Set column name for apply result over groupby

Question

This is a fairly trivial problem, but its triggering my OCD and I haven’t been able to find a suitable solution for the past half hour.

For background, I’m looking to calculate a value (let’s call it F) for each group in a DataFrame derived from different aggregated measures of columns in the existing DataFrame.

Here’s a toy example of what I’m trying to do:

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': ['X', 'Y', 'X', 'Y', 'Y', 'Y', 'Y', 'X', 'Y', 'X'],
                'B': ['N', 'N', 'N', 'M', 'N', 'M', 'M', 'N', 'M', 'N'],
                'C': [69, 83, 28, 25, 11, 31, 14, 37, 14,  0],
                'D': [ 0.3,  0.1,  0.1,  0.8,  0.8,  0. ,  0.8,  0.8,  0.1,  0.8],
                'E': [11, 11, 12, 11, 11, 12, 12, 11, 12, 12]
                })

df_grp = df.groupby(['A','B'])
df_grp.apply(lambda x: x['C'].sum() * x['D'].mean() / x['E'].max())

What I’d like to do is assign a name to the result of apply (or lambda). Is there anyway to do this without moving lambda to a named function or renaming the column after running the last line?

Asked By: MrT

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Source

Answer 1

You could convert your series to a dataframe using reset_index() and provide name='yout_col_name' — The name of the column corresponding to the Series values

(df_grp.apply(lambda x: x['C'].sum() * x['D'].mean() / x['E'].max())
      .reset_index(name='your_col_name'))

   A  B  your_col_name
0  X  N   5.583333
1  Y  M   2.975000
2  Y  N   3.845455

Answered By: Zero

Answer 2

Have the lambda function return a new Series:

df_grp.apply(lambda x: pd.Series({'new_name':
                    x['C'].sum() * x['D'].mean() / x['E'].max()}))
# or df_grp.apply(lambda x: x['C'].sum() * x['D'].mean() / x['E'].max()).to_frame('new_name')

     new_name
A B          
X N  5.583333
Y M  2.975000
  N  3.845455

Answered By: Alexander

Answer 3

The accepted answer seems work for the current version of Pandas, but name is not one of the parameters of reset_index according to the documentation. There is a names argument, but it serves a different purpose IMO.

Since the output of apply is a series, we can simply use pandas.Series.rename() to achive the result.

df = pd.DataFrame({'A': ['X', 'Y', 'X', 'Y', 'Y', 'Y', 'Y', 'X', 'Y', 'X'],
                'B': ['N', 'N', 'N', 'M', 'N', 'M', 'M', 'N', 'M', 'N'],
                'C': [69, 83, 28, 25, 11, 31, 14, 37, 14,  0],
                'D': [ 0.3,  0.1,  0.1,  0.8,  0.8,  0. ,  0.8,  0.8,  0.1,  0.8],
                'E': [11, 11, 12, 11, 11, 12, 12, 11, 12, 12]
                })

df_grp = df.groupby(['A','B'])
df_grp.apply(lambda x: x['C'].sum() * x['D'].mean() / x['E'].max()).rename("your_col_name")

Answered By: Sudip Sinha

Set column name for apply result over groupby

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