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()).
Advertisement
Answer
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