How to Find Rows with Maximum Values within Groups in Pandas?

Get Rows with Maximum Values in Groups Using Groupby

When performing data analysis, it often becomes necessary to identify rows that possess the highest value for a specific column within each group defined by other columns. This operation can be conveniently executed using the groupby() and transform() methods of pandas, a widely-used Python library for data manipulation.

Problem Statement

Given a pandas DataFrame with columns such as 'Sp', 'Mt', 'Value', and 'count', we aim to extract rows that have the maximum 'count' value within each group defined by 'Sp' and 'Mt' columns.

Solution

To retrieve the desired rows, we can employ the following steps:

1. Calculate Maximum Count for Each Group:

   • Utilize the groupby() method to group the DataFrame by 'Sp' and 'Mt' columns and then apply the max() function to the 'count' column to determine the maximum count value for each group.

2. Identify Rows with Maximum Count:

   • Utilize the transform() method to return a True/False boolean Series for each row, where 'True' indicates that the row has the maximum count value within its group.
   • Retrieve the original DataFrame rows corresponding to the True values using indexing.

Example 1

Consider the following 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

Applying the aforementioned steps results in the following 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

With a different 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

The output becomes:

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

Alternative Approach

An alternative approach involves adding a column to the DataFrame that represents the maximum count for each group. This can be achieved using the following steps:

1. Calculate the maximum count for each group using the df.groupby([‘Sp’, ‘Mt’])[‘count’].max() expression.
2. Add a new column called ‘count_max’ to the DataFrame using the df[‘count_max’] = df.groupby([‘Sp’, ‘Mt’])[‘count’].transform(max) expression.
3. Filter the DataFrame to include only rows where the ‘count’ column equals the ‘count_max’ column.

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