How to Find Rows with the Maximum Count in Pandas GroupBy Operations?

Get Rows with Maximum Count in Pandas Groups

Problem:

How to identify rows with the highest value for the 'count' column in a pandas DataFrame when grouping by multiple columns?

Solution:

Step 1: Find Maximum Count for Each Group

To determine the maximum count for each group, use the groupby() and max() functions:

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

This will create a Series containing the maximum count for each group.

Step 2: Identify Rows with Maximum Count

To get the indices of the rows with maximum count in the original DataFrame, use the transform() method:

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

This will create a boolean Series where True indicates rows with maximum count.

Step 3: Filter Rows Based on Maximum Count

Finally, filter the DataFrame using the boolean indexing to select only the rows with maximum count:

result = df[idx]

This will return a new DataFrame containing only the rows with the highest value for the 'count' column within each group.

Examples:

Example 1:

df = pd.DataFrame({
    'Sp': ['MM1', 'MM1', 'MM1', 'MM2', 'MM2', 'MM2', 'MM4', 'MM4', 'MM4'],
    'Mt': ['S1', 'S1', 'S3', 'S3', 'S4', 'S4', 'S2', 'S2', 'S2'],
    'Value': ['a', 'n', 'cb', 'mk', 'bg', 'dgd', 'rd', 'cb', 'uyi'],
    'count': [3, 2, 5, 8, 10, 1, 2, 2, 7]
})

Output:

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

Example 2:

df = pd.DataFrame({
    'Sp': ['MM2', 'MM2', 'MM4', 'MM4', 'MM4'],
    'Mt': ['S4', 'S4', 'S2', 'S2', 'S2'],
    'Value': ['bg', 'dgd', 'rd', 'cb', 'uyi'],
    'count': [10, 1, 2, 8, 8]
})

Output:

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

Note: If multiple rows within a group have the maximum count, all of those rows will be returned.

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