["python","pandas","dataframe","group-by","aggregate"]
Multiple aggregations of the same column using pandas GroupBy.agg()

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?)
As of 2022-06-20, the below is the accepted practice for aggregations: df.groupby('dummy').agg( Mean=('returns', np.mean), Sum=('returns', np.sum)) see this answer for more information. Below the fold included for historical versions of pandas. You can simply pass the functions as a list: In [20]: df.groupby("dummy").agg({"returns": [np.mean, np.sum]}) Out[20]: mean sum dummy 1 0.036901 0.369012 or as a dictionary: In [21]: df.groupby('dummy').agg({'returns': {'Mean': np.mean, 'Sum': np.sum}}) Out[21]: returns Mean Sum dummy 1 0.036901 0.369012