Python for data analysis - chapter 2 - example

movielens.py

import pandas as pd
unames = ['user_id', 'gender', 'age', 'occupation', 'zip']
users = pd.read_table('users.dat', sep='::', header=None,
names=unames)
rnames = ['user_id', 'movie_id', 'rating', 'timestamp']
ratings = pd.read_table('ratings.dat', sep='::', header=None,
names=rnames)
mnames = ['movie_id', 'title', 'genres']
movies = pd.read_table('movies.dat', sep='::', header=None,
names=mnames)
data = pd.merge(pd.merge(ratings, users), movies)
data.ix[0]
mean_ratings = data.pivot_table('rating', rows='title', cols='gender', aggfunc='mean')
mean_ratings[-15:]
ratings_by_title = data.groupby('title').size()
ratings_by_title[:10]
active_titles = ratings_by_title.index[ratings_by_title >= 250]
mean_ratings = mean_ratings.ix[active_titles]
top_female_ratings = mean_ratings.sort_index(by='F', ascending=False)
top_female_ratings[:10]
mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']
sorted_by_diff = mean_ratings.sort_index(by='diff')
sorted_by_diff[-10:]
sorted_by_diff[::-1][:15]
rating_std_by_title = data.groupby('title')['rating'].std()
rating_std_by_title = rating_std_by_title.ix[active_titles]
rating_std_by_title.order(ascending=False)[:10]