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?

Answer 1

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 

Answer 2

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