similarmovies.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 12 23:33:52 2018

@author: blaze
"""

import pandas as pd

r_cols = ['user_id', 'movie_id', 'rating']
ratings = pd.read_csv('u.data', sep='\t', names=r_cols, usecols=range(3), encoding="ISO-8859-1" )

m_cols = ['movie_id', 'title']
movies = pd.read_csv('u.item', sep='|', names=m_cols, usecols=range(2), encoding="ISO-8859-1")

ratings = pd.merge(movies, ratings)
ratings.head()

movieRatings=ratings.pivot_table(index=['user_id'], columns=['title'], values='rating')
movieRatings.head()

starWarsRatings=movieRatings['Star Wars (1977)']
starWarsRatings.head()

similarMovies=movieRatings.corrwith(starWarsRatings)
similarMovies.dropna()
df=pd.DataFrame(similarMovies)
df.head()

similarMovies.sort_values(ascending=False)

import numpy as np
movieStats = ratings.groupby('title').agg({'rating': [np.size, np.mean]})
movieStats.head()

popularMovies=movieStats['rating']['size']>=100
movieStats[popularMovies].sort_values([('rating','mean')],ascending=False)[:15]

r=pd.DataFrame(similarMovies, columns=['similarity'])
df = movieStats[popularMovies].join(pd.DataFrame(similarMovies, columns=['similarity']))
df.head()