在DataFrame数据表里面提取需要的行

在DataFrame数据表里面提取需要的行

代码功能：

在DataFrame表格中使用loc(),得到我们想要的行，然后根据某一列元素的值进行排序

此代码中还展示了为DataFrame添加列，即直接name_DataFrame['diff']=___即可，同时可以依据新添加的列元素的值，来对dataframe进行排序

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)
 
mean_ratings = pd.pivot_table(data,index=['title'],values='rating',columns='gender')
 
print(mean_ratings[:10])
 
ratings_by_title = data.groupby('title').size()
 
print(ratings_by_title[:10])
 
active_titles = ratings_by_title.index[ratings_by_title >= 250]
 
print(active_titles)
 
active_mean_ratings = mean_ratings.loc[active_titles]
 
top_female_ratings = active_mean_ratings.sort_index(by='F', ascending=False)
 
active_mean_ratings['diff'] = active_mean_ratings['M'] - active_mean_ratings['F']
 
sorted_by_diff = active_mean_ratings.sort_index(by='diff')
 
print(sorted_by_diff[::-1][:15]) #注意对dataframe进行倒序访问的方法