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

Pandas: Efficiently Obtaining Topmost Records Within Groups

When working with Pandas DataFrames, it is frequently necessary to extract the leading records from each group. A common approach is to utilize the 'groupby' and 'apply' functions to enumerate records within each group.

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

However, there exists a more streamlined approach:

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

This method directly fetches the topmost records without the need for intermediate calculations. Additionally, the generated DataFrame maintains its original index.

To flatten the resulting MultiIndex, use:

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

This will produce the following DataFrame:

id	value
1	1
1	2
2	1
2	2
3	1
4	1

Alternatively, you can use SQL's "row_number()" window function to efficiently enumerate records within groups. This feature, however, is currently unavailable in Pandas.

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