Pandas dataframe grouping by two columns, count and sum

user_13713369 0
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I have the following df, which I would like to group by 'Name' so there is an 'A' and 'B' count column and a 'total sales' sum column:

eg turn this:

data = {'A or B' : ['A','A','B','B','A','B'],
        'Name' : ['Ben','Ben','Ben','Sam','Sam','Sam'],
        'Sales ($)' : [10,5,2,5,6,7]
       }

df=pd.DataFrame(data, columns = ['A or B','Name','Sales ($)'])

so it looks like this:

grouped_data = {'A' : [2,1],
        'B' : [1,2],
        'Name' : ['Ben','Sam'],
        'Total Sales ($)' : [17,18]
       }

df=pd.DataFrame(grouped_data, columns = ['A','B','Name','Total Sales ($)'])
pandas dataframe python
3 Answers

You can try with pd.get_dummies, join and groupby+sum:

pd.get_dummies(df['A or B'])\
  .join(df.drop('A or B',1))\
  .groupby('Name',as_index=False).sum()

Output:

  Name  A  B  Sales ($)
0  Ben  2  1         17
1  Sam  1  2         18

Details:

First, use get_dummies to get categorical variable into dummy/indicator variables:

pd.get_dummies(df['A or B'])
#   A  B
#0  1  0
#1  1  0
#2  0  1
#3  0  1
#4  1  0
#5  0  1

Then use join, to concat the dummies with original df with 'A or B' column dropped:

pd.get_dummies(df['A or B']).join(df.drop('A or B',1))
#   A  B Name  Sales ($)
#0  1  0  Ben         10
#1  1  0  Ben          5
#2  0  1  Ben          2
#3  0  1  Sam          5
#4  1  0  Sam          6
#5  0  1  Sam          7

And finally, do the groupby+sum based on name:

pd.get_dummies(df['A or B']).join(df.drop('A or B',1)).groupby('Name',as_index=False).sum()
#  Name  A  B  Sales ($)
#0  Ben  2  1         17
#1  Sam  1  2         18
user_136762020

You can work with aggregations inside groupby

df.groupby(['Name']).agg(A = ('A or B', lambda x: (x=='A').sum())
                        ,B = ('A or B', lambda x: (x=='B').sum())
                        ,total = ('Sales ($)', 'sum')).reset_index()
#output
    Name    A   B   total
0   Ben     2   1   17
1   Sam     1   2   18
user_58478540

Step by step solution:

import pandas as pd
data = {'A or B' : ['A','A','B','B','A','B'],
        'Name' : ['Ben','Ben','Ben','Sam','Sam','Sam'],
        'Sales ($)' : [10,5,2,5,6,7]
       }

df=pd.DataFrame(data, columns = ['A or B','Name','Sales ($)'])

#first create dummy for 'A or B' column
y = pd.get_dummies(df['A or B'])

#concatenate with original data frame
df=pd.concat([y,df], axis=1)
#delete the column
del df['A or B']

#now do the group by
df=df.groupby('Name').agg({'A':'sum',
                         'B':'sum', 
                         'Sales ($)': 'sum'})

#reset the index
df.reset_index(level=0, inplace=True)
print(df)

Output:

  Name  A  B  Sales ($)
0  Ben  2  1         17
1  Sam  1  2         18
user_34361260
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