Pandas Groupy take only the first N Groups
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
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