Aggregate a pandas DataFrame on multiple functions

Setup

# Import libraries
import pandas as pd
import seaborn as sns

# Load sample data in a DataFrame
df = sns.load_dataset('iris')[['species', 'sepal_length']]
df

	species	sepal_length
0	setosa	5.1
1	setosa	4.9
2	setosa	4.7
3	setosa	4.6
4	setosa	5.0
...	...	...
145	virginica	6.7
146	virginica	6.3
147	virginica	6.5
148	virginica	6.2
149	virginica	5.9

150 rows × 2 columns

Group and aggregate

If we want to get the minimum, average and maximum of sepal_length for each species, a classic way would be:

(
    df
    .groupby('species')
    .agg({'sepal_length': ['min', 'mean', 'max']})
)

	sepal_length
	min	mean	max
species
setosa	4.3	5.006	5.8
versicolor	4.9	5.936	7.0
virginica	4.9	6.588	7.9

An alternative style for aggregation allows to aggregate the same column on multiple functions, without having to suffer the pain of multi-index columns:

(
    df
    .groupby('species')
    .agg(
        sepal_length_min=('sepal_length', 'min'),
        sepal_length_mean=('sepal_length', 'mean'),
        sepal_length_max=('sepal_length', 'max'),
    )
)

	sepal_length_min	sepal_length_mean	sepal_length_max
species
setosa	4.3	5.006	5.8
versicolor	4.9	5.936	7.0
virginica	4.9	6.588	7.9