实例分析
14.2 MovieLens 1M数据集
这个数据集是关于电影的评分数据。
import pandas as pd
pd.options.display.max_rows = 10
unames = ['user_id', 'gender', 'age', 'occupation', 'zip']
users = pd.read_table('datasets/movielens/users.dat', sep='::',
header=None, names=unames)
rnames = ['user_id', 'movie_id', 'rating', 'timestamp']
ratings = pd.read_table('datasets/movielens/ratings.dat', sep='::',
header=None, names=rnames)
mnames = ['movie_id', 'title', 'genres']
movies = pd.read_table('datasets/movielens/movies.dat', sep='::',
header=None, names=mnames)
E:\anaconda\lib\site-packages\ipykernel_launcher.py:7: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
import sys
E:\anaconda\lib\site-packages\ipykernel_launcher.py:11: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
# This is added back by InteractiveShellApp.init_path()
E:\anaconda\lib\site-packages\ipykernel_launcher.py:14: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
print(users[:5])
user_id gender age occupation zip
0 1 F 1 10 48067
1 2 M 56 16 70072
2 3 M 25 15 55117
3 4 M 45 7 02460
4 5 M 25 20 55455
print(ratings[:5]
)
user_id movie_id rating timestamp
0 1 1193 5 978300760
1 1 661 3 978302109
2 1 914 3 978301968
3 1 3408 4 978300275
4 1 2355 5 978824291
print(movies[:5])
movie_id title genres
0 1 Toy Story (1995) Animation|Children's|Comedy
1 2 Jumanji (1995) Adventure|Children's|Fantasy
2 3 Grumpier Old Men (1995) Comedy|Romance
3 4 Waiting to Exhale (1995) Comedy|Drama
4 5 Father of the Bride Part II (1995) Comedy
print(ratings)
user_id movie_id rating timestamp
0 1 1193 5 978300760
1 1 661 3 978302109
2 1 914 3 978301968
3 1 3408 4 978300275
4 1 2355 5 978824291
... ... ... ... ...
1000204 6040 1091 1 956716541
1000205 6040 1094 5 956704887
1000206 6040 562 5 956704746
1000207 6040 1096 4 956715648
1000208 6040 1097 4 956715569
[1000209 rows x 4 columns]
data = pd.merge(pd.merge(ratings, users), movies)
#pandas的merge可以把多个表合并在一起。
print(data)
user_id movie_id rating timestamp gender age occupation zip \
0 1 1193 5 978300760 F 1 10 48067
1 2 1193 5 978298413 M 56 16 70072
2 12 1193 4 978220179 M 25 12 32793
3 15 1193 4 978199279 M 25 7 22903
4 17 1193 5 978158471 M 50 1 95350
... ... ... ... ... ... ... ... ...
1000204 5949 2198 5 958846401 M 18 17 47901
1000205 5675 2703 3 976029116 M 35 14 30030
1000206 5780 2845 1 958153068 M 18 17 92886
1000207 5851 3607 5 957756608 F 18 20 55410
1000208 5938 2909 4 957273353 M 25 1 35401
title genres
0 One Flew Over the Cuckoo's Nest (1975) Drama
1 One Flew Over the Cuckoo's Nest (1975) Drama
2 One Flew Over the Cuckoo's Nest (1975) Drama
3 One Flew Over the Cuckoo's Nest (1975) Drama
4 One Flew Over the Cuckoo's Nest (1975) Drama
... ... ...
1000204 Modulations (1998) Documentary
1000205 Broken Vessels (1998) Drama
1000206 White Boys (1999) Drama
1000207 One Little Indian (1973) Comedy|Drama|Western
1000208 Five Wives, Three Secretaries and Me (1998) Documentary
[1000209 rows x 10 columns]
data.iloc[0]
user_id 1
movie_id 1193
rating 5
timestamp 978300760
gender F
age 1
occupation 10
zip 48067
title One Flew Over the Cuckoo's Nest (1975)
genres Drama
Name: 0, dtype: object
#用pivot_table方法可以添加参数计算相关的统计量
mean_ratings = data.pivot_table('rating', index='title',
columns='gender', aggfunc='mean')
print(mean_ratings[:5])
gender F M
title
$1,000,000 Duck (1971) 3.375000 2.761905
'Night Mother (1986) 3.388889 3.352941
'Til There Was You (1997) 2.675676 2.733333
'burbs, The (1989) 2.793478 2.962085
...And Justice for All (1979) 3.828571 3.689024
#在书中的例子中，去除了评论数少于250条的数据
ratings_by_title = data.groupby('title').size()#数据分组
active_titles = ratings_by_title.index[ratings_by_title >= 250]#数据判别
mean_ratings = mean_ratings.loc[active_titles]#子集在总计算中选取就好了
top_female_ratings = mean_ratings.sort_values(by='F', ascending=False)
#根据F（女生）列进行降序排列
print(top_female_ratings[:10])
gender F M
title
Close Shave, A (1995) 4.644444 4.473795
Wrong Trousers, The (1993) 4.588235 4.478261
Sunset Blvd. (a.k.a. Sunset Boulevard) (1950) 4.572650 4.464589
Wallace & Gromit: The Best of Aardman Animation... 4.563107 4.385075
Schindler's List (1993) 4.562602 4.491415
Shawshank Redemption, The (1994) 4.539075 4.560625
Grand Day Out, A (1992) 4.537879 4.293255
To Kill a Mockingbird (1962) 4.536667 4.372611
Creature Comforts (1990) 4.513889 4.272277
Usual Suspects, The (1995) 4.513317 4.518248
计算评分分歧
计算评分分歧，可以再添加一列，计算男女评分数据之差。
mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']
sorted_by_diff = mean_ratings.sort_values(by='diff')#按着新计算的数据列进行排序
print(sorted_by_diff[:10])
gender F M diff
title
Dirty Dancing (1987) 3.790378 2.959596 -0.830782
Jumpin' Jack Flash (1986) 3.254717 2.578358 -0.676359
Grease (1978) 3.975265 3.367041 -0.608224
Little Women (1994) 3.870588 3.321739 -0.548849
Steel Magnolias (1989) 3.901734 3.365957 -0.535777
Anastasia (1997) 3.800000 3.281609 -0.518391
Rocky Horror Picture Show, The (1975) 3.673016 3.160131 -0.512885
Color Purple, The (1985) 4.158192 3.659341 -0.498851
Age of Innocence, The (1993) 3.827068 3.339506 -0.487561
Free Willy (1993) 2.921348 2.438776 -0.482573
计算数据份方差和协方差
rating_std_by_title = data.groupby('title')['rating'].std()
rating_std_by_title = rating_std_by_title.loc[active_titles]
print(rating_std_by_title.sort_values(ascending=False)[:10])
title
Dumb & Dumber (1994) 1.321333
Blair Witch Project, The (1999) 1.316368
Natural Born Killers (1994) 1.307198
Tank Girl (1995) 1.277695
Rocky Horror Picture Show, The (1975) 1.260177
Eyes Wide Shut (1999) 1.259624
Evita (1996) 1.253631
Billy Madison (1995) 1.249970
Fear and Loathing in Las Vegas (1998) 1.246408
Bicentennial Man (1999) 1.245533
Name: rating, dtype: float64
说明：
放上参考链接，这个系列都是复现的这个链接中的内容。
放上原链接： https://www.jianshu.com/p/04d180d90a3f
作者在链接中放上了书籍，以及相关资源。因为平时杂七杂八的也学了一些，所以这次可能是对书中的部分内容的复现。也可能有我自己想到的内容，内容暂时都还不定。在此感谢原作者SeanCheney的分享。