How Can I Apply Multiple Aggregation Functions to the Same Column Using Pandas GroupBy.agg()?

Multiple Aggregations of the Same Column Using pandas GroupBy.agg()

Problem:

How can you apply multiple aggregating functions (f1, f2) to the same column ("returns") in a pandas DataFrame using GroupBy.agg() without calling agg() multiple times?

Intuition:

It would be convenient to have a syntax like:

df.groupby("dummy").agg({"returns": [f1, f2]})

Solution:

As of 2022-06-20, the following syntax is the accepted practice:

df.groupby('dummy').agg(
    Mean=('returns', np.mean),
    Sum=('returns', np.sum),
)

This syntax uses tuples to specify the (column, function) pairs for aggregation.

Historical Solutions:

In earlier versions of pandas, you could use one of the following solutions:

• List of Functions:

  df.groupby("dummy").agg({"returns": [np.mean, np.sum]})

• Dictionary with Function List:

  df.groupby('dummy').agg({'returns':
                               {'Mean': np.mean, 'Sum': np.sum}})

These earlier solutions are still valid, but the first option using tuples is now considered best practice.

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