Pandas – possible to aggregate two columns using two different aggregations?

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The agg method can accept a dict, in which case the keys indicate the column to which the function is applied:

grouped.agg({'numberA':'sum', 'numberB':'min'})

For example,

import numpy as np
import pandas as pd
df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
                         'foo', 'bar', 'foo', 'foo'],
                   'B': ['one', 'one', 'two', 'three',
                         'two', 'two', 'one', 'three'],
                   'number A': np.arange(8),
                   'number B': np.arange(8) * 2})
grouped = df.groupby('A')

print(grouped.agg({
    'number A': 'sum',
    'number B': 'min'}))

yields

     number B  number A
A                      
bar         2         9
foo         0        19

This also shows that Pandas can handle spaces in column names. I’m not sure what the origin of the problem was, but literal spaces should not have posed a problem. If you wish to investigate this further, 

print(df.columns)

without reassigning the column names, will show show us the repr of the names. Maybe there was a hard-to-see character in the column name that looked like a space (or some other character) but was actually a u'\xa0' (NO-BREAK SPACE), for example.

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