GroupBy

Published Jun 11, 2022

The GroupBy object is returned by calls to .groupby() on a Series or DataFrame. The .groupby() function reassembles the data into distinct groups, often for aggregation.

Example

The following example produces a GroupBy object from a DataFrame and uses it to produce some aggregate results.

import pandas as pd

df = pd.DataFrame({'Key' : ['A', 'A', 'A', 'B', 'B', 'C'],
                   'Value' : [15., 23., 17., 5., 8., 12.]})
print(df, end='\n\n')

group = df.groupby(['Key'], as_index=False)

print(group.count(), end='\n\n')

print(group.sum(), end='\n\n')

print(group.mean())

This produces the following output:

  Key  Value
0   A   15.0
1   A   23.0
2   A   17.0
3   B    5.0
4   B    8.0
5   C   12.0

  Key  Value
0   A      3
1   B      2
2   C      1

  Key  Value
0   A   55.0
1   B   13.0
2   C   12.0

  Key      Value
0   A  18.333333
1   B   6.500000
2   C  12.000000

Selected methods of the GroupBy object are listed below:

GroupBy

.count(): Produces a new Series or DataFrame with counts of the values for each group in a GroupBy object.
.max(): Produces a new Series or DataFrame with maximum values for the groups in a GroupBy object.
.mean(): Produces a new Series or DataFrame with aggregate mean values for the groups in a GroupBy object.
.min(): Produces a new Series or DataFrame with minimum values for the groups in a GroupBy object.
.sum(): Produces a new Series or DataFrame with aggregate sums for the groups in a GroupBy object.