《利用Python进行数据分析》笔记---第2章--MovieLens 1M数据集

写在前面的话：

实例中的所有数据都是在GitHub上下载的，打包下载即可。

地址是：http://github.com/pydata/pydata-book

还有一定要说明的：

我使用的是Python2.7，书中的代码有一些有错误，我使用自己的2.7版本调通。

# coding: utf-8
import pandas as pd
unames = ['user_id','gender','age','occupation','zip']
users = pd.read_table('D:\Source Code\pydata-book-master\ch02\movielens\users.dat', sep='::', header=None, names=unames)
rnmaes = ['user_id','movie_id','rating','timestamp']
ratings = pd.read_table('D:\Source Code\pydata-book-master\ch02\movielens\\ratings.dat', sep='::', header=None, names=rnmaes)
mnames = ['movie_id','title','genres']
movies = pd.read_table('D:\Source Code\pydata-book-master\ch02\movielens\movies.dat', sep='::', header=None, names=mnames)

users[:5]
ratings[:5]
movies[:5]

ratings

data = pd.merge(pd.merge(ratings, users), movies)
data.ix[0]
mean_rating = data.pivot_table('rating', index='title', columns='gender', aggfunc='mean')
mean_rating[:5]
ratings_by_title = data.groupby('title').size()
ratings_by_title[:10]

active_titles = ratings_by_title.index[ratings_by_title >= 250]
active_titles

mean_rating = mean_rating.ix[active_titles]
mean_rating

top_female_rating = mean_rating.sort_index(by='F', ascending=False)
top_female_rating[:10]

mean_rating['diff'] = mean_rating['M'] - mean_rating['F']
sorted_by_diff = mean_rating.sort_index(by='diff')
sorted_by_diff[:15]

sorted_by_diff[::-1][:15]

ratings_std_by_title = data.groupby('title')['rating'].std()
ratings_std_by_title = ratings_by_title.ix[active_titles]
ratings_std_by_title.order(ascending=False)[:10]
ratings_std_by_title