Pandas get topmost n records within each group

Smart sorting and grouping

When your dataset is massive, it's critical to evade sorting the entire DataFrame initially. To achieve this, groupby and nlargest provide a smart way out. You can directly get the top n records without a total sort:

# Like finding the needle in the haystack without dismantling the whole stack!
df.groupby('groupby_col').apply(lambda x: x.nlargest(n, 'sort_col')).reset_index(level=1, drop=True)


The code above can significantly boost performance especially with large datasets. It collates the top n records from each group, leveraging inbuilt optimizations of Pandas.

Catering to diverse scenarios

Different scenes require distinct approaches to group and extract records in pandas:

Ranks and indices

Sometimes, you want to rank values within each group and then filter out the top n. This is possible with rank() and boolean indexing:

# Similar to picking the top students based on rank.
df['rank'] = df.groupby('groupby_col')['sort_col'].rank(method='min', ascending=False)
top_n_by_rank = df[df['rank'] <= n]


Extricating specific positions

Often, positions like first, second, or third places within groups are vital. The groupby().nth() has got your back:

df_sorted = df.sort_values(['groupby_col', 'sort_col'], ascending=[True, False])
# Ready for some gold, silver, and bronze?
top_n_specific = df_sorted.groupby('groupby_col').nth([0, 1])  # For top 2


For nth() to work correctly, remember to sort your data by both the grouping and sorting columns.

Optimization using query()

For larger datasets, consider combining query() with groupby for a more adroit approach:

# Because who doesn't like some query magic?
top_n_query = df.query('rank() <= @n', engine='numexpr')


This strategy utilises the swiftness of numexpr and avoids creating extra DataFrame columns.

Perfecting the final output

Particular scenarios may call for refining the final output. Here are some trimmings:

Discarding full sorts

To bypass full sorting when optimal time efficiency is substantial, use:

# Because time is gold. Agreed?
df.groupby('groupby_col').apply(lambda x: x.nlargest(n, 'sort_col'))


When only the group's order is of interest, this approach can be a time-saver.

Bespoke grouping functions

For more complex groupings, ditch lambda functions:

def top_n_items(sub_df, n=2):
    # King Kong ain't got nothing on these top items!
    return sub_df.nlargest(n, 'sort_col')

df.groupby('groupby_col').apply(top_n_items)


This method offers a reusable and readable custom grouping logic.

Remembering efficiency

Although nlargest is nimble, for small n and large groups, a sort_values followed by head(n) for each group can sometimes be more efficient:

# Let's fly smart, not hard!
df.sort_values(['groupby_col', 'sort_col'], ascending=[True, False]).groupby('groupby_col').head(n)


By understanding the size and construction of your data, you can pick from the vast array of tools pandas equips you with to achieve peak efficiency.