So we will start with some easy R Code
library(psych)## Warning: package 'psych' was built under R version 3.5.1library(reticulate)## Warning: package 'reticulate' was built under R version 3.5.1mytitanic=as.data.frame(Titanic)
describe(mytitanic)## vars n mean sd median trimmed mad min max range skew
## Class* 1 32 2.50 1.14 2.5 2.50 1.48 1 4 3 0.00
## Sex* 2 32 1.50 0.51 1.5 1.50 0.74 1 2 1 0.00
## Age* 3 32 1.50 0.51 1.5 1.50 0.74 1 2 1 0.00
## Survived* 4 32 1.50 0.51 1.5 1.50 0.74 1 2 1 0.00
## Freq 5 32 68.78 136.00 13.5 36.62 20.02 0 670 670 3.07
## kurtosis se
## Class* -1.46 0.20
## Sex* -2.06 0.09
## Age* -2.06 0.09
## Survived* -2.06 0.09
## Freq 9.93 24.04Now for some basic Python
for x in range(1,5):
for y in range(5):
try:
x/y
except:
print ("Div by Zero")
else:
print ("Yay, it worked.")## Div by Zero
## Yay, it worked.
## Yay, it worked.
## Yay, it worked.
## Yay, it worked.
## Div by Zero
## Yay, it worked.
## Yay, it worked.
## Yay, it worked.
## Yay, it worked.
## Div by Zero
## Yay, it worked.
## Yay, it worked.
## Yay, it worked.
## Yay, it worked.
## Div by Zero
## Yay, it worked.
## Yay, it worked.
## Yay, it worked.
## Yay, it worked.Not the best way..
import pandas as pd
import io
import requests
mydf=pd.read_csv("C:/Users/lfult/Desktop/Python Intro Course/MDSO.csv")
print(mydf)## Date Open High ... Close Adj Close Volume
## 0 2017-10-23 77.040001 78.500000 ... 72.260002 72.260002 3900700
## 1 2017-10-30 72.550003 75.639999 ... 71.779999 71.779999 3421800
## 2 2017-11-06 71.690002 72.000000 ... 65.690002 65.690002 5218600
## 3 2017-11-13 66.260002 70.639999 ... 68.639999 68.639999 2823000
## 4 2017-11-20 68.959999 71.029999 ... 70.150002 70.150002 969600
## 5 2017-11-27 70.050003 70.089996 ... 67.349998 67.349998 3768100
## 6 2017-12-04 68.150002 68.720001 ... 66.230003 66.230003 1897600
## 7 2017-12-11 66.169998 67.320000 ... 66.639999 66.639999 3302400
## 8 2017-12-18 67.199997 67.629997 ... 65.940002 65.940002 1505000
## 9 2017-12-25 65.669998 66.169998 ... 63.369999 63.369999 1125800
## 10 2018-01-01 63.070000 66.379997 ... 64.870003 64.870003 1612300
## 11 2018-01-08 64.870003 67.019997 ... 66.900002 66.900002 2274300
## 12 2018-01-15 67.230003 68.080002 ... 66.860001 66.860001 1948700
## 13 2018-01-22 66.470001 71.820000 ... 71.739998 71.739998 3195800
## 14 2018-01-29 71.260002 71.720001 ... 68.230003 68.230003 2088200
## 15 2018-02-05 67.370003 68.199997 ... 64.970001 64.970001 6800600
## 16 2018-02-12 66.650002 69.050003 ... 67.980003 67.980003 3820000
## 17 2018-02-19 67.470001 68.930000 ... 67.330002 67.330002 1860000
## 18 2018-02-26 67.660004 68.419998 ... 66.500000 66.500000 2072200
## 19 2018-03-05 66.290001 70.290001 ... 70.059998 70.059998 2165400
## 20 2018-03-12 69.989998 70.839996 ... 66.870003 66.870003 2539100
## 21 2018-03-19 66.300003 67.379997 ... 63.759998 63.759998 2642900
## 22 2018-03-26 64.800003 65.830002 ... 62.810001 62.810001 1990600
## 23 2018-04-02 62.570000 65.129997 ... 63.740002 63.740002 2340200
## 24 2018-04-09 64.339996 67.400002 ... 66.440002 66.440002 3838500
## 25 2018-04-16 67.040001 74.760002 ... 73.139999 73.139999 5849400
## 26 2018-04-23 73.050003 73.800003 ... 71.199997 71.199997 2700600
## 27 2018-04-30 70.919998 75.919998 ... 75.110001 75.110001 3709100
## 28 2018-05-07 75.629997 79.720001 ... 77.680000 77.680000 3755000
## 29 2018-05-14 77.519997 78.580002 ... 77.540001 77.540001 3051000
## 30 2018-05-21 75.519997 78.010002 ... 75.330002 75.330002 2513900
## 31 2018-05-28 74.970001 79.800003 ... 79.099998 79.099998 2246400
## 32 2018-06-04 79.139999 81.459999 ... 80.389999 80.389999 2399900
## 33 2018-06-11 80.360001 82.160004 ... 82.129997 82.129997 2517900
## 34 2018-06-18 82.379997 85.690002 ... 82.389999 82.389999 2960300
## 35 2018-06-25 81.959999 82.129997 ... 80.559998 80.559998 3466900
## 36 2018-07-02 79.389999 84.879997 ... 84.400002 84.400002 2462800
## 37 2018-07-09 84.660004 85.860001 ... 84.750000 84.750000 2278100
## 38 2018-07-16 84.589996 88.379997 ... 87.000000 87.000000 2902800
## 39 2018-07-23 86.760002 88.870003 ... 74.529999 74.529999 10948700
## 40 2018-07-30 74.610001 76.550003 ... 76.120003 76.120003 3323900
## 41 2018-08-06 75.849998 78.669998 ... 77.629997 77.629997 2068100
## 42 2018-08-13 77.830002 80.669998 ... 78.209999 78.209999 2151200
## 43 2018-08-20 78.519997 81.870003 ... 81.669998 81.669998 1800900
## 44 2018-08-27 82.379997 85.139999 ... 84.980003 84.980003 2175500
## 45 2018-09-03 84.540001 84.949997 ... 83.370003 83.370003 1488400
## 46 2018-09-10 83.989998 84.559998 ... 80.529999 80.529999 2216200
## 47 2018-09-17 80.309998 80.919998 ... 74.070000 74.070000 3715400
## 48 2018-09-24 73.910004 74.519997 ... 73.309998 73.309998 3144200
## 49 2018-10-01 73.519997 73.730003 ... 71.019997 71.019997 3348700
## 50 2018-10-08 71.000000 71.540001 ... 66.510002 66.510002 3906800
## 51 2018-10-15 66.360001 75.000000 ... 70.059998 70.059998 6243500
## 52 2018-10-22 69.809998 71.889999 ... 71.699997 71.699997 3468600
## 53 2018-10-25 68.019997 71.870003 ... 71.699997 71.699997 871256
##
## [54 rows x 7 columns]mydf2=pd.DataFrame.to_csv(mydf, "test.csv")
idx = pd.date_range('2017-10-30', '2018-10-25', freq='W')
myseries=pd.Series(mydf['Adj Close'])
myseries=myseries[2:53]
myseries.index=idxHere, we use reticulate.
mypandas=import("pandas")
mydf2=read.csv("test.csv")
mydf3=read.csv("test.csv")
describe(mydf2)# R describe## vars n mean sd median trimmed mad
## X 1 54 26.50 15.73 2.650e+01 26.50 20.02
## Date* 2 54 27.50 15.73 2.750e+01 27.50 20.02
## Open 3 54 72.68 6.66 7.113e+01 72.28 7.03
## High 4 54 74.93 6.71 7.377e+01 74.58 8.09
## Low 5 54 69.93 6.56 6.772e+01 69.57 6.36
## Close 6 54 72.65 6.69 7.170e+01 72.28 7.70
## Adj.Close 7 54 72.65 6.69 7.170e+01 72.28 7.70
## Volume 8 54 3014941.78 1630812.52 2.591e+06 2767863.64 1041378.24
## min max range skew kurtosis se
## X 0.00 53.00 53.00 0.00 -1.27 2.14
## Date* 1.00 54.00 53.00 0.00 -1.27 2.14
## Open 62.57 86.76 24.19 0.45 -1.05 0.91
## High 65.13 88.87 23.74 0.40 -1.11 0.91
## Low 59.60 83.59 23.99 0.44 -1.12 0.89
## Close 62.81 87.00 24.19 0.44 -1.03 0.91
## Adj.Close 62.81 87.00 24.19 0.44 -1.03 0.91
## Volume 871256.00 10948700.00 10077444.00 2.46 8.64 221925.47mypandas$DataFrame$describe(mydf3) #Python describe## X Open High Low Close Adj.Close Volume
## count 54.00000 54.00000 54.000000 54.000000 54.000000 54.000000 54
## mean 26.50000 72.67815 74.928333 69.931296 72.652593 72.652593 3014942
## std 15.73213 6.65523 6.711973 6.563521 6.692131 6.692131 1630813
## min 0.00000 62.57000 65.129997 59.599998 62.810001 62.810001 871256
## 25% 13.25000 67.08000 68.960001 64.727499 66.862501 66.862501 2103950
## 50% 26.50000 71.13000 73.765003 67.715000 71.699997 71.699997 2591000
## 75% 39.75000 77.75250 80.452499 75.057503 77.667499 77.667499 3468175
## max 53.00000 86.76000 88.870003 83.589996 87.000000 87.000000 10948700