Numpy matrix to Pandas DataFrame - pandas

Question

I have a numpy user-item matrix, each row corresponds to an user and each columns corresponds to an item.
I want to convert the matrix in a pandas DataFrame like the one as follows:
user item rating
0 1 1907 4.0
1 1 1028 5.0
2 1 608 4.0
3 1 2692 4.0
4 1 1193 5.0
I use the following code to generate a DataFrame:
predictions = pd.DataFrame(data=pred)
predictions = predictions.stack().reset_index(name='rating')
predictions.columns = ['user', 'item', 'rating']
and I obtain a df like this:
user item rating
0 0 0 5.000000
1 0 1 0.000000
2 0 2 0.000000
3 0 3 0.000000
Is there a way in pandas to map each values in user and items columns to a value stored in list? User with value 0 should be mapped with the 1st value in user list, user with value 5 with the 6th element in user list and so on...
I'm trying using:
predictions[["user"]].apply(lambda value: users[value])
but I got an IndexError I don't understand because my users list is of size 96
IndexError: ('index 96 is out of bounds for axis 1 with size 96', 'occurred at index user')

Answer 1

my fault was in this code:
while not session.should_stop():
predictions = session.run(decoder_op)
pred = np.vstack((pred, predictions))
just replaced with:
np.vstack((pred, predictions))
and it works like a charm with:
predictions['user'] = predictions['user'].map(lambda value: users[value])
predictions['item'] = predictions['item'].map(lambda value: items[value])