Data manipulation using Pandas

Pandas is an open source,BSD-licensed library providing high performance,easy to use data structures and data analysis tools for Python.

Agenda

Data Frames
Data Series
Different operations in Pandas

#import pandas

import pandas as pd
import numpy as np

Playing with dataframe

#Playing with dataframe

df=pd.DataFrame(np.arange(0,20).reshape(5,4),index=['Row1','Row2','Row3','Row4','Row5']
               ,columns=['Col1','Col2','Col3','Col4'])

df.head()

##       Col1  Col2  Col3  Col4
## Row1     0     1     2     3
## Row2     4     5     6     7
## Row3     8     9    10    11
## Row4    12    13    14    15
## Row5    16    17    18    19

#Accessing elements
#iloc #loc
df.loc['Row1']

## Col1    0
## Col2    1
## Col3    2
## Col4    3
## Name: Row1, dtype: int32

#store file in excel file
df.to_csv('Test.csv')

type(df.loc['Row1'])     #data series
#series is either be only one row or one column
#dataframe is the combination of multiple rows and columns

## <class 'pandas.core.series.Series'>

df.iloc[:,:]

##       Col1  Col2  Col3  Col4
## Row1     0     1     2     3
## Row2     4     5     6     7
## Row3     8     9    10    11
## Row4    12    13    14    15
## Row5    16    17    18    19

df.iloc[0:2,0:1]  #col1 is present

##       Col1
## Row1     0
## Row2     4

type(df.iloc[0:2,0:1])   #DataFrame

## <class 'pandas.core.frame.DataFrame'>

df.iloc[0:2,0]

## Row1    0
## Row2    4
## Name: Col1, dtype: int32

type(df.iloc[0:2,0])

## <class 'pandas.core.series.Series'>

# Take elemnents from column 2
df.iloc[:,1:]

##       Col2  Col3  Col4
## Row1     1     2     3
## Row2     5     6     7
## Row3     9    10    11
## Row4    13    14    15
## Row5    17    18    19

#convert dataframes into array
df.iloc[:,1:].values

## array([[ 1,  2,  3],
##        [ 5,  6,  7],
##        [ 9, 10, 11],
##        [13, 14, 15],
##        [17, 18, 19]])

#check null values
df.isnull().sum()

## Col1    0
## Col2    0
## Col3    0
## Col4    0
## dtype: int64

df['Col1'].value_counts()

## 12    1
## 4     1
## 16    1
## 8     1
## 0     1
## Name: Col1, dtype: int64

df['Col1'].unique()

## array([ 0,  4,  8, 12, 16])

df['Col3']

## Row1     2
## Row2     6
## Row3    10
## Row4    14
## Row5    18
## Name: Col3, dtype: int32

df[['Col3','Col2']]

##       Col3  Col2
## Row1     2     1
## Row2     6     5
## Row3    10     9
## Row4    14    13
## Row5    18    17

df=pd.read_csv("B:\R_Softanbees\DATASETS\mercedes.csv")

df.head()

##    ID  X0 X1  X2 X3 X4 X5 X6  ... X377  X378  X379  X380  X382  X383  X384  X385
## 0   1  az  v   n  f  d  t  a  ...    0     1     0     0     0     0     0     0
## 1   2   t  b  ai  a  d  b  g  ...    1     0     0     0     0     0     0     0
## 2   3  az  v  as  f  d  a  j  ...    0     1     0     0     0     0     0     0
## 3   4  az  l   n  f  d  z  l  ...    0     1     0     0     0     0     0     0
## 4   5   w  s  as  c  d  y  i  ...    0     0     0     0     0     0     0     0
## 
## [5 rows x 377 columns]

df.info()

## <class 'pandas.core.frame.DataFrame'>
## RangeIndex: 4209 entries, 0 to 4208
## Columns: 377 entries, ID to X385
## dtypes: int64(369), object(8)
## memory usage: 12.1+ MB

df.describe()   #summary

##                 ID          X10  ...         X384         X385
## count  4209.000000  4209.000000  ...  4209.000000  4209.000000
## mean   4211.039202     0.019007  ...     0.000713     0.001663
## std    2423.078926     0.136565  ...     0.026691     0.040752
## min       1.000000     0.000000  ...     0.000000     0.000000
## 25%    2115.000000     0.000000  ...     0.000000     0.000000
## 50%    4202.000000     0.000000  ...     0.000000     0.000000
## 75%    6310.000000     0.000000  ...     0.000000     0.000000
## max    8416.000000     1.000000  ...     1.000000     1.000000
## 
## [8 rows x 369 columns]

test1=pd.read_csv("test1.csv",sep=";")
test1.head()

##    row1  1   2   3   4
## 0  row2  5   6   7   8
## 1  row3  9  10  11  12

find no. of occurance of each categories in a column,like table() in R

df["X0"].value_counts()

## ak    432
## y     348
## z     335
## x     302
## ay    299
## t     293
## o     246
## f     213
## w     198
## j     171
## n     167
## aj    162
## az    161
## s     116
## ap    108
## al     88
## h      64
## d      61
## e      48
## v      40
## ai     38
## af     34
## m      34
## am     28
## i      25
## at     21
## u      20
## ba     19
## a      18
## b      13
## ad     12
## k      12
## aw     11
## aq     11
## r      10
## ax      8
## bc      6
## l       6
## c       6
## as      6
## ao      5
## au      5
## g       3
## ae      1
## av      1
## an      1
## p       1
## ag      1
## bb      1
## Name: X0, dtype: int64

df[df['ID']>100]  #display the rows where ID>100

##         ID  X0  X1  X2 X3 X4  X5  ... X378 X379  X380  X382  X383  X384  X385
## 60     101  ay   i  as  c  d   j  ...    0    0     0     0     0     0     0
## 61     103   x   b   m  c  d   j  ...    0    0     0     0     0     0     0
## 62     104   n   l  ak  f  d   j  ...    0    0     0     0     0     0     0
## 63     105   j  aa  as  c  d   j  ...    0    0     0     0     0     0     0
## 64     110   z   b   r  c  d   j  ...    0    0     0     0     0     0     0
## ...    ...  ..  ..  .. .. ..  ..  ...  ...  ...   ...   ...   ...   ...   ...
## 4204  8410  aj   h  as  f  d  aa  ...    0    0     0     0     0     0     0
## 4205  8411   t  aa  ai  d  d  aa  ...    0    0     0     0     0     0     0
## 4206  8413   y   v  as  f  d  aa  ...    0    0     0     0     0     0     0
## 4207  8414  ak   v  as  a  d  aa  ...    0    0     0     0     0     0     0
## 4208  8416   t  aa  ai  c  d  aa  ...    0    0     0     0     0     0     0
## 
## [4149 rows x 377 columns]

df.corr()

##             ID       X10       X11  ...      X383      X384      X385
## ID    1.000000 -0.016166 -0.020253  ... -0.001625  0.013948  0.026357
## X10  -0.016166  1.000000 -0.002146  ... -0.003035 -0.003717 -0.005681
## X11  -0.020253 -0.002146  1.000000  ... -0.000336 -0.000412 -0.000629
## X12   0.043162 -0.039453 -0.004369  ... -0.006180 -0.007570 -0.011569
## X13   0.017910 -0.035496 -0.003931  ... -0.005560 -0.006811 -0.010408
## ...        ...       ...       ...  ...       ...       ...       ...
## X380  0.012520 -0.012561 -0.001391  ... -0.001968 -0.002410 -0.003683
## X382 -0.021581 -0.013108 -0.001452  ... -0.002053 -0.002515 -0.003844
## X383 -0.001625 -0.003035 -0.000336  ...  1.000000 -0.000582 -0.000890
## X384  0.013948 -0.003717 -0.000412  ... -0.000582  1.000000 -0.001090
## X385  0.026357 -0.005681 -0.000629  ... -0.000890 -0.001090  1.000000
## 
## [369 rows x 369 columns]

CSV

from io import StringIO,BytesIO

data=('col1,col2,col3\n'
     'x,y,1\n'
     'a,b,2\n'
     'c,d,3')

type(data)

## <class 'str'>

StringIO()  #Text I/O implementation using an in-memory buffer.

## <_io.StringIO object at 0x000000002BAE4AF8>

pd.read_csv(StringIO(data))

##   col1 col2  col3
## 0    x    y     1
## 1    a    b     2
## 2    c    d     3

df=pd.read_csv(StringIO(data),usecols=['col1','col3'])
# only pick col1 and col3

df

##   col1  col3
## 0    x     1
## 1    a     2
## 2    c     3

df.to_csv("Testcsv.csv")

# Specifying columns data types

data=('a,b,c,d\n'
     '1,2,3,4\n'
     '5,6,7,8\n'
     '9,10,11')

print(data)

## a,b,c,d
## 1,2,3,4
## 5,6,7,8
## 9,10,11

df=pd.read_csv(StringIO(data),dtype=object) 
#all the values converted into string

df

##    a   b   c    d
## 0  1   2   3    4
## 1  5   6   7    8
## 2  9  10  11  NaN

df['a']

## 0    1
## 1    5
## 2    9
## Name: a, dtype: object

df['a'][1]

## '5'

df=pd.read_csv(StringIO(data),dtype={'b':int,'c':float,'a':"Int64"})

df

##    a   b     c    d
## 0  1   2   3.0  4.0
## 1  5   6   7.0  8.0
## 2  9  10  11.0  NaN

data=('index,a,b,c\n'
     '4,apple,bat,5.7\n'
     '8,orange,cow,10')

pd.read_csv(StringIO(data))

##    index       a    b     c
## 0      4   apple  bat   5.7
## 1      8  orange  cow  10.0

pd.read_csv(StringIO(data),index_col=0)

##             a    b     c
## index                   
## 4       apple  bat   5.7
## 8      orange  cow  10.0

data=('a,b,c\n'
     '4,apple,bat,\n'
     '8,orange,cow,')

pd.read_csv(StringIO(data))

##         a    b   c
## 4   apple  bat NaN
## 8  orange  cow NaN

pd.read_csv(StringIO(data),index_col=False)

##    a       b    c
## 0  4   apple  bat
## 1  8  orange  cow

# Combine usecols and indexcols
pd.read_csv(StringIO(data),usecols=['b','c'],index_col=False)

##         b    c
## 0   apple  bat
## 1  orange  cow

#Quoting and escape characters.useful in NLP

data='a,b\n"hello,\\"Bob\\",nice to see you",5'

pd.read_csv(StringIO(data),escapechar="\\")

##                              a  b
## 0  hello,"Bob",nice to see you  5

# URL to csv

df=pd.read_csv("https://download.bls.gov/pub/time.series/cu/cu.item",sep="\t")

df.head()

##   item_code  ... sort_sequence
## 0       AA0  ...             2
## 1      AA0R  ...           399
## 2       SA0  ...             1
## 3      SA0E  ...           374
## 4     SA0L1  ...           358
## 
## [5 rows x 5 columns]

Read JSON to CSV

data='{"employee_name":"James","email":"james@gmail.com","job_profile":[{"title1":"team lead","title2":"sr.developer"}]}'
df1=pd.read_json(data)

df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', header=None)

df.head()

##    0      1     2     3     4    5   ...    8     9     10    11    12    13
## 0   1  14.23  1.71  2.43  15.6  127  ...  0.28  2.29  5.64  1.04  3.92  1065
## 1   1  13.20  1.78  2.14  11.2  100  ...  0.26  1.28  4.38  1.05  3.40  1050
## 2   1  13.16  2.36  2.67  18.6  101  ...  0.30  2.81  5.68  1.03  3.17  1185
## 3   1  14.37  1.95  2.50  16.8  113  ...  0.24  2.18  7.80  0.86  3.45  1480
## 4   1  13.24  2.59  2.87  21.0  118  ...  0.39  1.82  4.32  1.04  2.93   735
## 
## [5 rows x 14 columns]

# convert json to csv
df.to_csv("wine.csv")

df1.to_json(orient="index")

## '{"0":{"employee_name":"James","email":"james@gmail.com","job_profile":{"title1":"team lead","title2":"sr.developer"}}}'

df.to_json(orient="records")

## 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Reading HTML content

url = 'https://www.fdic.gov/bank/individual/failed/banklist.html'

dfs = pd.read_html(url)  # read table from HTML page

dfs[0]

##                              Bank Name  ...       Closing Date
## 0                 The First State Bank  ...      April 3, 2020
## 1                   Ericson State Bank  ...  February 14, 2020
## 2     City National Bank of New Jersey  ...   November 1, 2019
## 3                        Resolute Bank  ...   October 25, 2019
## 4                Louisa Community Bank  ...   October 25, 2019
## ..                                 ...  ...                ...
## 556                 Superior Bank, FSB  ...      July 27, 2001
## 557                Malta National Bank  ...        May 3, 2001
## 558    First Alliance Bank & Trust Co.  ...   February 2, 2001
## 559  National State Bank of Metropolis  ...  December 14, 2000
## 560                   Bank of Honolulu  ...   October 13, 2000
## 
## [561 rows x 6 columns]

url_mcc = 'https://en.wikipedia.org/wiki/Mobile_country_code'
dfs = pd.read_html(url_mcc, match='Country', header=0)

dfs[0]

##      Mobile country code  ...                   Remarks
## 0                    289  ...  MCC is not listed by ITU
## 1                    412  ...                       NaN
## 2                    276  ...                       NaN
## 3                    603  ...                       NaN
## 4                    544  ...                       NaN
## ..                   ...  ...                       ...
## 247                  452  ...                       NaN
## 248                  543  ...                       NaN
## 249                  421  ...                       NaN
## 250                  645  ...                       NaN
## 251                  648  ...                       NaN
## 
## [252 rows x 6 columns]

Reading Excel Files

df_excel=pd.read_excel("graph.xlsx")

df_excel.head()

##        Age Percentage cured Unnamed: 2 Unnamed: 3
## 0      NaN                A          B          C
## 1  infants          0.99852   0.859971   0.584281
## 2       10         0.836235   0.530928   0.830369
## 3       11         0.500711   0.840163   0.405765
## 4       12         0.224256  0.0981093   0.899363

Pickling

All pandas objects are equipped with to_pickle methods which use Python’s cPickle module to save data structures to disk using the pickle format

df_excel.to_pickle('df_excel')

df=pd.read_pickle('df_excel')

df.head()

##        Age Percentage cured Unnamed: 2 Unnamed: 3
## 0      NaN                A          B          C
## 1  infants          0.99852   0.859971   0.584281
## 2       10         0.836235   0.530928   0.830369
## 3       11         0.500711   0.840163   0.405765
## 4       12         0.224256  0.0981093   0.899363

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