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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?

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()))

 new_name
A B 
X N 5.583333
Y M 2.975000
 N 3.845455

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