Python pandas groupby aggregate on multiple columns, then pivot

Edited for Pandas 0.22+ considering the deprecation of the use of dictionaries in a group by aggregation.

We set up a very similar dictionary where we use the keys of the dictionary to specify our functions and the dictionary itself to rename the columns.

rnm_cols = dict(size='Size', sum='Sum', mean='Mean', std='Std')df.set_index(['Category', 'Item']).stack().groupby('Category') \  .agg(rnm_cols.keys()).rename(columns=rnm_cols)            Size   Sum        Mean        StdCategory                                     Books          3    58   19.333333   2.081666Clothes        3   148   49.333333   4.041452Technology     6  1800  300.000000  70.710678

option 1
use agg ← link to docs

agg_funcs = dict(Size='size', Sum='sum', Mean='mean', Std='std')df.set_index(['Category', 'Item']).stack().groupby(level=0).agg(agg_funcs)                  Std   Sum        Mean  SizeCategory                                     Books        2.081666    58   19.333333     3Clothes      4.041452   148   49.333333     3Technology  70.710678  1800  300.000000     6

option 2
more for less
use describe ← link to docs

df.set_index(['Category', 'Item']).stack().groupby(level=0).describe().unstack()            count        mean        std    min    25%    50%    75%    maxCategory                                                                   Books         3.0   19.333333   2.081666   17.0   18.5   20.0   20.5   21.0Clothes       3.0   49.333333   4.041452   45.0   47.5   50.0   51.5   53.0Technology    6.0  300.000000  70.710678  200.0  262.5  300.0  337.5  400.0

df.groupby('Category').agg({'Item':'size','shop1':['sum','mean','std'],'shop2':['sum','mean','std'],'shop3':['sum','mean','std']})

Or if you want it across all shops then:

df1 = df.set_index(['Item','Category']).stack().reset_index().rename(columns={'level_2':'Shops',0:'costs'})df1.groupby('Category').agg({'Item':'size','costs':['sum','mean','std']})

If I understand correctly, you want to calculate aggregate metrics for all shops, not for each individually. To do that, you can first stack your dataframe and then group by Category:

stacked = df.set_index(['Item', 'Category']).stack().reset_index()stacked.columns = ['Item', 'Category', 'Shop', 'Price']stacked.groupby('Category').agg({'Price':['count','sum','mean','std']})

Which results in

           Price                                        count   sum        mean        stdCategory                                     Books          3    58   19.333333   2.081666Clothes        3   148   49.333333   4.041452Technology     6  1800  300.000000  70.710678