数据分析实例-MovieLens 1M 数据集

缺乏、安全感 2022-05-18 08:52 260阅读 0赞

MovieLens 1M数据集含有来自6000名用户对4000部电影的100万条评分数据。分为三个表：评分，用户信息，电影信息。这些数据都是dat文件格式。

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### 读取3个数据集： ###

#coding=gbk
    # MovieLens 1M数据集含有来自6000名用户对4000部电影的100万条评分数据。
    # 分为三个表：评分，用户信息，电影信息。这些数据都是dat文件格式
    # ，可以通过pandas.read_table将各个表分别读到一个pandas DataFrame对象中
    import pandas as pd
    import time 
    start = time.clock()
    filename1 =r'D:\datasets\users.dat'
    filename2 = r'D:\datasets\ratings.dat'
    filename3 = r'D:\datasets\movies.dat'
    pd.options.display.max_rows = 10
    uname = ['user_id','gender','age','occupation','zip']
    users = pd.read_table(filename1, sep='::', header = None, names=uname, engine='python')
    print(users.head()) #年龄和职业都是使用编码的形式给出来的
    #    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(users.shape)  # (6040, 5)
    
    rnames = ['user_id','movie_id','rating','timestamp']
    ratings = pd.read_table(filename2, header =None, sep='::',names=rnames, engine= 'python')
    print(ratings.head())
    #    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(ratings.shape)  #(1000209, 4)
    mnames = ['movie_id','title','genres']  # genres 表示影片的体裁是什么
    movies = pd.read_table(filename3, header = None, sep='::', names = mnames, engine='python')
    # print(movies.head())
    #    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(movies.shape) #(3883, 3)

> **年龄和职业都是使用编码的形式给出来的：**

- Age is chosen from the following ranges:
    
    	*  1:  "Under 18"
    	* 18:  "18-24"
    	* 25:  "25-34"
    	* 35:  "35-44"
    	* 45:  "45-49"
    	* 50:  "50-55"
    	* 56:  "56+"
    
    - Occupation is chosen from the following choices:
    
    	*  0:  "other" or not specified
    	*  1:  "academic/educator"
    	*  2:  "artist"
    	*  3:  "clerical/admin"
    	*  4:  "college/grad student"
    	*  5:  "customer service"
    	*  6:  "doctor/health care"
    	*  7:  "executive/managerial"
    	*  8:  "farmer"
    	*  9:  "homemaker"
    	* 10:  "K-12 student"
    	* 11:  "lawyer"
    	* 12:  "programmer"
    	* 13:  "retired"
    	* 14:  "sales/marketing"
    	* 15:  "scientist"
    	* 16:  "self-employed"
    	* 17:  "technician/engineer"
    	* 18:  "tradesman/craftsman"
    	* 19:  "unemployed"
    	* 20:  "writer"

### 使用merge 函数将3个表进行合并 ###

#使用merge 函数将3个表进行合并
    data = pd.merge(pd.merge(ratings, users), movies)
    # print(data.head())
    #    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 
    # print(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

### 使用透视表，按性别计算每部电影的平均得分 ###

#index  表示索引，values表示所要进行分析的数据， columns允许选择一个或多个列,以columns作为分组的列
    mean_ratings = data.pivot_table(values ='rating', index='title', columns ='gender', aggfunc='mean')
    # print(mean_ratings.head())
    # 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

### 使用选择的数据进行分析 ###

#过滤掉评分数据不足250 条的电影
    ratings_by_title = data.groupby('title').size()
    print(ratings_by_title[:3])
    # title
    # $1,000,000 Duck (1971)       37
    # 'Night Mother (1986)         70
    # 'Til There Was You (1997)    52
    # dtype: int64
    active_titles = ratings_by_title.index[ratings_by_title >= 250] #找出其评论大于250 的索引
    print(active_titles[:3])
    # Index([''burbs, The (1989)', '10 Things I Hate About You (1999)',
    #        '101 Dalmatians (1961)'],
    #       dtype='object', name='title')
    
    #可以以active_titles 中的电影作为索引，选择出 mean_ratings 中的电影
    mean_ratings = mean_ratings.loc[active_titles]
    print(mean_ratings[:5])
    # gender                                    F         M
    # title                                                
    # 'burbs, The (1989)                 2.793478  2.962085
    # 10 Things I Hate About You (1999)  3.646552  3.311966
    # 101 Dalmatians (1961)              3.791444  3.500000
    # 101 Dalmatians (1996)              3.240000  2.911215
    # 12 Angry Men (1957)                4.184397  4.328421
    
    #查看女性观众喜欢的电影，可以按 F 列进行降序排列
    top_ratings = mean_ratings.sort_values(by="F", ascending = False)
    print(top_ratings[:3])
    # 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
    
    #计算男性观众和女性观众分歧最大的电影
    mean_ratings['diff'] = mean_ratings['M'] - mean_ratings['F']
    sort_by_diff = mean_ratings.sort_values(by='diff')
    print(sort_by_diff[:3])
    # 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
    #对行进行反序操作， 取出前3行，得到是男性更喜欢的电影，而女性观众相反
    print(sort_by_diff[::-1][:3])
    # gender                                         F         M      diff
    # title                                                               
    # Good, The Bad and The Ugly, The (1966)  3.494949  4.221300  0.726351
    # Kentucky Fried Movie, The (1977)        2.878788  3.555147  0.676359
    # Dumb & Dumber (1994)                    2.697987  3.336595  0.638608
    
    #计算得分数据的标准差，找出分歧最大的电影
    rating_std = data.groupby('title')['rating'].std()
    rating_std = rating_std.loc[active_titles]
    print(rating_std.sort_values(ascending=False)[:3])
    # title
    # Dumb & Dumber (1994)               1.321333
    # Blair Witch Project, The (1999)    1.316368
    # Natural Born Killers (1994)        1.307198
    # Name: rating, dtype: float64
    
    end = time.clock()
    spending_time = end - start
    print('花费的时间为：%.2f'%spending_time + 's')
    # 花费的时间为：11.13s

[20200423154601830.PNG]: /images/20220518/c2cd345fc4e54fdd9e955af6b76dd82d.png