rec_in_pandas.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('dark')
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)

ratings_data = pd.read_csv(r'./ml-latest-small/ratings.csv')
#print(ratings_data.head())

movie_names = pd.read_csv('.//ml-latest-small//movies.csv')
#print(movie_names.head())

movie_data = pd.merge(ratings_data, movie_names, on='movieId')
#print(movie_data.head())

#print(movie_data.groupby('title')['rating'].mean().head())
'''
print(movie_data.groupby('title')['rating'].mean().sort_values(ascending=False).head())
print('~~~~~~~~~~')
print(movie_data.groupby('title')['rating'].count().sort_values(ascending=False).head())
'''

ratings_mean_count = pd.DataFrame(movie_data.groupby('title')['rating'].mean())
ratings_mean_count['rating_counts'] = pd.DataFrame(movie_data.groupby('title')['rating'].count())
#print(ratings_mean_count.head())

plt.figure(figsize=(8,6))
plt.rcParams['patch.force_edgecolor'] = True
#ratings_mean_count['rating'].hist(bins=50)
#sns.jointplot(x='rating', y='rating_counts', data=ratings_mean_count, alpha=0.4)
#plt.show()

user_movie_rating = movie_data.pivot_table(index='userId', columns='title', values='rating')
#print(user_movie_rating.head())
forrest_gump_ratings = user_movie_rating['Forrest Gump (1994)']
#print(forrest_gump_ratings.head())

movies_like_forest_gump = user_movie_rating.corrwith(forrest_gump_ratings)
corr_forrest_gump = pd.DataFrame(movies_like_forest_gump, columns=['Correlation'])
corr_forrest_gump.dropna(inplace=True)
#print(corr_forrest_gump.head())
#print(corr_forrest_gump.sort_values('Correlation', ascending=False).head(10))

corr_forrest_gump = corr_forrest_gump.join(ratings_mean_count['rating_counts'])
#print(corr_forrest_gump.head())

print(corr_forrest_gump[corr_forrest_gump['rating_counts']>50].sort_values('Correlation',ascending=False).head())