CODEWITHSUNDEEP
pythonpandas
DisneylandSC
DisneylandSC

Reputation: 966

Collapsing headers after groupby with different aggregations per column

I am using a groupby with different aggregations per column like so

df = pd.DataFrame({'A': [1, 1, 2, 2],
                   'B': [1, 2, 3, 4],
                   'C': np.random.randn(4)})

df

   A  B         C
0  1  1  0.362838
1  1  2  0.227877
2  2  3  1.267767
3  2  4 -0.562860

then

df.groupby('A').agg({'B': ['min', 'max'], 'C': 'sum'})

gives the output

       B         C
  | min max |   sum
A |         |
1 |  1   2  | 0.590716
2 |  3   4  | 0.704907

I can now access then minimum of column B via

df['B']['min']

What I would like to do is simplify the headers to be

  | B |   C
1 | 1 | 0.590716
2 | 3 | 0.704907

where B is set to df['B']['min'] so I can call my columns again using df['B'] and df['C']

Upvotes: 1

Views: 122

Answers (1)

Matus Hmelar
Matus Hmelar

Reputation: 448

Improved answer with help of DisneylandSC

Multiindex column can be first removed by using .drop() and then .droplevel() function.

df.drop(columns=['B','max'],level=1).droplevel(level=1,axis=1)

Old answer:

To remove multiindex column from your final dataset you can use .drop() and specify level.

df.drop(columns=['B','max'],level=1).T.droplevel(1).T

Upvotes: 1

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