How Can I Efficiently Extract the Top N Records from Each Group in a Pandas DataFrame?

Pandas: Efficiently Extract Top Records Within Each Group

Obtaining the top records within each group of a DataFrame is a common task in data manipulation. This article presents multiple approaches to achieve this objective, including a solution inspired by SQL window functions.

Problem Statement:
Given a DataFrame with a grouping column and a value column, we want to extract the top n records for each group.

Naive Approach with Grouping and Row Numbering:
One way to approach this problem is to apply a grouping operation, followed by a window function-like approach. This involves adding a row number to each record within each group and then filtering for the top rows based on that row number.

Practical Solution:
A more efficient solution involves using the head() method on the grouped DataFrame. By default, head() returns the first n records in each group. This aligns well with the objective of obtaining the top records.

df.groupby('id').head(2)

Removing MultiIndex:
To remove the MultiIndex introduced by the grouping operation, we use reset_index(drop=True):

df.groupby('id').head(2).reset_index(drop=True)

Output:

   id  value
0   1      1
1   1      2
2   2      1
3   2      2
4   3      1
5   4      1

Elegant Approach for Row Numbering:
While Python lacks the row_number() function of SQL, we can replicate its functionality using a combination of groupby() and cumcount(). Here's how:

df['row_num'] = df.groupby('id').cumcount() + 1

This approach assigns a unique row number within each group without introducing additional columns or multi-index.

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