How to count number of products previously used in dataframe using groupby?
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I have a dataframe where i am given with a user Id, products and product category.
i want to make a feature for dataframe where it tells how many times that user has previously consumed this product.
user 6 has taken product 8, two times previously in last row.
user_id products last_used
1 8 0
2 2 0
4 2 0
6 8 0
6 2 0
1 8 1
2 2 1
6 8 1
6 8 2
I want this last used column in my dataframe.
Thanks in advance.
python pandas dataframe
asked Nov 17 '18 at 5:03
kanav anandkanav anand
134110
add a comment |
I have a dataframe where i am given with a user Id, products and product category.
i want to make a feature for dataframe where it tells how many times that user has previously consumed this product.
user 6 has taken product 8, two times previously in last row.
user_id products last_used
1 8 0
2 2 0
4 2 0
6 8 0
6 2 0
1 8 1
2 2 1
6 8 1
6 8 2
I want this last used column in my dataframe.
Thanks in advance.
python pandas dataframe
asked Nov 17 '18 at 5:03
kanav anandkanav anand
134110
add a comment |
I have a dataframe where i am given with a user Id, products and product category.
i want to make a feature for dataframe where it tells how many times that user has previously consumed this product.
user 6 has taken product 8, two times previously in last row.
user_id products last_used
1 8 0
2 2 0
4 2 0
6 8 0
6 2 0
1 8 1
2 2 1
6 8 1
6 8 2
I want this last used column in my dataframe.
Thanks in advance.
python pandas dataframe
asked Nov 17 '18 at 5:03
kanav anandkanav anand
134110
I have a dataframe where i am given with a user Id, products and product category.
i want to make a feature for dataframe where it tells how many times that user has previously consumed this product.
user 6 has taken product 8, two times previously in last row.
user_id products last_used
1 8 0
2 2 0
4 2 0
6 8 0
6 2 0
1 8 1
2 2 1
6 8 1
6 8 2
I want this last used column in my dataframe.
Thanks in advance.
python pandas dataframe
python pandas dataframe
asked Nov 17 '18 at 5:03
kanav anandkanav anand
134110
asked Nov 17 '18 at 5:03
kanav anandkanav anand
134110
asked Nov 17 '18 at 5:03
kanav anandkanav anand
134110
asked Nov 17 '18 at 5:03
kanav anandkanav anand
134110
asked Nov 17 '18 at 5:03
kanav anandkanav anand
134110
134110
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Use groupby and cumcount:
df['last_used'] = df.groupby(['user_id','products'])['products'].cumcount()
print(df)
user_id products last_used
0 1 8 0
1 2 2 0
2 4 2 0
3 6 8 0
4 6 2 0
5 1 8 1
6 2 2 1
7 6 8 1
8 6 8 2
answered Nov 17 '18 at 5:09
Sandeep KadapaSandeep Kadapa
7,433831
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Use groupby and cumcount:
df['last_used'] = df.groupby(['user_id','products'])['products'].cumcount()
print(df)
user_id products last_used
0 1 8 0
1 2 2 0
2 4 2 0
3 6 8 0
4 6 2 0
5 1 8 1
6 2 2 1
7 6 8 1
8 6 8 2
answered Nov 17 '18 at 5:09
Sandeep KadapaSandeep Kadapa
7,433831
add a comment |
Use groupby and cumcount:
df['last_used'] = df.groupby(['user_id','products'])['products'].cumcount()
print(df)
user_id products last_used
0 1 8 0
1 2 2 0
2 4 2 0
3 6 8 0
4 6 2 0
5 1 8 1
6 2 2 1
7 6 8 1
8 6 8 2
answered Nov 17 '18 at 5:09
Sandeep KadapaSandeep Kadapa
7,433831
add a comment |
Use groupby and cumcount:
df['last_used'] = df.groupby(['user_id','products'])['products'].cumcount()
print(df)
user_id products last_used
0 1 8 0
1 2 2 0
2 4 2 0
3 6 8 0
4 6 2 0
5 1 8 1
6 2 2 1
7 6 8 1
8 6 8 2
answered Nov 17 '18 at 5:09
Sandeep KadapaSandeep Kadapa
7,433831
Use groupby and cumcount:
df['last_used'] = df.groupby(['user_id','products'])['products'].cumcount()
print(df)
user_id products last_used
0 1 8 0
1 2 2 0
2 4 2 0
3 6 8 0
4 6 2 0
5 1 8 1
6 2 2 1
7 6 8 1
8 6 8 2
answered Nov 17 '18 at 5:09
Sandeep KadapaSandeep Kadapa
7,433831
answered Nov 17 '18 at 5:09
Sandeep KadapaSandeep Kadapa
7,433831
answered Nov 17 '18 at 5:09
Sandeep KadapaSandeep Kadapa
7,433831
answered Nov 17 '18 at 5:09
Sandeep KadapaSandeep Kadapa
7,433831
7,433831
add a comment |
add a comment |
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