Pandas Groupy take only the first N Groups [duplicate]

Answer 1

One method is to use Counter to get the top 3 unique items from the list, filter your DataFrame based on those items, and then perform a groupby operation on this filtered DataFrame.

from collections import Counter

c = Counter(df.item_id)
most_common = [item for item, _ in c.most_common(3)]

>>> df[df.item_id.isin(most_common)].groupby('item_id').sum()
         user_id
item_id         
a              3
b              5
c              1

Answer 2

Here is one way using list(grouped).

result = [g[1] for g in list(grouped)[:3]]

# 1st
result[0]

  item_id  user_id
0       a        1
1       a        2

# 2nd
result[1]

  item_id  user_id
2       b        1
3       b        1
4       b        3