netflix.py

f = ['count','mean']

df_movie_summary = df.groupby('Movie_Id')['Rating'].agg(f)
df_movie_summary.index = df_movie_summary.index.map(int)
movie_benchmark = round(df_movie_summary['count'].quantile(0.7),0)
drop_movie_list = df_movie_summary[df_movie_summary['count'] < movie_benchmark].index

print('Movie minimum times of review: {}'.format(movie_benchmark))

df_cust_summary = df.groupby('Cust_Id')['Rating'].agg(f)
df_cust_summary.index = df_cust_summary.index.map(int)
cust_benchmark = round(df_cust_summary['count'].quantile(0.7),0)
drop_cust_list = df_cust_summary[df_cust_summary['count'] < cust_benchmark].index

print('Original Shape: {}'.format(df.shape))
df = df[~df['Movie_Id'].isin(drop_movie_list)]
df = df[~df['Cust_Id'].isin(drop_cust_list)]
print('After Trim Shape: {}'.format(df.shape))
print('-Data Examples-')
print(df.iloc[::5000000, :])