["python","pandas","group-by","greatest-n-per-group","window-functions"]
Pandas get topmost n records within each group

Suppose I have pandas DataFrame like this: df = pd.DataFrame({'id':[1,1,1,2,2,2,2,3,4], 'value':[1,2,3,1,2,3,4,1,1]}) which looks like: id value 0 1 1 1 1 2 2 1 3 3 2 1 4 2 2 5 2 3 6 2 4 7 3 1 8 4 1 I want to get a new DataFrame with top 2 records for each id, like this: id value 0 1 1 1 1 2 3 2 1 4 2 2 7 3 1 8 4 1 I can do it with numbering records within group after groupby: dfN = df.groupby('id').apply(lambda x:x['value'].reset_index()).reset_index() which looks like: id level_1 index value 0 1 0 0 1 1 1 1 1 2 2 1 2 2 3 3 2 0 3 1 4 2 1 4 2 5 2 2 5 3 6 2 3 6 4 7 3 0 7 1 8 4 0 8 1 then for the desired output: dfN[dfN['level_1'] <= 1][['id', 'value']] Output: id value 0 1 1 1 1 2 3 2 1 4 2 2 7 3 1 8 4 1 But is there more effective/elegant approach to do this? And also is there more elegant approach to number records within each group (like SQL window function row_number()).
Did you try df.groupby('id').head(2) Output generated: 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 (Keep in mind that you might need to order/sort before, depending on your data) EDIT: As mentioned by the questioner, use df.groupby('id').head(2).reset_index(drop=True) to remove the MultiIndex and flatten the results: id value 0 1 1 1 1 2 2 2 1 3 2 2 4 3 1 5 4 1