user_user.py

s = """
    users   artist             gender      plays    age
0   a       aesop rock         m           72       28.0
1   b       air                m           178      28.0
2   c       amon tobin         m           106      28.0
3   d       animal collective  m           203      28.0
4   e       annie              m           75       28.0
"""

import io
import pandas as pd
import numpy as np

userArtist = pd.read_fwf(io.StringIO(s), header=1)
userArtist.set_index("Unnamed: 0", inplace=True)

music_mat = userArtist.pivot_table(index='artist', columns='users', values='plays').fillna(0).apply(np.sign)
# user-item collaberative filtering of random user 
random_user = np.random.choice(userArtist.users)
# Check df for values of random user
random_user_rating = music_mat[random_user]
# Pairwise correlation of top five similar users 
user_corr = music_mat.corrwith(random_user_rating)[:5]

rating_c = userArtist[(userArtist.artist.isnull().values) & (userArtist.users != random_user)]
rating_c['similarity'] = rating_c['users'].map(user_corr.get)
rating_c['sim_rating'] = rating_c.similarity * rating_c.plays

recommendation = rating_c.groupby('artist').apply(lambda s: s.sim_rating.sum() / s.similarity.sum())
recommendation