How to Efficiently Get the Top N Records within Each Group of a Pandas DataFrame?

Get Topmost n Records within Each Group in DataFrame

To obtain the top n records for each group in a DataFrame, consider utilizing Pandas' efficient methods. Suppose we have the following DataFrame with 'id' and 'value' columns:

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

Using the groupby() and head() functions, we can retrieve the top 2 records for each 'id':

df_top2 = df.groupby('id').head(2)

Output:

       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 flatten the MultiIndex and eliminate duplicate row indices, apply reset_index():

df_top2 = df.groupby('id').head(2).reset_index(drop=True)

Result:

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

Alternatively, if the records need to be ordered before selecting the top n for each group, apply sorting first:

df_sorted = df.sort_values('value', ascending=False)
df_top2 = df_sorted.groupby('id').head(2)

This provides a more efficient and elegant approach to obtain the top records within each group in a DataFrame.

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