How Can I Efficiently Retrieve the Top N Records Within Groups in a Pandas DataFrame?

Pandas: Efficiently Retrieving Top Records Within Groups

Consider the task of retrieving the top two records within each distinct value of a specific column in a pandas DataFrame. As an example, consider the following DataFrame:

df = pd.DataFrame({'id':[1,1,1,2,2,2,2,3,4], 'value':[1,2,3,1,2,3,4,1,1]})

Traditionally, you might approach this problem by numbering records within each group after grouping by the desired column:

dfN = df.groupby('id').apply(lambda x:x['value'].reset_index()).reset_index()

However, a more efficient and elegant approach is to leverage pandas' head function:

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

This directly returns the top two records for each group, without the need for additional column numbering.

       id  value
id             
1  0   1      1
   1   1      2 
2  3   2      1
   4   2      2
3  7   3      1
4  8   4      1

To remove the MultiIndex and flatten the results:

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

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

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