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

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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