Groupby sum and count on multiple columns in pytho

Question

I have a pandas dataframe that looks like this

ID     country   month   revenue  profit   ebit
234    USA       201409   10        5       3
344    USA       201409    9        7       2
532    UK        201410    20       10      5
129    Canada    201411    15       10      5

I want to group by ID, country, month and count the IDs per month and country and sum the revenue, profit, ebit. The output for the above data would be:

 country   month    revenue   profit  ebit   count
   USA     201409     19        12      5      2
   UK      201409     20        10      5      1
   Canada  201411     15        10      5      1

I have tried different variations of groupby, sum and count functions of pandas but I am unable to figure out how to apply groupby sum and count all together to give the result as shown. Please share any ideas that you might have. Thanks!

Answer 1

It can be done using pivot_table this way:

>>> df1=pd.pivot_table(df, index=['country','month'],values=['revenue','profit','ebit'],aggfunc=np.sum)
>>> df1 
                ebit  profit  revenue
country month                        
Canada  201411     5      10       15
UK      201410     5      10       20
USA     201409     5      12       19

>>> df2=pd.pivot_table(df, index=['country','month'], values='ID',aggfunc=len).rename('count')
>>> df2

country  month 
Canada   201411    1
UK       201410    1
USA      201409    2

>>> pd.concat([df1,df2],axis=1)

                ebit  profit  revenue  count
country month                               
Canada  201411     5      10       15      1
UK      201410     5      10       20      1
USA     201409     5      12       19      2

Answer 2

You can do the groupby, and then map the counts of each country to a new column.

g = df.groupby(['country', 'month'])['revenue', 'profit', 'ebit'].sum().reset_index()
g['count'] = g['country'].map(df['country'].value_counts())
g

Out[3]:


    country  month   revenue  profit  ebit  count
0   Canada   201411  15       10      5     1
1   UK       201410  20       10      5     1
2   USA      201409  19       12      5     2

Edit

To get the counts per country and month, you can do another groupby, and then join the two DataFrames together.

g = df.groupby(['country', 'month'])['revenue', 'profit', 'ebit'].sum()
j = df.groupby(['country', 'month']).size().to_frame('count')
pd.merge(g, j, left_index=True, right_index=True).reset_index()

Out[6]:

    country  month   revenue  profit  ebit  count
0   Canada   201411  15       10      5     1
1   UK       201410  20       10      5     1
2   UK       201411  10       5       2     1
3   USA      201409  19       12      5     2

I added another record for the UK with a different date - notice how there are now two UK entries in the merged DataFrame, with the appropriate counts.