Python pandas groupby aggregate on multiple columns, then pivot

Question

In Python, I have a pandas DataFrame similar to the following:

Item | shop1 | shop2 | shop3 | Category
------------------------------------
Shoes| 45    | 50    | 53    | Clothes
TV   | 200   | 300   | 250   | Technology
Book | 20    | 17    | 21    | Books
phone| 300   | 350   | 400   | Technology

Where shop1, shop2 and shop3 are the costs of every item in different shops.
Now, I need to return a DataFrame, after some data cleaning, like this one:

Category (index)| size| sum| mean | std
----------------------------------------

where size is the number of items in each Category and sum, mean and std are related to the same functions applied to the 3 shops. How can I do these operations with the split-apply-combine pattern (groupby, aggregate, apply,…) ?

Can someone help me out? I’m going crazy with this one…thank you!

Asked By: Davide Tamburrino

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Source

Answer 1

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']})

Answered By: Scott Boston

Answer 2

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        std
Category                                     
Books          3    58   19.333333   2.081666
Clothes        3   148   49.333333   4.041452
Technology     6  1800  300.000000  70.710678

Answered By: foglerit

Answer 3

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        Std
Category                                     
Books          3    58   19.333333   2.081666
Clothes        3   148   49.333333   4.041452
Technology     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  Size
Category                                     
Books        2.081666    58   19.333333     3
Clothes      4.041452   148   49.333333     3
Technology  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%    max
Category                                                                   
Books         3.0   19.333333   2.081666   17.0   18.5   20.0   20.5   21.0
Clothes       3.0   49.333333   4.041452   45.0   47.5   50.0   51.5   53.0
Technology    6.0  300.000000  70.710678  200.0  262.5  300.0  337.5  400.0

Answered By: piRSquared

Python pandas groupby aggregate on multiple columns, then pivot

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