Multiple aggregations of the same column using pandas GroupBy.agg()

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Is there a pandas built-in way to apply two different aggregating functions f1, f2 to the same column df["returns"], without having to call agg() multiple times?

Example dataframe:

import pandas as pd
import datetime as dt
import numpy as np

pd.np.random.seed(0)
df = pd.DataFrame({
         "date"    :  [dt.date(2012, x, 1) for x in range(1, 11)], 
         "returns" :  0.05 * np.random.randn(10), 
         "dummy"   :  np.repeat(1, 10)
})

The syntactically wrong, but intuitively right, way to do it would be:

# Assume `f1` and `f2` are defined for aggregating.
df.groupby("dummy").agg({"returns": f1, "returns": f2})

Obviously, Python doesn’t allow duplicate keys. Is there any other manner for expressing the input to agg()? Perhaps a list of tuples [(column, function)] would work better, to allow multiple functions applied to the same column? But agg() seems like it only accepts a dictionary.

Is there a workaround for this besides defining an auxiliary function that just applies both of the functions inside of it? (How would this work with aggregation anyway?)

3 Answers
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