How Can I Perform Multiple Aggregations on the Same Column Using Pandas GroupBy.agg()?

Multiple Aggregations on the Same Column with Pandas GroupBy.agg()

In pandas, GroupBy.agg() allows for convenient aggregation of data by applying a function to each column. However, it becomes necessary to call agg() multiple times when applying different functions to the same column.

Traditional (Incorrect) Approach:

The intuitively straightforward approach would be:

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

Unfortunately, this results in an error due to duplicate keys.

Solution:

Since agg() expects a dictionary, the straightforward solution is to create a dictionary with the column name and a list of functions:

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

This will result in a multi-index DataFrame with the output of both aggregations.

Example:

Consider the following 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)
})

To apply both mean and sum to the "returns" column:

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

This will produce:

           returns          
           mean       sum
dummy                    
1      0.036901  0.369012

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