How to Get Rows with the Maximum Count in Grouped Pandas DataFrames?

Get Rows with Max Count in Grouped Pandas DataFrames

Problem:

Retrieve all rows from a pandas DataFrame that have the maximum value for the "count" column, grouped by the "Sp" and "Mt" columns.

Examples:

Example 1:

Input DataFrame:

Sp	Mt	Value	count
MM1	S1	a	3
MM1	S1	n	2
MM1	S3	cb	5
MM2	S3	mk	8
MM2	S4	bg	10
MM2	S4	dgd	1
MM4	S2	rd	2
MM4	S2	cb	2
MM4	S2	uyi	7

Expected Output:

Sp	Mt	Value	count
MM1	S1	a	3
MM1	S3	cb	5
MM2	S3	mk	8
MM2	S4	bg	10
MM4	S2	uyi	7

Example 2:

Input DataFrame:

Sp	Mt	Value	count
MM2	S4	bg	10
MM2	S4	dgd	1
MM4	S2	rd	2
MM4	S2	cb	8
MM4	S2	uyi	8

Expected Output:

Sp	Mt	Value	count
MM2	S4	bg	10
MM4	S2	cb	8
MM4	S2	uyi	8

Solution:

1. Get the Max Count per Group:

import pandas as pd

df.groupby(['Sp', 'Mt'])['count'].max()

This returns a Series with the maximum count for each unique combination of "Sp" and "Mt".

2. Create a Boolean Mask:

idx = df.groupby(['Sp', 'Mt'])['count'].transform(max) == df['count']

This creates a Boolean mask where True indicates rows with the maximum count within their group.

3. Filter the DataFrame:

df[idx]

This filters the DataFrame to include only rows where the idx mask is True, resulting in the desired output.

Note: Multiple rows with equal maximum count will be returned if they occur in the same group.

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