Pandas groupby and transpose related problem

Question

Currently I am working on a pandas data frame. Facing a problem related to group by operation. My data frame is -

Name    A          Job       B
A      Online      Govt      22
B      Offline     Pvt       50
C      Others      Other     33
A      Others      Govt      62
B      Online      Pvt       18
C      Offline     Other     35
A      Offline     Govt      53
B      Online      Pvt       75
C      Others      Other     74

My final output should be -

Name   Offline    Online   Others   Govt   Pvt  Other
A       53        20        62        1      0    0
B       50        18        75        0      1    0
C       35        74        33        0      0    1

I have done the following code -

data = pd.read_csv('/Users../pandas_data/abc1.csv')
df3 = data.groupby(['Name', 'A'])['B']\
   .first()\
   .unstack(fill_value='NA')\
   .rename_axis(None, 1)

Thanks in advance.

Answer 1

You should take a look at pandas' pivot_table , for the second part of the output you can use

pd.pivot_table(
    data=df.drop(columns='A'),
    index=['Name'],
    columns=['Job'],
    aggfunc=lambda x: int(len(x) > 0),
    fill_value=0
)

which would yield

        B          
Job  Govt Other Pvt
Name               
A       1     0   0
B       0     0   1
C       0     1   0

then do something similar for the first part (don't know how to interpret .. ), like

pd.pivot_table(
    data=df.drop(columns='Job'),
    index=['Name'],
    columns=['A'],
    aggfunc= # depends on what you expect
)

and finally concatenate using concat over axis=1 .