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

Groupby Data and Retrieve Rows with Maximum Count Values

To determine the rows with maximum count values within groups, groupby operations can be employed on a pandas DataFrame.

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

Example 1:

Our goal is to identify rows within each group (defined by ['Sp', 'Mt']) that have the highest count value. In Example 1, we have the following 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

To achieve this, we can first obtain the maximum count value for each group:

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

Which produces:

Sp	Mt
MM1	S1	3
MM1	S3	5
MM2	S3	8
MM2	S4	10
MM4	S2	7

Then, to extract the indices of the original DataFrame where the count value equals the maximum value within each group:

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

Which yields the desired output.

Example 2:

In Example 2, the expected output is as follows:

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

In this scenario, there are multiple rows within the MM4 group with the maximum count value (8). Using the same approach as before, we obtain both rows:

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

Update:

If the goal is to add a new column to the DataFrame indicating the maximum count value within each group, we can use the transform method to calculate the value for each row:

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

This will result in a DataFrame that includes the count_max column, which contains the maximum count value for each group:

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

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