Pandas multi-index truncated frame

Question

I want to crop multi-index pandas dataframe

here is the code to get my test data:

import pandas as pd testdf = { 'Name': { 0: 'H', 1: 'H', 2: 'H', 3: 'H', 4: 'H'}, 'Division': { 0: 'C', 1: 'C', 2: 'C', 3: 'C', 4: 'C'}, 'EmployeeId': { 0: 14, 1: 14, 2: 14, 3: 14, 4: 14}, 'Amt1': { 0: 124.39, 1: 186.78, 2: 127.94, 3: 258.35000000000002, 4: 284.77999999999997}, 'Amt2': { 0: 30.0, 1: 30.0, 2: 30.0, 3: 30.0, 4: 60.0}, 'Employer': { 0: 'Z', 1: 'Z', 2: 'Z', 3: 'Z', 4: 'Z'}, 'PersonId': { 0: 14, 1: 14, 2: 14, 3: 14, 4: 15}, 'Provider': { 0: 'A', 1: 'A', 2: 'A', 3: 'A', 4: 'B'}, 'Year': { 0: 2012, 1: 2012, 2: 2013, 3: 2013, 4: 2012}} testdf = pd.DataFrame(testdf) testdf grouper_keys = [ 'Employer', 'Year', 'Division', 'Name', 'EmployeeId', 'PersonId'] testdf2 = pd.pivot_table(data=testdf, values='Amt1', index=grouper_keys, columns='Provider', fill_value=None, margins=False, dropna=True, aggfunc=('sum', 'count'), ) print(testdf2) 

gives:

Now I can only get sum for A or B using

 testdf2.loc[:, slice(None, ('sum', 'A'))] 

which gives

How can I get both sum and count only for A or B

Answer 1

You can use:

 idx = pd.IndexSlice df = testdf2.loc[:, idx[['sum', 'count'], 'A']] print (df) sum count Provider AA Employer Year Division Name EmployeeId PersonId Z 2012 CH 14 14 311.17 2.0 15 NaN NaN 2013 CH 14 14 386.29 2.0 

Another solution:

 df = testdf2.loc[:, (slice('sum','count'), ['A'])] print (df) sum count Provider AA Employer Year Division Name EmployeeId PersonId Z 2012 CH 14 14 311.17 2.0 15 NaN NaN 2013 CH 14 14 386.29 2.0 

Answer 2

Use xs for cross section

 testdf2.xs('A', axis=1, level=1) 

Or save the column level with drop_level=False

 testdf2.xs('A', axis=1, level=1, drop_level=False)