netflix.py

df_p = pd.pivot_table(df,values='Rating',index='Cust_Id',columns='Movie_Id')
df_title = pd.read_csv('../input/movie_titles.csv', encoding = "ISO-8859-1", header = None, names = ['Movie_Id', 'Year', 'Name'])
df_title.set_index('Movie_Id', inplace = True)
def recommend(movie_title, min_count):
    print("For movie ({})".format(movie_title))
    print("- Top 10 movies recommended based on Pearsons'R correlation - ")
    i = int(df_title.index[df_title['Name'] == movie_title][0])
    target = df_p[i]
    similar_to_target = df_p.corrwith(target)
    corr_target = pd.DataFrame(similar_to_target, columns = ['PearsonR'])
    corr_target.dropna(inplace = True)
    corr_target = corr_target.sort_values('PearsonR', ascending = False)
    corr_target.index = corr_target.index.map(int)
    corr_target = corr_target.join(df_title).join(df_movie_summary)[['PearsonR', 'Name', 'count', 'mean']]
    print(corr_target[corr_target['count']>min_count][:10].to_string(index=False))
   
recommend("What the #$*! Do We Know!?", 0)