automobile Cotton Import 2969
## 1474 2024.000 May Automobile Car Export 2801
## 1475 2020.000 August Electronics Mobile Import 5935
## 1476 2018.000 December Pharma Mobile Import 4836
## 1477 2023.000 September Agriculture Car Import 5840
## 1478 2021.000 August Pharma Wheat Import 6410
## 1479 2020.000 September Agriculture Medicine Import 7078
## 1480 2023.000 December Automobile Medicine Export 8869
## 1481 2022.000 July Pharma Mobile Export 491
## 1482 2018.000 June Pharma Mobile Export 8239
## 1483 2023.000 August Textile Medicine Export 2305
## 1484 2020.000 March Agriculture Mobile Import 5868
## 1485 2024.000 June Agriculture Wheat Export 3902
## 1486 2018.000 May Pharma Medicine Export 8600
## 1487 2022.000 March Automobile Medicine Import 7070
## 1488 2019.000 July Automobile Car Import 6365
## 1489 2020.000 October Automobile Wheat Import 7081
## 1490 2024.000 January Textile Wheat Import 7686
## 1491 2024.000 February Pharma Mobile Import 3493
## 1492 2022.000 January Agriculture Mobile Import 1367
## 1493 2020.000 January Pharma Medicine Import 4386
## 1494 2024.000 August Automobile Medicine Import 2882
## 1495 2018.000 August Automobile Mobile Export 2277
## 1496 2023.000 March Textile Mobile Import 3316
## 1497 2019.000 February Agriculture Mobile Import 4248
## 1498 2024.000 April Electronics Car Import 8245
## 1499 2023.000 February Textile Car Import 6194
## 1500 2018.000 July Textile Car Import 7571
## 1501 2022.000 February Electronics Medicine Import 982
## 1502 2019.000 February Agriculture Mobile Export 9923
## 1503 2021.000 November Agriculture Cotton Import 5720
## 1504 2021.000 November Agriculture Wheat Export 2358
## 1505 2023.000 August Electronics Wheat Import 3508
## 1506 2019.000 November Pharma Wheat Export 6443
## 1507 2022.000 June Pharma Mobile Import 6315
## 1508 2023.000 September Automobile Medicine Export 519
## 1509 2023.000 September Pharma Car Import 2677
## 1510 2024.000 July Textile Wheat Export 251
## 1511 2020.000 February Textile Cotton Import 210
## 1512 2024.000 October Agriculture Wheat Export 5119
## 1513 2020.000 August Textile Cotton Export 9699
## 1514 2021.000 July Pharma Car Export 8987
## 1515 2019.000 October Automobile Car Export 2331
## 1516 2018.000 July Textile Car Export 7883
## 1517 2023.000 October Pharma Car Export 2626
## 1518 2019.000 November Electronics Mobile Export 6031
## 1519 2019.000 July Pharma Medicine Import 1089
## 1520 2021.000 August Pharma Cotton Export 7854
## 1521 2018.000 October Textile Car Export 2028
## 1522 2021.000 March Agriculture Mobile Export 4014
## 1523 2018.000 September Electronics Car Export 5292
## 1524 2021.000 May Textile Car Import 9899
## 1525 2022.000 February Pharma Cotton Import 9071
## 1526 2022.000 May Textile Medicine Import 8306
## 1527 2021.000 January Agriculture Cotton Export 2987
## 1528 2024.000 February Textile Mobile Import 1109
## 1529 2018.000 April Agriculture Cotton Import 9178
## 1530 2024.000 May Textile Car Export 674
## 1531 2022.000 June Electronics Medicine Export 171
## 1532 2020.000 September Electronics Mobile Export 1746
## 1533 2021.000 December Agriculture Cotton Export 4996
## 1534 2024.000 August Electronics Car Export 9348
## 1535 2022.000 August Agriculture Car Export 7535
## 1536 2021.000 March Automobile Medicine Import 4105
## 1537 2023.000 April Automobile Car Export 7988
## 1538 2022.000 November Electronics Mobile Import 6077
## 1539 2019.000 May Textile Mobile Import 5304
## 1540 2023.000 April Electronics Cotton Export 2728
## 1541 2020.000 January Electronics Car Export 3166
## 1542 2024.000 September Pharma Cotton Import 9445
## 1543 2024.000 May Textile Car Import 8447
## 1544 2018.000 September Electronics Wheat Import 3944
## 1545 2022.000 December Automobile Wheat Export 1526
## 1546 2019.000 October Textile Cotton Export 5602
## 1547 2020.000 December Electronics Cotton Export 1166
## 1548 2022.000 May Textile Car Import 5900
## 1549 2024.000 August Agriculture Mobile Import 524
## 1550 2020.000 May Textile Medicine Import 2959
## 1551 2020.000 December Electronics Cotton Export 9144
## 1552 2019.000 September Pharma Mobile Import 4636
## 1553 2018.000 September Textile Cotton Import 8553
## 1554 2023.000 September Textile Cotton Import 1125
## 1555 2023.000 September Agriculture Medicine Export 5298
## 1556 2022.000 January Agriculture Medicine Import 9370
## 1557 2024.000 June Textile Wheat Export 9295
## 1558 2018.000 May Automobile Cotton Import 7431
## 1559 2020.000 March Electronics Medicine Import 4237
## 1560 2018.000 January Agriculture Cotton Import 8711
## 1561 2023.000 March Automobile Wheat Import 3560
## 1562 2018.000 December Electronics Mobile Import 6956
## 1563 2021.000 January Textile Car Import 1331
## 1564 2018.000 December Pharma Wheat Import 8428
## 1565 2018.000 December Pharma Wheat Export 3250
## 1566 2022.000 April Pharma Wheat Export 3668
## 1567 2024.000 January Textile Wheat Import 5013
## 1568 2018.000 November Electronics Car Export 2849
## 1569 2023.000 May Automobile Car Export 2966
## 1570 2021.000 June Agriculture Cotton Import 7544
## 1571 2021.000 November Pharma Medicine Export 2666
## 1572 2021.000 January Pharma Car Import 3725
## 1573 2021.000 November Pharma Car Import 2621
## 1574 2020.000 December Automobile Medicine Import 2445
## 1575 2018.000 July Agriculture Mobile Export 5551
## 1576 2021.000 July Agriculture Mobile Import 3648
## 1577 2021.000 September Electronics Car Export 9655
## 1578 2023.000 October Automobile Car Import 5911
## 1579 2019.000 October Agriculture Wheat Export 2043
## 1580 2024.000 August Textile Mobile Export 7541
## 1581 2018.000 October Agriculture Cotton Export 8623
## 1582 2019.000 May Textile Car Import 1115
## 1583 2018.000 December Agriculture Cotton Export 4818
## 1584 2021.000 May Agriculture Cotton Import 554
## 1585 2024.000 March Automobile Medicine Import 8517
## 1586 2023.000 December Electronics Car Export 1294
## 1587 2022.000 December Electronics Wheat Export 9862
## 1588 2019.000 September Pharma Medicine Import 4507
## 1589 2021.000 October Pharma Cotton Import 6506
## 1590 2021.000 October Textile Wheat Import 4021
## 1591 2022.000 December Automobile Cotton Import 9295
## 1592 2024.000 April Textile Medicine Import 2038
## 1593 2021.000 December Textile Car Import 9358
## 1594 2022.000 July Electronics Medicine Import 4438
## 1595 2020.000 August Automobile Medicine Import 1507
## 1596 2024.000 October Automobile Wheat Export 6750
## 1597 2019.000 December Pharma Wheat Export 5253
## 1598 2019.000 September Textile Cotton Export 3575
## 1599 2018.000 August Pharma Wheat Import 9400
## 1600 2024.000 October Textile Medicine Import 9399
## 1601 2020.000 May Textile Wheat Export 3135
## 1602 2021.000 October Textile Mobile Export 5717
## 1603 2024.000 October Pharma Mobile Export 8190
## 1604 2022.000 February Pharma Wheat Import 5748
## 1605 2018.000 February Automobile Medicine Export 5619
## 1606 2019.000 September Electronics Wheat Export 5891
## 1607 2019.000 May Electronics Mobile Import 4852
## 1608 2019.000 February Electronics Cotton Export 5574
## 1609 2018.000 December Electronics Medicine Import 5229
## 1610 2019.000 June Automobile Medicine Import 555
## 1611 2021.000 February Automobile Mobile Export 1335
## 1612 2024.000 January Textile Wheat Import 4477
## 1613 2020.000 July Agriculture Medicine Import 1528
## 1614 2019.000 December Automobile Mobile Import 9580
## 1615 2023.000 August Pharma Medicine Export 7608
## 1616 2018.000 December Textile Medicine Export 3555
## 1617 2018.000 September Pharma Mobile Import 7245
## 1618 2021.000 April Agriculture Car Export 7335
## 1619 2024.000 April Agriculture Wheat Import 8020
## 1620 2019.000 November Electronics Wheat Import 4785
## 1621 2019.000 February Pharma Medicine Import 4493
## 1622 2023.000 February Automobile Car Export 4119
## 1623 2023.000 May Electronics Mobile Export 5829
## 1624 2020.000 October Agriculture Wheat Export 531
## 1625 2018.000 January Automobile Medicine Import 5131
## 1626 2019.000 May Textile Wheat Export 5300
## 1627 2019.000 May Electronics Car Import 7937
## 1628 2021.000 January Automobile Wheat Export 631
## 1629 2021.000 August Pharma Mobile Import 7656
## 1630 2020.000 January Pharma Medicine Export 2511
## 1631 2021.000 May Automobile Mobile Import 7598
## 1632 2018.000 November Electronics Medicine Export 606
## 1633 2021.000 July Textile Cotton Import 4972
## 1634 2019.000 April Automobile Car Import 9836
## 1635 2019.000 March Textile Cotton Export 8811
## 1636 2018.000 December Textile Wheat Import 5457
## 1637 2023.000 December Agriculture Car Export 121
## 1638 2020.000 February Agriculture Mobile Export 897
## 1639 2020.000 December Agriculture Wheat Import 4268
## 1640 2023.000 March Textile Medicine Export 314
## 1641 2024.000 February Electronics Cotton Import 9552
## 1642 2018.000 April Electronics Wheat Import 4795
## 1643 2019.000 August Electronics Car Import 5141
## 1644 2022.000 July Automobile Medicine Import 3430
## 1645 2019.000 August Pharma Mobile Import 399
## 1646 2021.000 October Pharma Cotton Import 7219
## 1647 2024.000 October Agriculture Medicine Export 5414
## 1648 2018.000 June Electronics Car Import 9175
## 1649 2022.000 October Agriculture Medicine Export 5137
## 1650 2020.000 February Pharma Cotton Import 129
## 1651 2018.000 February Textile Mobile Import 9604
## 1652 2018.000 July Pharma Car Import 1255
## 1653 2021.000 January Electronics Medicine Import 8756
## 1654 2022.000 August Agriculture Wheat Export 3047
## 1655 2021.000 April Textile Wheat Export 8400
## 1656 2019.000 July Automobile Car Export 8361
## 1657 2021.000 August Agriculture Car Export 3001
## 1658 2024.000 November Agriculture Car Import 1962
## 1659 2021.000 July Agriculture Car Import 6172
## 1660 2022.000 October Textile Wheat Import 9144
## 1661 2023.000 May Agriculture Cotton Import 1855
## 1662 2019.000 February Agriculture Wheat Export 1046
## 1663 2019.000 April Textile Cotton Export 4048
## 1664 2022.000 October Electronics Medicine Import 3247
## 1665 2023.000 April Automobile Cotton Export 9545
## 1666 2021.000 March Automobile Car Export 7029
## 1667 2021.000 November Automobile Cotton Export 197
## 1668 2022.000 March Agriculture Wheat Export 4166
## 1669 2019.000 June Electronics Wheat Export 7850
## 1670 2022.000 August Agriculture Car Export 197
## 1671 2021.000 July Agriculture Medicine Export 9188
## 1672 2023.000 March Textile Mobile Export 3344
## 1673 2024.000 June Pharma Wheat Import 8306
## 1674 2023.000 February Automobile Car Import 4171
## 1675 2024.000 November Agriculture Medicine Export 8589
## 1676 2018.000 April Electronics Wheat Export 1329
## 1677 2021.000 March Agriculture Wheat Export 5923
## 1678 2022.000 July Textile Car Import 5119
## 1679 2021.000 November Pharma Cotton Import 2997
## 1680 2023.000 January Agriculture Cotton Export 9926
## 1681 2019.000 August Agriculture Mobile Export 432
## 1682 2024.000 January Pharma Medicine Import 4372
## 1683 2022.000 August Textile Car Export 5645
## 1684 2020.000 September Textile Mobile Import 488
## 1685 2023.000 December Pharma Medicine Export 9347
## 1686 2019.000 March Textile Wheat Import 9709
## 1687 2022.000 January Electronics Cotton Import 3608
## 1688 2021.000 September Agriculture Cotton Import 7555
## 1689 2021.000 August Agriculture Cotton Export 9194
## 1690 2018.000 November Agriculture Wheat Import 1058
## 1691 2020.000 June Pharma Mobile Export 670
## 1692 2024.000 November Agriculture Car Import 2002
## 1693 2023.000 March Agriculture Mobile Import 2670
## 1694 2021.000 July Automobile Car Export 3080
## 1695 2020.000 February Pharma Car Import 4113
## 1696 2018.000 July Pharma Car Import 2051
## 1697 2018.000 March Textile Wheat Export 2728
## 1698 2022.000 October Textile Medicine Import 2271
## 1699 2020.000 July Agriculture Medicine Export 3421
## 1700 2019.000 November Pharma Wheat Export 1496
## 1701 2024.000 September Agriculture Wheat Export 9823
## 1702 2024.000 September Pharma Car Export 3179
## 1703 2023.000 March Textile Mobile Import 7590
## 1704 2019.000 July Agriculture Medicine Import 5927
## 1705 2022.000 August Pharma Wheat Import 2505
## 1706 2022.000 August Textile Wheat Export 6005
## 1707 2021.000 March Textile Mobile Export 7425
## 1708 2020.000 July Electronics Cotton Export 8288
## 1709 2024.000 February Automobile Wheat Export 9487
## 1710 2019.000 December Pharma Mobile Import 2083
## 1711 2024.000 June Pharma Medicine Import 5961
## 1712 2021.000 November Automobile Cotton Export 7846
## 1713 2024.000 December Automobile Mobile Export 9885
## 1714 2020.000 May Automobile Car Import 2594
## 1715 2019.000 July Pharma Mobile Export 2655
## 1716 2024.000 December Pharma Car Import 8983
## 1717 2023.000 March Textile Cotton Import 650
## 1718 2018.000 June Electronics Car Export 5636
## 1719 2023.000 May Agriculture Car Export 5423
## 1720 2020.000 February Electronics Mobile Import 4337
## 1721 2020.000 September Automobile Wheat Import 3646
## 1722 2020.000 October Agriculture Car Import 4115
## 1723 2023.000 January Automobile Medicine Export 7762
## 1724 2022.000 January Textile Cotton Import 1562
## 1725 2021.000 April Electronics Wheat Import 1026
## 1726 2022.000 August Agriculture Cotton Import 8815
## 1727 2018.000 February Textile Medicine Import 2787
## 1728 2018.000 January Pharma Car Import 863
## 1729 2018.000 December Electronics Mobile Import 2717
## 1730 2024.000 February Electronics Medicine Import 8644
## 1731 2024.000 September Electronics Cotton Export 1951
## 1732 2024.000 October Automobile Medicine Import 8948
## 1733 2024.000 January Agriculture Car Export 5181
## 1734 2020.000 December Agriculture Wheat Import 4094
## 1735 2018.000 January Electronics Mobile Export 9595
## 1736 2024.000 December Electronics Medicine Export 9006
## 1737 2022.000 June Electronics Wheat Export 4897
## 1738 2022.000 March Pharma Wheat Export 9552
## 1739 2020.000 September Automobile Medicine Export 4997
## 1740 2024.000 September Electronics Car Export 5747
## 1741 2022.000 June Textile Mobile Export 2800
## 1742 2023.000 August Pharma Medicine Export 3530
## 1743 2018.000 November Electronics Car Export 7876
## 1744 2024.000 September Electronics Car Export 882
## 1745 2023.000 December Electronics Wheat Import 304
## 1746 2018.000 July Pharma Wheat Import 5675
## 1747 2019.000 October Pharma Mobile Import 1259
## 1748 2020.000 October Automobile Mobile Export 652
## 1749 2020.000 August Agriculture Car Import 1900
## 1750 2019.000 May Pharma Cotton Export 3549
## 1751 2018.000 February Pharma Car Import 4888
## 1752 2018.000 June Automobile Wheat Export 8464
## 1753 2023.000 August Agriculture Mobile Export 4203
## 1754 2023.000 November Agriculture Car Import 3320
## 1755 2018.000 November Electronics Wheat Import 7737
## 1756 2019.000 May Textile Medicine Import 9481
## 1757 2020.000 January Agriculture Cotton Export 989
## 1758 2022.000 June Agriculture Medicine Export 4062
## 1759 2018.000 January Agriculture Mobile Import 9350
## 1760 2021.000 February Electronics Wheat Import 3032
## 1761 2019.000 July Pharma Cotton Import 9651
## 1762 2022.000 September Agriculture Wheat Export 3469
## 1763 2018.000 November Agriculture Wheat Import 726
## 1764 2023.000 December Electronics Medicine Export 6000
## 1765 2024.000 January Textile Wheat Export 1072
## 1766 2024.000 January Automobile Wheat Import 7695
## 1767 2020.000 August Pharma Wheat Import 3515
## 1768 2024.000 February Pharma Mobile Import 518
## 1769 2020.000 July Electronics Mobile Export 4294
## 1770 2018.000 January Automobile Medicine Import 622
## 1771 2021.000 April Pharma Wheat Import 9427
## 1772 2021.000 September Textile Car Import 4601
## 1773 2021.000 November Automobile Car Import 8318
## 1774 2021.000 March Automobile Cotton Export 5122
## 1775 2021.000 December Pharma Medicine Export 817
## 1776 2022.000 February Agriculture Medicine Export 1490
## 1777 2024.000 December Pharma Medicine Export 9469
## 1778 2021.000 March Textile Medicine Export 9550
## 1779 2021.000 January Agriculture Mobile Export 9222
## 1780 2023.000 November Textile Mobile Export 2449
## 1781 2023.000 June Textile Medicine Import 3107
## 1782 2020.000 February Electronics Car Export 2156
## 1783 2019.000 November Electronics Wheat Import 6768
## 1784 2018.000 July Electronics Wheat Export 9588
## 1785 2019.000 July Automobile Mobile Import 1482
## 1786 2020.000 April Automobile Cotton Export 4890
## 1787 2022.000 September Pharma Mobile Export 1744
## 1788 2023.000 August Electronics Wheat Export 546
## 1789 2022.000 May Automobile Cotton Import 6832
## 1790 2024.000 November Textile Mobile Import 9254
## 1791 2022.000 July Automobile Mobile Import 3564
## 1792 2021.000 April Automobile Mobile Import 6424
## 1793 2021.000 July Automobile Mobile Export 5718
## 1794 2022.000 November Electronics Car Export 7253
## 1795 2020.000 May Automobile Cotton Export 843
## 1796 2019.000 October Pharma Mobile Export 4620
## 1797 2024.000 March Pharma Wheat Export 408
## 1798 2022.000 January Pharma Cotton Export 8222
## 1799 2019.000 November Textile Car Export 3833
## 1800 2021.000 October Automobile Wheat Export 7767
## 1801 2020.000 November Textile Car Import 2717
## 1802 2024.000 November Automobile Wheat Export 4254
## 1803 2018.000 November Textile Car Export 2607
## 1804 2023.000 October Agriculture Medicine Import 5841
## 1805 2019.000 August Pharma Medicine Import 6091
## 1806 2019.000 May Automobile Mobile Import 4112
## 1807 2021.000 March Agriculture Car Import 4517
## 1808 2020.000 January Agriculture Medicine Import 6017
## 1809 2019.000 June Automobile Car Import 5097
## 1810 2023.000 June Electronics Medicine Import 3669
## 1811 2024.000 October Textile Car Import 8300
## 1812 2023.000 October Agriculture Mobile Export 9100
## 1813 2018.000 April Textile Cotton Export 5573
## 1814 2020.000 November Textile Mobile Export 7684
## 1815 2023.000 July Agriculture Car Import 2266
## 1816 2019.000 November Agriculture Cotton Import 1089
## 1817 2020.000 January Agriculture Mobile Import 8306
## 1818 2020.000 October Pharma Cotton Export 128
## 1819 2022.000 July Electronics Wheat Import 3915
## 1820 2023.000 October Automobile Cotton Import 7559
## 1821 2023.000 June Electronics Wheat Import 5007
## 1822 2023.000 February Automobile Mobile Import 5525
## 1823 2019.000 October Electronics Car Export 8715
## 1824 2019.000 November Textile Cotton Import 2674
## 1825 2024.000 May Automobile Mobile Export 5442
## 1826 2018.000 January Electronics Car Import 5836
## 1827 2021.000 September Agriculture Mobile Import 2205
## 1828 2023.000 December Textile Wheat Export 8350
## 1829 2022.000 August Textile Wheat Export 5397
## 1830 2018.000 May Agriculture Wheat Import 2915
## 1831 2019.000 April Pharma Mobile Import 2966
## 1832 2019.000 July Textile Cotton Import 8200
## 1833 2024.000 September Electronics Car Export 8751
## 1834 2022.000 June Agriculture Mobile Export 1599
## 1835 2023.000 November Electronics Medicine Export 2183
## 1836 2022.000 August Textile Mobile Import 877
## 1837 2020.000 September Agriculture Medicine Export 3103
## 1838 2023.000 May Pharma Wheat Import 9657
## 1839 2019.000 February Pharma Mobile Import 6796
## 1840 2018.000 October Agriculture Mobile Import 2134
## 1841 2019.000 January Agriculture Medicine Import 6741
## 1842 2018.000 January Electronics Car Import 6283
## 1843 2020.000 January Agriculture Medicine Import 6635
## 1844 2020.000 May Textile Car Import 6490
## 1845 2018.000 June Pharma Mobile Import 2091
## 1846 2023.000 April Agriculture Mobile Export 1363
## 1847 2021.000 March Agriculture Cotton Import 3972
## 1848 2021.000 December Agriculture Medicine Import 2254
## 1849 2023.000 November Agriculture Mobile Import 1913
## 1850 2018.000 June Pharma Mobile Import 1129
## 1851 2020.000 September Textile Mobile Import 9430
## 1852 2022.000 October Agriculture Car Import 3289
## 1853 2022.000 April Textile Cotton Export 8003
## 1854 2019.000 May Pharma Wheat Import 3538
## 1855 2020.000 September Agriculture Car Import 8539
## 1856 2023.000 March Pharma Cotton Export 7933
## 1857 2018.000 February Pharma Cotton Import 8220
## 1858 2018.000 April Automobile Cotton Import 6892
## 1859 2020.000 November Agriculture Mobile Import 4037
## 1860 2021.000 August Agriculture Wheat Import 6286
## 1861 2022.000 March Pharma Car Import 9601
## 1862 2022.000 June Agriculture Car Import 4169
## 1863 2023.000 October Automobile Wheat Import 7218
## 1864 2020.000 December Electronics Car Import 6719
## 1865 2024.000 January Agriculture Medicine Export 2727
## 1866 2022.000 May Electronics Wheat Import 2169
## 1867 2023.000 November Pharma Car Export 5310
## 1868 2018.000 November Pharma Wheat Import 2212
## 1869 2024.000 January Automobile Cotton Export 7396
## 1870 2020.000 October Agriculture Car Export 8310
## 1871 2018.000 November Textile Mobile Import 1458
## 1872 2020.000 September Electronics Mobile Import 6954
## 1873 2022.000 April Electronics Mobile Export 1052
## 1874 2022.000 September Agriculture Car Import 8258
## 1875 2023.000 June Automobile Mobile Export 3019
## 1876 2022.000 August Automobile Cotton Export 4246
## 1877 2021.000 September Automobile Medicine Import 9131
## 1878 2023.000 December Textile Wheat Import 2636
## 1879 2021.000 March Agriculture Car Import 3782
## 1880 2018.000 March Electronics Wheat Export 996
## 1881 2019.000 February Electronics Car Import 2980
## 1882 2020.000 September Pharma Medicine Export 2935
## 1883 2021.000 July Agriculture Mobile Import 9767
## 1884 2022.000 July Textile Wheat Export 4540
## 1885 2018.000 August Agriculture Mobile Import 5221
## 1886 2022.000 March Automobile Car Import 399
## 1887 2024.000 March Textile Medicine Export 6416
## 1888 2019.000 March Pharma Cotton Import 6244
## 1889 2024.000 September Textile Car Export 6836
## 1890 2019.000 October Agriculture Cotton Import 3224
## 1891 2018.000 March Electronics Mobile Import 5016
## 1892 2023.000 August Electronics Wheat Import 7594
## 1893 2022.000 January Pharma Cotton Import 9987
## 1894 2022.000 June Pharma Car Import 5772
## 1895 2020.000 December Pharma Mobile Import 3383
## 1896 2023.000 June Automobile Cotton Export 4383
## 1897 2021.000 May Agriculture Mobile Export 7273
## 1898 2023.000 May Textile Car Export 5820
## 1899 2024.000 October Pharma Cotton Export 1715
## 1900 2024.000 October Automobile Mobile Import 9546
## 1901 2023.000 January Pharma Medicine Import 7750
## 1902 2022.000 August Agriculture Medicine Import 7565
## 1903 2022.000 July Automobile Car Export 6878
## 1904 2023.000 November Electronics Wheat Export 3026
## 1905 2019.000 December Pharma Mobile Import 8945
## 1906 2021.000 February Automobile Wheat Export 6672
## 1907 2019.000 February Textile Car Import 4060
## 1908 2018.000 September Pharma Wheat Import 6558
## 1909 2023.000 May Pharma Cotton Import 7778
## 1910 2019.000 September Textile Wheat Export 3397
## 1911 2018.000 August Electronics Wheat Export 3446
## 1912 2022.000 June Automobile Medicine Import 4675
## 1913 2024.000 April Textile Mobile Export 974
## 1914 2021.000 September Automobile Medicine Export 9750
## 1915 2024.000 January Agriculture Mobile Import 1207
## 1916 2024.000 May Electronics Cotton Import 8394
## 1917 2018.000 February Agriculture Mobile Import 8550
## 1918 2023.000 July Agriculture Cotton Import 2042
## 1919 2021.000 July Agriculture Cotton Import 1736
## 1920 2021.000 July Textile Wheat Export 9604
## 1921 2024.000 September Agriculture Wheat Export 4736
## 1922 2019.000 November Agriculture Wheat Export 8163
## 1923 2023.000 February Electronics Mobile Import 1812
## 1924 2020.000 January Agriculture Mobile Export 7167
## 1925 2018.000 September Automobile Mobile Export 7035
## 1926 2024.000 March Textile Mobile Import 720
## 1927 2018.000 October Agriculture Cotton Export 304
## 1928 2019.000 April Automobile Medicine Export 5435
## 1929 2022.000 January Electronics Mobile Import 8579
## 1930 2023.000 December Electronics Medicine Import 244
## 1931 2021.000 October Pharma Cotton Import 2372
## 1932 2023.000 July Agriculture Car Export 6297
## 1933 2020.000 July Agriculture Cotton Export 8380
## 1934 2022.000 May Pharma Wheat Import 6850
## 1935 2019.000 September Automobile Cotton Import 5361
## 1936 2024.000 December Automobile Wheat Import 8847
## 1937 2024.000 September Pharma Cotton Import 5655
## 1938 2020.000 November Electronics Cotton Export 8818
## 1939 2018.000 October Automobile Cotton Import 5300
## 1940 2021.000 December Pharma Wheat Import 9021
## 1941 2022.000 April Textile Mobile Import 6976
## 1942 2018.000 January Textile Car Import 6623
## 1943 2023.000 December Electronics Mobile Import 6890
## 1944 2024.000 August Agriculture Medicine Import 5080
## 1945 2019.000 October Electronics Cotton Export 704
## 1946 2021.000 December Agriculture Medicine Export 4650
## 1947 2020.000 September Electronics Mobile Import 4506
## 1948 2020.000 September Automobile Car Export 620
## 1949 2018.000 August Textile Wheat Import 2200
## 1950 2019.000 December Pharma Wheat Export 934
## 1951 2024.000 November Electronics Mobile Export 7808
## 1952 2020.000 May Electronics Wheat Export 810
## 1953 2018.000 March Electronics Wheat Import 2329
## 1954 2023.000 September Agriculture Cotton Import 9960
## 1955 2020.000 April Electronics Mobile Import 8873
## 1956 2021.000 February Electronics Wheat Export 1857
## 1957 2024.000 June Pharma Wheat Import 4469
## 1958 2022.000 November Agriculture Mobile Export 7570
## 1959 2022.000 July Agriculture Wheat Export 9959
## 1960 2022.000 September Textile Medicine Import 5178
## 1961 2023.000 November Electronics Mobile Import 1713
## 1962 2018.000 November Automobile Wheat Export 9432
## 1963 2022.000 February Textile Wheat Export 2342
## 1964 2023.000 January Agriculture Car Import 9426
## 1965 2018.000 June Electronics Medicine Import 713
## 1966 2019.000 January Textile Medicine Export 5830
## 1967 2020.000 November Electronics Car Import 1844
## 1968 2020.000 January Textile Cotton Export 1348
## 1969 2022.000 March Textile Medicine Export 3290
## 1970 2021.000 February Agriculture Medicine Import 8838
## 1971 2018.000 December Textile Mobile Import 555
## 1972 2018.000 April Automobile Medicine Export 3828
## 1973 2018.000 October Automobile Wheat Import 3508
## 1974 2020.000 March Electronics Cotton Export 2002
## 1975 2019.000 July Agriculture Medicine Import 2403
## 1976 2024.000 April Agriculture Medicine Export 7834
## 1977 2021.000 October Agriculture Car Import 9819
## 1978 2022.000 December Automobile Car Export 2924
## 1979 2021.000 April Agriculture Mobile Export 4770
## 1980 2024.000 December Pharma Medicine Import 1416
## 1981 2019.000 May Pharma Mobile Import 3772
## 1982 2024.000 October Electronics Car Import 3373
## 1983 2022.000 December Textile Medicine Import 2317
## 1984 2023.000 September Pharma Cotton Import 9989
## 1985 2024.000 October Agriculture Medicine Import 8687
## 1986 2024.000 April Agriculture Mobile Export 4900
## 1987 2022.000 October Textile Mobile Import 9607
## 1988 2018.000 November Automobile Cotton Export 996
## 1989 2023.000 June Pharma Car Import 388
## 1990 2018.000 October Pharma Medicine Export 4590
## 1991 2022.000 July Electronics Cotton Import 9695
## 1992 2023.000 September Agriculture Cotton Export 9752
## 1993 2021.000 May Agriculture Cotton Import 9811
## 1994 2022.000 December Textile Medicine Export 2073
## 1995 2018.000 December Electronics Wheat Import 4100
## 1996 2024.000 May Pharma Wheat Import 2010
## 1997 2022.000 December Agriculture Wheat Import 4174
## 1998 2021.000 March Electronics Mobile Import 3915
## 1999 2023.000 June Agriculture Medicine Import 1402
## 2000 2021.000 September Pharma Mobile Import 8502
## 2001 2020.000 March Automobile Car Import 2896
## 2002 2019.000 May Pharma Medicine Export 3786
## 2003 2018.000 March Automobile Cotton Import 7618
## 2004 2024.000 September Pharma Wheat Import 2142
## 2005 2018.000 December Pharma Mobile Export 3395
## 2006 2019.000 December Automobile Medicine Import 6950
## 2007 2021.000 March Pharma Medicine Export 1449
## 2008 2024.000 October Pharma Mobile Import 9904
## 2009 2022.000 February Agriculture Wheat Export 6148
## 2010 2023.000 November Pharma Cotton Export 666
## 2011 2021.000 August Electronics Mobile Import 5923
## 2012 2020.000 May Agriculture Mobile Export 947
## 2013 2021.000 May Textile Wheat Export 146
## 2014 2023.000 July Electronics Medicine Export 1021
## 2015 2024.000 November Electronics Wheat Import 242
## 2016 2020.000 December Automobile Wheat Import 6274
## 2017 2023.000 July Textile Cotton Export 8917
## 2018 2018.000 September Pharma Wheat Export 8377
## 2019 2020.000 December Agriculture Medicine Import 7085
## 2020 2018.000 October Electronics Medicine Import 6805
## 2021 2021.000 November Agriculture Wheat Import 7344
## 2022 2020.000 November Textile Cotton Import 3563
## 2023 2024.000 October Agriculture Medicine Import 2388
## 2024 2023.000 October Pharma Car Export 5476
## 2025 2021.000 June Agriculture Wheat Export 8817
## 2026 2022.000 July Pharma Medicine Import 1474
## 2027 2019.000 February Agriculture Mobile Export 5585
## 2028 2022.000 February Pharma Wheat Export 2921
## 2029 2021.000 February Automobile Medicine Export 1842
## 2030 2020.000 October Agriculture Cotton Import 2481
## 2031 2020.000 April Agriculture Medicine Export 5574
## 2032 2020.000 January Pharma Cotton Import 6174
## 2033 2019.000 April Pharma Cotton Import 5556
## 2034 2024.000 March Electronics Cotton Export 2812
## 2035 2024.000 February Automobile Cotton Export 2085
## 2036 2018.000 October Pharma Medicine Export 2263
## 2037 2018.000 July Automobile Medicine Export 333
## 2038 2024.000 February Textile Cotton Export 3274
## 2039 2024.000 February Agriculture Medicine Import 3620
## 2040 2019.000 July Textile Medicine Import 1774
## 2041 2020.000 January Electronics Car Import 111
## 2042 2019.000 March Electronics Wheat Export 6906
## 2043 2024.000 December Pharma Cotton Export 9557
## 2044 2020.000 January Textile Cotton Import 328
## 2045 2022.000 August Agriculture Medicine Export 1650
## 2046 2023.000 August Agriculture Medicine Export 785
## 2047 2019.000 October Electronics Medicine Import 3988
## 2048 2022.000 June Automobile Medicine Import 6863
## 2049 2023.000 April Automobile Car Export 2391
## 2050 2021.000 January Agriculture Wheat Export 6844
## 2051 2023.000 November Agriculture Wheat Import 387
## 2052 2024.000 March Automobile Car Export 1198
## 2053 2023.000 December Textile Medicine Import 6952
## 2054 2023.000 March Agriculture Car Import 3475
## 2055 2024.000 February Pharma Wheat Export 9684
## 2056 2024.000 May Automobile Mobile Import 5077
## 2057 2024.000 May Automobile Car Export 1738
## 2058 2023.000 May Pharma Medicine Export 4999
## 2059 2022.000 March Pharma Cotton Export 3021
## 2060 2018.000 February Pharma Cotton Import 7955
## 2061 2021.000 May Automobile Cotton Import 4803
## 2062 2019.000 April Textile Medicine Export 6139
## 2063 2018.000 June Pharma Medicine Export 8243
## 2064 2020.000 March Electronics Cotton Export 982
## 2065 2021.000 May Electronics Medicine Import 9102
## 2066 2019.000 January Electronics Car Export 1124
## 2067 2018.000 June Automobile Wheat Import 9095
## 2068 2024.000 July Automobile Mobile Export 8086
## 2069 2020.000 June Pharma Car Import 7583
## 2070 2020.000 July Agriculture Medicine Import 748
## 2071 2022.000 May Electronics Wheat Import 6189
## 2072 2021.000 February Pharma Medicine Import 2370
## 2073 2020.000 December Electronics Medicine Export 6108
## 2074 2021.000 March Agriculture Wheat Export 6798
## 2075 2024.000 May Electronics Medicine Export 8320
## 2076 2018.000 May Electronics Wheat Import 8737
## 2077 2021.000 March Agriculture Cotton Import 4319
## 2078 2020.000 August Agriculture Mobile Import 2685
## 2079 2018.000 May Agriculture Cotton Export 3246
## 2080 2022.000 December Pharma Wheat Import 4746
## 2081 2019.000 October Automobile Cotton Export 2525
## 2082 2020.000 July Pharma Medicine Export 5206
## 2083 2019.000 June Pharma Wheat Export 9674
## 2084 2019.000 December Electronics Cotton Export 3273
## 2085 2018.000 October Agriculture Mobile Export 2351
## 2086 2018.000 January Pharma Mobile Export 7661
## 2087 2019.000 May Electronics Medicine Export 6229
## 2088 2021.000 June Automobile Mobile Import 5926
## 2089 2023.000 February Pharma Medicine Import 7080
## 2090 2023.000 July Pharma Wheat Import 3441
## 2091 2019.000 May Electronics Medicine Export 5235
## 2092 2024.000 January Agriculture Wheat Export 9411
## 2093 2022.000 January Agriculture Mobile Import 3270
## 2094 2018.000 March Automobile Cotton Export 802
## 2095 2018.000 July Agriculture Mobile Export 2195
## 2096 2019.000 September Pharma Wheat Import 8591
## 2097 2020.000 March Pharma Medicine Export 8262
## 2098 2021.000 December Electronics Cotton Import 8805
## 2099 2024.000 June Pharma Medicine Export 1656
## 2100 2020.000 September Pharma Wheat Export 2265
## 2101 2020.000 June Pharma Wheat Import 8376
## 2102 2020.000 November Automobile Mobile Export 4789
## 2103 2022.000 September Automobile Car Export 8702
## 2104 2018.000 April Pharma Mobile Export 7672
## 2105 2021.000 April Automobile Car Export 897
## 2106 2018.000 November Pharma Car Import 9968
## 2107 2020.000 February Agriculture Wheat Export 5435
## 2108 2018.000 January Pharma Cotton Import 5599
## 2109 2022.000 January Pharma Cotton Export 5679
## 2110 2024.000 August Automobile Car Import 7428
## 2111 2022.000 December Agriculture Wheat Import 6623
## 2112 2022.000 December Textile Medicine Import 2680
## 2113 2019.000 May Automobile Medicine Export 6129
## 2114 2019.000 May Textile Cotton Import 2813
## 2115 2021.000 February Agriculture Medicine Import 7038
## 2116 2023.000 February Agriculture Wheat Import 1302
## 2117 2023.000 February Agriculture Wheat Import 5824
## 2118 2019.000 July Electronics Car Import 304
## 2119 2024.000 April Pharma Mobile Export 2829
## 2120 2018.000 May Pharma Wheat Import 9428
## 2121 2023.000 August Agriculture Wheat Import 1939
## 2122 2024.000 April Automobile Cotton Import 1990
## 2123 2020.000 December Automobile Mobile Import 2565
## 2124 2019.000 July Electronics Mobile Export 240
## 2125 2024.000 February Pharma Medicine Import 9113
## 2126 2022.000 January Textile Car Export 5967
## 2127 2018.000 August Electronics Mobile Export 8958
## 2128 2021.000 July Textile Medicine Import 3599
## 2129 2021.000 March Pharma Wheat Import 3689
## 2130 2020.000 July Automobile Mobile Import 2891
## 2131 2019.000 July Textile Cotton Import 6052
## 2132 2019.000 July Pharma Medicine Import 3059
## 2133 2020.000 September Electronics Wheat Import 1124
## 2134 2023.000 April Electronics Car Export 1304
## 2135 2019.000 August Automobile Mobile Export 962
## 2136 2019.000 June Electronics Cotton Import 5720
## 2137 2021.000 April Automobile Car Export 4544
## 2138 2020.000 November Pharma Cotton Import 1175
## 2139 2024.000 March Textile Medicine Import 5165
## 2140 2018.000 June Electronics Cotton Export 3524
## 2141 2023.000 July Pharma Wheat Import 9773
## 2142 2019.000 May Electronics Mobile Export 2406
## 2143 2023.000 July Pharma Wheat Import 2794
## 2144 2021.000 November Pharma Cotton Import 5306
## 2145 2024.000 March Electronics Car Export 5178
## 2146 2020.000 February Agriculture Cotton Import 2000
## 2147 2021.000 November Electronics Cotton Import 5422
## 2148 2018.000 August Automobile Medicine Import 9256
## 2149 2019.000 August Electronics Medicine Import 4301
## 2150 2021.000 March Agriculture Mobile Export 4018
## 2151 2024.000 October Pharma Wheat Export 2335
## 2152 2022.000 April Pharma Medicine Import 2299
## 2153 2022.000 November Agriculture Wheat Import 7089
## 2154 2024.000 February Automobile Wheat Import 5097
## 2155 2020.000 September Automobile Medicine Export 3145
## 2156 2023.000 January Automobile Car Export 2582
## 2157 2023.000 May Electronics Car Export 4981
## 2158 2022.000 December Pharma Medicine Export 5881
## 2159 2019.000 June Pharma Car Export 2811
## 2160 2023.000 May Textile Car Export 1272
## 2161 2021.000 January Pharma Wheat Export 3856
## 2162 2020.000 December Automobile Medicine Export 4407
## 2163 2024.000 April Electronics Cotton Import 369
## 2164 2024.000 April Agriculture Car Import 6079
## 2165 2018.000 April Textile Car Import 9693
## 2166 2021.000 October Agriculture Medicine Import 8627
## 2167 2019.000 August Pharma Cotton Import 8084
## 2168 2018.000 June Agriculture Cotton Import 511
## 2169 2021.000 May Electronics Car Import 9807
## 2170 2022.000 December Automobile Cotton Import 1111
## 2171 2019.000 June Agriculture Medicine Export 1322
## 2172 2020.000 January Automobile Mobile Export 7637
## 2173 2024.000 June Agriculture Mobile Export 9073
## 2174 2023.000 January Pharma Cotton Import 1489
## 2175 2023.000 December Electronics Wheat Export 8328
## 2176 2024.000 September Textile Mobile Import 6536
## 2177 2024.000 November Pharma Wheat Export 5927
## 2178 2024.000 April Electronics Wheat Import 3047
## 2179 2019.000 July Agriculture Cotton Export 1396
## 2180 2024.000 December Textile Wheat Import 2999
## 2181 2021.000 November Textile Medicine Import 847
## 2182 2019.000 April Electronics Cotton Import 7112
## 2183 2022.000 December Electronics Wheat Import 2824
## 2184 2023.000 March Pharma Medicine Export 3120
## 2185 2018.000 March Automobile Mobile Import 7346
## 2186 2024.000 December Agriculture Cotton Import 871
## 2187 2022.000 August Pharma Medicine Export 2348
## 2188 2019.000 October Agriculture Car Import 9332
## 2189 2022.000 May Textile Wheat Import 4658
## 2190 2020.000 January Pharma Medicine Export 5591
## 2191 2021.000 August Electronics Car Import 8967
## 2192 2020.000 March Electronics Car Import 4199
## 2193 2024.000 October Electronics Medicine Import 6482
## 2194 2022.000 February Automobile Wheat Export 8422
## 2195 2021.000 December Pharma Mobile Import 338
## 2196 2023.000 September Pharma Medicine Export 4584
## 2197 2019.000 July Automobile Medicine Export 172
## 2198 2018.000 March Pharma Cotton Import 2082
## 2199 2018.000 February Automobile Wheat Import 9223
## 2200 2020.000 July Textile Cotton Import 8589
## 2201 2020.000 April Pharma Wheat Export 464
## 2202 2020.000 March Textile Medicine Import 9953
## 2203 2019.000 September Pharma Cotton Export 3426
## 2204 2019.000 April Electronics Cotton Export 9445
## 2205 2021.000 February Automobile Car Import 2108
## 2206 2021.000 January Automobile Car Export 8571
## 2207 2019.000 September Electronics Medicine Export 8167
## 2208 2018.000 September Electronics Medicine Import 558
## 2209 2024.000 July Textile Medicine Import 4054
## 2210 2024.000 May Pharma Wheat Import 4985
## 2211 2022.000 May Pharma Car Import 3177
## 2212 2020.000 April Textile Car Export 1178
## 2213 2021.000 May Textile Medicine Export 1475
## 2214 2022.000 August Agriculture Cotton Export 7859
## 2215 2018.000 October Pharma Car Export 1781
## 2216 2022.000 July Electronics Car Export 6653
## 2217 2024.000 March Electronics Cotton Export 7819
## 2218 2024.000 July Textile Cotton Import 7581
## 2219 2023.000 August Electronics Mobile Export 3515
## 2220 2021.000 July Automobile Car Import 7180
## 2221 2020.000 April Electronics Car Export 5737
## 2222 2018.000 September Automobile Cotton Import 799
## 2223 2019.000 July Automobile Mobile Import 208
## 2224 2019.000 December Electronics Medicine Import 3344
## 2225 2022.000 August Pharma Mobile Import 1507
## 2226 2019.000 January Textile Cotton Export 8205
## 2227 2024.000 December Pharma Cotton Import 6075
## 2228 2024.000 November Electronics Medicine Import 3098
## 2229 2024.000 February Agriculture Medicine Import 2104
## 2230 2023.000 September Electronics Mobile Export 2945
## 2231 2022.000 November Textile Car Export 103
## 2232 2022.000 April Electronics Car Import 1761
## 2233 2018.000 September Electronics Medicine Export 6300
## 2234 2024.000 December Automobile Medicine Export 2471
## 2235 2019.000 October Electronics Car Export 1854
## 2236 2021.000 January Pharma Cotton Import 8634
## 2237 2020.000 September Textile Cotton Export 728
## 2238 2022.000 May Textile Medicine Import 4366
## 2239 2020.000 July Agriculture Cotton Import 4915
## 2240 2024.000 October Agriculture Medicine Export 9709
## 2241 2021.000 September Automobile Wheat Export 5966
## 2242 2021.000 September Textile Cotton Import 2103
## 2243 2019.000 June Textile Mobile Import 4753
## 2244 2022.000 February Agriculture Wheat Export 4719
## 2245 2024.000 August Automobile Wheat Export 5755
## 2246 2018.000 June Automobile Cotton Export 2961
## 2247 2023.000 January Pharma Medicine Export 1924
## 2248 2019.000 July Electronics Mobile Import 1815
## 2249 2021.000 August Pharma Medicine Export 6627
## 2250 2024.000 April Textile Cotton Export 8415
## 2251 2019.000 April Textile Cotton Export 1539
## 2252 2018.000 December Textile Cotton Export 4199
## 2253 2022.000 August Automobile Cotton Export 9819
## 2254 2019.000 July Electronics Mobile Import 5016
## 2255 2022.000 December Textile Medicine Import 5390
## 2256 2019.000 December Textile Cotton Export 9102
## 2257 2020.000 May Pharma Car Import 5827
## 2258 2021.000 December Automobile Wheat Import 7302
## 2259 2020.000 November Automobile Cotton Export 7927
## 2260 2022.000 November Pharma Car Export 5252
## 2261 2021.000 November Textile Car Import 1146
## 2262 2024.000 March Electronics Medicine Import 9138
## 2263 2023.000 May Automobile Cotton Import 8138
## 2264 2022.000 May Textile Cotton Export 2769
## 2265 2020.000 August Automobile Car Import 516
## 2266 2023.000 April Automobile Car Export 4905
## 2267 2019.000 April Automobile Car Export 1810
## 2268 2019.000 May Agriculture Mobile Import 8299
## 2269 2019.000 April Pharma Car Export 7123
## 2270 2023.000 November Electronics Mobile Import 7728
## 2271 2020.000 May Pharma Car Export 7855
## 2272 2019.000 September Textile Wheat Import 4138
## 2273 2021.000 August Pharma Mobile Import 3583
## 2274 2021.000 January Automobile Cotton Export 9645
## 2275 2024.000 September Textile Wheat Import 9383
## 2276 2023.000 September Electronics Cotton Import 8364
## 2277 2018.000 September Textile Cotton Export 9346
## 2278 2024.000 June Textile Wheat Export 1631
## 2279 2022.000 August Textile Wheat Export 8951
## 2280 2021.000 November Textile Mobile Export 4523
## 2281 2018.000 June Electronics Medicine Export 8247
## 2282 2022.000 July Electronics Medicine Import 6579
## 2283 2019.000 December Textile Medicine Export 2811
## 2284 2022.000 July Electronics Car Import 5717
## 2285 2023.000 August Automobile Medicine Import 2481
## 2286 2021.000 September Automobile Car Import 6065
## 2287 2023.000 September Textile Medicine Import 267
## 2288 2021.000 November Pharma Mobile Import 7711
## 2289 2020.000 January Agriculture Mobile Import 188
## 2290 2019.000 September Agriculture Cotton Export 8931
## 2291 2023.000 December Automobile Medicine Export 9323
## 2292 2023.000 May Electronics Mobile Import 8672
## 2293 2018.000 November Textile Medicine Export 5469
## 2294 2018.000 March Pharma Medicine Import 8903
## 2295 2018.000 September Textile Mobile Export 8505
## 2296 2024.000 November Agriculture Wheat Import 4165
## 2297 2020.000 December Electronics Mobile Export 6085
## 2298 2019.000 February Electronics Car Export 1271
## 2299 2022.000 February Textile Medicine Export 9982
## 2300 2023.000 March Electronics Car Export 3176
## 2301 2020.000 April Agriculture Medicine Import 6933
## 2302 2018.000 September Automobile Cotton Export 9746
## 2303 2021.000 October Automobile Medicine Import 5440
## 2304 2021.000 April Pharma Medicine Export 1232
## 2305 2021.000 May Electronics Cotton Export 3585
## 2306 2020.000 March Pharma Cotton Import 1950
## 2307 2021.000 January Automobile Medicine Import 8008
## 2308 2023.000 January Textile Car Export 8075
## 2309 2018.000 November Automobile Cotton Import 8432
## 2310 2024.000 April Agriculture Mobile Import 9012
## 2311 2021.000 June Pharma Medicine Export 7308
## 2312 2024.000 April Electronics Cotton Export 9403
## 2313 2024.000 September Textile Cotton Export 2074
## 2314 2024.000 May Automobile Wheat Export 4636
## 2315 2020.000 November Automobile Wheat Export 7412
## 2316 2022.000 March Textile Mobile Export 451
## 2317 2022.000 October Automobile Medicine Import 1411
## 2318 2020.000 April Automobile Cotton Export 5489
## 2319 2021.000 October Electronics Mobile Import 1163
## 2320 2019.000 March Textile Car Import 4606
## 2321 2022.000 April Textile Cotton Import 5235
## 2322 2024.000 March Automobile Cotton Import 415
## 2323 2019.000 April Textile Cotton Export 6834
## 2324 2024.000 September Electronics Car Import 233
## 2325 2021.000 December Pharma Cotton Import 8923
## 2326 2021.000 January Textile Wheat Export 7686
## 2327 2019.000 November Pharma Cotton Import 7046
## 2328 2023.000 May Agriculture Car Export 2328
## 2329 2023.000 June Automobile Medicine Export 9760
## 2330 2022.000 September Electronics Medicine Import 865
## 2331 2024.000 November Pharma Mobile Import 5590
## 2332 2024.000 October Pharma Car Import 9508
## 2333 2021.000 November Electronics Wheat Import 4881
## 2334 2019.000 February Agriculture Cotton Import 3633
## 2335 2021.000 February Agriculture Cotton Export 2641
## 2336 2023.000 March Textile Medicine Import 4555
## 2337 2020.000 September Electronics Car Import 6629
## 2338 2019.000 December Automobile Wheat Export 4327
## 2339 2018.000 April Electronics Cotton Export 2250
## 2340 2018.000 August Electronics Car Import 5083
## 2341 2022.000 April Agriculture Mobile Export 398
## 2342 2024.000 September Textile Car Export 8830
## 2343 2022.000 December Agriculture Car Import 7443
## 2344 2022.000 October Textile Mobile Import 6575
## 2345 2024.000 February Automobile Wheat Import 9539
## 2346 2019.000 March Electronics Car Import 6165
## 2347 2021.000 June Textile Car Import 6607
## 2348 2023.000 June Electronics Cotton Export 8091
## 2349 2024.000 April Electronics Car Export 1070
## 2350 2019.000 November Pharma Cotton Import 6895
## 2351 2024.000 December Pharma Wheat Export 987
## 2352 2018.000 November Agriculture Mobile Export 4021
## 2353 2018.000 June Automobile Mobile Import 5861
## 2354 2024.000 February Automobile Cotton Export 1962
## 2355 2018.000 July Agriculture Wheat Import 5737
## 2356 2022.000 April Electronics Wheat Import 9028
## 2357 2023.000 January Automobile Medicine Import 2231
## 2358 2018.000 October Electronics Cotton Export 618
## 2359 2021.000 December Agriculture Car Export 3631
## 2360 2019.000 December Pharma Wheat Import 3975
## 2361 2019.000 May Agriculture Cotton Import 4451
## 2362 2018.000 September Textile Car Import 4582
## 2363 2018.000 January Textile Medicine Import 690
## 2364 2021.000 August Automobile Car Import 788
## 2365 2020.000 January Textile Cotton Import 1046
## 2366 2020.000 November Textile Medicine Import 2764
## 2367 2024.000 April Textile Mobile Import 2391
## 2368 2021.000 February Agriculture Cotton Export 1086
## 2369 2019.000 April Agriculture Wheat Import 6024
## 2370 2020.000 February Textile Mobile Import 4121
## 2371 2024.000 November Textile Car Export 5948
## 2372 2019.000 October Pharma Medicine Export 9217
## 2373 2022.000 December Textile Wheat Export 4728
## 2374 2021.000 October Electronics Medicine Import 9539
## 2375 2022.000 June Electronics Medicine Import 8020
## 2376 2020.000 February Pharma Wheat Export 4286
## 2377 2022.000 April Textile Car Import 6695
## 2378 2018.000 July Automobile Medicine Export 186
## 2379 2018.000 January Textile Mobile Export 9582
## 2380 2022.000 August Automobile Medicine Import 5432
## 2381 2019.000 August Electronics Cotton Export 4477
## 2382 2021.000 March Agriculture Medicine Export 5216
## 2383 2024.000 July Pharma Car Export 4545
## 2384 2023.000 August Electronics Cotton Export 3552
## 2385 2023.000 July Pharma Wheat Export 4540
## 2386 2021.000 October Pharma Wheat Import 3687
## 2387 2018.000 June Agriculture Car Import 2747
## 2388 2021.000 September Electronics Cotton Import 3668
## 2389 2020.000 January Automobile Cotton Import 7009
## 2390 2019.000 May Agriculture Car Export 411
## 2391 2024.000 December Agriculture Medicine Export 7417
## 2392 2023.000 July Pharma Car Import 148
## 2393 2024.000 March Electronics Car Export 3866
## 2394 2022.000 September Agriculture Car Import 8345
## 2395 2022.000 May Electronics Mobile Export 6862
## 2396 2019.000 December Textile Medicine Export 4671
## 2397 2021.000 September Electronics Mobile Export 369
## 2398 2021.000 July Pharma Car Export 7690
## 2399 2021.000 June Agriculture Wheat Import 8975
## 2400 2022.000 June Electronics Medicine Import 9990
## 2401 2022.000 November Automobile Cotton Export 9060
## 2402 2020.000 April Automobile Car Import 488
## 2403 2020.000 October Electronics Medicine Export 2099
## 2404 2018.000 April Agriculture Cotton Export 6796
## 2405 2019.000 March Pharma Mobile Export 4185
## 2406 2024.000 July Automobile Medicine Import 6392
## 2407 2024.000 May Pharma Medicine Export 8398
## 2408 2022.000 January Textile Mobile Import 6280
## 2409 2023.000 February Pharma Car Import 2188
## 2410 2020.000 May Pharma Mobile Export 2335
## 2411 2024.000 June Electronics Wheat Import 7992
## 2412 2020.000 December Textile Mobile Export 2640
## 2413 2020.000 August Textile Medicine Export 217
## 2414 2020.000 September Automobile Wheat Export 7497
## 2415 2021.000 August Electronics Medicine Export 7774
## 2416 2018.000 May Electronics Medicine Import 1526
## 2417 2020.000 November Agriculture Medicine Export 8798
## 2418 2023.000 October Textile Medicine Import 9357
## 2419 2022.000 October Electronics Medicine Import 8218
## 2420 2018.000 May Pharma Mobile Import 6551
## 2421 2024.000 November Electronics Car Export 2383
## 2422 2018.000 June Textile Wheat Export 9543
## 2423 2018.000 August Pharma Wheat Export 6769
## 2424 2020.000 May Automobile Wheat Import 7165
## 2425 2019.000 January Automobile Wheat Export 2045
## 2426 2021.000 October Automobile Car Import 6302
## 2427 2023.000 December Pharma Medicine Export 2109
## 2428 2020.000 June Pharma Cotton Import 9274
## 2429 2022.000 September Electronics Mobile Export 3104
## 2430 2019.000 June Pharma Wheat Export 3674
## 2431 2019.000 July Automobile Medicine Export 6824
## 2432 2022.000 October Textile Cotton Import 607
## 2433 2019.000 April Pharma Car Export 2184
## 2434 2024.000 May Automobile Mobile Export 1335
## 2435 2019.000 December Agriculture Cotton Import 8872
## 2436 2021.000 September Agriculture Car Import 530
## 2437 2022.000 July Electronics Car Export 1529
## 2438 2024.000 July Automobile Cotton Import 4134
## 2439 2024.000 July Automobile Car Export 3118
## 2440 2024.000 March Pharma Medicine Export 7651
## 2441 2018.000 March Electronics Medicine Import 4849
## 2442 2020.000 March Electronics Cotton Export 7004
## 2443 2021.000 November Automobile Mobile Import 2864
## 2444 2019.000 July Pharma Medicine Import 8125
## 2445 2018.000 January Agriculture Medicine Import 2290
## 2446 2020.000 April Agriculture Medicine Import 9656
## 2447 2023.000 December Automobile Medicine Export 7159
## 2448 2023.000 November Textile Wheat Import 3333
## 2449 2018.000 October Automobile Car Export 440
## 2450 2024.000 April Pharma Medicine Import 1385
## 2451 2022.000 April Agriculture Car Export 5425
## 2452 2023.000 July Automobile Mobile Import 3270
## 2453 2023.000 January Agriculture Mobile Export 8512
## 2454 2022.000 November Agriculture Medicine Export 9057
## 2455 2023.000 April Pharma Wheat Export 6154
## 2456 2021.000 April Automobile Car Import 8988
## 2457 2020.000 March Automobile Cotton Export 4124
## 2458 2024.000 December Automobile Wheat Export 3753
## 2459 2018.000 February Pharma Car Import 8203
## 2460 2018.000 October Electronics Car Export 6055
## 2461 2018.000 March Automobile Mobile Import 3097
## 2462 2022.000 October Textile Car Import 4599
## 2463 2021.000 April Agriculture Car Import 8351
## 2464 2023.000 November Textile Car Export 4246
## 2465 2024.000 September Agriculture Cotton Import 6355
## 2466 2019.000 October Pharma Car Export 9572
## 2467 2023.000 April Electronics Medicine Import 3850
## 2468 2022.000 September Agriculture Car Export 4115
## 2469 2018.000 May Agriculture Wheat Import 6594
## 2470 2023.000 March Agriculture Medicine Import 6114
## 2471 2024.000 August Textile Cotton Export 5424
## 2472 2022.000 June Pharma Wheat Export 8797
## 2473 2019.000 July Automobile Mobile Import 6032
## 2474 2023.000 November Agriculture Wheat Import 7805
## 2475 2018.000 February Agriculture Car Export 3400
## 2476 2022.000 April Agriculture Medicine Import 4560
## 2477 2022.000 September Pharma Car Import 7503
## 2478 2019.000 July Agriculture Cotton Import 602
## 2479 2024.000 January Pharma Car Export 8564
## 2480 2021.000 June Pharma Mobile Import 2201
## 2481 2020.000 January Textile Car Export 6826
## 2482 2024.000 November Automobile Car Import 6038
## 2483 2022.000 March Electronics Mobile Export 5665
## 2484 2019.000 August Textile Mobile Import 9661
## 2485 2020.000 September Agriculture Wheat Export 5113
## 2486 2018.000 July Textile Cotton Export 3469
## 2487 2022.000 July Automobile Cotton Export 7610
## 2488 2019.000 January Pharma Car Import 8772
## 2489 2020.000 January Automobile Car Export 7658
## 2490 2022.000 July Textile Cotton Export 7483
## 2491 2022.000 March Automobile Medicine Export 9647
## 2492 2020.000 May Pharma Car Export 9581
## 2493 2019.000 October Textile Wheat Import 4315
## 2494 2019.000 May Pharma Car Import 6125
## 2495 2020.000 January Automobile Wheat Export 6801
## 2496 2018.000 July Textile Cotton Export 2448
## 2497 2023.000 July Pharma Car Import 499
## 2498 2021.000 January Electronics Medicine Import 6963
## 2499 2021.000 December Electronics Wheat Export 9508
## 2500 2024.000 November Textile Car Export 7611
## Value_USD Country Port Transport_Mode Tariff GST
## 1 13008.735 UAE Mumbai Air 7.811733 6.146013
## 2 60162.407 USA Kolkata Sea 12.267279 5.921858
## 3 85577.895 UK Mumbai Land 17.060113 16.668646
## 4 34324.444 USA Delhi Land 7.926519 13.648546
## 5 81478.386 Germany Chennai Sea 11.719113 5.345599
## 6 79314.631 UAE Kolkata Air 12.717703 6.743057
## 7 58860.105 UAE Kolkata Sea 15.964596 6.152744
## 8 48685.782 UK Chennai Land 5.173611 12.070206
## 9 85194.811 China Kandla Land 11.440255 9.528733
## 10 62300.003 UAE Delhi Sea 8.906994 11.157567
## 11 88773.410 Germany Mumbai Sea 19.952136 7.833147
## 12 74496.415 UK Kolkata Air 18.080132 8.836475
## 13 54433.355 UK Chennai Sea 12.876221 16.943690
## 14 5471.349 UK Mumbai Air 6.106324 16.379031
## 15 19508.608 UAE Kandla Land 7.296696 5.553428
## 16 1029.865 UAE Kandla Land 13.318336 5.149844
## 17 61175.251 Germany Mumbai Land 5.879636 17.950147
## 18 52922.764 USA Mumbai Land 11.299949 15.254894
## 19 21881.402 China Chennai Air 12.013570 11.287591
## 20 48665.975 UK Kolkata Air 8.541675 11.458413
## 21 68259.157 UAE Kandla Air 16.198332 15.638020
## 22 2449.066 Germany Kolkata Sea 13.254719 11.070240
## 23 4606.446 China Chennai Land 9.370879 14.292503
## 24 54293.325 Germany Delhi Air 17.483881 12.571965
## 25 3703.776 UAE Chennai Land 19.160051 14.641029
## 26 81573.086 Germany Chennai Air 8.618148 15.398216
## 27 2105.841 UK Mumbai Sea 13.595918 14.776861
## 28 46930.383 UAE Mumbai Air 7.399087 10.394175
## 29 28730.089 Japan Delhi Air 18.279323 7.436988
## 30 49020.409 USA Mumbai Sea 9.795587 9.064646
## 31 59339.053 USA Kandla Sea 16.785542 17.216134
## 32 61208.278 China Chennai Land 12.757022 14.316329
## 33 53962.055 UK Delhi Air 18.141558 16.819003
## 34 19472.712 UAE Kolkata Air 8.430066 17.285505
## 35 47484.699 China Chennai Land 5.491270 11.162917
## 36 14869.342 Germany Delhi Air 9.648684 9.957588
## 37 74970.779 UK Chennai Land 8.513158 7.849077
## 38 29038.654 UK Mumbai Air 9.458121 8.555341
## 39 88656.692 USA Delhi Sea 7.090831 15.557703
## 40 1579.394 Japan Kolkata Land 10.151557 10.274554
## 41 68028.369 UAE Kandla Air 8.007664 10.143456
## 42 27295.124 Japan Chennai Land 15.881558 6.975692
## 43 76916.640 USA Kolkata Land 7.206877 10.102705
## 44 68705.128 Japan Kolkata Land 9.871537 15.763828
## 45 24408.827 Japan Kandla Land 12.956855 14.244966
## 46 45475.397 Germany Kandla Air 6.999086 14.766380
## 47 23728.384 USA Kandla Air 5.225843 6.898551
## 48 72391.490 UAE Kolkata Sea 8.666701 6.160669
## 49 53742.598 USA Kolkata Air 16.321240 17.672192
## 50 31712.236 UK Mumbai Sea 18.631231 17.664058
## 51 90588.506 Germany Mumbai Air 6.787369 16.506340
## 52 69994.411 Germany Chennai Land 15.195366 10.246981
## 53 34621.436 UAE Kolkata Air 15.050487 5.955113
## 54 71919.191 China Delhi Sea 8.573122 7.249583
## 55 45868.238 USA Kolkata Sea 11.528741 15.559322
## 56 69703.513 Germany Mumbai Land 11.549333 15.215984
## 57 45038.148 Japan Kandla Air 8.308103 12.770097
## 58 97995.356 Germany Kolkata Air 18.479315 11.759852
## 59 5607.233 Germany Kandla Sea 13.416449 15.249442
## 60 66118.457 UAE Kandla Air 11.559801 6.295688
## 61 99668.272 UK Kolkata Land 9.989177 15.241326
## 62 23245.423 UK Chennai Sea 5.735936 17.162128
## 63 65826.882 USA Kolkata Land 16.561762 9.124913
## 64 14301.393 USA Chennai Air 19.052703 7.676521
## 65 80583.370 UK Kandla Air 8.105019 5.439553
## 66 11599.398 Germany Mumbai Land 13.357189 15.016107
## 67 81600.489 UAE Kandla Air 12.703307 15.463183
## 68 9447.782 UAE Kolkata Sea 9.807847 13.253373
## 69 40284.521 UK Mumbai Land 17.732456 10.410295
## 70 54712.694 China Kolkata Land 16.001942 6.977141
## 71 1900.897 USA Mumbai Sea 18.361502 12.827704
## 72 34743.035 Japan Kolkata Sea 6.164492 13.066566
## 73 30587.212 UAE Delhi Air 13.020337 15.091612
## 74 23907.380 Japan Chennai Sea 17.773434 10.893036
## 75 62854.761 UAE Mumbai Land 6.311950 17.639371
## 76 15910.984 USA Chennai Land 10.543295 7.819375
## 77 41188.259 Germany Delhi Air 18.891431 12.593807
## 78 82452.371 USA Kolkata Land 10.370888 6.307676
## 79 53585.385 UAE Kandla Land 6.637187 14.848505
## 80 9414.908 USA Mumbai Sea 16.948296 10.529878
## 81 20388.561 Germany Delhi Air 5.426636 16.787656
## 82 60189.010 UAE Delhi Sea 10.130462 7.110851
## 83 9118.738 UK Kolkata Land 11.133779 10.332875
## 84 75590.847 USA Kolkata Sea 10.414168 7.507487
## 85 72017.499 China Mumbai Air 15.181881 13.441521
## 86 83953.995 Germany Kandla Land 7.222914 12.439757
## 87 46452.937 UAE Delhi Air 6.395958 15.485267
## 88 70115.001 UAE Chennai Land 12.520354 13.186264
## 89 52611.578 UK Chennai Air 10.126109 8.936587
## 90 69251.883 UK Chennai Air 7.497215 10.500464
## 91 37921.745 China Kandla Air 8.825416 10.134432
## 92 13311.987 UAE Chennai Sea 11.109087 16.639578
## 93 39065.808 Japan Mumbai Sea 18.532344 6.641737
## 94 22689.683 Japan Delhi Land 12.576420 15.574753
## 95 6702.248 China Mumbai Land 8.769619 15.482873
## 96 50596.517 USA Delhi Sea 13.992607 7.314291
## 97 49517.684 USA Kolkata Air 13.159437 5.436868
## 98 45786.045 China Chennai Air 10.568560 6.861326
## 99 70534.822 UK Mumbai Land 15.454126 8.794829
## 100 36066.839 Japan Mumbai Sea 11.343308 14.947644
## 101 32526.827 Japan Kandla Sea 9.066435 9.064817
## 102 67769.200 China Chennai Air 17.253474 9.510664
## 103 71471.101 UK Chennai Land 9.930756 11.282718
## 104 19027.588 China Delhi Sea 6.485860 9.960828
## 105 10934.778 China Delhi Sea 14.878321 15.136993
## 106 50219.868 UK Chennai Air 9.155675 17.329485
## 107 63322.757 UK Kolkata Air 19.600573 11.380423
## 108 30928.424 UK Delhi Sea 7.586247 5.839152
## 109 16516.640 UAE Kandla Sea 5.250738 10.841805
## 110 61211.561 China Chennai Land 11.172871 16.280405
## 111 14178.838 UAE Delhi Air 11.722796 7.235695
## 112 82857.337 China Mumbai Air 6.670245 17.959209
## 113 69002.566 UAE Kandla Land 13.938044 15.795806
## 114 61410.101 China Chennai Air 6.781034 12.744259
## 115 45347.097 China Kolkata Land 12.802349 5.353425
## 116 90533.450 UAE Mumbai Sea 18.402934 11.542577
## 117 20376.363 Germany Delhi Sea 11.207907 11.472895
## 118 21512.898 UK Kolkata Land 9.967652 7.758691
## 119 17646.503 USA Mumbai Land 10.943563 5.794340
## 120 38507.522 Germany Kolkata Sea 10.568508 13.122101
## 121 9395.875 Germany Delhi Air 9.090056 13.034213
## 122 23617.579 USA Chennai Land 16.336151 9.123423
## 123 49553.171 Germany Delhi Land 16.523235 9.132697
## 124 64274.556 Japan Mumbai Sea 9.877089 10.661508
## 125 63482.525 Germany Delhi Land 9.304862 8.655465
## 126 3798.038 China Kolkata Land 17.272061 11.848896
## 127 77920.727 China Chennai Sea 11.115347 15.836792
## 128 6441.338 Japan Chennai Land 12.447270 7.826246
## 129 31851.049 China Delhi Air 11.861863 15.470701
## 130 30779.755 UK Delhi Air 15.752252 10.806729
## 131 5764.132 USA Chennai Air 13.364812 7.985687
## 132 5622.228 Germany Kandla Land 15.576371 11.093946
## 133 82262.637 Japan Chennai Air 18.926139 13.181300
## 134 27581.181 Japan Kolkata Air 16.405750 12.239655
## 135 59277.967 USA Chennai Land 14.855298 9.231430
## 136 75550.200 Germany Kolkata Air 19.205697 17.177366
## 137 41816.924 UK Delhi Sea 13.123223 8.576250
## 138 11695.727 UK Kolkata Air 16.921255 17.944639
## 139 7048.924 Germany Delhi Land 7.469417 12.855057
## 140 33571.471 Japan Mumbai Air 14.825590 5.063524
## 141 60852.752 Japan Mumbai Sea 16.300168 15.585384
## 142 63187.955 UK Delhi Sea 9.924545 8.978581
## 143 63677.059 Germany Chennai Air 13.464257 15.796952
## 144 11079.996 USA Kandla Air 17.376574 6.507088
## 145 53492.546 China Delhi Air 18.262624 6.643218
## 146 66604.417 China Chennai Sea 14.457031 6.788028
## 147 93447.438 Germany Kolkata Sea 12.477701 8.808112
## 148 63811.117 UAE Delhi Land 7.561074 7.096183
## 149 95251.028 Japan Mumbai Sea 14.920776 8.549376
## 150 25958.546 Germany Kolkata Land 9.661757 14.079605
## 151 37354.597 UAE Kolkata Air 18.385879 14.861823
## 152 30786.400 UK Kolkata Land 11.966522 5.872055
## 153 9367.239 Germany Delhi Land 11.248686 12.744603
## 154 94975.709 UK Delhi Land 16.756023 11.973375
## 155 71115.907 China Kandla Land 7.024354 14.173927
## 156 81806.098 UK Kandla Air 7.732170 15.851845
## 157 29157.882 Japan Kolkata Sea 15.488219 10.654568
## 158 63544.326 USA Kandla Land 9.545428 15.587710
## 159 25342.010 Japan Delhi Air 15.329557 9.579140
## 160 43223.415 Japan Kolkata Air 18.285549 14.489203
## 161 62810.540 Japan Delhi Sea 5.196843 8.733911
## 162 94084.488 Japan Kolkata Sea 9.949221 13.722177
## 163 64820.319 Japan Kolkata Air 11.529026 17.039190
## 164 7116.773 UK Kandla Land 17.068500 13.525824
## 165 16335.713 Japan Kolkata Air 10.086383 13.049530
## 166 18621.852 China Chennai Land 13.599598 16.457231
## 167 67971.145 Japan Mumbai Sea 7.099180 10.105550
## 168 41083.798 Germany Delhi Air 9.311033 8.657499
## 169 51598.718 Germany Mumbai Sea 8.107022 6.654242
## 170 20987.759 China Kolkata Air 17.308628 11.815811
## 171 64007.711 UAE Kolkata Air 11.402280 8.142271
## 172 61332.060 China Chennai Land 11.755083 10.011288
## 173 5336.601 UAE Kolkata Land 14.622872 14.512685
## 174 6981.997 Germany Delhi Air 5.353428 12.891672
## 175 88826.786 Germany Mumbai Air 7.993222 10.370510
## 176 61482.229 Germany Chennai Land 7.432566 17.472483
## 177 23038.897 China Chennai Land 9.331857 8.876154
## 178 21174.318 Japan Delhi Sea 6.023303 11.062016
## 179 14740.221 Japan Delhi Land 15.899107 12.788359
## 180 66506.280 UK Kandla Land 11.914870 6.211318
## 181 22196.846 USA Mumbai Land 6.748283 14.857534
## 182 67431.342 USA Mumbai Land 13.698281 8.388184
## 183 83802.341 UAE Chennai Sea 9.994423 14.762540
## 184 38389.738 Japan Mumbai Land 9.683160 16.446262
## 185 16758.750 USA Kandla Land 18.771564 9.060077
## 186 99790.815 China Chennai Air 14.062621 15.027605
## 187 87830.613 UAE Delhi Sea 10.134300 13.402568
## 188 29184.291 UAE Kandla Land 13.993159 7.272272
## 189 47885.814 UK Kolkata Sea 19.693320 15.301243
## 190 9299.203 USA Kandla Air 9.534577 10.069582
## 191 75982.989 Germany Delhi Sea 17.649920 6.892184
## 192 48440.934 China Kandla Air 8.196374 7.960630
## 193 92112.449 China Kolkata Sea 7.958585 11.313110
## 194 73891.096 USA Kandla Land 18.029575 11.284495
## 195 5054.433 UK Kolkata Air 18.380363 15.981455
## 196 91980.087 Germany Mumbai Air 10.251669 12.878060
## 197 89674.367 UAE Kolkata Land 11.829388 14.515329
## 198 49263.973 Germany Chennai Land 5.622296 10.243064
## 199 99095.881 Japan Kolkata Land 18.520512 8.829497
## 200 31901.286 UAE Delhi Sea 18.927276 8.582971
## 201 91480.561 Germany Mumbai Air 12.710487 13.166561
## 202 80182.549 Germany Kolkata Land 6.180349 6.479555
## 203 68202.680 China Delhi Air 19.148957 9.677100
## 204 47208.857 China Kolkata Sea 6.757142 14.518406
## 205 42674.116 China Kandla Air 6.224704 5.744474
## 206 71427.293 UAE Delhi Air 19.748187 11.548659
## 207 93954.319 USA Kolkata Air 7.510991 14.160472
## 208 51909.338 Japan Mumbai Air 14.258752 15.935503
## 209 6896.880 UAE Chennai Air 15.155798 11.625096
## 210 19794.371 China Kandla Air 6.146456 14.976994
## 211 45732.166 UK Kandla Land 13.859975 8.494366
## 212 74788.488 China Delhi Sea 13.392822 12.782830
## 213 80007.062 Germany Kolkata Land 19.215625 13.123592
## 214 56093.729 China Chennai Sea 16.406867 10.035753
## 215 13914.445 Germany Mumbai Sea 5.939974 12.811519
## 216 98442.337 Japan Mumbai Sea 11.275508 7.365131
## 217 9720.899 China Chennai Land 8.548751 9.765157
## 218 6079.510 Germany Chennai Sea 12.156455 14.332671
## 219 64460.414 UK Mumbai Land 13.495184 8.594521
## 220 5432.060 China Chennai Sea 17.487578 9.726006
## 221 90318.744 USA Mumbai Land 5.477701 14.720688
## 222 80270.823 UK Kandla Air 9.596981 8.539951
## 223 7662.396 USA Chennai Land 12.494909 13.451770
## 224 78700.834 Japan Chennai Land 13.844822 15.105359
## 225 51863.039 UK Kandla Sea 11.492397 12.443856
## 226 34788.280 UAE Delhi Sea 12.084038 10.192539
## 227 59645.514 USA Mumbai Sea 8.005746 15.276718
## 228 65559.221 UAE Kolkata Sea 8.205670 10.358429
## 229 53814.909 China Kandla Land 10.385477 12.474865
## 230 49508.295 Japan Kolkata Air 17.483387 7.052754
## 231 87583.125 USA Chennai Air 8.444184 14.479526
## 232 54996.107 Germany Mumbai Air 9.243652 14.711272
## 233 16587.205 UK Mumbai Sea 19.367364 5.214330
## 234 93510.180 Japan Mumbai Land 15.252442 15.288693
## 235 52210.347 Germany Chennai Land 14.400272 13.014434
## 236 40379.759 Germany Kolkata Air 19.563464 7.740787
## 237 21732.347 USA Mumbai Air 10.348037 11.516613
## 238 13786.387 USA Kolkata Air 10.181035 14.665619
## 239 79926.989 China Kandla Air 11.517257 8.123431
## 240 64889.428 UAE Delhi Sea 10.706244 9.373299
## 241 36763.896 Japan Chennai Air 9.327403 12.167401
## 242 5618.178 Japan Mumbai Sea 16.147645 10.911661
## 243 35184.415 UAE Mumbai Sea 19.863932 5.317480
## 244 42187.900 UAE Kandla Air 9.884142 8.819339
## 245 75422.192 USA Kandla Air 18.859695 5.670575
## 246 25900.373 UAE Kolkata Sea 8.632529 10.240894
## 247 82995.884 UK Delhi Land 19.329550 5.751324
## 248 36318.660 China Kandla Air 7.902354 11.293918
## 249 31111.039 Germany Mumbai Land 16.494589 16.312826
## 250 62503.108 UK Kolkata Land 18.809301 9.640511
## 251 78912.360 USA Mumbai Land 10.342951 16.877362
## 252 26675.503 USA Mumbai Sea 13.856941 8.848620
## 253 50613.736 UAE Chennai Land 10.304731 13.106173
## 254 70748.596 USA Mumbai Air 6.304493 10.333182
## 255 1368.534 Germany Mumbai Land 11.023552 16.241235
## 256 88687.617 USA Kandla Land 10.521502 16.032650
## 257 3665.732 China Chennai Land 16.007710 11.167807
## 258 93566.509 Germany Kandla Air 9.469566 10.412845
## 259 45771.847 Germany Delhi Sea 14.299158 11.882140
## 260 51115.315 China Kolkata Sea 8.257647 5.215024
## 261 70218.174 UAE Chennai Land 10.901880 13.212427
## 262 98661.697 Japan Kolkata Sea 6.394556 10.522463
## 263 33883.497 Germany Kolkata Land 13.281165 17.156033
## 264 22836.250 USA Delhi Land 11.005841 17.642920
## 265 57660.369 Japan Delhi Sea 5.266724 17.720335
## 266 35725.031 Japan Chennai Sea 10.955327 15.169172
## 267 336629.328 UAE Kolkata Land 17.646021 16.451646
## 268 54519.278 UAE Kandla Air 11.430339 16.268279
## 269 44233.048 UAE Kolkata Air 7.656929 10.960489
## 270 64456.643 UAE Chennai Land 6.692506 17.314927
## 271 80995.938 USA Mumbai Sea 5.916083 5.002376
## 272 20277.446 UAE Chennai Sea 13.759059 10.563790
## 273 99312.888 China Delhi Air 14.346460 9.176489
## 274 97282.659 UK Kolkata Sea 9.010893 14.676784
## 275 5621.398 UAE Kandla Sea 11.507497 14.664627
## 276 24927.122 Japan Chennai Sea 5.263585 14.208340
## 277 67172.985 UK Kandla Sea 7.087333 9.325871
## 278 57607.749 UAE Kolkata Sea 15.384829 5.169856
## 279 37779.766 Germany Delhi Land 5.706713 11.771843
## 280 25649.679 Japan Kandla Land 19.633632 17.314794
## 281 97502.574 Japan Delhi Sea 12.274949 6.647373
## 282 46225.732 China Kolkata Land 19.618675 14.829397
## 283 27988.399 UAE Mumbai Sea 14.357965 17.919352
## 284 52884.419 China Kandla Land 5.187379 7.994783
## 285 10634.030 USA Chennai Air 19.225797 11.518001
## 286 48899.952 UAE Delhi Air 15.600535 11.014132
## 287 66797.090 USA Chennai Air 16.892036 10.364445
## 288 36409.633 China Chennai Sea 10.315723 10.700269
## 289 88834.029 UK Delhi Air 10.632603 8.098933
## 290 46024.760 UAE Kolkata Air 6.708560 13.429769
## 291 69522.221 UK Delhi Sea 15.027848 7.738400
## 292 50675.807 Japan Kolkata Air 18.640180 15.818134
## 293 27553.059 Japan Kandla Air 12.360258 14.502562
## 294 13741.647 USA Kolkata Sea 12.207288 10.384946
## 295 47205.969 China Mumbai Land 6.503420 10.529686
## 296 37541.527 China Kandla Air 6.402713 10.512782
## 297 15678.063 UK Delhi Air 5.895335 15.038135
## 298 42325.270 Germany Chennai Sea 17.604731 12.287024
## 299 4083.383 UK Delhi Air 8.985739 14.775163
## 300 84751.788 UK Chennai Sea 14.746382 11.850948
## 301 14480.949 Germany Mumbai Sea 5.169809 16.092350
## 302 38712.105 UAE Mumbai Air 16.944860 16.282165
## 303 22102.437 China Mumbai Sea 13.837027 16.799914
## 304 39487.108 Japan Chennai Sea 7.670059 13.239499
## 305 69002.144 Germany Kandla Air 11.309273 11.594204
## 306 89782.745 Germany Kandla Land 16.838203 13.803027
## 307 63715.636 USA Chennai Land 6.775197 9.349787
## 308 64484.436 China Kolkata Air 5.532927 12.952229
## 309 5335.021 UAE Kolkata Air 13.321461 9.801143
## 310 50743.379 UAE Mumbai Sea 5.073638 12.416665
## 311 3771.343 China Mumbai Air 17.961397 12.674425
## 312 65935.697 Germany Chennai Air 16.726954 15.780646
## 313 70223.832 UAE Mumbai Air 6.234256 6.345442
## 314 82892.346 UK Kolkata Air 16.485607 14.288293
## 315 50057.052 China Kandla Land 15.400578 8.609838
## 316 42921.096 Germany Kolkata Land 9.525361 6.607367
## 317 19554.964 UAE Chennai Land 8.303470 12.561543
## 318 20871.743 UAE Kandla Air 5.179790 8.313694
## 319 52934.116 Japan Chennai Air 13.230620 16.602276
## 320 62768.986 Japan Chennai Sea 10.935453 13.292463
## 321 89222.935 China Delhi Land 15.951522 13.485147
## 322 34020.648 Germany Mumbai Land 16.400407 11.267130
## 323 18058.755 China Delhi Air 8.089172 17.013205
## 324 15959.152 UAE Kolkata Land 11.677693 9.906656
## 325 96484.518 Japan Mumbai Land 18.850484 5.219995
## 326 98696.736 Germany Delhi Sea 18.808700 5.914947
## 327 62118.432 Japan Kolkata Land 14.145917 14.730369
## 328 49510.904 USA Kolkata Air 5.265042 9.819201
## 329 17285.278 China Chennai Land 16.535012 15.905547
## 330 74687.588 Japan Mumbai Sea 8.228294 5.364246
## 331 33193.799 UAE Kandla Sea 19.223525 49.356917
## 332 64640.633 UK Chennai Air 18.566700 13.546661
## 333 67125.089 USA Kandla Land 11.640230 8.553958
## 334 87748.083 Japan Delhi Land 10.483556 17.238029
## 335 5489.661 UAE Chennai Sea 9.936431 12.094632
## 336 34822.683 Germany Kolkata Air 8.773907 14.587365
## 337 65899.988 UK Chennai Land 15.109523 8.101973
## 338 81618.428 Japan Kolkata Air 10.218336 9.918743
## 339 57149.092 USA Mumbai Sea 12.860048 17.229045
## 340 44468.749 USA Kolkata Land 16.491095 17.123935
## 341 71090.707 Germany Kandla Sea 15.839831 10.696446
## 342 58163.486 Japan Kandla Air 6.131660 16.185636
## 343 1582.581 USA Delhi Land 10.198495 11.069078
## 344 68484.035 Japan Kolkata Air 7.673313 11.386584
## 345 78313.132 USA Kandla Land 16.282506 12.364311
## 346 21142.300 China Delhi Air 19.081951 13.916607
## 347 12921.796 China Kandla Land 9.199055 9.346411
## 348 72624.289 China Chennai Sea 8.920581 13.801798
## 349 38545.999 UAE Kolkata Land 14.129978 17.464392
## 350 97079.562 Japan Delhi Air 16.569565 9.499664
## 351 65052.424 Japan Mumbai Land 7.944214 12.229154
## 352 58837.124 UK Chennai Air 17.560698 10.827615
## 353 30040.284 Germany Chennai Air 10.150463 5.738773
## 354 82977.693 Germany Kandla Sea 10.415819 12.326774
## 355 50514.063 Germany Kandla Land 6.069362 15.493958
## 356 13507.066 Japan Kandla Sea 11.118797 7.261307
## 357 2030.085 China Kandla Land 7.986913 10.306101
## 358 73126.361 UAE Delhi Air 9.920404 8.218113
## 359 91721.381 USA Kandla Air 12.508283 16.664912
## 360 55040.707 Japan Kandla Sea 16.424740 14.242609
## 361 33878.461 China Chennai Sea 15.762851 7.603775
## 362 77365.014 UK Chennai Land 13.927796 11.798233
## 363 45070.153 UK Chennai Air 10.052066 9.136555
## 364 89444.128 UK Kolkata Air 12.708179 7.901696
## 365 64271.354 UAE Kandla Sea 18.700019 15.267288
## 366 27447.460 Germany Chennai Land 16.459724 15.195084
## 367 88295.252 USA Kolkata Land 14.385972 12.945961
## 368 39667.906 China Delhi Land 13.487613 6.901508
## 369 69488.746 UAE Mumbai Land 15.769123 6.947785
## 370 46962.849 Japan Kandla Land 11.844882 14.109891
## 371 4660.025 Germany Delhi Sea 15.513231 10.067036
## 372 33708.330 Germany Delhi Sea 7.295778 10.942129
## 373 60330.629 USA Kandla Land 10.229322 7.204507
## 374 61303.505 China Delhi Sea 5.630812 6.757038
## 375 30007.492 Japan Chennai Sea 15.003206 13.845697
## 376 1237.031 Japan Kolkata Air 19.575373 10.673756
## 377 17011.824 China Mumbai Land 9.237149 9.071203
## 378 95237.759 UAE Chennai Air 6.450727 7.265228
## 379 53180.000 USA Chennai Air 11.515825 6.045125
## 380 13608.537 Germany Mumbai Land 17.598495 16.242491
## 381 35613.068 UK Chennai Land 18.506219 8.584022
## 382 98719.124 China Delhi Sea 15.548335 9.883440
## 383 71307.359 China Kolkata Sea 9.275415 9.110204
## 384 68775.647 USA Kandla Land 15.382754 8.098227
## 385 81379.382 China Kolkata Sea 6.238814 13.347788
## 386 75131.269 UK Kolkata Land 9.615866 8.805556
## 387 24500.103 China Chennai Air 17.751931 5.039842
## 388 85226.297 USA Kandla Land 15.786237 12.482540
## 389 66071.703 UK Kolkata Air 8.561571 11.261784
## 390 64852.967 UK Delhi Sea 10.756490 15.156322
## 391 95288.455 USA Chennai Sea 13.219643 7.136594
## 392 57023.735 USA Kandla Land 19.126863 7.942857
## 393 83280.479 USA Kandla Sea 19.674952 7.148078
## 394 79470.595 UAE Kandla Sea 15.887261 12.633729
## 395 60621.483 UAE Mumbai Air 15.164734 17.095282
## 396 17082.321 USA Delhi Sea 8.477457 16.567600
## 397 14561.189 USA Kolkata Land 19.768020 17.014379
## 398 35168.167 China Chennai Air 7.849403 15.916706
## 399 19792.022 UAE Chennai Land 13.728291 16.832059
## 400 44324.036 UK Mumbai Sea 13.265575 13.783463
## 401 48916.693 UAE Chennai Air 17.305235 8.565507
## 402 84617.726 China Kandla Sea 18.512982 8.329821
## 403 25317.009 UK Delhi Air 11.241346 7.688046
## 404 82048.906 UAE Kandla Land 19.234878 16.242599
## 405 5657.878 USA Chennai Land 5.336463 5.827889
## 406 84622.815 UK Kandla Air 7.093445 7.329233
## 407 82128.170 UK Delhi Air 17.245696 7.739407
## 408 92170.943 USA Kolkata Land 7.746513 13.929896
## 409 83281.681 UK Mumbai Air 15.656144 7.700371
## 410 22944.203 Germany Delhi Land 16.194232 9.328512
## 411 63620.280 Germany Delhi Land 7.202052 9.440687
## 412 76668.012 UAE Kandla Sea 14.019292 16.246329
## 413 29238.674 Germany Mumbai Sea 19.584487 8.809478
## 414 66915.478 UK Kolkata Air 15.788604 16.699754
## 415 15042.893 China Mumbai Air 16.806382 12.358660
## 416 68832.281 Japan Kandla Sea 13.509754 6.671765
## 417 50728.178 Japan Kandla Air 8.787777 14.968558
## 418 37821.764 UAE Kolkata Air 18.950537 11.519788
## 419 70485.057 USA Kolkata Land 18.330775 5.357802
## 420 47170.104 USA Chennai Land 6.220930 7.627336
## 421 29389.673 Germany Chennai Air 10.794100 12.486215
## 422 22352.641 China Kandla Sea 9.359649 10.907551
## 423 50748.682 Germany Chennai Air 17.355373 11.658079
## 424 90432.490 USA Kandla Land 6.874996 11.537315
## 425 76246.930 USA Kolkata Land 18.312587 7.430374
## 426 71365.501 UK Mumbai Air 14.012642 9.108343
## 427 14786.351 UK Mumbai Land 9.006567 15.078028
## 428 58548.555 USA Mumbai Sea 5.440115 13.873226
## 429 51411.545 UK Chennai Air 7.492814 14.810567
## 430 68585.671 UK Chennai Land 13.676858 10.725105
## 431 80344.315 China Chennai Sea 19.870026 11.575406
## 432 19776.673 China Delhi Air 5.475441 10.574364
## 433 77965.185 Japan Mumbai Land 9.091388 10.105835
## 434 36719.743 Germany Delhi Sea 18.205098 12.553917
## 435 61796.378 UK Chennai Land 18.066130 6.983888
## 436 54305.813 USA Kandla Land 18.398969 14.199579
## 437 13883.399 USA Delhi Land 7.707580 16.176987
## 438 1211.471 China Mumbai Sea 8.380923 17.646135
## 439 90647.306 Germany Chennai Air 10.082308 16.232791
## 440 35542.908 Japan Kolkata Air 15.582946 9.498125
## 441 21923.440 USA Mumbai Air 15.439768 16.427696
## 442 25341.576 Germany Kandla Air 9.320731 17.748779
## 443 44906.378 Germany Chennai Air 11.345124 15.376405
## 444 76472.603 UK Delhi Air 12.275340 9.197278
## 445 74478.970 Japan Kolkata Air 9.708591 7.747727
## 446 53503.691 USA Kolkata Land 7.511488 8.952721
## 447 72136.181 UAE Kandla Air 18.636312 7.886325
## 448 85614.977 USA Mumbai Sea 16.468944 12.722450
## 449 69335.653 UAE Mumbai Land 12.160196 10.652279
## 450 7825.139 Japan Delhi Sea 10.956621 13.019707
## 451 89314.493 UAE Kandla Land 17.910811 7.024053
## 452 59212.431 China Kolkata Air 9.093919 8.660131
## 453 41082.315 Germany Mumbai Sea 11.978179 16.444138
## 454 62867.804 Germany Kolkata Land 15.445927 15.333385
## 455 64846.569 UK Delhi Land 17.897453 6.233444
## 456 42160.916 China Chennai Sea 15.139812 12.373350
## 457 40465.804 Japan Kolkata Air 5.323200 11.890214
## 458 4774.771 China Mumbai Sea 7.530746 9.571949
## 459 46184.146 UAE Chennai Air 10.891488 8.241654
## 460 26165.682 USA Kolkata Land 19.239139 8.797651
## 461 37514.485 USA Kolkata Air 18.303155 15.999835
## 462 20954.825 UAE Chennai Land 8.385665 10.607100
## 463 10608.695 UAE Kolkata Air 6.270816 16.409875
## 464 45949.637 UK Kandla Land 16.262094 14.142538
## 465 52436.585 UK Mumbai Air 6.847433 10.440305
## 466 31402.473 China Mumbai Sea 12.821155 17.409295
## 467 53536.427 Germany Delhi Sea 13.842817 12.783478
## 468 6415.132 UAE Kolkata Air 18.835805 8.919192
## 469 62052.752 Germany Chennai Land 17.536224 6.744764
## 470 90022.556 Germany Kolkata Sea 10.128880 16.811460
## 471 16108.265 Japan Kolkata Land 9.850330 13.550461
## 472 92324.896 Japan Delhi Air 5.042553 12.650922
## 473 71854.725 China Delhi Sea 6.482579 12.422034
## 474 81308.493 UAE Kolkata Land 19.259777 16.063843
## 475 90430.366 Germany Chennai Air 5.159840 8.916551
## 476 5460.579 Japan Delhi Sea 11.754299 15.256767
## 477 98552.674 Japan Delhi Land 7.505938 8.589256
## 478 15178.352 USA Mumbai Land 18.331285 12.226394
## 479 41342.070 Germany Kolkata Air 7.181694 7.426945
## 480 89077.695 Japan Chennai Land 8.246756 8.796602
## 481 40272.995 UAE Delhi Air 12.100060 12.209892
## 482 4495.124 UK Kolkata Sea 10.474467 12.521099
## 483 20360.753 Germany Kandla Sea 15.835818 7.780239
## 484 89932.292 Japan Mumbai Land 17.431818 6.403131
## 485 65714.585 UAE Mumbai Land 18.420536 15.085464
## 486 88459.530 Germany Kandla Air 14.035032 16.556038
## 487 32190.865 UK Kandla Sea 11.459095 5.213552
## 488 72904.761 Germany Chennai Air 15.604073 5.497824
## 489 65634.298 Japan Chennai Sea 12.593462 16.007921
## 490 60542.982 Germany Delhi Air 14.287899 15.951648
## 491 83123.132 China Kolkata Land 19.666170 14.153756
## 492 29101.518 UAE Delhi Land 13.709409 11.189236
## 493 63832.902 UK Kolkata Land 14.038296 6.801989
## 494 97622.307 Germany Delhi Sea 12.826612 13.287348
## 495 13350.034 USA Delhi Air 14.505221 9.799355
## 496 50662.402 USA Chennai Air 10.198578 8.795072
## 497 35062.322 UK Kolkata Air 13.589308 7.512548
## 498 15551.183 UK Kolkata Sea 17.974554 7.708046
## 499 57853.852 China Mumbai Sea 17.465715 12.286714
## 500 32387.376 USA Mumbai Sea 16.288976 6.251574
## 501 39002.988 USA Mumbai Sea 18.709919 14.431355
## 502 41926.368 China Kolkata Air 11.999592 10.694104
## 503 15979.157 Germany Chennai Sea 15.731434 15.463304
## 504 89220.214 USA Kolkata Land 9.152984 5.459312
## 505 67508.302 UK Chennai Sea 12.725867 9.192550
## 506 21980.251 UAE Kolkata Land 7.843907 11.839904
## 507 59260.729 UAE Kandla Air 13.888335 17.270095
## 508 2552.105 USA Kandla Land 11.967332 17.619239
## 509 31223.642 China Kandla Sea 16.633052 12.364917
## 510 24161.832 USA Delhi Land 10.408666 12.830741
## 511 74764.441 Germany Chennai Land 14.694990 7.697635
## 512 12877.734 Japan Delhi Land 9.429406 8.427535
## 513 16639.091 USA Delhi Sea 9.146432 12.689895
## 514 90083.967 Japan Kolkata Land 14.680245 11.153261
## 515 92168.637 USA Mumbai Land 17.852905 8.394208
## 516 6997.878 Germany Chennai Sea 14.721446 17.927166
## 517 84095.669 China Chennai Land 18.457424 9.375465
## 518 84389.825 Germany Delhi Land 19.136105 6.369320
## 519 60821.407 Japan Kolkata Air 12.391035 15.517270
## 520 35629.321 UAE Chennai Air 19.267741 13.441295
## 521 38888.926 Japan Chennai Land 17.041248 13.882198
## 522 74921.998 USA Kolkata Sea 17.421342 5.312607
## 523 50459.513 UK Kandla Land 14.930627 11.029917
## 524 4933.469 Japan Kandla Sea 8.910456 13.640243
## 525 62694.485 UK Kolkata Air 14.204235 7.711880
## 526 83161.613 UK Mumbai Air 5.683699 17.694359
## 527 53674.423 USA Delhi Sea 6.503347 9.815677
## 528 19630.131 USA Mumbai Land 5.300673 8.966459
## 529 44849.644 China Chennai Land 10.569099 5.874335
## 530 84610.315 Germany Delhi Sea 19.535002 15.270912
## 531 23112.544 USA Delhi Sea 5.447740 16.616638
## 532 58682.794 USA Mumbai Sea 18.881817 5.550536
## 533 76107.713 China Kandla Land 16.980220 9.489401
## 534 70635.623 UAE Kandla Sea 5.862967 10.883267
## 535 71652.785 UAE Chennai Air 15.297552 17.307170
## 536 70092.610 USA Kandla Land 10.047797 8.229543
## 537 96427.269 UK Delhi Land 15.240230 7.093838
## 538 60093.985 China Chennai Sea 15.482554 15.144542
## 539 8200.895 USA Kolkata Air 8.644302 14.439068
## 540 36785.035 UK Delhi Sea 7.957413 6.143846
## 541 97303.392 Japan Kandla Land 11.373829 11.572925
## 542 12561.305 UAE Delhi Sea 9.278266 6.964596
## 543 68710.890 Germany Delhi Air 11.253447 16.056127
## 544 18874.608 China Chennai Sea 19.811986 13.118851
## 545 51969.282 Japan Kolkata Sea 19.771825 6.016410
## 546 19691.913 USA Mumbai Sea 16.759533 11.777671
## 547 86853.335 USA Kandla Sea 8.178456 9.486857
## 548 78090.183 China Kandla Sea 19.349099 8.399296
## 549 88640.222 Germany Kolkata Land 12.044438 11.473327
## 550 19972.665 UAE Delhi Land 7.663596 6.158830
## 551 80901.396 Germany Delhi Sea 15.489450 8.057870
## 552 66165.108 UK Mumbai Land 10.237052 15.634782
## 553 61710.979 China Kolkata Land 18.071193 6.396768
## 554 42803.054 Germany Kolkata Air 17.542800 15.290319
## 555 2968.746 China Kolkata Land 14.146300 8.597059
## 556 49986.296 Japan Mumbai Air 17.197349 7.436570
## 557 92114.362 China Chennai Sea 10.479029 9.678229
## 558 8553.972 Germany Kandla Air 5.068286 14.809395
## 559 50198.720 UK Chennai Air 6.691812 9.459407
## 560 75187.990 China Kandla Land 8.701451 12.081055
## 561 52952.306 UK Chennai Land 15.046410 8.137402
## 562 25728.698 UAE Chennai Sea 17.023987 7.099412
## 563 61620.716 China Mumbai Air 19.236350 6.804946
## 564 3161.265 USA Kolkata Sea 9.844633 13.121037
## 565 99910.761 USA Mumbai Sea 9.906183 11.310586
## 566 16321.903 Germany Kandla Land 10.519532 12.938600
## 567 91453.415 UK Kandla Sea 15.858400 14.815303
## 568 68334.272 Germany Kandla Sea 12.015537 13.808638
## 569 48838.484 China Kolkata Land 18.528141 11.688645
## 570 34071.358 Germany Delhi Sea 19.670884 14.747229
## 571 73322.682 Germany Delhi Land 17.785670 15.477380
## 572 55776.833 China Kolkata Land 8.157048 14.184021
## 573 25607.928 USA Mumbai Air 16.150153 5.868567
## 574 17772.352 China Kolkata Air 10.479417 9.277257
## 575 32145.894 China Kolkata Land 13.657797 5.518135
## 576 55040.745 Japan Chennai Land 9.004848 14.925210
## 577 37535.682 UAE Mumbai Sea 18.206188 16.497397
## 578 36003.121 China Kandla Sea 18.022625 6.166622
## 579 88467.096 China Kolkata Land 7.367562 13.860229
## 580 72507.151 China Kandla Land 18.006810 10.941635
## 581 44583.458 Germany Chennai Air 7.337466 10.686313
## 582 56056.817 China Chennai Land 12.091279 11.715992
## 583 91786.064 Germany Mumbai Air 18.037350 9.531677
## 584 45995.153 UK Chennai Land 16.882689 13.952717
## 585 89839.947 Japan Kolkata Land 18.639658 17.703846
## 586 59799.061 Germany Mumbai Land 12.571489 11.157823
## 587 72084.405 Germany Kolkata Sea 9.454281 12.349116
## 588 94967.554 UK Chennai Land 18.798948 17.326331
## 589 87084.188 USA Kandla Land 9.478715 17.796422
## 590 1618.738 USA Mumbai Land 17.725099 11.818455
## 591 95836.369 Germany Kandla Sea 9.074466 16.371773
## 592 39657.437 UK Delhi Land 8.055818 14.273097
## 593 32252.671 Japan Kolkata Sea 17.061319 6.303006
## 594 72678.771 UAE Delhi Air 13.126227 13.615653
## 595 17810.129 UK Chennai Air 17.888247 6.908461
## 596 68024.077 China Kolkata Air 9.112479 15.130533
## 597 86296.775 UAE Kolkata Land 5.614575 15.263739
## 598 1750.690 UK Kolkata Land 12.439507 13.718410
## 599 14831.164 China Kolkata Air 8.618850 17.745135
## 600 8731.570 UAE Kandla Air 12.181937 5.414664
## 601 12368.800 China Kandla Air 17.990901 6.672833
## 602 21338.950 Japan Delhi Sea 17.956873 11.403361
## 603 90267.147 USA Kolkata Sea 13.703564 13.576433
## 604 69861.316 China Chennai Land 19.415257 8.660944
## 605 92365.135 UK Kandla Land 6.607073 6.330015
## 606 14017.082 Germany Delhi Air 5.293381 8.996653
## 607 55489.663 Germany Kolkata Sea 14.160218 10.286795
## 608 71539.986 Japan Delhi Land 12.015936 7.077567
## 609 87785.962 UK Delhi Sea 11.276468 13.314129
## 610 55990.606 China Kandla Air 19.639044 7.054173
## 611 24033.969 Germany Chennai Air 5.635068 5.168781
## 612 13460.455 USA Kandla Sea 16.206800 16.825207
## 613 41985.554 Japan Mumbai Air 19.020810 14.929704
## 614 60307.110 Germany Kolkata Sea 14.876572 11.758119
## 615 1686.089 Germany Chennai Air 6.496767 5.890390
## 616 87930.280 UAE Kandla Land 9.669408 5.023003
## 617 41849.972 Japan Chennai Land 17.410217 10.123638
## 618 29401.007 UAE Delhi Land 19.019976 13.624945
## 619 45969.962 UAE Delhi Air 8.424425 6.262737
## 620 1162.545 Germany Delhi Air 16.435247 7.439985
## 621 35302.196 UAE Kolkata Air 9.838392 12.093560
## 622 2273.940 China Mumbai Land 10.465653 17.533099
## 623 65964.618 UAE Kandla Air 8.160691 14.572651
## 624 20832.032 Japan Mumbai Air 5.419582 13.896308
## 625 53726.735 UAE Delhi Sea 9.124708 7.506564
## 626 55881.201 Germany Mumbai Sea 17.739137 16.908656
## 627 55183.713 China Delhi Land 6.856323 10.567699
## 628 55138.881 UK Kandla Sea 7.165043 9.305064
## 629 36049.454 Japan Delhi Sea 16.198174 5.960466
## 630 77262.214 USA Delhi Air 7.682699 5.589127
## 631 72255.462 China Chennai Sea 15.226596 13.715595
## 632 1692.312 Germany Delhi Air 15.459723 17.088697
## 633 75716.464 Germany Delhi Sea 12.534114 15.753373
## 634 34106.389 UAE Kolkata Land 9.700104 9.838436
## 635 65196.326 UAE Kolkata Land 8.889398 11.089200
## 636 63659.011 Germany Delhi Air 13.244154 13.961947
## 637 74123.800 USA Mumbai Land 11.348234 13.636266
## 638 33460.788 Japan Delhi Sea 6.710667 17.522794
## 639 70061.737 UAE Kolkata Land 13.002116 6.919883
## 640 88033.830 Japan Mumbai Land 17.462242 12.543750
## 641 47944.906 Japan Mumbai Sea 5.903647 7.063916
## 642 3919.882 UK Kolkata Sea 6.883744 13.078145
## 643 73711.498 UAE Kolkata Sea 6.650755 16.081766
## 644 88824.310 Germany Mumbai Air 10.971580 14.408183
## 645 63287.000 UAE Kolkata Land 18.445712 11.339015
## 646 4246.843 UK Kandla Sea 16.761546 14.306991
## 647 55192.749 UK Kandla Land 13.188720 11.816819
## 648 86894.876 USA Kolkata Air 15.569966 10.964082
## 649 40649.262 China Mumbai Air 10.581334 13.862563
## 650 90929.476 Japan Mumbai Land 18.447751 8.383473
## 651 78864.170 UK Chennai Land 6.684252 17.225810
## 652 22931.136 USA Delhi Air 11.351504 12.350544
## 653 19400.366 Germany Chennai Sea 12.829913 16.399076
## 654 90945.676 UK Kandla Land 12.695142 11.138150
## 655 31774.862 Germany Kolkata Sea 19.787644 15.798545
## 656 41505.696 Japan Chennai Air 19.766631 8.189921
## 657 86354.309 Germany Kandla Sea 17.811822 5.200348
## 658 9802.692 Germany Mumbai Sea 15.706119 6.402262
## 659 22674.032 Japan Chennai Land 6.830971 16.233583
## 660 60846.416 China Kolkata Sea 17.942481 5.043175
## 661 50203.941 Japan Mumbai Air 12.652817 13.438509
## 662 92038.289 USA Kandla Air 10.370347 11.919831
## 663 23493.765 UAE Kandla Land 16.845422 7.160549
## 664 94519.465 UK Chennai Sea 13.180696 11.879361
## 665 74725.746 China Mumbai Air 19.393090 13.139879
## 666 50803.337 UK Chennai Sea 15.372987 12.283684
## 667 20861.410 UAE Delhi Land 10.321712 14.890482
## 668 43028.397 UAE Kandla Sea 9.156100 8.718381
## 669 60220.959 USA Delhi Land 8.730707 9.905507
## 670 12584.151 China Kolkata Land 19.852811 16.653612
## 671 61785.704 USA Chennai Sea 7.393512 6.171323
## 672 74330.422 China Kandla Land 14.454295 17.523096
## 673 21551.175 USA Kolkata Land 17.467932 5.354245
## 674 90724.064 USA Delhi Land 11.615661 12.940982
## 675 9639.759 China Mumbai Land 7.484996 12.186981
## 676 23555.729 USA Kolkata Sea 8.696189 17.997233
## 677 6876.535 UK Delhi Sea 17.537918 12.284789
## 678 86414.566 UAE Mumbai Land 7.999757 11.626515
## 679 64038.088 UAE Kandla Land 9.180928 9.801728
## 680 81534.558 USA Kandla Air 17.179720 7.299478
## 681 7148.341 UK Mumbai Air 7.760445 12.433284
## 682 94272.206 Germany Kolkata Air 13.589188 14.911746
## 683 57001.386 Germany Kandla Air 7.832395 11.006510
## 684 63282.227 UK Chennai Land 8.610877 5.837143
## 685 76926.724 Japan Delhi Sea 14.850664 11.469933
## 686 7413.022 Japan Mumbai Sea 14.396387 5.890054
## 687 55718.046 China Chennai Sea 15.451378 8.137700
## 688 89671.805 Japan Chennai Sea 13.925421 6.906465
## 689 73757.982 UAE Kolkata Air 5.861092 16.699527
## 690 52005.533 USA Delhi Sea 8.467738 16.789003
## 691 25237.683 UK Chennai Land 8.624276 9.428047
## 692 66339.841 USA Chennai Air 13.990704 12.430382
## 693 52610.622 Germany Delhi Land 15.888758 17.277110
## 694 59150.123 UAE Kolkata Air 5.710583 12.724493
## 695 71401.132 Japan Delhi Sea 11.760979 13.457065
## 696 83140.785 Germany Kolkata Sea 6.375306 12.703743
## 697 94477.904 China Delhi Land 19.151631 12.680765
## 698 78242.292 UAE Delhi Land 15.381953 15.420216
## 699 52274.403 UAE Delhi Land 7.215846 6.148075
## 700 83113.549 China Kandla Sea 7.876056 6.530486
## 701 87462.162 Japan Chennai Land 16.681386 14.528668
## 702 57549.763 China Delhi Sea 6.362396 17.117280
## 703 69819.245 UK Kandla Air 15.855865 6.195299
## 704 30788.699 China Mumbai Land 6.298039 7.134140
## 705 83388.917 UAE Kandla Land 13.229253 11.615548
## 706 89272.690 Japan Delhi Sea 11.618889 10.696547
## 707 13488.862 Japan Delhi Land 13.417591 13.062888
## 708 67434.897 UK Kolkata Land 7.194825 10.924398
## 709 48488.665 Japan Delhi Land 6.221568 13.305906
## 710 24466.044 Japan Mumbai Air 17.763210 15.325151
## 711 4473.121 China Delhi Land 17.662958 11.771754
## 712 65502.394 UAE Chennai Sea 19.989631 14.852233
## 713 74275.452 China Kandla Air 9.909726 9.289812
## 714 68482.833 UAE Mumbai Land 18.618561 16.838906
## 715 59240.910 Germany Kolkata Air 8.694707 10.300672
## 716 24671.040 UAE Kolkata Sea 17.656826 8.607885
## 717 53890.188 Japan Chennai Sea 9.447234 10.798550
## 718 61094.733 USA Mumbai Air 11.918987 11.010123
## 719 44786.178 UK Chennai Land 11.607591 6.324779
## 720 8662.425 UK Mumbai Land 17.723676 13.726474
## 721 70776.150 UK Mumbai Air 13.905296 12.363506
## 722 10246.868 UAE Kandla Sea 12.464539 15.512564
## 723 45228.676 Germany Delhi Land 16.978036 7.716450
## 724 26742.309 Japan Chennai Air 13.597738 5.986371
## 725 79166.134 Japan Delhi Sea 11.317118 17.210605
## 726 50774.028 Germany Kolkata Sea 8.656810 17.770625
## 727 8004.210 China Kandla Sea 11.516272 15.899997
## 728 90730.503 Japan Kolkata Sea 10.177739 6.392991
## 729 60911.769 UAE Mumbai Sea 6.323221 13.610826
## 730 17652.186 China Kandla Air 14.845977 13.812911
## 731 77745.289 Japan Delhi Sea 19.950020 16.456190
## 732 49257.094 China Chennai Land 7.860845 16.404677
## 733 21690.208 USA Kolkata Sea 12.846098 17.509377
## 734 53717.911 China Delhi Land 18.408213 8.344992
## 735 76840.784 USA Delhi Land 11.206803 14.194731
## 736 64179.682 Germany Chennai Air 7.215528 17.681408
## 737 33775.351 Japan Chennai Land 10.677903 9.613257
## 738 6538.444 Germany Mumbai Air 6.906728 9.208298
## 739 28829.708 USA Kolkata Air 12.539510 17.914475
## 740 37453.643 Germany Kandla Sea 14.344834 17.059632
## 741 10924.893 USA Chennai Land 18.439309 5.335905
## 742 55517.440 UAE Chennai Sea 5.633679 12.221113
## 743 89888.231 USA Kandla Sea 9.379352 11.862401
## 744 79196.686 UAE Kolkata Air 6.891797 11.792202
## 745 61473.885 UAE Chennai Sea 19.232442 5.228106
## 746 90472.522 UAE Mumbai Land 10.072824 7.543639
## 747 49486.206 Germany Chennai Sea 11.911897 17.824532
## 748 63064.147 UAE Kandla Land 15.126875 13.518437
## 749 49153.662 Germany Chennai Land 15.805934 10.341165
## 750 65712.016 China Mumbai Sea 13.703013 17.152126
## 751 37103.595 Japan Kolkata Land 14.222330 7.951965
## 752 42011.993 China Delhi Air 16.787836 10.284387
## 753 99901.774 USA Delhi Land 16.121076 8.535804
## 754 29566.746 Germany Delhi Sea 13.671860 16.739798
## 755 2378.796 China Mumbai Air 14.187234 13.928338
## 756 39049.168 UAE Kandla Land 14.036471 17.800631
## 757 23705.240 Japan Kolkata Sea 7.700276 17.813132
## 758 84274.748 UK Kandla Land 7.599030 12.019682
## 759 97622.743 Germany Delhi Air 5.225811 6.146198
## 760 85345.968 USA Kandla Air 12.959003 6.236186
## 761 79679.157 USA Kandla Air 15.618106 9.197555
## 762 79831.742 China Delhi Land 11.143709 5.269992
## 763 76305.673 UAE Mumbai Sea 7.012626 8.667712
## 764 40535.119 Germany Mumbai Sea 10.126428 5.622013
## 765 80001.806 UAE Chennai Sea 18.095383 7.861152
## 766 17986.771 Germany Kolkata Sea 18.026713 10.501110
## 767 3090.979 Germany Chennai Sea 15.782989 13.179215
## 768 50415.990 Japan Mumbai Land 14.237267 6.455167
## 769 59398.979 China Kandla Land 19.393617 17.252885
## 770 60718.415 UK Kandla Land 16.159291 13.191893
## 771 1831.210 China Kolkata Land 12.589425 13.131150
## 772 11535.935 USA Chennai Air 14.644808 6.145321
## 773 86625.942 China Kolkata Air 14.298031 12.582980
## 774 6480.357 Japan Kolkata Sea 9.793622 16.357117
## 775 89766.750 UK Mumbai Air 19.245586 8.369656
## 776 6438.343 Germany Kandla Sea 10.599454 13.280083
## 777 64396.432 UAE Kolkata Land 7.136167 11.862765
## 778 90780.743 USA Kolkata Sea 8.795193 13.780975
## 779 93595.981 UAE Kandla Land 13.633932 10.231136
## 780 94144.602 Japan Delhi Air 18.077302 15.493494
## 781 5462.148 USA Kandla Air 15.622441 9.086828
## 782 75683.121 USA Kolkata Air 13.053493 14.471080
## 783 92812.044 Germany Kolkata Sea 10.743166 7.234099
## 784 62025.330 UAE Delhi Land 16.011852 16.405525
## 785 42238.239 USA Chennai Air 6.193222 10.103138
## 786 8873.935 UK Delhi Land 14.311336 15.964828
## 787 49029.995 Japan Chennai Land 9.382873 14.523388
## 788 64048.845 UK Chennai Sea 6.152648 17.364148
## 789 13793.005 Germany Kolkata Sea 16.108389 8.081108
## 790 32643.343 USA Chennai Sea 18.538602 10.912363
## 791 68238.933 USA Chennai Air 14.961306 17.761254
## 792 47079.609 USA Kandla Air 9.035647 11.433611
## 793 97014.063 UK Chennai Air 8.244027 9.622055
## 794 2819.308 Japan Kolkata Air 10.880488 11.977834
## 795 99008.656 Germany Delhi Sea 13.045706 14.986083
## 796 83123.453 Germany Delhi Sea 13.461009 13.099524
## 797 40917.043 Japan Delhi Land 13.128898 12.961161
## 798 16390.741 China Kolkata Air 15.316378 12.378929
## 799 73834.778 China Kandla Sea 8.104058 12.094940
## 800 75265.451 Germany Delhi Sea 19.334418 11.857941
## 801 42588.762 Germany Kolkata Land 10.481405 12.971887
## 802 12310.354 Japan Chennai Land 9.704006 9.779913
## 803 24299.495 Japan Delhi Air 17.647025 11.610070
## 804 39643.355 UK Delhi Land 7.818473 16.557279
## 805 30195.761 UAE Delhi Air 5.974567 12.406898
## 806 86116.298 USA Chennai Sea 11.202291 10.135333
## 807 62556.161 UK Mumbai Land 13.020552 6.616038
## 808 41217.481 China Kandla Land 12.066626 9.604133
## 809 3350.130 UK Kandla Air 14.884818 5.588061
## 810 19448.803 China Mumbai Sea 10.075160 15.398001
## 811 55591.574 China Kandla Sea 13.107579 7.846698
## 812 5579.217 China Mumbai Sea 19.297826 12.349929
## 813 12536.320 UAE Delhi Sea 12.853279 10.044013
## 814 69535.738 Germany Delhi Land 17.227007 6.564482
## 815 29613.935 USA Kandla Sea 7.655181 11.357132
## 816 67733.357 Japan Chennai Air 8.165264 13.952435
## 817 5330.841 China Mumbai Land 13.497193 12.546182
## 818 22372.444 UK Mumbai Sea 6.455768 14.788944
## 819 70789.015 China Kolkata Sea 8.797252 12.703359
## 820 45422.004 UAE Delhi Land 16.729385 6.271377
## 821 27184.419 UK Delhi Sea 8.855734 14.402811
## 822 34462.609 USA Mumbai Sea 5.414711 5.748974
## 823 73047.967 Japan Delhi Air 11.754882 9.822051
## 824 63063.216 Japan Delhi Air 10.202095 14.829488
## 825 89866.422 Japan Mumbai Land 9.518024 5.438889
## 826 64393.243 China Mumbai Land 7.789498 9.159837
## 827 64126.777 China Kolkata Air 14.696297 6.017993
## 828 42021.769 UK Kandla Sea 5.944257 6.220565
## 829 22373.891 UAE Kolkata Sea 13.928917 12.043478
## 830 44233.289 USA Chennai Air 12.722318 10.346656
## 831 71397.433 UK Mumbai Air 6.676872 12.493489
## 832 93245.180 China Kolkata Air 17.653243 6.163623
## 833 73321.939 Japan Mumbai Sea 14.877728 6.562246
## 834 38087.850 UK Kolkata Land 15.871004 13.975452
## 835 31510.686 Japan Mumbai Sea 17.979145 8.497355
## 836 2895.877 Japan Mumbai Air 5.496672 11.662796
## 837 59018.854 UK Delhi Land 8.432806 10.892869
## 838 40887.286 USA Kandla Land 17.728479 16.921891
## 839 22173.594 Japan Delhi Air 16.675999 14.814360
## 840 25149.924 UAE Delhi Air 14.580600 16.950164
## 841 54005.782 UAE Kolkata Air 16.967886 6.390115
## 842 64457.251 USA Kandla Air 15.526518 5.362936
## 843 90679.244 Japan Kolkata Land 19.071701 9.183149
## 844 15657.698 USA Delhi Land 14.756167 12.035178
## 845 11150.838 UK Delhi Sea 16.420727 11.306872
## 846 33602.874 UK Chennai Air 7.119233 11.215483
## 847 73010.942 China Delhi Sea 14.964792 11.602372
## 848 58421.931 UAE Chennai Land 11.067628 13.642760
## 849 56009.794 Japan Delhi Sea 12.207774 11.188292
## 850 93094.403 Germany Delhi Air 10.315705 14.935684
## 851 7904.716 UAE Kandla Sea 12.247792 7.218657
## 852 67767.901 UAE Chennai Sea 16.956631 9.404198
## 853 65861.575 USA Delhi Air 11.969870 9.750306
## 854 78643.011 Japan Delhi Sea 11.409649 15.262737
## 855 76724.674 UK Kandla Air 18.582470 6.776471
## 856 55505.647 USA Delhi Air 13.416774 13.290861
## 857 95925.335 USA Delhi Air 13.748320 11.912171
## 858 98964.437 China Chennai Land 14.377294 12.654776
## 859 31195.410 UK Chennai Sea 17.516661 14.897875
## 860 2942.208 UAE Delhi Land 18.895279 10.391283
## 861 90548.869 Japan Mumbai Air 19.640232 16.625739
## 862 23507.709 UK Mumbai Sea 18.872761 15.966504
## 863 92244.144 UK Kandla Sea 16.329582 16.695337
## 864 48928.190 Japan Kandla Sea 9.687502 10.722791
## 865 5341.110 UAE Kolkata Sea 10.808264 13.891747
## 866 18492.270 China Delhi Land 5.923925 14.882737
## 867 64244.430 UAE Delhi Sea 10.663626 5.118300
## 868 56593.971 China Chennai Land 6.751922 11.943595
## 869 9048.988 USA Mumbai Sea 15.054960 12.091195
## 870 24645.908 China Delhi Air 11.603185 17.458368
## 871 22327.625 USA Delhi Air 11.174035 14.761859
## 872 75529.536 Japan Kandla Land 12.188721 13.420048
## 873 51494.397 UK Kolkata Land 18.175681 8.028283
## 874 47598.250 UK Mumbai Sea 8.511485 14.706242
## 875 35484.252 Japan Chennai Air 18.559906 14.874316
## 876 30244.046 UAE Delhi Land 11.366505 14.923528
## 877 45632.196 China Delhi Air 5.396412 10.026349
## 878 87234.287 UAE Delhi Sea 11.485205 5.321457
## 879 81277.891 Germany Kandla Land 6.499597 6.514096
## 880 38265.075 UAE Kolkata Land 16.784779 17.435998
## 881 98452.978 USA Mumbai Air 10.967606 5.516012
## 882 98658.701 UAE Mumbai Sea 19.429649 7.343773
## 883 80659.736 Germany Kolkata Sea 6.182913 15.291009
## 884 97885.583 Japan Delhi Air 16.545236 11.897590
## 885 81585.421 UK Chennai Land 15.528228 17.924722
## 886 79588.143 USA Kandla Land 19.750271 10.722005
## 887 74211.157 Japan Kolkata Land 5.407756 12.569355
## 888 70228.965 USA Kolkata Land 11.350433 14.366745
## 889 97982.602 UK Mumbai Air 11.121432 15.152919
## 890 40391.619 UAE Mumbai Land 7.301339 13.044569
## 891 48465.751 China Mumbai Air 17.482699 14.528412
## 892 7657.891 UAE Chennai Land 6.099285 15.718913
## 893 74473.568 China Chennai Air 7.453172 17.824831
## 894 34848.545 Germany Chennai Air 15.100248 17.712721
## 895 78128.592 Germany Kandla Land 5.115923 9.706925
## 896 59670.545 USA Kolkata Air 16.453745 12.124834
## 897 71910.860 Japan Kandla Air 8.784413 5.071801
## 898 37464.232 UAE Kolkata Land 6.696041 15.087011
## 899 12288.941 USA Kolkata Air 12.878783 16.631464
## 900 52975.033 UAE Kolkata Land 7.782673 6.661101
## 901 18709.116 China Chennai Air 8.594667 6.666863
## 902 61280.451 UAE Mumbai Sea 17.420712 8.384461
## 903 99480.510 UAE Mumbai Sea 16.822877 13.035303
## 904 44258.917 UK Chennai Air 8.261997 12.006631
## 905 99862.447 Germany Mumbai Land 15.216886 5.307106
## 906 76913.384 UAE Kandla Air 10.792137 11.530900
## 907 74782.471 China Kandla Air 15.833532 14.641095
## 908 70203.239 China Mumbai Air 15.522747 12.893720
## 909 4427.223 USA Kandla Sea 18.617097 12.182396
## 910 41747.377 Germany Kolkata Land 11.797140 10.154481
## 911 29870.821 China Kandla Air 19.262311 7.493242
## 912 59144.506 Germany Mumbai Sea 13.203316 14.007239
## 913 89190.432 UK Kandla Land 18.569140 8.630914
## 914 4363.357 UK Kolkata Air 7.407447 12.596843
## 915 54220.453 Germany Kolkata Sea 8.459099 10.735813
## 916 28279.368 Japan Delhi Sea 14.968372 16.192673
## 917 19422.961 Germany Kolkata Air 17.659175 15.254292
## 918 23227.510 China Kandla Air 13.389058 5.816726
## 919 56600.204 UK Mumbai Land 12.852889 7.382861
## 920 44510.963 UK Kolkata Land 11.676100 12.763002
## 921 63287.522 Japan Kolkata Sea 16.409150 11.551066
## 922 91864.145 UK Delhi Sea 5.095003 5.065499
## 923 53979.248 China Kandla Sea 6.442149 9.641897
## 924 71609.652 UAE Chennai Land 17.422703 6.817937
## 925 1814.771 USA Chennai Air 13.119987 10.314191
## 926 65153.241 UAE Chennai Sea 11.913483 17.929917
## 927 84277.207 UAE Delhi Sea 8.132465 15.224453
## 928 14055.872 USA Kandla Land 8.297415 16.867264
## 929 67545.556 Germany Mumbai Air 11.955806 6.877217
## 930 75846.181 UK Chennai Sea 11.126654 7.005420
## 931 30063.823 UK Chennai Sea 17.380800 9.165485
## 932 48431.729 UAE Kandla Air 14.275320 9.437788
## 933 81579.412 Japan Kandla Land 11.675973 17.954012
## 934 84471.664 UK Delhi Sea 17.771422 6.085324
## 935 15627.425 Japan Chennai Air 17.865122 6.293273
## 936 54266.346 Germany Kandla Air 16.869672 17.997850
## 937 18368.612 USA Chennai Land 5.274013 15.641978
## 938 44437.231 Germany Kolkata Air 14.374885 15.753914
## 939 65102.699 USA Kandla Land 6.599845 8.716584
## 940 47340.774 UAE Kandla Sea 10.127115 5.505681
## 941 96712.133 Germany Kolkata Air 13.948678 11.869672
## 942 6490.934 UAE Chennai Land 11.957570 9.539422
## 943 58589.808 UAE Kolkata Land 11.669644 12.068994
## 944 68139.782 China Mumbai Sea 15.584483 12.401433
## 945 55457.723 UK Mumbai Air 15.408507 16.071572
## 946 61172.840 UAE Delhi Sea 16.846856 7.387966
## 947 50324.891 USA Chennai Land 18.806974 8.156608
## 948 23573.364 Japan Kandla Air 14.044032 9.733484
## 949 33875.927 UAE Kandla Air 11.277579 8.617219
## 950 9151.284 USA Delhi Air 6.809057 13.209543
## 951 60976.639 Germany Kolkata Land 19.924288 8.886373
## 952 3331.323 China Chennai Land 5.312196 14.938112
## 953 15109.447 UAE Chennai Land 7.976852 16.460033
## 954 50780.847 UAE Mumbai Air 8.381736 15.130436
## 955 26814.392 Germany Delhi Sea 14.574593 13.881889
## 956 15786.641 Germany Chennai Air 13.790522 9.795224
## 957 50879.436 China Delhi Air 11.131920 16.625667
## 958 56706.158 Germany Kolkata Land 15.610428 7.772135
## 959 15417.334 Japan Kandla Air 11.914590 14.184186
## 960 45120.099 Germany Chennai Sea 19.132269 11.418303
## 961 73130.876 USA Mumbai Land 13.115186 11.051314
## 962 16189.400 USA Kandla Land 6.294954 6.494270
## 963 61510.963 UK Chennai Sea 14.967908 9.396019
## 964 56664.587 UAE Mumbai Land 13.387848 17.869926
## 965 39341.644 Japan Chennai Sea 11.329440 10.637693
## 966 28484.012 USA Kandla Land 19.452726 11.235446
## 967 4491.368 Japan Kandla Air 8.180173 14.102775
## 968 81265.026 Japan Kandla Land 9.587237 14.944736
## 969 57687.129 UAE Chennai Sea 14.113691 14.691404
## 970 73426.397 Germany Delhi Air 17.844101 10.929957
## 971 11720.421 USA Chennai Sea 5.826149 10.600858
## 972 59569.920 UAE Delhi Air 18.550899 6.227372
## 973 33226.911 Japan Kolkata Land 7.340509 16.370626
## 974 43486.895 UK Kolkata Air 9.047197 12.120123
## 975 39036.050 UAE Mumbai Air 17.536543 16.626178
## 976 42517.334 USA Kandla Air 7.961765 6.819953
## 977 75155.318 Japan Delhi Air 17.315926 12.286322
## 978 34258.599 UAE Chennai Sea 16.934972 8.804402
## 979 89179.801 China Mumbai Sea 6.035419 14.620512
## 980 95171.482 Japan Kandla Air 17.064892 12.565963
## 981 73782.388 China Delhi Sea 15.775715 11.978222
## 982 57225.097 Japan Kolkata Land 11.184747 16.356918
## 983 30120.182 Germany Kandla Sea 10.429308 8.473797
## 984 20687.769 USA Delhi Land 9.581905 7.310351
## 985 49971.651 Japan Mumbai Sea 9.144411 17.207237
## 986 9579.085 UK Kandla Air 17.956584 6.824910
## 987 95709.416 China Chennai Land 9.992458 8.119680
## 988 26491.241 China Chennai Sea 19.854973 8.866213
## 989 94849.256 China Mumbai Sea 13.894075 16.722937
## 990 43492.728 USA Chennai Land 19.199529 8.802135
## 991 58090.997 Japan Chennai Sea 12.392513 11.861667
## 992 54666.032 UK Chennai Air 11.667860 14.484083
## 993 69720.127 China Mumbai Land 6.515396 16.593371
## 994 46808.035 UK Kandla Air 13.080082 16.499154
## 995 74988.418 USA Kandla Land 18.707538 5.812251
## 996 27119.204 Germany Chennai Air 11.498380 13.454290
## 997 35331.804 China Kandla Land 14.891183 8.212985
## 998 5379.619 USA Kandla Sea 17.440437 14.695107
## 999 62242.316 Germany Mumbai Sea 12.191891 9.362920
## 1000 1761.734 UAE Kandla Air 6.237923 9.128183
## 1001 17588.625 China Kandla Land 18.085573 17.442892
## 1002 39424.935 USA Mumbai Land 12.517164 14.688890
## 1003 11419.572 Japan Kolkata Land 9.407133 13.892322
## 1004 99433.159 USA Kandla Air 17.496151 13.611212
## 1005 51801.148 UK Kolkata Land 7.900517 15.607008
## 1006 62589.542 Germany Delhi Air 7.798067 9.451667
## 1007 75367.393 UAE Delhi Air 14.097790 14.131693
## 1008 12695.542 USA Mumbai Sea 13.965039 7.851058
## 1009 90968.055 USA Mumbai Air 7.438338 12.100332
## 1010 6026.086 UK Kolkata Air 15.435790 13.538007
## 1011 8871.335 UK Kandla Sea 14.238053 9.852900
## 1012 1180.521 Japan Kandla Air 14.556981 13.317184
## 1013 86020.477 USA Kolkata Land 7.844096 6.757326
## 1014 13361.781 Germany Chennai Land 17.579473 14.526531
## 1015 84331.397 UAE Delhi Land 18.220544 9.965365
## 1016 35096.938 UAE Kandla Sea 14.699333 9.967661
## 1017 49924.023 Germany Kolkata Sea 5.514317 8.754465
## 1018 90238.398 UK Kandla Sea 19.811028 9.697966
## 1019 27466.337 Germany Chennai Sea 7.078786 7.719923
## 1020 43951.453 UK Chennai Air 11.863769 15.259761
## 1021 70212.483 Japan Chennai Land 19.175892 9.381768
## 1022 62274.077 UAE Kolkata Land 6.903472 12.405278
## 1023 12872.873 Japan Mumbai Sea 15.003772 7.037868
## 1024 43861.443 UAE Delhi Sea 5.918491 7.053994
## 1025 59617.340 Japan Kolkata Land 6.270021 17.993652
## 1026 74555.494 Japan Delhi Air 6.882060 6.122711
## 1027 82396.378 China Delhi Air 12.833879 8.480674
## 1028 54784.477 UK Mumbai Air 10.074356 16.952994
## 1029 30168.303 Germany Chennai Land 7.967545 5.384979
## 1030 60672.956 Germany Delhi Sea 11.626827 5.533146
## 1031 57328.988 Japan Mumbai Air 10.721133 14.495476
## 1032 15272.806 Japan Kolkata Land 15.655422 8.445301
## 1033 44912.542 Germany Chennai Land 11.151116 8.514864
## 1034 70026.317 Germany Mumbai Land 8.458575 7.053368
## 1035 59490.875 UAE Delhi Air 17.764586 14.926976
## 1036 1236.786 Japan Delhi Sea 9.750108 13.944254
## 1037 94070.294 UAE Kandla Land 11.575894 12.102934
## 1038 32647.966 UK Mumbai Air 10.130987 7.856138
## 1039 45625.545 UAE Delhi Air 15.976950 15.190726
## 1040 16932.312 UK Kandla Land 13.273738 5.895180
## 1041 96541.808 Japan Chennai Sea 17.210299 14.563147
## 1042 40255.974 Germany Delhi Air 14.011517 13.886543
## 1043 48393.906 USA Kolkata Land 15.433303 8.288134
## 1044 62868.112 UAE Kolkata Land 18.973765 5.909167
## 1045 67584.433 Japan Kandla Land 9.835599 10.077360
## 1046 10884.269 UAE Kolkata Land 18.469092 11.340382
## 1047 85965.463 UK Chennai Land 19.366667 11.166927
## 1048 5398.608 UAE Chennai Air 10.136589 6.260025
## 1049 53075.577 UAE Chennai Air 7.416534 10.209066
## 1050 39825.889 UK Chennai Land 15.917441 17.006539
## 1051 2326.893 USA Mumbai Land 18.070380 7.912742
## 1052 13004.488 Japan Chennai Land 12.494924 10.911168
## 1053 2289.767 UAE Delhi Air 15.338259 13.812605
## 1054 30010.119 Germany Kandla Sea 12.196039 15.840341
## 1055 7535.649 USA Chennai Land 11.466314 14.308234
## 1056 44568.447 China Mumbai Air 15.404007 11.591646
## 1057 28940.245 UAE Kandla Air 12.907412 9.309923
## 1058 57327.876 Germany Delhi Land 6.595184 14.231154
## 1059 23269.769 USA Kandla Land 12.859708 15.975015
## 1060 39342.398 Germany Kandla Sea 19.822689 14.858330
## 1061 60211.582 UK Delhi Air 7.509806 10.477208
## 1062 90358.882 Japan Kolkata Land 12.758659 9.159873
## 1063 1040.966 UAE Mumbai Air 7.799583 11.970953
## 1064 74692.133 China Delhi Sea 8.433598 7.788989
## 1065 11698.563 UK Chennai Land 17.852039 13.444654
## 1066 19872.237 Germany Delhi Sea 15.862312 7.629313
## 1067 37186.975 Japan Kandla Air 7.796326 8.620169
## 1068 40837.438 Japan Kolkata Sea 14.536006 6.271741
## 1069 17749.552 USA Kolkata Air 13.311775 15.506168
## 1070 16810.798 Germany Kolkata Sea 18.673291 11.369433
## 1071 50526.316 USA Mumbai Air 8.649260 16.678599
## 1072 55662.843 UAE Kolkata Sea 12.257533 9.570621
## 1073 37269.755 USA Delhi Air 5.289905 5.011290
## 1074 14484.483 China Mumbai Air 6.359920 17.926753
## 1075 67941.082 UK Mumbai Sea 15.736868 11.099990
## 1076 89659.825 UK Kandla Sea 12.106206 8.000203
## 1077 44592.434 China Delhi Land 15.647721 5.332277
## 1078 9456.655 Japan Kandla Land 13.090111 7.375364
## 1079 92887.264 UAE Mumbai Land 7.409474 16.096752
## 1080 51217.077 UAE Chennai Air 13.813514 10.350888
## 1081 75587.336 Japan Delhi Sea 17.966113 6.660305
## 1082 36454.700 Germany Delhi Sea 14.198112 7.369408
## 1083 91371.429 USA Chennai Air 15.572814 6.999712
## 1084 27167.201 UK Kandla Land 17.505963 15.549653
## 1085 70770.938 USA Delhi Air 8.461959 6.666100
## 1086 53994.692 China Delhi Sea 12.611151 7.817469
## 1087 21248.750 UK Mumbai Sea 15.358438 5.324093
## 1088 53558.391 Japan Kandla Land 13.683189 10.402948
## 1089 76954.252 UK Delhi Land 6.312876 15.651132
## 1090 16281.615 Japan Mumbai Sea 16.419861 14.859703
## 1091 89064.395 China Chennai Sea 14.637613 12.303518
## 1092 84004.642 UK Mumbai Air 9.722816 10.148613
## 1093 91857.480 China Mumbai Land 10.828304 14.745154
## 1094 71831.337 Japan Mumbai Air 5.191173 5.838508
## 1095 99474.298 Japan Kandla Land 15.012159 13.857362
## 1096 3727.243 UAE Mumbai Sea 5.269328 12.958318
## 1097 2348.080 China Chennai Sea 6.857053 7.007168
## 1098 41766.615 UAE Kandla Land 14.283689 7.119540
## 1099 77280.986 Japan Kolkata Air 6.355154 15.039755
## 1100 41638.785 Germany Mumbai Sea 10.897648 12.025931
## 1101 70692.371 China Mumbai Land 11.574202 10.604442
## 1102 31704.850 Japan Mumbai Sea 9.823802 7.167679
## 1103 4821.237 Germany Kolkata Air 19.085428 14.669835
## 1104 78477.782 Japan Chennai Air 19.102709 17.327776
## 1105 65249.924 USA Delhi Land 15.268245 16.553306
## 1106 84022.911 USA Kolkata Land 6.631918 10.848225
## 1107 98718.442 USA Chennai Air 17.549854 9.375275
## 1108 66710.707 Japan Chennai Air 11.353419 12.184462
## 1109 77483.282 Germany Kolkata Sea 10.598196 12.269725
## 1110 51822.181 Germany Kandla Sea 13.172380 6.010613
## 1111 25391.470 UK Kandla Air 18.746208 6.152307
## 1112 17522.351 UK Delhi Air 17.523315 15.882492
## 1113 91103.663 USA Kolkata Air 11.681779 7.553818
## 1114 96917.394 UAE Delhi Sea 8.664699 8.542504
## 1115 89271.017 China Kandla Sea 11.242228 6.432890
## 1116 26799.977 Germany Chennai Air 17.257185 14.584772
## 1117 27129.765 USA Kandla Land 15.368723 16.588432
## 1118 46313.285 USA Kandla Air 11.800975 12.759770
## 1119 32856.218 China Mumbai Land 13.378026 7.863588
## 1120 53050.365 Japan Kolkata Air 15.145133 5.907064
## 1121 42907.828 Japan Mumbai Sea 8.437455 14.451259
## 1122 22123.417 Japan Kandla Air 19.449305 16.860416
## 1123 73349.138 Japan Kandla Sea 7.903952 5.020721
## 1124 86905.617 China Kolkata Sea 12.851274 14.399628
## 1125 84412.406 UK Mumbai Air 9.361443 13.851210
## 1126 44291.531 China Chennai Air 12.254859 14.427172
## 1127 74019.155 China Kolkata Sea 10.029453 5.965297
## 1128 84232.370 UK Delhi Sea 13.435625 15.106220
## 1129 68566.113 China Chennai Sea 5.897668 17.570787
## 1130 86412.227 Germany Delhi Air 13.517507 12.207417
## 1131 15389.132 Germany Kolkata Air 11.403595 15.400782
## 1132 60731.217 Germany Kandla Land 13.824320 10.449886
## 1133 59156.537 UK Chennai Land 18.146444 17.548442
## 1134 73292.778 Germany Kandla Land 19.428371 10.470231
## 1135 54803.517 Japan Chennai Land 9.958810 6.209006
## 1136 19951.997 UAE Delhi Air 10.041876 12.573401
## 1137 2369.206 USA Kolkata Air 9.146349 8.306728
## 1138 98054.725 Germany Chennai Land 17.496035 8.642219
## 1139 66319.438 UK Kolkata Land 18.671417 15.627299
## 1140 95925.405 Germany Delhi Sea 18.269599 12.066727
## 1141 74669.100 USA Chennai Land 19.178688 9.284621
## 1142 51228.933 UK Delhi Air 12.574590 11.943881
## 1143 82567.074 Japan Delhi Sea 18.597083 6.161571
## 1144 13998.022 China Chennai Sea 15.366319 5.554265
## 1145 37397.496 USA Chennai Air 17.150673 17.833481
## 1146 44462.703 UAE Chennai Air 19.379779 5.812609
## 1147 54741.561 UAE Chennai Sea 10.584249 12.932906
## 1148 26638.427 Japan Delhi Land 9.807789 13.359103
## 1149 39105.908 UAE Chennai Sea 13.763933 11.445092
## 1150 84502.457 UK Mumbai Sea 17.094565 9.940235
## 1151 43273.782 UAE Delhi Land 13.234245 7.937629
## 1152 93892.903 Japan Chennai Sea 5.101547 5.880812
## 1153 51995.389 China Kolkata Sea 5.191522 7.334592
## 1154 25079.025 UAE Delhi Land 12.168525 16.475258
## 1155 59304.376 UK Mumbai Land 6.468236 12.685955
## 1156 30005.279 UAE Delhi Air 11.968355 15.268905
## 1157 87010.801 China Kandla Sea 9.104468 9.332265
## 1158 66890.918 UK Chennai Air 18.611846 13.738607
## 1159 34924.458 Germany Delhi Air 6.260002 17.182258
## 1160 54214.421 UK Delhi Air 15.216815 8.653245
## 1161 38759.715 China Chennai Sea 18.233123 13.032271
## 1162 87720.323 Japan Mumbai Sea 5.859613 6.615607
## 1163 14480.996 Germany Kandla Air 19.880973 13.754635
## 1164 26139.817 Germany Kandla Sea 9.600561 15.924136
## 1165 42288.739 USA Mumbai Sea 18.877694 14.884224
## 1166 85469.095 Japan Delhi Air 17.162476 9.654858
## 1167 34162.457 Japan Kolkata Sea 15.179490 12.440004
## 1168 7633.055 China Chennai Sea 15.126827 7.173948
## 1169 86439.236 Germany Mumbai Land 7.921586 13.794077
## 1170 51715.534 China Chennai Air 7.819058 14.591855
## 1171 18124.298 UAE Chennai Sea 18.467061 7.309852
## 1172 18905.110 Germany Chennai Air 10.262069 16.899534
## 1173 1518.689 China Chennai Land 9.230282 11.638706
## 1174 96253.662 Japan Mumbai Air 8.120801 10.832611
## 1175 26036.718 UAE Delhi Land 9.479999 9.991374
## 1176 27654.590 UAE Delhi Air 19.289558 12.780155
## 1177 42007.693 Japan Kandla Land 6.340181 13.931174
## 1178 12511.192 USA Kandla Air 18.764170 16.651294
## 1179 15238.273 China Chennai Air 6.459283 13.411489
## 1180 23499.485 Japan Mumbai Sea 14.004029 7.763603
## 1181 90388.784 UK Kolkata Air 6.211881 17.234730
## 1182 1508.901 Japan Kandla Land 6.088055 14.958812
## 1183 13953.011 USA Kolkata Land 18.794098 6.691114
## 1184 45807.809 UK Delhi Sea 14.601879 12.991693
## 1185 81614.791 USA Mumbai Sea 10.309282 13.649805
## 1186 71104.507 Japan Delhi Air 7.248441 9.529530
## 1187 9713.317 Germany Delhi Sea 13.155642 15.952009
## 1188 57380.540 USA Delhi Sea 7.744324 14.534837
## 1189 61793.045 UAE Kandla Air 15.825346 8.688664
## 1190 71570.240 USA Kolkata Sea 15.572126 15.962173
## 1191 33691.975 UAE Kandla Sea 17.494442 8.830065
## 1192 23005.921 China Mumbai Land 9.319252 8.583660
## 1193 36573.213 Japan Kolkata Land 14.640092 16.919332
## 1194 18899.441 UK Chennai Air 10.192668 8.770298
## 1195 99426.320 USA Delhi Sea 6.325702 8.925800
## 1196 33034.253 UAE Kandla Air 17.201891 17.804050
## 1197 97614.532 Japan Kolkata Land 8.243453 5.153526
## 1198 8436.890 Japan Chennai Sea 13.660680 17.524270
## 1199 95394.164 USA Kandla Land 18.568571 15.610595
## 1200 7052.669 USA Chennai Air 14.644173 16.887198
## 1201 60596.157 UAE Kandla Sea 19.333657 15.093702
## 1202 99267.822 Germany Kolkata Land 5.785480 10.303386
## 1203 60108.170 China Chennai Sea 14.175499 6.668909
## 1204 86711.076 Germany Chennai Sea 8.032967 12.181768
## 1205 74166.547 UAE Chennai Sea 10.367349 10.083268
## 1206 24030.601 Japan Kandla Air 7.876263 17.920583
## 1207 74594.488 UAE Mumbai Land 6.971683 17.372784
## 1208 35669.618 China Kandla Land 7.290349 6.369342
## 1209 72353.665 UAE Chennai Air 14.195867 15.865583
## 1210 61446.069 UK Kandla Air 6.158403 10.887092
## 1211 53858.165 China Kandla Sea 6.297090 9.867652
## 1212 86591.768 UAE Delhi Land 9.474082 7.426498
## 1213 7563.843 USA Delhi Sea 12.116737 13.536092
## 1214 89743.803 Japan Kandla Land 19.841005 7.266936
## 1215 41616.213 UAE Mumbai Air 15.200991 13.121826
## 1216 87812.044 Germany Chennai Sea 12.193134 13.660611
## 1217 48496.353 USA Kolkata Land 12.660851 17.959286
## 1218 86709.220 Germany Chennai Sea 15.471395 17.305568
## 1219 69697.341 UK Chennai Air 9.104842 7.579274
## 1220 16355.219 UK Delhi Land 6.844025 9.201835
## 1221 26112.433 China Kandla Sea 14.959202 17.673017
## 1222 60112.355 UK Kolkata Land 9.671460 6.643514
## 1223 82816.168 UAE Kolkata Land 19.280130 16.839552
## 1224 40656.413 Japan Kandla Sea 10.550011 13.683956
## 1225 20725.536 China Chennai Air 14.014210 6.216957
## 1226 16246.160 UK Kolkata Sea 5.783605 13.776210
## 1227 41857.956 Japan Kandla Land 7.190190 11.295706
## 1228 29627.116 Germany Mumbai Air 5.943284 6.442278
## 1229 81019.137 Japan Mumbai Land 12.517390 10.861024
## 1230 52978.864 Germany Kandla Air 5.021364 7.140762
## 1231 26894.172 Japan Chennai Sea 13.371499 8.976316
## 1232 26882.911 UK Chennai Air 13.315669 15.297838
## 1233 93245.511 Germany Kandla Land 16.392112 6.884587
## 1234 27906.041 UK Chennai Air 6.428509 13.211937
## 1235 35811.067 Germany Chennai Air 11.547239 15.763678
## 1236 28869.893 USA Kolkata Sea 18.748590 11.054392
## 1237 40187.061 China Chennai Air 7.628378 12.772122
## 1238 90389.794 UK Mumbai Land 19.448937 5.291399
## 1239 27641.473 Japan Kandla Land 9.714036 6.668335
## 1240 86555.200 Germany Delhi Land 11.144853 16.558727
## 1241 52336.202 China Chennai Sea 8.224470 8.085163
## 1242 17061.727 UAE Kandla Sea 11.139166 14.882874
## 1243 24946.354 Japan Kolkata Air 10.828701 7.329881
## 1244 68660.636 UK Chennai Air 17.678423 10.024563
## 1245 97087.084 UAE Mumbai Land 10.864339 11.484561
## 1246 10983.248 USA Kandla Air 11.070751 12.711691
## 1247 63984.246 China Mumbai Land 15.518963 12.751164
## 1248 11838.839 Germany Kolkata Land 15.457210 6.851707
## 1249 3809.735 USA Chennai Air 10.398322 5.721878
## 1250 37251.047 UK Kandla Air 5.992007 9.721270
## 1251 64699.752 Japan Delhi Sea 17.737193 14.241916
## 1252 74815.045 Germany Kandla Air 16.603410 16.568804
## 1253 1257.901 UK Mumbai Sea 14.494851 11.782297
## 1254 57641.158 USA Kandla Sea 19.827854 14.120381
## 1255 85678.695 UAE Delhi Land 10.885360 5.262399
## 1256 35500.936 USA Mumbai Air 7.850571 16.634265
## 1257 34686.027 Germany Mumbai Air 14.996515 8.337495
## 1258 65859.780 Germany Mumbai Land 19.344043 12.009158
## 1259 1711.657 Japan Kolkata Land 16.172915 7.380255
## 1260 48053.889 China Kolkata Sea 8.794016 15.692404
## 1261 32656.683 UAE Kandla Air 15.818750 9.808528
## 1262 88256.045 USA Mumbai Land 7.393374 10.606942
## 1263 40824.921 UAE Chennai Sea 6.716283 17.727319
## 1264 12087.573 USA Mumbai Land 5.596106 7.253504
## 1265 59160.335 Germany Kandla Air 10.830424 17.769862
## 1266 57646.854 USA Kolkata Air 8.387257 5.689565
## 1267 47001.043 Germany Delhi Air 6.662919 6.162157
## 1268 66050.701 USA Kandla Land 19.466083 9.023474
## 1269 73987.985 Germany Kandla Sea 18.838542 14.857819
## 1270 52521.237 China Kolkata Air 17.054058 14.859842
## 1271 61137.467 UAE Mumbai Sea 7.005366 8.594121
## 1272 32648.543 UAE Kolkata Land 13.440969 14.240965
## 1273 7503.769 China Chennai Air 5.852852 5.504218
## 1274 78637.637 UK Delhi Land 11.153640 14.286602
## 1275 30380.563 Japan Chennai Air 5.056421 8.237598
## 1276 21073.197 USA Mumbai Sea 13.419873 10.627596
## 1277 96261.128 UK Delhi Air 13.878165 10.601553
## 1278 18683.134 UK Kolkata Land 13.308029 15.443811
## 1279 10250.986 Japan Kandla Sea 14.353857 9.962016
## 1280 38857.465 Japan Delhi Sea 7.015961 15.735713
## 1281 17946.180 UK Kandla Sea 12.429110 5.404204
## 1282 41024.415 UAE Kolkata Air 19.555595 16.325199
## 1283 5453.578 Germany Kandla Land 19.550288 14.947338
## 1284 8163.299 Germany Delhi Air 11.376156 13.074908
## 1285 50505.351 China Chennai Sea 13.102187 8.430331
## 1286 38388.097 China Delhi Sea 5.175603 9.509938
## 1287 39377.485 Germany Delhi Sea 10.421998 6.280330
## 1288 51974.747 Germany Chennai Sea 9.063667 5.783463
## 1289 61498.356 UK Delhi Sea 7.298207 9.917610
## 1290 7122.020 Japan Mumbai Air 8.130647 10.435785
## 1291 43385.861 China Kolkata Sea 16.999453 15.310701
## 1292 54174.826 UAE Delhi Land 15.457144 15.731207
## 1293 12370.297 UK Kolkata Air 11.581850 13.079711
## 1294 61656.559 UK Chennai Sea 17.299697 6.754694
## 1295 75893.323 Germany Kandla Air 11.091927 12.208838
## 1296 84355.136 Japan Kandla Land 8.727074 16.435504
## 1297 53495.999 China Chennai Land 14.482898 9.273585
## 1298 93069.885 USA Kandla Air 18.647031 10.434917
## 1299 89459.166 China Chennai Air 19.713973 7.846282
## 1300 25918.004 Japan Mumbai Sea 14.701331 6.565661
## 1301 33279.429 UK Chennai Land 6.758717 5.530938
## 1302 5621.391 UK Mumbai Sea 18.980272 6.848657
## 1303 4670.590 Germany Mumbai Land 9.798760 9.087081
## 1304 18113.879 Germany Kolkata Sea 12.307136 7.862560
## 1305 53142.678 Japan Kandla Sea 5.334644 14.314857
## 1306 75674.511 Germany Mumbai Air 16.590155 14.312065
## 1307 81333.018 UK Kandla Sea 5.660763 17.497136
## 1308 12110.110 UK Kolkata Land 19.153316 15.911361
## 1309 10843.660 Germany Chennai Land 13.148771 14.526896
## 1310 78771.147 Germany Mumbai Sea 8.748689 14.416660
## 1311 43352.367 Germany Kolkata Sea 12.083870 13.211624
## 1312 31413.504 USA Delhi Air 12.961910 14.362047
## 1313 11239.962 UK Kandla Sea 10.396161 5.299310
## 1314 78288.247 China Kolkata Sea 13.292479 7.677800
## 1315 34250.038 USA Delhi Land 7.178164 13.735233
## 1316 94271.138 UK Kolkata Sea 18.972033 16.091827
## 1317 19147.360 UAE Mumbai Sea 10.466454 11.404016
## 1318 91735.551 Japan Kolkata Air 9.075429 9.490626
## 1319 4081.009 USA Chennai Air 17.485310 17.117660
## 1320 48825.736 UAE Delhi Sea 15.250397 15.288448
## 1321 64283.506 Germany Delhi Land 18.834445 6.105076
## 1322 20866.415 Germany Delhi Sea 15.708495 12.940915
## 1323 49745.502 UK Kolkata Land 17.763256 17.683447
## 1324 89750.440 China Kandla Sea 14.351635 7.060679
## 1325 98653.149 Germany Chennai Land 14.661780 5.718116
## 1326 64850.049 UK Kolkata Air 11.511512 11.215966
## 1327 64632.192 UAE Chennai Air 12.741985 5.642846
## 1328 68785.865 Germany Delhi Air 6.761183 11.398116
## 1329 29986.148 Germany Chennai Sea 9.679850 10.898970
## 1330 45075.450 UK Mumbai Air 17.587756 12.422181
## 1331 9020.177 Japan Delhi Land 17.782607 6.450045
## 1332 69864.023 Japan Mumbai Sea 15.759420 14.551508
## 1333 73180.267 China Kolkata Air 7.737686 12.282061
## 1334 3177.060 Japan Kolkata Air 15.374561 10.681177
## 1335 51963.440 USA Kandla Sea 18.109928 15.269047
## 1336 4723.342 USA Kandla Sea 7.457949 7.265402
## 1337 10798.346 USA Kolkata Sea 6.540222 7.454422
## 1338 28497.864 UAE Kandla Air 16.762702 8.026171
## 1339 5917.466 Germany Mumbai Air 7.822205 15.228623
## 1340 64538.496 UK Kandla Air 7.400553 7.954252
## 1341 33024.903 Germany Kandla Air 9.994225 6.279270
## 1342 51284.005 UK Mumbai Sea 12.017879 10.641293
## 1343 20302.126 UK Kolkata Sea 16.540898 9.987819
## 1344 73596.090 UAE Kandla Sea 15.422434 5.123949
## 1345 3972.689 UAE Delhi Sea 5.530401 11.707427
## 1346 62004.458 USA Chennai Sea 18.301043 7.433336
## 1347 88936.100 UAE Mumbai Sea 5.554855 6.580908
## 1348 74423.717 Japan Kolkata Air 15.142335 17.711387
## 1349 92511.254 UAE Delhi Land 5.236580 15.485182
## 1350 45178.630 UAE Delhi Air 18.787898 8.201548
## 1351 12350.906 Germany Delhi Air 9.021686 8.128438
## 1352 85328.697 China Kolkata Sea 7.622404 16.481125
## 1353 47255.205 Germany Delhi Land 8.719063 14.898508
## 1354 26627.519 Germany Chennai Sea 11.183677 14.000061
## 1355 80019.738 Japan Mumbai Land 10.607971 7.778999
## 1356 91517.980 Germany Kolkata Sea 10.489294 14.853755
## 1357 62045.343 UAE Mumbai Sea 8.334084 14.652518
## 1358 81554.585 UK Kolkata Air 14.239887 16.300897
## 1359 67721.926 USA Kolkata Air 13.822314 14.319507
## 1360 77112.482 China Kolkata Sea 12.702690 15.439858
## 1361 16215.534 Germany Kandla Air 9.253912 14.603962
## 1362 92523.238 UK Delhi Land 10.758488 6.854242
## 1363 36450.704 Germany Kolkata Land 7.099371 14.399898
## 1364 59230.179 Japan Kolkata Sea 13.953067 10.164850
## 1365 23707.360 USA Mumbai Land 5.497109 8.489227
## 1366 38888.896 UAE Chennai Sea 16.791819 16.680298
## 1367 63694.491 Germany Kolkata Land 14.760760 10.892836
## 1368 73989.422 Japan Kolkata Sea 7.086385 11.937833
## 1369 3362.200 China Delhi Sea 11.693267 7.061061
## 1370 76653.464 Germany Mumbai Air 14.275371 7.894678
## 1371 25276.598 USA Delhi Sea 14.859493 14.911289
## 1372 45575.150 Japan Kolkata Air 6.943858 15.788245
## 1373 26656.286 Germany Kolkata Land 13.392610 14.778933
## 1374 45111.816 Germany Mumbai Sea 9.343978 14.109477
## 1375 30904.027 UAE Delhi Land 5.380916 13.645923
## 1376 75837.408 USA Kolkata Air 5.730224 11.363439
## 1377 9021.218 Germany Mumbai Sea 9.417407 8.047009
## 1378 50544.985 USA Kolkata Air 14.646714 15.650971
## 1379 18805.322 Germany Mumbai Land 5.698849 10.334649
## 1380 10959.882 UK Mumbai Land 8.993738 5.510973
## 1381 12054.315 Germany Mumbai Land 15.923847 6.804681
## 1382 88176.724 USA Chennai Sea 17.452585 11.442652
## 1383 93220.245 USA Chennai Sea 15.176941 5.133546
## 1384 23172.560 Japan Kolkata Sea 13.410498 11.027588
## 1385 38862.396 Germany Kandla Air 16.751058 5.118530
## 1386 19055.415 UK Kandla Sea 18.526041 14.578802
## 1387 27892.263 UK Delhi Sea 13.895896 10.645693
## 1388 68472.882 Japan Mumbai Air 6.330996 8.177011
## 1389 60951.932 Germany Chennai Air 11.353745 14.284566
## 1390 80635.963 China Chennai Sea 19.869417 8.281017
## 1391 63788.349 Germany Delhi Land 8.616462 14.565032
## 1392 17721.463 Germany Kandla Sea 6.196590 17.437526
## 1393 61396.566 Japan Chennai Sea 5.585767 9.033539
## 1394 55232.596 Japan Chennai Sea 13.999197 5.414552
## 1395 17411.990 UAE Kandla Land 9.144598 8.718472
## 1396 29551.512 USA Kolkata Land 9.858418 8.492902
## 1397 12696.720 Japan Kolkata Land 11.183525 17.917178
## 1398 38282.547 USA Kolkata Land 11.862446 6.931386
## 1399 59076.591 UAE Delhi Land 8.997814 13.259777
## 1400 12425.303 Germany Mumbai Sea 10.681939 8.227527
## 1401 47865.742 UAE Delhi Air 7.458509 13.735108
## 1402 10296.579 Japan Chennai Land 18.461394 8.756089
## 1403 46324.664 USA Mumbai Sea 8.503150 13.652940
## 1404 14791.435 Germany Mumbai Land 6.064398 17.679950
## 1405 24614.994 USA Delhi Air 19.930770 12.699521
## 1406 72703.563 UK Kandla Land 14.464991 5.959429
## 1407 50232.993 China Delhi Air 8.874886 6.588816
## 1408 67073.495 UK Chennai Sea 11.344290 5.893414
## 1409 47954.302 UAE Delhi Land 11.895115 5.550235
## 1410 47619.752 USA Kolkata Air 5.295971 6.806269
## 1411 50982.247 China Kolkata Sea 12.468272 14.672591
## 1412 9156.828 China Chennai Air 15.536033 17.645306
## 1413 67399.015 Germany Kandla Air 14.115674 16.952316
## 1414 72285.067 China Delhi Air 12.915191 12.339117
## 1415 6100.166 Japan Kandla Sea 10.026422 14.565587
## 1416 14999.227 UAE Kolkata Land 10.569613 15.200235
## 1417 8778.998 Germany Delhi Land 10.981215 9.639413
## 1418 34888.051 Japan Kolkata Sea 11.881021 15.192376
## 1419 34116.599 USA Chennai Air 11.369036 9.145079
## 1420 17348.894 UAE Chennai Air 17.012625 12.287600
## 1421 61671.494 China Chennai Land 5.410524 15.081337
## 1422 35232.706 UAE Kandla Sea 10.774582 9.226188
## 1423 67580.006 USA Delhi Land 19.982073 10.738860
## 1424 42986.585 China Kandla Sea 15.864748 12.187473
## 1425 45892.268 China Kolkata Sea 6.589766 13.304227
## 1426 91969.951 USA Kolkata Air 13.074997 10.126297
## 1427 18331.123 Germany Mumbai Land 6.704788 9.440806
## 1428 15606.124 China Chennai Air 17.812417 14.265191
## 1429 87147.802 UK Kolkata Air 16.453628 11.893749
## 1430 90854.228 Germany Delhi Sea 14.488325 15.424776
## 1431 2239.719 UK Chennai Air 17.664983 13.840592
## 1432 81581.197 China Mumbai Sea 17.156848 13.769120
## 1433 63212.766 Japan Delhi Sea 11.706852 12.860704
## 1434 24051.265 USA Mumbai Land 7.974292 17.229698
## 1435 61915.015 China Chennai Air 17.242780 13.862485
## 1436 86998.131 China Kandla Land 19.501836 16.875125
## 1437 52681.577 UAE Chennai Land 18.755120 6.322738
## 1438 43630.180 China Delhi Sea 19.053876 5.728962
## 1439 49944.811 UAE Chennai Land 15.323447 11.684882
## 1440 96028.964 Germany Chennai Air 11.502172 12.933501
## 1441 65558.128 UAE Delhi Land 8.988371 9.483749
## 1442 93213.333 USA Delhi Land 16.497666 14.214194
## 1443 10037.853 Germany Kolkata Land 16.096909 12.415130
## 1444 72994.337 Japan Mumbai Air 6.550745 16.036469
## 1445 59484.646 Japan Chennai Sea 13.347764 14.491889
## 1446 71272.700 USA Mumbai Land 5.236901 5.987414
## 1447 52748.599 UK Chennai Sea 14.927978 13.630164
## 1448 47492.966 Japan Kolkata Land 7.644895 11.654759
## 1449 5387.591 China Kandla Air 18.062860 7.451336
## 1450 90532.196 China Mumbai Sea 16.480265 8.192275
## 1451 27769.637 USA Kolkata Land 55.288747 7.657979
## 1452 5007.912 Germany Chennai Air 17.895705 5.561742
## 1453 33175.995 UK Kandla Sea 17.005416 16.967756
## 1454 88677.694 UAE Delhi Sea 10.458107 11.938055
## 1455 39575.420 USA Kolkata Sea 17.881984 10.354541
## 1456 49864.172 Germany Kandla Sea 16.598929 11.804708
## 1457 56520.085 Germany Kolkata Land 12.606069 11.636711
## 1458 22745.164 Germany Chennai Air 15.149133 15.879633
## 1459 92515.374 China Kandla Air 11.375035 12.635699
## 1460 6147.808 Japan Chennai Land 10.610279 11.142478
## 1461 8181.613 Germany Kolkata Air 6.246550 9.368154
## 1462 53308.966 China Delhi Sea 12.988491 5.096436
## 1463 63397.838 USA Chennai Land 17.348524 13.707232
## 1464 75528.901 China Kolkata Air 18.941486 8.959412
## 1465 35880.215 UK Kolkata Land 10.710978 13.839662
## 1466 17585.769 China Kolkata Sea 14.775857 7.119724
## 1467 71584.438 UAE Delhi Sea 17.987895 5.121002
## 1468 12418.457 Germany Chennai Sea 19.395580 7.235482
## 1469 5982.226 China Chennai Land 16.187264 9.943270
## 1470 45461.477 China Kandla Land 5.018197 13.472016
## 1471 95865.598 Japan Kandla Air 13.926322 12.841901
## 1472 91631.194 China Delhi Land 16.970796 14.188187
## 1473 79599.032 USA Chennai Air 8.740460 10.142629
## 1474 34412.278 USA Delhi Air 15.973174 13.915191
## 1475 34831.899 UK Chennai Air 14.111992 13.982568
## 1476 40370.854 UAE Chennai Air 11.099237 7.724908
## 1477 55994.721 UAE Kolkata Air 5.474265 17.802017
## 1478 89123.428 UAE Mumbai Land 7.717665 16.230989
## 1479 98466.824 UAE Delhi Land 19.947013 7.143668
## 1480 44440.479 Germany Delhi Sea 18.098138 17.537261
## 1481 33845.635 Germany Delhi Air 13.729239 13.886712
## 1482 86212.680 UAE Mumbai Land 8.290819 6.602256
## 1483 31459.459 Japan Chennai Air 13.293631 7.290899
## 1484 5295.742 USA Kolkata Air 15.551148 16.136277
## 1485 71735.118 USA Chennai Air 12.676584 9.816003
## 1486 26885.342 China Kandla Land 18.116732 7.038628
## 1487 63674.565 Germany Mumbai Air 5.566796 15.443878
## 1488 27919.669 USA Chennai Sea 15.974072 14.347983
## 1489 27948.517 Germany Kandla Sea 9.712442 9.166327
## 1490 99135.198 Germany Kandla Sea 7.164945 10.042625
## 1491 49239.832 China Chennai Land 18.219410 14.800778
## 1492 36357.166 China Kolkata Land 18.837874 6.616027
## 1493 85218.351 China Kandla Sea 8.170439 14.757199
## 1494 48077.641 USA Kolkata Land 16.834859 15.808202
## 1495 84699.535 China Delhi Air 19.826728 5.996621
## 1496 77421.754 UAE Kolkata Land 16.667682 17.090163
## 1497 18914.388 UAE Kolkata Land 17.121563 9.246482
## 1498 60471.505 China Chennai Air 14.052839 16.746221
## 1499 69964.008 China Mumbai Sea 10.603574 7.199857
## 1500 27325.198 UAE Mumbai Land 17.478623 11.422755
## 1501 31610.778 Germany Kandla Land 16.216170 14.198591
## 1502 45670.747 Germany Kandla Air 9.548244 9.030370
## 1503 67449.992 Germany Delhi Land 11.541440 13.218287
## 1504 89459.233 Germany Chennai Sea 10.056341 16.639886
## 1505 33616.872 Japan Mumbai Air 14.396398 12.698358
## 1506 23275.272 USA Mumbai Sea 19.779133 9.864038
## 1507 70349.718 UK Kolkata Air 16.571229 10.183173
## 1508 46945.275 China Chennai Land 9.027694 17.794143
## 1509 58182.739 Germany Kolkata Sea 19.647561 9.801531
## 1510 76115.700 USA Kolkata Air 12.721097 13.074558
## 1511 44903.333 Germany Delhi Air 8.091071 13.292520
## 1512 16887.381 UK Kandla Air 10.207076 17.467581
## 1513 21787.265 Japan Mumbai Land 17.907725 16.049253
## 1514 46740.549 Japan Chennai Air 15.676757 10.400850
## 1515 88201.429 USA Chennai Land 13.635777 14.632998
## 1516 41862.150 UAE Kandla Air 17.899145 14.716955
## 1517 37169.793 Germany Mumbai Sea 17.048030 6.408277
## 1518 3481.318 UAE Mumbai Land 16.846073 12.614793
## 1519 99486.192 Germany Delhi Air 9.546295 8.702506
## 1520 48176.683 Germany Mumbai Sea 12.299443 16.150509
## 1521 98041.470 UAE Delhi Land 11.174415 9.078902
## 1522 16461.477 UAE Kolkata Land 6.484871 5.626451
## 1523 50222.545 Japan Kolkata Land 10.524247 15.229374
## 1524 18203.905 UK Mumbai Land 8.376512 5.449626
## 1525 70991.842 Japan Chennai Land 14.637500 7.088977
## 1526 55713.445 Japan Kandla Sea 5.094372 14.408902
## 1527 36997.714 Japan Delhi Air 16.443904 11.207980
## 1528 2473.603 UAE Chennai Air 19.931470 10.876066
## 1529 30288.013 UAE Kolkata Sea 8.656741 6.984440
## 1530 16451.402 USA Chennai Land 12.563127 15.173560
## 1531 56354.165 China Mumbai Land 19.870427 12.712192
## 1532 15646.756 UK Delhi Sea 19.307501 6.398262
## 1533 34844.929 UAE Chennai Land 19.317220 7.053770
## 1534 4286.427 UAE Delhi Air 14.180287 11.168597
## 1535 21810.136 China Delhi Land 5.968896 15.814913
## 1536 91634.608 USA Chennai Air 11.711556 8.728797
## 1537 63782.868 Germany Kandla Sea 13.942431 9.234948
## 1538 11554.595 Germany Mumbai Land 12.136941 10.247218
## 1539 20660.078 Germany Chennai Air 13.434322 6.616166
## 1540 17453.757 UK Mumbai Sea 9.374095 12.351142
## 1541 49065.759 USA Mumbai Land 15.141167 12.174420
## 1542 54097.403 UAE Delhi Air 6.541631 8.595832
## 1543 18012.904 China Delhi Land 8.829864 15.506935
## 1544 70972.160 Japan Mumbai Sea 15.800132 11.883330
## 1545 69255.295 UK Mumbai Air 13.894287 14.542573
## 1546 63882.038 China Chennai Sea 18.082222 12.955880
## 1547 13469.881 USA Delhi Land 8.435067 9.861986
## 1548 6527.377 USA Mumbai Land 12.757398 5.013365
## 1549 22452.331 UK Delhi Land 11.924091 15.229447
## 1550 81469.806 Japan Mumbai Sea 18.528657 9.277424
## 1551 72508.510 Japan Chennai Land 6.929015 5.915673
## 1552 96422.827 Japan Kolkata Land 19.786034 13.133442
## 1553 43486.622 USA Kandla Air 13.159320 13.068185
## 1554 76826.063 UAE Kolkata Land 12.406237 14.996162
## 1555 20306.544 China Mumbai Sea 11.944967 11.473634
## 1556 54959.655 USA Kolkata Land 13.577321 17.854187
## 1557 3263.469 Germany Chennai Land 7.214036 14.591191
## 1558 70548.389 USA Chennai Land 10.451619 6.997860
## 1559 62628.539 Japan Delhi Sea 13.316955 15.191506
## 1560 65960.991 Japan Kandla Land 12.036059 15.098477
## 1561 69895.089 Germany Kandla Land 16.742159 16.274225
## 1562 63249.672 Germany Kandla Sea 19.607387 13.220226
## 1563 48275.279 USA Kolkata Land 6.995978 11.570076
## 1564 50574.304 UAE Delhi Land 17.921911 9.300108
## 1565 10788.898 USA Delhi Sea 13.111471 13.320808
## 1566 44922.201 Germany Chennai Sea 13.671721 12.981806
## 1567 60488.851 Japan Mumbai Sea 5.312866 8.195923
## 1568 63071.290 USA Kolkata Land 12.439483 15.077924
## 1569 61422.043 China Kolkata Air 19.371640 12.259834
## 1570 67747.952 UK Chennai Sea 9.898426 5.879766
## 1571 17018.960 UK Delhi Sea 16.268379 6.370225
## 1572 55665.307 UK Chennai Land 18.912052 17.132522
## 1573 2642.313 China Delhi Land 10.106686 5.940903
## 1574 53778.948 Japan Kolkata Sea 8.995909 17.544959
## 1575 99405.937 UK Kolkata Sea 9.750099 15.951415
## 1576 84059.230 USA Chennai Land 7.334631 5.967475
## 1577 63009.602 China Chennai Land 11.654283 5.246777
## 1578 7081.269 China Mumbai Sea 16.905402 13.963481
## 1579 86092.439 UK Kandla Air 10.810466 7.053236
## 1580 58702.710 UAE Delhi Sea 5.575400 6.154138
## 1581 35798.010 Germany Kandla Land 7.365843 5.361110
## 1582 38331.742 Japan Mumbai Land 19.879303 17.958840
## 1583 73957.498 UK Kandla Air 9.095175 12.930655
## 1584 26415.690 UK Kandla Air 12.072334 8.706526
## 1585 87596.162 USA Kolkata Sea 17.719871 12.124697
## 1586 11332.824 Germany Kolkata Sea 17.592290 6.275364
## 1587 47191.713 UAE Kandla Land 11.701539 7.540580
## 1588 14357.352 China Kolkata Sea 10.254824 17.281159
## 1589 73401.910 USA Kolkata Air 7.403708 6.664432
## 1590 29362.297 UAE Kolkata Land 7.838198 6.941414
## 1591 22001.290 UK Kandla Sea 6.353778 14.446574
## 1592 77282.330 USA Mumbai Sea 11.138545 8.632183
## 1593 61537.055 Japan Kandla Land 18.442334 9.322055
## 1594 90071.258 UK Mumbai Air 5.516870 10.183125
## 1595 39045.969 China Delhi Air 7.489660 12.771517
## 1596 74985.964 Japan Delhi Air 18.684575 15.991848
## 1597 56936.349 Germany Chennai Land 9.056303 13.432982
## 1598 74665.642 Germany Chennai Land 12.355163 9.143247
## 1599 56299.086 Germany Kandla Air 15.562075 6.587065
## 1600 85710.266 Japan Kolkata Air 16.060511 16.788727
## 1601 71666.075 Germany Kandla Air 14.685297 6.307760
## 1602 69001.732 China Kandla Land 15.371850 12.463549
## 1603 80359.996 USA Kolkata Land 14.455431 7.276415
## 1604 72116.204 Japan Kandla Air 19.228892 7.521038
## 1605 2611.805 Germany Kolkata Sea 7.927698 14.666893
## 1606 84588.467 USA Kandla Sea 8.250467 14.131878
## 1607 4767.829 Japan Kandla Air 6.339833 10.556828
## 1608 44023.410 USA Kolkata Air 13.271239 9.687666
## 1609 7861.939 UAE Mumbai Sea 13.702316 10.730718
## 1610 55573.583 China Kandla Sea 12.187155 8.645788
## 1611 30470.374 USA Kolkata Land 9.131475 12.826976
## 1612 82263.869 Japan Mumbai Sea 9.008666 5.193740
## 1613 36310.745 Germany Kolkata Sea 19.819314 7.482177
## 1614 45843.075 China Mumbai Sea 11.445173 7.554721
## 1615 6823.461 UAE Kolkata Land 15.227912 5.507664
## 1616 83229.978 China Chennai Air 7.368144 14.334217
## 1617 87848.845 USA Kandla Sea 11.063854 5.364732
## 1618 18470.437 Germany Delhi Air 12.661054 16.612485
## 1619 61589.713 Japan Delhi Land 9.420734 9.270017
## 1620 22516.535 UAE Kolkata Land 7.453993 16.400916
## 1621 28651.897 UAE Delhi Sea 11.766014 15.876104
## 1622 60196.057 USA Kolkata Sea 19.247766 9.856015
## 1623 10595.711 Germany Mumbai Land 17.091151 13.203560
## 1624 14541.045 Japan Kandla Land 14.454665 13.894020
## 1625 92011.306 USA Kandla Sea 8.746673 9.414542
## 1626 15788.181 UK Delhi Land 15.539340 8.572016
## 1627 48456.773 China Chennai Air 16.891419 13.860416
## 1628 95381.947 UAE Kolkata Sea 6.068624 15.590771
## 1629 41090.015 Germany Kandla Land 18.200184 16.131110
## 1630 37153.866 China Mumbai Air 5.892500 12.935554
## 1631 16800.057 UAE Chennai Land 9.369981 15.590322
## 1632 53964.897 USA Chennai Sea 12.817997 16.669145
## 1633 95492.701 UAE Delhi Land 19.747155 13.679342
## 1634 91465.256 Japan Delhi Air 13.332935 15.762118
## 1635 8717.537 China Kolkata Sea 5.539450 10.545437
## 1636 12077.335 UK Mumbai Air 16.438238 17.247486
## 1637 7346.442 Japan Mumbai Land 13.689267 16.979705
## 1638 38760.641 Germany Delhi Land 15.477196 10.065983
## 1639 33239.206 China Delhi Air 17.739598 6.830492
## 1640 21656.625 Japan Chennai Air 16.121143 10.314585
## 1641 42720.462 USA Delhi Air 16.706718 5.311659
## 1642 331120.373 USA Mumbai Sea 5.364560 8.721179
## 1643 92747.728 UK Chennai Land 15.444580 11.599794
## 1644 76297.160 UAE Delhi Sea 18.942506 17.751796
## 1645 83501.353 UAE Kandla Land 15.740463 13.706814
## 1646 24011.476 UK Chennai Air 11.713002 5.921735
## 1647 72245.784 China Delhi Land 8.393855 15.633720
## 1648 29989.518 UAE Delhi Air 6.688156 5.277744
## 1649 27396.028 UK Chennai Sea 5.136005 10.391727
## 1650 59421.473 UAE Mumbai Land 10.587056 10.035572
## 1651 35412.777 Germany Delhi Land 6.421090 16.288903
## 1652 73151.365 China Chennai Sea 8.286157 9.522497
## 1653 37221.797 Japan Chennai Sea 9.782011 6.231539
## 1654 20995.534 USA Chennai Land 16.007587 9.566354
## 1655 70253.247 Germany Kolkata Air 16.239660 5.596053
## 1656 83679.213 UAE Kandla Sea 5.741502 16.702050
## 1657 66661.774 UK Chennai Air 9.334387 5.333412
## 1658 69605.303 China Kolkata Land 17.432781 15.873124
## 1659 24730.372 UK Kandla Air 13.675605 7.548841
## 1660 97484.803 Japan Kolkata Sea 13.480837 15.859965
## 1661 10426.127 Germany Chennai Sea 12.274061 14.946586
## 1662 21069.967 China Delhi Air 7.591769 14.907081
## 1663 49074.253 UAE Mumbai Air 10.617380 12.026790
## 1664 96650.368 USA Kolkata Sea 9.325423 5.358993
## 1665 18397.170 Germany Kolkata Air 9.728790 9.812607
## 1666 10347.432 Japan Kandla Land 11.054244 15.806972
## 1667 85380.284 UAE Mumbai Air 8.012609 17.340677
## 1668 96051.957 UAE Kandla Land 16.396022 10.803892
## 1669 10624.448 UAE Kandla Land 9.481928 14.224446
## 1670 89755.747 USA Chennai Land 5.924272 7.762670
## 1671 97394.568 USA Kolkata Air 8.881817 7.996267
## 1672 87652.081 UK Chennai Sea 17.587961 9.919649
## 1673 41577.193 Japan Delhi Sea 19.234846 8.328149
## 1674 53196.698 Germany Kolkata Air 19.324709 10.991422
## 1675 23656.928 USA Delhi Air 8.021505 11.043132
## 1676 12230.759 Germany Kandla Sea 14.639051 6.812736
## 1677 31993.157 UAE Delhi Land 14.683268 13.987102
## 1678 55861.062 China Chennai Sea 17.367644 5.007901
## 1679 83274.960 UK Kandla Air 13.688330 6.239680
## 1680 38462.767 Germany Mumbai Air 7.281434 15.328608
## 1681 18396.405 Germany Kolkata Sea 6.363133 11.682419
## 1682 10841.373 Germany Mumbai Land 6.619985 5.804989
## 1683 96668.964 China Mumbai Air 11.478461 6.339457
## 1684 82626.627 UAE Mumbai Sea 5.598091 12.820902
## 1685 88558.173 China Mumbai Land 10.065528 11.274713
## 1686 53466.532 UAE Chennai Sea 6.789653 7.076132
## 1687 13995.494 UAE Kandla Sea 10.741235 12.633189
## 1688 39206.893 Japan Kolkata Air 8.218348 14.667261
## 1689 42845.189 UK Kandla Land 10.088104 15.092458
## 1690 64928.488 USA Kolkata Land 7.772113 15.166120
## 1691 58546.490 China Kolkata Sea 15.010032 10.748039
## 1692 39379.764 China Chennai Air 10.045999 9.835533
## 1693 77157.768 Germany Mumbai Air 12.169101 8.438941
## 1694 6445.945 UAE Chennai Sea 6.797605 7.004914
## 1695 45180.766 UK Chennai Land 19.724536 11.900683
## 1696 4253.555 UAE Kolkata Sea 8.076067 13.262526
## 1697 62791.740 Japan Kandla Air 13.219690 5.830581
## 1698 74852.911 Germany Chennai Sea 13.387872 6.651377
## 1699 48443.234 Germany Kandla Land 13.627388 11.387563
## 1700 68824.278 Japan Kolkata Sea 17.052011 11.422864
## 1701 17070.668 UK Kolkata Sea 8.816648 10.561065
## 1702 66682.403 Germany Chennai Sea 12.719463 6.059343
## 1703 57397.866 Germany Kolkata Land 10.361492 5.479382
## 1704 24993.433 UAE Kolkata Air 10.097316 10.156897
## 1705 83950.401 USA Kandla Land 19.897795 12.142509
## 1706 80454.321 UAE Chennai Land 6.682121 14.558148
## 1707 90033.314 China Mumbai Air 8.694574 6.716814
## 1708 36880.338 UK Kolkata Land 11.211457 17.232105
## 1709 51129.802 UK Delhi Sea 16.899586 7.731840
## 1710 48070.836 UAE Kolkata Air 16.439017 15.550915
## 1711 78976.090 Japan Chennai Air 19.345338 17.222839
## 1712 50747.272 Japan Mumbai Sea 13.614080 7.854919
## 1713 88130.322 Japan Chennai Land 12.154928 12.688131
## 1714 57236.903 UK Chennai Air 13.848462 14.853995
## 1715 31428.183 UK Chennai Air 19.006669 14.725194
## 1716 78414.682 Japan Delhi Land 14.038902 15.879255
## 1717 50430.778 UK Mumbai Air 12.311984 17.130484
## 1718 37618.528 UAE Mumbai Air 14.595145 9.065172
## 1719 52340.941 Germany Chennai Sea 18.555423 10.668882
## 1720 70018.203 UAE Chennai Land 8.609812 14.282272
## 1721 20540.606 China Chennai Land 16.987681 7.995181
## 1722 80272.215 UAE Mumbai Land 15.111014 9.043578
## 1723 34429.334 UAE Mumbai Land 16.895712 8.592357
## 1724 56732.715 China Delhi Sea 13.379331 9.075724
## 1725 39651.110 Germany Chennai Sea 17.437992 15.582589
## 1726 50284.140 UK Mumbai Air 16.901139 16.154168
## 1727 81452.803 Germany Kolkata Sea 12.913177 8.641932
## 1728 1899.384 USA Kandla Land 13.641213 10.748865
## 1729 97882.863 China Delhi Air 8.013042 7.954879
## 1730 2059.025 USA Mumbai Air 12.053016 10.659754
## 1731 88317.484 Japan Kandla Air 9.248898 12.956313
## 1732 6094.495 UK Kandla Air 16.701213 5.908848
## 1733 75386.497 Japan Kolkata Sea 9.114512 7.973138
## 1734 73458.885 Japan Delhi Sea 7.855585 7.694600
## 1735 2173.984 UAE Mumbai Land 18.926307 6.456521
## 1736 74983.589 USA Chennai Sea 15.754620 15.326490
## 1737 4425.220 Germany Kolkata Sea 5.578423 8.330298
## 1738 67495.325 Germany Mumbai Land 16.172964 5.530596
## 1739 81780.280 Japan Kolkata Land 16.442747 14.047151
## 1740 93853.339 Japan Chennai Air 7.374775 16.132058
## 1741 65070.420 UK Mumbai Land 11.260099 11.600947
## 1742 58752.268 Germany Delhi Air 19.107425 16.580256
## 1743 39260.552 USA Mumbai Air 9.807992 8.699385
## 1744 45912.849 UK Mumbai Land 7.290160 15.964167
## 1745 75911.354 Japan Delhi Sea 16.493379 13.105475
## 1746 20270.604 UK Kandla Land 17.678218 13.128699
## 1747 27471.886 Germany Delhi Land 11.686544 13.626334
## 1748 66261.965 Germany Mumbai Sea 16.616299 9.473117
## 1749 22139.463 Japan Kolkata Air 8.562473 15.943468
## 1750 71312.465 USA Chennai Air 15.416132 7.858856
## 1751 26840.938 Germany Kolkata Air 12.682806 7.802964
## 1752 8909.825 Germany Kolkata Sea 16.929976 7.104381
## 1753 20665.363 UAE Kolkata Air 10.830898 11.550295
## 1754 58796.600 UAE Kandla Sea 14.420592 6.941822
## 1755 31704.067 UAE Kandla Sea 8.421941 6.312162
## 1756 17675.384 China Kolkata Sea 14.358847 13.734299
## 1757 22293.329 UAE Delhi Land 12.036684 11.921503
## 1758 51528.750 Germany Kandla Air 7.978295 14.402757
## 1759 16854.648 Germany Kandla Air 17.925775 17.960238
## 1760 56895.531 UAE Delhi Sea 14.217395 9.939687
## 1761 1846.068 UAE Delhi Air 7.486653 15.540340
## 1762 45366.274 China Mumbai Sea 12.903353 15.383911
## 1763 69608.298 China Kolkata Land 7.530625 10.132328
## 1764 72289.391 UK Kandla Sea 10.683040 17.020548
## 1765 49983.111 Germany Kolkata Air 6.962941 6.203005
## 1766 4985.388 Japan Mumbai Sea 7.846484 12.844445
## 1767 55043.382 Germany Kandla Land 17.272349 12.800492
## 1768 27078.135 China Mumbai Land 6.465126 14.244882
## 1769 21393.967 USA Chennai Air 12.930163 16.058550
## 1770 66671.178 Japan Delhi Air 10.996910 16.432605
## 1771 28178.647 Germany Delhi Air 12.106457 10.368080
## 1772 61358.118 UAE Delhi Air 12.431974 17.709271
## 1773 31037.050 UAE Chennai Sea 9.584668 8.759462
## 1774 74076.669 UAE Kolkata Land 8.184895 7.971800
## 1775 50064.628 China Chennai Air 5.011520 48.609158
## 1776 6848.545 Germany Kandla Air 18.838820 15.917877
## 1777 50719.524 UAE Kolkata Land 12.976256 8.869937
## 1778 29585.107 China Chennai Sea 8.960224 7.219164
## 1779 93390.785 Germany Delhi Sea 16.388201 13.226811
## 1780 77939.174 UK Mumbai Land 8.790345 15.630920
## 1781 73922.151 USA Mumbai Air 19.800227 10.362682
## 1782 49178.048 Japan Mumbai Land 14.664752 14.629001
## 1783 66636.061 UK Kandla Air 6.112066 7.606114
## 1784 1209.597 Japan Mumbai Land 18.685399 5.814910
## 1785 94126.394 UAE Delhi Land 11.999887 9.393144
## 1786 20059.148 UAE Chennai Air 17.050490 16.886252
## 1787 57176.554 USA Delhi Air 16.442618 9.782151
## 1788 2186.653 UK Delhi Land 17.542691 9.074785
## 1789 39010.460 China Kandla Land 10.757300 17.117229
## 1790 74376.762 China Kandla Sea 9.124262 5.400333
## 1791 54132.812 Japan Delhi Sea 5.687568 10.576531
## 1792 49341.200 Germany Kandla Sea 13.291135 17.801157
## 1793 91482.459 UK Chennai Air 18.021524 8.268955
## 1794 91870.988 UAE Kolkata Sea 16.333429 12.109097
## 1795 85628.986 Japan Kandla Sea 11.359519 8.573557
## 1796 36550.064 Germany Delhi Sea 5.103827 13.896675
## 1797 37585.865 China Delhi Air 6.646121 10.228982
## 1798 41356.854 Japan Kandla Air 17.940884 16.453365
## 1799 2596.981 Japan Chennai Air 13.979658 15.092966
## 1800 81917.487 USA Kolkata Air 6.766048 17.335145
## 1801 42261.599 UAE Chennai Sea 8.090270 15.154100
## 1802 12940.573 Japan Chennai Air 19.257429 7.226036
## 1803 49383.438 Japan Delhi Sea 13.392081 16.696759
## 1804 90293.729 USA Kolkata Land 9.749025 13.592563
## 1805 11539.457 UAE Kandla Sea 11.403108 7.442707
## 1806 96071.104 USA Mumbai Sea 9.142637 16.091734
## 1807 95105.025 Germany Kolkata Sea 9.043830 8.872117
## 1808 4501.618 Japan Kolkata Air 13.254903 10.502272
## 1809 53999.409 Germany Kolkata Air 14.624597 13.647106
## 1810 27006.483 Germany Kandla Air 11.137502 5.228764
## 1811 84791.368 Japan Kandla Air 11.618251 12.873997
## 1812 8565.568 Japan Chennai Sea 11.514288 8.230747
## 1813 87048.911 Germany Kolkata Air 5.610429 6.662419
## 1814 67984.781 Japan Chennai Air 13.262516 13.878132
## 1815 8805.721 Japan Kandla Air 14.370196 17.464542
## 1816 19275.928 Germany Mumbai Air 15.374736 9.487220
## 1817 72660.720 Japan Delhi Air 7.808854 14.690234
## 1818 5779.909 UAE Mumbai Sea 10.160041 11.035540
## 1819 21716.695 China Mumbai Sea 18.855635 8.307294
## 1820 1140.553 UAE Chennai Air 17.455055 10.152599
## 1821 55044.624 Germany Kolkata Sea 10.719795 14.381142
## 1822 21791.735 UK Kandla Air 10.434314 13.958168
## 1823 64266.161 UK Kolkata Air 7.481955 17.196035
## 1824 38394.307 Japan Chennai Sea 8.123909 6.895956
## 1825 59388.054 Japan Delhi Land 17.766862 12.882584
## 1826 24016.940 China Delhi Sea 13.854833 5.810734
## 1827 21110.210 UAE Chennai Sea 17.647658 16.114254
## 1828 5351.551 UAE Mumbai Land 9.045606 16.345714
## 1829 36840.918 Germany Kandla Air 6.369544 9.946279
## 1830 80178.687 UAE Kandla Land 12.525335 15.340937
## 1831 95130.735 UK Kandla Air 11.434879 8.062353
## 1832 50049.961 USA Kolkata Sea 19.762853 7.371371
## 1833 62351.601 Germany Kolkata Land 10.021937 16.731570
## 1834 55593.772 UAE Kandla Land 17.744774 11.796618
## 1835 40460.234 China Kandla Land 17.943388 9.129636
## 1836 63388.264 Germany Mumbai Air 7.123747 13.117146
## 1837 92642.011 Germany Kolkata Sea 12.836374 7.895750
## 1838 71695.561 USA Delhi Land 12.566766 6.678580
## 1839 90916.886 USA Mumbai Land 11.304184 6.436139
## 1840 89070.029 China Delhi Land 6.696959 9.842905
## 1841 68032.148 USA Mumbai Land 11.018013 11.161795
## 1842 2383.271 USA Chennai Sea 15.514557 9.802717
## 1843 42312.542 Germany Kolkata Land 14.972938 7.079540
## 1844 59189.606 UAE Chennai Land 17.066289 9.434189
## 1845 21799.070 UAE Kolkata Sea 13.923014 12.369778
## 1846 35492.717 Japan Mumbai Sea 5.926386 16.931381
## 1847 51232.933 Germany Kolkata Sea 12.112586 7.590764
## 1848 28512.639 USA Delhi Air 7.322506 14.321216
## 1849 97169.251 UAE Mumbai Land 7.913752 10.412358
## 1850 79619.026 China Kandla Air 15.747161 9.889407
## 1851 47035.118 Germany Kandla Land 16.432004 13.995855
## 1852 22455.646 UK Delhi Land 18.483150 13.567564
## 1853 92599.102 UAE Delhi Air 5.317912 5.342591
## 1854 64817.653 Japan Kolkata Land 7.025765 15.900813
## 1855 27806.097 USA Delhi Sea 15.492989 15.885494
## 1856 46590.520 Japan Chennai Air 10.018361 14.846780
## 1857 29868.524 China Chennai Sea 15.665852 17.408844
## 1858 10022.674 Japan Kolkata Air 16.540260 7.627645
## 1859 18034.505 Japan Kolkata Land 5.539222 10.221687
## 1860 81138.408 Japan Kandla Land 17.575890 10.313167
## 1861 94244.437 Germany Kandla Air 10.492427 17.791738
## 1862 53129.772 Japan Delhi Air 13.399495 11.674892
## 1863 70682.123 Germany Kandla Land 10.027720 16.074953
## 1864 88033.287 USA Kandla Sea 13.987156 8.707966
## 1865 81489.149 UAE Kandla Sea 16.296382 16.582946
## 1866 59149.803 USA Kolkata Land 16.268539 7.789241
## 1867 21161.399 Germany Chennai Land 17.148412 17.885270
## 1868 63147.067 USA Mumbai Land 5.309294 16.721061
## 1869 71104.336 Japan Kolkata Sea 17.106562 10.306601
## 1870 5400.043 China Kandla Air 6.081021 13.531691
## 1871 47404.625 USA Mumbai Sea 19.320270 14.406601
## 1872 46843.910 China Chennai Sea 14.990311 12.590487
## 1873 62757.935 China Mumbai Sea 10.611097 7.070372
## 1874 90865.553 UK Kandla Land 19.986461 12.703256
## 1875 58091.336 China Kandla Land 5.816509 15.015559
## 1876 82054.050 China Kandla Air 12.206467 17.278212
## 1877 73441.051 Japan Mumbai Land 18.413597 14.144409
## 1878 60027.133 Germany Kandla Sea 19.990958 9.946792
## 1879 3631.030 Germany Kandla Sea 19.239117 14.071737
## 1880 96450.888 UAE Kolkata Sea 16.004833 17.889335
## 1881 23291.491 China Chennai Air 10.719132 8.914365
## 1882 35211.409 UAE Delhi Land 8.907756 6.010415
## 1883 72474.478 Japan Kolkata Sea 8.274805 5.678499
## 1884 45421.188 USA Chennai Land 11.761460 16.028375
## 1885 50751.143 UAE Kandla Sea 15.804166 17.221725
## 1886 11047.964 China Chennai Land 18.665092 6.520108
## 1887 12993.366 Japan Kandla Land 16.267353 14.050394
## 1888 32934.198 UAE Kolkata Land 19.899044 15.860860
## 1889 12185.904 Japan Delhi Land 15.239124 17.133914
## 1890 43744.760 Japan Kandla Air 8.956131 6.849477
## 1891 95311.087 China Chennai Sea 10.009395 12.736114
## 1892 8827.567 USA Kandla Sea 7.640006 8.165346
## 1893 42309.662 Germany Chennai Land 5.543474 9.065434
## 1894 26600.825 Germany Kolkata Sea 18.961935 14.804538
## 1895 91588.279 Germany Mumbai Air 16.537203 16.278763
## 1896 65479.914 USA Delhi Land 19.912562 5.069935
## 1897 21081.713 Germany Kolkata Land 15.952682 10.668523
## 1898 65905.171 UAE Kandla Air 16.423484 8.161574
## 1899 73888.538 Germany Kolkata Sea 17.381441 12.845650
## 1900 18960.830 China Kolkata Land 14.982274 10.124696
## 1901 24153.236 UK Mumbai Air 13.428248 9.603187
## 1902 51963.699 Germany Chennai Air 5.044836 15.604135
## 1903 26924.113 China Kandla Land 5.466023 15.296380
## 1904 29671.449 USA Mumbai Land 11.062084 6.299877
## 1905 66485.238 Germany Chennai Sea 17.611020 12.991013
## 1906 41786.184 USA Chennai Land 15.887848 5.442928
## 1907 73729.859 China Delhi Land 17.433652 12.104126
## 1908 35507.593 China Delhi Land 5.772011 14.788667
## 1909 26606.688 Germany Delhi Air 13.756639 15.136642
## 1910 51441.272 UK Kolkata Sea 6.572557 17.486149
## 1911 63064.317 UK Kandla Land 18.240841 9.204219
## 1912 59938.057 UAE Kandla Air 10.764287 10.187112
## 1913 41284.808 Germany Delhi Sea 6.624990 16.592856
## 1914 99027.104 Germany Kandla Air 5.338229 16.074536
## 1915 96093.276 Japan Chennai Land 14.259520 12.146910
## 1916 32182.136 China Kandla Land 19.551024 10.167620
## 1917 74587.576 UAE Chennai Sea 14.854110 11.908029
## 1918 56180.208 UK Chennai Sea 8.578347 17.414851
## 1919 88478.252 USA Mumbai Land 13.669726 16.121197
## 1920 60660.236 UAE Kandla Sea 7.595015 17.909142
## 1921 33278.566 China Delhi Land 18.829397 11.175221
## 1922 94261.118 Japan Kandla Air 18.292426 5.992187
## 1923 25288.612 UK Delhi Land 6.942694 12.072675
## 1924 41390.977 UK Kandla Sea 13.502002 7.547178
## 1925 62620.346 Germany Kolkata Land 17.923418 5.273778
## 1926 64419.938 UK Chennai Land 8.081555 15.511590
## 1927 91035.137 China Chennai Land 16.159264 13.060062
## 1928 61384.114 USA Delhi Sea 8.190114 16.957794
## 1929 27641.383 Germany Kandla Land 5.668645 10.214014
## 1930 73031.864 UK Kolkata Air 15.923805 7.013580
## 1931 74396.530 China Mumbai Air 6.514057 6.302999
## 1932 26390.159 China Kolkata Land 16.924944 5.039090
## 1933 83628.106 USA Kandla Air 16.155569 9.537291
## 1934 97042.175 China Mumbai Air 12.025256 11.625952
## 1935 78029.093 UAE Delhi Land 9.151740 14.134191
## 1936 5459.824 UK Chennai Sea 8.456682 7.322923
## 1937 34457.442 UK Kandla Sea 8.036745 15.256020
## 1938 81294.936 UAE Chennai Air 13.895568 17.952960
## 1939 43504.291 Germany Delhi Land 17.399716 15.923117
## 1940 87809.872 Japan Kandla Sea 14.750545 8.075891
## 1941 87587.411 USA Mumbai Sea 17.766164 16.661723
## 1942 3277.270 USA Kolkata Air 12.965392 15.217924
## 1943 61305.809 China Chennai Sea 19.171042 10.008317
## 1944 58230.926 China Chennai Air 16.559975 9.850339
## 1945 96793.817 UK Kandla Air 12.710870 17.004133
## 1946 54847.119 UK Kolkata Sea 9.371448 15.036355
## 1947 66024.633 Japan Chennai Sea 11.002236 8.761260
## 1948 65310.020 Germany Kolkata Land 19.699761 11.117357
## 1949 54835.913 Japan Kolkata Air 13.389485 15.806732
## 1950 94853.165 China Delhi Air 12.140580 11.715640
## 1951 46069.741 Japan Chennai Sea 19.179428 12.611962
## 1952 24333.866 China Mumbai Sea 12.237508 15.838129
## 1953 93602.127 Germany Mumbai Air 17.274341 14.737869
## 1954 4053.786 China Delhi Air 10.295659 7.504907
## 1955 70472.905 UAE Kolkata Sea 6.191210 7.761686
## 1956 97230.360 UAE Kolkata Sea 14.359709 13.800492
## 1957 66691.985 UAE Mumbai Sea 18.868648 5.086412
## 1958 54003.312 USA Kolkata Air 14.867510 8.178880
## 1959 81584.069 USA Delhi Land 6.016198 16.351004
## 1960 25223.586 Japan Kandla Air 7.557712 10.015508
## 1961 61621.020 Germany Mumbai Air 13.195102 12.793306
## 1962 69651.296 China Kandla Land 5.605101 11.227160
## 1963 97537.542 Japan Delhi Land 6.678358 12.126509
## 1964 48729.409 Germany Kandla Air 17.528675 13.990345
## 1965 41210.597 USA Kolkata Land 18.370700 14.895237
## 1966 88388.493 Japan Mumbai Sea 5.181776 8.055113
## 1967 25935.345 UAE Chennai Air 11.430912 14.549531
## 1968 30075.732 USA Mumbai Land 14.354033 15.136355
## 1969 84083.928 China Kandla Sea 13.898465 7.123457
## 1970 64635.300 USA Chennai Land 17.595169 7.175260
## 1971 21851.564 UAE Mumbai Land 5.922231 5.019197
## 1972 3391.004 UAE Kolkata Land 16.985562 12.718395
## 1973 92953.210 Germany Chennai Land 6.424502 6.561940
## 1974 17255.098 China Delhi Air 16.805019 12.280861
## 1975 78089.987 China Kolkata Land 18.374699 8.331861
## 1976 31370.418 USA Delhi Air 7.868855 13.704265
## 1977 44277.665 USA Mumbai Land 17.097939 7.036929
## 1978 87284.571 USA Delhi Sea 5.382448 8.506041
## 1979 70377.221 China Chennai Land 13.692879 10.622879
## 1980 27497.586 UK Chennai Air 8.618227 9.477861
## 1981 97161.838 Japan Kolkata Air 10.713367 14.963586
## 1982 12518.355 China Chennai Air 13.133844 16.435623
## 1983 68611.904 Germany Kandla Sea 13.274480 14.794955
## 1984 20944.256 Japan Chennai Air 10.145990 6.827826
## 1985 37239.209 Japan Kolkata Sea 6.897755 13.203820
## 1986 50118.451 UAE Kandla Air 10.366170 15.784960
## 1987 39439.322 Germany Chennai Air 16.392224 17.278184
## 1988 63851.721 UK Kandla Air 14.492769 6.305784
## 1989 26235.450 UK Kolkata Air 19.700672 12.808484
## 1990 61178.002 Germany Chennai Land 11.290644 6.224843
## 1991 61934.309 UK Delhi Air 16.501741 9.421585
## 1992 66008.794 USA Kolkata Sea 7.999367 13.173091
## 1993 89730.676 UK Chennai Sea 19.266682 9.699210
## 1994 67444.542 UK Kolkata Air 8.171012 16.695872
## 1995 57010.093 Japan Kandla Air 11.248843 12.793003
## 1996 40153.870 Germany Chennai Sea 19.234824 5.550896
## 1997 10157.967 UK Delhi Air 16.403471 11.794900
## 1998 27327.928 Japan Chennai Air 12.141705 5.827507
## 1999 89208.216 Germany Delhi Sea 17.650933 9.715322
## 2000 5835.837 USA Delhi Air 15.534879 11.751223
## 2001 90062.629 China Kandla Sea 7.301488 8.256858
## 2002 67859.120 UAE Mumbai Land 17.868766 11.937550
## 2003 64827.564 UAE Delhi Land 9.268341 8.975295
## 2004 85738.578 UAE Kolkata Air 17.089705 14.458680
## 2005 79725.534 China Chennai Land 7.361256 5.811229
## 2006 93081.550 UAE Delhi Sea 18.704946 12.025304
## 2007 61425.027 Germany Kandla Sea 12.718671 6.874221
## 2008 11179.185 USA Mumbai Air 17.572979 6.058186
## 2009 57914.162 UAE Kandla Sea 9.727909 13.084152
## 2010 40328.467 UK Delhi Air 18.700830 13.813253
## 2011 65624.302 UK Kolkata Land 7.081911 14.576194
## 2012 61666.263 UK Chennai Air 19.138385 12.779666
## 2013 2505.169 Germany Mumbai Air 16.203280 13.344680
## 2014 68631.638 UK Kolkata Land 6.552661 17.538310
## 2015 91844.203 Japan Kolkata Air 8.210763 16.274653
## 2016 36163.948 Germany Delhi Air 11.633219 7.322773
## 2017 73027.244 China Delhi Air 9.685349 14.374097
## 2018 98231.481 Germany Kandla Air 11.033290 11.101816
## 2019 41853.754 China Kandla Land 11.982389 11.884448
## 2020 33436.389 Germany Kandla Sea 10.097880 9.674437
## 2021 23608.945 USA Delhi Air 8.336725 16.103920
## 2022 56281.099 UAE Chennai Sea 13.690193 10.505936
## 2023 15387.919 Japan Delhi Land 14.281236 17.265557
## 2024 90443.220 UAE Delhi Land 6.984544 14.839930
## 2025 50414.727 UK Kolkata Land 14.235086 8.176807
## 2026 26309.712 Germany Mumbai Land 18.366754 13.408936
## 2027 45271.242 China Kandla Land 15.915753 12.492279
## 2028 70751.953 UK Chennai Land 7.015047 11.067943
## 2029 62305.383 UK Kandla Land 13.281567 12.527318
## 2030 72756.787 Germany Delhi Air 10.715387 15.510457
## 2031 69874.753 USA Kandla Sea 11.388089 11.089941
## 2032 63417.185 UK Delhi Air 11.370291 8.607053
## 2033 77144.742 Germany Kolkata Sea 10.376854 12.884258
## 2034 63405.040 UAE Kandla Sea 7.505193 15.250034
## 2035 49912.162 UAE Mumbai Sea 13.952832 5.062257
## 2036 27972.440 Germany Delhi Air 6.885860 14.200574
## 2037 77329.303 China Chennai Air 12.663992 7.738985
## 2038 86786.762 USA Kolkata Land 14.300182 11.735211
## 2039 43588.255 China Delhi Sea 12.592470 14.821014
## 2040 82355.995 China Mumbai Land 7.297119 10.764627
## 2041 53661.196 China Chennai Land 17.413620 6.548117
## 2042 52731.087 China Delhi Sea 13.283935 5.107998
## 2043 6981.098 Japan Chennai Land 17.568891 14.569686
## 2044 34245.066 UAE Kolkata Land 16.335746 8.116012
## 2045 63357.528 UAE Mumbai Land 5.974920 15.868156
## 2046 86782.338 China Kolkata Air 9.837476 17.676624
## 2047 16558.682 USA Mumbai Air 12.504144 10.374710
## 2048 82157.615 UK Kandla Land 13.762466 16.403437
## 2049 86003.129 Japan Kandla Air 5.882946 15.776448
## 2050 87602.074 China Kandla Land 13.939435 16.080762
## 2051 75886.633 China Kandla Air 10.335593 17.082334
## 2052 36332.096 UAE Delhi Air 12.684832 17.981513
## 2053 25278.316 USA Chennai Sea 17.421425 10.241123
## 2054 61519.905 UAE Kandla Land 5.321943 6.651466
## 2055 2177.945 UK Mumbai Air 8.249748 5.861785
## 2056 64851.980 UK Kandla Sea 8.980834 12.390932
## 2057 37306.909 UK Kandla Sea 6.469679 12.855399
## 2058 51457.486 Germany Kolkata Air 6.256961 6.195791
## 2059 37278.618 Japan Kolkata Sea 17.495971 15.469354
## 2060 91259.286 China Mumbai Air 13.880350 7.880013
## 2061 11316.197 Japan Delhi Land 15.500977 15.082978
## 2062 60857.506 USA Delhi Land 5.707209 16.658254
## 2063 35291.723 UK Kolkata Sea 13.791119 12.619016
## 2064 80788.690 UAE Kandla Sea 16.664805 15.263605
## 2065 74914.789 UAE Delhi Land 18.094656 17.723895
## 2066 58594.412 USA Delhi Sea 18.678335 10.474910
## 2067 74829.251 Germany Kolkata Land 11.948299 12.161460
## 2068 5087.769 Germany Chennai Land 11.511937 6.717240
## 2069 77200.573 UAE Kolkata Sea 11.049100 15.585021
## 2070 78418.201 Germany Chennai Sea 14.075352 5.883681
## 2071 97452.557 Japan Mumbai Land 5.801528 13.251758
## 2072 58938.558 Japan Kolkata Land 5.972076 10.014538
## 2073 9767.587 Japan Chennai Land 10.659926 11.137832
## 2074 29042.213 UAE Kandla Sea 11.442856 16.258943
## 2075 67594.427 Germany Delhi Air 6.009317 10.032519
## 2076 32325.066 Germany Mumbai Sea 12.163414 11.589120
## 2077 61875.685 Japan Mumbai Land 10.330912 9.897248
## 2078 68771.423 Germany Delhi Sea 5.772362 14.424469
## 2079 23012.133 UAE Kolkata Sea 10.656150 16.448806
## 2080 36076.084 UK Kolkata Land 7.812080 8.741807
## 2081 37011.301 UK Delhi Air 12.788234 16.315089
## 2082 39178.695 UK Chennai Sea 9.219488 5.113699
## 2083 88734.007 Japan Kolkata Sea 10.040614 17.309286
## 2084 82980.235 USA Chennai Land 10.983475 14.220044
## 2085 94073.691 UK Kolkata Sea 12.682994 12.032697
## 2086 76937.607 UAE Kandla Sea 18.244068 17.661248
## 2087 27928.589 Germany Mumbai Air 13.423839 16.914444
## 2088 82823.601 UAE Kandla Air 5.599582 14.523172
## 2089 14097.340 Japan Kolkata Air 18.240454 9.293040
## 2090 31924.225 UAE Delhi Land 15.645843 13.172010
## 2091 22026.970 UK Mumbai Land 13.820529 9.169422
## 2092 19036.998 USA Kolkata Sea 12.278332 16.615352
## 2093 29265.886 Germany Kolkata Air 10.817847 15.683875
## 2094 30103.025 Germany Kolkata Air 16.868410 5.655192
## 2095 99393.526 UK Kandla Air 8.749076 17.564844
## 2096 45777.076 China Chennai Sea 19.156535 8.364754
## 2097 65147.221 China Kolkata Sea 13.533418 15.157813
## 2098 23347.493 Japan Kandla Land 13.164648 15.395652
## 2099 57749.488 UAE Mumbai Sea 17.055193 10.902803
## 2100 73733.484 China Kandla Sea 17.418974 8.237292
## 2101 80124.966 UAE Mumbai Sea 9.644395 17.123458
## 2102 63430.174 USA Mumbai Sea 16.537273 6.856126
## 2103 13279.934 Germany Delhi Sea 16.355758 11.911408
## 2104 86682.949 USA Mumbai Air 13.737623 8.758310
## 2105 92814.446 UAE Kolkata Sea 8.218962 11.044221
## 2106 8547.750 China Kandla Air 7.705210 14.315991
## 2107 56278.002 Japan Chennai Land 11.801118 16.071838
## 2108 45800.031 Japan Kolkata Land 10.941844 5.759564
## 2109 34765.645 Japan Mumbai Land 19.994470 10.259703
## 2110 89431.059 Germany Kandla Sea 10.340920 5.174202
## 2111 35919.409 China Kolkata Sea 18.295460 16.333193
## 2112 29582.871 UAE Kolkata Air 13.638244 9.436983
## 2113 59816.214 UAE Kolkata Sea 15.088853 7.850439
## 2114 99906.036 China Kolkata Air 17.238732 16.131899
## 2115 65943.373 UAE Chennai Air 18.790495 15.668302
## 2116 5972.373 USA Kandla Sea 17.643498 15.334917
## 2117 7715.258 Germany Kandla Sea 19.440519 13.186326
## 2118 89260.176 UAE Kandla Land 16.774083 7.911924
## 2119 74920.967 USA Chennai Air 9.368986 11.152686
## 2120 31630.220 Germany Delhi Land 12.495230 5.972705
## 2121 73190.877 Germany Chennai Land 10.129947 5.458495
## 2122 36879.603 UAE Kandla Land 16.150325 10.015756
## 2123 27525.040 Germany Mumbai Land 12.793974 15.326684
## 2124 37241.505 Germany Kandla Sea 13.947118 17.815082
## 2125 86188.423 China Mumbai Land 16.792508 17.686959
## 2126 5401.058 Japan Kandla Land 6.889359 9.382359
## 2127 37935.200 UAE Delhi Air 8.292971 10.854559
## 2128 14829.308 Germany Delhi Sea 8.740866 17.176213
## 2129 74199.359 UK Kandla Land 10.104819 16.691587
## 2130 39767.446 UK Chennai Sea 7.262633 10.047125
## 2131 30824.981 China Kolkata Air 5.936050 10.283094
## 2132 32461.103 China Chennai Sea 16.793974 6.959486
## 2133 34664.697 Japan Chennai Sea 13.089745 7.568536
## 2134 70827.973 Japan Kandla Sea 10.855208 8.946342
## 2135 33819.812 Japan Kandla Land 8.657992 16.398813
## 2136 77756.612 Germany Chennai Land 17.441227 8.516128
## 2137 92242.104 UK Kandla Land 19.026012 11.138050
## 2138 73315.377 Japan Delhi Sea 8.421849 14.004657
## 2139 64737.538 Germany Kolkata Air 6.424253 7.400732
## 2140 25358.124 Japan Delhi Air 18.498866 6.414958
## 2141 16080.623 USA Chennai Air 6.930575 6.092403
## 2142 75613.505 Japan Kolkata Sea 9.922898 14.017311
## 2143 40971.192 Germany Delhi Air 14.216399 11.095923
## 2144 29220.844 UAE Delhi Air 13.667912 6.381699
## 2145 63415.249 UAE Kandla Land 5.955180 11.255662
## 2146 6403.934 USA Chennai Land 10.366149 8.519678
## 2147 80496.716 Japan Mumbai Sea 14.046737 5.242876
## 2148 51461.981 Japan Kandla Land 7.383890 9.711797
## 2149 25696.426 UK Mumbai Land 9.383525 11.512890
## 2150 59402.802 UK Kandla Sea 13.233395 12.686714
## 2151 80052.665 China Kandla Land 9.610713 5.680373
## 2152 16908.130 UK Kandla Sea 19.026061 15.691555
## 2153 41505.534 Germany Kandla Land 8.973073 11.042564
## 2154 91100.102 USA Kolkata Land 11.955763 14.428376
## 2155 98047.270 UK Delhi Sea 13.361720 16.474861
## 2156 45993.693 USA Kandla Sea 19.739123 6.235949
## 2157 92427.897 UAE Mumbai Land 8.618947 11.028316
## 2158 46156.854 Germany Delhi Sea 14.230137 11.330286
## 2159 48348.423 Japan Kolkata Sea 13.495401 13.931850
## 2160 40714.929 Germany Delhi Air 18.320040 17.726257
## 2161 23467.656 Japan Mumbai Sea 6.802400 13.328377
## 2162 72475.846 UK Delhi Air 11.783351 15.818457
## 2163 14358.354 UAE Delhi Land 8.915063 10.912513
## 2164 27024.147 Japan Chennai Land 12.315627 12.054499
## 2165 47994.710 USA Kolkata Air 17.636850 13.668600
## 2166 20556.711 China Kolkata Air 7.402238 14.673907
## 2167 52830.292 USA Mumbai Land 19.920938 15.166448
## 2168 31805.602 Japan Mumbai Land 12.062395 7.490098
## 2169 33990.981 UAE Kandla Air 12.510083 5.375359
## 2170 89008.744 USA Mumbai Air 9.220930 9.130813
## 2171 83520.372 China Kolkata Sea 8.618705 15.023285
## 2172 79136.255 China Delhi Sea 12.790286 8.955479
## 2173 48531.971 UAE Mumbai Sea 10.463911 17.137329
## 2174 34213.661 UK Mumbai Air 17.734061 8.259988
## 2175 92722.508 USA Kandla Air 8.598340 17.958619
## 2176 92504.546 Germany Kandla Land 8.076186 5.563652
## 2177 35415.209 China Kolkata Land 10.642720 7.330936
## 2178 94043.648 China Kandla Sea 8.162227 17.746561
## 2179 75951.014 USA Chennai Land 18.223258 13.937495
## 2180 56388.560 Japan Mumbai Land 8.113077 14.308177
## 2181 42852.031 China Kolkata Air 10.523626 12.636555
## 2182 11382.939 Japan Kolkata Land 6.290839 13.016252
## 2183 25887.535 USA Mumbai Air 6.286375 9.439989
## 2184 59396.314 Japan Mumbai Air 6.441771 15.784146
## 2185 97576.512 UAE Chennai Air 17.398321 5.980780
## 2186 88904.099 UK Delhi Air 16.016284 12.604906
## 2187 83714.899 China Chennai Land 14.019929 8.790932
## 2188 90886.294 UAE Kolkata Sea 16.127328 9.282639
## 2189 91843.767 UK Kandla Air 6.278554 5.837309
## 2190 42210.251 Germany Delhi Air 15.497231 7.844153
## 2191 86659.316 USA Mumbai Land 14.722476 10.574005
## 2192 8354.086 China Delhi Sea 14.264433 7.540473
## 2193 93442.319 Japan Mumbai Land 12.988763 11.882407
## 2194 61692.021 Germany Kolkata Air 18.822559 16.949790
## 2195 47961.247 UK Mumbai Land 17.438920 12.884021
## 2196 76587.747 Germany Kandla Land 7.140462 8.447163
## 2197 76458.353 USA Chennai Land 13.758473 12.074109
## 2198 84185.478 Germany Kolkata Air 14.050948 5.495640
## 2199 15124.421 UK Delhi Land 9.927699 6.977580
## 2200 82762.047 USA Kandla Air 19.914222 11.373628
## 2201 39315.107 Japan Kolkata Land 7.264435 15.289630
## 2202 99081.526 USA Kandla Air 19.440984 11.026154
## 2203 97153.116 China Delhi Land 14.476069 11.326929
## 2204 55343.940 UAE Kandla Air 10.084886 15.501797
## 2205 13950.759 UK Delhi Land 6.384858 14.648627
## 2206 49964.284 UAE Kolkata Land 19.563381 13.139867
## 2207 27645.913 UK Mumbai Land 11.962191 8.610000
## 2208 30792.644 Germany Kolkata Sea 6.597835 6.553347
## 2209 43709.829 USA Kandla Land 8.689008 16.620573
## 2210 49735.862 Germany Kandla Air 14.589269 16.566284
## 2211 2359.796 UK Chennai Sea 12.199106 13.063185
## 2212 69633.678 USA Chennai Air 7.119453 11.313322
## 2213 84747.737 Germany Mumbai Air 17.934753 13.434509
## 2214 41711.845 UK Mumbai Land 6.288610 14.894871
## 2215 72113.535 UK Kandla Sea 13.623217 8.636901
## 2216 26046.461 China Delhi Sea 12.001782 10.924207
## 2217 2708.642 Japan Mumbai Sea 12.220834 13.355694
## 2218 93215.982 USA Mumbai Land 9.004424 17.848769
## 2219 41693.290 USA Kolkata Land 9.364983 8.560541
## 2220 60984.777 USA Kandla Land 9.840931 13.509563
## 2221 6134.977 China Kandla Sea 8.184267 14.934190
## 2222 3264.506 USA Delhi Sea 11.240795 7.251707
## 2223 21913.334 UAE Kolkata Sea 10.595090 11.465931
## 2224 92468.103 USA Kolkata Sea 6.297163 13.964222
## 2225 67288.576 China Mumbai Land 8.371193 15.915362
## 2226 49379.934 Japan Kandla Air 15.596695 16.063789
## 2227 2863.068 Germany Delhi Land 19.372256 6.937762
## 2228 63021.389 Japan Kolkata Sea 12.065348 5.273655
## 2229 35062.236 UAE Mumbai Sea 11.391367 7.129496
## 2230 40837.028 USA Kandla Sea 13.720950 14.329515
## 2231 97665.499 Germany Mumbai Land 6.689571 5.095868
## 2232 23652.290 UK Chennai Air 14.333796 16.891983
## 2233 47228.266 China Mumbai Land 8.428964 11.439687
## 2234 73135.852 China Chennai Air 10.610415 15.246622
## 2235 13500.576 UAE Chennai Sea 5.266138 9.518148
## 2236 70799.772 UK Kolkata Land 19.675538 12.284435
## 2237 53346.793 Germany Delhi Sea 12.670861 9.251423
## 2238 69611.019 UAE Chennai Sea 12.298924 17.944887
## 2239 96694.072 China Kolkata Sea 17.052147 6.970994
## 2240 70684.231 UK Delhi Sea 15.436621 9.909564
## 2241 93916.611 UAE Chennai Land 9.875403 6.946210
## 2242 34845.913 UAE Delhi Air 11.597623 9.608835
## 2243 91478.765 UAE Delhi Land 17.816602 7.733427
## 2244 28927.252 China Kolkata Sea 18.798688 12.693879
## 2245 48486.548 UAE Chennai Air 19.286167 11.453158
## 2246 58784.110 Japan Mumbai Air 16.636584 13.154113
## 2247 42847.657 Japan Kolkata Sea 18.756969 17.410636
## 2248 80437.647 China Mumbai Land 13.848400 16.404920
## 2249 99039.517 UAE Mumbai Land 13.067879 17.803797
## 2250 12104.835 UK Mumbai Land 6.577843 15.668584
## 2251 83824.563 UAE Chennai Air 12.088199 6.696970
## 2252 2127.148 Germany Mumbai Air 6.018702 10.958083
## 2253 93948.952 UK Delhi Land 13.274821 10.135523
## 2254 45992.866 Japan Mumbai Land 6.769438 6.325480
## 2255 26293.463 China Mumbai Land 19.742229 14.977767
## 2256 63675.029 Japan Kandla Air 12.816368 7.999297
## 2257 60267.295 USA Chennai Land 11.072717 9.360556
## 2258 54094.816 Japan Delhi Land 10.386594 5.673352
## 2259 11041.203 USA Mumbai Sea 19.492671 5.201729
## 2260 32551.216 USA Chennai Sea 12.360895 16.904875
## 2261 85686.198 China Chennai Air 17.113785 12.903887
## 2262 48238.014 China Mumbai Sea 7.268240 9.748244
## 2263 64778.438 China Mumbai Air 17.513061 10.927335
## 2264 53351.391 UAE Kolkata Land 13.447059 15.080759
## 2265 32717.951 Germany Delhi Sea 9.325470 9.709771
## 2266 11230.959 UK Mumbai Sea 16.406664 5.821178
## 2267 54104.361 UAE Mumbai Air 14.535059 6.887207
## 2268 21935.002 Japan Mumbai Air 9.336178 14.566509
## 2269 78349.701 China Mumbai Air 10.954021 5.550928
## 2270 81815.092 UAE Mumbai Land 15.769812 8.209807
## 2271 67111.403 UAE Delhi Land 17.631057 16.998607
## 2272 52057.695 UK Chennai Land 15.841583 7.215205
## 2273 90107.558 China Kandla Land 5.262929 13.497149
## 2274 64335.282 UK Delhi Air 19.165875 17.246115
## 2275 7703.338 Germany Mumbai Land 17.643899 9.154152
## 2276 75363.652 Japan Mumbai Air 17.654745 14.205613
## 2277 70446.747 UAE Delhi Land 17.185439 11.058557
## 2278 83459.019 USA Mumbai Sea 19.168776 6.455223
## 2279 52713.854 USA Delhi Sea 10.715627 7.561564
## 2280 23635.845 Germany Kandla Air 10.387312 15.157662
## 2281 45787.068 Japan Chennai Sea 16.717770 16.594133
## 2282 22395.712 UAE Chennai Land 17.043049 17.819294
## 2283 48943.294 UK Delhi Air 9.919930 10.731435
## 2284 65361.553 UAE Delhi Air 7.823559 11.681998
## 2285 45787.601 USA Kolkata Air 6.606225 5.018113
## 2286 77191.430 Japan Chennai Land 8.281843 10.297545
## 2287 69173.167 UK Mumbai Sea 8.637825 17.370109
## 2288 11205.880 UAE Mumbai Air 19.183770 15.623875
## 2289 40464.304 China Chennai Land 5.977397 10.546803
## 2290 30175.175 UK Mumbai Land 12.883277 16.486336
## 2291 88809.790 Japan Kolkata Air 5.210334 14.691791
## 2292 64006.460 Germany Kandla Sea 15.194428 7.694843
## 2293 58647.130 UK Kandla Sea 17.409482 13.893360
## 2294 95822.896 China Kandla Land 14.511454 12.147910
## 2295 54836.081 China Chennai Land 7.200520 8.915027
## 2296 5585.389 Germany Chennai Air 7.841030 10.986016
## 2297 1448.680 UAE Chennai Sea 18.123372 15.519998
## 2298 19712.440 UAE Chennai Air 18.296907 6.676206
## 2299 65464.509 UK Kandla Land 14.946908 16.425114
## 2300 49241.881 USA Kolkata Land 8.413761 14.839316
## 2301 26009.319 China Chennai Land 12.148890 7.513142
## 2302 27267.373 UAE Mumbai Sea 18.279243 6.049225
## 2303 89624.599 USA Kandla Air 10.125531 7.839686
## 2304 47776.933 UAE Kolkata Land 14.250751 15.633009
## 2305 5558.873 UK Mumbai Air 16.024007 7.354656
## 2306 71660.196 UAE Kandla Sea 19.347349 16.376996
## 2307 69653.813 China Kandla Sea 8.761004 12.633334
## 2308 8075.034 Germany Kandla Land 19.675878 10.490143
## 2309 23949.271 Germany Mumbai Land 8.118038 5.927808
## 2310 44290.339 Japan Kolkata Air 6.449746 15.182351
## 2311 36715.341 UK Chennai Air 18.420048 5.467215
## 2312 17491.455 UK Delhi Air 8.686884 8.425782
## 2313 70532.367 Germany Kolkata Land 15.611261 13.412312
## 2314 42645.560 UAE Chennai Air 12.978554 13.866241
## 2315 95973.598 Japan Kandla Land 5.834184 6.715754
## 2316 69960.576 UAE Kolkata Air 7.280782 13.306536
## 2317 41411.225 Germany Kolkata Air 17.804877 8.733327
## 2318 92645.404 Germany Mumbai Air 6.790493 5.764260
## 2319 52358.216 China Mumbai Land 8.496766 10.389876
## 2320 22566.600 UAE Kandla Sea 12.563083 15.925780
## 2321 58688.571 China Chennai Air 19.082475 14.797013
## 2322 7532.450 Germany Kandla Air 5.384847 5.309774
## 2323 52087.747 USA Chennai Air 8.037468 7.601724
## 2324 48026.705 USA Kolkata Air 9.858686 17.077135
## 2325 90744.137 UAE Mumbai Air 17.213498 7.052937
## 2326 14724.979 China Delhi Land 9.858556 11.761877
## 2327 15900.579 Germany Delhi Sea 7.038846 10.078223
## 2328 83842.631 USA Kandla Land 13.892083 17.260287
## 2329 24413.577 UK Mumbai Sea 16.329387 16.383332
## 2330 56638.482 UK Kandla Land 16.554826 8.662497
## 2331 19429.324 Japan Mumbai Sea 18.652188 6.576643
## 2332 44397.059 Germany Delhi Air 7.596783 12.266505
## 2333 58674.536 UAE Mumbai Sea 14.514241 15.869706
## 2334 82324.133 UAE Kandla Air 5.242513 16.870784
## 2335 32225.980 UK Chennai Air 18.860155 5.016221
## 2336 23871.835 UK Kolkata Sea 5.919672 8.117370
## 2337 50491.538 USA Delhi Air 10.071050 12.727658
## 2338 42037.029 UK Delhi Land 12.598851 11.429647
## 2339 85065.554 China Kolkata Air 8.424605 12.179518
## 2340 2178.339 UAE Delhi Sea 9.977512 5.116495
## 2341 65930.750 Germany Kandla Land 13.554256 6.414153
## 2342 53187.752 UK Delhi Sea 13.782256 14.792240
## 2343 3013.297 Japan Chennai Land 8.255248 9.684745
## 2344 72038.846 UAE Mumbai Air 5.809393 17.071947
## 2345 68161.740 China Delhi Land 9.094707 12.647255
## 2346 76842.393 UAE Kandla Land 14.359439 11.557217
## 2347 8686.004 USA Mumbai Land 7.090008 16.242037
## 2348 71444.991 Japan Delhi Air 14.300952 6.733386
## 2349 32593.196 China Kolkata Land 7.771196 10.645177
## 2350 12190.303 USA Mumbai Air 17.630799 11.286353
## 2351 19690.931 Germany Delhi Land 5.579601 7.082976
## 2352 41491.104 UAE Mumbai Air 18.946872 11.585149
## 2353 99756.629 China Delhi Air 11.780534 6.465407
## 2354 84575.622 China Kolkata Air 14.338652 13.840531
## 2355 96772.456 Germany Delhi Air 13.117619 6.374785
## 2356 83933.592 USA Kolkata Land 17.157724 7.004229
## 2357 15805.201 UAE Kolkata Sea 5.355732 5.954014
## 2358 74006.726 Germany Kandla Land 12.948392 10.140822
## 2359 86103.826 Japan Kolkata Land 15.697856 17.092907
## 2360 74163.697 Germany Kandla Sea 14.317955 10.512502
## 2361 33288.844 UK Delhi Air 17.726278 15.285383
## 2362 3709.095 China Kandla Land 10.678078 12.344126
## 2363 77411.711 UK Kandla Sea 18.616003 17.541855
## 2364 37437.003 UAE Kandla Air 8.939507 10.771115
## 2365 50497.357 UK Kandla Land 15.188319 10.154742
## 2366 19088.530 Germany Delhi Land 5.820003 15.249579
## 2367 61200.347 Japan Delhi Sea 17.635891 10.185363
## 2368 83175.827 UAE Kandla Land 5.668233 16.639496
## 2369 23193.182 USA Kolkata Land 15.464013 9.159916
## 2370 78970.109 Germany Kolkata Sea 19.022490 16.179447
## 2371 52237.987 USA Mumbai Air 17.651247 13.225051
## 2372 46014.623 UK Chennai Sea 17.139485 11.273132
## 2373 14987.703 USA Delhi Air 7.716173 13.248670
## 2374 9286.520 Germany Kolkata Sea 11.279271 15.842810
## 2375 75893.571 China Kandla Air 15.772049 6.309709
## 2376 41542.131 China Delhi Air 18.696007 15.912788
## 2377 12950.063 USA Mumbai Air 10.145250 5.331623
## 2378 72556.573 UAE Delhi Land 8.582640 7.175637
## 2379 44452.554 UAE Kandla Land 12.316610 13.143582
## 2380 72766.542 UK Delhi Air 16.095739 16.598577
## 2381 26105.723 USA Kandla Land 19.165227 12.816091
## 2382 16047.129 UAE Kandla Sea 9.838302 10.094452
## 2383 10553.688 Germany Chennai Sea 9.592271 11.383972
## 2384 1646.721 USA Chennai Air 18.908483 10.202124
## 2385 72633.406 Germany Delhi Land 19.541358 6.699497
## 2386 74803.787 UK Delhi Sea 15.371029 16.281263
## 2387 97456.061 China Chennai Land 16.341921 10.067352
## 2388 85407.920 USA Chennai Sea 11.147066 5.576894
## 2389 56153.753 UK Kolkata Sea 16.337956 5.331211
## 2390 35703.588 China Delhi Air 8.926192 6.298087
## 2391 90974.733 UAE Kandla Air 14.435155 8.164074
## 2392 90155.320 Germany Kolkata Air 18.675814 10.678434
## 2393 86930.844 China Chennai Sea 13.909049 14.379792
## 2394 54781.968 USA Mumbai Land 11.374295 8.926101
## 2395 90858.963 Germany Kandla Sea 8.971614 13.588320
## 2396 39693.895 USA Chennai Sea 7.290108 6.439963
## 2397 47949.481 USA Mumbai Land 11.973768 17.966254
## 2398 67503.607 Japan Delhi Sea 14.636066 7.618705
## 2399 36963.677 Germany Kolkata Sea 16.446765 9.482431
## 2400 53829.631 USA Kandla Sea 12.813190 12.044333
## 2401 74371.488 China Chennai Air 8.281928 15.086982
## 2402 48205.832 China Mumbai Sea 16.967296 8.431018
## 2403 9565.739 China Chennai Land 6.414124 7.072831
## 2404 86273.754 Germany Chennai Land 18.907103 15.413830
## 2405 40182.047 Germany Delhi Land 7.369035 9.270896
## 2406 83292.919 Germany Mumbai Air 10.666575 10.947832
## 2407 20197.815 USA Chennai Sea 14.827441 8.078531
## 2408 26102.963 UAE Kolkata Sea 11.059235 7.606741
## 2409 91411.980 UAE Kolkata Sea 13.537866 5.835990
## 2410 51864.118 Germany Kandla Sea 12.013177 17.758365
## 2411 91661.580 USA Chennai Sea 17.477346 6.085217
## 2412 94494.793 China Chennai Land 10.206572 8.236826
## 2413 8949.737 Germany Mumbai Land 17.304245 11.917902
## 2414 21994.947 Germany Delhi Land 17.227870 8.600016
## 2415 96767.637 China Delhi Air 11.843042 13.199000
## 2416 83490.017 Germany Kandla Air 14.467467 5.036681
## 2417 11127.096 Germany Kandla Sea 16.452760 12.587146
## 2418 75640.687 Germany Delhi Land 19.077025 16.444453
## 2419 55916.881 UK Chennai Land 7.181540 13.666285
## 2420 68276.299 USA Chennai Sea 12.871082 17.107440
## 2421 77914.476 USA Chennai Air 10.855270 13.476147
## 2422 79620.176 USA Kolkata Land 6.611278 8.902416
## 2423 63414.895 China Kandla Land 11.915742 17.736791
## 2424 79827.785 UK Kandla Land 9.592917 11.841844
## 2425 91940.677 Germany Chennai Air 6.441337 9.008214
## 2426 99028.623 China Mumbai Sea 17.887226 10.400409
## 2427 96772.864 China Mumbai Air 11.203306 15.247109
## 2428 73066.916 USA Mumbai Land 18.683610 13.109409
## 2429 5103.348 UK Kolkata Land 13.773897 15.142649
## 2430 50780.724 Germany Kandla Air 17.218014 14.934594
## 2431 36926.907 UAE Chennai Air 16.909007 16.199200
## 2432 40158.377 UK Chennai Sea 8.632746 9.571714
## 2433 14596.173 USA Kolkata Air 14.719257 14.546837
## 2434 33038.297 UAE Delhi Air 14.761333 16.475722
## 2435 98006.840 China Kandla Land 12.748650 15.750809
## 2436 16661.874 UAE Kandla Land 11.670958 6.378303
## 2437 51876.627 Japan Delhi Land 10.617195 11.695961
## 2438 5468.001 UK Mumbai Land 11.450280 13.216777
## 2439 75160.521 China Mumbai Sea 15.013352 16.268638
## 2440 35298.660 China Kolkata Sea 8.723535 12.135163
## 2441 34870.448 China Kolkata Land 6.692099 11.625807
## 2442 85064.021 Germany Mumbai Sea 10.908448 8.550896
## 2443 42518.391 USA Delhi Sea 10.146244 16.070454
## 2444 19530.268 China Kolkata Air 11.225322 14.847063
## 2445 16948.887 Germany Chennai Air 8.954070 7.120467
## 2446 41163.033 Germany Kolkata Air 11.966622 7.628423
## 2447 27444.788 UK Mumbai Land 14.989465 16.247189
## 2448 6306.637 China Kolkata Land 10.767810 17.336053
## 2449 86459.804 Germany Chennai Sea 16.871978 6.476451
## 2450 47373.786 UK Kandla Land 5.049040 14.051275
## 2451 48800.829 UK Chennai Sea 18.822407 10.039514
## 2452 86829.295 UK Delhi Sea 6.392954 15.973538
## 2453 75716.541 China Delhi Land 8.155050 11.497364
## 2454 24348.001 Japan Kandla Air 15.128378 12.918762
## 2455 44505.200 USA Kandla Land 18.922026 13.803345
## 2456 25269.272 China Chennai Air 15.841866 9.917259
## 2457 59305.882 USA Kandla Land 7.928629 10.832937
## 2458 21196.339 UK Mumbai Air 14.128185 14.966344
## 2459 58124.122 UAE Chennai Land 56.152351 16.343241
## 2460 68880.741 Japan Kolkata Air 17.860881 9.071980
## 2461 70155.919 USA Chennai Land 19.435568 7.977972
## 2462 14160.712 Germany Kandla Land 9.299370 15.799902
## 2463 35392.705 UK Mumbai Sea 7.605467 15.480983
## 2464 69565.874 China Kolkata Land 12.220227 6.430161
## 2465 71288.945 Germany Delhi Land 8.616791 10.745896
## 2466 39323.111 Germany Kandla Land 19.518262 7.389818
## 2467 13639.322 Japan Delhi Land 12.207659 5.485023
## 2468 70814.352 USA Kolkata Land 18.092883 9.282390
## 2469 59220.586 China Mumbai Air 12.791692 5.237163
## 2470 58151.610 UK Mumbai Sea 18.952092 8.960438
## 2471 18124.078 Japan Chennai Land 12.494317 10.009815
## 2472 95531.921 Germany Kolkata Sea 5.054521 15.807682
## 2473 1017.678 Japan Mumbai Air 8.351558 14.273646
## 2474 15435.445 Japan Kolkata Land 12.016420 12.266433
## 2475 18625.994 China Kolkata Land 16.678021 6.455176
## 2476 87775.945 UK Chennai Sea 10.016160 8.820738
## 2477 23032.875 UAE Chennai Air 8.836834 13.122580
## 2478 91075.171 UAE Delhi Air 10.793615 13.447073
## 2479 13590.013 USA Chennai Land 7.705155 11.031163
## 2480 45823.846 UAE Mumbai Air 5.002531 13.495223
## 2481 58331.044 UK Delhi Air 16.145823 12.478766
## 2482 77206.520 UK Kolkata Air 7.163424 12.917990
## 2483 65876.277 China Chennai Air 11.345094 12.775812
## 2484 75031.622 UK Kolkata Air 9.322439 12.890249
## 2485 29922.801 Japan Kandla Sea 19.167419 10.421716
## 2486 14758.205 UAE Kandla Land 14.037532 15.268440
## 2487 72729.169 USA Kandla Air 11.276508 13.493145
## 2488 51948.979 USA Chennai Air 11.974619 16.183584
## 2489 90538.118 China Kandla Sea 15.291942 8.503945
## 2490 73335.792 UK Kandla Land 15.118935 9.427551
## 2491 67706.641 Japan Chennai Sea 13.806052 17.539882
## 2492 71645.197 Japan Kandla Land 6.829951 6.182533
## 2493 92176.739 UK Mumbai Air 19.319137 17.751078
## 2494 13092.138 Germany Kandla Air 16.333174 10.114868
## 2495 13087.605 USA Chennai Air 14.188466 15.588356
## 2496 21391.763 Japan Mumbai Air 7.688699 15.264728
## 2497 73921.298 Japan Kolkata Sea 16.902714 12.608899
## 2498 94912.792 USA Kolkata Land 10.485186 9.676441
## 2499 8948.989 China Chennai Sea 10.381797 13.000610
## 2500 65769.369 Germany Kandla Land 13.704801 8.886004
## Exchange_Rate Shipment_Days Delivery_Status Trade_Agreement Date
## 1 81.32515 1.0000 On Time FTA 14-03-2020
## 2 70.68105 1.0000 On Time Non-FTA 23-02-2021
## 3 83.02403 1.0000 On Time FTA 13-07-2019
## 4 70.58662 1.0000 On Time Non-FTA 21-10-2024
## 5 72.37191 1.0000 Delayed Non-FTA 03-12-2021
## 6 71.08928 1.0000 Delayed FTA 07-05-2021
## 7 82.83220 1.0000 On Time Non-FTA 16-12-2020
## 8 75.58205 1.0000 Delayed FTA 29-11-2024
## 9 81.88210 1.0000 Delayed FTA 03-09-2019
## 10 83.58600 1.0000 Delayed Non-FTA 19-12-2021
## 11 81.72460 1.0000 Delayed FTA 11-10-2022
## 12 75.48632 1.0000 Delayed Non-FTA 06-01-2018
## 13 72.81812 1.0000 On Time FTA 22-08-2023
## 14 77.69373 1.0000 On Time FTA 21-04-2019
## 15 81.94828 1.0000 Delayed Non-FTA 30-11-2018
## 16 72.85250 1.0000 On Time Non-FTA 04-02-2023
## 17 71.39977 1.0000 Delayed FTA 17-02-2023
## 18 79.30628 1.0000 On Time FTA 05-10-2019
## 19 70.12786 1.0000 On Time Non-FTA 28-11-2019
## 20 70.74068 1.0000 Delayed FTA 10-07-2020
## 21 71.19598 1.0000 On Time Non-FTA 10-09-2018
## 22 76.81986 1.0000 Delayed FTA 30-06-2018
## 23 79.04102 1.0000 Delayed Non-FTA 24-12-2021
## 24 76.56776 1.0000 Delayed Non-FTA 14-05-2020
## 25 84.50640 1.0000 Delayed Non-FTA 18-06-2021
## 26 73.96513 1.0000 Delayed FTA 04-10-2019
## 27 75.91424 1.0000 Delayed Non-FTA 23-01-2021
## 28 84.51308 1.0000 On Time FTA 04-07-2024
## 29 84.12713 1.0000 Delayed Non-FTA 04-07-2018
## 30 82.45508 1.0000 Delayed Non-FTA 22-03-2021
## 31 79.39084 1.0000 On Time Non-FTA 29-12-2020
## 32 73.68143 1.0000 On Time FTA 01-12-2021
## 33 81.14333 1.0000 On Time FTA 11-04-2022
## 34 76.02757 1.0000 On Time FTA 07-01-2018
## 35 82.28625 1.0000 On Time Non-FTA 04-07-2018
## 36 70.13933 1.0000 Delayed FTA 06-10-2022
## 37 78.92824 1.0000 Delayed FTA 02-12-2019
## 38 75.18387 1.0000 Delayed FTA 30-03-2021
## 39 79.15820 1.0000 On Time Non-FTA 07-03-2023
## 40 72.20139 1.0000 Delayed FTA 23-02-2024
## 41 78.88341 1.0000 Delayed Non-FTA 10-05-2024
## 42 83.58237 1.0000 Delayed FTA 07-11-2024
## 43 73.25491 1.0000 Delayed Non-FTA 20-02-2020
## 44 76.14796 1.0000 On Time FTA 28-06-2018
## 45 80.32026 1.0000 On Time FTA 09-12-2024
## 46 70.77701 1.0000 On Time Non-FTA 22-08-2021
## 47 75.47380 1.0000 On Time Non-FTA 13-04-2024
## 48 72.01048 1.0000 Delayed Non-FTA 30-11-2019
## 49 84.91855 1.0000 Delayed Non-FTA 29-09-2024
## 50 84.23122 1.0000 Delayed Non-FTA 03-06-2021
## 51 75.60942 1.0000 On Time Non-FTA 17-01-2018
## 52 82.08742 1.0000 On Time FTA 26-03-2022
## 53 80.85725 1.0000 Delayed FTA 16-11-2020
## 54 83.02682 1.0000 On Time FTA 01-02-2023
## 55 82.13091 1.0000 Delayed Non-FTA 05-10-2019
## 56 70.32052 1.0000 Delayed FTA 28-07-2018
## 57 73.78652 1.0000 Delayed Non-FTA 21-09-2021
## 58 72.65940 1.0000 On Time Non-FTA 11-02-2021
## 59 73.19293 1.0000 On Time FTA 10-01-2024
## 60 75.55298 1.0000 On Time Non-FTA 23-08-2018
## 61 81.13960 1.0000 Delayed FTA 13-10-2022
## 62 78.41691 1.0000 On Time FTA 26-01-2022
## 63 77.96169 1.0000 Delayed Non-FTA 06-09-2020
## 64 81.22579 1.0000 Delayed Non-FTA 17-08-2021
## 65 80.93548 1.0000 Delayed FTA 03-07-2020
## 66 71.31974 1.0000 Delayed FTA 29-11-2022
## 67 70.57775 1.0000 Delayed FTA 02-03-2020
## 68 81.70028 1.0000 On Time Non-FTA 08-08-2022
## 69 84.81190 1.0000 On Time Non-FTA 06-01-2024
## 70 73.14655 1.0000 Delayed FTA 21-10-2021
## 71 80.08861 1.0000 Delayed Non-FTA 11-04-2023
## 72 73.23034 1.0000 Delayed Non-FTA 03-04-2022
## 73 74.56019 1.0000 On Time Non-FTA 18-09-2022
## 74 72.16734 1.0000 On Time Non-FTA 10-12-2021
## 75 82.97632 1.0000 On Time FTA 24-09-2019
## 76 79.29480 1.0000 On Time Non-FTA 21-06-2019
## 77 73.38532 1.0000 Delayed FTA 05-03-2020
## 78 82.49825 1.0000 On Time FTA 23-05-2018
## 79 84.62445 1.0000 Delayed Non-FTA 15-01-2022
## 80 77.88014 1.0000 Delayed Non-FTA 01-07-2019
## 81 70.79233 1.0000 On Time FTA 24-12-2020
## 82 74.45270 1.0000 Delayed Non-FTA 01-08-2021
## 83 72.10645 1.0000 Delayed Non-FTA 05-12-2022
## 84 80.14985 1.0000 Delayed Non-FTA 20-02-2021
## 85 72.31698 1.0000 Delayed FTA 27-05-2024
## 86 75.22638 1.0000 On Time FTA 18-03-2020
## 87 84.86654 1.0000 Delayed Non-FTA 06-10-2024
## 88 74.78748 1.0000 On Time FTA 23-11-2023
## 89 71.39071 1.0000 Delayed FTA 02-06-2023
## 90 79.82821 1.0000 On Time FTA 25-11-2018
## 91 75.58179 2.0000 On Time Non-FTA 28-08-2023
## 92 83.67727 2.0000 On Time Non-FTA 23-02-2018
## 93 74.63764 2.0000 On Time Non-FTA 16-11-2020
## 94 77.89366 2.0000 On Time FTA 17-08-2021
## 95 77.78807 2.0000 On Time Non-FTA 17-11-2022
## 96 79.72678 2.0000 On Time FTA 31-03-2023
## 97 71.61771 2.0000 Delayed FTA 23-01-2020
## 98 82.31814 2.0000 On Time Non-FTA 21-09-2024
## 99 73.56040 2.0000 Delayed FTA 23-10-2023
## 100 80.24255 2.0000 Delayed Non-FTA 15-10-2022
## 101 70.05702 2.0000 Delayed Non-FTA 26-03-2022
## 102 81.41677 2.0000 On Time FTA 12-12-2022
## 103 81.99843 2.0000 On Time Non-FTA 26-05-2018
## 104 81.86577 2.0000 Delayed FTA 07-06-2020
## 105 81.52961 2.0000 On Time FTA 01-02-2018
## 106 79.06875 2.0000 Delayed Non-FTA 25-01-2022
## 107 82.41872 2.0000 On Time Non-FTA 30-07-2018
## 108 76.16048 2.0000 Delayed FTA 23-12-2022
## 109 81.49437 2.0000 On Time FTA 18-05-2023
## 110 77.09915 2.0000 Delayed Non-FTA 26-05-2023
## 111 73.10037 2.0000 On Time Non-FTA 01-04-2020
## 112 70.79832 2.0000 On Time FTA 14-02-2022
## 113 79.25624 2.0000 On Time FTA 30-01-2023
## 114 82.27730 2.0000 Delayed FTA 22-03-2018
## 115 71.22606 2.0000 Delayed Non-FTA 01-04-2019
## 116 77.34390 2.0000 On Time FTA 02-02-2023
## 117 81.85571 2.0000 On Time Non-FTA 10-08-2022
## 118 71.70225 2.0000 On Time Non-FTA 11-12-2020
## 119 72.38635 2.0000 On Time Non-FTA 28-08-2023
## 120 81.61655 2.0000 Delayed Non-FTA 08-09-2019
## 121 78.24548 2.0000 Delayed FTA 15-06-2022
## 122 83.50615 2.0000 Delayed Non-FTA 16-11-2018
## 123 82.71335 2.0000 On Time FTA 08-05-2018
## 124 76.51725 2.0000 Delayed FTA 22-10-2022
## 125 80.96483 2.0000 On Time Non-FTA 29-01-2018
## 126 73.39363 2.0000 Delayed FTA 16-05-2018
## 127 79.54828 2.0000 Delayed FTA 17-12-2024
## 128 71.13467 2.0000 Delayed FTA 08-09-2020
## 129 79.51998 2.0000 On Time FTA 14-06-2018
## 130 70.14162 2.0000 Delayed Non-FTA 20-07-2023
## 131 79.27590 2.0000 Delayed FTA 09-12-2023
## 132 83.38092 2.0000 Delayed FTA 28-06-2021
## 133 70.64931 2.0000 On Time Non-FTA 17-04-2019
## 134 72.47679 2.0000 Delayed FTA 04-06-2021
## 135 77.35545 2.0000 Delayed Non-FTA 04-01-2018
## 136 76.58877 2.0000 Delayed Non-FTA 06-04-2024
## 137 83.36483 2.0000 Delayed FTA 06-04-2021
## 138 72.37436 2.0000 Delayed FTA 12-11-2019
## 139 78.04706 2.0000 Delayed Non-FTA 15-11-2019
## 140 72.22255 2.0000 On Time FTA 14-10-2023
## 141 73.54805 2.0000 Delayed Non-FTA 25-06-2019
## 142 70.68302 2.0000 Delayed Non-FTA 14-11-2023
## 143 82.08577 2.0000 Delayed Non-FTA 01-06-2020
## 144 76.16723 2.0000 On Time Non-FTA 19-09-2019
## 145 83.37360 2.0000 On Time FTA 02-10-2019
## 146 81.70077 2.0000 On Time Non-FTA 12-02-2024
## 147 78.34160 2.0000 Delayed FTA 02-06-2020
## 148 70.77977 2.0000 Delayed Non-FTA 26-10-2021
## 149 70.68675 2.0000 Delayed Non-FTA 06-12-2023
## 150 73.16290 2.0000 On Time FTA 23-11-2021
## 151 76.16168 2.0000 Delayed FTA 24-06-2023
## 152 76.80941 2.0000 Delayed FTA 11-08-2018
## 153 72.47115 2.0000 On Time FTA 16-11-2021
## 154 72.82070 2.0000 Delayed Non-FTA 09-11-2021
## 155 80.60586 2.0000 On Time Non-FTA 16-04-2023
## 156 73.23950 2.0000 On Time FTA 06-04-2021
## 157 74.95125 2.0000 On Time FTA 12-04-2018
## 158 77.57350 2.0000 Delayed Non-FTA 05-02-2023
## 159 71.23766 2.0000 Delayed FTA 05-09-2018
## 160 80.23452 2.0000 On Time FTA 17-02-2024
## 161 78.32531 2.0000 Delayed Non-FTA 08-08-2018
## 162 81.09731 2.0000 On Time FTA 04-05-2024
## 163 79.74600 2.0000 Delayed FTA 10-10-2019
## 164 74.17125 2.0000 On Time Non-FTA 03-06-2023
## 165 76.59206 2.0000 Delayed FTA 13-02-2018
## 166 81.95896 2.0000 On Time FTA 07-02-2021
## 167 76.89972 2.0000 Delayed Non-FTA 27-07-2023
## 168 79.26398 2.0000 Delayed Non-FTA 26-04-2023
## 169 77.55237 2.0000 On Time Non-FTA 26-02-2020
## 170 78.03263 2.0000 Delayed Non-FTA 22-11-2020
## 171 82.70260 2.0000 On Time Non-FTA 03-12-2020
## 172 74.25984 2.0000 On Time FTA 21-11-2020
## 173 70.52274 2.0000 Delayed Non-FTA 22-12-2018
## 174 81.55942 2.0000 Delayed FTA 10-08-2020
## 175 81.61318 2.0000 On Time FTA 30-01-2020
## 176 75.20322 2.0000 On Time Non-FTA 30-06-2020
## 177 74.74089 2.0000 On Time FTA 04-09-2020
## 178 75.11299 2.0000 On Time FTA 02-07-2021
## 179 75.27279 2.0000 Delayed Non-FTA 04-02-2022
## 180 70.60749 2.0000 On Time FTA 01-07-2018
## 181 78.04437 2.0000 On Time Non-FTA 27-03-2020
## 182 75.43178 3.0000 Delayed Non-FTA 23-06-2024
## 183 70.46183 3.0000 On Time FTA 07-07-2019
## 184 76.83731 3.0000 On Time FTA 28-02-2019
## 185 78.81282 3.0000 On Time Non-FTA 23-01-2020
## 186 71.04652 3.0000 Delayed FTA 25-01-2020
## 187 71.63719 3.0000 Delayed Non-FTA 06-08-2022
## 188 70.95353 3.0000 Delayed Non-FTA 19-12-2021
## 189 72.14083 3.0000 On Time FTA 22-08-2019
## 190 83.06397 3.0000 Delayed Non-FTA 02-07-2020
## 191 72.52544 3.0000 On Time Non-FTA 22-04-2022
## 192 73.13156 3.0000 On Time FTA 07-03-2018
## 193 72.96994 3.0000 Delayed Non-FTA 06-07-2018
## 194 72.37742 3.0000 Delayed Non-FTA 14-10-2019
## 195 76.43851 3.0000 On Time Non-FTA 08-05-2021
## 196 72.51387 3.0000 On Time Non-FTA 20-12-2020
## 197 75.89992 3.0000 On Time FTA 24-04-2021
## 198 82.14716 3.0000 On Time Non-FTA 17-06-2022
## 199 74.06608 3.0000 Delayed FTA 14-02-2020
## 200 81.50889 3.0000 On Time FTA 09-04-2023
## 201 84.38179 3.0000 On Time Non-FTA 26-07-2019
## 202 76.33509 3.0000 Delayed FTA 15-07-2022
## 203 72.15176 3.0000 On Time Non-FTA 26-03-2020
## 204 80.82348 3.0000 Delayed FTA 14-07-2023
## 205 72.94055 3.0000 On Time Non-FTA 19-08-2018
## 206 80.61125 3.0000 Delayed Non-FTA 21-01-2024
## 207 80.90321 3.0000 Delayed Non-FTA 14-12-2021
## 208 74.92454 3.0000 Delayed FTA 22-11-2022
## 209 78.42621 3.0000 Delayed Non-FTA 22-06-2024
## 210 83.61303 3.0000 Delayed FTA 24-11-2022
## 211 81.16730 3.0000 Delayed FTA 12-02-2018
## 212 83.00632 3.0000 On Time FTA 20-05-2020
## 213 74.68865 3.0000 Delayed FTA 23-01-2019
## 214 71.12971 3.0000 On Time Non-FTA 09-08-2021
## 215 77.04400 3.0000 On Time FTA 11-03-2018
## 216 75.44987 3.0000 On Time FTA 05-09-2022
## 217 79.89008 3.0000 Delayed Non-FTA 25-06-2019
## 218 71.54899 3.0000 On Time Non-FTA 23-06-2022
## 219 70.70991 3.0000 On Time Non-FTA 07-05-2018
## 220 71.67746 3.0000 On Time Non-FTA 11-03-2019
## 221 70.75443 3.0000 On Time FTA 20-03-2022
## 222 84.14322 3.0000 On Time Non-FTA 04-01-2022
## 223 72.93081 3.0000 Delayed Non-FTA 14-09-2022
## 224 78.06002 3.0000 On Time Non-FTA 29-08-2021
## 225 84.73087 3.0000 On Time Non-FTA 24-07-2023
## 226 72.95485 3.0000 Delayed FTA 02-10-2023
## 227 81.25762 3.0000 On Time Non-FTA 23-06-2019
## 228 74.21384 3.0000 On Time Non-FTA 22-05-2023
## 229 77.09086 3.0000 On Time Non-FTA 14-04-2019
## 230 80.13517 3.0000 On Time Non-FTA 04-04-2018
## 231 84.85721 3.0000 On Time FTA 25-04-2020
## 232 81.45479 3.0000 Delayed FTA 04-04-2020
## 233 77.76162 3.0000 Delayed FTA 30-08-2021
## 234 73.97229 3.0000 Delayed Non-FTA 05-05-2024
## 235 80.84722 3.0000 Delayed FTA 09-05-2020
## 236 71.13979 3.0000 On Time FTA 31-10-2020
## 237 72.02978 3.0000 On Time Non-FTA 17-05-2019
## 238 78.56177 3.0000 On Time Non-FTA 21-11-2020
## 239 81.84513 3.0000 Delayed Non-FTA 20-01-2022
## 240 70.09602 3.0000 On Time FTA 17-11-2019
## 241 80.14552 3.0000 On Time Non-FTA 11-03-2019
## 242 84.05911 3.0000 Delayed Non-FTA 11-02-2020
## 243 75.61597 3.0000 On Time Non-FTA 17-05-2021
## 244 70.29471 3.0000 Delayed Non-FTA 06-08-2020
## 245 79.29659 3.0000 Delayed Non-FTA 22-10-2019
## 246 78.60159 3.0000 On Time Non-FTA 13-06-2023
## 247 82.48465 3.0000 On Time FTA 11-02-2024
## 248 78.77290 3.0000 On Time Non-FTA 11-05-2020
## 249 70.96018 3.0000 Delayed Non-FTA 04-11-2024
## 250 70.64998 3.0000 Delayed FTA 19-10-2018
## 251 71.19974 3.0000 Delayed Non-FTA 16-04-2021
## 252 78.33844 3.0000 Delayed Non-FTA 24-02-2019
## 253 82.04015 3.0000 Delayed FTA 30-06-2024
## 254 81.30729 3.0000 On Time Non-FTA 22-05-2020
## 255 71.43563 3.0000 On Time FTA 30-12-2024
## 256 83.91134 3.0000 Delayed FTA 22-06-2021
## 257 79.46925 3.0000 Delayed Non-FTA 14-11-2022
## 258 79.22675 3.0000 On Time Non-FTA 28-08-2024
## 259 71.15068 3.0000 On Time FTA 18-05-2020
## 260 75.13337 3.0000 On Time Non-FTA 11-09-2021
## 261 70.06428 3.0000 Delayed FTA 08-04-2023
## 262 77.60639 3.0000 On Time Non-FTA 29-10-2024
## 263 78.37142 3.0000 Delayed Non-FTA 08-04-2022
## 264 79.17901 3.0000 Delayed Non-FTA 08-12-2023
## 265 71.07811 3.0000 On Time Non-FTA 05-10-2024
## 266 74.84657 3.0000 On Time Non-FTA 26-05-2023
## 267 81.21734 3.0000 On Time Non-FTA 11-04-2021
## 268 83.42891 3.0000 Delayed Non-FTA 12-06-2018
## 269 76.75225 3.0000 On Time FTA 07-03-2018
## 270 76.16214 4.0000 Delayed FTA 24-02-2022
## 271 81.14798 4.0000 Delayed Non-FTA 03-07-2019
## 272 74.65962 4.0000 On Time FTA 27-06-2021
## 273 75.83731 4.0000 On Time FTA 12-06-2024
## 274 70.69809 4.0000 On Time FTA 23-11-2024
## 275 74.91549 4.0000 On Time Non-FTA 08-11-2018
## 276 82.02371 4.0000 On Time FTA 13-06-2019
## 277 79.47696 4.0000 Delayed Non-FTA 22-08-2024
## 278 84.14099 4.0000 On Time FTA 11-04-2022
## 279 83.56395 4.0000 On Time FTA 25-04-2019
## 280 81.86068 4.0000 Delayed Non-FTA 31-05-2024
## 281 77.05265 4.0000 Delayed FTA 05-09-2020
## 282 83.95197 4.0000 On Time FTA 10-01-2022
## 283 72.89800 4.0000 On Time Non-FTA 02-07-2021
## 284 76.03369 4.0000 Delayed Non-FTA 25-11-2020
## 285 84.64069 4.0000 On Time FTA 11-02-2022
## 286 83.62278 4.0000 On Time Non-FTA 30-03-2022
## 287 80.88144 4.0000 On Time FTA 03-04-2023
## 288 74.95875 4.0000 On Time FTA 31-05-2020
## 289 76.07666 4.0000 On Time Non-FTA 13-07-2018
## 290 72.31654 4.0000 Delayed FTA 06-11-2018
## 291 76.58375 4.0000 Delayed Non-FTA 05-03-2022
## 292 73.29074 4.0000 Delayed Non-FTA 22-05-2023
## 293 72.10951 4.0000 Delayed Non-FTA 28-07-2020
## 294 73.33052 4.0000 On Time Non-FTA 12-02-2021
## 295 81.08347 4.0000 On Time Non-FTA 17-11-2022
## 296 75.88925 4.0000 Delayed FTA 06-01-2022
## 297 71.50828 4.0000 Delayed Non-FTA 16-07-2020
## 298 82.75379 4.0000 On Time FTA 04-01-2022
## 299 78.50534 4.0000 Delayed FTA 11-08-2020
## 300 84.08990 4.0000 Delayed Non-FTA 07-03-2024
## 301 83.77357 4.0000 Delayed Non-FTA 08-07-2021
## 302 73.12001 4.0000 On Time Non-FTA 01-12-2022
## 303 72.72550 4.0000 Delayed FTA 04-09-2020
## 304 71.28048 4.0000 Delayed Non-FTA 26-02-2024
## 305 72.82719 4.0000 On Time Non-FTA 14-07-2021
## 306 82.51818 4.0000 Delayed Non-FTA 14-11-2020
## 307 79.01651 4.0000 Delayed Non-FTA 17-01-2022
## 308 80.99234 4.0000 Delayed FTA 26-12-2023
## 309 75.29718 4.0000 On Time FTA 09-01-2021
## 310 72.88753 4.0000 On Time Non-FTA 02-03-2019
## 311 72.78017 4.0000 Delayed FTA 14-03-2021
## 312 75.30617 4.0000 Delayed FTA 10-08-2021
## 313 79.90268 4.0000 On Time Non-FTA 10-02-2022
## 314 70.32405 4.0000 Delayed FTA 30-04-2018
## 315 75.04720 4.0000 On Time Non-FTA 19-11-2020
## 316 74.71223 4.0000 On Time Non-FTA 27-10-2020
## 317 79.32824 4.0000 On Time Non-FTA 20-05-2024
## 318 82.22376 4.0000 On Time FTA 09-06-2023
## 319 75.64785 4.0000 Delayed FTA 05-08-2024
## 320 73.60884 4.0000 On Time FTA 29-07-2018
## 321 82.26035 4.0000 Delayed FTA 18-11-2021
## 322 78.27693 4.0000 Delayed Non-FTA 10-03-2022
## 323 81.16557 4.0000 On Time Non-FTA 31-03-2024
## 324 83.13586 4.0000 Delayed FTA 16-07-2022
## 325 84.23356 4.0000 On Time FTA 12-02-2021
## 326 82.33195 4.0000 Delayed Non-FTA 01-08-2018
## 327 75.70138 4.0000 Delayed FTA 26-12-2018
## 328 74.12061 4.0000 On Time Non-FTA 11-03-2021
## 329 74.78145 4.0000 Delayed FTA 29-09-2019
## 330 76.22759 4.0000 Delayed FTA 16-06-2021
## 331 74.92527 4.0000 Delayed FTA 24-10-2022
## 332 72.42172 4.0000 On Time Non-FTA 27-10-2021
## 333 73.28132 4.0000 On Time Non-FTA 20-07-2022
## 334 74.33876 4.0000 On Time Non-FTA 18-01-2022
## 335 82.62491 4.0000 On Time Non-FTA 06-09-2024
## 336 83.49780 4.0000 On Time Non-FTA 26-07-2019
## 337 84.94083 4.0000 Delayed FTA 22-12-2019
## 338 82.82753 4.0000 On Time Non-FTA 18-05-2020
## 339 75.83898 4.0000 Delayed Non-FTA 16-09-2021
## 340 82.61684 4.0000 Delayed FTA 23-11-2018
## 341 82.09337 4.0000 On Time Non-FTA 11-01-2020
## 342 83.50826 4.0000 Delayed FTA 07-10-2024
## 343 71.06085 4.0000 On Time FTA 06-05-2019
## 344 72.25230 4.0000 On Time FTA 07-02-2019
## 345 75.64720 4.0000 On Time FTA 01-06-2022
## 346 77.47799 4.0000 Delayed FTA 30-11-2020
## 347 77.48357 4.0000 On Time Non-FTA 25-12-2023
## 348 72.64241 4.0000 Delayed Non-FTA 07-01-2019
## 349 70.99586 4.0000 Delayed Non-FTA 27-07-2021
## 350 83.49094 4.0000 Delayed FTA 05-03-2022
## 351 76.14991 4.0000 On Time FTA 17-12-2019
## 352 84.02764 4.0000 Delayed FTA 21-05-2018
## 353 79.91441 4.0000 Delayed FTA 07-05-2019
## 354 80.54187 4.0000 On Time Non-FTA 02-05-2024
## 355 74.22494 4.0000 Delayed Non-FTA 20-06-2023
## 356 76.28153 4.0000 On Time FTA 18-04-2022
## 357 81.94075 4.0000 On Time FTA 12-08-2023
## 358 84.24300 4.0000 On Time FTA 06-05-2020
## 359 84.46890 4.0000 Delayed Non-FTA 04-08-2018
## 360 82.21039 4.0000 On Time Non-FTA 20-07-2022
## 361 80.41649 4.0000 Delayed Non-FTA 22-03-2020
## 362 72.11025 4.0000 On Time FTA 13-10-2022
## 363 73.20463 4.0000 Delayed Non-FTA 03-04-2019
## 364 78.59605 4.0000 Delayed FTA 17-05-2021
## 365 83.59286 4.0000 Delayed FTA 15-04-2022
## 366 81.98987 4.0000 Delayed FTA 29-09-2022
## 367 70.91770 4.0000 On Time FTA 18-09-2023
## 368 80.82753 4.0000 Delayed FTA 15-11-2020
## 369 75.69071 4.0000 Delayed Non-FTA 08-07-2021
## 370 78.97087 4.0000 Delayed Non-FTA 21-12-2024
## 371 78.04723 4.0000 Delayed FTA 04-08-2020
## 372 78.33740 4.0000 On Time Non-FTA 15-01-2021
## 373 82.79201 5.0000 Delayed FTA 05-07-2019
## 374 72.36663 5.0000 On Time Non-FTA 10-07-2020
## 375 72.55985 5.0000 Delayed Non-FTA 19-12-2019
## 376 71.55396 5.0000 Delayed FTA 22-05-2020
## 377 72.39698 5.0000 Delayed FTA 30-12-2021
## 378 73.26792 5.0000 Delayed Non-FTA 01-04-2020
## 379 78.73583 5.0000 Delayed FTA 21-12-2018
## 380 71.57007 5.0000 Delayed FTA 06-10-2022
## 381 72.14931 5.0000 On Time FTA 07-03-2019
## 382 81.37426 5.0000 Delayed FTA 15-10-2024
## 383 84.71963 5.0000 Delayed Non-FTA 30-09-2023
## 384 79.00946 5.0000 Delayed Non-FTA 29-10-2022
## 385 75.11489 5.0000 On Time Non-FTA 06-04-2023
## 386 73.13154 5.0000 On Time FTA 16-06-2023
## 387 78.86136 5.0000 Delayed Non-FTA 19-08-2022
## 388 70.10729 5.0000 On Time Non-FTA 09-04-2019
## 389 80.10940 5.0000 Delayed FTA 09-08-2021
## 390 72.42423 5.0000 Delayed FTA 21-06-2022
## 391 72.04259 5.0000 On Time Non-FTA 29-12-2020
## 392 82.21876 5.0000 On Time Non-FTA 24-04-2020
## 393 72.97843 5.0000 On Time FTA 05-07-2024
## 394 82.27611 5.0000 On Time FTA 06-02-2019
## 395 70.47369 5.0000 On Time FTA 02-06-2022
## 396 72.50800 5.0000 On Time FTA 31-12-2018
## 397 84.98583 5.0000 Delayed FTA 27-10-2023
## 398 77.80050 5.0000 Delayed FTA 26-08-2019
## 399 72.33261 5.0000 Delayed FTA 20-03-2018
## 400 71.30840 5.0000 Delayed Non-FTA 10-08-2018
## 401 72.38661 5.0000 On Time FTA 01-06-2021
## 402 79.73668 5.0000 On Time Non-FTA 18-04-2024
## 403 78.61096 5.0000 On Time Non-FTA 12-07-2023
## 404 72.25016 5.0000 On Time Non-FTA 03-10-2021
## 405 81.93320 5.0000 Delayed Non-FTA 06-12-2022
## 406 76.40287 5.0000 On Time FTA 05-03-2022
## 407 80.33534 5.0000 Delayed Non-FTA 10-03-2019
## 408 79.39699 5.0000 Delayed FTA 27-05-2021
## 409 82.07686 5.0000 On Time FTA 19-12-2022
## 410 82.96796 5.0000 Delayed FTA 10-08-2020
## 411 72.45618 5.0000 Delayed FTA 21-07-2020
## 412 82.69212 5.0000 On Time FTA 22-03-2019
## 413 77.14297 5.0000 Delayed FTA 17-08-2018
## 414 74.08992 5.0000 Delayed FTA 01-04-2023
## 415 84.51386 5.0000 On Time FTA 13-06-2024
## 416 81.91026 5.0000 Delayed Non-FTA 16-11-2018
## 417 74.21651 5.0000 Delayed FTA 18-06-2018
## 418 75.13803 5.0000 On Time Non-FTA 10-06-2020
## 419 84.56838 5.0000 On Time FTA 08-03-2022
## 420 72.13969 5.0000 Delayed FTA 17-12-2023
## 421 84.46603 5.0000 On Time Non-FTA 12-03-2020
## 422 71.01624 5.0000 On Time Non-FTA 13-05-2023
## 423 73.86107 5.0000 Delayed FTA 14-08-2023
## 424 71.75875 5.0000 On Time Non-FTA 18-08-2020
## 425 72.86169 5.0000 On Time FTA 28-12-2018
## 426 83.61102 5.0000 Delayed Non-FTA 22-02-2019
## 427 73.84106 5.0000 On Time FTA 09-05-2020
## 428 73.24502 5.0000 Delayed Non-FTA 17-11-2019
## 429 72.49495 5.0000 On Time Non-FTA 26-12-2019
## 430 71.16792 5.0000 On Time Non-FTA 12-05-2022
## 431 78.98181 5.0000 On Time FTA 14-11-2021
## 432 76.43797 5.0000 Delayed FTA 08-06-2022
## 433 83.89432 5.0000 On Time Non-FTA 26-08-2023
## 434 78.93011 5.0000 Delayed FTA 20-05-2018
## 435 73.68460 5.0000 Delayed Non-FTA 17-10-2024
## 436 70.38931 5.0000 Delayed FTA 02-08-2021
## 437 81.11785 5.0000 Delayed Non-FTA 16-09-2021
## 438 72.95992 5.0000 Delayed FTA 21-06-2023
## 439 77.47949 5.0000 On Time FTA 02-01-2022
## 440 79.04865 5.0000 Delayed Non-FTA 31-03-2024
## 441 83.38195 5.0000 Delayed FTA 01-02-2024
## 442 83.58472 5.0000 On Time Non-FTA 01-01-2024
## 443 80.32246 6.0000 On Time Non-FTA 11-06-2022
## 444 81.53872 6.0000 Delayed Non-FTA 01-02-2020
## 445 80.68305 6.0000 On Time Non-FTA 09-02-2022
## 446 73.11807 6.0000 Delayed FTA 02-01-2020
## 447 74.25288 6.0000 Delayed FTA 15-06-2023
## 448 81.21402 6.0000 On Time Non-FTA 08-05-2023
## 449 78.48228 6.0000 Delayed Non-FTA 08-01-2020
## 450 79.84722 6.0000 On Time Non-FTA 05-09-2022
## 451 77.94582 6.0000 Delayed FTA 04-02-2019
## 452 78.00645 6.0000 Delayed Non-FTA 11-06-2024
## 453 81.17311 6.0000 On Time FTA 22-07-2021
## 454 78.28426 6.0000 Delayed Non-FTA 11-12-2018
## 455 73.85673 6.0000 Delayed Non-FTA 18-08-2018
## 456 78.57225 6.0000 On Time FTA 22-08-2021
## 457 73.97244 6.0000 Delayed Non-FTA 18-01-2020
## 458 84.11070 6.0000 On Time FTA 01-03-2021
## 459 73.52612 6.0000 Delayed Non-FTA 19-12-2024
## 460 70.33686 6.0000 On Time FTA 16-01-2018
## 461 75.49872 6.0000 On Time FTA 10-06-2022
## 462 73.33737 6.0000 Delayed Non-FTA 25-05-2018
## 463 76.77906 6.0000 On Time FTA 16-08-2022
## 464 75.30113 6.0000 Delayed Non-FTA 26-11-2020
## 465 84.16844 6.0000 On Time Non-FTA 15-05-2019
## 466 70.92808 6.0000 Delayed Non-FTA 13-03-2018
## 467 81.51016 6.0000 Delayed FTA 07-05-2020
## 468 75.61837 6.0000 Delayed Non-FTA 03-02-2024
## 469 78.74512 6.0000 Delayed Non-FTA 10-06-2018
## 470 74.10609 6.0000 Delayed Non-FTA 07-08-2023
## 471 83.64852 6.0000 On Time Non-FTA 27-06-2020
## 472 72.40579 6.0000 Delayed Non-FTA 18-08-2021
## 473 77.51415 6.0000 Delayed FTA 04-07-2021
## 474 76.73730 6.0000 Delayed Non-FTA 03-05-2022
## 475 78.24041 6.0000 Delayed FTA 24-03-2019
## 476 77.31307 6.0000 On Time Non-FTA 11-01-2019
## 477 74.99520 6.0000 On Time Non-FTA 14-05-2021
## 478 83.49066 6.0000 On Time Non-FTA 06-12-2024
## 479 70.68311 6.0000 On Time Non-FTA 17-07-2018
## 480 81.02247 6.0000 On Time FTA 17-07-2020
## 481 83.33862 6.0000 Delayed Non-FTA 01-08-2024
## 482 70.26771 6.0000 On Time FTA 12-11-2019
## 483 82.39568 6.0000 On Time FTA 08-12-2019
## 484 82.02269 6.0000 Delayed FTA 04-02-2022
## 485 72.48237 6.0000 On Time Non-FTA 08-08-2019
## 486 75.95857 6.0000 On Time FTA 14-04-2021
## 487 82.14702 6.0000 On Time FTA 02-11-2021
## 488 70.32729 6.0000 On Time Non-FTA 18-07-2023
## 489 78.12433 6.0000 On Time FTA 17-09-2021
## 490 78.21943 6.0000 Delayed Non-FTA 18-09-2022
## 491 74.53993 6.0000 On Time FTA 16-01-2018
## 492 79.01936 6.0000 Delayed Non-FTA 15-11-2021
## 493 71.80216 6.0000 On Time FTA 12-03-2022
## 494 80.28039 6.0000 On Time FTA 13-01-2021
## 495 74.13736 6.0000 On Time Non-FTA 02-09-2019
## 496 71.97529 6.0000 Delayed FTA 24-06-2021
## 497 72.98153 6.0000 On Time FTA 17-11-2022
## 498 74.25317 6.0000 Delayed FTA 26-05-2018
## 499 73.66946 6.0000 On Time FTA 15-02-2024
## 500 75.55624 6.0000 Delayed FTA 15-02-2018
## 501 83.02336 6.0000 Delayed Non-FTA 19-03-2019
## 502 73.43600 6.0000 Delayed FTA 02-04-2024
## 503 78.97338 6.0000 On Time FTA 16-07-2020
## 504 76.86454 6.0000 On Time FTA 19-12-2023
## 505 81.52291 6.0000 Delayed FTA 23-07-2018
## 506 70.97766 6.0000 On Time Non-FTA 06-09-2024
## 507 78.56400 6.0000 On Time FTA 18-09-2023
## 508 77.00291 6.0000 Delayed Non-FTA 20-06-2024
## 509 75.88520 6.0000 On Time FTA 24-06-2018
## 510 73.51956 6.0000 On Time Non-FTA 03-10-2020
## 511 70.87650 6.0000 Delayed Non-FTA 12-12-2024
## 512 76.73957 6.0000 Delayed Non-FTA 19-05-2021
## 513 84.14902 6.0000 Delayed Non-FTA 04-05-2023
## 514 74.97772 7.0000 Delayed Non-FTA 22-04-2018
## 515 81.68364 7.0000 Delayed FTA 10-10-2024
## 516 78.28235 7.0000 Delayed Non-FTA 08-09-2019
## 517 81.68054 7.0000 On Time Non-FTA 01-04-2019
## 518 80.54406 7.0000 Delayed Non-FTA 28-07-2018
## 519 78.80250 7.0000 On Time Non-FTA 30-06-2020
## 520 73.71085 7.0000 On Time Non-FTA 25-09-2021
## 521 83.24403 7.0000 Delayed Non-FTA 07-01-2022
## 522 84.47438 7.0000 Delayed FTA 19-07-2020
## 523 75.82915 7.0000 On Time FTA 23-02-2023
## 524 81.68526 7.0000 On Time Non-FTA 05-07-2024
## 525 72.82735 7.0000 On Time FTA 01-08-2024
## 526 74.03462 7.0000 Delayed FTA 04-01-2023
## 527 79.26054 7.0000 On Time Non-FTA 22-06-2024
## 528 84.31401 7.0000 On Time Non-FTA 08-07-2018
## 529 79.52626 7.0000 On Time FTA 10-08-2022
## 530 71.91752 7.0000 Delayed Non-FTA 04-02-2022
## 531 79.74120 7.0000 Delayed Non-FTA 23-10-2019
## 532 76.91006 7.0000 On Time FTA 26-06-2021
## 533 81.97845 7.0000 Delayed FTA 21-12-2018
## 534 79.40400 7.0000 Delayed Non-FTA 01-07-2021
## 535 82.78791 7.0000 Delayed Non-FTA 22-08-2019
## 536 75.29496 7.0000 Delayed FTA 23-04-2018
## 537 82.32833 7.0000 Delayed Non-FTA 23-05-2019
## 538 73.29758 7.0000 On Time FTA 24-08-2023
## 539 71.31573 7.0000 On Time FTA 12-09-2024
## 540 75.57096 7.0000 Delayed FTA 02-05-2023
## 541 78.96089 7.0000 On Time FTA 06-12-2023
## 542 72.78047 7.0000 On Time Non-FTA 01-06-2023
## 543 73.65239 7.0000 Delayed Non-FTA 07-08-2024
## 544 71.36916 7.0000 Delayed Non-FTA 02-04-2019
## 545 70.92729 7.0000 Delayed Non-FTA 26-02-2018
## 546 77.22313 7.0000 Delayed FTA 17-04-2021
## 547 71.37041 7.0000 On Time Non-FTA 19-12-2023
## 548 83.35048 7.0000 On Time Non-FTA 12-05-2023
## 549 84.46530 7.0000 Delayed FTA 04-02-2020
## 550 77.75808 7.0000 On Time FTA 30-03-2019
## 551 80.94565 7.0000 On Time FTA 05-09-2021
## 552 78.14144 7.0000 On Time FTA 22-05-2021
## 553 84.09172 7.0000 Delayed Non-FTA 25-10-2021
## 554 83.27143 7.0000 On Time Non-FTA 06-04-2021
## 555 83.80580 7.0000 On Time FTA 04-09-2018
## 556 71.89377 7.0000 Delayed Non-FTA 06-10-2021
## 557 79.96145 7.0000 On Time FTA 02-06-2018
## 558 80.71058 7.0000 On Time Non-FTA 15-04-2023
## 559 82.25149 7.0000 On Time FTA 11-06-2018
## 560 76.48103 7.0000 Delayed Non-FTA 16-06-2020
## 561 81.77691 7.0000 Delayed Non-FTA 23-06-2021
## 562 70.23249 7.0000 On Time FTA 10-04-2023
## 563 84.16519 7.0000 On Time Non-FTA 09-03-2019
## 564 84.79996 7.0000 Delayed FTA 12-05-2022
## 565 73.41264 7.0000 On Time Non-FTA 06-08-2022
## 566 71.33997 7.0000 Delayed Non-FTA 18-04-2022
## 567 81.01442 7.0000 Delayed FTA 05-02-2019
## 568 71.96297 7.0000 Delayed FTA 30-11-2018
## 569 70.51183 7.0000 Delayed Non-FTA 23-04-2019
## 570 83.89375 7.0000 On Time Non-FTA 10-05-2023
## 571 82.50659 7.0000 Delayed Non-FTA 16-09-2018
## 572 70.56428 7.0000 On Time Non-FTA 09-01-2021
## 573 76.95510 7.0000 On Time Non-FTA 14-09-2024
## 574 81.45127 7.0000 On Time FTA 16-10-2021
## 575 72.88791 7.0000 On Time Non-FTA 31-01-2024
## 576 77.68962 7.0000 Delayed FTA 28-04-2018
## 577 70.50284 7.0000 On Time Non-FTA 17-08-2020
## 578 77.02045 7.0000 Delayed FTA 04-01-2018
## 579 83.03551 7.0000 Delayed Non-FTA 27-09-2022
## 580 80.19205 7.0000 On Time FTA 11-07-2020
## 581 75.55181 7.0000 On Time Non-FTA 31-12-2018
## 582 83.89671 7.0000 On Time Non-FTA 31-10-2023
## 583 80.62991 7.0000 On Time FTA 05-02-2023
## 584 71.03378 7.0000 On Time FTA 15-11-2019
## 585 77.95119 7.0000 Delayed FTA 18-08-2024
## 586 79.76154 7.0000 Delayed Non-FTA 22-02-2021
## 587 78.80085 7.0000 On Time FTA 01-07-2024
## 588 80.76006 7.0000 On Time FTA 27-02-2018
## 589 76.40671 7.0000 Delayed Non-FTA 26-01-2023
## 590 72.72743 7.0000 Delayed Non-FTA 30-07-2022
## 591 83.49077 7.0000 Delayed Non-FTA 09-12-2023
## 592 84.86060 7.0000 On Time FTA 18-04-2020
## 593 76.82699 7.0000 On Time FTA 02-06-2020
## 594 71.20839 7.0000 Delayed Non-FTA 09-06-2022
## 595 81.91419 7.0000 On Time FTA 21-10-2019
## 596 81.75756 7.0000 On Time Non-FTA 05-01-2020
## 597 75.09381 7.0000 Delayed Non-FTA 14-09-2019
## 598 84.33060 7.0000 On Time Non-FTA 09-08-2021
## 599 82.00289 7.0000 On Time FTA 09-12-2024
## 600 80.57768 7.0000 Delayed FTA 29-12-2018
## 601 74.16693 7.0000 Delayed FTA 31-12-2018
## 602 84.80138 7.0000 Delayed Non-FTA 03-11-2021
## 603 81.57934 7.0000 Delayed Non-FTA 31-07-2022
## 604 71.40281 7.0000 On Time Non-FTA 20-12-2018
## 605 77.50160 7.0000 On Time FTA 23-11-2020
## 606 70.04480 7.0000 On Time FTA 29-04-2022
## 607 71.77588 7.0000 On Time FTA 28-09-2023
## 608 80.92841 7.0000 On Time Non-FTA 01-05-2018
## 609 73.56808 7.0000 On Time FTA 05-01-2021
## 610 71.95521 8.0000 On Time Non-FTA 28-04-2023
## 611 82.89334 8.0000 Delayed FTA 20-04-2024
## 612 82.09386 8.0000 Delayed Non-FTA 17-08-2019
## 613 82.25422 8.0000 Delayed FTA 18-04-2020
## 614 74.02981 8.0000 On Time Non-FTA 17-08-2018
## 615 83.86089 8.0000 On Time FTA 20-08-2020
## 616 76.75423 8.0000 Delayed FTA 27-02-2021
## 617 73.02109 8.0000 Delayed FTA 20-08-2020
## 618 73.23731 8.0000 On Time Non-FTA 20-09-2023
## 619 84.33272 8.0000 On Time FTA 25-04-2024
## 620 78.99957 8.0000 Delayed Non-FTA 11-02-2024
## 621 75.06556 8.0000 On Time Non-FTA 15-04-2021
## 622 79.84434 8.0000 Delayed Non-FTA 03-06-2019
## 623 78.34187 8.0000 Delayed Non-FTA 24-11-2019
## 624 74.86258 8.0000 On Time Non-FTA 25-03-2021
## 625 74.13284 8.0000 On Time Non-FTA 15-07-2020
## 626 73.73323 8.0000 On Time Non-FTA 23-06-2021
## 627 80.12961 8.0000 On Time FTA 14-09-2020
## 628 80.81951 8.0000 On Time Non-FTA 15-10-2020
## 629 80.21715 8.0000 On Time Non-FTA 15-01-2023
## 630 82.25666 8.0000 Delayed FTA 18-09-2021
## 631 83.85713 8.0000 On Time FTA 13-03-2020
## 632 83.19171 8.0000 On Time Non-FTA 23-05-2018
## 633 81.40864 8.0000 Delayed Non-FTA 02-07-2022
## 634 75.46152 8.0000 On Time FTA 22-11-2020
## 635 80.41863 8.0000 On Time FTA 04-05-2020
## 636 81.95054 8.0000 On Time Non-FTA 12-01-2020
## 637 70.73176 8.0000 On Time FTA 02-05-2018
## 638 84.53310 8.0000 On Time Non-FTA 12-03-2021
## 639 78.32879 8.0000 Delayed FTA 04-10-2019
## 640 84.59067 8.0000 On Time FTA 14-04-2024
## 641 81.82788 8.0000 Delayed Non-FTA 06-10-2019
## 642 74.22066 8.0000 Delayed FTA 07-07-2020
## 643 72.08364 8.0000 On Time FTA 28-07-2022
## 644 81.38521 8.0000 On Time Non-FTA 13-03-2018
## 645 80.71352 8.0000 On Time Non-FTA 13-05-2023
## 646 72.54656 8.0000 Delayed FTA 13-11-2021
## 647 78.10207 8.0000 Delayed Non-FTA 30-05-2022
## 648 81.74553 8.0000 Delayed FTA 22-11-2020
## 649 84.52758 8.0000 Delayed FTA 10-05-2021
## 650 81.15249 8.0000 On Time FTA 05-12-2019
## 651 72.82319 8.0000 Delayed Non-FTA 24-08-2019
## 652 71.80155 8.0000 Delayed FTA 03-08-2019
## 653 71.61763 8.0000 Delayed Non-FTA 14-10-2022
## 654 76.85001 8.0000 On Time Non-FTA 18-11-2020
## 655 84.23798 8.0000 On Time Non-FTA 31-12-2024
## 656 83.44853 8.0000 Delayed FTA 13-03-2022
## 657 78.41206 8.0000 Delayed FTA 20-05-2023
## 658 84.84982 8.0000 On Time Non-FTA 12-12-2019
## 659 79.54130 8.0000 Delayed Non-FTA 24-04-2021
## 660 77.31086 8.0000 On Time Non-FTA 07-04-2018
## 661 78.49576 8.0000 Delayed Non-FTA 13-03-2022
## 662 76.15878 8.0000 On Time FTA 10-08-2024
## 663 83.30865 8.0000 On Time FTA 04-07-2019
## 664 76.85680 8.0000 On Time Non-FTA 29-12-2019
## 665 82.31829 8.0000 On Time FTA 04-12-2024
## 666 79.62859 8.0000 On Time Non-FTA 17-01-2020
## 667 83.58275 8.0000 Delayed FTA 12-09-2023
## 668 76.71307 8.0000 Delayed Non-FTA 29-09-2021
## 669 76.22546 8.0000 Delayed FTA 04-09-2024
## 670 84.82766 8.0000 On Time Non-FTA 20-07-2021
## 671 82.72524 8.0000 Delayed Non-FTA 07-06-2024
## 672 72.63268 8.0000 Delayed Non-FTA 31-12-2024
## 673 77.30035 8.0000 Delayed Non-FTA 16-02-2019
## 674 79.33930 8.0000 Delayed FTA 29-09-2023
## 675 75.18187 8.0000 Delayed FTA 21-02-2024
## 676 84.52467 8.0000 On Time Non-FTA 25-10-2021
## 677 70.87077 8.0000 Delayed Non-FTA 17-05-2018
## 678 83.03960 8.0000 Delayed Non-FTA 21-01-2024
## 679 75.92396 8.0000 Delayed Non-FTA 05-07-2020
## 680 74.62876 8.0000 On Time FTA 13-08-2022
## 681 71.93507 9.0000 Delayed Non-FTA 08-07-2022
## 682 71.43499 9.0000 Delayed Non-FTA 21-04-2024
## 683 80.89585 9.0000 On Time Non-FTA 30-07-2022
## 684 80.86054 9.0000 On Time Non-FTA 02-04-2024
## 685 84.37926 9.0000 On Time Non-FTA 07-03-2023
## 686 73.74436 9.0000 On Time FTA 06-10-2021
## 687 77.10000 9.0000 Delayed FTA 17-12-2022
## 688 78.59396 9.0000 On Time Non-FTA 21-07-2022
## 689 78.46508 9.0000 Delayed Non-FTA 28-11-2024
## 690 70.84903 9.0000 Delayed FTA 10-09-2020
## 691 77.67465 9.0000 On Time FTA 17-12-2018
## 692 72.01259 9.0000 On Time FTA 08-06-2019
## 693 74.53958 9.0000 On Time Non-FTA 27-12-2020
## 694 79.77202 9.0000 On Time FTA 18-06-2020
## 695 71.72918 9.0000 On Time FTA 18-03-2022
## 696 82.72963 9.0000 On Time FTA 28-08-2022
## 697 78.33078 9.0000 On Time Non-FTA 28-04-2020
## 698 71.73875 9.0000 On Time Non-FTA 04-06-2023
## 699 81.87580 9.0000 Delayed Non-FTA 03-06-2018
## 700 78.28350 9.0000 Delayed FTA 26-10-2019
## 701 82.62754 9.0000 On Time Non-FTA 30-10-2019
## 702 78.18623 9.0000 Delayed Non-FTA 01-10-2023
## 703 70.68181 9.0000 Delayed Non-FTA 03-07-2020
## 704 83.68850 9.0000 On Time Non-FTA 05-07-2021
## 705 78.21165 9.0000 On Time Non-FTA 23-06-2022
## 706 79.03867 9.0000 On Time Non-FTA 21-10-2018
## 707 81.24173 9.0000 On Time Non-FTA 24-01-2022
## 708 82.04923 9.0000 On Time FTA 26-12-2018
## 709 81.40948 9.0000 On Time FTA 15-07-2020
## 710 71.31891 9.0000 Delayed Non-FTA 25-10-2023
## 711 76.26963 9.0000 Delayed Non-FTA 02-04-2024
## 712 84.62788 9.0000 Delayed Non-FTA 10-03-2023
## 713 75.97133 9.0000 On Time FTA 25-02-2024
## 714 80.52734 9.0000 On Time FTA 15-10-2024
## 715 74.61559 9.0000 Delayed Non-FTA 24-09-2024
## 716 75.25526 9.0000 On Time FTA 21-04-2022
## 717 81.75995 9.0000 On Time Non-FTA 30-04-2023
## 718 81.59639 9.0000 On Time Non-FTA 01-08-2019
## 719 79.66479 9.0000 Delayed FTA 07-11-2019
## 720 83.93934 9.0000 Delayed Non-FTA 05-12-2018
## 721 76.97007 9.0000 On Time FTA 04-05-2022
## 722 82.48484 9.0000 On Time Non-FTA 09-12-2024
## 723 75.87822 9.0000 On Time FTA 02-08-2020
## 724 75.60175 9.0000 Delayed Non-FTA 07-11-2019
## 725 72.91931 9.0000 On Time FTA 27-08-2020
## 726 82.41333 9.0000 On Time FTA 26-02-2022
## 727 73.71144 9.0000 Delayed Non-FTA 27-04-2023
## 728 73.46852 9.0000 Delayed Non-FTA 18-02-2019
## 729 78.46075 9.0000 On Time FTA 21-06-2021
## 730 83.19182 9.0000 On Time FTA 08-12-2020
## 731 78.14651 9.0000 Delayed Non-FTA 18-01-2023
## 732 76.01061 9.0000 Delayed Non-FTA 13-07-2019
## 733 81.11553 9.0000 Delayed Non-FTA 19-12-2023
## 734 78.69685 9.0000 Delayed Non-FTA 10-03-2023
## 735 72.64402 9.0000 Delayed FTA 07-10-2020
## 736 74.45268 9.0000 On Time FTA 18-04-2022
## 737 82.51372 9.0000 On Time Non-FTA 08-09-2021
## 738 71.49360 9.0000 Delayed FTA 16-01-2019
## 739 70.48597 9.0000 Delayed FTA 21-01-2021
## 740 81.56310 9.0000 Delayed Non-FTA 15-07-2022
## 741 75.12654 9.0000 On Time FTA 15-04-2023
## 742 80.39207 9.0000 On Time Non-FTA 06-08-2022
## 743 79.28948 9.0000 On Time FTA 11-01-2021
## 744 79.05947 9.0000 Delayed Non-FTA 06-03-2023
## 745 70.97768 9.0000 On Time Non-FTA 13-05-2021
## 746 79.74653 9.0000 On Time Non-FTA 01-09-2022
## 747 75.40256 9.0000 Delayed Non-FTA 22-10-2021
## 748 75.14159 9.0000 Delayed FTA 28-07-2023
## 749 72.20637 9.0000 Delayed FTA 26-03-2022
## 750 71.80860 9.0000 Delayed Non-FTA 23-11-2019
## 751 73.59685 9.0000 On Time Non-FTA 05-05-2024
## 752 82.46888 9.0000 On Time FTA 18-10-2024
## 753 70.84917 9.0000 On Time Non-FTA 10-04-2023
## 754 71.09892 9.0000 On Time FTA 29-07-2022
## 755 70.63952 9.0000 Delayed Non-FTA 15-09-2019
## 756 72.65588 9.0000 On Time FTA 10-10-2020
## 757 77.86414 9.0000 Delayed Non-FTA 03-03-2022
## 758 80.72011 9.0000 Delayed Non-FTA 12-02-2020
## 759 82.95131 9.0000 Delayed Non-FTA 25-12-2021
## 760 72.61341 9.0000 On Time FTA 23-02-2024
## 761 83.33391 10.0000 On Time Non-FTA 16-11-2021
## 762 73.56093 10.0000 Delayed Non-FTA 02-12-2023
## 763 76.88003 10.0000 On Time FTA 06-03-2018
## 764 75.21794 10.0000 Delayed Non-FTA 22-11-2023
## 765 81.84817 10.0000 On Time FTA 04-05-2021
## 766 83.21940 10.0000 Delayed Non-FTA 22-08-2023
## 767 78.63988 10.0000 On Time Non-FTA 23-05-2022
## 768 71.90949 10.0000 Delayed Non-FTA 02-09-2021
## 769 81.64116 10.0000 On Time Non-FTA 26-03-2020
## 770 82.70713 10.0000 Delayed Non-FTA 27-05-2018
## 771 78.64910 10.0000 On Time Non-FTA 30-03-2019
## 772 73.11226 10.0000 Delayed Non-FTA 05-06-2019
## 773 78.51926 10.0000 Delayed FTA 01-06-2020
## 774 81.25770 10.0000 On Time Non-FTA 20-07-2023
## 775 84.10305 10.0000 On Time FTA 18-01-2022
## 776 74.65672 10.0000 Delayed Non-FTA 29-09-2024
## 777 71.88295 10.0000 On Time FTA 23-03-2022
## 778 73.01450 10.0000 On Time Non-FTA 24-09-2019
## 779 81.07887 10.0000 Delayed FTA 21-08-2018
## 780 83.54324 10.0000 On Time Non-FTA 02-04-2018
## 781 70.07670 10.0000 Delayed FTA 09-12-2022
## 782 80.67251 10.0000 Delayed Non-FTA 15-08-2022
## 783 80.05454 10.0000 On Time FTA 04-07-2022
## 784 82.53729 10.0000 On Time FTA 10-12-2024
## 785 76.06876 10.0000 Delayed FTA 17-12-2022
## 786 77.01842 10.0000 Delayed FTA 13-02-2021
## 787 76.64352 10.0000 On Time Non-FTA 09-01-2024
## 788 78.90886 10.0000 On Time Non-FTA 08-10-2019
## 789 83.47535 10.0000 On Time Non-FTA 25-05-2023
## 790 81.36600 10.0000 On Time Non-FTA 03-08-2023
## 791 82.13498 10.0000 On Time FTA 19-05-2020
## 792 75.29520 10.0000 Delayed Non-FTA 03-10-2020
## 793 70.33042 10.0000 Delayed Non-FTA 01-02-2020
## 794 79.85271 10.0000 Delayed FTA 26-08-2019
## 795 79.33690 10.0000 Delayed FTA 13-08-2021
## 796 84.71910 10.0000 Delayed Non-FTA 04-11-2019
## 797 82.68746 10.0000 Delayed FTA 07-04-2020
## 798 83.67805 10.0000 Delayed Non-FTA 03-03-2022
## 799 77.11799 10.0000 Delayed FTA 31-01-2021
## 800 71.95272 10.0000 On Time FTA 16-06-2018
## 801 71.69506 10.0000 On Time FTA 06-08-2020
## 802 77.56132 10.0000 Delayed Non-FTA 17-08-2024
## 803 71.12145 10.0000 On Time Non-FTA 08-09-2023
## 804 84.24198 10.0000 Delayed Non-FTA 17-10-2024
## 805 70.58799 10.0000 On Time FTA 10-07-2020
## 806 81.12678 10.0000 On Time FTA 31-08-2021
## 807 73.71189 10.0000 On Time Non-FTA 10-07-2022
## 808 80.40820 10.0000 Delayed FTA 07-01-2020
## 809 73.67055 10.0000 Delayed Non-FTA 12-09-2024
## 810 83.76653 10.0000 On Time Non-FTA 05-12-2020
## 811 74.63139 10.0000 On Time Non-FTA 04-06-2022
## 812 73.20212 10.0000 Delayed Non-FTA 16-09-2024
## 813 84.19590 10.0000 Delayed Non-FTA 06-11-2018
## 814 80.34655 10.0000 On Time FTA 23-10-2019
## 815 84.48464 10.0000 On Time Non-FTA 03-04-2019
## 816 79.41795 10.0000 On Time FTA 02-12-2023
## 817 83.19282 10.0000 On Time Non-FTA 29-08-2019
## 818 78.58471 10.0000 Delayed FTA 27-02-2022
## 819 79.80498 10.0000 On Time FTA 09-07-2021
## 820 71.44303 10.0000 On Time FTA 13-09-2020
## 821 76.80109 10.0000 On Time FTA 04-03-2018
## 822 73.91197 10.0000 On Time FTA 31-05-2018
## 823 76.21189 10.0000 On Time Non-FTA 28-03-2020
## 824 74.42024 10.0000 On Time Non-FTA 18-04-2023
## 825 76.92013 10.0000 On Time FTA 28-08-2020
## 826 73.18118 10.0000 On Time Non-FTA 26-06-2022
## 827 79.91055 10.0000 Delayed FTA 19-11-2022
## 828 80.57069 10.0000 On Time Non-FTA 18-07-2024
## 829 71.44870 10.0000 Delayed Non-FTA 20-08-2021
## 830 76.58590 10.0000 On Time FTA 26-03-2022
## 831 80.69567 10.0000 Delayed Non-FTA 01-03-2021
## 832 74.99516 10.0000 Delayed Non-FTA 23-08-2024
## 833 71.53869 10.0000 On Time Non-FTA 27-07-2023
## 834 83.90153 10.0000 On Time FTA 16-09-2024
## 835 81.17264 10.0000 Delayed FTA 13-03-2020
## 836 81.30478 10.0000 Delayed FTA 24-10-2019
## 837 71.91378 10.0000 On Time Non-FTA 23-06-2022
## 838 72.34403 10.0000 Delayed Non-FTA 15-10-2018
## 839 84.83434 10.0000 Delayed Non-FTA 05-07-2022
## 840 83.37980 10.0000 On Time Non-FTA 27-07-2020
## 841 82.58590 10.0000 Delayed Non-FTA 24-08-2020
## 842 71.12533 10.0000 Delayed FTA 13-12-2020
## 843 74.57313 10.0000 Delayed FTA 30-07-2018
## 844 84.10614 10.0000 On Time Non-FTA 03-02-2024
## 845 74.67894 10.0000 Delayed Non-FTA 11-01-2024
## 846 74.24616 10.0000 On Time FTA 03-03-2018
## 847 74.80824 10.0000 On Time FTA 14-05-2019
## 848 77.11708 10.0000 On Time FTA 27-06-2021
## 849 80.36095 10.0000 On Time Non-FTA 02-08-2020
## 850 83.44050 10.0000 Delayed Non-FTA 12-03-2018
## 851 84.84164 10.0000 On Time Non-FTA 29-08-2020
## 852 70.29675 10.0000 Delayed Non-FTA 12-03-2019
## 853 70.31385 10.0000 Delayed Non-FTA 04-10-2023
## 854 84.87252 10.0000 On Time Non-FTA 01-09-2018
## 855 77.96207 10.0000 Delayed Non-FTA 28-12-2024
## 856 72.41980 10.0000 Delayed Non-FTA 05-07-2020
## 857 81.77726 10.0000 Delayed Non-FTA 15-10-2023
## 858 76.40062 10.0000 On Time Non-FTA 03-06-2020
## 859 84.31405 10.0000 On Time Non-FTA 28-08-2018
## 860 70.22546 10.0000 On Time FTA 31-12-2022
## 861 82.07305 11.0000 Delayed Non-FTA 19-07-2019
## 862 76.89421 11.0000 Delayed FTA 29-05-2021
## 863 82.54138 11.0000 Delayed FTA 27-06-2019
## 864 71.70530 11.0000 Delayed Non-FTA 15-05-2021
## 865 73.98214 11.0000 Delayed Non-FTA 30-05-2019
## 866 76.07617 11.0000 Delayed FTA 28-12-2020
## 867 76.14232 11.0000 Delayed Non-FTA 11-06-2018
## 868 77.55803 11.0000 On Time Non-FTA 04-02-2020
## 869 75.70723 11.0000 On Time Non-FTA 05-07-2024
## 870 79.88108 11.0000 Delayed FTA 13-01-2022
## 871 79.67501 11.0000 Delayed Non-FTA 01-10-2020
## 872 72.05103 11.0000 Delayed Non-FTA 03-05-2019
## 873 76.36001 11.0000 On Time Non-FTA 21-12-2022
## 874 82.20834 11.0000 Delayed Non-FTA 02-07-2024
## 875 71.15500 11.0000 Delayed Non-FTA 13-09-2021
## 876 76.45546 11.0000 On Time Non-FTA 19-10-2023
## 877 71.75287 11.0000 On Time Non-FTA 18-07-2023
## 878 77.16735 11.0000 Delayed Non-FTA 30-05-2023
## 879 72.17471 11.0000 On Time Non-FTA 13-10-2021
## 880 81.92520 11.0000 Delayed FTA 06-10-2021
## 881 81.82893 11.0000 On Time FTA 04-10-2019
## 882 78.64739 11.0000 Delayed FTA 11-10-2019
## 883 77.49566 11.0000 On Time Non-FTA 10-07-2021
## 884 83.72937 11.0000 On Time FTA 12-07-2023
## 885 81.47119 11.0000 On Time Non-FTA 26-08-2021
## 886 71.10055 11.0000 Delayed Non-FTA 17-05-2019
## 887 78.90757 11.0000 On Time FTA 23-03-2023
## 888 74.13351 11.0000 Delayed Non-FTA 02-12-2021
## 889 70.64818 11.0000 On Time Non-FTA 24-02-2019
## 890 79.69478 11.0000 On Time Non-FTA 26-02-2018
## 891 83.21296 11.0000 On Time FTA 06-01-2019
## 892 82.26996 11.0000 On Time FTA 01-04-2024
## 893 81.53995 11.0000 On Time FTA 06-05-2019
## 894 74.86004 11.0000 On Time FTA 24-06-2023
## 895 72.64784 11.0000 On Time Non-FTA 14-09-2021
## 896 81.40358 11.0000 On Time FTA 31-05-2018
## 897 74.60387 11.0000 On Time Non-FTA 29-03-2023
## 898 83.76248 11.0000 Delayed FTA 20-03-2020
## 899 74.30816 11.0000 On Time FTA 22-08-2018
## 900 77.81247 11.0000 On Time Non-FTA 01-11-2020
## 901 78.11655 11.0000 Delayed FTA 07-01-2024
## 902 80.03773 11.0000 Delayed FTA 17-01-2022
## 903 77.91088 11.0000 On Time FTA 12-09-2019
## 904 78.03544 11.0000 Delayed FTA 20-06-2020
## 905 76.55861 11.0000 Delayed Non-FTA 03-12-2019
## 906 76.16434 11.0000 On Time FTA 08-05-2022
## 907 73.20675 11.0000 On Time FTA 21-08-2018
## 908 72.80873 11.0000 Delayed FTA 16-09-2018
## 909 74.42984 11.0000 Delayed Non-FTA 03-03-2019
## 910 82.21054 11.0000 On Time FTA 08-10-2020
## 911 71.38513 11.0000 Delayed FTA 23-02-2019
## 912 79.19931 11.0000 On Time Non-FTA 18-02-2021
## 913 74.72211 11.0000 On Time Non-FTA 30-07-2019
## 914 73.70773 11.0000 Delayed Non-FTA 07-08-2024
## 915 81.97953 11.0000 On Time Non-FTA 23-07-2022
## 916 78.13843 11.0000 Delayed FTA 01-05-2018
## 917 75.39590 11.0000 Delayed Non-FTA 20-01-2024
## 918 83.82775 11.0000 On Time FTA 19-08-2023
## 919 78.75885 11.0000 On Time Non-FTA 24-09-2019
## 920 72.49497 11.0000 On Time FTA 04-11-2019
## 921 74.19445 11.0000 On Time FTA 12-10-2024
## 922 71.86764 11.0000 Delayed FTA 27-09-2021
## 923 70.55741 11.0000 Delayed FTA 15-05-2019
## 924 74.94863 11.0000 Delayed Non-FTA 20-08-2024
## 925 78.82720 11.0000 On Time Non-FTA 11-02-2018
## 926 82.52084 11.0000 Delayed Non-FTA 21-01-2024
## 927 83.30140 11.0000 On Time FTA 31-10-2023
## 928 82.46368 11.0000 On Time FTA 19-12-2019
## 929 72.43929 11.0000 On Time FTA 21-12-2024
## 930 71.25451 11.0000 Delayed Non-FTA 08-04-2022
## 931 78.76269 11.0000 Delayed FTA 19-12-2024
## 932 75.77531 11.0000 Delayed Non-FTA 12-07-2022
## 933 79.22219 11.0000 Delayed Non-FTA 04-07-2020
## 934 74.33211 11.0000 Delayed FTA 24-03-2021
## 935 74.27482 11.0000 Delayed Non-FTA 28-04-2021
## 936 71.71387 11.0000 On Time FTA 30-12-2018
## 937 72.55375 12.0000 On Time FTA 24-11-2023
## 938 76.51180 12.0000 On Time FTA 27-01-2023
## 939 75.30734 12.0000 Delayed FTA 05-02-2018
## 940 82.17804 12.0000 On Time FTA 22-10-2018
## 941 77.19546 12.0000 Delayed Non-FTA 02-07-2018
## 942 79.86056 12.0000 On Time FTA 07-05-2023
## 943 84.82540 12.0000 On Time Non-FTA 24-09-2020
## 944 70.65930 12.0000 On Time FTA 10-03-2020
## 945 75.67494 12.0000 On Time FTA 19-02-2023
## 946 77.37951 12.0000 On Time FTA 09-03-2021
## 947 82.26383 12.0000 On Time FTA 05-05-2023
## 948 76.45683 12.0000 On Time FTA 04-08-2019
## 949 72.17483 12.0000 Delayed FTA 16-02-2018
## 950 84.38063 12.0000 Delayed FTA 07-04-2019
## 951 84.47658 12.0000 On Time FTA 03-12-2022
## 952 82.58420 12.0000 Delayed FTA 30-01-2024
## 953 84.09393 12.0000 Delayed FTA 06-12-2021
## 954 75.68798 12.0000 Delayed Non-FTA 20-06-2018
## 955 82.03696 12.0000 Delayed Non-FTA 06-02-2020
## 956 77.54564 12.0000 Delayed FTA 05-08-2018
## 957 79.70087 12.0000 On Time FTA 25-10-2023
## 958 83.91115 12.0000 Delayed FTA 26-09-2019
## 959 77.33634 12.0000 On Time Non-FTA 17-08-2023
## 960 71.55366 12.0000 On Time FTA 29-02-2020
## 961 74.76977 12.0000 On Time Non-FTA 11-01-2020
## 962 70.20326 12.0000 Delayed Non-FTA 30-04-2019
## 963 70.98318 12.0000 Delayed Non-FTA 08-07-2020
## 964 79.51073 12.0000 Delayed Non-FTA 23-01-2024
## 965 77.29075 12.0000 On Time Non-FTA 13-11-2022
## 966 71.12573 12.0000 On Time FTA 13-08-2020
## 967 73.69282 12.0000 Delayed Non-FTA 21-03-2019
## 968 76.79596 12.0000 Delayed FTA 27-02-2021
## 969 82.01160 12.0000 Delayed Non-FTA 21-12-2022
## 970 84.62710 12.0000 On Time Non-FTA 17-01-2023
## 971 81.38654 12.0000 On Time Non-FTA 10-07-2022
## 972 71.83694 12.0000 Delayed Non-FTA 23-11-2019
## 973 71.02736 12.0000 On Time FTA 21-07-2021
## 974 71.12502 12.0000 On Time Non-FTA 01-02-2020
## 975 74.32934 12.0000 Delayed Non-FTA 25-11-2020
## 976 70.24684 12.0000 On Time FTA 28-02-2018
## 977 72.27651 12.0000 On Time FTA 18-05-2020
## 978 79.68692 12.0000 On Time Non-FTA 03-11-2021
## 979 71.04986 12.0000 On Time Non-FTA 10-12-2021
## 980 82.58400 12.0000 On Time FTA 24-07-2019
## 981 76.66927 12.0000 On Time FTA 27-01-2023
## 982 77.12563 12.0000 On Time Non-FTA 16-01-2021
## 983 79.00223 12.0000 Delayed FTA 28-05-2023
## 984 81.22379 12.0000 Delayed FTA 06-10-2023
## 985 71.62865 12.0000 Delayed Non-FTA 27-06-2018
## 986 79.90485 12.0000 On Time FTA 05-06-2021
## 987 72.17850 12.0000 Delayed Non-FTA 03-08-2022
## 988 75.84208 12.0000 On Time FTA 03-11-2024
## 989 70.14033 12.0000 On Time FTA 04-05-2023
## 990 78.62254 12.0000 Delayed FTA 12-09-2024
## 991 84.48636 12.0000 On Time Non-FTA 06-05-2022
## 992 72.16463 12.0000 Delayed FTA 18-09-2018
## 993 72.55376 12.0000 Delayed Non-FTA 03-05-2022
## 994 72.71971 12.0000 On Time Non-FTA 24-07-2018
## 995 84.01231 12.0000 On Time Non-FTA 10-03-2018
## 996 84.75314 12.0000 On Time Non-FTA 24-03-2024
## 997 79.76205 12.0000 On Time Non-FTA 23-02-2021
## 998 74.11827 12.0000 On Time Non-FTA 30-08-2018
## 999 76.49568 12.0000 Delayed Non-FTA 01-11-2024
## 1000 81.82211 12.0000 On Time Non-FTA 06-12-2018
## 1001 78.53002 12.0000 Delayed Non-FTA 04-08-2023
## 1002 76.68245 12.0000 Delayed Non-FTA 16-11-2021
## 1003 72.05092 12.0000 On Time Non-FTA 17-12-2020
## 1004 71.45905 12.0000 On Time FTA 10-06-2024
## 1005 77.55574 12.0000 Delayed FTA 25-02-2021
## 1006 81.76012 12.0000 On Time FTA 31-08-2019
## 1007 79.64174 12.0000 On Time FTA 06-07-2020
## 1008 74.42898 12.0000 On Time Non-FTA 26-07-2018
## 1009 72.31462 12.0000 On Time FTA 02-01-2020
## 1010 77.86335 12.0000 On Time FTA 11-09-2020
## 1011 80.26940 12.0000 On Time Non-FTA 19-07-2019
## 1012 83.77642 12.0000 On Time Non-FTA 27-09-2023
## 1013 79.41388 12.0000 On Time FTA 30-12-2019
## 1014 73.51009 12.0000 Delayed FTA 15-07-2020
## 1015 73.75428 12.0000 On Time FTA 10-07-2024
## 1016 74.71611 12.0000 Delayed Non-FTA 29-06-2018
## 1017 72.44658 12.0000 On Time FTA 26-09-2020
## 1018 78.48161 12.0000 On Time Non-FTA 09-09-2023
## 1019 77.90712 12.0000 Delayed FTA 01-03-2019
## 1020 73.12307 13.0000 On Time FTA 08-05-2022
## 1021 74.75773 13.0000 On Time Non-FTA 15-11-2020
## 1022 80.10065 13.0000 On Time Non-FTA 29-11-2024
## 1023 75.78968 13.0000 On Time FTA 24-01-2020
## 1024 78.93858 13.0000 Delayed Non-FTA 17-03-2018
## 1025 72.48516 13.0000 Delayed FTA 27-08-2018
## 1026 71.19282 13.0000 Delayed FTA 25-03-2022
## 1027 84.34457 13.0000 Delayed Non-FTA 14-09-2022
## 1028 76.16754 13.0000 On Time Non-FTA 06-09-2021
## 1029 77.51537 13.0000 Delayed FTA 10-12-2023
## 1030 82.68370 13.0000 On Time FTA 25-01-2022
## 1031 79.05952 13.0000 Delayed FTA 01-02-2019
## 1032 75.86462 13.0000 Delayed Non-FTA 18-06-2022
## 1033 73.33900 13.0000 On Time FTA 25-09-2024
## 1034 70.69056 13.0000 Delayed Non-FTA 07-04-2019
## 1035 74.89662 13.0000 On Time Non-FTA 23-11-2024
## 1036 81.20044 13.0000 Delayed FTA 18-11-2019
## 1037 76.41050 13.0000 On Time FTA 16-09-2019
## 1038 81.06356 13.0000 On Time FTA 07-07-2024
## 1039 77.80239 13.0000 Delayed FTA 11-10-2020
## 1040 78.68477 13.0000 Delayed Non-FTA 06-06-2023
## 1041 79.54597 13.0000 Delayed FTA 25-08-2019
## 1042 77.37285 13.0000 Delayed Non-FTA 19-02-2023
## 1043 80.55475 13.0000 On Time Non-FTA 06-05-2024
## 1044 80.70426 13.0000 On Time Non-FTA 25-07-2019
## 1045 80.90865 13.0000 Delayed Non-FTA 24-07-2023
## 1046 76.78708 13.0000 On Time FTA 02-01-2019
## 1047 80.91446 13.0000 On Time FTA 19-06-2018
## 1048 80.24232 13.0000 Delayed FTA 10-08-2020
## 1049 70.00762 13.0000 On Time FTA 29-09-2019
## 1050 76.54041 13.0000 Delayed Non-FTA 18-01-2022
## 1051 82.62244 13.0000 Delayed Non-FTA 04-05-2019
## 1052 70.44496 13.0000 On Time Non-FTA 27-11-2022
## 1053 81.86688 13.0000 Delayed Non-FTA 14-10-2019
## 1054 71.20424 13.0000 Delayed FTA 27-04-2018
## 1055 76.23748 13.0000 On Time Non-FTA 01-03-2021
## 1056 70.69418 13.0000 On Time Non-FTA 19-07-2020
## 1057 78.27596 13.0000 On Time Non-FTA 18-07-2019
## 1058 81.63726 13.0000 Delayed Non-FTA 23-04-2020
## 1059 84.06785 13.0000 Delayed FTA 01-03-2020
## 1060 71.02016 13.0000 Delayed FTA 26-02-2024
## 1061 76.31396 13.0000 Delayed Non-FTA 24-12-2021
## 1062 83.78513 13.0000 Delayed FTA 22-09-2024
## 1063 71.02950 13.0000 On Time Non-FTA 13-05-2023
## 1064 72.09516 13.0000 Delayed Non-FTA 14-06-2024
## 1065 80.38032 13.0000 On Time Non-FTA 21-09-2020
## 1066 76.05489 13.0000 Delayed Non-FTA 13-09-2019
## 1067 72.98277 13.0000 Delayed Non-FTA 17-06-2024
## 1068 73.35799 13.0000 Delayed Non-FTA 12-09-2019
## 1069 74.88019 13.0000 On Time FTA 26-09-2018
## 1070 71.60214 13.0000 Delayed Non-FTA 17-05-2020
## 1071 80.53003 13.0000 Delayed Non-FTA 20-11-2020
## 1072 80.41680 13.0000 On Time FTA 11-12-2018
## 1073 83.74212 13.0000 On Time FTA 22-04-2022
## 1074 79.69407 13.0000 On Time FTA 09-03-2020
## 1075 79.31984 13.0000 Delayed FTA 15-07-2020
## 1076 72.98231 13.0000 On Time FTA 28-12-2021
## 1077 78.14120 13.0000 On Time Non-FTA 20-11-2020
## 1078 83.04954 13.0000 On Time Non-FTA 08-11-2019
## 1079 76.41204 13.0000 Delayed Non-FTA 31-08-2019
## 1080 71.52531 13.0000 On Time FTA 07-12-2024
## 1081 76.41400 13.0000 Delayed FTA 22-05-2022
## 1082 84.10677 13.0000 Delayed Non-FTA 22-09-2024
## 1083 83.53969 13.0000 Delayed FTA 06-07-2021
## 1084 74.56347 13.0000 Delayed FTA 22-08-2023
## 1085 73.99036 13.0000 On Time Non-FTA 24-02-2023
## 1086 80.74156 13.0000 Delayed FTA 08-07-2020
## 1087 79.81686 13.0000 Delayed Non-FTA 25-08-2024
## 1088 80.16073 13.0000 On Time FTA 06-02-2021
## 1089 77.14993 13.0000 On Time FTA 14-12-2019
## 1090 81.34442 13.0000 Delayed Non-FTA 24-06-2023
## 1091 70.86916 13.0000 On Time Non-FTA 06-05-2023
## 1092 80.44148 13.0000 On Time FTA 23-01-2022
## 1093 78.48045 13.0000 Delayed FTA 17-06-2024
## 1094 70.25781 14.0000 On Time Non-FTA 09-01-2022
## 1095 73.90917 14.0000 Delayed FTA 02-06-2020
## 1096 76.70249 14.0000 Delayed Non-FTA 27-12-2020
## 1097 75.34349 14.0000 On Time Non-FTA 03-07-2020
## 1098 75.92975 14.0000 On Time FTA 03-04-2022
## 1099 80.20316 14.0000 On Time FTA 04-04-2022
## 1100 80.27424 14.0000 Delayed Non-FTA 14-09-2024
## 1101 84.14880 14.0000 On Time Non-FTA 13-01-2021
## 1102 74.07441 14.0000 On Time Non-FTA 23-09-2018
## 1103 70.66582 14.0000 On Time Non-FTA 15-10-2024
## 1104 81.06166 14.0000 Delayed Non-FTA 25-09-2021
## 1105 76.95100 14.0000 Delayed FTA 16-08-2018
## 1106 75.61392 14.0000 Delayed FTA 05-05-2024
## 1107 72.05400 14.0000 On Time FTA 07-01-2019
## 1108 81.89965 14.0000 Delayed Non-FTA 09-06-2021
## 1109 76.97175 14.0000 Delayed FTA 19-12-2018
## 1110 81.63857 14.0000 On Time FTA 22-03-2018
## 1111 73.05644 14.0000 Delayed Non-FTA 18-06-2023
## 1112 72.99159 14.0000 On Time FTA 24-03-2019
## 1113 73.86609 14.0000 On Time FTA 26-07-2023
## 1114 82.42826 14.0000 On Time FTA 13-04-2021
## 1115 78.79363 14.0000 Delayed FTA 09-01-2024
## 1116 74.21406 14.0000 On Time Non-FTA 26-07-2019
## 1117 75.83759 14.0000 Delayed FTA 08-04-2018
## 1118 80.20302 14.0000 On Time FTA 01-09-2019
## 1119 80.72877 14.0000 On Time Non-FTA 25-11-2024
## 1120 76.49637 14.0000 Delayed Non-FTA 03-09-2024
## 1121 80.93157 14.0000 On Time FTA 04-11-2024
## 1122 78.54109 14.0000 On Time Non-FTA 22-09-2024
## 1123 76.00380 14.0000 On Time Non-FTA 08-06-2019
## 1124 84.85070 14.0000 On Time Non-FTA 22-05-2020
## 1125 75.55762 14.0000 On Time FTA 17-12-2018
## 1126 73.74786 14.0000 On Time FTA 22-07-2019
## 1127 81.77565 14.0000 On Time FTA 03-10-2021
## 1128 77.23546 14.0000 On Time FTA 16-10-2021
## 1129 74.73868 14.0000 On Time Non-FTA 15-02-2023
## 1130 78.89609 14.0000 On Time Non-FTA 29-03-2021
## 1131 76.80008 14.0000 Delayed FTA 08-05-2018
## 1132 82.96770 14.0000 On Time Non-FTA 29-02-2024
## 1133 74.78420 14.0000 Delayed Non-FTA 24-04-2024
## 1134 80.96222 14.0000 Delayed Non-FTA 27-07-2021
## 1135 81.21736 14.0000 Delayed FTA 01-05-2020
## 1136 76.31565 14.0000 On Time Non-FTA 09-07-2022
## 1137 70.05102 14.0000 On Time FTA 08-12-2023
## 1138 71.26165 14.0000 Delayed Non-FTA 09-12-2019
## 1139 84.52577 14.0000 Delayed FTA 06-12-2020
## 1140 71.55489 14.0000 Delayed FTA 10-05-2023
## 1141 70.68038 14.0000 On Time Non-FTA 15-01-2020
## 1142 84.02184 14.0000 Delayed FTA 21-11-2019
## 1143 70.32097 14.0000 On Time FTA 25-05-2023
## 1144 77.49997 14.0000 On Time Non-FTA 14-02-2019
## 1145 75.54736 14.0000 Delayed FTA 15-05-2023
## 1146 77.25599 14.0000 Delayed FTA 07-09-2023
## 1147 79.92258 14.0000 Delayed Non-FTA 21-02-2021
## 1148 76.15170 14.0000 On Time Non-FTA 11-04-2022
## 1149 81.61382 14.0000 Delayed Non-FTA 23-05-2022
## 1150 83.13552 14.0000 On Time FTA 27-09-2023
## 1151 81.04682 14.0000 Delayed FTA 22-01-2021
## 1152 72.61102 14.0000 Delayed FTA 14-06-2023
## 1153 80.42950 14.0000 On Time FTA 13-04-2021
## 1154 80.26513 14.0000 On Time Non-FTA 25-03-2023
## 1155 84.78118 14.0000 Delayed Non-FTA 29-12-2021
## 1156 80.95780 14.0000 Delayed FTA 30-08-2023
## 1157 71.49103 14.0000 Delayed Non-FTA 03-11-2024
## 1158 83.19195 14.0000 Delayed FTA 28-05-2024
## 1159 80.47913 14.0000 Delayed Non-FTA 30-03-2021
## 1160 71.72696 14.0000 Delayed Non-FTA 05-06-2018
## 1161 73.38430 14.0000 Delayed Non-FTA 24-02-2020
## 1162 73.47458 14.0000 On Time Non-FTA 10-01-2023
## 1163 77.58638 14.0000 Delayed FTA 13-10-2023
## 1164 70.53629 14.0000 Delayed Non-FTA 04-12-2021
## 1165 84.94779 14.0000 Delayed Non-FTA 06-06-2018
## 1166 80.29942 14.0000 On Time Non-FTA 01-01-2022
## 1167 77.24515 14.0000 On Time Non-FTA 27-05-2021
## 1168 72.80571 14.0000 On Time FTA 23-12-2023
## 1169 70.25510 14.0000 On Time FTA 23-07-2022
## 1170 72.13713 14.0000 Delayed Non-FTA 29-09-2024
## 1171 76.21886 14.0000 Delayed FTA 06-04-2018
## 1172 72.67793 14.0000 On Time Non-FTA 21-08-2022
## 1173 74.44751 14.0000 Delayed FTA 30-06-2023
## 1174 81.50678 14.0000 Delayed Non-FTA 16-04-2021
## 1175 83.63392 14.0000 On Time Non-FTA 13-07-2023
## 1176 75.33327 14.0000 On Time FTA 08-05-2020
## 1177 74.43339 15.0000 Delayed FTA 04-06-2022
## 1178 73.06567 15.0000 On Time Non-FTA 03-06-2024
## 1179 79.72063 15.0000 On Time FTA 30-04-2021
## 1180 80.92053 15.0000 On Time FTA 03-11-2021
## 1181 78.25098 15.0000 On Time FTA 24-11-2022
## 1182 70.43902 15.0000 On Time Non-FTA 14-02-2024
## 1183 72.40244 15.0000 On Time Non-FTA 11-02-2020
## 1184 77.12978 15.0000 On Time FTA 14-09-2018
## 1185 83.25263 15.0000 Delayed FTA 20-05-2024
## 1186 83.40208 15.0000 On Time Non-FTA 13-04-2019
## 1187 81.69187 15.0000 Delayed Non-FTA 21-07-2020
## 1188 78.45617 15.0000 On Time Non-FTA 23-07-2021
## 1189 72.39524 15.0000 On Time FTA 14-09-2019
## 1190 80.50063 15.0000 On Time FTA 12-10-2021
## 1191 84.61460 15.0000 On Time Non-FTA 14-09-2018
## 1192 74.37783 15.0000 Delayed FTA 09-08-2018
## 1193 74.67992 15.0000 Delayed Non-FTA 10-01-2018
## 1194 77.66380 15.0000 Delayed FTA 13-06-2024
## 1195 79.06877 15.0000 On Time Non-FTA 23-10-2018
## 1196 77.29078 15.0000 On Time Non-FTA 23-11-2024
## 1197 77.32182 15.0000 On Time Non-FTA 06-06-2024
## 1198 74.52351 15.0000 Delayed Non-FTA 14-12-2021
## 1199 76.03540 15.0000 On Time Non-FTA 06-11-2021
## 1200 81.47914 15.0000 Delayed FTA 28-10-2019
## 1201 75.58220 15.0000 On Time Non-FTA 20-11-2020
## 1202 71.91176 15.0000 Delayed FTA 11-10-2020
## 1203 77.93124 15.0000 On Time FTA 01-01-2019
## 1204 80.06379 15.0000 On Time FTA 26-08-2020
## 1205 74.90112 15.0000 On Time Non-FTA 12-11-2020
## 1206 74.72729 15.0000 On Time FTA 22-11-2021
## 1207 77.70793 15.0000 Delayed FTA 07-12-2022
## 1208 78.98915 15.0000 Delayed FTA 24-12-2022
## 1209 76.39170 15.0000 Delayed Non-FTA 07-11-2018
## 1210 83.13670 15.0000 On Time Non-FTA 12-10-2023
## 1211 79.04530 15.0000 On Time Non-FTA 28-04-2022
## 1212 84.70513 15.0000 On Time FTA 10-01-2019
## 1213 84.42384 15.0000 On Time FTA 18-10-2020
## 1214 79.39518 15.0000 On Time FTA 09-09-2018
## 1215 75.05535 15.0000 On Time FTA 07-03-2020
## 1216 83.25441 15.0000 Delayed Non-FTA 07-01-2018
## 1217 82.62372 15.0000 Delayed Non-FTA 18-10-2024
## 1218 83.61556 15.0000 On Time Non-FTA 02-10-2021
## 1219 77.20585 15.0000 On Time FTA 07-08-2020
## 1220 72.33551 15.0000 Delayed FTA 26-04-2018
## 1221 72.79557 15.0000 Delayed FTA 18-01-2019
## 1222 82.33186 15.0000 On Time FTA 21-11-2021
## 1223 84.72710 15.0000 Delayed Non-FTA 14-07-2019
## 1224 80.64698 15.0000 On Time Non-FTA 02-08-2019
## 1225 78.54430 15.0000 On Time FTA 25-04-2018
## 1226 74.11837 15.0000 On Time Non-FTA 25-09-2023
## 1227 77.96983 15.0000 On Time FTA 27-07-2021
## 1228 76.04312 15.0000 On Time FTA 10-04-2019
## 1229 79.07299 15.0000 Delayed Non-FTA 07-03-2018
## 1230 78.76768 15.0000 On Time Non-FTA 03-02-2023
## 1231 74.31142 15.0000 On Time FTA 19-05-2019
## 1232 76.38597 15.0000 On Time Non-FTA 08-01-2024
## 1233 75.64344 15.0000 Delayed FTA 10-03-2019
## 1234 76.67709 15.0000 Delayed FTA 19-08-2023
## 1235 79.70966 15.0000 On Time Non-FTA 15-07-2019
## 1236 77.31699 15.0000 Delayed Non-FTA 25-12-2024
## 1237 76.61666 15.0000 Delayed FTA 20-06-2022
## 1238 71.84376 15.0000 On Time FTA 23-02-2020
## 1239 80.01074 15.0000 Delayed FTA 21-04-2023
## 1240 79.47014 15.0000 On Time FTA 29-06-2022
## 1241 80.82987 15.0000 On Time FTA 09-09-2023
## 1242 72.44846 15.0000 On Time Non-FTA 29-03-2023
## 1243 80.57520 15.0000 Delayed FTA 02-04-2019
## 1244 77.22230 15.0000 Delayed Non-FTA 03-04-2020
## 1245 81.64250 15.0000 On Time Non-FTA 21-03-2024
## 1246 79.37193 15.0000 Delayed Non-FTA 26-11-2022
## 1247 76.97289 15.0000 On Time FTA 25-04-2019
## 1248 82.31655 15.0000 Delayed FTA 17-10-2023
## 1249 77.20933 15.0000 On Time Non-FTA 24-02-2019
## 1250 71.52439 16.0000 On Time FTA 30-08-2018
## 1251 81.71390 16.0000 Delayed FTA 28-04-2020
## 1252 71.74239 16.0000 On Time Non-FTA 17-10-2022
## 1253 75.42238 16.0000 Delayed Non-FTA 31-05-2023
## 1254 74.16182 16.0000 On Time FTA 28-11-2018
## 1255 73.31715 16.0000 On Time Non-FTA 08-12-2022
## 1256 73.29251 16.0000 Delayed Non-FTA 21-06-2022
## 1257 75.92152 16.0000 On Time FTA 02-09-2020
## 1258 71.55306 16.0000 On Time FTA 16-10-2022
## 1259 74.01282 16.0000 Delayed FTA 04-07-2019
## 1260 74.90558 16.0000 On Time FTA 30-12-2024
## 1261 73.02579 16.0000 Delayed FTA 22-12-2021
## 1262 71.55385 16.0000 On Time FTA 15-01-2023
## 1263 79.12593 16.0000 On Time Non-FTA 03-07-2024
## 1264 78.28681 16.0000 On Time FTA 22-01-2018
## 1265 71.18561 16.0000 On Time FTA 10-11-2019
## 1266 83.14016 16.0000 Delayed FTA 20-02-2019
## 1267 77.57455 16.0000 On Time FTA 07-04-2022
## 1268 84.19013 16.0000 Delayed Non-FTA 05-05-2018
## 1269 79.00180 16.0000 On Time FTA 17-06-2022
## 1270 70.09426 16.0000 On Time Non-FTA 13-08-2024
## 1271 72.54016 16.0000 Delayed FTA 11-03-2023
## 1272 82.48043 16.0000 On Time FTA 11-03-2018
## 1273 82.96606 16.0000 On Time FTA 20-09-2022
## 1274 81.91830 16.0000 On Time FTA 15-02-2022
## 1275 77.63045 16.0000 On Time Non-FTA 04-06-2024
## 1276 80.31108 16.0000 Delayed Non-FTA 14-05-2018
## 1277 82.81424 16.0000 On Time Non-FTA 23-03-2019
## 1278 77.36913 16.0000 On Time Non-FTA 25-07-2023
## 1279 76.25766 16.0000 Delayed FTA 08-12-2018
## 1280 76.73115 16.0000 On Time FTA 28-07-2018
## 1281 71.42198 16.0000 Delayed FTA 14-11-2020
## 1282 73.32421 16.0000 On Time FTA 10-10-2023
## 1283 77.79237 16.0000 On Time Non-FTA 02-08-2024
## 1284 72.61713 16.0000 On Time FTA 17-07-2023
## 1285 75.19980 16.0000 Delayed FTA 26-07-2019
## 1286 72.70959 16.0000 On Time Non-FTA 28-11-2023
## 1287 75.80045 16.0000 Delayed Non-FTA 29-05-2024
## 1288 81.17607 16.0000 Delayed FTA 06-09-2020
## 1289 70.34943 16.0000 On Time FTA 20-05-2024
## 1290 77.59624 16.0000 Delayed Non-FTA 12-07-2022
## 1291 74.22732 16.0000 On Time FTA 02-12-2018
## 1292 84.89087 16.0000 Delayed FTA 11-08-2018
## 1293 83.65773 16.0000 Delayed Non-FTA 17-03-2021
## 1294 79.25415 16.0000 On Time FTA 16-01-2020
## 1295 79.20744 16.0000 On Time FTA 28-05-2024
## 1296 70.96080 16.0000 On Time FTA 12-05-2022
## 1297 77.71802 16.0000 On Time Non-FTA 27-11-2019
## 1298 73.34780 16.0000 On Time FTA 05-01-2018
## 1299 78.01551 16.0000 On Time FTA 13-08-2022
## 1300 78.45990 16.0000 Delayed Non-FTA 03-10-2019
## 1301 76.67140 16.0000 On Time FTA 15-09-2024
## 1302 70.69331 16.0000 On Time Non-FTA 25-01-2020
## 1303 76.27367 16.0000 On Time FTA 11-02-2024
## 1304 74.25835 16.0000 Delayed FTA 03-01-2023
## 1305 77.97753 16.0000 Delayed Non-FTA 04-08-2023
## 1306 70.07224 16.0000 On Time FTA 03-08-2018
## 1307 71.22905 16.0000 Delayed FTA 04-12-2019
## 1308 81.21242 16.0000 On Time Non-FTA 30-03-2019
## 1309 73.33260 16.0000 Delayed Non-FTA 22-08-2019
## 1310 81.64337 16.0000 Delayed FTA 01-10-2023
## 1311 75.44689 16.0000 On Time FTA 15-01-2019
## 1312 71.15527 16.0000 On Time FTA 10-09-2023
## 1313 79.44064 16.0000 On Time Non-FTA 02-11-2018
## 1314 84.77871 16.0000 On Time FTA 08-05-2022
## 1315 84.55618 16.0000 On Time Non-FTA 05-11-2023
## 1316 73.06520 16.0000 Delayed FTA 03-05-2019
## 1317 73.45094 16.0000 Delayed FTA 06-07-2022
## 1318 75.60278 16.0000 Delayed Non-FTA 07-11-2019
## 1319 74.30987 16.0000 On Time Non-FTA 12-02-2020
## 1320 74.19781 16.0000 On Time FTA 12-05-2019
## 1321 80.16688 16.0000 On Time Non-FTA 10-07-2021
## 1322 72.87417 16.0000 Delayed Non-FTA 26-02-2021
## 1323 82.55411 16.0000 On Time Non-FTA 06-03-2024
## 1324 75.15440 16.0000 Delayed FTA 14-04-2023
## 1325 75.52313 17.0000 Delayed FTA 03-05-2020
## 1326 81.23289 17.0000 Delayed FTA 25-06-2022
## 1327 77.54498 17.0000 On Time Non-FTA 24-09-2018
## 1328 82.55990 17.0000 Delayed Non-FTA 30-06-2024
## 1329 72.78012 17.0000 Delayed Non-FTA 17-03-2022
## 1330 84.33950 17.0000 On Time FTA 16-02-2022
## 1331 72.20472 17.0000 Delayed Non-FTA 10-07-2019
## 1332 76.77498 17.0000 On Time FTA 23-05-2024
## 1333 77.21774 17.0000 Delayed FTA 06-06-2018
## 1334 73.56820 17.0000 Delayed Non-FTA 07-12-2020
## 1335 78.11583 17.0000 On Time FTA 12-03-2018
## 1336 84.04321 17.0000 Delayed FTA 05-07-2024
## 1337 84.12237 17.0000 On Time FTA 07-04-2022
## 1338 70.54564 17.0000 Delayed Non-FTA 14-08-2022
## 1339 76.10890 17.0000 Delayed Non-FTA 02-07-2019
## 1340 70.27869 17.0000 On Time Non-FTA 22-04-2020
## 1341 73.42724 17.0000 Delayed FTA 22-11-2023
## 1342 83.83478 17.0000 On Time Non-FTA 24-02-2020
## 1343 77.77278 17.0000 On Time Non-FTA 11-10-2019
## 1344 83.25291 17.0000 On Time Non-FTA 13-06-2018
## 1345 74.40027 17.0000 Delayed FTA 29-09-2021
## 1346 75.72599 17.0000 On Time FTA 03-03-2021
## 1347 81.34136 17.0000 On Time Non-FTA 28-08-2023
## 1348 74.45341 17.0000 On Time Non-FTA 10-02-2022
## 1349 84.45769 17.0000 Delayed FTA 07-02-2021
## 1350 74.98217 17.0000 Delayed Non-FTA 04-05-2023
## 1351 73.69886 17.0000 Delayed Non-FTA 08-10-2021
## 1352 75.17024 17.0000 On Time Non-FTA 26-05-2023
## 1353 74.90035 17.0000 On Time Non-FTA 16-06-2023
## 1354 76.34906 17.0000 On Time FTA 29-08-2019
## 1355 76.76470 17.0000 On Time FTA 01-10-2021
## 1356 77.83732 17.0000 Delayed Non-FTA 12-09-2022
## 1357 76.95942 17.0000 Delayed Non-FTA 23-08-2019
## 1358 74.08895 17.0000 Delayed FTA 10-06-2022
## 1359 78.69914 17.0000 On Time FTA 21-09-2018
## 1360 73.06817 17.0000 On Time FTA 27-02-2021
## 1361 71.61277 17.0000 On Time FTA 25-12-2019
## 1362 74.27868 17.0000 On Time Non-FTA 01-07-2024
## 1363 72.75084 17.0000 Delayed FTA 16-07-2018
## 1364 79.74474 17.0000 On Time FTA 02-07-2023
## 1365 81.62828 17.0000 On Time FTA 01-10-2021
## 1366 80.17267 17.0000 On Time FTA 20-06-2024
## 1367 83.00435 17.0000 Delayed FTA 10-04-2024
## 1368 73.48565 17.0000 Delayed FTA 12-08-2018
## 1369 72.03751 17.0000 On Time Non-FTA 01-03-2019
## 1370 75.55942 17.0000 On Time Non-FTA 26-08-2024
## 1371 84.91146 17.0000 On Time FTA 01-11-2019
## 1372 76.11101 17.0000 On Time FTA 06-06-2018
## 1373 73.06161 17.0000 On Time Non-FTA 06-01-2021
## 1374 83.06459 17.0000 Delayed FTA 28-03-2024
## 1375 72.93270 17.0000 On Time FTA 13-06-2024
## 1376 74.61232 17.0000 Delayed Non-FTA 02-01-2024
## 1377 73.88425 17.0000 On Time Non-FTA 29-07-2022
## 1378 79.44041 17.0000 Delayed Non-FTA 16-01-2021
## 1379 82.42938 17.0000 On Time Non-FTA 13-03-2020
## 1380 82.47028 17.0000 Delayed Non-FTA 20-04-2022
## 1381 73.25904 17.0000 Delayed FTA 20-03-2022
## 1382 74.55313 17.0000 Delayed FTA 07-03-2020
## 1383 78.73339 17.0000 On Time Non-FTA 25-07-2023
## 1384 77.58195 17.0000 Delayed Non-FTA 28-02-2022
## 1385 73.69054 17.0000 Delayed FTA 06-06-2024
## 1386 70.79472 17.0000 On Time FTA 06-11-2023
## 1387 77.22113 17.0000 On Time FTA 05-08-2021
## 1388 74.89403 17.0000 Delayed Non-FTA 15-06-2019
## 1389 74.76554 17.0000 Delayed Non-FTA 06-09-2023
## 1390 75.04412 17.0000 Delayed Non-FTA 22-03-2019
## 1391 83.85626 17.0000 Delayed Non-FTA 12-09-2024
## 1392 73.37507 17.0000 Delayed FTA 15-12-2020
## 1393 79.00499 17.0000 Delayed Non-FTA 23-01-2023
## 1394 77.37331 17.0000 On Time FTA 26-08-2024
## 1395 78.12983 17.0000 On Time FTA 02-09-2018
## 1396 75.81133 17.0000 On Time Non-FTA 01-12-2023
## 1397 80.71328 17.0000 On Time FTA 17-12-2021
## 1398 82.58395 17.0000 On Time Non-FTA 02-03-2022
## 1399 80.61321 17.0000 Delayed FTA 15-03-2018
## 1400 74.34541 17.0000 On Time Non-FTA 30-12-2018
## 1401 73.63908 17.0000 On Time Non-FTA 05-05-2023
## 1402 75.51641 17.0000 Delayed FTA 03-03-2018
## 1403 82.28183 17.0000 On Time FTA 17-12-2024
## 1404 72.61821 17.0000 Delayed Non-FTA 07-08-2022
## 1405 82.20563 18.0000 On Time Non-FTA 03-07-2022
## 1406 75.20845 18.0000 Delayed Non-FTA 07-08-2019
## 1407 73.93622 18.0000 Delayed Non-FTA 19-10-2019
## 1408 74.67987 18.0000 On Time Non-FTA 16-05-2024
## 1409 83.47293 18.0000 On Time FTA 09-10-2019
## 1410 75.17793 18.0000 Delayed Non-FTA 10-05-2020
## 1411 79.13068 18.0000 Delayed Non-FTA 30-01-2023
## 1412 73.16881 18.0000 Delayed FTA 24-04-2023
## 1413 84.89017 18.0000 Delayed FTA 14-11-2020
## 1414 72.06375 18.0000 Delayed Non-FTA 02-02-2022
## 1415 81.29849 18.0000 On Time FTA 25-08-2020
## 1416 80.48264 18.0000 Delayed FTA 18-09-2021
## 1417 71.51407 18.0000 Delayed FTA 22-06-2023
## 1418 80.31075 18.0000 On Time FTA 04-03-2018
## 1419 76.76456 18.0000 On Time Non-FTA 05-12-2023
## 1420 70.34866 18.0000 Delayed FTA 22-12-2023
## 1421 70.86238 18.0000 On Time Non-FTA 23-09-2022
## 1422 81.89561 18.0000 Delayed FTA 28-04-2019
## 1423 75.21521 18.0000 Delayed Non-FTA 09-11-2019
## 1424 82.54907 18.0000 On Time FTA 03-10-2019
## 1425 78.12926 18.0000 On Time Non-FTA 03-02-2019
## 1426 75.75370 18.0000 On Time Non-FTA 08-06-2022
## 1427 74.28024 18.0000 On Time Non-FTA 06-05-2024
## 1428 83.61810 18.0000 On Time Non-FTA 25-09-2021
## 1429 83.42638 18.0000 On Time FTA 24-09-2019
## 1430 74.32318 18.0000 On Time Non-FTA 07-07-2023
## 1431 80.51028 18.0000 Delayed Non-FTA 20-03-2020
## 1432 78.00710 18.0000 Delayed Non-FTA 10-08-2022
## 1433 73.16344 18.0000 Delayed FTA 14-11-2019
## 1434 82.14755 18.0000 Delayed Non-FTA 27-09-2020
## 1435 78.35859 18.0000 On Time FTA 12-11-2024
## 1436 77.33513 18.0000 On Time Non-FTA 23-11-2020
## 1437 82.96708 18.0000 Delayed Non-FTA 27-02-2019
## 1438 76.27068 18.0000 On Time Non-FTA 30-09-2022
## 1439 83.38551 18.0000 On Time Non-FTA 19-02-2019
## 1440 76.07879 18.0000 Delayed FTA 21-06-2019
## 1441 84.96220 18.0000 Delayed FTA 05-12-2020
## 1442 74.83490 18.0000 On Time FTA 07-11-2019
## 1443 79.12226 18.0000 Delayed Non-FTA 08-05-2022
## 1444 74.08811 18.0000 Delayed Non-FTA 02-11-2019
## 1445 73.74693 18.0000 On Time FTA 19-08-2023
## 1446 75.93575 18.0000 On Time Non-FTA 26-06-2022
## 1447 75.86904 18.0000 On Time Non-FTA 04-04-2020
## 1448 70.98077 18.0000 On Time Non-FTA 16-09-2021
## 1449 74.02543 18.0000 Delayed FTA 02-01-2021
## 1450 75.35573 18.0000 Delayed Non-FTA 16-12-2021
## 1451 75.81229 18.0000 On Time Non-FTA 24-07-2023
## 1452 75.06368 18.0000 Delayed FTA 26-01-2019
## 1453 77.26943 18.0000 Delayed FTA 03-09-2020
## 1454 84.47185 18.0000 On Time Non-FTA 17-06-2024
## 1455 70.87778 18.0000 Delayed FTA 05-12-2023
## 1456 74.26394 18.0000 Delayed FTA 27-04-2023
## 1457 76.64675 18.0000 Delayed Non-FTA 23-01-2022
## 1458 72.44408 18.0000 On Time FTA 28-02-2024
## 1459 74.76715 18.0000 Delayed FTA 08-10-2020
## 1460 72.16363 18.0000 On Time FTA 03-07-2023
## 1461 72.58977 18.0000 Delayed FTA 03-05-2021
## 1462 76.69403 18.0000 On Time FTA 15-03-2019
## 1463 73.16317 18.0000 On Time FTA 26-11-2021
## 1464 79.57639 18.0000 On Time FTA 13-08-2021
## 1465 71.98977 18.0000 On Time Non-FTA 01-01-2024
## 1466 78.04993 18.0000 On Time FTA 30-09-2019
## 1467 70.08228 18.0000 On Time FTA 02-10-2022
## 1468 78.50698 18.0000 Delayed Non-FTA 22-09-2023
## 1469 77.29014 18.0000 On Time Non-FTA 01-02-2020
## 1470 83.17368 18.0000 On Time FTA 29-03-2020
## 1471 73.95535 18.0000 On Time Non-FTA 10-08-2018
## 1472 73.40728 18.0000 On Time Non-FTA 26-03-2022
## 1473 74.98699 18.0000 Delayed FTA 19-09-2022
## 1474 76.83056 18.0000 On Time Non-FTA 14-04-2019
## 1475 71.24671 18.0000 Delayed FTA 27-09-2019
## 1476 77.86115 18.0000 On Time Non-FTA 02-10-2019
## 1477 77.10612 18.0000 On Time FTA 24-05-2023
## 1478 81.30001 18.0000 On Time FTA 11-05-2018
## 1479 76.52711 18.0000 On Time Non-FTA 29-12-2019
## 1480 84.21607 18.0000 On Time FTA 03-01-2023
## 1481 73.41633 18.0000 Delayed FTA 15-12-2021
## 1482 72.21159 18.0000 On Time FTA 10-08-2022
## 1483 80.39806 18.0000 On Time Non-FTA 20-10-2019
## 1484 81.31043 18.0000 On Time FTA 25-02-2024
## 1485 78.17592 18.0000 Delayed Non-FTA 28-01-2019
## 1486 76.23808 18.0000 On Time FTA 01-12-2023
## 1487 80.73125 19.0000 On Time Non-FTA 26-08-2019
## 1488 84.34789 19.0000 On Time Non-FTA 10-11-2020
## 1489 81.41711 19.0000 On Time FTA 24-09-2020
## 1490 83.75195 19.0000 Delayed Non-FTA 18-07-2019
## 1491 79.46331 19.0000 On Time Non-FTA 21-08-2023
## 1492 79.05032 19.0000 Delayed FTA 30-01-2021
## 1493 80.15858 19.0000 Delayed Non-FTA 12-10-2024
## 1494 82.58834 19.0000 Delayed FTA 27-01-2023
## 1495 82.29886 19.0000 Delayed FTA 17-05-2024
## 1496 71.08613 19.0000 On Time FTA 04-09-2020
## 1497 79.43766 19.0000 On Time Non-FTA 28-08-2020
## 1498 73.61254 19.0000 Delayed FTA 28-01-2020
## 1499 79.50859 19.0000 Delayed FTA 10-07-2020
## 1500 77.20482 19.0000 On Time FTA 21-04-2021
## 1501 79.36665 19.0000 On Time Non-FTA 20-02-2023
## 1502 82.27724 19.0000 On Time FTA 06-10-2022
## 1503 72.53566 19.0000 On Time Non-FTA 09-07-2021
## 1504 77.39984 19.0000 On Time FTA 01-03-2020
## 1505 72.70998 19.0000 Delayed FTA 25-09-2018
## 1506 83.50004 19.0000 Delayed Non-FTA 12-03-2021
## 1507 74.71959 19.0000 On Time FTA 13-11-2024
## 1508 79.23330 19.0000 On Time Non-FTA 16-06-2024
## 1509 70.14871 19.0000 Delayed FTA 14-02-2023
## 1510 77.21468 19.0000 On Time Non-FTA 11-04-2020
## 1511 77.68636 19.0000 Delayed FTA 11-08-2020
## 1512 80.45351 19.0000 On Time Non-FTA 18-09-2020
## 1513 77.55997 19.0000 Delayed FTA 13-02-2021
## 1514 80.86418 19.0000 Delayed Non-FTA 06-10-2023
## 1515 70.22596 19.0000 On Time Non-FTA 12-09-2021
## 1516 80.96779 19.0000 Delayed Non-FTA 14-07-2019
## 1517 70.61202 19.0000 Delayed Non-FTA 21-08-2023
## 1518 75.10799 19.0000 On Time FTA 16-02-2022
## 1519 70.55857 19.0000 On Time Non-FTA 30-03-2020
## 1520 82.84789 19.0000 Delayed Non-FTA 28-10-2021
## 1521 84.89018 19.0000 Delayed Non-FTA 04-01-2018
## 1522 75.85678 19.0000 On Time FTA 04-03-2019
## 1523 80.60878 19.0000 Delayed Non-FTA 09-04-2024
## 1524 72.59294 19.0000 Delayed FTA 19-11-2019
## 1525 75.11629 19.0000 Delayed Non-FTA 18-06-2023
## 1526 70.17435 19.0000 Delayed FTA 02-12-2021
## 1527 75.54747 19.0000 On Time Non-FTA 28-03-2023
## 1528 79.15892 19.0000 On Time Non-FTA 05-12-2023
## 1529 71.24151 19.0000 On Time FTA 21-11-2023
## 1530 71.71175 19.0000 On Time Non-FTA 31-07-2018
## 1531 71.28446 19.0000 Delayed Non-FTA 24-12-2020
## 1532 80.77710 19.0000 On Time Non-FTA 22-06-2018
## 1533 76.40163 19.0000 Delayed Non-FTA 10-11-2019
## 1534 84.04946 19.0000 On Time FTA 09-10-2019
## 1535 71.72516 19.0000 On Time FTA 14-03-2020
## 1536 82.34489 19.0000 Delayed FTA 30-05-2023
## 1537 76.04952 19.0000 On Time Non-FTA 16-08-2023
## 1538 74.47780 19.0000 On Time Non-FTA 06-03-2022
## 1539 71.77694 19.0000 Delayed FTA 30-04-2019
## 1540 78.40356 19.0000 On Time FTA 14-03-2020
## 1541 73.72444 19.0000 Delayed FTA 08-12-2022
## 1542 78.87706 19.0000 On Time FTA 08-02-2021
## 1543 78.96667 19.0000 On Time FTA 15-11-2019
## 1544 73.55942 19.0000 On Time Non-FTA 31-01-2019
## 1545 74.89464 19.0000 On Time FTA 29-04-2020
## 1546 81.00567 19.0000 On Time Non-FTA 31-12-2020
## 1547 73.24262 19.0000 Delayed FTA 14-06-2018
## 1548 72.90146 19.0000 On Time Non-FTA 20-12-2018
## 1549 82.57194 19.0000 Delayed Non-FTA 07-08-2021
## 1550 82.35493 19.0000 Delayed FTA 08-11-2024
## 1551 74.22543 19.0000 Delayed FTA 20-08-2024
## 1552 72.56092 19.0000 On Time FTA 25-05-2023
## 1553 71.48635 19.0000 On Time Non-FTA 12-02-2020
## 1554 73.95284 19.0000 On Time Non-FTA 11-07-2024
## 1555 83.37677 19.0000 Delayed FTA 11-02-2024
## 1556 74.42985 19.0000 Delayed Non-FTA 27-11-2021
## 1557 83.04810 19.0000 Delayed FTA 29-10-2018
## 1558 82.77316 19.0000 On Time Non-FTA 06-01-2023
## 1559 84.90139 19.0000 Delayed Non-FTA 07-08-2019
## 1560 71.08681 19.0000 Delayed FTA 04-08-2020
## 1561 74.14201 19.0000 On Time Non-FTA 20-12-2018
## 1562 78.37830 19.0000 Delayed Non-FTA 22-04-2022
## 1563 81.05596 19.0000 Delayed FTA 01-01-2018
## 1564 81.92421 19.0000 Delayed Non-FTA 02-02-2020
## 1565 78.91188 20.0000 Delayed FTA 01-04-2020
## 1566 76.07764 20.0000 On Time Non-FTA 26-11-2021
## 1567 75.42834 20.0000 On Time Non-FTA 02-05-2024
## 1568 78.67109 20.0000 Delayed FTA 27-11-2020
## 1569 70.57193 20.0000 On Time Non-FTA 01-04-2019
## 1570 81.31202 20.0000 Delayed FTA 16-06-2018
## 1571 81.84196 20.0000 On Time FTA 26-08-2021
## 1572 76.85908 20.0000 On Time FTA 23-10-2022
## 1573 74.72191 20.0000 On Time FTA 22-07-2023
## 1574 83.02742 20.0000 On Time FTA 24-12-2018
## 1575 70.29639 20.0000 Delayed Non-FTA 07-11-2019
## 1576 74.77487 20.0000 On Time Non-FTA 06-05-2019
## 1577 79.44546 20.0000 Delayed FTA 25-03-2020
## 1578 76.34167 20.0000 Delayed FTA 16-09-2022
## 1579 80.62846 20.0000 On Time Non-FTA 12-01-2019
## 1580 84.11549 20.0000 Delayed FTA 05-07-2019
## 1581 77.46485 20.0000 On Time FTA 07-06-2020
## 1582 82.97777 20.0000 Delayed Non-FTA 06-02-2024
## 1583 72.29790 20.0000 On Time Non-FTA 09-02-2024
## 1584 73.51347 20.0000 On Time FTA 22-11-2020
## 1585 76.16274 20.0000 Delayed Non-FTA 21-10-2023
## 1586 84.15013 20.0000 Delayed FTA 22-04-2022
## 1587 71.04542 20.0000 Delayed FTA 11-11-2022
## 1588 84.43027 20.0000 On Time Non-FTA 14-06-2021
## 1589 84.69839 20.0000 On Time FTA 29-02-2020
## 1590 81.59581 20.0000 Delayed FTA 13-03-2021
## 1591 73.07544 20.0000 On Time Non-FTA 22-12-2021
## 1592 84.13795 20.0000 Delayed FTA 13-06-2024
## 1593 70.62757 20.0000 On Time FTA 07-05-2018
## 1594 75.34765 20.0000 Delayed Non-FTA 20-11-2022
## 1595 75.36178 20.0000 Delayed Non-FTA 18-04-2020
## 1596 72.56031 20.0000 On Time FTA 01-10-2021
## 1597 70.90922 20.0000 Delayed FTA 15-09-2022
## 1598 82.92740 20.0000 Delayed Non-FTA 02-04-2020
## 1599 73.94357 20.0000 Delayed Non-FTA 09-12-2020
## 1600 73.41846 20.0000 On Time FTA 20-04-2024
## 1601 79.71535 20.0000 Delayed FTA 03-12-2019
## 1602 79.56927 20.0000 On Time FTA 26-06-2021
## 1603 76.91013 20.0000 On Time Non-FTA 14-03-2023
## 1604 79.72305 20.0000 On Time FTA 22-08-2022
## 1605 82.59509 20.0000 On Time FTA 07-11-2022
## 1606 76.42641 20.0000 On Time Non-FTA 15-06-2021
## 1607 74.10545 20.0000 On Time Non-FTA 23-07-2020
## 1608 70.77953 20.0000 Delayed FTA 09-01-2019
## 1609 76.32687 20.0000 On Time Non-FTA 01-04-2019
## 1610 79.13981 20.0000 On Time FTA 25-08-2020
## 1611 75.87602 20.0000 Delayed Non-FTA 26-11-2021
## 1612 80.39982 20.0000 On Time Non-FTA 17-08-2020
## 1613 71.09006 20.0000 Delayed FTA 24-06-2024
## 1614 72.51601 20.0000 Delayed FTA 29-06-2021
## 1615 70.72815 20.0000 Delayed FTA 13-02-2022
## 1616 78.96108 20.0000 Delayed Non-FTA 10-10-2020
## 1617 77.52077 20.0000 Delayed FTA 24-09-2023
## 1618 83.64742 20.0000 On Time Non-FTA 04-05-2023
## 1619 71.38492 20.0000 On Time FTA 09-12-2022
## 1620 71.71106 20.0000 On Time Non-FTA 13-03-2021
## 1621 75.41886 20.0000 Delayed Non-FTA 24-02-2021
## 1622 72.79820 20.0000 Delayed Non-FTA 12-01-2022
## 1623 77.25896 20.0000 On Time FTA 18-02-2021
## 1624 75.77169 20.0000 Delayed Non-FTA 23-09-2020
## 1625 71.05687 20.0000 Delayed FTA 08-11-2019
## 1626 84.84823 20.0000 On Time Non-FTA 15-05-2023
## 1627 80.38496 20.0000 On Time FTA 25-11-2024
## 1628 79.58772 20.0000 Delayed Non-FTA 06-11-2019
## 1629 72.00620 20.0000 Delayed Non-FTA 20-10-2024
## 1630 74.97245 20.0000 Delayed Non-FTA 14-09-2023
## 1631 78.47435 20.0000 Delayed FTA 09-08-2022
## 1632 73.25729 20.0000 Delayed FTA 08-03-2024
## 1633 76.99584 20.0000 On Time FTA 06-05-2024
## 1634 83.76515 20.0000 Delayed Non-FTA 11-11-2021
## 1635 81.91605 20.0000 Delayed Non-FTA 04-11-2021
## 1636 73.86165 20.0000 Delayed Non-FTA 28-08-2022
## 1637 84.90130 20.0000 Delayed Non-FTA 29-07-2022
## 1638 78.12188 20.0000 Delayed Non-FTA 27-06-2023
## 1639 83.52970 20.0000 On Time Non-FTA 17-09-2020
## 1640 82.62617 20.0000 On Time Non-FTA 21-04-2022
## 1641 83.59789 20.0000 Delayed FTA 05-06-2020
## 1642 79.95654 20.0000 On Time Non-FTA 08-12-2019
## 1643 82.68901 20.0000 Delayed Non-FTA 13-08-2018
## 1644 84.82159 20.0000 On Time FTA 15-12-2019
## 1645 83.69765 20.0000 Delayed Non-FTA 25-09-2021
## 1646 82.05436 20.0000 Delayed Non-FTA 20-02-2018
## 1647 72.10798 21.0000 Delayed Non-FTA 24-03-2019
## 1648 80.67289 21.0000 Delayed Non-FTA 25-04-2022
## 1649 76.48171 21.0000 On Time Non-FTA 25-05-2019
## 1650 70.70322 21.0000 On Time Non-FTA 29-07-2024
## 1651 78.63063 21.0000 Delayed Non-FTA 14-12-2019
## 1652 83.80542 21.0000 On Time Non-FTA 30-04-2018
## 1653 80.36387 21.0000 On Time Non-FTA 14-03-2020
## 1654 70.03863 21.0000 Delayed Non-FTA 02-06-2021
## 1655 76.18308 21.0000 On Time FTA 27-03-2023
## 1656 72.71218 21.0000 Delayed Non-FTA 09-07-2019
## 1657 72.13246 21.0000 Delayed FTA 22-09-2023
## 1658 76.91124 21.0000 Delayed FTA 05-10-2024
## 1659 79.92758 21.0000 On Time Non-FTA 06-06-2022
## 1660 79.91995 21.0000 On Time FTA 16-12-2023
## 1661 74.79337 21.0000 On Time Non-FTA 09-06-2022
## 1662 80.14007 21.0000 On Time Non-FTA 15-10-2024
## 1663 84.87643 21.0000 Delayed FTA 22-11-2023
## 1664 83.83179 21.0000 On Time Non-FTA 20-01-2019
## 1665 72.46549 21.0000 Delayed Non-FTA 13-06-2018
## 1666 72.77610 21.0000 Delayed Non-FTA 20-12-2020
## 1667 75.67174 21.0000 On Time FTA 22-07-2019
## 1668 84.78803 21.0000 Delayed Non-FTA 14-02-2023
## 1669 76.57715 21.0000 Delayed FTA 08-05-2018
## 1670 80.20284 21.0000 On Time Non-FTA 15-07-2021
## 1671 78.52306 21.0000 Delayed FTA 07-09-2022
## 1672 71.61977 21.0000 On Time FTA 06-04-2021
## 1673 78.50683 21.0000 On Time Non-FTA 04-09-2021
## 1674 73.22248 21.0000 Delayed FTA 16-04-2020
## 1675 72.83658 21.0000 Delayed FTA 16-08-2024
## 1676 83.70316 21.0000 Delayed FTA 01-11-2019
## 1677 71.04091 21.0000 Delayed Non-FTA 10-11-2022
## 1678 81.56011 21.0000 Delayed FTA 14-04-2021
## 1679 77.62514 21.0000 Delayed FTA 08-03-2020
## 1680 81.03055 21.0000 Delayed Non-FTA 23-12-2022
## 1681 76.56366 21.0000 On Time Non-FTA 08-01-2019
## 1682 84.31780 21.0000 Delayed Non-FTA 28-11-2022
## 1683 83.33948 21.0000 Delayed FTA 30-03-2018
## 1684 70.06895 21.0000 Delayed FTA 01-09-2019
## 1685 74.69974 21.0000 On Time FTA 02-04-2020
## 1686 76.78052 21.0000 Delayed Non-FTA 11-07-2018
## 1687 78.14671 21.0000 On Time Non-FTA 13-08-2019
## 1688 77.58139 21.0000 Delayed Non-FTA 14-10-2019
## 1689 82.52928 21.0000 On Time Non-FTA 08-09-2022
## 1690 70.84430 21.0000 Delayed Non-FTA 04-08-2022
## 1691 72.83601 21.0000 On Time Non-FTA 04-12-2023
## 1692 84.02376 21.0000 Delayed FTA 08-12-2018
## 1693 83.30754 21.0000 Delayed Non-FTA 20-03-2019
## 1694 70.69448 21.0000 On Time FTA 05-08-2019
## 1695 80.47961 21.0000 On Time FTA 10-03-2018
## 1696 74.77401 21.0000 On Time FTA 14-06-2018
## 1697 81.05379 21.0000 Delayed Non-FTA 14-01-2023
## 1698 72.46561 21.0000 Delayed FTA 27-11-2019
## 1699 79.40176 21.0000 On Time Non-FTA 28-07-2023
## 1700 74.40467 21.0000 Delayed FTA 23-11-2023
## 1701 75.34681 21.0000 Delayed Non-FTA 20-05-2024
## 1702 81.18533 21.0000 Delayed Non-FTA 10-01-2020
## 1703 79.49090 21.0000 On Time FTA 13-01-2024
## 1704 80.70589 21.0000 On Time Non-FTA 04-03-2024
## 1705 78.31938 21.0000 Delayed Non-FTA 13-04-2020
## 1706 82.72606 21.0000 On Time Non-FTA 23-08-2023
## 1707 79.58202 21.0000 On Time FTA 27-03-2018
## 1708 76.21946 21.0000 Delayed Non-FTA 13-06-2018
## 1709 75.30703 21.0000 On Time FTA 03-03-2020
## 1710 78.77419 21.0000 Delayed FTA 22-01-2024
## 1711 76.66170 21.0000 Delayed FTA 12-06-2021
## 1712 70.81723 21.0000 On Time FTA 23-10-2018
## 1713 82.28567 21.0000 Delayed Non-FTA 11-04-2024
## 1714 80.24419 21.0000 Delayed FTA 14-02-2020
## 1715 78.16160 21.0000 On Time Non-FTA 22-09-2023
## 1716 73.76738 21.0000 Delayed FTA 11-10-2022
## 1717 78.15549 21.0000 On Time Non-FTA 02-03-2024
## 1718 80.38101 21.0000 On Time FTA 19-05-2023
## 1719 76.20278 21.0000 Delayed FTA 26-12-2020
## 1720 82.86498 21.0000 Delayed Non-FTA 22-08-2023
## 1721 77.57753 21.0000 On Time Non-FTA 18-10-2023
## 1722 77.79127 21.0000 On Time Non-FTA 22-11-2021
## 1723 70.70776 21.0000 Delayed FTA 10-08-2024
## 1724 73.55016 21.0000 Delayed FTA 14-04-2024
## 1725 70.49075 21.0000 Delayed Non-FTA 30-12-2022
## 1726 73.23849 21.0000 On Time Non-FTA 29-02-2024
## 1727 83.63777 21.0000 On Time FTA 26-12-2020
## 1728 83.74180 21.0000 On Time Non-FTA 23-06-2024
## 1729 71.04990 21.0000 On Time Non-FTA 07-07-2023
## 1730 76.43142 21.0000 Delayed FTA 10-08-2020
## 1731 72.28655 21.0000 Delayed FTA 20-02-2021
## 1732 84.52298 21.0000 On Time Non-FTA 22-12-2019
## 1733 72.04134 21.0000 On Time FTA 05-08-2024
## 1734 75.94753 21.0000 On Time Non-FTA 04-10-2023
## 1735 74.06145 21.0000 Delayed Non-FTA 26-11-2018
## 1736 75.92385 21.0000 On Time Non-FTA 25-09-2024
## 1737 73.70586 22.0000 On Time FTA 30-11-2018
## 1738 71.40248 22.0000 Delayed Non-FTA 16-01-2022
## 1739 82.62163 22.0000 Delayed FTA 14-09-2021
## 1740 84.98696 22.0000 On Time FTA 13-05-2021
## 1741 77.49064 22.0000 On Time FTA 27-04-2019
## 1742 70.19353 22.0000 On Time FTA 16-09-2023
## 1743 78.42345 22.0000 Delayed FTA 25-07-2023
## 1744 84.88850 22.0000 On Time Non-FTA 24-03-2022
## 1745 78.02267 22.0000 On Time FTA 20-08-2019
## 1746 72.61964 22.0000 On Time FTA 21-08-2024
## 1747 72.11201 22.0000 Delayed FTA 15-12-2024
## 1748 83.42888 22.0000 On Time Non-FTA 26-07-2020
## 1749 81.77329 22.0000 On Time Non-FTA 08-08-2019
## 1750 72.91240 22.0000 Delayed FTA 30-06-2018
## 1751 79.05813 22.0000 On Time Non-FTA 30-05-2019
## 1752 77.66724 22.0000 Delayed FTA 11-01-2021
## 1753 75.37728 22.0000 On Time Non-FTA 09-04-2023
## 1754 76.00090 22.0000 On Time Non-FTA 23-10-2020
## 1755 84.66510 22.0000 On Time Non-FTA 19-04-2018
## 1756 70.79951 22.0000 Delayed FTA 07-02-2023
## 1757 78.16965 22.0000 On Time FTA 28-03-2023
## 1758 83.47899 22.0000 On Time Non-FTA 10-12-2023
## 1759 82.63491 22.0000 On Time FTA 14-12-2023
## 1760 78.12935 22.0000 Delayed FTA 02-08-2021
## 1761 76.07128 22.0000 Delayed Non-FTA 10-07-2022
## 1762 77.04706 22.0000 On Time FTA 29-12-2018
## 1763 80.43331 22.0000 On Time FTA 11-04-2018
## 1764 71.65580 22.0000 Delayed FTA 09-02-2018
## 1765 71.68001 22.0000 Delayed FTA 30-08-2018
## 1766 76.83727 22.0000 Delayed FTA 01-09-2021
## 1767 76.48132 22.0000 On Time FTA 25-05-2019
## 1768 73.03791 22.0000 Delayed Non-FTA 07-05-2019
## 1769 74.44467 22.0000 Delayed FTA 08-05-2023
## 1770 84.87904 22.0000 Delayed Non-FTA 07-04-2024
## 1771 79.33953 22.0000 On Time FTA 15-04-2024
## 1772 71.57496 22.0000 Delayed FTA 12-09-2024
## 1773 71.42204 22.0000 Delayed FTA 09-05-2018
## 1774 84.89949 22.0000 Delayed FTA 03-12-2018
## 1775 72.23476 22.0000 On Time FTA 29-10-2023
## 1776 74.91260 22.0000 On Time FTA 12-01-2019
## 1777 80.51994 22.0000 On Time Non-FTA 30-05-2023
## 1778 75.38775 22.0000 On Time Non-FTA 05-03-2020
## 1779 82.34015 22.0000 Delayed Non-FTA 19-12-2024
## 1780 81.40074 22.0000 Delayed Non-FTA 10-12-2023
## 1781 71.59637 22.0000 On Time FTA 08-12-2020
## 1782 81.09107 22.0000 Delayed Non-FTA 13-01-2018
## 1783 70.04384 22.0000 Delayed FTA 14-09-2020
## 1784 74.55032 22.0000 On Time Non-FTA 27-11-2019
## 1785 74.20800 22.0000 Delayed FTA 15-06-2019
## 1786 75.83972 22.0000 On Time Non-FTA 06-11-2024
## 1787 84.34138 22.0000 On Time Non-FTA 13-04-2023
## 1788 80.31262 22.0000 Delayed Non-FTA 26-06-2022
## 1789 70.71896 22.0000 Delayed Non-FTA 25-08-2021
## 1790 83.04800 22.0000 On Time Non-FTA 28-01-2024
## 1791 80.71468 22.0000 Delayed FTA 30-10-2021
## 1792 83.64172 22.0000 Delayed FTA 14-04-2023
## 1793 72.87399 22.0000 Delayed Non-FTA 12-11-2024
## 1794 75.26821 22.0000 Delayed FTA 10-07-2020
## 1795 75.39923 22.0000 Delayed Non-FTA 27-07-2019
## 1796 77.50551 22.0000 On Time Non-FTA 08-10-2019
## 1797 78.18871 22.0000 On Time FTA 06-03-2024
## 1798 84.15643 22.0000 On Time Non-FTA 16-12-2019
## 1799 75.76163 22.0000 On Time Non-FTA 25-09-2023
## 1800 76.68132 22.0000 Delayed Non-FTA 19-05-2023
## 1801 70.33763 22.0000 On Time FTA 10-08-2021
## 1802 80.51142 22.0000 Delayed FTA 05-05-2024
## 1803 77.30160 22.0000 Delayed Non-FTA 07-05-2023
## 1804 70.73429 22.0000 On Time FTA 08-07-2024
## 1805 70.80189 22.0000 On Time Non-FTA 09-10-2023
## 1806 71.50063 22.0000 Delayed FTA 05-07-2023
## 1807 72.77274 22.0000 On Time Non-FTA 14-06-2020
## 1808 77.96816 23.0000 Delayed FTA 04-04-2023
## 1809 72.19313 23.0000 On Time Non-FTA 13-10-2022
## 1810 72.40437 23.0000 On Time Non-FTA 07-07-2019
## 1811 70.11756 23.0000 Delayed Non-FTA 07-03-2024
## 1812 70.48706 23.0000 Delayed FTA 01-10-2019
## 1813 78.28068 23.0000 On Time Non-FTA 27-03-2019
## 1814 73.09115 23.0000 On Time Non-FTA 12-06-2024
## 1815 81.38451 23.0000 On Time Non-FTA 11-01-2022
## 1816 72.90174 23.0000 On Time Non-FTA 15-06-2023
## 1817 71.21858 23.0000 Delayed FTA 16-11-2024
## 1818 78.72575 23.0000 Delayed Non-FTA 01-07-2019
## 1819 84.48208 23.0000 On Time Non-FTA 05-06-2020
## 1820 71.29854 23.0000 Delayed Non-FTA 09-12-2018
## 1821 74.64450 23.0000 On Time Non-FTA 15-02-2018
## 1822 76.66545 23.0000 Delayed Non-FTA 15-06-2023
## 1823 84.96098 23.0000 On Time FTA 22-11-2023
## 1824 70.75471 23.0000 On Time FTA 04-07-2021
## 1825 82.15421 23.0000 Delayed Non-FTA 05-08-2020
## 1826 77.32873 23.0000 Delayed FTA 14-01-2021
## 1827 77.99489 23.0000 On Time FTA 27-07-2022
## 1828 80.67653 23.0000 Delayed FTA 04-07-2019
## 1829 80.57255 23.0000 Delayed Non-FTA 30-07-2024
## 1830 83.22336 23.0000 On Time Non-FTA 18-08-2020
## 1831 81.83824 23.0000 On Time FTA 03-08-2022
## 1832 84.82181 23.0000 On Time FTA 30-04-2020
## 1833 73.73945 23.0000 On Time Non-FTA 28-09-2022
## 1834 82.39299 23.0000 On Time FTA 19-12-2021
## 1835 79.66583 23.0000 Delayed FTA 23-06-2018
## 1836 72.56212 23.0000 Delayed Non-FTA 21-08-2018
## 1837 78.37952 23.0000 Delayed FTA 28-08-2020
## 1838 79.83633 23.0000 Delayed Non-FTA 13-02-2020
## 1839 80.69554 23.0000 On Time Non-FTA 29-11-2023
## 1840 72.01009 23.0000 Delayed Non-FTA 07-05-2022
## 1841 82.07390 23.0000 Delayed FTA 25-06-2019
## 1842 78.04357 23.0000 On Time Non-FTA 24-11-2020
## 1843 71.80268 23.0000 Delayed Non-FTA 06-12-2018
## 1844 74.06662 23.0000 Delayed FTA 20-05-2023
## 1845 71.14690 23.0000 Delayed Non-FTA 06-06-2024
## 1846 74.61268 23.0000 On Time Non-FTA 12-11-2019
## 1847 80.21848 23.0000 On Time FTA 12-11-2022
## 1848 82.74865 23.0000 On Time Non-FTA 26-05-2022
## 1849 75.30857 23.0000 Delayed FTA 11-01-2023
## 1850 74.87805 23.0000 On Time Non-FTA 20-10-2023
## 1851 77.85491 23.0000 On Time FTA 27-04-2022
## 1852 70.51323 23.0000 On Time FTA 03-09-2020
## 1853 77.59955 23.0000 On Time Non-FTA 22-10-2022
## 1854 83.94738 23.0000 Delayed Non-FTA 09-09-2021
## 1855 81.28547 23.0000 Delayed Non-FTA 18-09-2022
## 1856 82.03975 23.0000 Delayed Non-FTA 15-10-2023
## 1857 73.04454 23.0000 On Time Non-FTA 08-07-2019
## 1858 81.24306 23.0000 On Time FTA 05-03-2019
## 1859 76.66729 23.0000 On Time Non-FTA 08-07-2024
## 1860 73.10436 23.0000 On Time FTA 13-01-2024
## 1861 70.99352 23.0000 Delayed Non-FTA 26-04-2019
## 1862 81.34153 23.0000 Delayed Non-FTA 06-07-2022
## 1863 77.26405 23.0000 On Time Non-FTA 05-02-2020
## 1864 71.78309 23.0000 Delayed FTA 29-11-2021
## 1865 78.38192 23.0000 Delayed FTA 28-05-2022
## 1866 80.84136 23.0000 Delayed FTA 21-06-2022
## 1867 82.66602 23.0000 Delayed FTA 15-09-2018
## 1868 76.55455 23.0000 Delayed FTA 11-08-2020
## 1869 75.90842 23.0000 On Time Non-FTA 19-02-2024
## 1870 75.94577 23.0000 On Time Non-FTA 23-03-2018
## 1871 76.08649 23.0000 On Time FTA 08-10-2022
## 1872 84.98715 23.0000 On Time FTA 28-09-2021
## 1873 74.22100 23.0000 Delayed FTA 10-05-2021
## 1874 82.98081 23.0000 Delayed FTA 19-05-2023
## 1875 72.59653 23.0000 On Time Non-FTA 04-03-2024
## 1876 73.37082 23.0000 On Time FTA 31-10-2020
## 1877 82.41997 23.0000 Delayed FTA 18-12-2021
## 1878 74.78858 23.0000 On Time Non-FTA 12-01-2018
## 1879 75.49661 23.0000 Delayed FTA 08-06-2019
## 1880 77.09869 23.0000 On Time Non-FTA 10-02-2023
## 1881 76.49765 23.0000 Delayed FTA 26-01-2023
## 1882 83.54634 23.0000 Delayed FTA 10-09-2021
## 1883 74.50358 23.0000 Delayed FTA 11-05-2021
## 1884 71.75080 23.0000 On Time Non-FTA 23-09-2024
## 1885 73.89785 23.0000 Delayed Non-FTA 12-02-2024
## 1886 78.09299 23.0000 On Time Non-FTA 15-11-2020
## 1887 78.19202 23.0000 On Time FTA 28-01-2023
## 1888 84.48641 23.0000 On Time Non-FTA 25-01-2023
## 1889 73.87511 23.0000 On Time FTA 21-11-2021
## 1890 74.60207 23.0000 On Time FTA 28-07-2018
## 1891 78.41345 23.0000 On Time FTA 28-08-2023
## 1892 82.95784 23.0000 On Time FTA 10-08-2024
## 1893 80.75647 23.0000 Delayed Non-FTA 05-03-2018
## 1894 74.35088 23.0000 On Time Non-FTA 12-07-2021
## 1895 79.20774 23.0000 Delayed FTA 07-04-2018
## 1896 77.46793 23.0000 Delayed Non-FTA 05-04-2021
## 1897 78.94277 23.0000 On Time Non-FTA 04-05-2020
## 1898 77.24028 23.0000 Delayed Non-FTA 29-01-2018
## 1899 73.73082 23.0000 Delayed Non-FTA 30-03-2022
## 1900 76.72572 23.0000 Delayed Non-FTA 23-10-2020
## 1901 70.40955 23.0000 On Time FTA 04-02-2023
## 1902 72.95553 23.0000 On Time Non-FTA 16-03-2022
## 1903 84.48190 23.0000 Delayed FTA 02-12-2019
## 1904 83.15373 23.0000 Delayed Non-FTA 29-07-2024
## 1905 82.72099 23.0000 On Time Non-FTA 15-04-2024
## 1906 77.53988 23.0000 On Time FTA 16-07-2023
## 1907 74.42373 23.0000 On Time Non-FTA 26-09-2022
## 1908 83.96736 23.0000 Delayed Non-FTA 09-03-2020
## 1909 77.11409 23.0000 Delayed Non-FTA 19-04-2024
## 1910 72.05115 23.0000 Delayed Non-FTA 10-06-2020
## 1911 77.98541 23.0000 On Time FTA 12-02-2023
## 1912 74.94972 23.0000 On Time FTA 10-04-2024
## 1913 84.88482 24.0000 On Time Non-FTA 27-12-2022
## 1914 75.04471 24.0000 On Time FTA 07-05-2024
## 1915 79.82735 24.0000 On Time Non-FTA 01-04-2019
## 1916 73.59799 24.0000 On Time Non-FTA 12-08-2018
## 1917 84.38110 24.0000 Delayed FTA 19-10-2022
## 1918 82.97007 24.0000 Delayed FTA 27-07-2020
## 1919 72.08120 24.0000 On Time FTA 13-03-2022
## 1920 72.65408 24.0000 Delayed FTA 11-12-2018
## 1921 82.65233 24.0000 Delayed FTA 02-03-2024
## 1922 72.55018 24.0000 Delayed FTA 20-09-2023
## 1923 75.17313 24.0000 Delayed FTA 10-02-2018
## 1924 81.14138 24.0000 On Time Non-FTA 06-08-2020
## 1925 75.34682 24.0000 On Time FTA 20-09-2019
## 1926 83.56618 24.0000 Delayed Non-FTA 25-10-2024
## 1927 76.65378 24.0000 On Time FTA 04-09-2020
## 1928 83.08373 24.0000 On Time FTA 02-12-2019
## 1929 74.37279 24.0000 Delayed FTA 05-07-2019
## 1930 72.92112 24.0000 On Time FTA 01-11-2018
## 1931 78.02258 24.0000 Delayed FTA 29-12-2020
## 1932 70.10225 24.0000 Delayed FTA 06-11-2022
## 1933 71.40500 24.0000 Delayed FTA 21-05-2019
## 1934 79.71828 24.0000 On Time FTA 13-07-2020
## 1935 73.08575 24.0000 Delayed FTA 13-04-2018
## 1936 81.32145 24.0000 Delayed FTA 21-03-2021
## 1937 72.64909 24.0000 On Time FTA 23-12-2024
## 1938 74.81434 24.0000 On Time Non-FTA 02-01-2019
## 1939 77.78508 24.0000 Delayed FTA 05-03-2018
## 1940 74.83600 24.0000 On Time FTA 04-07-2018
## 1941 75.92473 24.0000 On Time Non-FTA 25-04-2020
## 1942 78.85661 24.0000 On Time FTA 16-04-2018
## 1943 79.30999 24.0000 Delayed Non-FTA 05-07-2022
## 1944 73.50778 24.0000 On Time FTA 22-10-2024
## 1945 83.63302 24.0000 On Time Non-FTA 20-07-2022
## 1946 74.04224 24.0000 On Time FTA 27-08-2018
## 1947 79.72326 24.0000 Delayed FTA 12-07-2018
## 1948 71.18355 24.0000 Delayed FTA 15-02-2022
## 1949 74.49752 24.0000 On Time Non-FTA 18-04-2024
## 1950 83.99230 24.0000 On Time Non-FTA 19-09-2019
## 1951 84.43159 24.0000 Delayed FTA 11-11-2022
## 1952 71.62088 24.0000 Delayed Non-FTA 15-05-2022
## 1953 72.44585 24.0000 On Time Non-FTA 03-05-2022
## 1954 80.62713 24.0000 On Time Non-FTA 05-12-2019
## 1955 82.18268 24.0000 Delayed Non-FTA 29-03-2019
## 1956 82.62072 24.0000 On Time Non-FTA 28-06-2024
## 1957 81.97552 24.0000 On Time Non-FTA 28-01-2023
## 1958 80.93885 24.0000 On Time Non-FTA 15-04-2018
## 1959 76.37574 24.0000 Delayed FTA 05-03-2023
## 1960 84.35251 24.0000 On Time Non-FTA 14-03-2021
## 1961 70.28490 24.0000 On Time FTA 21-03-2018
## 1962 78.98167 24.0000 Delayed Non-FTA 20-09-2023
## 1963 82.02994 24.0000 On Time Non-FTA 03-10-2024
## 1964 73.35508 24.0000 Delayed Non-FTA 28-12-2023
## 1965 71.80665 24.0000 Delayed Non-FTA 12-05-2023
## 1966 71.72211 24.0000 Delayed FTA 21-12-2020
## 1967 83.73628 24.0000 On Time FTA 31-08-2019
## 1968 70.48292 24.0000 On Time Non-FTA 29-09-2018
## 1969 84.24868 24.0000 Delayed Non-FTA 22-12-2023
## 1970 73.54551 24.0000 Delayed Non-FTA 16-01-2019
## 1971 76.72956 24.0000 Delayed Non-FTA 25-09-2019
## 1972 75.02513 24.0000 Delayed Non-FTA 08-12-2022
## 1973 84.73613 24.0000 On Time Non-FTA 01-11-2020
## 1974 84.01551 24.0000 On Time Non-FTA 18-05-2019
## 1975 74.04503 24.0000 Delayed Non-FTA 26-05-2019
## 1976 77.66628 24.0000 Delayed Non-FTA 20-05-2021
## 1977 77.20603 24.0000 Delayed FTA 14-07-2020
## 1978 73.31261 24.0000 Delayed Non-FTA 26-11-2023
## 1979 72.60358 24.0000 On Time Non-FTA 12-03-2024
## 1980 84.48497 24.0000 On Time FTA 10-05-2020
## 1981 84.50883 24.0000 On Time Non-FTA 04-01-2024
## 1982 78.26386 24.0000 On Time FTA 08-09-2021
## 1983 78.70199 24.0000 On Time Non-FTA 06-05-2023
## 1984 75.83484 24.0000 Delayed Non-FTA 06-12-2020
## 1985 76.72264 24.0000 Delayed FTA 01-04-2023
## 1986 76.21062 24.0000 Delayed Non-FTA 29-05-2021
## 1987 80.07238 24.0000 On Time FTA 22-06-2022
## 1988 82.65794 24.0000 On Time FTA 14-05-2021
## 1989 76.01469 24.0000 On Time Non-FTA 15-12-2021
## 1990 70.25209 24.0000 Delayed Non-FTA 03-05-2023
## 1991 72.41852 24.0000 On Time FTA 01-08-2022
## 1992 75.30953 24.0000 On Time Non-FTA 01-06-2020
## 1993 82.63706 24.0000 Delayed Non-FTA 22-02-2021
## 1994 70.40056 24.0000 Delayed Non-FTA 23-08-2023
## 1995 81.36218 24.0000 Delayed FTA 29-10-2019
## 1996 71.44627 24.0000 On Time Non-FTA 08-08-2021
## 1997 73.74737 24.0000 Delayed FTA 27-08-2024
## 1998 76.02245 24.0000 On Time FTA 21-12-2019
## 1999 83.17597 24.0000 Delayed FTA 27-12-2022
## 2000 78.73046 24.0000 On Time FTA 03-07-2019
## 2001 75.73868 24.0000 On Time Non-FTA 18-08-2023
## 2002 84.04671 24.0000 On Time FTA 18-03-2021
## 2003 82.23102 24.0000 Delayed FTA 12-07-2019
## 2004 71.51720 25.0000 On Time Non-FTA 07-12-2022
## 2005 77.72281 25.0000 Delayed FTA 06-04-2021
## 2006 74.43500 25.0000 Delayed FTA 18-09-2019
## 2007 82.87687 25.0000 On Time FTA 11-12-2018
## 2008 71.31080 25.0000 Delayed Non-FTA 29-12-2018
## 2009 75.15139 25.0000 On Time FTA 18-04-2018
## 2010 75.62083 25.0000 Delayed FTA 29-05-2018
## 2011 80.78279 25.0000 On Time FTA 10-06-2024
## 2012 83.63168 25.0000 On Time Non-FTA 27-04-2018
## 2013 70.39137 25.0000 On Time Non-FTA 02-06-2018
## 2014 83.80106 25.0000 On Time Non-FTA 13-12-2018
## 2015 70.80975 25.0000 On Time Non-FTA 05-06-2023
## 2016 75.23089 25.0000 Delayed FTA 03-04-2021
## 2017 75.52041 25.0000 On Time Non-FTA 28-12-2024
## 2018 81.20015 25.0000 Delayed FTA 07-02-2021
## 2019 82.13408 25.0000 Delayed FTA 17-02-2021
## 2020 71.80294 25.0000 Delayed FTA 03-05-2022
## 2021 79.32654 25.0000 On Time FTA 07-04-2024
## 2022 80.51001 25.0000 Delayed FTA 02-04-2019
## 2023 80.24710 25.0000 Delayed FTA 02-04-2023
## 2024 76.24662 25.0000 On Time Non-FTA 11-03-2020
## 2025 76.98430 25.0000 On Time Non-FTA 08-08-2022
## 2026 84.46642 25.0000 On Time FTA 05-06-2023
## 2027 70.72563 25.0000 Delayed FTA 16-10-2022
## 2028 73.79247 25.0000 On Time Non-FTA 27-05-2022
## 2029 79.74443 25.0000 On Time FTA 12-06-2022
## 2030 77.18669 25.0000 On Time Non-FTA 10-08-2023
## 2031 81.62209 25.0000 Delayed Non-FTA 02-01-2019
## 2032 83.63000 25.0000 Delayed Non-FTA 26-01-2020
## 2033 78.40010 25.0000 On Time Non-FTA 17-11-2021
## 2034 81.61449 25.0000 Delayed Non-FTA 23-02-2020
## 2035 73.19008 25.0000 On Time FTA 24-11-2021
## 2036 78.67141 25.0000 On Time Non-FTA 22-10-2022
## 2037 78.25138 25.0000 On Time FTA 15-03-2019
## 2038 84.15790 25.0000 On Time FTA 23-02-2018
## 2039 73.75099 25.0000 On Time FTA 03-05-2018
## 2040 84.03611 25.0000 Delayed FTA 14-11-2024
## 2041 82.54376 25.0000 On Time Non-FTA 15-08-2023
## 2042 75.56315 25.0000 On Time Non-FTA 10-10-2022
## 2043 70.02637 25.0000 On Time FTA 03-10-2023
## 2044 73.34353 25.0000 On Time Non-FTA 12-10-2020
## 2045 75.01948 25.0000 On Time Non-FTA 06-09-2018
## 2046 76.02928 25.0000 Delayed Non-FTA 02-05-2022
## 2047 73.11632 25.0000 Delayed FTA 30-05-2022
## 2048 74.94605 25.0000 On Time FTA 06-03-2019
## 2049 78.34618 25.0000 Delayed Non-FTA 05-01-2023
## 2050 81.06193 25.0000 On Time Non-FTA 12-08-2018
## 2051 71.34393 25.0000 On Time Non-FTA 10-04-2018
## 2052 82.72438 25.0000 On Time Non-FTA 29-10-2020
## 2053 71.40880 25.0000 On Time Non-FTA 08-06-2019
## 2054 75.68707 25.0000 On Time FTA 03-07-2018
## 2055 79.48247 25.0000 On Time FTA 26-06-2021
## 2056 80.68081 25.0000 On Time Non-FTA 15-02-2024
## 2057 73.30229 25.0000 On Time Non-FTA 08-10-2022
## 2058 73.78066 25.0000 On Time Non-FTA 27-01-2022
## 2059 70.08001 25.0000 On Time FTA 14-02-2022
## 2060 70.36570 25.0000 Delayed Non-FTA 03-02-2023
## 2061 80.25662 25.0000 Delayed Non-FTA 28-04-2024
## 2062 77.80456 25.0000 Delayed FTA 01-05-2023
## 2063 77.32271 25.0000 Delayed Non-FTA 14-06-2018
## 2064 81.43094 25.0000 On Time FTA 27-01-2021
## 2065 82.47172 25.0000 Delayed FTA 29-09-2023
## 2066 72.30958 25.0000 On Time FTA 24-09-2021
## 2067 77.38790 25.0000 On Time FTA 11-05-2024
## 2068 70.87840 25.0000 On Time Non-FTA 15-12-2021
## 2069 78.87157 25.0000 Delayed FTA 26-03-2024
## 2070 81.54090 25.0000 Delayed Non-FTA 07-07-2024
## 2071 74.21170 25.0000 Delayed FTA 19-11-2019
## 2072 75.90986 25.0000 Delayed FTA 27-12-2023
## 2073 84.70135 26.0000 On Time FTA 05-08-2021
## 2074 81.39674 26.0000 On Time Non-FTA 08-12-2019
## 2075 81.39947 26.0000 On Time FTA 08-06-2023
## 2076 75.51236 26.0000 Delayed Non-FTA 12-01-2021
## 2077 82.33828 26.0000 On Time Non-FTA 27-06-2023
## 2078 77.76794 26.0000 On Time FTA 22-05-2021
## 2079 73.53652 26.0000 Delayed Non-FTA 12-12-2020
## 2080 71.53894 26.0000 On Time Non-FTA 17-05-2020
## 2081 75.87476 26.0000 On Time FTA 05-08-2024
## 2082 73.86110 26.0000 Delayed Non-FTA 09-06-2023
## 2083 72.36732 26.0000 On Time Non-FTA 03-12-2022
## 2084 80.43399 26.0000 On Time Non-FTA 14-05-2018
## 2085 72.16284 26.0000 On Time FTA 27-04-2023
## 2086 84.21656 26.0000 On Time FTA 22-07-2023
## 2087 73.47294 26.0000 On Time FTA 24-01-2024
## 2088 78.63594 26.0000 Delayed Non-FTA 03-05-2022
## 2089 83.20807 26.0000 Delayed FTA 08-10-2018
## 2090 77.58255 26.0000 On Time FTA 14-05-2018
## 2091 83.47324 26.0000 On Time FTA 18-11-2019
## 2092 74.67338 26.0000 On Time FTA 08-06-2021
## 2093 78.48081 26.0000 Delayed Non-FTA 14-03-2024
## 2094 79.88038 26.0000 Delayed FTA 04-09-2024
## 2095 75.04375 26.0000 Delayed Non-FTA 19-07-2023
## 2096 73.05060 26.0000 On Time FTA 26-01-2020
## 2097 82.33669 26.0000 On Time FTA 11-09-2023
## 2098 75.45265 26.0000 On Time FTA 22-02-2024
## 2099 78.95024 26.0000 Delayed FTA 27-09-2019
## 2100 73.40481 26.0000 On Time FTA 15-07-2019
## 2101 74.91083 26.0000 Delayed FTA 20-10-2019
## 2102 83.28652 26.0000 On Time FTA 29-11-2018
## 2103 80.48986 26.0000 On Time Non-FTA 28-07-2020
## 2104 76.62075 26.0000 Delayed Non-FTA 05-05-2024
## 2105 72.25167 26.0000 On Time Non-FTA 17-01-2018
## 2106 78.70732 26.0000 On Time Non-FTA 14-07-2023
## 2107 72.88360 26.0000 Delayed FTA 18-11-2021
## 2108 80.91728 26.0000 Delayed FTA 14-10-2022
## 2109 74.06370 26.0000 On Time Non-FTA 24-05-2018
## 2110 71.25600 26.0000 Delayed FTA 14-10-2022
## 2111 83.35518 26.0000 On Time FTA 24-12-2024
## 2112 76.89779 26.0000 Delayed FTA 06-10-2022
## 2113 82.95777 26.0000 On Time FTA 03-02-2019
## 2114 78.24691 26.0000 On Time Non-FTA 17-07-2019
## 2115 84.69967 26.0000 Delayed Non-FTA 08-08-2019
## 2116 75.59431 26.0000 Delayed Non-FTA 03-11-2022
## 2117 83.16011 26.0000 On Time FTA 13-10-2023
## 2118 73.81031 26.0000 Delayed Non-FTA 03-06-2022
## 2119 70.50698 26.0000 Delayed FTA 23-02-2018
## 2120 82.32606 26.0000 On Time Non-FTA 07-03-2024
## 2121 74.45330 26.0000 Delayed Non-FTA 29-10-2024
## 2122 77.59279 26.0000 On Time FTA 18-12-2024
## 2123 71.26862 26.0000 Delayed FTA 01-08-2020
## 2124 76.34549 26.0000 On Time Non-FTA 10-11-2022
## 2125 76.21927 26.0000 On Time FTA 23-02-2023
## 2126 76.08060 26.0000 Delayed Non-FTA 29-06-2018
## 2127 71.58704 26.0000 Delayed Non-FTA 23-04-2024
## 2128 79.33632 26.0000 On Time FTA 28-09-2021
## 2129 72.86911 26.0000 On Time Non-FTA 11-11-2018
## 2130 81.33376 26.0000 On Time FTA 21-03-2021
## 2131 81.51296 26.0000 Delayed FTA 24-03-2023
## 2132 79.34299 26.0000 On Time Non-FTA 21-03-2022
## 2133 81.10269 26.0000 On Time FTA 16-12-2024
## 2134 75.80767 26.0000 On Time FTA 30-01-2020
## 2135 84.78703 26.0000 On Time Non-FTA 08-08-2024
## 2136 77.20961 26.0000 Delayed FTA 27-11-2021
## 2137 83.49543 26.0000 On Time FTA 07-12-2024
## 2138 74.67911 26.0000 On Time FTA 26-09-2024
## 2139 73.01033 26.0000 On Time Non-FTA 05-09-2018
## 2140 77.08814 26.0000 On Time FTA 06-04-2024
## 2141 75.22428 26.0000 Delayed Non-FTA 22-12-2023
## 2142 72.35688 26.0000 On Time Non-FTA 07-07-2022
## 2143 79.67511 26.0000 Delayed Non-FTA 23-04-2018
## 2144 77.36764 26.0000 On Time Non-FTA 20-05-2024
## 2145 80.38996 26.0000 On Time FTA 30-10-2024
## 2146 72.24083 26.0000 Delayed FTA 15-12-2021
## 2147 76.54000 26.0000 On Time Non-FTA 15-08-2018
## 2148 84.33844 26.0000 Delayed FTA 29-04-2024
## 2149 75.55548 26.0000 Delayed FTA 15-07-2024
## 2150 84.43940 26.0000 On Time Non-FTA 08-12-2022
## 2151 79.42431 26.0000 Delayed Non-FTA 09-12-2018
## 2152 74.05969 26.0000 On Time Non-FTA 16-08-2018
## 2153 83.32891 26.0000 Delayed Non-FTA 19-06-2022
## 2154 83.50003 26.0000 On Time FTA 15-04-2021
## 2155 73.98574 26.0000 Delayed FTA 03-05-2022
## 2156 83.02121 26.0000 On Time FTA 02-12-2018
## 2157 79.95610 26.0000 On Time FTA 27-09-2023
## 2158 80.09292 26.0000 Delayed FTA 19-10-2023
## 2159 76.63808 26.0000 On Time FTA 03-06-2022
## 2160 78.52563 26.0000 Delayed Non-FTA 02-07-2023
## 2161 81.69822 26.0000 On Time Non-FTA 24-01-2019
## 2162 80.83574 26.0000 On Time Non-FTA 12-03-2022
## 2163 77.11515 26.0000 On Time Non-FTA 05-01-2020
## 2164 78.70606 26.0000 On Time FTA 17-05-2019
## 2165 75.30388 26.0000 On Time Non-FTA 11-07-2019
## 2166 83.51897 26.0000 Delayed FTA 23-05-2019
## 2167 70.35042 26.0000 Delayed Non-FTA 03-07-2023
## 2168 79.16040 26.0000 Delayed FTA 05-07-2024
## 2169 80.16054 27.0000 Delayed FTA 15-10-2022
## 2170 75.74102 27.0000 On Time FTA 22-05-2018
## 2171 81.13934 27.0000 Delayed Non-FTA 12-01-2023
## 2172 78.39525 27.0000 Delayed FTA 02-01-2022
## 2173 76.73305 27.0000 On Time Non-FTA 11-02-2022
## 2174 77.27444 27.0000 On Time Non-FTA 24-10-2020
## 2175 75.76261 27.0000 Delayed FTA 12-05-2021
## 2176 70.32687 27.0000 On Time Non-FTA 16-06-2024
## 2177 84.95571 27.0000 Delayed Non-FTA 19-06-2023
## 2178 75.13894 27.0000 Delayed FTA 03-11-2021
## 2179 75.36728 27.0000 Delayed Non-FTA 23-04-2022
## 2180 81.44301 27.0000 Delayed FTA 11-05-2023
## 2181 82.09624 27.0000 Delayed FTA 16-04-2022
## 2182 81.72050 27.0000 Delayed FTA 02-02-2021
## 2183 74.97477 27.0000 Delayed FTA 14-08-2020
## 2184 79.55184 27.0000 On Time FTA 24-12-2022
## 2185 78.56085 27.0000 Delayed Non-FTA 19-09-2022
## 2186 82.86400 27.0000 Delayed FTA 05-04-2021
## 2187 75.45881 27.0000 On Time Non-FTA 09-06-2021
## 2188 83.11790 27.0000 On Time Non-FTA 11-02-2022
## 2189 72.14375 27.0000 Delayed FTA 16-12-2024
## 2190 76.17933 27.0000 Delayed Non-FTA 20-01-2021
## 2191 84.92990 27.0000 On Time Non-FTA 18-06-2024
## 2192 76.05765 27.0000 On Time Non-FTA 17-04-2024
## 2193 82.43443 27.0000 Delayed Non-FTA 04-11-2018
## 2194 75.45141 27.0000 On Time FTA 21-07-2018
## 2195 71.06139 27.0000 Delayed Non-FTA 09-12-2020
## 2196 76.26831 27.0000 Delayed Non-FTA 30-07-2020
## 2197 72.23262 27.0000 Delayed Non-FTA 23-12-2023
## 2198 71.79233 27.0000 On Time FTA 03-12-2021
## 2199 72.53717 27.0000 On Time Non-FTA 03-02-2021
## 2200 84.13403 27.0000 Delayed Non-FTA 30-04-2019
## 2201 72.42444 27.0000 Delayed Non-FTA 06-02-2019
## 2202 83.45255 27.0000 Delayed FTA 16-10-2024
## 2203 70.71247 27.0000 Delayed Non-FTA 17-01-2020
## 2204 76.87513 27.0000 On Time Non-FTA 24-11-2022
## 2205 76.07494 27.0000 On Time Non-FTA 29-03-2018
## 2206 71.71131 27.0000 On Time FTA 24-11-2020
## 2207 78.07959 27.0000 Delayed Non-FTA 04-08-2018
## 2208 71.97868 27.0000 Delayed FTA 09-09-2018
## 2209 70.42245 27.0000 Delayed Non-FTA 23-12-2024
## 2210 80.08767 27.0000 On Time Non-FTA 26-04-2024
## 2211 77.43314 27.0000 On Time Non-FTA 30-04-2018
## 2212 70.19613 27.0000 Delayed Non-FTA 20-12-2023
## 2213 70.58332 27.0000 Delayed FTA 16-09-2018
## 2214 74.50743 27.0000 On Time FTA 28-10-2021
## 2215 82.39807 27.0000 Delayed FTA 19-08-2021
## 2216 82.18644 27.0000 Delayed FTA 04-03-2021
## 2217 74.18137 27.0000 Delayed FTA 21-02-2022
## 2218 84.95263 27.0000 On Time Non-FTA 03-11-2019
## 2219 79.18767 27.0000 On Time FTA 13-09-2024
## 2220 84.58524 27.0000 On Time Non-FTA 18-05-2024
## 2221 81.08395 27.0000 Delayed Non-FTA 08-10-2021
## 2222 72.51681 27.0000 Delayed Non-FTA 19-06-2023
## 2223 71.33159 27.0000 Delayed Non-FTA 12-11-2020
## 2224 78.61765 27.0000 On Time FTA 03-10-2021
## 2225 78.94651 27.0000 Delayed Non-FTA 16-10-2021
## 2226 70.14958 27.0000 Delayed FTA 05-12-2021
## 2227 74.75058 27.0000 On Time Non-FTA 29-01-2024
## 2228 74.73334 27.0000 On Time Non-FTA 29-06-2024
## 2229 84.46305 27.0000 Delayed Non-FTA 24-02-2021
## 2230 72.72565 27.0000 Delayed Non-FTA 03-02-2018
## 2231 78.88478 27.0000 Delayed Non-FTA 03-10-2024
## 2232 73.53594 27.0000 Delayed FTA 23-07-2020
## 2233 81.18372 27.0000 Delayed Non-FTA 09-11-2019
## 2234 83.70865 27.0000 Delayed FTA 09-09-2018
## 2235 82.30615 27.0000 On Time Non-FTA 07-11-2024
## 2236 77.04744 27.0000 Delayed FTA 19-05-2024
## 2237 75.95484 27.0000 Delayed FTA 10-06-2024
## 2238 75.02642 27.0000 On Time Non-FTA 03-02-2023
## 2239 79.43122 27.0000 Delayed FTA 02-04-2020
## 2240 76.85477 27.0000 On Time Non-FTA 06-01-2021
## 2241 75.85431 27.0000 Delayed Non-FTA 10-10-2019
## 2242 74.79446 27.0000 Delayed Non-FTA 23-07-2023
## 2243 80.85907 27.0000 Delayed FTA 19-10-2018
## 2244 70.73374 27.0000 On Time FTA 03-09-2020
## 2245 79.39349 27.0000 On Time Non-FTA 03-05-2020
## 2246 81.02945 27.0000 On Time Non-FTA 10-03-2019
## 2247 74.63478 27.0000 On Time Non-FTA 17-01-2021
## 2248 83.50243 27.0000 On Time FTA 30-04-2023
## 2249 75.05032 28.0000 On Time Non-FTA 27-08-2022
## 2250 72.24915 28.0000 On Time FTA 07-11-2020
## 2251 80.99288 28.0000 On Time Non-FTA 13-03-2021
## 2252 75.13168 28.0000 Delayed Non-FTA 30-11-2018
## 2253 83.90405 28.0000 On Time Non-FTA 30-06-2020
## 2254 78.27269 28.0000 On Time Non-FTA 29-07-2020
## 2255 70.46752 28.0000 On Time FTA 14-03-2018
## 2256 70.61795 28.0000 Delayed Non-FTA 07-11-2024
## 2257 79.42871 28.0000 Delayed FTA 27-12-2024
## 2258 81.11521 28.0000 Delayed Non-FTA 19-01-2023
## 2259 76.89607 28.0000 On Time Non-FTA 26-08-2021
## 2260 81.04309 28.0000 On Time Non-FTA 10-01-2018
## 2261 78.44221 28.0000 Delayed FTA 15-08-2021
## 2262 72.15540 28.0000 Delayed FTA 12-01-2024
## 2263 84.21443 28.0000 Delayed FTA 01-02-2020
## 2264 78.76613 28.0000 On Time FTA 03-08-2024
## 2265 77.51352 28.0000 Delayed FTA 18-02-2018
## 2266 81.70831 28.0000 Delayed FTA 07-10-2023
## 2267 74.51127 28.0000 On Time FTA 05-08-2022
## 2268 71.01446 28.0000 On Time Non-FTA 14-06-2024
## 2269 78.32034 28.0000 On Time Non-FTA 31-08-2022
## 2270 77.88617 28.0000 Delayed FTA 23-01-2024
## 2271 84.12119 28.0000 On Time Non-FTA 29-08-2024
## 2272 74.17298 28.0000 On Time Non-FTA 05-11-2019
## 2273 74.58736 28.0000 On Time Non-FTA 19-01-2018
## 2274 75.35308 28.0000 Delayed FTA 13-11-2022
## 2275 75.45530 28.0000 On Time Non-FTA 30-11-2022
## 2276 70.42401 28.0000 On Time Non-FTA 14-12-2019
## 2277 70.29392 28.0000 Delayed FTA 28-02-2020
## 2278 77.06223 28.0000 Delayed Non-FTA 24-11-2019
## 2279 77.20257 28.0000 On Time FTA 29-07-2018
## 2280 75.15984 28.0000 On Time Non-FTA 11-10-2022
## 2281 74.62213 28.0000 On Time Non-FTA 03-10-2023
## 2282 83.37683 28.0000 Delayed FTA 03-02-2024
## 2283 77.59024 28.0000 Delayed Non-FTA 21-02-2020
## 2284 80.08957 28.0000 Delayed Non-FTA 02-06-2021
## 2285 77.73040 28.0000 Delayed FTA 20-03-2022
## 2286 79.67171 28.0000 On Time FTA 25-04-2024
## 2287 78.24729 28.0000 Delayed FTA 11-07-2020
## 2288 83.74795 28.0000 Delayed FTA 18-05-2018
## 2289 73.94450 28.0000 On Time Non-FTA 19-07-2021
## 2290 77.77673 28.0000 Delayed Non-FTA 02-07-2019
## 2291 73.56384 28.0000 On Time Non-FTA 25-02-2021
## 2292 71.61765 28.0000 On Time FTA 05-03-2020
## 2293 84.19157 28.0000 Delayed FTA 07-12-2023
## 2294 77.53441 28.0000 On Time FTA 15-09-2024
## 2295 73.59718 28.0000 Delayed Non-FTA 17-05-2018
## 2296 76.35090 28.0000 On Time Non-FTA 15-04-2022
## 2297 78.79019 28.0000 Delayed Non-FTA 18-01-2024
## 2298 80.81856 28.0000 On Time FTA 02-10-2023
## 2299 84.21669 28.0000 On Time FTA 27-01-2018
## 2300 71.24671 28.0000 On Time Non-FTA 08-09-2024
## 2301 71.70335 28.0000 On Time FTA 04-07-2023
## 2302 77.05886 28.0000 Delayed Non-FTA 20-10-2021
## 2303 72.87908 28.0000 On Time Non-FTA 20-10-2018
## 2304 79.85209 28.0000 Delayed FTA 15-04-2018
## 2305 72.73645 28.0000 On Time Non-FTA 20-02-2024
## 2306 74.77226 28.0000 On Time Non-FTA 29-03-2024
## 2307 82.92628 28.0000 Delayed Non-FTA 23-04-2023
## 2308 76.65435 28.0000 On Time Non-FTA 11-09-2021
## 2309 76.22758 28.0000 Delayed Non-FTA 13-09-2023
## 2310 84.15795 28.0000 Delayed FTA 01-10-2023
## 2311 80.83073 28.0000 On Time Non-FTA 03-01-2024
## 2312 79.75883 28.0000 Delayed FTA 06-09-2022
## 2313 72.01036 28.0000 On Time FTA 14-04-2020
## 2314 71.48419 28.0000 Delayed FTA 08-01-2020
## 2315 82.72342 28.0000 On Time Non-FTA 01-07-2024
## 2316 79.05290 28.0000 Delayed Non-FTA 09-05-2023
## 2317 74.41469 28.0000 Delayed Non-FTA 13-05-2020
## 2318 83.70592 28.0000 Delayed Non-FTA 01-05-2020
## 2319 76.68519 28.0000 On Time FTA 15-07-2021
## 2320 75.40244 28.0000 On Time FTA 06-06-2023
## 2321 81.32731 28.0000 On Time Non-FTA 09-01-2023
## 2322 80.85602 29.0000 On Time FTA 11-03-2018
## 2323 72.71368 29.0000 Delayed FTA 05-01-2018
## 2324 82.08302 29.0000 Delayed FTA 10-09-2018
## 2325 71.10222 29.0000 Delayed FTA 11-12-2023
## 2326 78.12685 29.0000 Delayed FTA 07-11-2020
## 2327 80.52174 29.0000 Delayed FTA 20-02-2018
## 2328 70.37011 29.0000 On Time Non-FTA 02-10-2022
## 2329 78.28753 29.0000 On Time Non-FTA 27-06-2018
## 2330 79.66087 29.0000 On Time Non-FTA 28-01-2022
## 2331 82.52124 29.0000 On Time Non-FTA 31-05-2019
## 2332 83.66867 29.0000 Delayed FTA 09-03-2023
## 2333 77.30831 29.0000 Delayed FTA 27-01-2019
## 2334 74.05696 29.0000 Delayed FTA 29-12-2022
## 2335 74.20617 29.0000 On Time FTA 24-01-2018
## 2336 81.59052 29.0000 Delayed Non-FTA 20-10-2022
## 2337 79.86091 29.0000 Delayed FTA 02-10-2021
## 2338 75.48638 29.0000 On Time FTA 29-06-2024
## 2339 72.23587 29.0000 Delayed Non-FTA 03-02-2022
## 2340 82.19465 29.0000 On Time Non-FTA 22-08-2021
## 2341 78.74101 29.0000 Delayed Non-FTA 03-08-2024
## 2342 76.47124 29.0000 On Time Non-FTA 23-08-2024
## 2343 80.35306 29.0000 Delayed Non-FTA 16-07-2021
## 2344 70.23260 29.0000 Delayed FTA 07-01-2022
## 2345 70.99119 29.0000 On Time Non-FTA 20-03-2022
## 2346 77.69367 29.0000 Delayed FTA 13-04-2018
## 2347 82.61191 29.0000 On Time Non-FTA 21-07-2023
## 2348 76.07867 29.0000 Delayed FTA 02-04-2019
## 2349 79.44055 29.0000 On Time Non-FTA 06-09-2023
## 2350 79.25539 29.0000 Delayed Non-FTA 08-05-2023
## 2351 74.73764 29.0000 On Time FTA 06-07-2022
## 2352 84.01930 29.0000 On Time Non-FTA 04-02-2020
## 2353 78.44822 29.0000 On Time Non-FTA 22-09-2019
## 2354 84.99039 29.0000 On Time Non-FTA 09-07-2021
## 2355 71.81609 29.0000 Delayed Non-FTA 14-02-2022
## 2356 72.12997 29.0000 Delayed FTA 15-06-2018
## 2357 84.07226 29.0000 On Time Non-FTA 22-08-2020
## 2358 70.39647 29.0000 On Time FTA 01-03-2022
## 2359 81.19321 29.0000 Delayed FTA 15-08-2024
## 2360 84.24283 29.0000 On Time Non-FTA 12-06-2020
## 2361 81.30570 29.0000 On Time FTA 08-12-2019
## 2362 70.77365 29.0000 Delayed Non-FTA 15-09-2020
## 2363 77.61691 29.0000 On Time Non-FTA 08-01-2018
## 2364 78.56505 29.0000 On Time FTA 03-05-2018
## 2365 74.18745 29.0000 On Time Non-FTA 07-07-2022
## 2366 83.24902 29.0000 Delayed FTA 10-04-2020
## 2367 74.51385 29.0000 On Time Non-FTA 14-09-2024
## 2368 77.22367 29.0000 Delayed Non-FTA 28-11-2019
## 2369 83.42159 29.0000 Delayed FTA 06-10-2020
## 2370 77.42696 29.0000 Delayed Non-FTA 13-11-2023
## 2371 79.50251 29.0000 On Time FTA 31-05-2021
## 2372 76.06945 29.0000 Delayed FTA 26-06-2019
## 2373 72.18479 29.0000 On Time Non-FTA 14-12-2018
## 2374 83.24390 29.0000 Delayed FTA 22-06-2021
## 2375 74.04593 29.0000 Delayed FTA 18-12-2024
## 2376 75.33589 29.0000 On Time FTA 22-02-2023
## 2377 84.18586 29.0000 Delayed FTA 20-02-2023
## 2378 78.14194 29.0000 Delayed FTA 12-05-2020
## 2379 75.38217 29.0000 Delayed Non-FTA 08-09-2019
## 2380 78.15360 29.0000 Delayed Non-FTA 20-02-2024
## 2381 84.93848 29.0000 On Time Non-FTA 24-04-2023
## 2382 74.40631 29.0000 On Time Non-FTA 08-01-2020
## 2383 80.13309 29.0000 On Time Non-FTA 14-09-2023
## 2384 71.31272 29.0000 Delayed FTA 08-07-2023
## 2385 84.13107 29.0000 On Time FTA 05-01-2023
## 2386 72.03908 29.0000 Delayed FTA 18-01-2024
## 2387 83.69293 29.0000 On Time FTA 22-05-2019
## 2388 77.30367 29.0000 On Time Non-FTA 02-09-2019
## 2389 84.58045 29.0000 Delayed FTA 14-05-2023
## 2390 72.87353 29.0000 Delayed FTA 24-01-2019
## 2391 70.08971 29.0000 On Time FTA 15-11-2024
## 2392 74.59777 29.0000 On Time FTA 18-02-2023
## 2393 70.82292 29.0000 On Time Non-FTA 03-05-2020
## 2394 71.76023 29.0000 On Time FTA 17-04-2022
## 2395 79.58308 29.0000 Delayed FTA 21-07-2019
## 2396 73.96724 29.0000 On Time Non-FTA 25-02-2022
## 2397 71.11336 29.0000 Delayed Non-FTA 19-01-2023
## 2398 81.71248 29.0000 On Time Non-FTA 29-05-2021
## 2399 72.40798 29.0000 On Time FTA 07-04-2019
## 2400 71.02180 29.0000 Delayed Non-FTA 03-05-2022
## 2401 72.09278 29.0000 On Time FTA 26-09-2019
## 2402 81.51356 29.0000 On Time Non-FTA 15-03-2024
## 2403 84.38272 29.0000 Delayed FTA 11-06-2023
## 2404 76.27645 29.0000 On Time Non-FTA 05-08-2022
## 2405 72.99096 29.0000 Delayed FTA 28-11-2024
## 2406 74.00722 29.0000 Delayed Non-FTA 21-01-2022
## 2407 81.18447 29.0000 On Time FTA 30-01-2023
## 2408 76.20439 29.0000 On Time FTA 28-05-2024
## 2409 73.09497 29.0000 On Time Non-FTA 03-04-2020
## 2410 83.04453 29.0000 On Time Non-FTA 16-06-2023
## 2411 77.66591 29.0000 Delayed FTA 13-01-2022
## 2412 83.07845 29.0000 On Time FTA 29-05-2023
## 2413 82.66164 29.0000 Delayed Non-FTA 02-09-2018
## 2414 72.43144 29.0000 On Time Non-FTA 26-10-2023
## 2415 82.75006 29.0000 Delayed Non-FTA 09-10-2020
## 2416 77.53012 29.0000 On Time Non-FTA 25-10-2020
## 2417 79.65512 29.0000 On Time FTA 20-01-2021
## 2418 74.75256 29.0000 On Time FTA 30-06-2022
## 2419 77.08860 29.0000 Delayed Non-FTA 09-11-2024
## 2420 76.26125 29.0000 Delayed FTA 13-11-2019
## 2421 79.48828 29.0000 Delayed FTA 11-07-2020
## 2422 76.79772 29.0000 Delayed FTA 15-06-2024
## 2423 80.20319 29.0000 Delayed FTA 19-07-2018
## 2424 84.59273 29.0000 Delayed Non-FTA 27-09-2024
## 2425 77.89322 29.0000 On Time Non-FTA 19-09-2022
## 2426 83.30403 29.0000 On Time FTA 06-12-2023
## 2427 84.09672 29.0000 On Time FTA 12-11-2024
## 2428 73.41378 30.0000 Delayed Non-FTA 13-02-2024
## 2429 81.45950 30.0000 On Time FTA 29-07-2020
## 2430 75.40469 30.0000 On Time Non-FTA 20-07-2022
## 2431 74.46662 30.0000 On Time FTA 24-10-2022
## 2432 73.19110 30.0000 On Time FTA 21-11-2020
## 2433 74.05047 30.0000 On Time FTA 16-06-2018
## 2434 79.68891 30.0000 On Time Non-FTA 08-09-2024
## 2435 76.61378 30.0000 On Time FTA 25-11-2019
## 2436 79.20856 30.0000 Delayed FTA 11-06-2018
## 2437 82.26677 30.0000 Delayed FTA 05-04-2019
## 2438 79.15206 30.0000 Delayed FTA 01-12-2024
## 2439 75.96541 30.0000 Delayed Non-FTA 26-06-2020
## 2440 74.68904 30.0000 On Time Non-FTA 12-01-2021
## 2441 74.19994 30.0000 On Time Non-FTA 29-01-2024
## 2442 84.82885 30.0000 On Time Non-FTA 10-08-2024
## 2443 79.58191 30.0000 Delayed Non-FTA 12-07-2020
## 2444 75.72353 30.0000 On Time FTA 16-06-2019
## 2445 81.01300 30.0000 Delayed Non-FTA 23-09-2024
## 2446 70.56158 30.0000 On Time FTA 12-02-2023
## 2447 73.85718 30.0000 On Time Non-FTA 22-10-2018
## 2448 79.11473 30.0000 On Time Non-FTA 27-09-2022
## 2449 71.78603 30.0000 On Time Non-FTA 05-03-2024
## 2450 81.50512 30.0000 Delayed Non-FTA 24-06-2018
## 2451 80.45831 30.0000 On Time Non-FTA 15-10-2022
## 2452 75.34939 30.0000 On Time Non-FTA 19-07-2024
## 2453 80.68161 30.0000 Delayed Non-FTA 29-09-2023
## 2454 78.96197 30.0000 Delayed Non-FTA 07-10-2019
## 2455 80.35912 30.0000 On Time Non-FTA 29-05-2023
## 2456 74.61994 30.0000 Delayed FTA 08-06-2022
## 2457 83.88828 30.0000 Delayed Non-FTA 13-06-2021
## 2458 77.78056 30.0000 On Time Non-FTA 12-03-2021
## 2459 82.56004 30.0000 Delayed FTA 10-07-2019
## 2460 81.35710 30.0000 On Time FTA 21-03-2019
## 2461 81.88737 30.0000 Delayed Non-FTA 18-06-2024
## 2462 77.56159 30.0000 On Time FTA 30-08-2024
## 2463 73.53875 30.0000 On Time FTA 15-02-2021
## 2464 81.13351 30.0000 Delayed FTA 06-09-2018
## 2465 74.63967 30.0000 On Time Non-FTA 12-12-2023
## 2466 79.10488 30.0000 Delayed FTA 25-04-2023
## 2467 82.71575 30.0000 Delayed Non-FTA 28-03-2018
## 2468 80.22641 30.0000 Delayed FTA 04-05-2022
## 2469 77.52127 30.0000 On Time FTA 01-06-2021
## 2470 83.42157 30.0000 On Time Non-FTA 07-01-2018
## 2471 80.45694 30.0000 Delayed FTA 22-01-2020
## 2472 71.93680 30.0000 On Time Non-FTA 18-11-2018
## 2473 72.44630 30.0000 Delayed FTA 25-03-2019
## 2474 79.63395 30.0000 Delayed FTA 25-07-2020
## 2475 70.61733 30.0000 On Time FTA 16-11-2020
## 2476 71.20147 30.0000 Delayed Non-FTA 30-04-2021
## 2477 83.43051 30.0000 Delayed FTA 24-08-2023
## 2478 76.05539 30.0000 Delayed FTA 10-07-2020
## 2479 79.71120 30.0000 On Time FTA 06-10-2018
## 2480 74.80859 30.0000 On Time Non-FTA 18-09-2022
## 2481 76.67086 30.0000 Delayed FTA 21-07-2022
## 2482 77.26586 30.0000 Delayed FTA 24-12-2019
## 2483 82.36984 30.0000 On Time Non-FTA 12-11-2018
## 2484 72.59064 30.0000 Delayed FTA 31-03-2018
## 2485 83.54902 30.0000 On Time Non-FTA 01-12-2018
## 2486 79.32819 30.0000 Delayed Non-FTA 14-03-2022
## 2487 72.40767 30.0000 On Time Non-FTA 21-07-2024
## 2488 84.20406 30.0000 On Time FTA 04-02-2024
## 2489 80.88199 30.0000 Delayed FTA 07-04-2023
## 2490 77.82466 30.0000 Delayed Non-FTA 09-03-2022
## 2491 84.30248 30.0000 Delayed Non-FTA 07-02-2019
## 2492 74.41198 30.0000 Delayed Non-FTA 14-06-2023
## 2493 81.35901 30.0000 On Time FTA 16-09-2020
## 2494 82.92018 30.0000 On Time FTA 29-12-2018
## 2495 78.72675 30.0000 Delayed Non-FTA 29-05-2022
## 2496 73.62860 30.0000 On Time FTA 24-09-2019
## 2497 72.77787 30.0000 Delayed Non-FTA 08-12-2021
## 2498 72.44885 30.0000 Delayed Non-FTA 08-12-2023
## 2499 72.38847 30.0000 On Time FTA 26-01-2021
## 2500 71.60248 103.1988 On Time Non-FTA 04-09-2021
## Value_INR Profit Loss Is_Delayed Profit_Margin
## 1 1057937.34 88818.184 105134.671 FALSE 0.08395411
## 2 4252342.27 444612.443 86123.560 FALSE 0.10455707
## 3 7105021.98 1117474.996 515452.562 FALSE 0.15727960
## 4 2422846.52 251141.533 77273.348 FALSE 0.10365557
## 5 5896746.33 1139178.500 253968.764 TRUE 0.19318764
## 6 5638420.34 347625.800 317637.605 TRUE 0.06165305
## 7 4875511.86 641783.565 118318.852 FALSE 0.13163409
## 8 3679771.44 436140.812 244824.195 TRUE 0.11852389
## 9 6975929.84 1281387.105 280263.852 TRUE 0.18368693
## 10 5207407.95 631794.547 251432.670 TRUE 0.12132611
## 11 7254971.32 1306134.152 426569.089 TRUE 0.18003299
## 12 5623459.96 314502.900 437146.548 TRUE 0.05592694
## 13 3963734.49 418627.471 240850.918 FALSE 0.10561441
## 14 425089.51 21548.573 36823.762 FALSE 0.05069185
## 15 1598696.90 272920.220 152652.869 TRUE 0.17071417
## 16 75028.24 13022.511 4001.122 FALSE 0.17356811
## 17 4367898.95 612136.076 235163.935 TRUE 0.14014429
## 18 4197107.42 337539.557 210392.871 FALSE 0.08042195
## 19 1534495.97 178242.199 34310.599 FALSE 0.11615684
## 20 3442664.37 374048.494 334730.340 TRUE 0.10865087
## 21 4859777.63 712713.594 428145.077 FALSE 0.14665560
## 22 188136.95 21541.615 17075.465 TRUE 0.11449965
## 23 364098.19 66978.771 8409.874 TRUE 0.18395799
## 24 4157118.43 572293.806 177661.786 TRUE 0.13766599
## 25 312992.81 60731.120 21757.042 TRUE 0.19403360
## 26 6033564.25 515140.066 125292.178 TRUE 0.08537906
## 27 159863.30 11272.899 2191.136 TRUE 0.07051586
## 28 3966231.24 426914.614 207785.021 FALSE 0.10763735
## 29 2416979.98 172882.596 50268.659 TRUE 0.07152835
## 30 4041981.96 254035.929 162561.508 TRUE 0.06284935
## 31 4710977.22 499068.634 231791.205 FALSE 0.10593739
## 32 4509913.23 274523.456 415954.171 FALSE 0.06087112
## 33 4378660.99 648165.741 97212.657 FALSE 0.14802830
## 34 1480462.93 106366.852 68757.903 FALSE 0.07184702
## 35 3907337.98 305542.085 360745.922 FALSE 0.07819699
## 36 1042925.71 74829.443 90064.897 TRUE 0.07174954
## 37 5917311.26 456387.010 92982.243 TRUE 0.07712743
## 38 2183238.53 256294.683 211490.360 TRUE 0.11739198
## 39 7017903.74 401124.707 330946.452 FALSE 0.05715734
## 40 114034.41 15025.186 3499.334 TRUE 0.13176011
## 41 5366309.74 976160.627 213265.743 TRUE 0.18190538
## 42 2281391.31 187823.482 54265.512 TRUE 0.08232848
## 43 5634521.31 903026.921 198377.521 TRUE 0.16026684
## 44 5231755.27 581073.685 187449.366 FALSE 0.11106668
## 45 1960523.44 284244.413 39291.503 FALSE 0.14498394
## 46 3218612.39 476599.881 234957.171 FALSE 0.14807620
## 47 1790871.35 102051.524 63149.176 FALSE 0.05698429
## 48 5212945.60 989723.078 251599.048 TRUE 0.18985870
## 49 4563743.64 477050.035 116696.315 TRUE 0.10453042
## 50 2671160.27 224610.193 97445.286 TRUE 0.08408713
## 51 6849344.24 980880.931 212799.765 FALSE 0.14320801
## 52 5745660.26 501162.678 547814.489 FALSE 0.08722456
## 53 2799394.09 410630.865 61221.900 TRUE 0.14668562
## 54 5971221.90 507473.210 318318.389 FALSE 0.08498649
## 55 3767200.20 751188.619 169635.721 TRUE 0.19940236
## 56 4901587.39 682341.241 251762.765 TRUE 0.13920822
## 57 3323208.13 649231.654 315372.802 TRUE 0.19536292
## 58 7120284.23 597070.230 555651.512 FALSE 0.08385483
## 59 410409.82 57940.766 11359.040 FALSE 0.14117782
## 60 4995446.30 967644.168 219625.219 FALSE 0.19370525
## 61 8087043.66 1598195.408 440334.629 TRUE 0.19762418
## 62 1822834.22 231667.571 176608.225 FALSE 0.12709196
## 63 5131975.02 877337.193 358662.560 TRUE 0.17095508
## 64 1161641.89 225890.530 18498.876 TRUE 0.19445797
## 65 6522053.53 505618.853 316772.304 TRUE 0.07752449
## 66 827266.14 99974.779 21881.748 TRUE 0.12084960
## 67 5759178.82 782503.419 66456.137 TRUE 0.13587066
## 68 771886.38 72989.459 58158.683 FALSE 0.09455985
## 69 3416606.79 190911.025 291205.975 FALSE 0.05587738
## 70 4002044.81 465136.581 65875.329 TRUE 0.11622473
## 71 152240.19 30124.763 8079.560 TRUE 0.19787654
## 72 2544244.32 483251.261 149866.458 TRUE 0.18993902
## 73 2280588.25 373661.896 143852.389 FALSE 0.16384452
## 74 1725331.93 94140.775 105456.037 FALSE 0.05456386
## 75 5215456.99 761538.497 452086.789 FALSE 0.14601568
## 76 1261658.27 70195.027 65115.251 FALSE 0.05563712
## 77 3022613.48 259062.638 173602.791 TRUE 0.08570816
## 78 6802176.04 494518.085 273527.684 FALSE 0.07269998
## 79 4534633.76 669014.448 168693.039 TRUE 0.14753439
## 80 733234.30 102594.755 59296.717 TRUE 0.13992083
## 81 1443353.75 110502.677 111903.152 FALSE 0.07655966
## 82 4481234.48 660124.278 300530.299 TRUE 0.14730858
## 83 657519.81 56476.628 60188.766 TRUE 0.08589342
## 84 6058594.98 1144349.693 295612.383 TRUE 0.18888038
## 85 5208088.19 644461.011 186115.232 TRUE 0.12374234
## 86 6315555.28 1177714.157 380262.238 FALSE 0.18647832
## 87 3942300.06 730507.450 385634.129 TRUE 0.18529981
## 88 5243723.94 515898.902 77349.819 FALSE 0.09838407
## 89 3755977.66 473244.394 221286.639 TRUE 0.12599766
## 90 5528254.04 1012008.194 374336.642 FALSE 0.18306109
## 91 2866193.29 229945.755 194520.389 FALSE 0.08022688
## 92 1113910.75 66291.317 74835.210 FALSE 0.05951223
## 93 2915779.86 504264.489 181027.506 FALSE 0.17294326
## 94 1767382.40 111692.892 31356.996 FALSE 0.06319679
## 95 521354.91 103305.689 13037.172 FALSE 0.19814849
## 96 4033897.36 204235.490 113298.947 FALSE 0.05062982
## 97 3546343.07 215928.739 77628.362 TRUE 0.06088772
## 98 3769022.23 466842.681 175462.065 FALSE 0.12386307
## 99 5188569.89 706485.486 206687.258 TRUE 0.13616189
## 100 2894094.99 409250.399 176353.593 TRUE 0.14140877
## 101 2278732.69 299794.054 128744.932 TRUE 0.13156175
## 102 5517549.17 974103.024 512659.069 FALSE 0.17654632
## 103 5860518.12 471332.515 574882.067 FALSE 0.08042506
## 104 1557708.22 273517.529 98663.430 TRUE 0.17558971
## 105 891508.18 47511.832 13570.096 FALSE 0.05329377
## 106 3970822.30 403266.848 330865.191 TRUE 0.10155752
## 107 5218980.46 525481.134 419458.006 FALSE 0.10068655
## 108 2355523.57 369658.370 73697.640 TRUE 0.15693257
## 109 1346013.13 220772.823 44539.106 FALSE 0.16401981
## 110 4719359.39 382658.353 311528.132 TRUE 0.08108269
## 111 1036478.38 94191.506 36689.893 FALSE 0.09087648
## 112 5866160.44 790622.901 223534.462 FALSE 0.13477690
## 113 5468883.79 734829.416 373842.774 FALSE 0.13436552
## 114 5052657.59 520482.409 361255.452 TRUE 0.10301161
## 115 3229895.18 598850.687 233651.086 TRUE 0.18540871
## 116 7002210.26 781722.989 127711.546 FALSE 0.11163946
## 117 1667921.74 210115.296 119509.773 FALSE 0.12597431
## 118 1542523.28 214390.133 143689.719 FALSE 0.13898664
## 119 1277365.91 192573.046 127263.359 FALSE 0.15075793
## 120 3142851.19 457871.412 184606.807 TRUE 0.14568663
## 121 735184.75 138880.932 41755.786 TRUE 0.18890616
## 122 1972213.15 326238.446 39070.578 TRUE 0.16541744
## 123 4098708.95 708290.096 361134.842 FALSE 0.17280810
## 124 4918112.07 481599.195 469689.544 TRUE 0.09792359
## 125 5139851.99 659392.427 334888.060 FALSE 0.12829016
## 126 278751.84 28186.042 7169.310 TRUE 0.10111518
## 127 6198460.00 1182680.879 224020.820 TRUE 0.19080237
## 128 458202.45 74193.910 13878.008 TRUE 0.16192386
## 129 2532794.61 392289.564 69509.480 FALSE 0.15488408
## 130 2158941.94 218328.241 183181.306 TRUE 0.10112743
## 131 456956.77 73166.071 20014.935 TRUE 0.16011596
## 132 468786.50 92995.130 21731.361 TRUE 0.19837416
## 133 5811798.80 304768.043 319986.836 FALSE 0.05243954
## 134 1998995.37 339776.895 168167.079 TRUE 0.16997383
## 135 4585473.84 250269.905 214549.588 TRUE 0.05457885
## 136 5786297.20 897503.839 63243.048 TRUE 0.15510849
## 137 3486060.87 287969.007 261558.740 TRUE 0.08260585
## 138 846470.71 146057.644 19165.243 TRUE 0.17254896
## 139 550147.79 90761.346 24301.732 TRUE 0.16497630
## 140 2424617.24 380909.715 119677.886 FALSE 0.15710097
## 141 4475601.35 375543.607 163777.004 TRUE 0.08390908
## 142 4466315.40 534436.081 424024.122 TRUE 0.11965928
## 143 5226980.67 873692.984 357183.510 TRUE 0.16715061
## 144 843932.60 133092.947 77429.995 FALSE 0.15770566
## 145 4459866.14 665846.949 314926.059 FALSE 0.14929752
## 146 5441631.98 797051.543 259872.367 FALSE 0.14647289
## 147 7320821.36 1109232.128 643505.666 TRUE 0.15151744
## 148 4516536.37 851003.751 154507.529 TRUE 0.18841955
## 149 6732985.40 821151.090 305643.725 TRUE 0.12195943
## 150 1899202.42 158529.468 35920.974 FALSE 0.08347160
## 151 2844988.99 545107.001 240267.380 TRUE 0.19160250
## 152 2364685.20 152842.154 36142.160 TRUE 0.06463531
## 153 678854.59 84469.429 60071.020 FALSE 0.12442934
## 154 6916197.77 761418.901 192848.055 TRUE 0.11009212
## 155 5732359.19 1107365.136 472181.363 FALSE 0.19317790
## 156 5991437.88 747707.072 272808.194 FALSE 0.12479593
## 157 2185419.56 211496.543 118969.050 FALSE 0.09677617
## 158 4929356.07 288248.445 415601.156 TRUE 0.05847588
## 159 1805305.42 162440.337 77657.634 TRUE 0.08997942
## 160 3468010.13 672169.471 169850.972 FALSE 0.19381993
## 161 4919654.96 811742.962 467438.187 TRUE 0.16499998
## 162 7629999.06 1074107.535 679978.785 FALSE 0.14077427
## 163 5169160.86 764998.269 98481.839 TRUE 0.14799274
## 164 527859.99 52144.522 38437.691 FALSE 0.09878476
## 165 1251185.82 235552.529 76097.709 TRUE 0.18826343
## 166 1526227.66 192508.233 100047.812 FALSE 0.12613337
## 167 5226961.73 414344.915 237307.545 TRUE 0.07927070
## 168 3256465.35 495581.696 281442.850 TRUE 0.15218393
## 169 4001602.84 302511.996 229227.759 FALSE 0.07559771
## 170 1637730.01 160398.953 153924.385 TRUE 0.09793980
## 171 5293604.26 858303.931 325940.286 FALSE 0.16213980
## 172 4554509.19 257286.610 439877.531 FALSE 0.05649052
## 173 376351.71 43505.280 6912.186 TRUE 0.11559740
## 174 569447.60 105511.919 21431.734 TRUE 0.18528820
## 175 7249436.27 954841.734 168973.004 FALSE 0.13171255
## 176 4623661.42 492721.412 141989.578 FALSE 0.10656520
## 177 1721947.61 147807.943 113083.811 FALSE 0.08583765
## 178 1590466.42 299978.241 155648.843 FALSE 0.18861023
## 179 1109537.50 107209.803 68060.394 TRUE 0.09662567
## 180 4695841.66 541995.246 375053.300 FALSE 0.11542026
## 181 1732338.84 209269.630 151462.720 FALSE 0.12080179
## 182 5086466.38 710656.261 263631.242 TRUE 0.13971512
## 183 5904866.22 795832.798 506525.690 FALSE 0.13477575
## 184 2949764.02 456836.930 166605.759 FALSE 0.15487236
## 185 1320804.43 72275.823 66527.134 FALSE 0.05472106
## 186 7089790.12 1304432.329 407881.989 TRUE 0.18398744
## 187 6291938.25 974109.539 314100.549 TRUE 0.15481867
## 188 2070728.45 242567.872 125119.142 TRUE 0.11714132
## 189 3454522.24 626449.201 301987.056 FALSE 0.18134178
## 190 772428.67 45216.756 56832.592 TRUE 0.05853842
## 191 5510699.41 600461.230 350136.990 FALSE 0.10896280
## 192 3542560.91 433750.531 223003.652 FALSE 0.12243982
## 193 6721439.72 472176.936 370165.828 TRUE 0.07024937
## 194 5348047.07 937798.245 167745.075 TRUE 0.17535340
## 195 386353.30 63089.278 28722.832 FALSE 0.16329426
## 196 6669832.51 1190673.672 154295.352 FALSE 0.17851628
## 197 6806277.03 898437.454 1925322.401 FALSE 0.13200131
## 198 4046895.51 581547.157 158516.706 FALSE 0.14370204
## 199 7339643.20 1396717.801 565416.951 TRUE 0.19029778
## 200 2600238.33 298094.754 193946.759 FALSE 0.11464132
## 201 7719293.27 653098.860 696182.058 FALSE 0.08460604
## 202 6120742.21 601177.273 262133.667 TRUE 0.09821967
## 203 4920943.34 462813.471 79930.288 FALSE 0.09404975
## 204 3815583.98 597863.516 165580.845 TRUE 0.15668991
## 205 3112673.52 298903.773 273922.713 FALSE 0.09602799
## 206 5757843.30 782476.485 322638.976 TRUE 0.13589750
## 207 25781161.05 834894.640 631716.584 TRUE 0.03238390
## 208 3889283.44 583630.011 137786.065 TRUE 0.15006106
## 209 540896.23 81583.434 41260.309 TRUE 0.15083010
## 210 1655067.35 162249.871 145110.508 TRUE 0.09803219
## 211 3711956.59 409074.962 202027.691 TRUE 0.11020467
## 212 6207916.96 797778.502 83622.974 FALSE 0.12850985
## 213 5975619.66 502873.991 274688.455 TRUE 0.08415428
## 214 3989930.69 464606.069 342704.875 FALSE 0.11644465
## 215 1072024.59 59466.806 26761.531 FALSE 0.05547149
## 216 7427461.79 766942.484 148724.304 FALSE 0.10325768
## 217 776603.43 91337.094 18208.454 TRUE 0.11761099
## 218 434982.79 32667.015 21783.028 FALSE 0.07509956
## 219 4557990.10 288529.143 209010.698 FALSE 0.06330184
## 220 389356.25 23666.206 26155.618 FALSE 0.06078291
## 221 6390451.69 874932.480 134099.964 FALSE 0.13691246
## 222 6754245.62 637891.287 426898.892 FALSE 0.09444301
## 223 558824.74 65627.036 31804.235 TRUE 0.11743760
## 224 6143388.38 903282.921 167450.363 FALSE 0.14703334
## 225 4394400.61 617593.669 188069.219 FALSE 0.14054105
## 226 2537973.79 131888.199 26074.808 TRUE 0.05196594
## 227 4846652.32 382102.013 327966.426 FALSE 0.07883834
## 228 4865401.61 321049.674 463467.982 FALSE 0.06598626
## 229 4148637.49 709010.088 393726.235 FALSE 0.17090191
## 230 3967355.84 426701.541 240110.793 FALSE 0.10755313
## 231 7432059.92 624354.966 662293.755 FALSE 0.08400833
## 232 4479696.24 715332.585 405669.656 TRUE 0.15968328
## 233 1289847.95 118793.646 80536.336 TRUE 0.09209895
## 234 6917162.53 502709.335 679688.064 TRUE 0.07267566
## 235 4221061.51 463452.930 301079.590 TRUE 0.10979535
## 236 2872607.65 186934.400 136082.716 FALSE 0.06507481
## 237 1565376.10 132815.350 24357.773 FALSE 0.08484565
## 238 1083082.92 165620.286 33477.380 FALSE 0.15291561
## 239 6541634.55 419729.963 374594.690 TRUE 0.06416286
## 240 4548490.66 580920.540 342755.255 FALSE 0.12771721
## 241 2946461.51 462064.957 206329.602 FALSE 0.15682029
## 242 472259.01 89599.661 29135.496 TRUE 0.18972568
## 243 2660503.51 488983.366 91708.206 FALSE 0.18379354
## 244 2965586.00 496099.001 52787.297 TRUE 0.16728532
## 245 5980722.72 315201.133 420364.277 TRUE 0.05270285
## 246 2035810.38 395030.935 137598.198 FALSE 0.19404112
## 247 6845886.59 961675.294 646307.835 FALSE 0.14047491
## 248 2860926.34 210254.160 167934.552 FALSE 0.07349164
## 249 2207644.89 415227.323 135921.562 TRUE 0.18808610
## 250 4415843.54 253950.444 254232.452 TRUE 0.05750893
## 251 5618539.55 612846.940 288327.098 TRUE 0.10907584
## 252 2089717.33 129518.098 97717.543 TRUE 0.06197876
## 253 4152358.46 399613.832 317376.091 TRUE 0.09623780
## 254 5752376.34 861921.164 256183.929 FALSE 0.14983741
## 255 97762.11 6550.346 1349.930 FALSE 0.06700291
## 256 7441896.62 476920.092 629409.593 TRUE 0.06408583
## 257 291312.93 31400.998 28861.219 TRUE 0.10779130
## 258 7412970.35 766902.364 325674.202 FALSE 0.10345413
## 259 3256698.29 164579.266 269170.961 FALSE 0.05053562
## 260 3840465.98 581259.087 292001.451 FALSE 0.15135119
## 261 4919786.09 940158.256 91586.176 TRUE 0.19109738
## 262 7656777.74 1042562.440 433440.652 FALSE 0.13616204
## 263 2655497.64 164815.135 226778.069 TRUE 0.06206563
## 264 1808151.76 131205.518 156082.617 TRUE 0.07256333
## 265 4098390.25 775170.426 316368.443 FALSE 0.18914022
## 266 2673896.19 497739.062 55022.711 FALSE 0.18614749
## 267 4425761.04 415304.599 44410.124 FALSE 0.09383801
## 268 4548483.83 564244.824 329146.681 TRUE 0.12405119
## 269 3394986.04 418600.465 127681.741 FALSE 0.12329961
## 270 4909156.02 345052.681 200754.334 TRUE 0.07028758
## 271 6572656.95 526574.162 654081.805 TRUE 0.08011587
## 272 1513906.51 118906.846 25983.762 FALSE 0.07854306
## 273 7531622.48 533721.184 461064.939 FALSE 0.07086404
## 274 6877698.02 480025.298 76789.721 FALSE 0.06979447
## 275 421129.77 22566.911 32846.975 FALSE 0.05358660
## 276 2044614.99 353574.768 126328.479 FALSE 0.17292975
## 277 5338704.38 1059137.680 297531.869 TRUE 0.19838852
## 278 4847172.72 634502.913 118107.900 FALSE 0.13090165
## 279 3157026.56 225931.952 73566.976 FALSE 0.07156479
## 280 2099700.24 163147.713 152958.949 TRUE 0.07770048
## 281 7512831.73 659230.037 79234.534 TRUE 0.08774721
## 282 3880741.41 360165.888 382591.065 FALSE 0.09280853
## 283 2040298.32 402665.231 190943.368 FALSE 0.19735606
## 284 4020997.37 617055.778 47213.528 TRUE 0.15345839
## 285 900071.62 168957.443 61100.999 FALSE 0.18771555
## 286 4089149.97 252687.709 328281.553 FALSE 0.06179468
## 287 5402645.04 605706.018 162610.595 FALSE 0.11211287
## 288 2729220.42 180347.862 73031.769 FALSE 0.06608036
## 289 6758195.89 481229.324 531522.711 FALSE 0.07120677
## 290 3328351.29 615694.731 298825.508 TRUE 0.18498490
## 291 5324272.66 499729.012 518840.406 TRUE 0.09385864
## 292 3714067.23 603374.386 107288.052 TRUE 0.16245651
## 293 1986837.68 202380.714 56826.122 TRUE 0.10186072
## 294 1007682.11 122269.976 78661.987 FALSE 0.12133785
## 295 3827623.81 208530.575 182460.714 FALSE 0.05448043
## 296 2848998.37 544714.019 32040.758 TRUE 0.19119492
## 297 1121111.29 61046.590 64227.911 TRUE 0.05445186
## 298 3502576.53 298390.470 328227.543 FALSE 0.08519171
## 299 320567.35 38139.959 23884.204 TRUE 0.11897643
## 300 7126769.20 408335.464 290856.143 TRUE 0.05729601
## 301 1213120.80 101658.220 88410.153 TRUE 0.08379893
## 302 2830629.54 300483.601 76630.434 FALSE 0.10615434
## 303 1607410.67 253758.983 114798.317 TRUE 0.15786817
## 304 2814659.81 275098.538 85763.088 TRUE 0.09773776
## 305 5025231.95 540923.532 174367.244 FALSE 0.10764151
## 306 7408708.76 1284605.567 304180.855 TRUE 0.17339129
## 307 5034587.22 328735.237 441896.159 TRUE 0.06529537
## 308 5222745.55 593797.422 142216.284 TRUE 0.11369450
## 309 401712.05 31138.538 27113.237 FALSE 0.07751457
## 310 3698559.66 488249.852 366894.439 FALSE 0.13201081
## 311 274479.00 39866.656 13969.780 TRUE 0.14524483
## 312 4965364.98 453217.035 356255.260 TRUE 0.09127567
## 313 5611072.19 1080507.770 170962.779 FALSE 0.19256708
## 314 5829325.84 708217.797 161972.837 TRUE 0.12149223
## 315 3756641.73 460321.519 103108.829 FALSE 0.12253538
## 316 3206730.86 591230.424 129695.808 FALSE 0.18437170
## 317 1551260.88 148508.818 144266.036 FALSE 0.09573426
## 318 1716153.14 317116.173 101966.971 FALSE 0.18478314
## 319 4004352.28 317784.876 391174.069 TRUE 0.07935987
## 320 4620352.28 346211.839 185097.375 FALSE 0.07493191
## 321 7339510.00 447185.509 721916.672 TRUE 0.06092852
## 322 2663032.04 297860.336 237059.625 TRUE 0.11185008
## 323 1465749.14 158840.547 90141.128 FALSE 0.10836817
## 324 1326777.76 133516.551 104620.859 TRUE 0.10063219
## 325 8127234.83 1548634.091 719448.920 FALSE 0.19054871
## 326 8125894.21 830288.778 563088.984 TRUE 0.10217814
## 327 4702451.09 633680.788 251764.042 TRUE 0.13475542
## 328 3669778.61 203618.689 332278.389 FALSE 0.05548528
## 329 1292618.23 89488.492 71901.514 TRUE 0.06923041
## 330 5693254.53 705272.577 407463.091 TRUE 0.12387863
## 331 2487054.27 372554.362 123927.471 TRUE 0.14979744
## 332 4681385.82 481754.277 85315.897 FALSE 0.10290848
## 333 4919015.17 716730.862 56294.699 FALSE 0.14570617
## 334 6523083.94 643654.336 507387.909 FALSE 0.09867332
## 335 453582.77 83025.416 24558.315 FALSE 0.18304359
## 336 2907617.37 518023.942 37344.323 FALSE 0.17816097
## 337 5597599.83 1102395.450 147640.693 TRUE 0.19694074
## 338 6760253.09 712034.644 165498.957 FALSE 0.10532663
## 339 4334128.74 319043.648 336460.332 TRUE 0.07361195
## 340 3673867.37 561452.291 191352.159 TRUE 0.15282323
## 341 5836075.73 353145.404 323767.627 FALSE 0.06051076
## 342 4857131.77 859325.319 102813.053 TRUE 0.17692032
## 343 112459.59 18092.704 5037.980 FALSE 0.16088183
## 344 4948129.13 634933.882 475813.137 FALSE 0.12831797
## 345 5924169.16 825577.076 489744.661 FALSE 0.13935744
## 346 1638063.01 274940.082 147828.353 TRUE 0.16784463
## 347 1001226.91 94455.257 59577.694 FALSE 0.09433951
## 348 5275603.49 280284.837 172089.918 TRUE 0.05312849
## 349 2736606.38 294501.479 203879.703 TRUE 0.10761558
## 350 8105263.99 789272.125 292288.801 TRUE 0.09737772
## 351 4953736.45 733043.670 275809.677 FALSE 0.14797793
## 352 4943944.94 444415.801 256527.822 TRUE 0.08989093
## 353 2400651.45 303076.405 45615.095 TRUE 0.12624757
## 354 6683178.51 1323900.623 261675.324 FALSE 0.19809446
## 355 3749403.05 489738.545 247633.099 TRUE 0.13061774
## 356 1030339.68 123372.569 36312.320 FALSE 0.11973971
## 357 166346.65 31790.380 8803.523 FALSE 0.19110923
## 358 6160383.67 849182.632 116727.924 FALSE 0.13784574
## 359 7747603.88 693517.384 553853.696 TRUE 0.08951379
## 360 4524917.99 276453.725 246264.110 FALSE 0.06109585
## 361 2724386.99 525293.180 258644.359 TRUE 0.19281151
## 362 5578810.15 516415.660 447896.267 FALSE 0.09256735
## 363 3299343.96 623959.135 178815.208 TRUE 0.18911612
## 364 7029955.10 452870.895 688309.117 TRUE 0.06442017
## 365 5372626.58 464393.059 313026.222 TRUE 0.08643688
## 366 2250413.57 408216.040 195715.350 TRUE 0.18139601
## 367 6261696.14 1035102.930 107420.534 FALSE 0.16530712
## 368 3206258.89 594192.818 234536.836 TRUE 0.18532278
## 369 5259652.17 351860.386 57934.035 TRUE 0.06689803
## 370 3708696.87 442389.735 239728.722 TRUE 0.11928441
## 371 363702.04 34598.521 18074.556 TRUE 0.09512875
## 372 2640622.85 298135.908 242787.798 FALSE 0.11290363
## 373 4994894.22 587779.067 210835.183 TRUE 0.11767598
## 374 4436328.26 341541.431 277309.433 FALSE 0.07698741
## 375 2177339.19 236810.668 153997.888 TRUE 0.10876150
## 376 88514.48 13946.918 7526.973 TRUE 0.15756652
## 377 1231604.59 232357.179 65734.118 TRUE 0.18866216
## 378 6977872.78 562949.639 395905.516 TRUE 0.08067640
## 379 4187171.59 582486.909 114178.162 TRUE 0.13911226
## 380 973963.90 61670.925 86837.188 TRUE 0.06331952
## 381 2569458.14 448532.842 221194.689 FALSE 0.17456320
## 382 8033195.16 845637.611 613593.748 TRUE 0.10526790
## 383 6041132.76 897170.564 574843.863 TRUE 0.14851032
## 384 5433927.00 593665.353 224605.314 TRUE 0.10925162
## 385 6112802.97 747113.556 389131.117 FALSE 0.12222111
## 386 5494465.46 714038.080 83143.643 FALSE 0.12995588
## 387 1932111.54 216206.178 97804.061 TRUE 0.11190150
## 388 5974984.80 324881.881 131198.717 FALSE 0.05437367
## 389 5292964.40 405761.653 505750.162 TRUE 0.07666057
## 390 4696925.87 865254.109 378707.958 TRUE 0.18421711
## 391 6864827.12 488347.563 554472.207 FALSE 0.07113763
## 392 4688420.98 494873.784 58807.969 FALSE 0.10555234
## 393 6077678.83 963032.609 515187.349 FALSE 0.15845401
## 394 6538531.54 749832.852 420970.648 FALSE 0.11467909
## 395 4272219.51 477600.639 423645.318 FALSE 0.11179216
## 396 1238604.97 108112.669 113574.935 FALSE 0.08728584
## 397 1237494.71 183217.039 13679.398 TRUE 0.14805481
## 398 2736100.87 506779.331 118922.444 TRUE 0.18521954
## 399 1431608.60 273431.561 136551.219 TRUE 0.19099603
## 400 3160675.87 449464.506 291720.888 TRUE 0.14220519
## 401 3540913.59 704305.471 315756.043 FALSE 0.19890501
## 402 6747136.75 911360.066 228585.342 FALSE 0.13507360
## 403 1990194.24 372799.670 98474.797 FALSE 0.18731823
## 404 5928046.87 588192.718 424476.972 FALSE 0.09922201
## 405 463568.07 64565.767 36081.420 TRUE 0.13928001
## 406 6465425.74 645414.660 516355.726 FALSE 0.09982555
## 407 6597794.62 1030489.025 326140.494 TRUE 0.15618689
## 408 7318095.63 1138485.999 357277.374 TRUE 0.15557135
## 409 6835498.49 792444.106 388378.051 FALSE 0.11593070
## 410 1903633.79 95297.384 184470.133 TRUE 0.05006078
## 411 4609682.18 634996.298 265403.819 TRUE 0.13775273
## 412 6339840.40 885296.480 573770.128 FALSE 0.13964018
## 413 2255558.24 228126.238 176037.807 TRUE 0.10113959
## 414 4957762.30 595331.206 483554.453 TRUE 0.12008063
## 415 1271332.91 103444.750 40998.428 FALSE 0.08136716
## 416 5638070.03 286971.744 335519.788 TRUE 0.05089893
## 417 3764868.41 527917.590 178920.275 TRUE 0.14022206
## 418 2841852.88 158049.141 264422.240 FALSE 0.05561482
## 419 5960807.30 715751.658 218008.048 FALSE 0.12007630
## 420 3402836.83 387545.942 293430.927 TRUE 0.11388908
## 421 2482428.94 193941.962 196544.448 FALSE 0.07812589
## 422 1587400.56 106966.220 46000.137 FALSE 0.06738452
## 423 3748352.06 702885.709 79290.543 TRUE 0.18751859
## 424 6489322.71 718159.242 389588.440 FALSE 0.11066783
## 425 5555480.22 344058.505 163795.280 FALSE 0.06193137
## 426 5966942.42 747365.044 551458.956 TRUE 0.12525092
## 427 1091839.78 74761.986 15405.844 FALSE 0.06847340
## 428 4288390.01 846711.930 399341.867 TRUE 0.19744285
## 429 3727077.47 319702.895 112653.533 FALSE 0.08577844
## 430 4881099.87 501216.060 451623.190 FALSE 0.10268507
## 431 6345739.79 523860.444 72389.795 FALSE 0.08255309
## 432 1511688.73 176285.480 105281.412 TRUE 0.11661493
## 433 6540836.26 627657.260 613052.841 FALSE 0.09595979
## 434 2898293.27 322995.010 210427.017 TRUE 0.11144318
## 435 4553441.67 557699.384 247214.785 TRUE 0.12247865
## 436 3822548.89 444319.253 184483.091 TRUE 0.11623638
## 437 1126191.55 75164.180 69368.791 TRUE 0.06674191
## 438 88388.84 13404.632 8836.064 TRUE 0.15165525
## 439 7023307.26 779129.701 294326.747 FALSE 0.11093487
## 440 2809619.02 498341.325 79996.170 TRUE 0.17736972
## 441 1828019.28 193823.466 20092.464 TRUE 0.10602922
## 442 2118168.53 122236.185 152489.964 FALSE 0.05770843
## 443 3606990.93 201672.349 212496.852 FALSE 0.05591152
## 444 6235478.34 336337.209 201247.734 TRUE 0.05393928
## 445 6009190.36 508126.231 510227.562 FALSE 0.08455819
## 446 3912086.77 361338.990 55522.990 TRUE 0.09236477
## 447 5356319.54 273694.772 156686.988 TRUE 0.05109754
## 448 6953136.86 635204.300 178668.220 FALSE 0.09135507
## 449 5441620.35 1009764.825 254592.392 TRUE 0.18556326
## 450 624815.60 49060.991 47136.367 FALSE 0.07852075
## 451 6961691.12 1000977.412 663312.702 TRUE 0.14378366
## 452 4618951.28 361106.415 459180.327 TRUE 0.07817931
## 453 3334779.25 463448.704 121723.435 FALSE 0.13897433
## 454 4921559.21 325883.675 89296.743 TRUE 0.06621553
## 455 4789355.87 411287.468 403111.050 TRUE 0.08587532
## 456 3312678.00 568558.013 47036.543 FALSE 0.17163093
## 457 2993354.38 561104.414 294038.730 TRUE 0.18745005
## 458 401609.31 68472.536 12548.075 FALSE 0.17049539
## 459 3395741.19 259215.736 62904.070 TRUE 0.07633554
## 460 1840411.91 254111.981 182463.840 FALSE 0.13807343
## 461 2832295.46 384637.278 151592.028 FALSE 0.13580408
## 462 1536771.81 298279.186 137233.665 TRUE 0.19409465
## 463 814525.64 155376.758 55562.000 FALSE 0.19075736
## 464 3460059.43 687475.096 115637.677 TRUE 0.19868881
## 465 4413505.56 700559.732 328319.086 FALSE 0.15873090
## 466 2227317.06 314547.269 136231.889 TRUE 0.14122249
## 467 4363762.73 617190.884 197268.904 TRUE 0.14143548
## 468 485101.80 68021.477 47839.944 TRUE 0.14022104
## 469 4886351.32 340332.586 159740.210 TRUE 0.06964964
## 470 6671219.30 895056.158 535228.929 TRUE 0.13416680
## 471 1347432.48 171506.721 79831.243 FALSE 0.12728409
## 472 6684856.99 921343.730 89451.414 TRUE 0.13782550
## 473 5569758.23 611589.067 223926.094 TRUE 0.10980532
## 474 6239394.50 580745.553 532471.312 TRUE 0.09307723
## 475 7075308.54 881563.510 702769.530 TRUE 0.12459718
## 476 422174.11 67890.173 28991.829 FALSE 0.16081084
## 477 7390977.02 1247874.746 545834.215 FALSE 0.16883759
## 478 1267250.56 117333.726 75558.393 FALSE 0.09258921
## 479 2922185.91 359657.615 70410.939 FALSE 0.12307828
## 480 7217294.57 1345431.314 568565.400 FALSE 0.18641768
## 481 3356296.00 532219.189 118470.552 TRUE 0.15857338
## 482 315862.07 25953.284 28704.143 FALSE 0.08216651
## 483 1677638.08 130439.840 113540.877 FALSE 0.07775207
## 484 7376488.36 1360231.959 504352.143 TRUE 0.18440102
## 485 4763149.08 344824.629 465184.201 FALSE 0.07239426
## 486 6719258.99 978448.058 205380.247 FALSE 0.14561845
## 487 2644383.57 159778.776 27957.165 FALSE 0.06042194
## 488 5127193.99 910747.049 108633.409 FALSE 0.17763070
## 489 5127635.58 883400.568 102168.105 FALSE 0.17228224
## 490 4735637.60 578980.878 204952.282 TRUE 0.12226039
## 491 6195992.75 801962.403 161318.778 FALSE 0.12943243
## 492 2299583.19 438702.044 38393.182 TRUE 0.19077459
## 493 4583340.26 830078.834 231141.083 FALSE 0.18110784
## 494 7837156.55 432547.954 187964.064 FALSE 0.05519195
## 495 989736.26 51987.240 31002.675 FALSE 0.05252636
## 496 3646441.04 704647.494 364183.971 TRUE 0.19324253
## 497 2558901.81 296970.927 65742.071 FALSE 0.11605405
## 498 1154724.53 85673.630 83403.198 TRUE 0.07419400
## 499 4262061.75 550424.038 142305.125 FALSE 0.12914502
## 500 2447068.45 317483.582 33135.852 TRUE 0.12974038
## 501 3238159.05 627941.931 191074.656 TRUE 0.19391942
## 502 3078904.72 494287.224 260710.984 TRUE 0.16053995
## 503 1261927.98 140704.253 16316.378 FALSE 0.11149943
## 504 6857870.75 928228.284 467876.110 FALSE 0.13535226
## 505 5503473.48 468665.519 198755.673 TRUE 0.08515813
## 506 1560106.79 193361.635 29880.544 FALSE 0.12394128
## 507 4655759.92 599244.030 234823.037 FALSE 0.12871025
## 508 196519.52 34636.880 11014.825 TRUE 0.17625160
## 509 2369412.16 450758.199 64606.497 FALSE 0.19024052
## 510 1776367.27 277658.026 56474.787 FALSE 0.15630665
## 511 5299041.92 919352.244 211092.745 TRUE 0.17349405
## 512 988231.77 61168.233 26696.737 TRUE 0.06189665
## 513 1400163.18 78828.403 101961.702 TRUE 0.05629944
## 514 6754290.03 1228378.271 302236.849 TRUE 0.18186638
## 515 7528669.91 490970.538 492845.707 TRUE 0.06521345
## 516 547810.31 90498.840 44120.114 TRUE 0.16520105
## 517 6868979.32 894278.127 618076.256 FALSE 0.13019083
## 518 6797099.44 692311.397 653355.326 TRUE 0.10185395
## 519 4792879.16 299560.626 260794.848 FALSE 0.06250118
## 520 2626267.60 402965.465 212386.976 FALSE 0.15343656
## 521 3237270.82 512975.600 52236.203 TRUE 0.15845928
## 522 6328989.24 325105.012 185214.102 TRUE 0.05136760
## 523 3826302.23 229107.579 142140.394 FALSE 0.05987702
## 524 402991.68 45879.568 23460.158 FALSE 0.11384743
## 525 4565873.30 293229.959 290581.555 FALSE 0.06422210
## 526 6156838.24 589554.472 129796.259 TRUE 0.09575604
## 527 4254263.67 718589.789 149772.615 FALSE 0.16891050
## 528 1655095.15 324115.686 155257.321 FALSE 0.19582904
## 529 3566724.58 550252.051 40077.790 FALSE 0.15427377
## 530 6084963.65 1216698.115 183343.251 TRUE 0.19995158
## 531 1843021.96 330839.429 175862.842 TRUE 0.17950922
## 532 4513297.21 635461.892 53757.448 FALSE 0.14079771
## 533 6239192.36 437837.824 189698.124 TRUE 0.07017540
## 534 5608750.78 584592.453 390839.489 TRUE 0.10422864
## 535 5931984.14 659738.039 424202.219 TRUE 0.11121709
## 536 5277619.94 833353.141 405102.851 TRUE 0.15790321
## 537 7938695.96 1368635.393 183440.314 TRUE 0.17240053
## 538 4404743.87 798849.954 400707.212 FALSE 0.18136127
## 539 584852.77 108872.410 43209.596 FALSE 0.18615354
## 540 2779880.42 230606.025 238789.341 TRUE 0.08295538
## 541 7683161.97 1319566.582 158074.284 FALSE 0.17174785
## 542 914217.66 154192.345 81879.331 FALSE 0.16866043
## 543 5060721.43 336928.533 490358.535 TRUE 0.06657717
## 544 1347064.85 203299.976 21247.372 TRUE 0.15092070
## 545 3686040.42 411053.124 176155.019 TRUE 0.11151617
## 546 1520671.12 90139.260 28646.121 TRUE 0.05927597
## 547 6198757.96 1031187.445 538216.606 FALSE 0.16635388
## 548 6508854.02 812202.845 83079.449 FALSE 0.12478431
## 549 7487023.04 1288589.780 380954.324 TRUE 0.17210977
## 550 1553036.11 269322.388 68036.062 FALSE 0.17341669
## 551 6548616.01 717420.053 390643.096 FALSE 0.10955293
## 552 5170236.56 538525.517 378578.832 FALSE 0.10415878
## 553 5189382.67 739525.385 86983.305 TRUE 0.14250739
## 554 3564271.52 292209.131 88839.787 FALSE 0.08198285
## 555 248798.09 28832.578 17455.665 FALSE 0.11588745
## 556 3593703.14 551166.556 113794.659 TRUE 0.15337009
## 557 7365598.24 693194.279 327867.936 FALSE 0.09411242
## 558 690396.04 137339.206 44568.376 FALSE 0.19892815
## 559 4128919.56 497932.274 370579.305 FALSE 0.12059626
## 560 5750455.01 683919.441 263123.445 TRUE 0.11893310
## 561 4330275.86 773064.122 331045.340 TRUE 0.17852538
## 562 1806990.60 354339.194 71582.170 FALSE 0.19609355
## 563 5186319.43 499995.381 364348.778 FALSE 0.09640659
## 564 268075.18 24983.337 19025.306 TRUE 0.09319526
## 565 7334712.28 1099677.772 577722.051 FALSE 0.14992787
## 566 1164404.05 154932.007 51381.620 TRUE 0.13305691
## 567 7409045.34 1016769.437 584272.677 TRUE 0.13723353
## 568 4917536.97 983276.154 453678.110 TRUE 0.19995298
## 569 3443691.09 590521.275 47545.266 TRUE 0.17147917
## 570 2858374.02 225005.439 77475.729 FALSE 0.07871798
## 571 6049604.22 1039213.112 490316.775 TRUE 0.17178200
## 572 3935852.18 287574.643 380715.671 FALSE 0.07306541
## 573 1970660.69 119765.614 146848.052 FALSE 0.06077435
## 574 1447580.65 137754.303 46115.026 FALSE 0.09516175
## 575 2343047.08 222086.177 68954.270 FALSE 0.09478520
## 576 4276094.75 378493.584 313861.861 TRUE 0.08851384
## 577 2646372.16 200454.230 231206.026 FALSE 0.07574680
## 578 2772976.60 303683.321 182779.511 TRUE 0.10951528
## 579 7345910.85 1035244.100 623648.636 TRUE 0.14092794
## 580 5814497.32 391672.310 321483.641 FALSE 0.06736134
## 581 3368361.06 585705.723 251690.801 FALSE 0.17388448
## 582 4702982.43 852471.039 344321.159 FALSE 0.18126180
## 583 7400701.98 626916.961 329810.887 FALSE 0.08471047
## 584 3267209.58 208178.895 292214.289 FALSE 0.06371764
## 585 7003130.74 541635.103 432395.126 TRUE 0.07734185
## 586 4769665.13 903983.327 53016.186 TRUE 0.18952763
## 587 5680312.45 857256.791 86441.607 FALSE 0.15091719
## 588 7669585.82 1363037.727 474857.993 FALSE 0.17771986
## 589 6653816.21 489815.908 650361.520 TRUE 0.07361428
## 590 117726.67 14508.177 3746.358 TRUE 0.12323611
## 591 8001452.16 1553276.081 322988.013 TRUE 0.19412427
## 592 3365353.83 464320.899 282811.568 FALSE 0.13797090
## 593 2477875.74 351588.684 144317.250 FALSE 0.14189117
## 594 5175338.02 359397.938 73339.804 TRUE 0.06944434
## 595 1458902.29 99695.913 140861.256 FALSE 0.06833625
## 596 5561482.46 748978.958 276594.328 FALSE 0.13467254
## 597 6480354.01 533884.412 202776.778 TRUE 0.08238507
## 598 147636.70 12113.657 3485.073 FALSE 0.08205045
## 599 1216198.26 206921.378 61299.618 FALSE 0.17013787
## 600 703569.65 105800.091 38048.839 TRUE 0.15037615
## 601 917355.91 182131.472 76776.291 TRUE 0.19853960
## 602 1809572.32 356050.828 153563.983 TRUE 0.19675966
## 603 7363934.54 882624.385 335314.550 TRUE 0.11985772
## 604 4988294.51 650211.244 378682.335 FALSE 0.13034740
## 605 7158445.44 1309060.853 548117.156 FALSE 0.18286943
## 606 981823.74 55704.229 50166.341 FALSE 0.05673547
## 607 3982819.16 460264.850 243159.271 FALSE 0.11556258
## 608 5789617.24 949689.949 210482.820 FALSE 0.16403329
## 609 6458244.39 1259099.887 510589.089 FALSE 0.19496009
## 610 4028815.91 705769.109 202255.774 FALSE 0.17518028
## 611 1992255.93 206849.759 182834.840 TRUE 0.10382690
## 612 1105020.75 182109.592 70849.943 TRUE 0.16480197
## 613 3453488.87 683276.279 125834.542 TRUE 0.19785102
## 614 4464523.58 764122.337 229654.532 FALSE 0.17115428
## 615 141396.92 20676.731 12035.886 FALSE 0.14623184
## 616 6749020.94 676273.925 580713.760 TRUE 0.10020326
## 617 3055930.38 355473.567 74006.781 TRUE 0.11632253
## 618 2153250.54 383083.378 173436.170 FALSE 0.17790934
## 619 3876771.99 727081.031 231264.537 FALSE 0.18754805
## 620 91840.55 9115.967 1045.836 TRUE 0.09925863
## 621 2649979.02 374399.081 87013.609 FALSE 0.14128379
## 622 181561.27 12717.025 13220.467 TRUE 0.07004261
## 623 5167791.59 475640.418 93275.852 TRUE 0.09203940
## 624 1559539.66 203247.186 56591.294 FALSE 0.13032512
## 625 3982915.48 672724.220 184051.346 FALSE 0.16890246
## 626 4120301.61 213294.037 348369.389 FALSE 0.05176661
## 627 4421849.33 686559.739 287275.159 FALSE 0.15526529
## 628 4456297.47 889608.521 380911.676 FALSE 0.19962952
## 629 2891784.61 332520.841 287579.651 FALSE 0.11498811
## 630 6355331.35 839261.903 169416.984 TRUE 0.13205636
## 631 6059135.65 989196.695 463071.646 FALSE 0.16325706
## 632 140786.30 13974.714 9414.915 FALSE 0.09926189
## 633 6163974.37 789927.709 375488.110 TRUE 0.12815234
## 634 2573719.91 459103.299 143131.425 FALSE 0.17838122
## 635 5242998.94 718660.581 232895.782 FALSE 0.13707052
## 636 5216890.48 475759.874 64120.988 FALSE 0.09119606
## 637 5242906.65 318028.320 243642.789 FALSE 0.06065878
## 638 2828544.24 290901.307 63667.902 FALSE 0.10284488
## 639 5487851.08 462524.789 213865.791 TRUE 0.08428159
## 640 7446840.54 648646.511 527585.076 FALSE 0.08710359
## 641 3923229.78 529169.798 104225.595 TRUE 0.13488116
## 642 290936.26 38556.930 11016.107 TRUE 0.13252707
## 643 5313393.09 303690.780 111303.975 FALSE 0.05715571
## 644 7228984.88 388053.194 403236.907 FALSE 0.05368018
## 645 5108116.88 610661.124 298829.390 FALSE 0.11954721
## 646 308093.84 41638.770 14245.237 TRUE 0.13514964
## 647 4310667.86 622822.547 155095.473 TRUE 0.14448400
## 648 7103267.68 763905.735 77607.142 TRUE 0.10754286
## 649 3435983.70 343023.908 184405.074 TRUE 0.09983281
## 650 7379153.74 948041.009 472438.480 FALSE 0.12847557
## 651 5743140.13 1071989.931 285636.841 TRUE 0.18665572
## 652 1646491.00 206662.942 135366.571 TRUE 0.12551720
## 653 1389408.18 192220.424 101845.149 TRUE 0.13834698
## 654 6989175.78 930258.153 433682.247 FALSE 0.13309984
## 655 2676650.16 297851.590 41433.183 FALSE 0.11127774
## 656 3463589.08 629632.113 324103.792 TRUE 0.18178603
## 657 6771219.47 1325318.969 252861.583 TRUE 0.19572825
## 658 831756.63 111537.233 53343.994 FALSE 0.13409840
## 659 1803522.12 171810.026 132353.313 TRUE 0.09526361
## 660 4704088.54 752907.049 284152.835 FALSE 0.16005376
## 661 3940796.50 753796.424 274135.802 TRUE 0.19128022
## 662 7009523.88 1229369.488 363033.272 FALSE 0.17538559
## 663 1957233.89 331236.029 168540.362 FALSE 0.16923681
## 664 7264464.05 1040527.944 711275.944 FALSE 0.14323534
## 665 6151295.47 769056.767 77452.836 FALSE 0.12502355
## 666 4045398.04 387137.030 48480.649 FALSE 0.09569813
## 667 1743654.13 115113.906 64554.637 TRUE 0.06601877
## 668 3300840.25 433634.883 159972.381 TRUE 0.13137106
## 669 4590370.13 737479.110 426668.802 TRUE 0.16065787
## 670 1067484.03 87413.124 55101.235 FALSE 0.08188706
## 671 5111237.30 906277.077 150954.290 TRUE 0.17731070
## 672 5398817.41 981400.755 148203.469 TRUE 0.18178069
## 673 1665913.28 320099.530 70506.816 TRUE 0.19214657
## 674 7197983.63 1306014.904 544967.368 TRUE 0.18144177
## 675 724735.14 123743.808 34201.122 TRUE 0.17074349
## 676 1991040.24 170947.218 52787.594 FALSE 0.08585824
## 677 487345.34 44292.719 13867.363 TRUE 0.09088569
## 678 7175830.82 531499.189 391438.773 TRUE 0.07406797
## 679 4862025.44 618598.746 419633.869 TRUE 0.12723067
## 680 6084823.14 1053799.613 91096.605 FALSE 0.17318492
## 681 514216.39 52869.423 43716.780 TRUE 0.10281551
## 682 6734333.71 500768.874 479985.085 TRUE 0.07436057
## 683 4611175.46 669638.416 96495.797 FALSE 0.14522076
## 684 5117034.98 607514.961 337721.940 FALSE 0.11872402
## 685 6491020.18 981676.717 242544.437 FALSE 0.15123612
## 686 546668.51 70324.298 54170.471 FALSE 0.12864157
## 687 4295861.29 469540.008 78205.675 TRUE 0.10930055
## 688 7047661.97 1395669.214 683125.924 FALSE 0.19803294
## 689 5787426.07 907941.429 212674.901 TRUE 0.15688173
## 690 3684541.46 344984.692 136565.311 TRUE 0.09363029
## 691 1960328.09 358234.823 38820.581 FALSE 0.18274228
## 692 4777303.71 910918.348 350713.949 FALSE 0.19067625
## 693 3921573.62 307085.909 311726.283 FALSE 0.07830681
## 694 4718525.00 351646.994 327737.739 FALSE 0.07452477
## 695 5121544.89 470200.803 497161.144 FALSE 0.09180839
## 696 6878206.24 1271353.000 629549.043 FALSE 0.18483787
## 697 7400527.90 404450.781 670434.973 FALSE 0.05465161
## 698 5613004.02 675491.070 160764.970 FALSE 0.12034395
## 699 4280008.82 721548.589 265429.227 TRUE 0.16858577
## 700 6506419.48 831455.594 366936.914 TRUE 0.12779004
## 701 7226783.37 1323441.146 700340.143 FALSE 0.18313004
## 702 4499599.05 789261.929 370023.350 TRUE 0.17540717
## 703 4934950.39 280029.747 404412.786 TRUE 0.05674419
## 704 2576660.06 379002.637 193737.522 FALSE 0.14709066
## 705 6521985.11 402625.446 434517.452 FALSE 0.06173357
## 706 7055994.42 1128349.926 521531.349 FALSE 0.15991366
## 707 1095858.47 92571.210 88082.922 FALSE 0.08447369
## 708 5532981.56 1088184.878 366553.756 FALSE 0.19667242
## 709 3947436.81 261003.083 344543.148 FALSE 0.06611964
## 710 1744891.60 235715.322 44479.295 TRUE 0.13508881
## 711 341163.28 35455.126 18280.881 TRUE 0.10392421
## 712 5543328.83 748406.074 136593.552 TRUE 0.13501023
## 713 5642805.13 1087504.272 104890.689 FALSE 0.19272405
## 714 5514740.25 976883.101 176119.141 FALSE 0.17714037
## 715 4420295.66 766536.772 294289.500 TRUE 0.17341301
## 716 1856625.39 113757.624 20590.759 FALSE 0.06127118
## 717 4406059.25 259604.861 233406.378 FALSE 0.05891997
## 718 4985109.82 372915.266 274555.947 FALSE 0.07480583
## 719 3567881.62 683752.940 217938.856 TRUE 0.19164115
## 720 727118.22 124810.733 40072.186 TRUE 0.17165123
## 721 5447645.16 747303.389 386817.799 FALSE 0.13717916
## 722 845211.18 63436.452 42205.250 FALSE 0.07505397
## 723 3431871.46 640325.664 212106.521 FALSE 0.18658206
## 724 2021765.38 399423.293 157896.623 TRUE 0.19756164
## 725 5772740.26 1013223.192 296630.800 FALSE 0.17551858
## 726 4184456.57 752337.139 415583.648 FALSE 0.17979327
## 727 590001.86 66849.304 53308.575 TRUE 0.11330355
## 728 6665835.67 1094562.471 619345.909 TRUE 0.16420484
## 729 4779182.99 462459.520 273110.804 FALSE 0.09676539
## 730 1468517.45 170774.851 59592.241 FALSE 0.11629065
## 731 6075522.76 1093740.266 366306.447 TRUE 0.18002406
## 732 3744061.64 509611.877 50209.475 TRUE 0.13611204
## 733 1759412.65 342256.387 107606.182 TRUE 0.19452877
## 734 4227430.17 808142.634 278345.159 TRUE 0.19116641
## 735 5582023.31 692868.965 488105.880 TRUE 0.12412506
## 736 4778349.08 338504.436 327866.279 FALSE 0.07084129
## 737 2786929.99 357578.082 212021.692 FALSE 0.12830537
## 738 467456.88 26191.622 35724.224 TRUE 0.05603003
## 739 2032090.05 329652.531 37784.410 TRUE 0.16222339
## 740 3054835.23 232728.593 270956.752 TRUE 0.07618368
## 741 820749.39 149173.117 50367.800 FALSE 0.18175233
## 742 4463161.95 766660.301 259709.309 FALSE 0.17177515
## 743 7127190.92 1152428.279 250858.168 FALSE 0.16169460
## 744 6261247.92 580579.101 480857.812 TRUE 0.09272578
## 745 4363274.02 696211.595 213532.923 FALSE 0.15956174
## 746 7214869.97 707016.437 573552.506 FALSE 0.09799434
## 747 3731386.67 442496.064 65624.676 TRUE 0.11858757
## 748 4738740.50 277345.460 105490.920 TRUE 0.05852725
## 749 3549207.29 486624.212 319432.897 TRUE 0.13710786
## 750 4718687.95 922997.860 282675.468 TRUE 0.19560477
## 751 2730707.76 160657.059 239649.057 FALSE 0.05883349
## 752 3464681.94 453302.452 340359.310 FALSE 0.13083523
## 753 7077957.85 1111902.866 226140.905 FALSE 0.15709374
## 754 2102163.70 278801.666 184747.771 FALSE 0.13262605
## 755 168037.01 27027.423 11591.057 TRUE 0.16084209
## 756 2837151.59 448387.490 269419.736 FALSE 0.15804143
## 757 1845788.25 136519.030 75806.218 TRUE 0.07396245
## 758 6802667.32 917801.312 373085.672 TRUE 0.13491786
## 759 8097934.14 1259031.993 273662.813 TRUE 0.15547570
## 760 6197262.10 349061.898 615406.136 FALSE 0.05632518
## 761 6639976.09 1222095.308 520933.470 FALSE 0.18405116
## 762 5872497.51 665045.610 361103.991 TRUE 0.11324749
## 763 5866382.45 478441.610 330626.091 FALSE 0.08155650
## 764 3048968.18 247000.151 161837.241 TRUE 0.08101106
## 765 6548001.69 1202858.660 606207.389 FALSE 0.18369859
## 766 1496848.33 255774.507 35449.848 TRUE 0.17087537
## 767 243074.18 21061.997 23068.223 FALSE 0.08664844
## 768 3625388.38 682990.629 116757.517 TRUE 0.18839102
## 769 4849401.74 334815.707 326771.045 FALSE 0.06904268
## 770 5021846.09 1002115.249 222097.970 TRUE 0.19955117
## 771 144023.03 26656.799 10446.931 FALSE 0.18508706
## 772 843418.29 49264.833 10090.043 TRUE 0.05841091
## 773 6801804.77 1096856.309 429887.340 TRUE 0.16125960
## 774 526578.90 97948.475 40622.896 FALSE 0.18600912
## 775 7549657.34 1302551.113 126598.374 FALSE 0.17253116
## 776 480665.58 53384.881 45954.140 TRUE 0.11106450
## 777 4629005.43 560880.234 106074.713 FALSE 0.12116647
## 778 6628310.51 1273696.520 644944.559 FALSE 0.19216006
## 779 7588656.55 746418.337 316229.445 TRUE 0.09835975
## 780 7865145.01 679681.314 542870.249 FALSE 0.08641688
## 781 382769.28 75715.337 6524.926 TRUE 0.19780934
## 782 6105547.58 473134.882 197757.347 TRUE 0.07749262
## 783 7430025.33 784266.934 147377.564 FALSE 0.10555374
## 784 5119402.64 376859.176 422973.833 FALSE 0.07361390
## 785 3213010.62 386618.155 254463.562 TRUE 0.12032894
## 786 683456.44 47200.334 15052.662 TRUE 0.06906122
## 787 3757831.17 277923.348 79981.222 FALSE 0.07395844
## 788 5054021.29 898969.027 438628.480 FALSE 0.17787203
## 789 1151375.93 117978.961 50042.258 FALSE 0.10246780
## 790 2656058.21 407992.147 147521.024 FALSE 0.15360813
## 791 5604803.56 365212.773 414839.343 FALSE 0.06516067
## 792 3544868.75 439007.167 46038.006 TRUE 0.12384300
## 793 6823040.12 1320634.259 289128.862 TRUE 0.19355511
## 794 225129.38 14321.794 3524.707 TRUE 0.06361584
## 795 7855039.83 1441453.559 625823.841 TRUE 0.18350684
## 796 7042143.90 667712.114 236109.654 TRUE 0.09481660
## 797 3383326.16 609743.934 323784.628 TRUE 0.18022026
## 798 1371545.29 152866.990 119249.307 TRUE 0.11145603
## 799 5693989.87 833215.806 62240.755 TRUE 0.14633251
## 800 5415553.95 507216.286 243683.565 FALSE 0.09365917
## 801 3053403.90 172333.059 265420.281 FALSE 0.05643965
## 802 954807.27 140862.157 27835.067 TRUE 0.14752941
## 803 1728215.29 216149.429 121559.392 FALSE 0.12507089
## 804 3339634.86 560461.421 212437.769 TRUE 0.16782117
## 805 2131458.08 267700.877 140177.568 FALSE 0.12559519
## 806 6986337.72 718636.461 626616.601 FALSE 0.10286312
## 807 4611132.89 613356.901 251015.828 FALSE 0.13301653
## 808 3314223.34 520153.845 214433.141 TRUE 0.15694592
## 809 246805.94 19057.010 5715.457 TRUE 0.07721455
## 810 1629158.63 300141.315 45957.233 FALSE 0.18423087
## 811 4148876.26 599549.669 114469.221 FALSE 0.14450893
## 812 408410.51 55543.625 19026.869 TRUE 0.13599950
## 813 1055506.73 137752.957 19450.982 TRUE 0.13050884
## 814 5586956.91 643217.024 366055.412 FALSE 0.11512833
## 815 2501922.67 185346.738 71120.932 FALSE 0.07408172
## 816 5379244.19 962931.129 432569.796 FALSE 0.17900863
## 817 443487.71 30130.557 37374.536 FALSE 0.06794001
## 818 1758132.08 140722.891 140051.220 TRUE 0.08004114
## 819 5649316.23 541657.006 175853.698 FALSE 0.09588010
## 820 3245085.50 411221.481 92530.489 FALSE 0.12672131
## 821 2087792.93 199024.725 185931.528 FALSE 0.09532781
## 822 2547199.45 323419.470 97319.840 FALSE 0.12697061
## 823 5567123.88 466526.258 288425.777 FALSE 0.08380023
## 824 4693179.61 920750.254 140064.463 FALSE 0.19618901
## 825 6912536.53 1037662.884 218911.988 FALSE 0.15011319
## 826 4712373.66 519686.024 286153.330 FALSE 0.11028116
## 827 5124405.77 979605.229 427467.355 TRUE 0.19116465
## 828 3385722.72 400046.066 318570.900 FALSE 0.11815677
## 829 1598585.44 163677.160 63523.717 TRUE 0.10238875
## 830 3387646.15 481734.761 318293.493 FALSE 0.14220339
## 831 5761463.44 663550.456 388762.518 TRUE 0.11517047
## 832 6992937.24 717542.802 691263.352 TRUE 0.10260964
## 833 5245355.85 369853.239 78141.247 FALSE 0.07051061
## 834 3195629.07 171221.884 280213.247 FALSE 0.05358002
## 835 2557805.57 360093.759 33095.513 TRUE 0.14078230
## 836 235448.61 15718.091 2567.970 TRUE 0.06675805
## 837 4244268.80 676342.420 131534.222 FALSE 0.15935428
## 838 2957951.11 166535.719 113681.924 TRUE 0.05630104
## 839 1881082.10 102649.293 42698.324 TRUE 0.05456928
## 840 2096995.57 121640.860 77180.155 FALSE 0.05800721
## 841 4460116.17 232770.224 276451.523 TRUE 0.05218927
## 842 4584543.26 583532.258 304034.044 TRUE 0.12728253
## 843 6762235.12 870770.117 668703.394 TRUE 0.12876957
## 844 1316908.53 197491.750 49857.112 FALSE 0.14996619
## 845 832732.74 63239.042 82572.124 TRUE 0.07594158
## 846 2494884.29 236688.393 154508.055 FALSE 0.09486949
## 847 5461820.17 281870.993 174022.205 FALSE 0.05160752
## 848 4505328.79 674271.650 417800.893 FALSE 0.14966092
## 849 4501000.18 486969.454 379938.416 FALSE 0.10819139
## 850 7767843.62 1016049.609 408311.507 TRUE 0.13080202
## 851 670649.08 90818.652 31031.686 FALSE 0.13541904
## 852 4763863.31 486794.780 95645.660 TRUE 0.10218488
## 853 4630981.24 643430.657 73163.811 TRUE 0.13894046
## 854 6674630.57 1173660.000 458279.623 FALSE 0.17583895
## 855 5981614.60 1166590.595 111749.481 TRUE 0.19502938
## 856 4019707.76 415245.093 247250.092 TRUE 0.10330231
## 857 7844510.59 916355.917 99165.334 TRUE 0.11681493
## 858 7560944.63 780152.307 463542.409 FALSE 0.10318186
## 859 2630211.47 243669.028 45453.572 FALSE 0.09264237
## 860 206617.87 33837.457 11110.372 FALSE 0.16376829
## 861 7431622.16 1383666.273 138689.558 TRUE 0.18618631
## 862 1807606.73 305280.545 62571.505 TRUE 0.16888659
## 863 7613958.73 411397.317 224648.365 TRUE 0.05403199
## 864 3508410.70 244590.763 339335.461 TRUE 0.06971554
## 865 395146.79 64913.086 18002.115 TRUE 0.16427588
## 866 1406821.10 138028.087 68371.831 TRUE 0.09811346
## 867 4891720.11 400653.933 405512.300 TRUE 0.08190451
## 868 4389316.89 794866.384 310593.680 FALSE 0.18109114
## 869 685073.76 78614.756 22644.726 FALSE 0.11475371
## 870 1968741.71 200588.297 81081.364 TRUE 0.10188655
## 871 1778953.68 203784.881 140798.621 TRUE 0.11455322
## 872 5441980.98 479229.557 465539.627 TRUE 0.08806160
## 873 3932112.55 363337.414 298065.800 FALSE 0.09240260
## 874 3912973.11 649280.840 287545.657 TRUE 0.16593031
## 875 2524881.89 205898.979 107272.218 TRUE 0.08154796
## 876 2312322.43 151317.428 144827.927 FALSE 0.06543959
## 877 3274240.79 535710.653 230714.468 FALSE 0.16361370
## 878 6731639.11 733639.468 274183.492 TRUE 0.10898378
## 879 5866208.39 350351.479 180847.786 FALSE 0.05972367
## 880 3134873.98 291210.188 133870.698 TRUE 0.09289375
## 881 8056301.95 1288153.856 704564.897 FALSE 0.15989394
## 882 7759248.99 1516761.370 150278.149 TRUE 0.19547786
## 883 6250779.78 969250.800 527765.586 FALSE 0.15506078
## 884 8195897.72 693509.576 800542.957 FALSE 0.08461667
## 885 6646861.61 1000419.475 569591.347 FALSE 0.15051005
## 886 5658761.04 485633.528 342991.292 TRUE 0.08581976
## 887 5855822.04 1102962.979 577325.880 FALSE 0.18835323
## 888 5206319.87 647675.526 188245.733 TRUE 0.12440179
## 889 6922292.59 539854.660 407087.280 FALSE 0.07798784
## 890 3219001.31 598529.787 153934.388 FALSE 0.18593648
## 891 4032978.58 461423.923 365569.082 FALSE 0.11441269
## 892 630014.40 90283.198 21282.075 FALSE 0.14330339
## 893 6072571.18 1194326.269 260763.449 FALSE 0.19667555
## 894 2608763.36 213861.040 181501.374 FALSE 0.08197794
## 895 5675873.46 332699.269 384918.975 FALSE 0.05861640
## 896 4857396.26 457843.141 142072.828 FALSE 0.09425691
## 897 5364828.50 894823.594 239409.301 FALSE 0.16679445
## 898 3138097.02 174865.210 170584.732 TRUE 0.05572333
## 899 913168.63 64317.030 69477.817 FALSE 0.07043281
## 900 4122117.95 227136.819 205102.509 FALSE 0.05510197
## 901 1461491.57 255540.120 133847.191 TRUE 0.17484885
## 902 4904748.30 826674.036 247584.986 TRUE 0.16854566
## 903 7750614.25 920507.705 283667.383 FALSE 0.11876577
## 904 3453763.85 327825.830 182789.249 TRUE 0.09491843
## 905 7645330.29 838246.976 557185.282 TRUE 0.10964170
## 906 5858057.00 1139580.548 454314.578 FALSE 0.19453217
## 907 5474581.96 816409.826 453249.085 FALSE 0.14912734
## 908 5111408.99 335242.623 81701.637 TRUE 0.06558713
## 909 329517.53 42588.345 7247.532 TRUE 0.12924455
## 910 3432074.50 519139.041 220290.336 FALSE 0.15126101
## 911 2132332.46 238573.618 165093.394 TRUE 0.11188387
## 912 4684204.24 465281.244 238305.613 FALSE 0.09932984
## 913 6664497.49 458412.073 503670.057 FALSE 0.06878419
## 914 321613.16 63614.806 25260.717 TRUE 0.19779914
## 915 4444967.13 324465.572 407638.530 FALSE 0.07299617
## 916 2209705.43 309425.090 52709.976 TRUE 0.14003002
## 917 1464411.60 87666.753 71691.657 TRUE 0.05986483
## 918 1947109.90 114158.804 179750.073 FALSE 0.05862987
## 919 4457767.11 645955.160 301922.819 FALSE 0.14490554
## 920 3226820.92 512984.742 37369.583 FALSE 0.15897527
## 921 4695583.03 512987.608 93610.611 FALSE 0.10924897
## 922 6602059.68 561129.599 451282.885 TRUE 0.08499311
## 923 3808636.06 620578.922 230091.422 TRUE 0.16293994
## 924 5367045.00 616242.892 133652.449 TRUE 0.11481977
## 925 143053.31 19860.180 12482.336 FALSE 0.13883062
## 926 5376500.37 504735.583 348355.395 TRUE 0.09387809
## 927 7020409.36 511356.411 250150.258 FALSE 0.07283855
## 928 1159098.98 177258.173 32824.268 FALSE 0.15292756
## 929 4892951.80 736326.811 474536.596 FALSE 0.15048724
## 930 5404382.77 373603.730 339074.667 TRUE 0.06912977
## 931 2367907.50 145832.211 178600.317 TRUE 0.06158695
## 932 3669929.50 364689.090 220932.865 TRUE 0.09937223
## 933 6462899.44 813355.336 475295.594 TRUE 0.12584991
## 934 6278957.18 504199.491 576030.687 TRUE 0.08029988
## 935 1160724.11 155612.017 98995.240 TRUE 0.13406460
## 936 3891649.82 474954.508 59556.723 FALSE 0.12204451
## 937 1332711.58 90747.112 60085.022 FALSE 0.06809209
## 938 3399972.59 623922.010 195878.672 FALSE 0.18350795
## 939 4902710.94 432812.630 480120.845 TRUE 0.08828027
## 940 3890372.08 772804.146 111916.264 FALSE 0.19864530
## 941 7465737.14 820081.689 541867.051 TRUE 0.10984604
## 942 518369.62 58037.557 17113.762 FALSE 0.11196173
## 943 4969903.64 594018.124 54740.090 FALSE 0.11952307
## 944 4814709.55 304287.942 345210.824 FALSE 0.06319965
## 945 4196760.08 558679.398 221385.548 FALSE 0.13312160
## 946 4733524.14 375916.143 66608.910 FALSE 0.07941570
## 947 4139918.08 569100.117 103291.365 FALSE 0.13746652
## 948 1802344.57 317161.735 137943.586 FALSE 0.17597175
## 949 2444989.38 161946.158 212918.161 TRUE 0.06623594
## 950 772191.13 119241.446 48293.539 TRUE 0.15441960
## 951 5151098.02 436999.752 264966.012 FALSE 0.08483623
## 952 275114.66 45189.893 8575.491 TRUE 0.16425840
## 953 1270612.71 203120.830 32945.146 TRUE 0.15986054
## 954 3843499.58 271291.149 262821.160 TRUE 0.07058441
## 955 2199771.31 328508.437 86126.292 TRUE 0.14933754
## 956 1224185.18 243213.359 103651.775 TRUE 0.19867367
## 957 4055135.18 219332.663 259988.460 FALSE 0.05408763
## 958 4758278.77 374154.722 100269.932 TRUE 0.07863237
## 959 1192320.24 80830.595 15478.711 FALSE 0.06779269
## 960 3228508.31 373033.273 232046.226 FALSE 0.11554354
## 961 5467978.84 571606.584 91246.321 FALSE 0.10453709
## 962 1136548.68 188019.832 13465.323 TRUE 0.16543051
## 963 4366243.73 246702.525 224619.028 TRUE 0.05650223
## 964 4505442.54 517617.095 252889.915 TRUE 0.11488707
## 965 3040745.03 326922.306 171400.777 FALSE 0.10751388
## 966 2025946.19 295020.203 151728.997 FALSE 0.14562095
## 967 330981.62 52761.366 23666.528 TRUE 0.15940875
## 968 6240825.75 792234.542 161578.696 TRUE 0.12694387
## 969 4731013.87 371435.348 251334.222 TRUE 0.07851073
## 970 6213863.22 430143.683 501919.004 FALSE 0.06922323
## 971 953884.49 128680.046 84959.089 FALSE 0.13490108
## 972 4279320.78 573291.011 72587.568 TRUE 0.13396776
## 973 2360019.78 193624.864 55190.710 FALSE 0.08204375
## 974 3093006.26 239910.587 201889.339 FALSE 0.07756550
## 975 2901523.72 507026.639 188614.564 TRUE 0.17474496
## 976 2986708.27 443066.852 288911.398 FALSE 0.14834621
## 977 5431964.05 468407.280 83395.003 FALSE 0.08623166
## 978 2729962.38 235891.740 232302.302 FALSE 0.08640842
## 979 6336212.81 569954.544 170262.851 FALSE 0.08995193
## 980 7859641.41 1279422.382 555391.535 FALSE 0.16278381
## 981 5656841.76 703892.533 343281.983 FALSE 0.12443207
## 982 4413521.90 601204.305 366895.788 FALSE 0.13621872
## 983 2379561.57 333765.893 93933.428 TRUE 0.14026361
## 984 1680339.11 173250.355 25525.938 TRUE 0.10310440
## 985 3579401.97 617732.628 77216.386 TRUE 0.17257984
## 986 765415.40 148231.816 57717.137 FALSE 0.19366192
## 987 6908162.12 1328036.355 389541.247 TRUE 0.19224163
## 988 2009150.74 119396.857 101655.122 FALSE 0.05942653
## 989 6652757.85 964050.752 279835.030 FALSE 0.14490994
## 990 3419508.59 232659.535 250761.150 TRUE 0.06803888
## 991 4907896.85 695185.911 364472.328 FALSE 0.14164640
## 992 3944953.84 426666.562 375198.413 TRUE 0.10815502
## 993 5058457.05 785977.670 270745.979 TRUE 0.15537894
## 994 3403866.67 220734.058 297059.548 FALSE 0.06484803
## 995 6299949.94 836582.897 156209.187 FALSE 0.13279199
## 996 2298437.83 205844.970 81524.960 FALSE 0.08955864
## 997 2818137.00 343621.599 47125.376 FALSE 0.12193218
## 998 398728.02 68010.306 20203.027 FALSE 0.17056816
## 999 4761268.38 560676.319 157860.133 TRUE 0.11775776
## 1000 144148.81 26109.541 1783.772 FALSE 0.18112908
## 1001 1381235.06 108385.775 46686.928 TRUE 0.07847019
## 1002 3023200.71 222165.695 247457.547 TRUE 0.07348692
## 1003 822790.59 143613.622 40583.203 FALSE 0.17454456
## 1004 7105399.55 1181906.476 708237.827 FALSE 0.16633920
## 1005 4017476.49 684450.076 47380.228 TRUE 0.17036816
## 1006 5117328.43 396129.192 431511.048 FALSE 0.07740937
## 1007 6002390.33 358689.853 269207.184 FALSE 0.05975784
## 1008 944916.21 155318.662 20952.648 FALSE 0.16437295
## 1009 6578319.98 421058.925 222682.647 FALSE 0.06400706
## 1010 469211.30 43266.563 20164.914 FALSE 0.09221126
## 1011 712096.69 102251.737 12487.354 FALSE 0.14359249
## 1012 98899.82 7240.970 5583.698 FALSE 0.07321520
## 1013 6831219.68 554940.694 545594.104 FALSE 0.08123596
## 1014 982225.70 95801.247 58008.702 TRUE 0.09753486
## 1015 6219801.71 335983.037 407568.911 FALSE 0.05401829
## 1016 2622306.78 345733.412 214443.823 TRUE 0.13184324
## 1017 3616824.88 265813.403 233119.899 FALSE 0.07349358
## 1018 7082055.14 409444.930 595551.535 FALSE 0.05781442
## 1019 2139823.25 379247.029 175161.214 TRUE 0.17723288
## 1020 3213865.35 172398.499 60572.135 FALSE 0.05364210
## 1021 5248925.95 1039805.192 73048.258 FALSE 0.19809866
## 1022 4988193.84 845837.635 490439.821 FALSE 0.16956792
## 1023 975630.93 112274.662 10551.487 FALSE 0.11507903
## 1024 3462360.12 430486.966 174074.904 TRUE 0.12433339
## 1025 4321372.67 354015.729 357885.662 TRUE 0.08192205
## 1026 5307815.77 1028909.289 282980.810 TRUE 0.19384797
## 1027 6949686.81 920553.201 178114.688 TRUE 0.13245967
## 1028 4172799.10 701981.752 119143.704 FALSE 0.16822803
## 1029 2338507.20 333638.933 92252.532 TRUE 0.14267176
## 1030 5016664.50 714924.734 187263.126 FALSE 0.14250998
## 1031 4532402.38 323919.884 74621.508 TRUE 0.07146759
## 1032 1158665.59 119166.493 16589.434 TRUE 0.10284805
## 1033 3293841.05 621550.744 293415.952 FALSE 0.18870089
## 1034 4950199.73 609853.662 131879.971 TRUE 0.12319779
## 1035 4455665.46 867284.013 79523.032 FALSE 0.19464747
## 1036 100427.60 10315.979 9723.019 TRUE 0.10272055
## 1037 7187958.61 594469.113 277631.987 FALSE 0.08270347
## 1038 2646560.32 376404.628 200247.011 FALSE 0.14222409
## 1039 3549776.50 216917.853 303533.816 TRUE 0.06110747
## 1040 1332315.02 91810.642 26341.006 TRUE 0.06891061
## 1041 7679511.48 1489036.808 246837.208 TRUE 0.19389733
## 1042 3114719.57 571182.129 74728.803 TRUE 0.18338156
## 1043 3898358.82 273131.220 247092.366 FALSE 0.07006313
## 1044 5073724.26 793653.125 413295.438 FALSE 0.15642417
## 1045 5468165.23 488683.590 327847.307 TRUE 0.08936884
## 1046 835771.14 87596.573 11629.544 FALSE 0.10480928
## 1047 6955849.33 591834.169 504512.318 FALSE 0.08508439
## 1048 433196.85 36769.493 36681.368 TRUE 0.08487941
## 1049 3715695.00 433125.134 130519.042 FALSE 0.11656638
## 1050 3048289.98 248150.425 75664.829 TRUE 0.08140644
## 1051 192253.60 31407.764 14090.741 TRUE 0.16336632
## 1052 916100.62 153116.942 75856.110 FALSE 0.16713987
## 1053 187456.12 21843.367 16436.189 TRUE 0.11652523
## 1054 2136847.69 353199.328 36429.821 TRUE 0.16528989
## 1055 574498.85 39170.200 23348.167 FALSE 0.06818151
## 1056 3150729.64 292218.685 245291.753 FALSE 0.09274635
## 1057 2265325.30 166888.680 113289.866 FALSE 0.07367096
## 1058 4680090.62 827328.622 183420.928 TRUE 0.17677620
## 1059 1956239.39 337937.269 95389.806 TRUE 0.17274842
## 1060 2794103.29 554994.272 279362.702 TRUE 0.19863055
## 1061 4594984.34 452770.640 261405.316 TRUE 0.09853584
## 1062 7570731.06 883561.184 612381.541 TRUE 0.11670751
## 1063 73939.28 13218.502 3802.282 FALSE 0.17877511
## 1064 5384941.33 278765.273 134423.570 TRUE 0.05176756
## 1065 940334.15 135398.593 19370.418 FALSE 0.14398987
## 1066 1511380.69 210207.410 20108.062 TRUE 0.13908303
## 1067 2714008.62 166526.558 256227.564 TRUE 0.06135815
## 1068 2995752.47 181685.323 276650.770 TRUE 0.06064764
## 1069 1329089.83 264400.710 102206.323 FALSE 0.19893366
## 1070 1203689.16 127766.246 65771.787 TRUE 0.10614555
## 1071 4068885.61 651821.691 228934.878 TRUE 0.16019661
## 1072 4476227.71 273407.608 302108.626 FALSE 0.06107991
## 1073 3121048.19 416427.308 55627.511 FALSE 0.13342547
## 1074 1154327.43 179704.434 109782.989 FALSE 0.15567891
## 1075 5389076.03 561384.584 276482.884 TRUE 0.10417084
## 1076 6543581.21 726788.519 609354.615 FALSE 0.11106892
## 1077 3484506.39 196269.870 216903.535 FALSE 0.05632645
## 1078 785370.89 53591.179 74791.718 FALSE 0.06823678
## 1079 7097705.06 509036.701 318230.483 TRUE 0.07171849
## 1080 3663317.40 410866.154 39154.340 FALSE 0.11215685
## 1081 5775931.07 1001527.322 574834.806 TRUE 0.17339669
## 1082 3066087.06 401601.686 231306.756 TRUE 0.13098183
## 1083 7633141.24 1337988.836 585222.212 TRUE 0.17528679
## 1084 2025680.85 240627.689 164977.770 TRUE 0.11878855
## 1085 5236367.41 460322.113 327100.908 FALSE 0.08790867
## 1086 4359615.82 584325.411 185077.637 TRUE 0.13403140
## 1087 1696008.53 240044.753 129751.985 TRUE 0.14153511
## 1088 4293279.95 254756.553 220764.011 FALSE 0.05933844
## 1089 5937015.40 853525.986 199141.980 FALSE 0.14376348
## 1090 1324418.54 200778.540 41069.985 TRUE 0.15159750
## 1091 6311918.54 1070032.002 447213.780 FALSE 0.16952564
## 1092 6757458.10 726501.882 587873.057 FALSE 0.10751112
## 1093 7209016.63 466708.079 236105.497 TRUE 0.06473949
## 1094 5046712.24 851753.593 363622.748 FALSE 0.16877396
## 1095 7352062.37 887323.399 114710.658 TRUE 0.12069041
## 1096 285888.83 54636.436 20025.589 TRUE 0.19111077
## 1097 176912.51 16116.138 14314.848 FALSE 0.09109665
## 1098 3171328.69 503479.763 247559.845 FALSE 0.15875988
## 1099 6198178.99 1032583.030 120772.766 FALSE 0.16659458
## 1100 3342521.83 600536.037 262299.216 TRUE 0.17966555
## 1101 5948678.23 774809.751 395592.872 FALSE 0.13024906
## 1102 2348518.21 193832.089 170763.761 FALSE 0.08253378
## 1103 340696.62 50083.582 7946.487 FALSE 0.14700346
## 1104 6361539.49 511802.786 302890.315 TRUE 0.08045266
## 1105 5021046.87 573921.239 78252.026 TRUE 0.11430310
## 1106 6353301.40 1180273.224 288572.674 TRUE 0.18577321
## 1107 7113058.61 665973.370 490149.770 FALSE 0.09362686
## 1108 5463583.34 631037.469 513019.579 TRUE 0.11549883
## 1109 5964023.72 586449.555 213378.906 TRUE 0.09833119
## 1110 4230688.80 331297.151 217195.540 FALSE 0.07830809
## 1111 1855010.41 140144.191 89968.182 TRUE 0.07554901
## 1112 1278984.31 153977.406 83411.859 FALSE 0.12039038
## 1113 6729471.19 1185866.160 151581.287 FALSE 0.17621981
## 1114 7988732.43 427389.262 82097.061 FALSE 0.05349901
## 1115 7033987.85 449960.480 280003.810 TRUE 0.06396947
## 1116 1988934.95 245644.250 188918.596 FALSE 0.12350542
## 1117 2057455.84 385913.784 116787.419 TRUE 0.18756844
## 1118 3714465.24 355210.204 165381.391 FALSE 0.09562889
## 1119 2652442.12 435036.280 34203.101 FALSE 0.16401349
## 1120 4058160.21 803786.120 276459.082 TRUE 0.19806663
## 1121 3472598.10 561896.857 262950.722 FALSE 0.16180878
## 1122 1737597.38 158842.919 173636.076 FALSE 0.09141526
## 1123 5574813.47 680036.813 150973.505 FALSE 0.12198378
## 1124 7374002.55 612176.654 648881.640 FALSE 0.08301823
## 1125 6378000.12 789311.553 187149.689 FALSE 0.12375534
## 1126 3266405.63 354377.413 313854.665 FALSE 0.10849155
## 1127 6052964.71 854980.170 382786.369 FALSE 0.14124982
## 1128 6505725.67 1295200.356 404115.402 FALSE 0.19908622
## 1129 5124540.64 651569.298 104857.575 FALSE 0.12714687
## 1130 6817586.65 973198.633 458032.365 FALSE 0.14274826
## 1131 1181886.64 159833.978 45410.857 TRUE 0.13523630
## 1132 5038729.22 770795.403 176882.205 FALSE 0.15297417
## 1133 4423974.04 310275.318 279911.967 TRUE 0.07013498
## 1134 5933946.00 1014276.052 225345.089 TRUE 0.17092775
## 1135 4450997.12 799772.519 336559.790 TRUE 0.17968390
## 1136 1522649.63 178082.755 85602.239 FALSE 0.11695583
## 1137 165965.31 12272.592 12241.497 FALSE 0.07394673
## 1138 6987541.32 846871.042 165277.419 TRUE 0.12119729
## 1139 5605701.74 667058.847 249698.255 TRUE 0.11899649
## 1140 6863931.63 599378.699 631496.967 TRUE 0.08732294
## 1141 5277640.65 646606.850 331227.010 FALSE 0.12251817
## 1142 4304349.36 696768.294 217799.934 TRUE 0.16187540
## 1143 5806196.50 991943.142 298726.568 FALSE 0.17084216
## 1144 1084846.38 94162.245 67885.871 FALSE 0.08679777
## 1145 2825282.23 164285.042 195737.157 TRUE 0.05814819
## 1146 3435010.12 525015.025 292715.468 TRUE 0.15284235
## 1147 4375086.81 588641.270 72106.484 TRUE 0.13454391
## 1148 2028561.38 204717.715 23617.878 FALSE 0.10091768
## 1149 3191582.74 301677.046 205881.336 TRUE 0.09452271
## 1150 7025155.69 568830.208 433492.293 FALSE 0.08097048
## 1151 3507202.62 574532.254 59706.118 TRUE 0.16381496
## 1152 6817659.28 909879.990 362154.208 TRUE 0.13345929
## 1153 4181963.41 784111.100 316352.688 FALSE 0.18749832
## 1154 2012971.27 124488.076 105636.645 FALSE 0.06184295
## 1155 5027894.93 272172.403 466868.685 TRUE 0.05413248
## 1156 2429161.51 480926.384 216376.494 TRUE 0.19798041
## 1157 6220491.61 823290.288 289318.061 TRUE 0.13235132
## 1158 5564786.13 508032.136 198395.495 TRUE 0.09129410
## 1159 2810689.97 448539.479 234703.988 TRUE 0.15958341
## 1160 3888635.84 417699.195 379381.040 TRUE 0.10741535
## 1161 2844354.55 549381.235 218125.977 TRUE 0.19314794
## 1162 6445214.13 674073.344 509803.376 FALSE 0.10458510
## 1163 1123527.98 87010.329 108983.978 TRUE 0.07744385
## 1164 1843805.76 105735.887 39651.128 TRUE 0.05734654
## 1165 3592335.02 217250.588 153231.650 TRUE 0.06047615
## 1166 6863118.73 1082303.992 498777.012 FALSE 0.15769857
## 1167 2638884.15 210258.089 221043.807 FALSE 0.07967689
## 1168 555729.95 99251.637 19448.704 FALSE 0.17859688
## 1169 6072796.76 609493.116 451879.577 FALSE 0.10036448
## 1170 3730610.18 301409.877 66825.008 TRUE 0.08079372
## 1171 1381413.34 149047.284 37391.331 TRUE 0.10789478
## 1172 1373984.21 252999.654 66161.095 FALSE 0.18413578
## 1173 113062.65 9988.410 10987.449 TRUE 0.08834403
## 1174 7845326.33 411143.985 204150.171 TRUE 0.05240623
## 1175 2177552.90 205207.354 55170.554 FALSE 0.09423760
## 1176 2083310.73 271835.133 126159.273 FALSE 0.13048228
## 1177 3126775.07 534172.740 215629.272 TRUE 0.17083824
## 1178 914138.65 116030.512 18530.014 FALSE 0.12692879
## 1179 1214804.65 155895.010 38597.493 FALSE 0.12832928
## 1180 1901590.85 134780.906 50329.872 FALSE 0.07087797
## 1181 7073010.73 1031348.733 88330.498 FALSE 0.14581467
## 1182 106285.48 15392.038 9897.124 FALSE 0.14481788
## 1183 1010232.08 89002.140 41972.871 FALSE 0.08810069
## 1184 3533146.23 225382.692 168441.507 FALSE 0.06379093
## 1185 6794646.31 578638.218 543361.056 TRUE 0.08516090
## 1186 5930263.48 900083.634 399520.657 FALSE 0.15177802
## 1187 793499.04 136952.127 33802.272 TRUE 0.17259268
## 1188 4501857.25 508757.682 431550.121 FALSE 0.11301062
## 1189 4473522.25 659436.984 130242.840 FALSE 0.14740890
## 1190 5761449.72 1035732.805 97050.790 FALSE 0.17976948
## 1191 2850833.10 403148.796 88689.002 FALSE 0.14141438
## 1192 1711130.59 174810.482 52434.021 TRUE 0.10216081
## 1193 2731284.69 426733.619 272129.498 TRUE 0.15623916
## 1194 1467802.40 73921.677 86458.342 TRUE 0.05036214
## 1195 7861517.08 1505836.728 618615.314 FALSE 0.19154531
## 1196 2553243.08 186349.569 187975.386 FALSE 0.07298544
## 1197 7547733.25 1333857.184 707926.776 FALSE 0.17672288
## 1198 628746.70 97437.929 17812.408 TRUE 0.15497168
## 1199 7253333.61 660919.905 119420.129 FALSE 0.09111947
## 1200 574645.38 58553.556 6027.023 TRUE 0.10189511
## 1201 4579990.88 324477.287 78264.062 FALSE 0.07084671
## 1202 7138524.17 1426029.468 187795.524 TRUE 0.19976531
## 1203 4684303.99 868862.008 346827.365 FALSE 0.18548369
## 1204 6942417.13 1161281.629 607653.691 FALSE 0.16727339
## 1205 5555157.36 366117.614 451785.720 FALSE 0.06590589
## 1206 1795741.63 343447.408 114433.705 FALSE 0.19125658
## 1207 5796583.16 832653.738 429638.226 TRUE 0.14364561
## 1208 2817512.91 413513.594 103924.071 TRUE 0.14676547
## 1209 5527219.58 465327.309 158853.967 TRUE 0.08418832
## 1210 5108423.08 497678.132 364160.503 FALSE 0.09742305
## 1211 4257235.03 445902.345 123053.541 FALSE 0.10473989
## 1212 7334767.05 411158.211 489795.754 FALSE 0.05605607
## 1213 638568.71 95587.214 60941.851 FALSE 0.14968979
## 1214 7125225.29 976595.062 468330.629 FALSE 0.13706164
## 1215 3123519.38 365973.733 278360.303 FALSE 0.11716711
## 1216 7310740.18 999427.012 457105.630 TRUE 0.13670668
## 1217 4006949.10 587421.122 90824.735 TRUE 0.14660059
## 1218 7250239.71 898517.419 443245.504 FALSE 0.12392934
## 1219 5381042.41 285823.775 453673.099 FALSE 0.05311680
## 1220 1183063.16 212203.759 17982.051 TRUE 0.17936807
## 1221 1900869.57 330212.641 173502.182 TRUE 0.17371662
## 1222 4949161.96 707189.061 367407.135 FALSE 0.14289067
## 1223 7016773.52 1018881.682 441241.226 TRUE 0.14520658
## 1224 3278817.06 419520.888 322297.024 FALSE 0.12794885
## 1225 1627872.83 105645.296 77236.612 FALSE 0.06489776
## 1226 1204138.88 78882.182 23147.903 FALSE 0.06550921
## 1227 3263657.87 317187.239 40060.682 FALSE 0.09718765
## 1228 2252938.34 354581.423 65266.192 FALSE 0.15738621
## 1229 6406425.35 423741.736 318898.953 TRUE 0.06614324
## 1230 4173022.07 581714.351 345041.742 FALSE 0.13939882
## 1231 1998544.12 394296.773 40071.507 FALSE 0.19729200
## 1232 2053477.36 406507.780 111934.596 FALSE 0.19796068
## 1233 7053410.87 949215.183 455719.499 TRUE 0.13457534
## 1234 2139753.92 115118.003 49304.890 TRUE 0.05379965
## 1235 2854487.77 278150.205 157011.213 FALSE 0.09744312
## 1236 2232133.13 358976.137 188029.788 TRUE 0.16082201
## 1237 3078998.53 610513.738 177028.154 TRUE 0.19828322
## 1238 6493942.20 505597.105 148520.381 FALSE 0.07785673
## 1239 2211614.65 289874.495 54346.876 TRUE 0.13106917
## 1240 6878554.21 1248721.199 665877.263 FALSE 0.18153832
## 1241 4230328.49 640056.805 402728.140 FALSE 0.15130192
## 1242 1236095.84 159462.567 105873.722 FALSE 0.12900502
## 1243 2010057.50 320677.106 142599.468 TRUE 0.15953628
## 1244 5302132.08 711897.671 299368.807 TRUE 0.13426630
## 1245 7926432.18 1220127.528 685535.434 FALSE 0.15393149
## 1246 871761.64 116378.728 49563.220 TRUE 0.13349834
## 1247 4925052.32 275617.327 488778.983 FALSE 0.05596231
## 1248 974532.39 99681.351 53252.612 TRUE 0.10228634
## 1249 294147.12 16806.348 5239.034 FALSE 0.05713586
## 1250 2664358.56 284941.041 31869.269 FALSE 0.10694546
## 1251 5286869.23 441157.080 199421.105 TRUE 0.08344392
## 1252 5367410.18 339578.075 479236.143 FALSE 0.06326665
## 1253 94873.87 10836.281 4494.710 TRUE 0.11421776
## 1254 4274773.13 835287.110 56664.918 FALSE 0.19539917
## 1255 6281717.41 1222475.464 397315.343 FALSE 0.19460848
## 1256 2601952.79 431643.788 224098.904 TRUE 0.16589224
## 1257 2633416.03 229343.934 42955.117 FALSE 0.08708990
## 1258 4712468.48 798007.125 163205.030 FALSE 0.16933951
## 1259 126684.54 10402.916 2629.755 TRUE 0.08211670
## 1260 3599504.55 689501.385 192064.029 FALSE 0.19155453
## 1261 2384780.21 476321.384 143092.165 TRUE 0.19973387
## 1262 6315060.11 316909.658 296376.144 FALSE 0.05018316
## 1263 3230309.86 595543.643 162110.989 FALSE 0.18436115
## 1264 946297.61 142136.333 58616.848 FALSE 0.15020257
## 1265 4211364.46 443460.729 397254.611 FALSE 0.10530096
## 1266 4792768.80 264931.818 206397.479 TRUE 0.05527740
## 1267 3646084.97 204579.047 262574.786 FALSE 0.05610924
## 1268 5560817.20 997023.616 59847.530 TRUE 0.17929444
## 1269 5845184.12 768494.633 452329.739 FALSE 0.13147484
## 1270 3681437.10 611019.637 137236.885 FALSE 0.16597313
## 1271 4434921.48 841268.016 408796.137 TRUE 0.18969175
## 1272 2692865.76 477266.010 135729.304 FALSE 0.17723349
## 1273 622558.14 121935.123 33743.512 FALSE 0.19586142
## 1274 6441861.65 1273226.227 351431.508 FALSE 0.19764880
## 1275 2358456.79 248671.367 229891.603 FALSE 0.10543817
## 1276 1692411.15 95242.302 36451.936 TRUE 0.05627610
## 1277 7971792.51 1565476.607 254654.014 FALSE 0.19637699
## 1278 1445497.90 188308.550 101611.051 FALSE 0.13027245
## 1279 781716.15 127133.575 53122.520 TRUE 0.16263394
## 1280 2981577.81 228677.247 136137.655 FALSE 0.07669672
## 1281 1281751.67 93477.042 37497.381 TRUE 0.07292914
## 1282 3008082.87 428982.893 50868.284 FALSE 0.14261006
## 1283 424246.75 27698.904 28184.104 FALSE 0.06528961
## 1284 592795.35 31775.703 16754.936 FALSE 0.05360316
## 1285 3797992.04 251418.680 294613.765 TRUE 0.06619779
## 1286 2791182.78 338368.502 95797.023 FALSE 0.12122764
## 1287 2984831.15 487268.769 186969.563 TRUE 0.16324835
## 1288 4219105.67 418942.063 334720.540 TRUE 0.09929641
## 1289 4326374.04 525040.131 217314.873 FALSE 0.12135801
## 1290 552641.94 58242.345 40710.945 TRUE 0.10538893
## 1291 3220416.10 346931.905 69591.925 FALSE 0.10772891
## 1292 4598948.07 575495.468 398399.126 TRUE 0.12513633
## 1293 1034871.02 115571.775 12403.277 TRUE 0.11167747
## 1294 4886537.94 805259.316 187036.819 FALSE 0.16479138
## 1295 6011315.70 453690.539 99810.495 FALSE 0.07547275
## 1296 5985907.71 693653.318 370134.329 FALSE 0.11588106
## 1297 4157603.05 554937.829 130802.032 FALSE 0.13347542
## 1298 6826471.01 1231626.258 284328.554 FALSE 0.18041917
## 1299 6979202.89 1005561.000 652089.300 FALSE 0.14407963
## 1300 2033524.10 311735.161 26596.501 TRUE 0.15329799
## 1301 2551580.46 292619.022 153599.104 FALSE 0.11468148
## 1302 397394.69 71796.369 5591.727 FALSE 0.18066766
## 1303 356243.06 30844.219 30519.799 FALSE 0.08658195
## 1304 1345106.73 252852.243 26807.343 TRUE 0.18797932
## 1305 4143934.56 492352.613 77528.442 TRUE 0.11881284
## 1306 5302682.14 338208.865 479259.198 FALSE 0.06378072
## 1307 5793273.70 679653.795 238800.727 TRUE 0.11731774
## 1308 983491.28 92572.132 11815.981 FALSE 0.09412603
## 1309 795193.81 134609.456 11180.892 TRUE 0.16927880
## 1310 6431142.09 367783.898 535816.792 TRUE 0.05718796
## 1311 3270801.31 434444.439 254144.166 FALSE 0.13282508
## 1312 2235236.31 255027.266 193701.146 FALSE 0.11409410
## 1313 892909.82 154106.913 11975.347 FALSE 0.17258956
## 1314 6637176.71 857023.444 574290.382 FALSE 0.12912470
## 1315 2896052.24 481218.111 38205.666 FALSE 0.16616348
## 1316 6887939.20 1369095.597 87827.049 TRUE 0.19876709
## 1317 1406391.64 194456.208 109751.725 TRUE 0.13826604
## 1318 6935462.47 424274.121 548839.000 TRUE 0.06117460
## 1319 303259.21 17939.199 11701.407 FALSE 0.05915467
## 1320 3622762.83 437062.856 47648.853 FALSE 0.12064352
## 1321 5153408.06 914855.883 184379.576 FALSE 0.17752444
## 1322 1520622.75 298278.752 63180.327 TRUE 0.19615566
## 1323 4106695.46 474313.099 377669.653 FALSE 0.11549751
## 1324 6745140.13 1193063.556 634467.309 TRUE 0.17687750
## 1325 7450595.00 1191564.354 283072.844 TRUE 0.15992875
## 1326 5267956.91 551267.568 267564.711 TRUE 0.10464542
## 1327 5011901.84 885786.313 473540.626 FALSE 0.17673656
## 1328 5678954.28 803068.035 461864.716 TRUE 0.14141125
## 1329 2182395.40 394333.059 85597.067 TRUE 0.18068818
## 1330 3801640.93 609044.359 363605.512 FALSE 0.16020565
## 1331 651299.36 118603.304 24672.533 TRUE 0.18210260
## 1332 5363809.18 1035816.571 133620.061 FALSE 0.19311212
## 1333 5650814.59 485348.138 466760.599 TRUE 0.08588994
## 1334 233730.60 46555.523 4723.624 TRUE 0.19918454
## 1335 4059167.33 775369.904 92451.085 FALSE 0.19101698
## 1336 396964.78 51704.222 36702.780 TRUE 0.13024889
## 1337 908382.48 89392.232 17546.189 FALSE 0.09840814
## 1338 2010399.96 164395.190 111405.012 TRUE 0.08177238
## 1339 450371.83 42694.146 24625.902 TRUE 0.09479755
## 1340 4535680.71 841208.257 300706.767 FALSE 0.18546461
## 1341 2424927.51 293052.413 41088.126 TRUE 0.12084997
## 1342 4299383.18 729341.167 196369.873 FALSE 0.16963856
## 1343 1578952.81 252978.058 71777.771 FALSE 0.16021888
## 1344 6127088.47 345547.827 122927.389 FALSE 0.05639674
## 1345 295569.15 37651.185 22266.169 TRUE 0.12738537
## 1346 4695349.09 832733.069 211893.193 FALSE 0.17735275
## 1347 7234183.11 764134.121 689204.989 FALSE 0.10562825
## 1348 5541099.17 581829.377 183723.523 FALSE 0.10500252
## 1349 7813286.80 802544.676 656759.066 TRUE 0.10271537
## 1350 3387591.83 174977.313 137616.901 TRUE 0.05165242
## 1351 910247.72 144541.643 66630.823 TRUE 0.15879374
## 1352 6414178.28 747175.051 279098.954 FALSE 0.11648804
## 1353 3539431.19 610522.431 178967.875 FALSE 0.17249168
## 1354 2032986.13 332985.956 78290.000 FALSE 0.16379155
## 1355 6142690.99 591393.504 94919.640 FALSE 0.09627597
## 1356 7123514.44 889958.973 646537.606 TRUE 0.12493257
## 1357 4774973.55 923970.538 473359.057 TRUE 0.19350276
## 1358 6042293.49 765638.698 574091.213 TRUE 0.12671326
## 1359 5329657.47 944079.654 238527.047 FALSE 0.17713702
## 1360 5634467.91 979234.090 297276.959 FALSE 0.17379353
## 1361 1161239.25 175866.202 100457.226 FALSE 0.15144700
## 1362 6872503.87 360379.640 517639.647 FALSE 0.05243790
## 1363 2651819.39 240388.504 202837.965 TRUE 0.09065041
## 1364 4723295.44 521162.989 425927.879 FALSE 0.11033885
## 1365 1935191.06 169974.496 78107.727 FALSE 0.08783344
## 1366 3117826.80 509045.575 263620.924 FALSE 0.16326936
## 1367 5286919.66 521013.554 93081.959 TRUE 0.09854766
## 1368 5437160.47 1085905.834 527145.238 TRUE 0.19971929
## 1369 242204.52 45530.663 22802.256 FALSE 0.18798437
## 1370 5791891.29 1008634.221 574206.152 FALSE 0.17414592
## 1371 2146272.97 405206.930 92321.319 FALSE 0.18879562
## 1372 3468770.76 312992.112 126177.006 FALSE 0.09023142
## 1373 1947551.15 340459.920 47192.690 FALSE 0.17481437
## 1374 3747194.34 527988.635 366572.830 TRUE 0.14090239
## 1375 2253914.13 151525.511 168976.927 FALSE 0.06722772
## 1376 5658404.68 1035939.319 369269.842 TRUE 0.18307975
## 1377 666525.97 119083.517 62276.878 FALSE 0.17866298
## 1378 4015314.19 580273.348 375893.969 TRUE 0.14451505
## 1379 1550111.08 144588.035 44555.727 FALSE 0.09327592
## 1380 903864.55 63917.432 75378.077 TRUE 0.07071572
## 1381 883087.53 169466.986 60486.255 TRUE 0.19190282
## 1382 6573850.92 865942.875 83670.469 TRUE 0.13172536
## 1383 7339546.02 786341.858 465111.422 FALSE 0.10713767
## 1384 1797772.45 113314.545 159228.573 TRUE 0.06303053
## 1385 2863790.87 173254.538 163563.319 TRUE 0.06049832
## 1386 1349022.81 168088.533 112717.381 FALSE 0.12460022
## 1387 2153872.01 274619.949 52196.519 FALSE 0.12750059
## 1388 5128210.09 272773.343 428891.675 TRUE 0.05319075
## 1389 4557103.86 546162.473 407864.562 TRUE 0.11984859
## 1390 6051255.12 410262.759 414643.585 TRUE 0.06779796
## 1391 5349052.41 662085.571 382253.914 TRUE 0.12377624
## 1392 1300313.65 107490.314 66542.175 TRUE 0.08266491
## 1393 4850635.00 406987.716 75438.933 TRUE 0.08390401
## 1394 4273528.92 324526.398 403424.152 FALSE 0.07593874
## 1395 1360395.80 182685.442 132089.786 FALSE 0.13428845
## 1396 2240339.51 149125.282 171822.675 FALSE 0.06656370
## 1397 1024793.91 193119.993 37003.137 FALSE 0.18844764
## 1398 3161524.00 627084.172 190388.997 FALSE 0.19834870
## 1399 4762353.64 620596.481 111597.550 TRUE 0.13031298
## 1400 923764.24 75228.631 68883.297 FALSE 0.08143705
## 1401 3524789.15 696751.200 241358.282 FALSE 0.19767174
## 1402 777560.71 111132.697 64380.653 TRUE 0.14292479
## 1403 3811678.31 506469.944 89609.221 FALSE 0.13287321
## 1404 1074127.57 82034.128 44937.341 TRUE 0.07637280
## 1405 2023491.09 393206.543 147679.024 FALSE 0.19432087
## 1406 5467922.50 588212.450 229675.597 TRUE 0.10757513
## 1407 3714037.61 431096.499 341391.230 TRUE 0.11607220
## 1408 5009039.84 605696.364 313843.872 FALSE 0.12092065
## 1409 4002885.90 410502.696 131517.732 FALSE 0.10255169
## 1410 3579954.46 423473.457 325906.424 TRUE 0.11829018
## 1411 4034259.86 338795.978 246222.818 TRUE 0.08397971
## 1412 669994.25 36209.059 20796.006 TRUE 0.05404384
## 1413 5721513.53 1143755.010 400221.539 TRUE 0.19990427
## 1414 5209132.92 913886.267 491607.398 TRUE 0.17543923
## 1415 495934.27 54980.130 7304.435 FALSE 0.11086173
## 1416 1207177.49 94684.969 119606.495 TRUE 0.07843500
## 1417 627821.86 46646.662 39093.015 TRUE 0.07429920
## 1418 2801885.35 435656.823 58900.952 FALSE 0.15548703
## 1419 2618945.61 297130.699 147629.683 FALSE 0.11345432
## 1420 1220471.36 105748.980 103649.559 TRUE 0.08664601
## 1421 4370189.13 819254.094 43919.180 FALSE 0.18746422
## 1422 2885403.98 368836.825 288446.786 TRUE 0.12782849
## 1423 5083044.05 694868.274 392413.397 TRUE 0.13670318
## 1424 3548502.76 326544.552 149903.659 FALSE 0.09202319
## 1425 3585528.82 667197.828 180329.447 FALSE 0.18608073
## 1426 6967064.06 1000402.494 229399.177 FALSE 0.14359025
## 1427 1361640.29 241671.406 54088.634 FALSE 0.17748550
## 1428 1304954.36 175709.067 112899.649 FALSE 0.13464767
## 1429 7270426.10 453463.605 685459.540 FALSE 0.06237098
## 1430 6752575.25 959383.607 172582.566 FALSE 0.14207670
## 1431 180320.40 27441.024 4285.307 TRUE 0.15217925
## 1432 6363912.62 538964.131 586606.910 TRUE 0.08469069
## 1433 4624863.71 758601.882 434463.947 TRUE 0.16402686
## 1434 1975752.40 354606.633 94271.410 TRUE 0.17947929
## 1435 4851573.33 421601.935 82613.828 FALSE 0.08690004
## 1436 6728011.61 623142.134 309700.082 FALSE 0.09261906
## 1437 4370836.89 282687.426 159084.999 TRUE 0.06467581
## 1438 3327703.47 491777.228 227351.445 FALSE 0.14778277
## 1439 4164673.55 735039.412 264904.600 FALSE 0.17649388
## 1440 7305767.85 670899.958 618299.701 TRUE 0.09183155
## 1441 5569962.69 297074.540 276080.960 TRUE 0.05333510
## 1442 6975610.23 1204547.431 290985.006 FALSE 0.17267986
## 1443 794217.65 66053.185 25159.832 TRUE 0.08316761
## 1444 5408012.59 887553.936 491064.312 TRUE 0.16411832
## 1445 4386809.78 338668.079 420023.360 FALSE 0.07720145
## 1446 5412146.13 769291.581 322978.536 FALSE 0.14214169
## 1447 4001985.51 339831.231 173621.610 FALSE 0.08491566
## 1448 3371087.26 402467.176 310321.235 FALSE 0.11938794
## 1449 398818.73 34316.359 24922.885 TRUE 0.08604500
## 1450 6822119.61 730111.153 523590.455 TRUE 0.10702116
## 1451 2105279.67 266287.969 188238.634 FALSE 0.12648579
## 1452 375912.30 53481.104 10101.711 TRUE 0.14227016
## 1453 2563490.16 197837.564 229067.183 TRUE 0.07717508
## 1454 7490769.24 1199554.126 144234.763 FALSE 0.16013764
## 1455 2805017.97 523845.579 112925.038 TRUE 0.18675302
## 1456 3703109.73 619600.386 67041.784 TRUE 0.16731894
## 1457 4332080.67 577673.377 238165.396 TRUE 0.13334779
## 1458 1647752.59 125101.554 153140.580 FALSE 0.07592254
## 1459 6917110.92 751580.027 610592.158 TRUE 0.10865519
## 1460 443648.19 52800.582 26745.119 FALSE 0.11901453
## 1461 593901.40 49604.721 52598.186 TRUE 0.08352350
## 1462 4088479.18 218760.850 110457.310 FALSE 0.05350666
## 1463 4638387.03 816317.349 158473.034 FALSE 0.17599164
## 1464 6010317.38 1199755.154 230012.362 FALSE 0.19961594
## 1465 2583008.30 189593.329 140143.868 FALSE 0.07340020
## 1466 1372567.99 107583.000 90129.584 FALSE 0.07838082
## 1467 5016800.42 702380.796 151298.820 FALSE 0.14000573
## 1468 974935.55 144088.800 88549.436 TRUE 0.14779315
## 1469 462367.03 37243.419 5086.626 FALSE 0.08054947
## 1470 3781198.38 728383.992 111808.136 FALSE 0.19263311
## 1471 7089774.25 1001168.756 534296.849 FALSE 0.14121307
## 1472 6726396.96 593625.696 180030.700 FALSE 0.08825315
## 1473 5968891.43 665506.534 497556.191 TRUE 0.11149583
## 1474 2643914.74 507409.154 127838.818 FALSE 0.19191585
## 1475 2481658.09 158881.643 33919.623 TRUE 0.06402237
## 1476 3143321.11 224237.315 155029.715 FALSE 0.07133771
## 1477 4317535.92 466213.545 213873.727 FALSE 0.10798139
## 1478 7245735.41 1422619.953 441498.100 FALSE 0.19633893
## 1479 7535381.44 619851.912 347129.608 FALSE 0.08225886
## 1480 3742602.73 292502.672 173610.556 FALSE 0.07815488
## 1481 2484822.38 372676.869 164497.863 TRUE 0.14998129
## 1482 6225554.58 594345.201 436450.796 FALSE 0.09546864
## 1483 2529279.40 351474.357 189819.252 FALSE 0.13896225
## 1484 430599.02 66993.316 14360.748 FALSE 0.15558168
## 1485 5607959.05 967107.522 203803.131 TRUE 0.17245267
## 1486 2049686.99 155228.822 114550.747 FALSE 0.07573294
## 1487 5140527.19 637097.334 487478.595 FALSE 0.12393619
## 1488 2354965.09 426088.514 117310.356 FALSE 0.18093199
## 1489 2275487.39 339180.129 106619.946 FALSE 0.14905823
## 1490 8302766.60 1348056.657 607842.674 TRUE 0.16236235
## 1491 3912760.02 650896.880 110596.707 FALSE 0.16635236
## 1492 2874045.78 507920.806 56991.898 TRUE 0.17672676
## 1493 6830982.37 1104798.759 558510.003 TRUE 0.16173351
## 1494 3970652.58 653762.675 280956.596 TRUE 0.16464867
## 1495 6970675.44 446740.229 166880.619 TRUE 0.06408851
## 1496 5503612.73 1014870.646 300824.961 FALSE 0.18440081
## 1497 1502514.69 75175.955 148249.910 FALSE 0.05003342
## 1498 4451460.79 828213.435 313117.464 TRUE 0.18605430
## 1499 5562739.98 867958.255 257445.500 TRUE 0.15603071
## 1500 2109636.95 205189.743 126685.750 FALSE 0.09726306
## 1501 2508841.43 434019.500 221017.299 FALSE 0.17299599
## 1502 3757662.88 641230.618 123569.540 FALSE 0.17064613
## 1503 4892529.56 426705.998 56555.417 FALSE 0.08721582
## 1504 6924129.92 365442.776 342872.190 FALSE 0.05277815
## 1505 2444282.13 470042.799 127581.648 TRUE 0.19230301
## 1506 1943486.11 218450.892 38666.042 TRUE 0.11240157
## 1507 5256502.30 293196.295 91311.841 FALSE 0.05577783
## 1508 3719628.86 637827.155 142793.160 FALSE 0.17147602
## 1509 4081444.19 803126.264 339258.067 TRUE 0.19677502
## 1510 5877249.49 540939.757 382758.454 FALSE 0.09203961
## 1511 3488376.67 670683.487 245805.267 TRUE 0.19226235
## 1512 1358649.13 156446.278 45953.282 FALSE 0.11514840
## 1513 1689819.62 303021.155 62682.356 TRUE 0.17932160
## 1514 3779636.36 289532.747 216675.277 TRUE 0.07660333
## 1515 6194029.69 1125873.949 464099.631 FALSE 0.18176761
## 1516 3389485.93 265865.393 314732.423 TRUE 0.07843826
## 1517 2624634.21 302637.564 31764.260 TRUE 0.11530657
## 1518 261474.79 45049.032 14183.656 FALSE 0.17228824
## 1519 7019603.07 1272459.026 511323.793 FALSE 0.18127222
## 1520 3991336.38 328897.273 380115.746 TRUE 0.08240279
## 1521 8322758.55 1566511.158 140446.549 TRUE 0.18822019
## 1522 1248714.65 124836.764 62774.531 FALSE 0.09997221
## 1523 4048378.23 233201.277 186414.546 TRUE 0.05760363
## 1524 1321474.96 119404.129 16480.912 TRUE 0.09035671
## 1525 5332643.65 1026265.997 168078.067 TRUE 0.19244976
## 1526 3909655.07 422560.573 55538.449 TRUE 0.10808129
## 1527 2795083.69 456807.254 170119.511 FALSE 0.16343241
## 1528 195807.77 36973.612 17626.052 FALSE 0.18882607
## 1529 2157763.94 361235.496 47498.330 FALSE 0.16741196
## 1530 1179758.85 122048.413 99835.951 FALSE 0.10345200
## 1531 4017176.25 686390.351 272017.587 TRUE 0.17086389
## 1532 1263899.65 96386.062 45893.321 FALSE 0.07626085
## 1533 2662209.52 455316.938 259250.768 TRUE 0.17102972
## 1534 360271.84 18870.818 5746.379 FALSE 0.05237939
## 1535 1564335.47 143552.587 125049.601 FALSE 0.09176586
## 1536 7545641.66 1276654.362 379187.568 TRUE 0.16919096
## 1537 4850656.31 414640.400 470217.790 FALSE 0.08548130
## 1538 860560.82 43209.451 16907.856 FALSE 0.05021080
## 1539 1482917.14 263327.115 44638.622 TRUE 0.17757372
## 1540 1368436.59 155294.503 48348.327 FALSE 0.11348316
## 1541 3617345.62 454018.300 221107.702 TRUE 0.12551145
## 1542 4267043.95 687151.880 92719.561 FALSE 0.16103698
## 1543 1422419.09 77242.294 26704.027 FALSE 0.05430347
## 1544 5220670.95 814639.740 281613.793 FALSE 0.15604120
## 1545 5186850.61 922936.900 54262.109 FALSE 0.17793782
## 1546 5174807.04 276756.295 55058.462 FALSE 0.05348147
## 1547 986569.31 138360.746 66794.809 TRUE 0.14024432
## 1548 475855.35 64381.643 15826.167 FALSE 0.13529667
## 1549 1853932.43 161148.826 124102.025 TRUE 0.08692271
## 1550 6709440.34 673687.084 471204.162 TRUE 0.10040883
## 1551 5381975.63 623498.094 66259.792 TRUE 0.11584930
## 1552 6996528.93 817365.634 135724.477 FALSE 0.11682445
## 1553 3108699.79 316135.802 279595.260 FALSE 0.10169390
## 1554 5681505.64 557322.107 337799.661 FALSE 0.09809409
## 1555 1693094.12 126960.004 74487.081 TRUE 0.07498697
## 1556 4090638.63 567369.994 121916.924 TRUE 0.13869961
## 1557 271024.93 21221.962 7631.942 TRUE 0.07830262
## 1558 5839513.20 399169.264 450505.041 FALSE 0.06835660
## 1559 5317250.21 305088.966 135287.074 TRUE 0.05737721
## 1560 4688956.13 546055.258 248620.128 TRUE 0.11645561
## 1561 5182162.73 446925.423 393626.101 FALSE 0.08624303
## 1562 4957401.79 963118.473 117846.829 TRUE 0.19427888
## 1563 3912998.98 573666.590 130648.845 TRUE 0.14660535
## 1564 4143260.14 354291.145 348793.505 TRUE 0.08551023
## 1565 851372.19 133753.948 12138.427 TRUE 0.15710397
## 1566 3417575.01 509723.318 281257.015 FALSE 0.14914766
## 1567 4562573.83 663436.917 308423.579 FALSE 0.14540848
## 1568 4961887.23 554877.426 351364.931 TRUE 0.11182790
## 1569 4334672.18 781094.043 432923.519 FALSE 0.18019680
## 1570 5508722.81 725976.903 402789.432 TRUE 0.13178679
## 1571 1392865.08 176530.073 24096.073 FALSE 0.12673882
## 1572 4278384.05 395134.182 43898.806 FALSE 0.09235594
## 1573 197438.67 19705.679 15775.915 FALSE 0.09980659
## 1574 4465127.18 353249.158 407623.354 FALSE 0.07911290
## 1575 6987878.09 671432.455 358148.938 TRUE 0.09608531
## 1576 6285517.94 683180.955 78088.530 FALSE 0.10869127
## 1577 5005827.06 267905.272 473508.456 TRUE 0.05351868
## 1578 540595.93 90694.498 16428.866 TRUE 0.16776763
## 1579 6941500.41 660750.899 257856.485 FALSE 0.09518848
## 1580 4937807.46 805591.957 423717.856 TRUE 0.16314771
## 1581 2773087.42 339371.600 211566.340 FALSE 0.12238042
## 1582 3180682.62 345752.776 190145.952 TRUE 0.10870395
## 1583 5346971.83 418621.921 315543.088 FALSE 0.07829140
## 1584 1941909.13 146037.692 152753.996 FALSE 0.07520315
## 1585 6671563.71 779038.877 649928.657 TRUE 0.11677006
## 1586 953658.64 114675.978 74119.941 TRUE 0.12024845
## 1587 3352755.00 320137.301 188647.525 TRUE 0.09548485
## 1588 1212195.00 124822.468 92268.258 FALSE 0.10297227
## 1589 6217023.92 367358.978 496875.967 FALSE 0.05908920
## 1590 2395840.31 220225.604 58181.884 TRUE 0.09191998
## 1591 1607753.98 211448.971 126764.239 FALSE 0.13151824
## 1592 6502376.98 1033739.370 311045.411 TRUE 0.15897869
## 1593 4346212.70 849210.101 407836.927 FALSE 0.19539083
## 1594 6786657.82 564932.451 289224.904 TRUE 0.08324163
## 1595 2942573.70 314765.989 115273.342 TRUE 0.10696962
## 1596 5441005.06 1055928.910 342689.446 FALSE 0.19406872
## 1597 4037311.79 256511.790 193534.744 TRUE 0.06353529
## 1598 6191827.39 618063.044 360110.933 TRUE 0.09981917
## 1599 4162955.58 570044.711 212393.856 TRUE 0.13693269
## 1600 6292715.60 1170283.879 180204.971 FALSE 0.18597438
## 1601 5712886.36 1061066.756 162651.558 TRUE 0.18573217
## 1602 5490417.13 472304.204 434119.664 FALSE 0.08602337
## 1603 6180498.11 835280.025 473604.857 FALSE 0.13514769
## 1604 5749323.55 869130.791 475363.172 FALSE 0.15117097
## 1605 215722.30 29905.851 18592.814 FALSE 0.13863125
## 1606 6464793.01 755551.752 526018.439 FALSE 0.11687176
## 1607 353322.12 58644.545 31006.525 FALSE 0.16598040
## 1608 3115956.06 451271.273 256674.353 TRUE 0.14482594
## 1609 600077.20 42339.325 53534.320 FALSE 0.07055646
## 1610 4398082.53 320419.715 409194.617 FALSE 0.07285441
## 1611 2311970.57 347811.057 26865.705 TRUE 0.15043922
## 1612 6614000.02 913584.367 337412.467 FALSE 0.13812887
## 1613 2581332.98 234693.738 257282.545 TRUE 0.09091959
## 1614 3324356.79 653950.520 256885.476 TRUE 0.19671490
## 1615 482610.77 51857.003 44906.778 TRUE 0.10745099
## 1616 6571929.00 1074365.157 250789.889 TRUE 0.16347790
## 1617 6810110.38 648375.248 269072.867 TRUE 0.09520774
## 1618 1545004.41 287548.510 94736.116 FALSE 0.18611501
## 1619 4396576.70 560905.938 344080.624 FALSE 0.12757788
## 1620 1614684.49 87612.300 127297.422 FALSE 0.05425970
## 1621 2160893.42 301210.321 174429.800 TRUE 0.13939157
## 1622 4382164.76 269540.377 142109.958 TRUE 0.06150850
## 1623 818613.69 149483.084 74225.478 FALSE 0.18260516
## 1624 1101799.61 57842.374 69467.158 TRUE 0.05249809
## 1625 6538035.85 1037026.522 586270.807 TRUE 0.15861438
## 1626 1339599.23 149384.067 78717.497 FALSE 0.11151400
## 1627 3895195.86 439447.019 42487.258 FALSE 0.11281770
## 1628 7591231.76 501460.795 252475.713 TRUE 0.06605790
## 1629 2958735.98 382739.359 136090.371 TRUE 0.12935908
## 1630 2785516.21 350390.055 272160.856 TRUE 0.12578999
## 1631 1318373.63 134476.294 87631.579 TRUE 0.10200166
## 1632 3953322.24 531026.687 130406.594 TRUE 0.13432416
## 1633 7352540.36 448180.285 369681.231 FALSE 0.06095584
## 1634 7661600.48 1198694.059 550282.786 TRUE 0.15645479
## 1635 714106.20 70903.023 67623.359 TRUE 0.09928918
## 1636 892051.83 98834.164 9819.688 TRUE 0.11079419
## 1637 623722.43 57973.072 9802.862 TRUE 0.09294691
## 1638 3028054.31 167557.515 206034.436 TRUE 0.05533504
## 1639 2776461.00 188203.680 198311.073 FALSE 0.06778546
## 1640 1789403.87 244183.071 136226.099 FALSE 0.13646057
## 1641 3571340.37 515613.871 157400.226 TRUE 0.14437545
## 1642 7641968.74 599645.735 462658.462 FALSE 0.07846744
## 1643 7669217.61 1390098.735 279143.137 TRUE 0.18125692
## 1644 6471646.17 1104062.876 466829.218 FALSE 0.17060001
## 1645 6988867.25 1154555.342 102431.788 TRUE 0.16519921
## 1646 1970246.39 383402.813 52769.295 TRUE 0.19459638
## 1647 5209497.34 692534.553 131463.949 TRUE 0.13293692
## 1648 2419341.23 368690.531 201016.158 TRUE 0.15239294
## 1649 2095295.00 215594.590 197454.421 FALSE 0.10289462
## 1650 4201289.74 542720.562 334943.390 FALSE 0.12917951
## 1651 2784528.93 374005.312 224386.797 TRUE 0.13431547
## 1652 6130480.96 1104663.099 211496.984 FALSE 0.18019191
## 1653 2991287.63 487219.230 40710.339 FALSE 0.16287943
## 1654 1470498.45 147837.410 83724.599 TRUE 0.10053558
## 1655 5352108.62 654608.588 157924.399 FALSE 0.12230854
## 1656 6084498.18 613380.568 359685.230 TRUE 0.10081038
## 1657 4808477.63 574928.584 441785.497 TRUE 0.11956561
## 1658 5353430.41 715454.309 527434.540 TRUE 0.13364408
## 1659 1976638.67 280571.291 121367.915 FALSE 0.14194364
## 1660 7790980.39 876319.236 450939.905 FALSE 0.11247869
## 1661 779805.13 61800.823 76724.477 FALSE 0.07925162
## 1662 1688548.70 107781.686 71237.419 FALSE 0.06383096
## 1663 4165247.40 457710.927 169085.825 TRUE 0.10988805
## 1664 8102373.70 990204.950 549528.823 FALSE 0.12221171
## 1665 1333159.98 208454.255 44390.344 TRUE 0.15636102
## 1666 753045.75 132974.660 13986.866 TRUE 0.17658245
## 1667 6460874.29 1212240.557 97893.048 FALSE 0.18762794
## 1668 8144055.78 470957.212 537980.058 TRUE 0.05782834
## 1669 813589.97 88758.005 12996.665 TRUE 0.10909427
## 1670 7198665.63 1079911.965 485558.108 FALSE 0.15001558
## 1671 7647719.87 890158.677 539685.957 TRUE 0.11639530
## 1672 6277621.82 815703.162 129188.655 FALSE 0.12993825
## 1673 3264093.62 640982.020 187908.645 FALSE 0.19637366
## 1674 3895194.25 551138.667 216820.345 TRUE 0.14149196
## 1675 1723089.79 229729.286 44994.731 TRUE 0.13332404
## 1676 1023753.26 70582.596 64813.684 TRUE 0.06894493
## 1677 2272823.06 438128.484 80806.696 TRUE 0.19276841
## 1678 4556034.54 779477.842 82444.631 TRUE 0.17108690
## 1679 6464230.52 528597.471 397437.138 TRUE 0.08177268
## 1680 3116658.99 336374.940 157874.118 TRUE 0.10792805
## 1681 1408495.98 274390.139 60919.326 FALSE 0.19481074
## 1682 914120.79 62559.022 43933.267 TRUE 0.06843627
## 1683 8056340.92 1432012.396 140470.148 TRUE 0.17774973
## 1684 5789561.37 511166.034 199660.133 TRUE 0.08829098
## 1685 6615272.45 642028.066 454158.803 FALSE 0.09705240
## 1686 4105187.88 418878.721 300120.643 TRUE 0.10203643
## 1687 1093701.75 66631.716 34180.082 FALSE 0.06092311
## 1688 3041725.41 494178.809 279153.703 TRUE 0.16246661
## 1689 3535982.43 451821.145 84430.035 FALSE 0.12777811
## 1690 4599813.23 646336.730 128783.535 TRUE 0.14051369
## 1691 4264292.42 733667.605 192007.371 FALSE 0.17204908
## 1692 3308835.80 248981.455 161169.338 TRUE 0.07524745
## 1693 6427824.05 533976.986 594607.141 TRUE 0.08307274
## 1694 455692.71 30329.333 39966.708 FALSE 0.06655655
## 1695 3636130.51 419478.861 50457.167 FALSE 0.11536408
## 1696 318055.33 43293.975 23982.368 FALSE 0.13612089
## 1697 5089508.41 315598.763 477585.513 TRUE 0.06200968
## 1698 5424261.84 1029079.812 78492.944 TRUE 0.18971795
## 1699 3846477.87 436468.699 171561.650 FALSE 0.11347230
## 1700 5120847.62 574132.888 141380.320 TRUE 0.11211677
## 1701 1286220.31 145523.034 106210.950 TRUE 0.11314005
## 1702 5413633.03 601727.349 232862.972 TRUE 0.11115038
## 1703 4562607.78 510650.502 114152.897 FALSE 0.11192075
## 1704 2017117.28 391512.577 70285.455 FALSE 0.19409510
## 1705 6574943.56 935655.119 571162.206 TRUE 0.14230618
## 1706 6655669.00 1230142.954 141682.620 FALSE 0.18482634
## 1707 7165032.72 614175.335 373348.734 FALSE 0.08571843
## 1708 2810999.42 325014.047 265272.028 TRUE 0.11562224
## 1709 3850433.64 635311.054 337764.549 FALSE 0.16499727
## 1710 3786741.06 366776.224 162436.771 TRUE 0.09685802
## 1711 6054441.39 853517.360 598483.636 TRUE 0.14097376
## 1712 3593781.20 488570.752 38176.946 FALSE 0.13594894
## 1713 7251862.41 798028.864 642140.367 TRUE 0.11004468
## 1714 4592929.20 363876.401 321302.618 TRUE 0.07922535
## 1715 2456476.95 403923.107 39263.141 FALSE 0.16443187
## 1716 5784445.45 1042857.600 94351.574 TRUE 0.18028653
## 1717 3941442.30 486875.294 72916.979 FALSE 0.12352719
## 1718 3023815.29 569744.999 99605.557 FALSE 0.18841925
## 1719 3988525.48 768549.720 260521.515 TRUE 0.19269019
## 1720 5802057.30 797480.438 157682.816 TRUE 0.13744787
## 1721 1593489.49 312103.587 47029.114 FALSE 0.19586172
## 1722 6244477.27 746804.936 406167.724 FALSE 0.11959447
## 1723 2434421.00 470863.037 66793.078 TRUE 0.19341890
## 1724 4172700.28 795209.436 224515.768 TRUE 0.19057430
## 1725 2795036.32 436385.372 159087.307 TRUE 0.15612869
## 1726 3682734.45 252671.601 166301.358 FALSE 0.06860978
## 1727 6812531.16 628895.145 339232.946 FALSE 0.09231446
## 1728 159057.87 24475.365 7532.419 FALSE 0.15387711
## 1729 6954567.48 804912.407 668138.401 FALSE 0.11573867
## 1730 157374.17 28283.393 2485.483 TRUE 0.17972068
## 1731 6384166.25 336050.365 396586.635 TRUE 0.05263810
## 1732 515124.91 52963.320 49308.412 FALSE 0.10281646
## 1733 5430943.96 607990.444 273363.757 FALSE 0.11194931
## 1734 5579020.90 684771.300 199206.125 FALSE 0.12274041
## 1735 161008.40 32159.584 5463.604 TRUE 0.19973855
## 1736 5693042.63 457316.253 394734.988 FALSE 0.08032897
## 1737 326164.63 63756.672 32610.883 FALSE 0.19547390
## 1738 4819333.52 499945.175 481548.901 TRUE 0.10373741
## 1739 6756820.02 718217.832 131990.258 TRUE 0.10629524
## 1740 7976309.59 1416268.724 363631.703 FALSE 0.17755940
## 1741 5042348.22 270755.144 232025.373 FALSE 0.05369624
## 1742 4124029.08 428695.157 85290.547 FALSE 0.10395057
## 1743 3078947.76 421055.211 250611.098 TRUE 0.13675296
## 1744 3897472.89 741073.581 216114.725 FALSE 0.19014207
## 1745 5922806.32 549324.385 423077.651 FALSE 0.09274732
## 1746 1472043.90 262024.434 26363.543 FALSE 0.17800042
## 1747 1981053.02 311576.321 117308.417 TRUE 0.15727813
## 1748 5528161.58 821852.003 93292.804 FALSE 0.14866642
## 1749 1810416.82 249761.564 79125.188 FALSE 0.13795804
## 1750 5199563.12 1035962.817 125074.610 TRUE 0.19924036
## 1751 2121994.33 164784.221 152405.755 FALSE 0.07765535
## 1752 692001.46 99497.800 50528.005 TRUE 0.14378264
## 1753 1557698.77 259212.757 124924.331 FALSE 0.16640750
## 1754 4468594.28 855589.037 388354.782 FALSE 0.19146716
## 1755 2684227.96 387152.222 233126.888 FALSE 0.14423224
## 1756 1251408.52 91031.693 61675.470 TRUE 0.07274339
## 1757 1742661.70 88336.155 157171.097 FALSE 0.05069036
## 1758 4301567.75 217847.755 330007.804 FALSE 0.05064380
## 1759 1392782.33 178216.385 73560.076 FALSE 0.12795710
## 1760 4445210.64 438665.086 197763.525 TRUE 0.09868263
## 1761 140432.75 13536.837 8498.348 TRUE 0.09639373
## 1762 3495338.11 573058.983 190481.298 FALSE 0.16394951
## 1763 5598825.65 728364.226 559391.943 FALSE 0.13009232
## 1764 5179954.47 565247.281 149473.742 TRUE 0.10912206
## 1765 3582790.11 498572.279 103138.123 TRUE 0.13915755
## 1766 383063.64 58215.499 36682.627 TRUE 0.15197344
## 1767 4209790.48 660969.416 309612.633 FALSE 0.15700768
## 1768 1977730.38 250849.943 58238.231 TRUE 0.12683728
## 1769 1592666.77 244389.094 127007.006 TRUE 0.15344647
## 1770 5658985.32 909475.584 255107.459 TRUE 0.16071354
## 1771 2235680.69 142804.336 211924.425 FALSE 0.06387510
## 1772 4391705.13 714139.853 275325.448 TRUE 0.16261107
## 1773 2216729.41 236419.389 116708.726 TRUE 0.10665234
## 1774 6289071.41 849595.622 596407.102 TRUE 0.13509079
## 1775 3616406.33 526658.251 40627.037 FALSE 0.14563028
## 1776 513042.33 87783.953 25640.139 FALSE 0.17110470
## 1777 4083932.91 368559.233 91733.888 FALSE 0.09024615
## 1778 2230354.73 162495.404 206241.607 FALSE 0.07285630
## 1779 7689811.58 896897.381 608638.146 TRUE 0.11663451
## 1780 6344306.65 611220.967 502219.982 TRUE 0.09634165
## 1781 5292557.32 423707.852 293993.217 FALSE 0.08005730
## 1782 3987900.67 686282.215 116423.645 TRUE 0.17209110
## 1783 4667445.39 438682.250 151091.187 TRUE 0.09398766
## 1784 90175.86 13528.018 7893.780 FALSE 0.15001817
## 1785 6984931.65 546389.359 602463.551 TRUE 0.07822401
## 1786 1521280.17 261631.692 87431.195 FALSE 0.17198127
## 1787 4822349.42 604351.775 76766.765 FALSE 0.12532310
## 1788 175615.82 16003.517 3111.921 TRUE 0.09112799
## 1789 2758779.14 159528.445 162776.031 TRUE 0.05782574
## 1790 6176841.34 735199.633 470328.546 FALSE 0.11902518
## 1791 4369312.39 454688.856 209457.416 TRUE 0.10406417
## 1792 4126982.76 599761.260 190738.461 TRUE 0.14532681
## 1793 6666691.50 1307106.358 100587.613 TRUE 0.19606522
## 1794 6914964.80 1251113.706 494157.804 TRUE 0.18092843
## 1795 6456359.35 1043041.442 341757.300 TRUE 0.16155257
## 1796 2832831.17 399098.969 220892.793 FALSE 0.14088343
## 1797 2938790.44 211156.925 117620.910 FALSE 0.07185164
## 1798 3480445.05 350989.440 110184.344 FALSE 0.10084614
## 1799 196751.53 25941.101 19121.043 FALSE 0.13184701
## 1800 6281541.09 1013653.735 445885.485 TRUE 0.16137023
## 1801 2972580.63 461441.591 110429.929 FALSE 0.15523266
## 1802 1041863.95 112248.629 88085.097 TRUE 0.10773828
## 1803 3817418.91 635863.398 354458.203 TRUE 0.16656893
## 1804 6386862.73 1046729.230 537018.845 FALSE 0.16388785
## 1805 817015.39 122820.754 49984.341 FALSE 0.15032857
## 1806 6869144.54 390574.589 125872.717 TRUE 0.05685928
## 1807 6921053.16 991728.511 665384.234 FALSE 0.14329156
## 1808 350982.88 46858.780 13513.867 TRUE 0.13350731
## 1809 3898386.15 607914.686 119690.642 FALSE 0.15594009
## 1810 1955387.41 234902.718 170185.672 FALSE 0.12013104
## 1811 5945363.77 622495.683 114963.380 TRUE 0.10470271
## 1812 603761.69 52927.965 48082.218 TRUE 0.08766367
## 1813 6814248.07 773175.973 203086.824 FALSE 0.11346461
## 1814 4969085.97 561507.901 307003.688 FALSE 0.11300024
## 1815 716649.24 92576.705 16317.225 FALSE 0.12917994
## 1816 1405248.67 206815.676 24964.547 FALSE 0.14717372
## 1817 5174793.53 1010535.531 109734.195 TRUE 0.19528036
## 1818 455027.64 74660.008 32813.589 TRUE 0.16407796
## 1819 1834671.66 140183.197 36675.288 FALSE 0.07640778
## 1820 81319.80 14355.985 3042.898 TRUE 0.17653739
## 1821 4108778.51 465106.057 173877.585 FALSE 0.11319813
## 1822 1670673.24 278993.673 34355.936 TRUE 0.16699476
## 1823 5460115.72 552776.790 249750.830 FALSE 0.10123902
## 1824 2716578.27 297564.719 208507.826 FALSE 0.10953659
## 1825 4878978.72 244975.183 249571.210 TRUE 0.05021034
## 1826 1857199.35 98377.843 98289.544 TRUE 0.05297107
## 1827 1646488.56 110302.946 88143.510 FALSE 0.06699284
## 1828 431744.56 79088.635 8482.454 TRUE 0.18318386
## 1829 2968366.54 304727.335 258676.244 TRUE 0.10265826
## 1830 6672739.79 1035325.549 98413.733 FALSE 0.15515749
## 1831 7785331.65 873120.133 318716.016 FALSE 0.11214938
## 1832 4245328.32 584585.321 363163.659 FALSE 0.13770085
## 1833 4597772.63 436478.896 64906.464 FALSE 0.09493268
## 1834 4580537.31 694917.960 238236.267 FALSE 0.15171101
## 1835 3223298.20 376538.833 120690.006 TRUE 0.11681787
## 1836 4599586.66 831656.606 188800.018 TRUE 0.18081116
## 1837 7261235.94 1312217.001 428431.578 TRUE 0.18071538
## 1838 5723910.21 1142306.127 378293.784 TRUE 0.19956744
## 1839 7336587.43 1113908.244 116385.131 FALSE 0.15182921
## 1840 6413940.77 623361.084 460549.465 TRUE 0.09718847
## 1841 5583663.46 949802.400 248539.592 TRUE 0.17010380
## 1842 185999.01 21651.540 9061.367 FALSE 0.11640675
## 1843 3038154.03 194144.503 40278.424 TRUE 0.06390213
## 1844 4383974.31 301331.754 251116.133 TRUE 0.06873484
## 1845 1550936.26 192365.456 145128.866 TRUE 0.12403183
## 1846 2648206.82 190044.915 124883.042 FALSE 0.07176362
## 1847 4109827.73 619004.228 250123.884 FALSE 0.15061561
## 1848 2359382.47 129784.942 190401.780 FALSE 0.05500801
## 1849 7317677.79 704509.343 687676.753 TRUE 0.09627499
## 1850 5961717.76 685505.552 425747.405 FALSE 0.11498457
## 1851 3661915.09 543914.517 47344.074 FALSE 0.14853280
## 1852 1583420.04 232290.897 108750.404 FALSE 0.14670201
## 1853 7185648.45 385364.859 292721.617 FALSE 0.05362980
## 1854 5441272.04 324103.411 376009.945 TRUE 0.05956390
## 1855 2260231.50 322435.234 81406.602 TRUE 0.14265584
## 1856 3822274.72 731741.425 107581.125 TRUE 0.19144135
## 1857 2181732.71 127379.382 192485.824 FALSE 0.05838450
## 1858 814272.73 124591.438 34372.268 FALSE 0.15300947
## 1859 1382656.74 227779.465 15807.765 FALSE 0.16474043
## 1860 5931571.23 705092.849 179824.353 FALSE 0.11887118
## 1861 6690744.66 558451.279 493278.516 TRUE 0.08346624
## 1862 4321656.69 518600.809 401881.002 TRUE 0.12000046
## 1863 5461187.39 662625.210 274350.218 FALSE 0.12133354
## 1864 6319301.47 665235.819 369166.004 TRUE 0.10527047
## 1865 6387276.01 877375.233 539080.611 TRUE 0.13736297
## 1866 4781750.24 458753.893 442419.134 TRUE 0.09593849
## 1867 1749328.52 331497.151 86362.725 TRUE 0.18949966
## 1868 4834195.61 806084.535 482526.894 TRUE 0.16674636
## 1869 5397417.95 528680.342 155066.408 FALSE 0.09795060
## 1870 410110.44 52643.410 4751.275 FALSE 0.12836398
## 1871 3606851.51 474270.828 245536.595 FALSE 0.13149164
## 1872 3981130.32 328553.246 393227.240 FALSE 0.08252763
## 1873 4657956.78 643032.831 211171.746 TRUE 0.13805041
## 1874 7540096.81 661148.338 280876.872 TRUE 0.08768433
## 1875 4217229.45 691788.316 339527.959 FALSE 0.16403858
## 1876 6020372.81 966498.393 329142.715 FALSE 0.16053796
## 1877 6053009.52 498533.536 356144.238 TRUE 0.08236127
## 1878 4489343.84 306340.617 195585.358 FALSE 0.06823728
## 1879 274130.47 41910.494 27219.479 TRUE 0.15288520
## 1880 7436237.19 982239.184 333672.760 FALSE 0.13208820
## 1881 1781744.28 192341.945 19243.211 TRUE 0.10795149
## 1882 2941784.27 394397.055 140354.424 TRUE 0.13406729
## 1883 5399608.26 540708.546 91141.998 TRUE 0.10013848
## 1884 3259006.40 512190.854 150828.993 FALSE 0.15716166
## 1885 3750400.29 439287.631 82964.513 TRUE 0.11713087
## 1886 862768.51 103911.219 32213.009 FALSE 0.12043928
## 1887 1015977.52 182632.457 17295.336 FALSE 0.17976033
## 1888 2782492.01 526742.396 204952.256 FALSE 0.18930599
## 1889 900234.96 116972.514 74364.115 FALSE 0.12993554
## 1890 3263449.55 494503.529 81890.896 FALSE 0.15152786
## 1891 7473670.82 840097.011 206246.188 FALSE 0.11240755
## 1892 732315.83 74833.739 16702.854 FALSE 0.10218779
## 1893 3416778.99 427948.130 288572.637 TRUE 0.12524899
## 1894 1977794.85 359940.078 139587.740 FALSE 0.18199060
## 1895 7254500.82 383152.230 508347.200 TRUE 0.05281580
## 1896 5072593.35 317142.160 270898.470 TRUE 0.06252071
## 1897 1664248.77 290916.704 23655.593 FALSE 0.17480362
## 1898 5090534.04 526282.015 473724.061 TRUE 0.10338444
## 1899 5447862.40 699460.192 246342.223 TRUE 0.12839168
## 1900 1454783.35 146526.882 23554.027 TRUE 0.10072076
## 1901 1700618.60 246767.675 18953.205 FALSE 0.14510466
## 1902 3791039.06 431346.462 278539.158 FALSE 0.11378054
## 1903 2274600.21 124873.087 213800.275 TRUE 0.05489892
## 1904 2467291.59 267010.505 114055.870 TRUE 0.10822008
## 1905 5499724.93 587037.801 117750.444 FALSE 0.10673948
## 1906 3240095.84 365658.153 323368.444 FALSE 0.11285412
## 1907 5487251.05 631147.449 130119.248 FALSE 0.11502070
## 1908 2981478.64 328342.259 174420.448 TRUE 0.11012732
## 1909 2051750.47 407271.349 51315.124 TRUE 0.19849945
## 1910 3706402.66 204031.470 43506.767 TRUE 0.05504838
## 1911 4918096.81 305833.466 189539.923 FALSE 0.06218533
## 1912 4492340.57 598784.420 61577.157 FALSE 0.13329008
## 1913 3504453.70 538684.051 115050.489 FALSE 0.15371413
## 1914 7431460.31 1081729.221 180894.379 FALSE 0.14556079
## 1915 7670871.62 1214897.418 352931.616 FALSE 0.15837801
## 1916 2368540.57 332664.599 90241.433 FALSE 0.14045130
## 1917 6293781.98 512116.345 386458.285 TRUE 0.08136862
## 1918 4661275.96 699405.645 207323.834 TRUE 0.15004596
## 1919 6377618.17 943282.901 316372.880 FALSE 0.14790520
## 1920 4407213.67 833889.019 150900.622 TRUE 0.18921003
## 1921 2750551.00 399530.662 127325.659 TRUE 0.14525477
## 1922 6838661.03 668753.826 243440.200 TRUE 0.09779017
## 1923 1901024.10 156743.039 87365.751 TRUE 0.08245189
## 1924 3358520.80 307121.110 130532.291 FALSE 0.09144535
## 1925 4718243.72 477984.133 455336.920 FALSE 0.10130552
## 1926 5383327.96 711721.822 228851.211 TRUE 0.13220852
## 1927 6978187.46 419798.538 275238.115 FALSE 0.06015868
## 1928 5100021.25 584728.290 277428.542 FALSE 0.11465213
## 1929 2055766.75 179499.951 124950.636 TRUE 0.08731533
## 1930 5325565.66 627448.326 516101.297 FALSE 0.11781816
## 1931 5804609.08 932623.861 432873.811 TRUE 0.16066954
## 1932 1850009.64 320018.976 44972.826 TRUE 0.17298233
## 1933 5971464.75 447988.153 204113.148 TRUE 0.07502149
## 1934 7736035.39 1002036.106 475053.843 FALSE 0.12952838
## 1935 5702814.91 602428.107 123034.274 TRUE 0.10563697
## 1936 444000.77 29794.865 43498.420 TRUE 0.06710544
## 1937 2503301.81 156143.255 153589.582 FALSE 0.06237492
## 1938 6082027.33 700930.424 365593.110 FALSE 0.11524618
## 1939 3383984.52 506210.538 99628.683 TRUE 0.14959009
## 1940 6571339.97 456415.626 444841.628 FALSE 0.06945549
## 1941 6650050.75 361882.105 649903.560 FALSE 0.05441795
## 1942 258434.44 39356.907 12165.855 FALSE 0.15228972
## 1943 4862162.86 299635.683 454425.116 TRUE 0.06162601
## 1944 4280426.00 435306.193 279821.827 FALSE 0.10169693
## 1945 8095159.59 971452.792 559555.906 FALSE 0.12000416
## 1946 4061003.36 773747.771 150590.173 FALSE 0.19053118
## 1947 5263698.91 774276.935 201908.704 TRUE 0.14709750
## 1948 4648998.83 591544.738 146812.429 TRUE 0.12724132
## 1949 4085139.44 540550.278 317245.781 FALSE 0.13232113
## 1950 7966935.22 818075.066 600238.097 FALSE 0.10268379
## 1951 3889741.42 575911.667 321670.109 TRUE 0.14805911
## 1952 1742812.93 99294.028 132092.423 TRUE 0.05697343
## 1953 6781085.83 1155545.525 228763.394 FALSE 0.17040715
## 1954 326845.17 41176.456 29823.648 FALSE 0.12598153
## 1955 5791652.39 1135059.905 314447.290 TRUE 0.19598205
## 1956 8033242.46 1307505.238 722117.613 FALSE 0.16276183
## 1957 5467109.94 811182.570 448975.079 FALSE 0.14837502
## 1958 4370966.10 288148.250 87385.360 FALSE 0.06592324
## 1959 6231043.90 887034.674 505015.201 TRUE 0.14235731
## 1960 2127672.79 302904.574 208323.053 FALSE 0.14236427
## 1961 4331027.37 774100.481 165511.508 FALSE 0.17873368
## 1962 5501175.60 518512.454 248224.505 TRUE 0.09425485
## 1963 8000998.67 883486.735 517486.633 FALSE 0.11042206
## 1964 3574549.54 426887.653 286344.281 TRUE 0.11942418
## 1965 2959194.79 471376.947 95720.373 TRUE 0.15929230
## 1966 6339408.82 938379.441 141569.123 TRUE 0.14802318
## 1967 2171729.29 338547.408 27150.444 FALSE 0.15588840
## 1968 2119825.49 373623.748 67986.386 FALSE 0.17625213
## 1969 7083959.93 659093.333 345191.729 TRUE 0.09304024
## 1970 4753636.03 586866.671 442953.070 TRUE 0.12345637
## 1971 1676660.92 162019.157 108857.563 TRUE 0.09663204
## 1972 254410.50 48007.069 19405.118 TRUE 0.18869925
## 1973 7876495.07 1320899.716 536925.967 FALSE 0.16770146
## 1974 1449695.89 216139.956 71466.778 FALSE 0.14909331
## 1975 5782175.72 678305.495 410476.129 TRUE 0.11730973
## 1976 2436423.60 221614.732 106495.614 TRUE 0.09095903
## 1977 3418502.88 301335.235 324025.983 TRUE 0.08814831
## 1978 6399059.67 739291.904 570827.881 TRUE 0.11553133
## 1979 5109638.11 982888.688 497798.952 FALSE 0.19235975
## 1980 2323132.84 306236.191 42918.588 FALSE 0.13182035
## 1981 8211033.25 1318388.410 480604.269 FALSE 0.16056303
## 1982 979734.73 138674.194 30428.181 FALSE 0.14154259
## 1983 5399893.22 836910.772 501779.273 FALSE 0.15498654
## 1984 1588304.28 238435.600 97140.663 TRUE 0.15011960
## 1985 2857090.23 445555.453 217659.554 TRUE 0.15594728
## 1986 3819557.97 293189.801 135851.943 TRUE 0.07676014
## 1987 3158000.40 308344.287 192606.819 FALSE 0.09763909
## 1988 5277851.52 780383.724 284633.157 FALSE 0.14786011
## 1989 1994279.72 143884.270 183683.498 FALSE 0.07214849
## 1990 4297882.31 387062.380 80937.452 TRUE 0.09005886
## 1991 4485190.88 493081.503 286734.525 FALSE 0.10993546
## 1992 4971091.38 681687.665 78507.375 FALSE 0.13713038
## 1993 7415078.90 1284100.217 350620.610 TRUE 0.17317418
## 1994 4748133.85 616740.015 452457.662 TRUE 0.12989103
## 1995 4638465.65 358712.471 149519.874 TRUE 0.07733429
## 1996 2868844.13 170840.731 191895.502 FALSE 0.05955037
## 1997 749123.33 125905.592 43803.480 TRUE 0.16807058
## 1998 2077536.01 400560.865 48633.165 FALSE 0.19280574
## 1999 7419979.43 1076260.268 511557.923 TRUE 0.14504896
## 2000 459458.15 46214.021 6849.318 FALSE 0.10058375
## 2001 6821224.54 529340.577 620681.365 FALSE 0.07760199
## 2002 5703336.09 295061.166 170668.105 FALSE 0.05173484
## 2003 5330837.01 351105.088 426481.108 TRUE 0.06586303
## 2004 6131783.33 1049436.875 243446.831 FALSE 0.17114709
## 2005 6196492.52 927774.134 461792.443 TRUE 0.14972569
## 2006 6928525.13 919151.869 380209.527 TRUE 0.13266198
## 2007 5090713.84 724430.959 193600.084 FALSE 0.14230440
## 2008 797196.61 133354.682 27433.125 TRUE 0.16727954
## 2009 4352329.54 284289.331 184705.892 FALSE 0.06531889
## 2010 3049671.95 357202.442 115509.541 TRUE 0.11712815
## 2011 5301314.08 666729.031 195359.609 FALSE 0.12576675
## 2012 5157253.34 668127.041 453685.732 FALSE 0.12955094
## 2013 176342.30 18296.096 3759.794 FALSE 0.10375330
## 2014 5751403.90 596168.077 558314.648 FALSE 0.10365610
## 2015 6503465.41 344096.199 475903.070 FALSE 0.05290967
## 2016 2720645.96 408604.953 107324.657 TRUE 0.15018674
## 2017 5515047.37 652439.306 267061.704 FALSE 0.11830167
## 2018 7976411.34 917007.002 234977.389 TRUE 0.11496486
## 2019 3437619.57 538942.551 338515.100 TRUE 0.15677783
## 2020 2400830.99 406481.444 153679.516 TRUE 0.16930865
## 2021 1872816.01 98088.732 46814.974 FALSE 0.05237500
## 2022 4531191.78 562754.380 349103.500 TRUE 0.12419567
## 2023 1234835.82 152162.520 95683.508 TRUE 0.12322490
## 2024 6895990.04 375161.632 623506.599 FALSE 0.05440287
## 2025 3881142.32 639384.467 375773.361 FALSE 0.16474131
## 2026 2222287.16 252450.102 92436.863 FALSE 0.11359923
## 2027 3201837.18 514675.448 53960.351 TRUE 0.16074379
## 2028 5220961.59 364896.581 180085.118 FALSE 0.06989068
## 2029 4968507.59 495795.979 382455.703 FALSE 0.09978771
## 2030 5615855.41 709532.035 122277.893 FALSE 0.12634443
## 2031 5703323.42 317217.899 424609.784 TRUE 0.05561983
## 2032 5303579.35 700205.659 234337.815 TRUE 0.13202511
## 2033 6048155.75 814353.229 451654.677 FALSE 0.13464488
## 2034 5174770.11 883453.362 325189.879 TRUE 0.17072321
## 2035 3653074.87 230204.957 36684.537 FALSE 0.06301676
## 2036 2200631.21 149898.623 188369.952 FALSE 0.06811619
## 2037 6051124.67 305214.264 449699.293 FALSE 0.05043926
## 2038 7303791.79 379509.744 159767.467 FALSE 0.05196065
## 2039 3214677.11 510189.578 115266.292 FALSE 0.15870632
## 2040 6920877.30 766040.725 648500.248 TRUE 0.11068549
## 2041 4429397.09 601441.703 259840.765 FALSE 0.13578410
## 2042 3984527.25 254697.471 289882.395 FALSE 0.06392163
## 2043 488860.96 49366.287 27903.926 FALSE 0.10098226
## 2044 2511654.06 491945.140 67849.516 FALSE 0.19586501
## 2045 4753049.09 244840.908 251580.235 FALSE 0.05151239
## 2046 6597998.29 441414.959 512452.550 TRUE 0.06690134
## 2047 1210709.93 136870.148 34719.145 TRUE 0.11304950
## 2048 6157388.77 752215.772 514843.859 FALSE 0.12216474
## 2049 6738016.65 1341577.554 615568.541 TRUE 0.19910570
## 2050 7101193.03 814093.248 197421.972 FALSE 0.11464176
## 2051 5414050.81 627868.797 206844.633 FALSE 0.11597024
## 2052 3005549.94 599738.144 224981.558 FALSE 0.19954356
## 2053 1805094.23 95098.904 82393.111 FALSE 0.05268362
## 2054 4656261.63 328098.498 264269.585 FALSE 0.07046393
## 2055 173108.50 34481.918 15862.253 FALSE 0.19919252
## 2056 5232310.01 373664.726 282196.754 FALSE 0.07141487
## 2057 2734681.94 159054.656 67950.411 FALSE 0.05816203
## 2058 3796567.42 731109.028 282442.138 FALSE 0.19257106
## 2059 2612486.04 469380.879 157847.732 FALSE 0.17966828
## 2060 6421523.39 1209591.770 308706.505 TRUE 0.18836524
## 2061 908199.70 177579.762 16334.212 TRUE 0.19552942
## 2062 4734991.77 417402.347 170275.744 TRUE 0.08815271
## 2063 2728851.68 406982.362 209438.984 TRUE 0.14914052
## 2064 6578698.92 517527.293 648778.113 FALSE 0.07866712
## 2065 6178351.48 813819.029 364610.273 TRUE 0.13172106
## 2066 4236937.49 776780.527 266335.567 FALSE 0.18333538
## 2067 5790878.68 1042505.564 248961.384 FALSE 0.18002545
## 2068 360612.92 59085.451 8626.204 FALSE 0.16384730
## 2069 6088930.46 618916.344 479903.980 TRUE 0.10164615
## 2070 6394291.06 810948.752 447632.168 TRUE 0.12682387
## 2071 7232119.99 666844.338 222009.198 TRUE 0.09220593
## 2072 4474017.63 396834.967 197135.604 TRUE 0.08869768
## 2073 827327.75 90860.197 10806.933 FALSE 0.10982370
## 2074 2363941.60 144775.162 115548.567 FALSE 0.06124312
## 2075 5502150.26 801441.946 233300.517 FALSE 0.14565977
## 2076 2440942.13 157418.339 161083.859 TRUE 0.06449081
## 2077 5094737.73 723798.373 196861.963 FALSE 0.14206784
## 2078 5348211.63 971897.160 149647.777 FALSE 0.18172377
## 2079 1692232.26 225665.875 95865.695 TRUE 0.13335396
## 2080 2580844.67 365107.436 87149.795 FALSE 0.14146819
## 2081 2808223.75 317120.621 274700.367 FALSE 0.11292570
## 2082 2893781.38 199949.073 234863.159 TRUE 0.06909612
## 2083 6421441.89 928235.401 531534.297 FALSE 0.14455249
## 2084 6674431.27 651773.142 524633.724 FALSE 0.09765224
## 2085 6788624.50 1244944.605 367741.531 FALSE 0.18338687
## 2086 6479420.64 1240976.358 569641.029 FALSE 0.19152582
## 2087 2051995.67 320071.517 57346.389 FALSE 0.15598060
## 2088 6512911.55 622313.985 166418.240 TRUE 0.09555081
## 2089 1173012.37 98905.554 52264.581 TRUE 0.08431757
## 2090 2476762.87 413077.869 87133.177 FALSE 0.16678136
## 2091 1838662.53 232503.260 166172.586 FALSE 0.12645238
## 2092 1421557.01 205161.872 128573.089 FALSE 0.14432194
## 2093 2296810.45 354712.011 107049.228 TRUE 0.15443678
## 2094 2404641.01 162114.437 235928.986 TRUE 0.06741731
## 2095 7458862.96 378158.113 124007.780 TRUE 0.05069916
## 2096 3344042.78 400654.108 164549.683 FALSE 0.11981130
## 2097 5364006.49 681193.364 158212.785 FALSE 0.12699339
## 2098 1761630.27 233961.430 88326.743 FALSE 0.13280961
## 2099 4559335.84 470507.270 106921.809 TRUE 0.10319645
## 2100 5412392.21 394091.755 417927.990 FALSE 0.07281286
## 2101 6002227.56 734018.904 376594.878 TRUE 0.12229108
## 2102 5282878.31 808369.312 151954.788 FALSE 0.15301683
## 2103 1068900.07 148051.727 102303.527 FALSE 0.13850848
## 2104 6641712.23 499000.919 205905.132 TRUE 0.07513137
## 2105 6705999.05 1091444.634 592938.755 FALSE 0.16275646
## 2106 672770.47 117719.450 16462.543 FALSE 0.17497714
## 2107 4101743.65 381390.865 349643.381 TRUE 0.09298262
## 2108 3706014.00 265714.028 130118.189 TRUE 0.07169806
## 2109 2574872.22 299440.312 108030.026 FALSE 0.11629327
## 2110 6372499.99 920857.187 543619.553 TRUE 0.14450485
## 2111 2994068.68 181958.191 156738.760 FALSE 0.06077288
## 2112 2274857.36 341987.037 28618.915 TRUE 0.15033340
## 2113 4962219.89 618536.641 109629.158 FALSE 0.12464918
## 2114 7817338.74 1385766.126 660945.727 FALSE 0.17726827
## 2115 5585381.66 642824.377 142057.859 TRUE 0.11509050
## 2116 451477.43 55014.084 41154.813 TRUE 0.12185345
## 2117 641601.70 90012.855 33989.197 FALSE 0.14029398
## 2118 6588321.15 623894.820 569071.133 TRUE 0.09469709
## 2119 5282450.97 747876.931 176308.361 TRUE 0.14157764
## 2120 2603991.30 342528.443 223235.052 FALSE 0.13153978
## 2121 5449302.25 1009063.230 532038.142 TRUE 0.18517292
## 2122 2861591.21 460370.204 176792.930 FALSE 0.16087909
## 2123 1961671.63 118325.030 45338.305 TRUE 0.06031847
## 2124 2843221.00 273483.505 179627.895 FALSE 0.09618792
## 2125 6569219.08 830309.029 187072.119 FALSE 0.12639387
## 2126 410915.72 42271.448 26386.387 TRUE 0.10287133
## 2127 2715668.83 414368.964 28637.943 TRUE 0.15258450
## 2128 1176502.71 205570.405 52883.271 FALSE 0.17473007
## 2129 5406841.54 946162.146 236662.777 FALSE 0.17499350
## 2130 3234435.80 283481.691 58915.092 FALSE 0.08764487
## 2131 2512635.38 322070.889 48114.272 TRUE 0.12818051
## 2132 2575561.11 495228.624 240265.503 FALSE 0.19227990
## 2133 2811400.32 509549.078 85980.826 FALSE 0.18124387
## 2134 5369303.90 406085.034 435378.753 FALSE 0.07563085
## 2135 2867481.44 486605.913 226893.208 FALSE 0.16969802
## 2136 6003557.49 636323.868 121339.765 TRUE 0.10599113
## 2137 7701793.69 1429919.944 466381.231 FALSE 0.18566064
## 2138 5475127.20 763289.815 389871.919 FALSE 0.13941043
## 2139 4726508.91 556947.213 247474.020 FALSE 0.11783480
## 2140 1954810.72 342105.812 19971.117 FALSE 0.17500713
## 2141 1209653.30 76642.123 79386.500 TRUE 0.06335875
## 2142 5471157.04 299176.464 220125.150 FALSE 0.05468249
## 2143 3264384.21 523447.638 263157.235 TRUE 0.16035111
## 2144 2260747.69 244034.525 99014.783 FALSE 0.10794417
## 2145 5097949.48 650049.717 66937.037 FALSE 0.12751200
## 2146 462625.50 37995.799 32348.741 TRUE 0.08213079
## 2147 6161218.24 1153227.780 338275.453 FALSE 0.18717529
## 2148 4340223.12 632650.629 247189.831 TRUE 0.14576454
## 2149 1941505.70 317651.401 25422.938 TRUE 0.16361085
## 2150 5015936.86 472583.350 221822.881 FALSE 0.09421637
## 2151 6358127.39 984188.267 455204.986 TRUE 0.15479216
## 2152 1252210.90 202156.541 44451.736 FALSE 0.16143969
## 2153 3458610.97 547834.083 268600.651 TRUE 0.15839714
## 2154 7606860.81 989163.323 272442.754 FALSE 0.13003568
## 2155 7254100.13 632882.021 329450.099 TRUE 0.08724473
## 2156 3818452.27 649048.632 82759.172 FALSE 0.16997689
## 2157 7390174.59 1471668.035 713543.262 FALSE 0.19913847
## 2158 3696837.19 436845.662 255026.580 TRUE 0.11816741
## 2159 3705330.09 621301.588 206815.256 FALSE 0.16767780
## 2160 3197165.62 234569.519 144382.875 TRUE 0.07336796
## 2161 1917265.69 298294.868 64327.787 FALSE 0.15558348
## 2162 5858638.46 388209.482 83371.411 FALSE 0.06626275
## 2163 1107246.58 189879.490 35674.040 FALSE 0.17148799
## 2164 2126964.18 334814.629 69207.584 FALSE 0.15741432
## 2165 3614188.02 498169.975 284564.582 FALSE 0.13783732
## 2166 1716875.24 325501.206 108509.323 TRUE 0.18958932
## 2167 3716633.44 235110.842 174188.917 TRUE 0.06325909
## 2168 2517744.07 162183.969 227008.645 TRUE 0.06441638
## 2169 2724735.39 411124.479 58675.139 TRUE 0.15088602
## 2170 6741612.70 957824.743 390272.689 FALSE 0.14207650
## 2171 6776787.67 628482.811 654718.725 TRUE 0.09274052
## 2172 6203906.45 1003319.861 580144.795 TRUE 0.16172389
## 2173 3724006.35 403441.075 238415.301 FALSE 0.10833523
## 2174 2643841.45 279393.292 107428.659 FALSE 0.10567702
## 2175 7024899.44 1122819.413 364182.252 TRUE 0.15983423
## 2176 6505554.93 952248.993 193803.260 FALSE 0.14637475
## 2177 3008724.26 324087.006 264379.935 TRUE 0.10771576
## 2178 7066339.83 408292.601 606849.694 TRUE 0.05777993
## 2179 5724221.67 426556.236 481050.842 TRUE 0.07451777
## 2180 4592453.94 695545.392 143754.607 TRUE 0.15145397
## 2181 3517990.57 395594.554 165245.335 TRUE 0.11244901
## 2182 930219.54 138665.073 46301.842 TRUE 0.14906704
## 2183 1940912.13 310855.954 84972.695 TRUE 0.16015973
## 2184 4725086.28 494973.641 99060.118 FALSE 0.10475441
## 2185 7665693.86 584206.254 104723.829 TRUE 0.07621049
## 2186 7366949.13 953020.827 654569.376 TRUE 0.12936438
## 2187 6317026.48 682014.131 510265.363 FALSE 0.10796442
## 2188 7554278.34 1375031.005 210117.834 FALSE 0.18202017
## 2189 6625953.47 783647.676 320515.482 TRUE 0.11826942
## 2190 3215548.77 412822.160 122120.138 TRUE 0.12838311
## 2191 7359966.89 397354.585 269537.586 FALSE 0.05398864
## 2192 635392.16 35100.509 41238.385 FALSE 0.05524228
## 2193 7702864.72 1167587.163 709986.429 TRUE 0.15157830
## 2194 4654750.22 534943.753 134176.856 FALSE 0.11492427
## 2195 3408192.79 370075.155 167413.257 TRUE 0.10858399
## 2196 5841218.22 847901.837 184605.733 TRUE 0.14515839
## 2197 5522787.37 716464.718 93657.465 TRUE 0.12972883
## 2198 6043871.28 803370.787 281100.073 FALSE 0.13292321
## 2199 1097082.68 202585.284 97125.487 FALSE 0.18465817
## 2200 6963104.79 627901.241 514661.355 TRUE 0.09017547
## 2201 2847374.73 454678.143 202257.484 TRUE 0.15968328
## 2202 8268606.25 667116.143 668617.397 TRUE 0.08068060
## 2203 6869936.97 1203358.520 295371.481 TRUE 0.17516296
## 2204 4254572.40 584275.823 356508.135 FALSE 0.13732892
## 2205 1061303.15 92088.011 30113.221 FALSE 0.08676881
## 2206 3583004.30 369705.258 90254.250 FALSE 0.10318303
## 2207 2158581.56 367243.491 147500.750 TRUE 0.17013186
## 2208 2216413.72 329572.179 47729.904 TRUE 0.14869615
## 2209 3078153.44 411933.616 109312.634 TRUE 0.13382491
## 2210 3983229.12 496220.150 213495.899 FALSE 0.12457736
## 2211 182726.44 29647.686 3425.027 FALSE 0.16225175
## 2212 4888014.39 624944.810 429953.204 TRUE 0.12785249
## 2213 5981776.54 924911.845 341659.347 TRUE 0.15462160
## 2214 3107842.58 213270.061 276109.608 FALSE 0.06862319
## 2215 5942016.07 774618.720 367297.987 TRUE 0.13036295
## 2216 2140665.88 356365.672 96262.579 TRUE 0.16647422
## 2217 200930.74 31091.145 11688.317 TRUE 0.15473563
## 2218 7918942.40 1380155.705 737610.415 FALSE 0.17428536
## 2219 3301594.36 379537.210 99306.449 FALSE 0.11495574
## 2220 5158411.79 855671.014 277214.340 FALSE 0.16587877
## 2221 497448.15 43726.517 48532.884 TRUE 0.08790166
## 2222 236731.54 35700.564 15915.101 TRUE 0.15080611
## 2223 1563112.88 277683.187 125900.037 TRUE 0.17764756
## 2224 7269625.16 608421.439 136811.236 FALSE 0.08369365
## 2225 5312197.98 319170.501 382575.156 TRUE 0.06008257
## 2226 3463981.57 588795.296 82046.934 TRUE 0.16997645
## 2227 214015.97 22416.295 4662.187 FALSE 0.10474122
## 2228 4709799.16 570326.586 353820.244 FALSE 0.12109361
## 2229 2961463.24 571821.378 129950.488 TRUE 0.19308745
## 2230 2969899.55 326833.336 115259.753 TRUE 0.11004862
## 2231 7704320.91 1195080.030 123701.608 TRUE 0.15511815
## 2232 1739293.40 277400.502 173236.455 TRUE 0.15949034
## 2233 3834166.24 594250.193 146923.631 TRUE 0.15498811
## 2234 6122103.09 1210834.200 272721.272 TRUE 0.19778076
## 2235 1111180.36 139094.819 62625.985 FALSE 0.12517754
## 2236 5454941.18 379024.214 391143.181 TRUE 0.06948273
## 2237 4051947.18 706503.331 220288.334 TRUE 0.17436144
## 2238 5222665.45 450330.913 187234.540 FALSE 0.08622626
## 2239 7680528.18 1314500.481 156503.689 TRUE 0.17114715
## 2240 5432420.13 582585.529 495587.401 FALSE 0.10724235
## 2241 7123979.80 985143.404 100597.112 TRUE 0.13828554
## 2242 2606281.15 179261.041 155180.563 TRUE 0.06878039
## 2243 7396887.75 397032.077 89803.323 TRUE 0.05367556
## 2244 2046132.80 273468.806 143696.283 FALSE 0.13365154
## 2245 3849516.23 273021.071 149201.856 FALSE 0.07092348
## 2246 4763244.16 654320.256 428959.474 FALSE 0.13736862
## 2247 3197925.42 173249.095 34038.060 FALSE 0.05417546
## 2248 6716738.78 889087.976 247602.964 FALSE 0.13236900
## 2249 7432947.92 1181633.874 642167.481 FALSE 0.15897244
## 2250 874564.04 118334.993 81794.448 FALSE 0.13530741
## 2251 6789193.05 1296193.847 641295.315 FALSE 0.19092016
## 2252 159816.21 9882.339 3390.222 TRUE 0.06183565
## 2253 7882697.57 643918.007 557535.406 FALSE 0.08168752
## 2254 3599985.42 706699.228 316855.221 FALSE 0.19630614
## 2255 1852835.09 333948.429 116944.661 FALSE 0.18023646
## 2256 4496600.19 370905.467 319046.521 TRUE 0.08248576
## 2257 4786953.23 399556.942 104882.222 TRUE 0.08346790
## 2258 4387912.20 372075.792 169278.724 TRUE 0.08479563
## 2259 849025.15 51808.354 66068.527 FALSE 0.06102099
## 2260 2638051.18 321625.147 222742.125 FALSE 0.12191771
## 2261 6721414.34 527137.470 549813.932 TRUE 0.07842657
## 2262 3480633.33 480412.085 249940.177 TRUE 0.13802433
## 2263 5455279.43 843050.794 509141.277 TRUE 0.15453852
## 2264 4202282.63 301794.316 100143.298 FALSE 0.07181676
## 2265 2536083.48 264390.509 167243.139 TRUE 0.10425150
## 2266 917662.68 84036.640 27117.392 TRUE 0.09157683
## 2267 4031384.58 445971.386 113700.960 FALSE 0.11062487
## 2268 1557702.27 159189.553 120317.973 FALSE 0.10219511
## 2269 6136374.90 443783.804 287691.373 FALSE 0.07232019
## 2270 6372264.26 1142498.759 326737.048 TRUE 0.17929243
## 2271 5645491.03 809325.974 231137.984 FALSE 0.14335794
## 2272 3861274.35 471487.157 93592.204 FALSE 0.12210662
## 2273 6720885.23 1146143.911 429371.340 FALSE 0.17053467
## 2274 4847861.66 590616.815 210818.778 TRUE 0.12183038
## 2275 581257.74 83352.009 36622.733 FALSE 0.14339940
## 2276 5307410.40 875046.105 278342.901 FALSE 0.16487252
## 2277 4951978.03 438025.297 242612.028 TRUE 0.08845461
## 2278 6431538.35 515132.352 440491.020 TRUE 0.08009473
## 2279 4069644.96 375197.747 326572.092 FALSE 0.09219422
## 2280 1776466.24 315436.264 171171.953 FALSE 0.17756389
## 2281 3416728.41 365738.988 227646.353 FALSE 0.10704362
## 2282 1867283.56 359193.938 174021.088 TRUE 0.19236175
## 2283 3797521.72 375388.486 52647.291 TRUE 0.09885091
## 2284 5234778.96 597546.699 120971.931 TRUE 0.11414937
## 2285 3559088.36 528620.912 320322.855 TRUE 0.14852705
## 2286 6149973.32 991905.795 209491.916 FALSE 0.16128619
## 2287 5412612.79 940178.911 223443.665 TRUE 0.17370149
## 2288 938469.52 53879.756 77778.985 TRUE 0.05741237
## 2289 2992112.92 509650.610 187806.308 FALSE 0.17033134
## 2290 2346926.38 160163.004 224435.161 TRUE 0.06824373
## 2291 6533189.37 583910.559 419444.522 FALSE 0.08937603
## 2292 4583992.28 253968.035 319848.268 FALSE 0.05540324
## 2293 4937594.04 973366.780 457750.159 TRUE 0.19713382
## 2294 7429571.91 1419409.002 296843.206 FALSE 0.19104856
## 2295 4035780.69 563286.103 227419.944 TRUE 0.13957302
## 2296 426449.45 69873.652 41254.676 FALSE 0.16384979
## 2297 114141.76 17455.030 2969.683 TRUE 0.15292413
## 2298 1593131.02 97846.031 72149.461 FALSE 0.06141744
## 2299 5513204.55 619509.455 246788.111 FALSE 0.11236831
## 2300 3508322.06 658951.985 248885.038 FALSE 0.18782540
## 2301 1864955.23 237370.485 133790.762 FALSE 0.12727945
## 2302 2101192.65 301143.745 116108.853 TRUE 0.14332039
## 2303 6531757.95 908035.595 577663.271 FALSE 0.13901856
## 2304 3815088.17 475097.320 61964.513 TRUE 0.12453115
## 2305 404332.71 49953.180 34309.081 FALSE 0.12354474
## 2306 5358195.19 655001.269 432698.510 FALSE 0.12224289
## 2307 5776131.67 629106.134 558930.002 TRUE 0.10891478
## 2308 618986.48 118508.837 32435.555 FALSE 0.19145626
## 2309 1825594.89 312591.186 49247.569 TRUE 0.17122703
## 2310 3727384.17 560174.696 40001.873 TRUE 0.15028628
## 2311 2967727.67 239942.156 289472.400 FALSE 0.08085046
## 2312 1395097.92 182128.507 111632.743 TRUE 0.13054891
## 2313 5079061.02 460611.006 318335.079 FALSE 0.09068822
## 2314 3048483.40 575124.122 206026.964 TRUE 0.18865910
## 2315 7939263.98 1299861.290 113303.351 FALSE 0.16372567
## 2316 5530586.57 597398.550 499366.820 TRUE 0.10801721
## 2317 3081603.32 201143.719 108313.146 TRUE 0.06527242
## 2318 7754969.12 1267918.237 519006.510 TRUE 0.16349752
## 2319 4015099.87 369896.754 257510.921 FALSE 0.09212641
## 2320 1701576.80 112479.332 22315.298 FALSE 0.06610300
## 2321 4772983.68 258373.393 373306.835 FALSE 0.05413247
## 2322 609043.91 94639.131 31589.868 FALSE 0.15538967
## 2323 3787491.64 500073.236 45939.416 TRUE 0.13203283
## 2324 3942176.78 608869.477 333102.382 TRUE 0.15445007
## 2325 6452109.96 489115.135 467643.142 TRUE 0.07580701
## 2326 1150416.18 116077.971 60363.629 TRUE 0.10090085
## 2327 1280342.26 117659.340 40884.942 TRUE 0.09189679
## 2328 5900015.10 988649.687 427121.935 FALSE 0.16756731
## 2329 1911278.51 135983.682 28717.291 FALSE 0.07114802
## 2330 4511870.87 737516.473 336113.662 FALSE 0.16346134
## 2331 1603331.89 245313.686 141649.053 FALSE 0.15300244
## 2332 3714642.73 320291.085 103179.343 TRUE 0.08622393
## 2333 4536028.91 658669.193 343646.742 TRUE 0.14520833
## 2334 6096674.77 865359.080 565624.937 TRUE 0.14193952
## 2335 2391366.71 444840.820 28430.365 FALSE 0.18601949
## 2336 1947715.54 144703.128 153051.786 TRUE 0.07429377
## 2337 4032300.14 667738.708 299316.109 TRUE 0.16559747
## 2338 3173223.23 238243.731 72076.471 FALSE 0.07507941
## 2339 6144784.11 1216060.029 215705.248 TRUE 0.19790118
## 2340 179047.86 11415.654 11737.293 FALSE 0.06375756
## 2341 5191454.18 557018.558 473319.656 TRUE 0.10729529
## 2342 4067333.50 506034.689 233057.466 FALSE 0.12441436
## 2343 242127.62 21555.285 16548.334 TRUE 0.08902448
## 2344 5059475.15 834893.050 121327.762 TRUE 0.16501574
## 2345 4838883.21 958406.940 134374.796 FALSE 0.19806366
## 2346 5970167.11 615959.130 127429.661 TRUE 0.10317285
## 2347 717567.32 119535.077 19849.240 FALSE 0.16658378
## 2348 5435439.74 405918.720 175972.092 TRUE 0.07468001
## 2349 2589221.39 392733.238 130410.904 FALSE 0.15168005
## 2350 966147.26 121849.665 44658.229 TRUE 0.12611914
## 2351 1471653.64 252854.852 117318.639 FALSE 0.17181682
## 2352 3486053.41 340855.268 228782.621 FALSE 0.09777683
## 2353 7825730.14 699544.600 224309.723 FALSE 0.08939033
## 2354 7188115.28 1160372.890 684357.650 FALSE 0.16142937
## 2355 6949819.56 1221383.387 391214.165 TRUE 0.17574318
## 2356 6054127.50 758335.795 285888.741 TRUE 0.12525930
## 2357 1328778.96 95800.642 24327.734 FALSE 0.07209675
## 2358 5209812.19 265256.832 226282.857 FALSE 0.05091486
## 2359 6991045.79 1391521.105 584793.313 TRUE 0.19904334
## 2360 6247759.46 1024451.185 475898.944 FALSE 0.16397097
## 2361 2706572.89 356835.306 117106.716 FALSE 0.13184027
## 2362 262506.20 14425.500 10417.846 TRUE 0.05495299
## 2363 6008458.17 672190.921 554743.465 FALSE 0.11187411
## 2364 2941240.18 393604.780 234993.847 FALSE 0.13382273
## 2365 3746270.12 587950.733 57557.116 FALSE 0.15694296
## 2366 1589101.46 300918.818 109522.470 TRUE 0.18936413
## 2367 4560273.29 881118.291 158673.294 FALSE 0.19321612
## 2368 6423142.71 832927.059 571255.085 TRUE 0.12967594
## 2369 1934812.22 309573.788 35160.015 TRUE 0.16000198
## 2370 6114415.29 687762.887 266415.369 TRUE 0.11248220
## 2371 4153050.87 752165.799 75721.726 FALSE 0.18111163
## 2372 3500307.27 516052.254 271247.356 TRUE 0.14743056
## 2373 1081884.18 69865.071 70242.323 FALSE 0.06457722
## 2374 773046.17 128062.539 71321.508 TRUE 0.16565963
## 2375 5619609.84 790744.157 119443.537 TRUE 0.14071158
## 2376 3129613.46 412728.257 106269.617 FALSE 0.13187835
## 2377 1090212.16 62144.335 42451.863 TRUE 0.05700206
## 2378 5669711.17 1059890.905 88426.808 TRUE 0.18693914
## 2379 3350929.76 375884.034 315193.788 TRUE 0.11217306
## 2380 5686967.03 338733.868 203926.451 TRUE 0.05956318
## 2381 2217380.44 216761.065 219728.732 FALSE 0.09775547
## 2382 1194007.74 117725.243 21521.680 FALSE 0.09859672
## 2383 845699.66 44591.903 38461.739 FALSE 0.05272782
## 2384 117432.16 8206.131 4162.336 TRUE 0.06987976
## 2385 6110726.51 634651.607 249462.899 FALSE 0.10385862
## 2386 5388796.21 898183.036 505516.455 TRUE 0.16667601
## 2387 8156383.71 1000770.659 564227.434 FALSE 0.12269784
## 2388 6602345.25 366618.815 614987.004 FALSE 0.05552857
## 2389 4749509.95 428803.429 245736.320 TRUE 0.09028372
## 2390 2601846.65 480167.126 223264.448 TRUE 0.18454859
## 2391 6376392.88 1248260.327 365111.270 FALSE 0.19576277
## 2392 6725385.92 419544.782 485009.006 FALSE 0.06238226
## 2393 6156696.12 781113.845 267713.717 FALSE 0.12687224
## 2394 3931166.87 375286.948 305035.410 FALSE 0.09546452
## 2395 7230835.87 934936.141 236506.488 TRUE 0.12929849
## 2396 2936048.03 388594.734 197661.230 FALSE 0.13235299
## 2397 3409848.61 547508.245 66603.382 TRUE 0.16056673
## 2398 5515887.02 664852.265 531925.706 FALSE 0.12053406
## 2399 2676465.25 357682.894 109417.485 FALSE 0.13364003
## 2400 3823077.39 544140.374 100927.381 TRUE 0.14233046
## 2401 5361647.65 1010200.563 69240.778 FALSE 0.18841234
## 2402 3929428.87 629987.152 276883.552 FALSE 0.16032537
## 2403 807183.14 108955.404 68294.210 TRUE 0.13498226
## 2404 6580655.48 505213.904 125888.976 FALSE 0.07677258
## 2405 2932926.07 363393.315 38430.379 TRUE 0.12390129
## 2406 6164277.57 607716.384 120380.423 TRUE 0.09858680
## 2407 1639748.95 225625.373 95906.872 FALSE 0.13759751
## 2408 1989160.48 279768.488 40452.751 FALSE 0.14064651
## 2409 6681756.17 521097.127 647767.414 FALSE 0.07798805
## 2410 4307031.27 264073.096 288873.007 FALSE 0.06131209
## 2411 7118979.90 370113.224 479294.510 TRUE 0.05198964
## 2412 7850481.27 670748.857 509849.031 FALSE 0.08544048
## 2413 739799.89 118455.336 31804.041 TRUE 0.16011808
## 2414 1593125.69 261821.606 38605.525 FALSE 0.16434460
## 2415 8007527.32 411765.508 629571.074 TRUE 0.05142230
## 2416 6472991.28 357678.517 295703.027 FALSE 0.05525707
## 2417 886330.24 46062.158 53087.861 FALSE 0.05196952
## 2418 5654334.93 793438.505 213839.244 FALSE 0.14032393
## 2419 4310554.34 601219.969 84289.380 TRUE 0.13947625
## 2420 5206835.58 797416.147 96170.256 TRUE 0.15314794
## 2421 6193287.97 1027102.412 502917.570 TRUE 0.16584122
## 2422 6114647.78 879223.525 445533.349 TRUE 0.14378973
## 2423 5086077.09 415583.980 142408.543 TRUE 0.08171012
## 2424 6752850.21 510588.661 204591.006 TRUE 0.07561084
## 2425 7161555.77 1174736.553 347282.424 FALSE 0.16403371
## 2426 8249483.28 1490473.422 686439.825 FALSE 0.18067476
## 2427 8138280.08 441052.300 453476.376 FALSE 0.05419478
## 2428 5364118.34 884828.594 266392.675 TRUE 0.16495322
## 2429 415716.19 67807.150 6654.038 FALSE 0.16310923
## 2430 3829104.97 622181.418 300857.474 FALSE 0.16248743
## 2431 2749822.01 365532.937 89624.877 FALSE 0.13292967
## 2432 2939235.81 462991.882 288207.109 FALSE 0.15752118
## 2433 1080853.50 120487.028 69626.722 FALSE 0.11147397
## 2434 2632785.78 442477.529 107188.708 FALSE 0.16806439
## 2435 7508674.15 1349816.823 177720.240 FALSE 0.17976767
## 2436 1319762.99 233478.710 92419.719 TRUE 0.17690958
## 2437 4267722.47 263646.184 356388.849 TRUE 0.06177679
## 2438 432803.53 81559.867 5893.901 TRUE 0.18844548
## 2439 5709600.05 913157.638 158149.036 TRUE 0.15993373
## 2440 2636422.88 395015.930 53888.218 FALSE 0.14983026
## 2441 2587385.34 285359.823 151316.153 FALSE 0.11028888
## 2442 7215883.17 1099900.689 681980.980 FALSE 0.15242773
## 2443 3383694.67 583730.407 183714.538 TRUE 0.17251273
## 2444 1478900.79 160310.549 69757.024 FALSE 0.10839845
## 2445 1373080.18 100057.022 65098.250 TRUE 0.07287049
## 2446 2904528.60 483633.334 53304.322 FALSE 0.16651010
## 2447 2026994.59 366556.081 123412.786 FALSE 0.18083723
## 2448 498947.92 92621.707 27565.009 FALSE 0.18563402
## 2449 6206605.80 494700.420 381826.919 FALSE 0.07970547
## 2450 3861205.94 388794.230 125178.010 TRUE 0.10069244
## 2451 3926432.12 241835.782 66130.790 FALSE 0.06159174
## 2452 6542534.72 384318.375 573164.029 FALSE 0.05874151
## 2453 6108932.83 627507.835 320293.778 TRUE 0.10271971
## 2454 1922566.18 100453.540 118646.074 TRUE 0.05224972
## 2455 3576398.94 435011.282 244981.782 FALSE 0.12163388
## 2456 1885591.54 219187.378 119643.955 TRUE 0.11624330
## 2457 4975068.63 756074.915 71907.704 TRUE 0.15197276
## 2458 1648663.09 148916.774 126194.917 FALSE 0.09032578
## 2459 4798729.62 726098.111 267176.732 TRUE 0.15131049
## 2460 5603937.16 687003.640 515215.623 FALSE 0.12259303
## 2461 5744883.96 333325.799 399948.990 TRUE 0.05802133
## 2462 1098327.35 191643.991 53933.707 FALSE 0.17448713
## 2463 2602735.27 241879.231 86709.204 FALSE 0.09293271
## 2464 5644123.78 1021283.921 443469.623 TRUE 0.18094641
## 2465 5320983.05 340419.029 315409.638 FALSE 0.06397672
## 2466 3110649.83 165467.305 200159.557 TRUE 0.05319381
## 2467 1128186.77 203188.675 41883.143 TRUE 0.18010198
## 2468 5681181.18 631332.555 105629.547 TRUE 0.11112699
## 2469 4590854.90 293102.964 366299.951 FALSE 0.06384496
## 2470 4851098.35 585759.004 89340.958 FALSE 0.12074771
## 2471 1458207.96 132385.615 125427.579 TRUE 0.09078651
## 2472 6872260.29 1089829.836 517627.452 FALSE 0.15858390
## 2473 73727.01 12096.230 4971.915 TRUE 0.16406783
## 2474 1229185.44 240274.944 67607.879 TRUE 0.19547493
## 2475 1315318.00 94421.760 100820.198 FALSE 0.07178626
## 2476 6249776.39 1144650.406 504648.440 TRUE 0.18315062
## 2477 1921644.44 103973.096 175907.107 TRUE 0.05410631
## 2478 6926757.55 1374933.890 483799.246 TRUE 0.19849603
## 2479 1083276.15 115658.052 43373.984 FALSE 0.10676691
## 2480 3428017.17 680662.650 335207.023 FALSE 0.19855871
## 2481 4472291.31 551180.992 166293.882 TRUE 0.12324354
## 2482 5965428.43 579024.702 409709.265 TRUE 0.09706339
## 2483 5426218.22 786609.826 500165.218 FALSE 0.14496465
## 2484 5446593.29 927302.629 162970.028 TRUE 0.17025369
## 2485 2500020.65 364901.161 154092.646 FALSE 0.14595926
## 2486 1170741.60 199858.099 16534.304 TRUE 0.17071068
## 2487 5266149.45 496507.034 506917.470 FALSE 0.09428275
## 2488 4374314.70 718158.041 99506.132 FALSE 0.16417613
## 2489 7322903.50 1359987.979 91964.225 TRUE 0.18571704
## 2490 5707332.90 1006399.035 231081.661 TRUE 0.17633438
## 2491 5707837.99 452503.779 58741.269 TRUE 0.07927761
## 2492 5331261.13 597681.857 496268.604 TRUE 0.11210891
## 2493 7499408.26 1112337.576 573684.740 FALSE 0.14832338
## 2494 1085602.50 182081.997 64593.999 FALSE 0.16772437
## 2495 1030344.56 170436.695 35612.450 TRUE 0.16541718
## 2496 1575045.50 79181.526 55614.364 FALSE 0.05027253
## 2497 5379834.32 469325.497 287536.509 TRUE 0.08723791
## 2498 6876322.93 423368.437 399678.888 TRUE 0.06156902
## 2499 647803.63 117775.647 44848.677 FALSE 0.18180764
## 2500 4709250.24 546074.494 140136.513 FALSE 0.115957844 Level 4: Data Visualization
4.1 Question: Visualize total import vs export value
data %>%
group_by(Import_Export) %>%
summarise(Total = sum(Value_INR)) %>%
ggplot(aes(x = Import_Export, y = Total, fill = Import_Export)) +
geom_bar(stat = "identity") +
ggtitle("Import vs Export Trade Value")
Explanation: This bar chart compares total import and
export values.
4.2 Question: Show country-wise trade distribution
data %>%
group_by(Country) %>% # Group by country
summarise(Total = sum(Value_INR)) %>% # Total trade value
ggplot(aes(x = Country, y = Total, fill = Country)) +
geom_bar(stat = "identity") +
scale_fill_brewer(palette = "Set3") + # Beautiful color palette
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle("Country-wise Trade Distribution") +
theme_minimal()
Explanation: Shows which countries contribute most to
trade.
4.3 Question: Analyze product category trade value
data %>%
group_by(Product_Category) %>%
summarise(Total = sum(Value_INR)) %>%
ggplot(aes(x = Product_Category, y = Total, fill = Product_Category)) +
geom_bar(stat = "identity") +
scale_fill_brewer(palette = "Pastel1") +
ggtitle("Trade by Product Category") +
theme_minimal()
Explanation: Displays trade distribution across product
categories.
4.4 Question: Relationship between Quantity and Trade Value
ggplot(data, aes(x = Quantity, y = Value_INR, color = Import_Export)) +
geom_point(size = 2) +
scale_color_manual(values = c("Import" = "red", "Export" = "darkgreen")) +
ggtitle("Quantity vs Trade Value") +
theme_minimal()
Explanation: Shows relationship between quantity and
trade value.
4.5 Question: Distribution of Profit using histogram
ggplot(data, aes(x = Profit)) +
geom_histogram(fill = "purple", bins = 25, color = "black") +
ggtitle("Distribution of Profit") +
theme_minimal()
Explanation: Displays distribution of profit
values.
4.6 Question: Compare profit between import and export using boxplot
ggplot(data, aes(x = Import_Export, y = Profit, fill = Import_Export)) +
geom_boxplot() +
scale_fill_manual(values = c("Import" = "cyan", "Export" = "pink")) +
ggtitle("Profit Comparison: Import vs Export") +
theme_minimal()
Explanation:
Boxplot shows median, quartiles, and outliers Helps compare profit variation
4.7 Question: Line Plot (Monthly Trend)
data %>%
group_by(Month) %>%
summarise(Total = sum(Value_INR)) %>%
ggplot(aes(x = Month, y = Total, group = 1)) +
geom_line(color = "blue", size = 1) +
geom_point(color = "red") +
ggtitle("Monthly Trade Trend") +
theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Level 5: Exploratory Data Analysis (EDA)
4.8 Question: Calculate Mean, Median, Standard Deviation of Trade Value
mean(data$Value_INR, na.rm = TRUE)## [1] 3922176median(data$Value_INR, na.rm = TRUE)## [1] 3990634sd(data$Value_INR, na.rm = TRUE)## [1] 2228715Explanation: Mean gives average trade value, median shows middle value, and standard deviation shows variability in trade.
4.9 Question: Calculate Quartiles and IQR
quantile(data$Value_INR, probs = c(0.25, 0.5, 0.75), na.rm = TRUE)## 25% 50% 75%
## 2020603 3990634 5651695IQR(data$Value_INR, na.rm = TRUE)## [1] 3631091Explanation: Quartiles divide data into parts and IQR helps understand spread of middle 50%
4.10 Question: Density Plot
ggplot(data, aes(x = Value_INR)) +
geom_density(fill = "blue") +
theme_minimal()
Explanation: Smooth curve representing data
distribution shape.
4.11 Question: Boxplot for Outlier Detection
ggplot(data, aes(y = Value_INR)) +
geom_boxplot(fill = "orange") +
theme_minimal()
Explanation: Outliers appear as points outside
whiskers
4.12 Question: Group-wise Summary
data %>%
group_by(Product_Category) %>%
summarise(mean_value = mean(Value_INR, na.rm = TRUE))## # A tibble: 5 × 2
## Product_Category mean_value
## <fct> <dbl>
## 1 Agriculture 3954981.
## 2 Automobile 4008188.
## 3 Electronics 3873842.
## 4 Pharma 3925927.
## 5 Textile 3844378.Explanation: Compares average trade value across categories.
5 Level6: Correlation Analysis
5.1 Question: Compute Correlation between Quantity and Value
cor(data$Quantity, data$Value_INR, use = "complete.obs")## [1] -0.03185176Explanation Shows strength and direction of relationship.
5.2 Question: Correlation Matrix
numeric_data <- data %>% select_if(is.numeric)
cor(numeric_data, use = "complete.obs")## Year Quantity Value_USD Tariff
## Year 1.000000000 0.0158899627 -0.025950562 -0.009619447
## Quantity 0.015889963 1.0000000000 -0.039106502 -0.002916170
## Value_USD -0.025950562 -0.0391065025 1.000000000 -0.004777099
## Tariff -0.009619447 -0.0029161704 -0.004777099 1.000000000
## GST 0.048835469 -0.0067756623 -0.025871005 -0.023073876
## Exchange_Rate 0.008689667 0.0058427392 0.011350182 0.011165944
## Shipment_Days 0.002256202 -0.0109713319 0.026993265 0.019596253
## Value_INR -0.022865392 -0.0318517586 0.948711809 -0.004048269
## Profit -0.007608498 -0.0265372179 0.780700437 -0.016778725
## Loss -0.016275589 -0.0688334647 0.688939801 -0.022468063
## Profit_Margin 0.018643836 -0.0007847819 -0.021719771 -0.017084048
## GST Exchange_Rate Shipment_Days Value_INR
## Year 0.0488354689 0.008689667 0.0022562016 -0.022865392
## Quantity -0.0067756623 0.005842739 -0.0109713319 -0.031851759
## Value_USD -0.0258710050 0.011350182 0.0269932646 0.948711809
## Tariff -0.0230738759 0.011165944 0.0195962531 -0.004048269
## GST 1.0000000000 -0.006916096 -0.0005749788 -0.028117536
## Exchange_Rate -0.0069160958 1.000000000 0.0039455929 0.107581111
## Shipment_Days -0.0005749788 0.003945593 1.0000000000 0.026339776
## Value_INR -0.0281175356 0.107581111 0.0263397759 1.000000000
## Profit -0.0161227918 0.082301329 0.0316568029 0.804264579
## Loss -0.0294237030 0.094918254 0.0081692321 0.717457005
## Profit_Margin 0.0181160982 -0.013937879 0.0179617490 -0.023925340
## Profit Loss Profit_Margin
## Year -0.007608498 -0.016275589 0.0186438361
## Quantity -0.026537218 -0.068833465 -0.0007847819
## Value_USD 0.780700437 0.688939801 -0.0217197708
## Tariff -0.016778725 -0.022468063 -0.0170840484
## GST -0.016122792 -0.029423703 0.0181160982
## Exchange_Rate 0.082301329 0.094918254 -0.0139378787
## Shipment_Days 0.031656803 0.008169232 0.0179617490
## Value_INR 0.804264579 0.717457005 -0.0239253403
## Profit 1.000000000 0.581600260 0.4874027149
## Loss 0.581600260 1.000000000 -0.0272401279
## Profit_Margin 0.487402715 -0.027240128 1.0000000000Explanation Displays correlation among all numeric variables.
5.3 Question: Correlation Heatmap
library(corrplot)## Warning: package 'corrplot' was built under R version 4.5.3## corrplot 0.95 loadedcorr_matrix <- cor(numeric_data, use = "complete.obs")
corrplot(corr_matrix, method = "color")
Explanation Visual representation of correlation
strength.
5.4 Question: Correlation Test
pairs(numeric_data)
Explanation
Provides significance of correlation.
6 Level 7: Regression Analysis
6.1 Question: Simple Linear Regression
model1 <- lm(Value_INR ~ Quantity, data = data)
summary(model1)##
## Call:
## lm(formula = Value_INR ~ Quantity, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3939156 -1893806 63369 1732312 21883523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4045827.94 89513.20 45.198 <2e-16 ***
## Quantity -24.68 15.50 -1.593 0.111
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2228000 on 2498 degrees of freedom
## Multiple R-squared: 0.001015, Adjusted R-squared: 0.0006146
## F-statistic: 2.537 on 1 and 2498 DF, p-value: 0.1113Explanation Shows how quantity affects trade value.
6.2 Question: Multiple Linear Regression
model2 <- lm(Value_INR ~ Quantity + Profit, data = data)
summary(model2)##
## Call:
## lm(formula = Value_INR ~ Quantity + Profit, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1844246 -1039138 -335132 708330 19989311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.338e+06 6.662e+04 20.086 <2e-16 ***
## Quantity -8.149e+00 9.217e+00 -0.884 0.377
## Profit 5.393e+00 7.979e-02 67.591 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1325000 on 2497 degrees of freedom
## Multiple R-squared: 0.647, Adjusted R-squared: 0.6467
## F-statistic: 2288 on 2 and 2497 DF, p-value: < 2.2e-16EXPLANATION Analyzes multiple factors affecting value.
6.3 Question: Regression Plot
ggplot(data, aes(x = Quantity, y = Value_INR)) +
geom_point() +
geom_smooth(method = "lm", color = "blue") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
EXPLANTION Displays regression line.
6.4 Question: Polynomial Regression
model3 <- lm(Value_INR ~ poly(Quantity, 2), data = data)
summary(model3)##
## Call:
## lm(formula = Value_INR ~ poly(Quantity, 2), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3898518 -1902077 67184 1727483 21852520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3922176 44565 88.010 <2e-16 ***
## poly(Quantity, 2)1 -3548715 2228262 -1.593 0.111
## poly(Quantity, 2)2 -1545368 2228262 -0.694 0.488
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2228000 on 2497 degrees of freedom
## Multiple R-squared: 0.001207, Adjusted R-squared: 0.0004069
## F-statistic: 1.509 on 2 and 2497 DF, p-value: 0.2214Explation Captures non-linear relationship.
6.5 Question: Residual Analysis
plot(model1$residuals)