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

Get the Rows with Maximum Value in Groups Using Groupby

Identifying all rows within a pandas DataFrame that exhibit the maximum value in a specific column within grouped data is a common task. This can be efficiently achieved using groupby operations and a series of transformations.

To find the rows with the maximum count within each group defined by the Sp and Mt columns, we follow these steps:

1. Calculate Group Maximum: First, calculate the maximum count for each group using the groupby function. This will return a Series containing the maximum count values indexed by the group keys.
2. Create a Boolean Mask: Create a boolean mask using transform and equality comparison to identify rows where the count equals the group maximum. This mask will have True values for rows with the maximum count.
3. Filter the DataFrame: Use the mask to filter the DataFrame, retaining only the rows with the maximum count.

Example 1:

Consider the following DataFrame:

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

By applying the above steps, we obtain the desired 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:

For another DataFrame:

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

The result will be:

   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 same maximum count, all those rows will be included in the output. If this is undesired, further filtering may be necessary.

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