zoukankan      html  css  js  c++  java
  • pandas之透视表和交叉表

    import pandas as pd
    import warnings
    
    warnings.filterwarnings('ignore')
    
    # 读取用户表
    users = pd.read_table('./users.dat', header=None, names=['UserID', 'Gender', 'Age', 'Occupation', 'Zip-code'], sep='::',
                          engine='python')
    # print(users.head())
    # 读取评分表
    ratings = pd.read_table('./ratings.dat', header=None, names=['UserID', 'MovieID', 'Rating', 'Timestamp'], sep='::',
                            engine='python')
    # print(ratings.head())
    # 读取电影详情表
    movies = pd.read_table('./movies.dat', header=None, names=['MovieID', 'Title', 'Genres'], sep='::', engine='python')
    # print(movies.head())
    # 将表进行合并
    data = pd.merge(pd.merge(ratings, users), movies)
    print(data.head())
    
    # 使用pivot_table方法查看,每一部电影不同性别的平均评分
    data_gender = pd.pivot_table(data, index='Title', columns='Gender', values='Rating', margins=True)
    # data_gender = data.pivot_table(index='Title', columns='Gender', values='Rating')
    print(data_gender.head())
    
    # 使用groupby方法
    data_gender = data.groupby(['Title', 'Gender']).agg({'Rating': 'mean'})
    print(data_gender.head())
    
    # 使用crosstab方法查看每一部电影不同性别的平均评分
    data_gender = pd.crosstab(data.Title, data.Gender, data.Rating, aggfunc='mean')
    print(data_gender.head())
    
    
    输出结果:
       UserID  MovieID  ...                                   Title  Genres
    0       1     1193  ...  One Flew Over the Cuckoo's Nest (1975)   Drama
    1       2     1193  ...  One Flew Over the Cuckoo's Nest (1975)   Drama
    2      12     1193  ...  One Flew Over the Cuckoo's Nest (1975)   Drama
    3      15     1193  ...  One Flew Over the Cuckoo's Nest (1975)   Drama
    4      17     1193  ...  One Flew Over the Cuckoo's Nest (1975)   Drama
    
    [5 rows x 10 columns]
    Gender                                F         M       All
    Title                                                      
    $1,000,000 Duck (1971)         3.375000  2.761905  3.027027
    'Night Mother (1986)           3.388889  3.352941  3.371429
    'Til There Was You (1997)      2.675676  2.733333  2.692308
    'burbs, The (1989)             2.793478  2.962085  2.910891
    ...And Justice for All (1979)  3.828571  3.689024  3.713568
                                        Rating
    Title                     Gender          
    $1,000,000 Duck (1971)    F       3.375000
                              M       2.761905
    'Night Mother (1986)      F       3.388889
                              M       3.352941
    'Til There Was You (1997) F       2.675676
    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
    import pandas as pd
    
    data = pd.DataFrame({'Sample': range(1, 11),
                         'Gender': ['Female', 'Male', 'Female', 'Male', 'Male', 'Male', 'Female', 'Female', 'Male',
                                    'Female'],
                         'Handedness': ['Right-handed', 'Left-handed', 'Right-handed', 'Right-handed', 'Left-handed',
                                        'Right-handed', 'Right-handed', 'Left-handed', 'Right-handed', 'Right-handed']})
    print(data)
    
    # 方法1 :使用pivot_table
    data1 = pd.pivot_table(data, index='Gender', columns='Handedness', aggfunc=len, margins=True)
    print(data1)
    
    # 方法2:使用crosstab
    data2 = pd.crosstab(data.Gender, data.Handedness, data.Sample, aggfunc=len, margins=True)
    print(data2)
    
    输出结果:
       Sample  Gender    Handedness
    0       1  Female  Right-handed
    1       2    Male   Left-handed
    2       3  Female  Right-handed
    3       4    Male  Right-handed
    4       5    Male   Left-handed
    5       6    Male  Right-handed
    6       7  Female  Right-handed
    7       8  Female   Left-handed
    8       9    Male  Right-handed
    9      10  Female  Right-handed
                    Sample                 
    Handedness Left-handed Right-handed All
    Gender                                 
    Female               1            4   5
    Male                 2            3   5
    All                  3            7  10
    Handedness  Left-handed  Right-handed  All
    Gender                                    
    Female                1             4    5
    Male                  2             3    5
    All                   3             7   10
  • 相关阅读:
    opencv 1.0 与 2.0的库对应表
    OpenCV SIFT原理与源码分析
    计算机杂志排名
    opencv Installation in Linux and hello world
    SSL 通信及 java keystore 工具介绍
    侧方位停车技巧图解 教你快速便捷停车(图)
    opencv 中文文档地址
    books
    Mysql processlist命令
    MYSQL优化之碎片整理
  • 原文地址:https://www.cnblogs.com/yuxiangyang/p/11266863.html
Copyright © 2011-2022 走看看