pandas – create sparse matrix for collaborative filtering
Question:
I have a pandas dataframe like this:
user_id
music_id
rating
A
a
5
B
a
3
and I would like to create a sparse matrix from it, putting music_id as column and user_id as a row like this:
->
a
b
A
5
B
3
what would be the way to achieve it by using pandas or numpy for this task?
Answers:
If we suppose you have the following dataset as describe in your question:
d = {'user_id':['A','B'],'music_id':['a','a'],'rating':[5,3]}
df = pd.DataFrame(d)
Then you can do:
df.set_index(['user_id','music_id']).unstack(level=-1).rating
or, equivalently:
pd.pivot_table(df,values='rating',index='user_id',columns=['music_id'])
I have a pandas dataframe like this:
| user_id | music_id | rating |
|---|---|---|
| A | a | 5 |
| B | a | 3 |
and I would like to create a sparse matrix from it, putting music_id as column and user_id as a row like this:
->
| a | b | |
|---|---|---|
| A | 5 | |
| B | 3 |
what would be the way to achieve it by using pandas or numpy for this task?
If we suppose you have the following dataset as describe in your question:
d = {'user_id':['A','B'],'music_id':['a','a'],'rating':[5,3]}
df = pd.DataFrame(d)
Then you can do:
df.set_index(['user_id','music_id']).unstack(level=-1).rating
or, equivalently:
pd.pivot_table(df,values='rating',index='user_id',columns=['music_id'])