• 2-1

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    lect02_eg01 Last Checkpoint: 11/06/2017 (autosaved)
     
     
     
     

    Pandas进阶及技巧

     

    1. 创建Pandas

     
     
     
     
     
     
    import pandas as pd
    ​
    country1 = pd.Series({'Name': '中国',
                        'Language': 'Chinese',
                        'Area': '9.597M km2',
                         'Happiness Rank': 79})
    ​
    country2 = pd.Series({'Name': '美国',
                        'Language': 'English (US)',
                        'Area': '9.834M km2',
                         'Happiness Rank': 14})
    ​
    country3 = pd.Series({'Name': '澳大利亚',
                        'Language': 'English (AU)',
                        'Area': '7.692M km2',
                         'Happiness Rank': 9})
    ​
    df = pd.DataFrame([country1, country2, country3], index=['CH', 'US', 'AU'])
     
     
     
     
     
     
     
     
    # 注意在jupyter中使用print和不使用print的区别
    print(df)
    df
     
     
     
              Area  Happiness Rank      Language  Name
CH  9.597M km2              79       Chinese    中国
US  9.834M km2              14  English (US)    美国
AU  7.692M km2               9  English (AU)  澳大利亚
     
     AreaHappiness RankLanguageName
    CH 9.597M km2 79 Chinese 中国
    US 9.834M km2 14 English (US) 美国
    AU 7.692M km2 9 English (AU) 澳大利亚
     
     
     
     
     
     
    # 添加数据
    # 如果个数为1，会自动进行“广播”操作
    # 如果大于要求的个数，会报错
    #如果刚好等于要求的个数，会依次填充字段属性值
    ​
    df['Location'] = '地球'
    print(df)
    ​
    df['Region'] = ['亚洲', '北美洲', '大洋洲']
    print(df)
    df
     
     
     
              Area  Happiness Rank      Language  Name Location
CH  9.597M km2              79       Chinese    中国       地球
US  9.834M km2              14  English (US)    美国       地球
AU  7.692M km2               9  English (AU)  澳大利亚       地球
          Area  Happiness Rank      Language  Name Location Region
CH  9.597M km2              79       Chinese    中国       地球     亚洲
US  9.834M km2              14  English (US)    美国       地球    北美洲
AU  7.692M km2               9  English (AU)  澳大利亚       地球    大洋洲
     
     AreaHappiness RankLanguageNameLocationRegion
    CH 9.597M km2 79 Chinese 中国 地球 亚洲
    US 9.834M km2 14 English (US) 美国 地球 北美洲
    AU 7.692M km2 9 English (AU) 澳大利亚 地球 大洋洲
     

    2. Pandas索引

     
     
     
     
     
     
    # 行索引
    print('loc:')
    print(df.loc['CH'])
    print(type(df.loc['CH']))
    ​
    print('iloc:')
    print(df.iloc[1])
     
     
     
    loc:
Area              9.597M km2
Happiness Rank            79
Language             Chinese
Name                      中国
Location                  地球
Region                    亚洲
Name: CH, dtype: object
<class 'pandas.core.series.Series'>
iloc:
Area                9.834M km2
Happiness Rank              14
Language          English (US)
Name                        美国
Location                    地球
Region                     北美洲
Name: US, dtype: object
     
     
     
     
     
     
    # 列索引
    print(df['Area'])
    print(type(df['Area']))
     
     
     
    CH    9.597M km2
US    9.834M km2
AU    7.692M km2
Name: Area, dtype: object
<class 'pandas.core.series.Series'>
     
     
     
     
     
     
    # 获取不连续的列数据
    print(df[['Name', 'Area']])
     
     
     
        Name        Area
CH    中国  9.597M km2
US    美国  9.834M km2
AU  澳大利亚  7.692M km2
     
     
     
     
     
     
    # 混合索引
    # 注意写法上的区别
    print('先取出列，再取行：')
    print(df['Area']['CH'])
    print(df['Area'].loc['CH'])
    print(df['Area'].iloc[0])
    ​
    print('先取出行，再取列：')
    print(df.loc['CH']['Area'])
    print(df.iloc[0]['Area'])
     
     
     
    先取出列，再取行：
9.597M km2
9.597M km2
9.597M km2
先取出行，再取列：
9.597M km2
9.597M km2
     
     
     
     
     
     
    # 转换行和列
    print(df.T)
     
     
     
                            CH            US            AU
Area            9.597M km2    9.834M km2    7.692M km2
Happiness Rank          79            14             9
Language           Chinese  English (US)  English (AU)
Name                    中国            美国          澳大利亚
Location                地球            地球            地球
Region                  亚洲           北美洲           大洋洲
     

    3. 删除数据

     
     
     
     
     
     
    print(df.drop(['CH']))
    # 注意drop操作只是将修改后的数据copy一份，而不会对原始数据进行修改
    print(df)
     
     
     
              Area  Happiness Rank      Language  Name Location Region
US  9.834M km2              14  English (US)    美国       地球    北美洲
AU  7.692M km2               9  English (AU)  澳大利亚       地球    大洋洲
          Area  Happiness Rank      Language  Name Location Region
CH  9.597M km2              79       Chinese    中国       地球     亚洲
US  9.834M km2              14  English (US)    美国       地球    北美洲
AU  7.692M km2               9  English (AU)  澳大利亚       地球    大洋洲
     
     
     
     
     
     
    print(df.drop(['CH'], inplace=True))
    # 如果使用了inplace=True，会在原始数据上进行修改，同时不会返回一个copy
    print(df)
     
     
     
    None
          Area  Happiness Rank      Language  Name Location Region
US  9.834M km2              14  English (US)    美国       地球    北美洲
AU  7.692M km2               9  English (AU)  澳大利亚       地球    大洋洲
     
     
     
     
     
     
    #  如果需要删除列，需要指定axis=1
    print(df.drop(['Area'], axis=1))
    print(df)
     
     
     
        Happiness Rank      Language  Name Location Region
CH              79       Chinese    中国       地球     亚洲
US              14  English (US)    美国       地球    北美洲
AU               9  English (AU)  澳大利亚       地球    大洋洲
          Area  Happiness Rank      Language  Name Location Region
CH  9.597M km2              79       Chinese    中国       地球     亚洲
US  9.834M km2              14  English (US)    美国       地球    北美洲
AU  7.692M km2               9  English (AU)  澳大利亚       地球    大洋洲
     
     
     
     
     
     
    # 也可直接使用del关键字
    del df['Name']
    print(df)
     
     
     
              Area  Happiness Rank      Language Location Region
US  9.834M km2              14  English (US)       地球    北美洲
AU  7.692M km2               9  English (AU)       地球    大洋洲
     

    4. DataFrame的操作与加载

     
     
     
     
     
     
    # 注意从DataFrame中取出的数据进行操作后，会对原始数据产生影响
    ranks = df['Happiness Rank']
    ranks += 2
    print(ranks)
    print(df)
     
     
     
    US    18
AU    13
Name: Happiness Rank, dtype: int64
          Area  Happiness Rank      Language Location Region
US  9.834M km2              18  English (US)       地球    北美洲
AU  7.692M km2              13  English (AU)       地球    大洋洲
     
     
     
     
     
     
    # 注意从DataFrame中取出的数据进行操作后，会对原始数据产生影响
    # 安全的操作是使用copy()
    ranks = df['Happiness Rank'].copy()
    ranks += 2
    print(ranks)
    print(df)
     
     
     
    US    20
AU    15
Name: Happiness Rank, dtype: int64
          Area  Happiness Rank      Language Location Region
US  9.834M km2              18  English (US)       地球    北美洲
AU  7.692M km2              13  English (AU)       地球    大洋洲
     
     
     
     
     
     
    # 加载csv文件数据
    reprot_2015_df = pd.read_csv('./2015.csv')
    print('2015年数据预览：')
    #print(reprot_2015_df.head())
    reprot_2015_df.head()
     
     
     
    2015年数据预览：
     
     CountryRegionHappiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
    0 Switzerland Western Europe 1 7.587 0.03411 1.39651 1.34951 0.94143 0.66557 0.41978 0.29678 2.51738
    1 Iceland Western Europe 2 7.561 0.04884 1.30232 1.40223 0.94784 0.62877 0.14145 0.43630 2.70201
    2 Denmark Western Europe 3 7.527 0.03328 1.32548 1.36058 0.87464 0.64938 0.48357 0.34139 2.49204
    3 Norway Western Europe 4 7.522 0.03880 1.45900 1.33095 0.88521 0.66973 0.36503 0.34699 2.46531
    4 Canada North America 5 7.427 0.03553 1.32629 1.32261 0.90563 0.63297 0.32957 0.45811 2.45176
     
     
     
     
     
     
    print(reprot_2015_df.info())
     
     
     
    <class 'pandas.core.frame.DataFrame'>
RangeIndex: 158 entries, 0 to 157
Data columns (total 12 columns):
Country                          158 non-null object
Region                           158 non-null object
Happiness Rank                   158 non-null int64
Happiness Score                  158 non-null float64
Standard Error                   158 non-null float64
Economy (GDP per Capita)         158 non-null float64
Family                           158 non-null float64
Health (Life Expectancy)         158 non-null float64
Freedom                          158 non-null float64
Trust (Government Corruption)    158 non-null float64
Generosity                       158 non-null float64
Dystopia Residual                158 non-null float64
dtypes: float64(9), int64(1), object(2)
memory usage: 14.9+ KB
None
     
     
     
     
     
     
    # 使用index_col指定索引列
    # 使用usecols指定需要读取的列
    reprot_2016_df = pd.read_csv('./2016.csv', 
                                 index_col='Country',
                                 usecols=['Country', 'Happiness Rank', 'Happiness Score', 'Region'])
    # 数据预览
    reprot_2016_df.head()
     
     
     
     RegionHappiness RankHappiness Score
    Country   
    Denmark Western Europe 1 7.526
    Switzerland Western Europe 2 7.509
    Iceland Western Europe 3 7.501
    Norway Western Europe 4 7.498
    Finland Western Europe 5 7.413
     
     
     
     
     
     
    print('列名(column)：', reprot_2016_df.columns)
    print('行名(index)：', reprot_2016_df.index)
     
     
     
    列名(column)： Index(['Region', 'Happiness Rank', 'Happiness Score'], dtype='object')
行名(index)： Index(['Denmark', 'Switzerland', 'Iceland', 'Norway', 'Finland', 'Canada',
       'Netherlands', 'New Zealand', 'Australia', 'Sweden',
       ...
       'Madagascar', 'Tanzania', 'Liberia', 'Guinea', 'Rwanda', 'Benin',
       'Afghanistan', 'Togo', 'Syria', 'Burundi'],
      dtype='object', name='Country', length=157)
     
     
     
     
     
     
    # 注意index是不可变的
    reprot_2016_df.index[0] = '丹麦'
     
     
     
    ---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-104-9214cb35e379> in <module>()
----> 1 reprot_2016_df.index[0] = '丹麦'

C:ProgramDataAnaconda3libsite-packagespandascoreindexesase.py in __setitem__(self, key, value)
   1618 
   1619     def __setitem__(self, key, value):
-> 1620         raise TypeError("Index does not support mutable operations")
   1621 
   1622     def __getitem__(self, key):

TypeError: Index does not support mutable operations
     
     
     
     
     
     
    # 重置index
    # 注意inplace加与不加的区别
    reprot_2016_df.reset_index().head()
     
     
     
     Country地区排名幸福指数
    0 Denmark Western Europe 1 7.526
    1 Switzerland Western Europe 2 7.509
    2 Iceland Western Europe 3 7.501
    3 Norway Western Europe 4 7.498
    4 Finland Western Europe 5 7.413
     
     
     
     
     
     
    # 重命名列名
    reprot_2016_df.rename(columns={'Region': '地区', 'Hapiness Rank': '排名', 'Hapiness Score': '幸福指数'})
    reprot_2016_df.head()
     
     
     
     地区排名幸福指数
    Country   
    Denmark Western Europe 1 7.526
    Switzerland Western Europe 2 7.509
    Iceland Western Europe 3 7.501
    Norway Western Europe 4 7.498
    Finland Western Europe 5 7.413
     
     
     
     
     
     
    # 重命名列名，注意inplace的使用
    reprot_2016_df.rename(columns={'Region': '地区', 'Happiness Rank': '排名', 'Happiness Score': '幸福指数'},
                         inplace=True)
    reprot_2016_df.head()
     
     
     
     地区排名幸福指数
    Country   
    Denmark Western Europe 1 7.526
    Switzerland Western Europe 2 7.509
    Iceland Western Europe 3 7.501
    Norway Western Europe 4 7.498
    Finland Western Europe 5 7.413
     

    5. Boolean Mask

     
     
     
     
     
     
    # 过滤 Western Europe 地区的国家
    only_western_europe = reprot_2016_df['地区'] == 'Western Europe'
    only_western_europe
     
     
     
    Country
Denmark                  True
Switzerland              True
Iceland                  True
Norway                   True
Finland                  True
Canada                  False
Netherlands              True
New Zealand             False
Australia               False
Sweden                   True
Israel                  False
Austria                  True
United States           False
Costa Rica              False
Puerto Rico             False
Germany                  True
Brazil                  False
Belgium                  True
Ireland                  True
Luxembourg               True
Mexico                  False
Singapore               False
United Kingdom           True
Chile                   False
Panama                  False
Argentina               False
Czech Republic          False
United Arab Emirates    False
Uruguay                 False
Malta                    True
                        ...  
Senegal                 False
Bulgaria                False
Mauritania              False
Zimbabwe                False
Malawi                  False
Sudan                   False
Gabon                   False
Mali                    False
Haiti                   False
Botswana                False
Comoros                 False
Ivory Coast             False
Cambodia                False
Angola                  False
Niger                   False
South Sudan             False
Chad                    False
Burkina Faso            False
Uganda                  False
Yemen                   False
Madagascar              False
Tanzania                False
Liberia                 False
Guinea                  False
Rwanda                  False
Benin                   False
Afghanistan             False
Togo                    False
Syria                   False
Burundi                 False
Name: 地区, Length: 157, dtype: bool
     
     
     
     
     
     
    # 过滤 Western Europe 地区的国家
    # 并且排名在10之外
    only_western_europe_10 = (reprot_2016_df['地区'] == 'Western Europe') & (reprot_2016_df['排名'] > 10)
    only_western_europe_10
     
     
     
    Country
Denmark                 False
Switzerland             False
Iceland                 False
Norway                  False
Finland                 False
Canada                  False
Netherlands             False
New Zealand             False
Australia               False
Sweden                  False
Israel                  False
Austria                  True
United States           False
Costa Rica              False
Puerto Rico             False
Germany                  True
Brazil                  False
Belgium                  True
Ireland                  True
Luxembourg               True
Mexico                  False
Singapore               False
United Kingdom           True
Chile                   False
Panama                  False
Argentina               False
Czech Republic          False
United Arab Emirates    False
Uruguay                 False
Malta                    True
                        ...  
Senegal                 False
Bulgaria                False
Mauritania              False
Zimbabwe                False
Malawi                  False
Sudan                   False
Gabon                   False
Mali                    False
Haiti                   False
Botswana                False
Comoros                 False
Ivory Coast             False
Cambodia                False
Angola                  False
Niger                   False
South Sudan             False
Chad                    False
Burkina Faso            False
Uganda                  False
Yemen                   False
Madagascar              False
Tanzania                False
Liberia                 False
Guinea                  False
Rwanda                  False
Benin                   False
Afghanistan             False
Togo                    False
Syria                   False
Burundi                 False
Length: 157, dtype: bool
     
     
     
     
     
     
    # 叠加 boolean mask 得到最终结果
    reprot_2016_df[only_western_europe_10]
     
     
     
     地区排名幸福指数
    Country   
    Austria Western Europe 12 7.119
    Germany Western Europe 16 6.994
    Belgium Western Europe 18 6.929
    Ireland Western Europe 19 6.907
    Luxembourg Western Europe 20 6.871
    United Kingdom Western Europe 23 6.725
    Malta Western Europe 30 6.488
    France Western Europe 32 6.478
    Spain Western Europe 37 6.361
    Italy Western Europe 50 5.977
    North Cyprus Western Europe 62 5.771
    Cyprus Western Europe 69 5.546
    Portugal Western Europe 94 5.123
    Greece Western Europe 99 5.033
     
     
     
     
     
     
    # 熟练以后可以写在一行中
    reprot_2016_df[(reprot_2016_df['地区'] == 'Western Europe') & (reprot_2016_df['排名'] > 10)]
     
     
     
     地区排名幸福指数
    Country   
    Austria Western Europe 12 7.119
    Germany Western Europe 16 6.994
    Belgium Western Europe 18 6.929
    Ireland Western Europe 19 6.907
    Luxembourg Western Europe 20 6.871
    United Kingdom Western Europe 23 6.725
    Malta Western Europe 30 6.488
    France Western Europe 32 6.478
    Spain Western Europe 37 6.361
    Italy Western Europe 50 5.977
    North Cyprus Western Europe 62 5.771
    Cyprus Western Europe 69 5.546
    Portugal Western Europe 94 5.123
    Greece Western Europe 99 5.033
     

    6. 层级索引

     
     
     
     
     
     
    reprot_2015_df.head()
     
     
     
     CountryRegionHappiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
    0 Switzerland Western Europe 1 7.587 0.03411 1.39651 1.34951 0.94143 0.66557 0.41978 0.29678 2.51738
    1 Iceland Western Europe 2 7.561 0.04884 1.30232 1.40223 0.94784 0.62877 0.14145 0.43630 2.70201
    2 Denmark Western Europe 3 7.527 0.03328 1.32548 1.36058 0.87464 0.64938 0.48357 0.34139 2.49204
    3 Norway Western Europe 4 7.522 0.03880 1.45900 1.33095 0.88521 0.66973 0.36503 0.34699 2.46531
    4 Canada North America 5 7.427 0.03553 1.32629 1.32261 0.90563 0.63297 0.32957 0.45811 2.45176
     
     
     
     
     
     
    # 设置层级索引
    report_2015_df2 = reprot_2015_df.set_index(['Region', 'Country'])
    report_2015_df2.head(20)
     
     
     
      Happiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
    RegionCountry          
    Western EuropeSwitzerland 1 7.587 0.03411 1.39651 1.34951 0.94143 0.66557 0.41978 0.29678 2.51738
    Iceland 2 7.561 0.04884 1.30232 1.40223 0.94784 0.62877 0.14145 0.43630 2.70201
    Denmark 3 7.527 0.03328 1.32548 1.36058 0.87464 0.64938 0.48357 0.34139 2.49204
    Norway 4 7.522 0.03880 1.45900 1.33095 0.88521 0.66973 0.36503 0.34699 2.46531
    North AmericaCanada 5 7.427 0.03553 1.32629 1.32261 0.90563 0.63297 0.32957 0.45811 2.45176
    Western EuropeFinland 6 7.406 0.03140 1.29025 1.31826 0.88911 0.64169 0.41372 0.23351 2.61955
    Netherlands 7 7.378 0.02799 1.32944 1.28017 0.89284 0.61576 0.31814 0.47610 2.46570
    Sweden 8 7.364 0.03157 1.33171 1.28907 0.91087 0.65980 0.43844 0.36262 2.37119
    Australia and New ZealandNew Zealand 9 7.286 0.03371 1.25018 1.31967 0.90837 0.63938 0.42922 0.47501 2.26425
    Australia 10 7.284 0.04083 1.33358 1.30923 0.93156 0.65124 0.35637 0.43562 2.26646
    Middle East and Northern AfricaIsrael 11 7.278 0.03470 1.22857 1.22393 0.91387 0.41319 0.07785 0.33172 3.08854
    Latin America and CaribbeanCosta Rica 12 7.226 0.04454 0.95578 1.23788 0.86027 0.63376 0.10583 0.25497 3.17728
    Western EuropeAustria 13 7.200 0.03751 1.33723 1.29704 0.89042 0.62433 0.18676 0.33088 2.53320
    Latin America and CaribbeanMexico 14 7.187 0.04176 1.02054 0.91451 0.81444 0.48181 0.21312 0.14074 3.60214
    North AmericaUnited States 15 7.119 0.03839 1.39451 1.24711 0.86179 0.54604 0.15890 0.40105 2.51011
    Latin America and CaribbeanBrazil 16 6.983 0.04076 0.98124 1.23287 0.69702 0.49049 0.17521 0.14574 3.26001
    Western EuropeLuxembourg 17 6.946 0.03499 1.56391 1.21963 0.91894 0.61583 0.37798 0.28034 1.96961
    Ireland 18 6.940 0.03676 1.33596 1.36948 0.89533 0.61777 0.28703 0.45901 1.97570
    Belgium 19 6.937 0.03595 1.30782 1.28566 0.89667 0.58450 0.22540 0.22250 2.41484
    Middle East and Northern AfricaUnited Arab Emirates 20 6.901 0.03729 1.42727 1.12575 0.80925 0.64157 0.38583 0.26428 2.24743
     
     
     
     
     
     
    # level0 索引
    report_2015_df2.loc['Western Europe']
     
     
     
     Happiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
    Country          
    Switzerland 1 7.587 0.03411 1.39651 1.34951 0.94143 0.66557 0.41978 0.29678 2.51738
    Iceland 2 7.561 0.04884 1.30232 1.40223 0.94784 0.62877 0.14145 0.43630 2.70201
    Denmark 3 7.527 0.03328 1.32548 1.36058 0.87464 0.64938 0.48357 0.34139 2.49204
    Norway 4 7.522 0.03880 1.45900 1.33095 0.88521 0.66973 0.36503 0.34699 2.46531
    Finland 6 7.406 0.03140 1.29025 1.31826 0.88911 0.64169 0.41372 0.23351 2.61955
    Netherlands 7 7.378 0.02799 1.32944 1.28017 0.89284 0.61576 0.31814 0.47610 2.46570
    Sweden 8 7.364 0.03157 1.33171 1.28907 0.91087 0.65980 0.43844 0.36262 2.37119
    Austria 13 7.200 0.03751 1.33723 1.29704 0.89042 0.62433 0.18676 0.33088 2.53320
    Luxembourg 17 6.946 0.03499 1.56391 1.21963 0.91894 0.61583 0.37798 0.28034 1.96961
    Ireland 18 6.940 0.03676 1.33596 1.36948 0.89533 0.61777 0.28703 0.45901 1.97570
    Belgium 19 6.937 0.03595 1.30782 1.28566 0.89667 0.58450 0.22540 0.22250 2.41484
    United Kingdom 21 6.867 0.01866 1.26637 1.28548 0.90943 0.59625 0.32067 0.51912 1.96994
    Germany 26 6.750 0.01848 1.32792 1.29937 0.89186 0.61477 0.21843 0.28214 2.11569
    France 29 6.575 0.03512 1.27778 1.26038 0.94579 0.55011 0.20646 0.12332 2.21126
    Spain 36 6.329 0.03468 1.23011 1.31379 0.95562 0.45951 0.06398 0.18227 2.12367
    Malta 37 6.302 0.04206 1.20740 1.30203 0.88721 0.60365 0.13586 0.51752 1.64880
    Italy 50 5.948 0.03914 1.25114 1.19777 0.95446 0.26236 0.02901 0.22823 2.02518
    North Cyprus 66 5.695 0.05635 1.20806 1.07008 0.92356 0.49027 0.14280 0.26169 1.59888
    Cyprus 67 5.689 0.05580 1.20813 0.89318 0.92356 0.40672 0.06146 0.30638 1.88931
    Portugal 88 5.102 0.04802 1.15991 1.13935 0.87519 0.51469 0.01078 0.13719 1.26462
    Greece 102 4.857 0.05062 1.15406 0.92933 0.88213 0.07699 0.01397 0.00000 1.80101
     
     
     
     
     
     
    # 两层索引
    report_2015_df2.loc['Western Europe', 'Switzerland']
     
     
     
    Happiness Rank                   1.00000
Happiness Score                  7.58700
Standard Error                   0.03411
Economy (GDP per Capita)         1.39651
Family                           1.34951
Health (Life Expectancy)         0.94143
Freedom                          0.66557
Trust (Government Corruption)    0.41978
Generosity                       0.29678
Dystopia Residual                2.51738
Name: (Western Europe, Switzerland), dtype: float64
     
     
     
     
     
     
    # 交换分层顺序
    report_2015_df2.swaplevel()
     
     
     
      Happiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
    CountryRegion          
    SwitzerlandWestern Europe 1 7.587 0.03411 1.39651 1.34951 0.94143 0.66557 0.41978 0.29678 2.51738
    IcelandWestern Europe 2 7.561 0.04884 1.30232 1.40223 0.94784 0.62877 0.14145 0.43630 2.70201
    DenmarkWestern Europe 3 7.527 0.03328 1.32548 1.36058 0.87464 0.64938 0.48357 0.34139 2.49204
    NorwayWestern Europe 4 7.522 0.03880 1.45900 1.33095 0.88521 0.66973 0.36503 0.34699 2.46531
    CanadaNorth America 5 7.427 0.03553 1.32629 1.32261 0.90563 0.63297 0.32957 0.45811 2.45176
    FinlandWestern Europe 6 7.406 0.03140 1.29025 1.31826 0.88911 0.64169 0.41372 0.23351 2.61955
    NetherlandsWestern Europe 7 7.378 0.02799 1.32944 1.28017 0.89284 0.61576 0.31814 0.47610 2.46570
    SwedenWestern Europe 8 7.364 0.03157 1.33171 1.28907 0.91087 0.65980 0.43844 0.36262 2.37119
    New ZealandAustralia and New Zealand 9 7.286 0.03371 1.25018 1.31967 0.90837 0.63938 0.42922 0.47501 2.26425
    AustraliaAustralia and New Zealand 10 7.284 0.04083 1.33358 1.30923 0.93156 0.65124 0.35637 0.43562 2.26646
    IsraelMiddle East and Northern Africa 11 7.278 0.03470 1.22857 1.22393 0.91387 0.41319 0.07785 0.33172 3.08854
    Costa RicaLatin America and Caribbean 12 7.226 0.04454 0.95578 1.23788 0.86027 0.63376 0.10583 0.25497 3.17728
    AustriaWestern Europe 13 7.200 0.03751 1.33723 1.29704 0.89042 0.62433 0.18676 0.33088 2.53320
    MexicoLatin America and Caribbean 14 7.187 0.04176 1.02054 0.91451 0.81444 0.48181 0.21312 0.14074 3.60214
    United StatesNorth America 15 7.119 0.03839 1.39451 1.24711 0.86179 0.54604 0.15890 0.40105 2.51011
    BrazilLatin America and Caribbean 16 6.983 0.04076 0.98124 1.23287 0.69702 0.49049 0.17521 0.14574 3.26001
    LuxembourgWestern Europe 17 6.946 0.03499 1.56391 1.21963 0.91894 0.61583 0.37798 0.28034 1.96961
    IrelandWestern Europe 18 6.940 0.03676 1.33596 1.36948 0.89533 0.61777 0.28703 0.45901 1.97570
    BelgiumWestern Europe 19 6.937 0.03595 1.30782 1.28566 0.89667 0.58450 0.22540 0.22250 2.41484
    United Arab EmiratesMiddle East and Northern Africa 20 6.901 0.03729 1.42727 1.12575 0.80925 0.64157 0.38583 0.26428 2.24743
    United KingdomWestern Europe 21 6.867 0.01866 1.26637 1.28548 0.90943 0.59625 0.32067 0.51912 1.96994
    OmanMiddle East and Northern Africa 22 6.853 0.05335 1.36011 1.08182 0.76276 0.63274 0.32524 0.21542 2.47489
    VenezuelaLatin America and Caribbean 23 6.810 0.06476 1.04424 1.25596 0.72052 0.42908 0.11069 0.05841 3.19131
    SingaporeSoutheastern Asia 24 6.798 0.03780 1.52186 1.02000 1.02525 0.54252 0.49210 0.31105 1.88501
    PanamaLatin America and Caribbean 25 6.786 0.04910 1.06353 1.19850 0.79661 0.54210 0.09270 0.24434 2.84848
    GermanyWestern Europe 26 6.750 0.01848 1.32792 1.29937 0.89186 0.61477 0.21843 0.28214 2.11569
    ChileLatin America and Caribbean 27 6.670 0.05800 1.10715 1.12447 0.85857 0.44132 0.12869 0.33363 2.67585
    QatarMiddle East and Northern Africa 28 6.611 0.06257 1.69042 1.07860 0.79733 0.64040 0.52208 0.32573 1.55674
    FranceWestern Europe 29 6.575 0.03512 1.27778 1.26038 0.94579 0.55011 0.20646 0.12332 2.21126
    ArgentinaLatin America and Caribbean 30 6.574 0.04612 1.05351 1.24823 0.78723 0.44974 0.08484 0.11451 2.83600
    ...... ... ... ... ... ... ... ... ... ... ...
    MyanmarSoutheastern Asia 129 4.307 0.04351 0.27108 0.70905 0.48246 0.44017 0.19034 0.79588 1.41805
    GeorgiaCentral and Eastern Europe 130 4.297 0.04221 0.74190 0.38562 0.72926 0.40577 0.38331 0.05547 1.59541
    MalawiSub-Saharan Africa 131 4.292 0.06130 0.01604 0.41134 0.22562 0.43054 0.06977 0.33128 2.80791
    Sri LankaSouthern Asia 132 4.271 0.03751 0.83524 1.01905 0.70806 0.53726 0.09179 0.40828 0.67108
    CameroonSub-Saharan Africa 133 4.252 0.04678 0.42250 0.88767 0.23402 0.49309 0.05786 0.20618 1.95071
    BulgariaCentral and Eastern Europe 134 4.218 0.04828 1.01216 1.10614 0.76649 0.30587 0.00872 0.11921 0.89991
    EgyptMiddle East and Northern Africa 135 4.194 0.03260 0.88180 0.74700 0.61712 0.17288 0.06324 0.11291 1.59927
    YemenMiddle East and Northern Africa 136 4.077 0.04367 0.54649 0.68093 0.40064 0.35571 0.07854 0.09131 1.92313
    AngolaSub-Saharan Africa 137 4.033 0.04758 0.75778 0.86040 0.16683 0.10384 0.07122 0.12344 1.94939
    MaliSub-Saharan Africa 138 3.995 0.05602 0.26074 1.03526 0.20583 0.38857 0.12352 0.18798 1.79293
    Congo (Brazzaville)Sub-Saharan Africa 139 3.989 0.06682 0.67866 0.66290 0.31051 0.41466 0.11686 0.12388 1.68135
    ComorosSub-Saharan Africa 140 3.956 0.04797 0.23906 0.79273 0.36315 0.22917 0.19900 0.17441 1.95812
    UgandaSub-Saharan Africa 141 3.931 0.04317 0.21102 1.13299 0.33861 0.45727 0.07267 0.29066 1.42766
    SenegalSub-Saharan Africa 142 3.904 0.03608 0.36498 0.97619 0.43540 0.36772 0.10713 0.20843 1.44395
    GabonSub-Saharan Africa 143 3.896 0.04547 1.06024 0.90528 0.43372 0.31914 0.11091 0.06822 0.99895
    NigerSub-Saharan Africa 144 3.845 0.03602 0.06940 0.77265 0.29707 0.47692 0.15639 0.19387 1.87877
    CambodiaSoutheastern Asia 145 3.819 0.05069 0.46038 0.62736 0.61114 0.66246 0.07247 0.40359 0.98195
    TanzaniaSub-Saharan Africa 146 3.781 0.05061 0.28520 1.00268 0.38215 0.32878 0.05747 0.34377 1.38079
    MadagascarSub-Saharan Africa 147 3.681 0.03633 0.20824 0.66801 0.46721 0.19184 0.08124 0.21333 1.85100
    Central African RepublicSub-Saharan Africa 148 3.678 0.06112 0.07850 0.00000 0.06699 0.48879 0.08289 0.23835 2.72230
    ChadSub-Saharan Africa 149 3.667 0.03830 0.34193 0.76062 0.15010 0.23501 0.05269 0.18386 1.94296
    GuineaSub-Saharan Africa 150 3.656 0.03590 0.17417 0.46475 0.24009 0.37725 0.12139 0.28657 1.99172
    Ivory CoastSub-Saharan Africa 151 3.655 0.05141 0.46534 0.77115 0.15185 0.46866 0.17922 0.20165 1.41723
    Burkina FasoSub-Saharan Africa 152 3.587 0.04324 0.25812 0.85188 0.27125 0.39493 0.12832 0.21747 1.46494
    AfghanistanSouthern Asia 153 3.575 0.03084 0.31982 0.30285 0.30335 0.23414 0.09719 0.36510 1.95210
    RwandaSub-Saharan Africa 154 3.465 0.03464 0.22208 0.77370 0.42864 0.59201 0.55191 0.22628 0.67042
    BeninSub-Saharan Africa 155 3.340 0.03656 0.28665 0.35386 0.31910 0.48450 0.08010 0.18260 1.63328
    SyriaMiddle East and Northern Africa 156 3.006 0.05015 0.66320 0.47489 0.72193 0.15684 0.18906 0.47179 0.32858
    BurundiSub-Saharan Africa 157 2.905 0.08658 0.01530 0.41587 0.22396 0.11850 0.10062 0.19727 1.83302
    TogoSub-Saharan Africa 158 2.839 0.06727 0.20868 0.13995 0.28443 0.36453 0.10731 0.16681 1.56726

    158 rows × 10 columns

     
     
     
     
     
     
    # 排序分层
    report_2015_df2.sort_index(level=0)
     
     
     
      Happiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
    RegionCountry          
    Australia and New ZealandAustralia 10 7.284 0.04083 1.33358 1.30923 0.93156 0.65124 0.35637 0.43562 2.26646
    New Zealand 9 7.286 0.03371 1.25018 1.31967 0.90837 0.63938 0.42922 0.47501 2.26425
    Central and Eastern EuropeAlbania 95 4.959 0.05013 0.87867 0.80434 0.81325 0.35733 0.06413 0.14272 1.89894
    Armenia 127 4.350 0.04763 0.76821 0.77711 0.72990 0.19847 0.03900 0.07855 1.75873
    Azerbaijan 80 5.212 0.03363 1.02389 0.93793 0.64045 0.37030 0.16065 0.07799 2.00073
    Belarus 59 5.813 0.03938 1.03192 1.23289 0.73608 0.37938 0.19090 0.11046 2.13090
    Bosnia and Herzegovina 96 4.949 0.06913 0.83223 0.91916 0.79081 0.09245 0.00227 0.24808 2.06367
    Bulgaria 134 4.218 0.04828 1.01216 1.10614 0.76649 0.30587 0.00872 0.11921 0.89991
    Croatia 62 5.759 0.04394 1.08254 0.79624 0.78805 0.25883 0.02430 0.05444 2.75414
    Czech Republic 31 6.505 0.04168 1.17898 1.20643 0.84483 0.46364 0.02652 0.10686 2.67782
    Estonia 73 5.429 0.04013 1.15174 1.22791 0.77361 0.44888 0.15184 0.08680 1.58782
    Georgia 130 4.297 0.04221 0.74190 0.38562 0.72926 0.40577 0.38331 0.05547 1.59541
    Hungary 104 4.800 0.06107 1.12094 1.20215 0.75905 0.32112 0.02758 0.12800 1.24074
    Kazakhstan 54 5.855 0.04114 1.12254 1.12241 0.64368 0.51649 0.08454 0.11827 2.24729
    Kosovo 69 5.589 0.05018 0.80148 0.81198 0.63132 0.24749 0.04741 0.28310 2.76579
    Kyrgyzstan 77 5.286 0.03823 0.47428 1.15115 0.65088 0.43477 0.04232 0.30030 2.23270
    Latvia 89 5.098 0.04640 1.11312 1.09562 0.72437 0.29671 0.06332 0.18226 1.62215
    Lithuania 56 5.833 0.03843 1.14723 1.25745 0.73128 0.21342 0.01031 0.02641 2.44649
    Macedonia 93 5.007 0.05376 0.91851 1.00232 0.73545 0.33457 0.05327 0.22359 1.73933
    Moldova 52 5.889 0.03799 0.59448 1.01528 0.61826 0.32818 0.01615 0.20951 3.10712
    Montenegro 82 5.192 0.05235 0.97438 0.90557 0.72521 0.18260 0.14296 0.16140 2.10017
    Poland 60 5.791 0.04263 1.12555 1.27948 0.77903 0.53122 0.04212 0.16759 1.86565
    Romania 86 5.124 0.06607 1.04345 0.88588 0.76890 0.35068 0.00649 0.13748 1.93129
    Russia 64 5.716 0.03135 1.13764 1.23617 0.66926 0.36679 0.03005 0.00199 2.27394
    Serbia 87 5.123 0.04864 0.92053 1.00964 0.74836 0.20107 0.02617 0.19231 2.02500
    Slovakia 45 5.995 0.04267 1.16891 1.26999 0.78902 0.31751 0.03431 0.16893 2.24639
    Slovenia 55 5.848 0.04251 1.18498 1.27385 0.87337 0.60855 0.03787 0.25328 1.61583
    Tajikistan 106 4.786 0.03198 0.39047 0.85563 0.57379 0.47216 0.15072 0.22974 2.11399
    Turkmenistan 70 5.548 0.04175 0.95847 1.22668 0.53886 0.47610 0.30844 0.16979 1.86984
    Ukraine 111 4.681 0.04412 0.79907 1.20278 0.67390 0.25123 0.02961 0.15275 1.57140
    ...... ... ... ... ... ... ... ... ... ... ...
    Sub-Saharan AfricaSomaliland region 91 5.057 0.06161 0.18847 0.95152 0.43873 0.46582 0.39928 0.50318 2.11032
    South Africa 113 4.642 0.04585 0.92049 1.18468 0.27688 0.33207 0.08884 0.11973 1.71956
    Sudan 118 4.550 0.06740 0.52107 1.01404 0.36878 0.10081 0.14660 0.19062 2.20857
    Swaziland 101 4.867 0.08742 0.71206 1.07284 0.07566 0.30658 0.03060 0.18259 2.48676
    Tanzania 146 3.781 0.05061 0.28520 1.00268 0.38215 0.32878 0.05747 0.34377 1.38079
    Togo 158 2.839 0.06727 0.20868 0.13995 0.28443 0.36453 0.10731 0.16681 1.56726
    Uganda 141 3.931 0.04317 0.21102 1.13299 0.33861 0.45727 0.07267 0.29066 1.42766
    Zambia 85 5.129 0.06988 0.47038 0.91612 0.29924 0.48827 0.12468 0.19591 2.63430
    Zimbabwe 115 4.610 0.04290 0.27100 1.03276 0.33475 0.25861 0.08079 0.18987 2.44191
    Western EuropeAustria 13 7.200 0.03751 1.33723 1.29704 0.89042 0.62433 0.18676 0.33088 2.53320
    Belgium 19 6.937 0.03595 1.30782 1.28566 0.89667 0.58450 0.22540 0.22250 2.41484
    Cyprus 67 5.689 0.05580 1.20813 0.89318 0.92356 0.40672 0.06146 0.30638 1.88931
    Denmark 3 7.527 0.03328 1.32548 1.36058 0.87464 0.64938 0.48357 0.34139 2.49204
    Finland 6 7.406 0.03140 1.29025 1.31826 0.88911 0.64169 0.41372 0.23351 2.61955
    France 29 6.575 0.03512 1.27778 1.26038 0.94579 0.55011 0.20646 0.12332 2.21126
    Germany 26 6.750 0.01848 1.32792 1.29937 0.89186 0.61477 0.21843 0.28214 2.11569
    Greece 102 4.857 0.05062 1.15406 0.92933 0.88213 0.07699 0.01397 0.00000 1.80101
    Iceland 2 7.561 0.04884 1.30232 1.40223 0.94784 0.62877 0.14145 0.43630 2.70201
    Ireland 18 6.940 0.03676 1.33596 1.36948 0.89533 0.61777 0.28703 0.45901 1.97570
    Italy 50 5.948 0.03914 1.25114 1.19777 0.95446 0.26236 0.02901 0.22823 2.02518
    Luxembourg 17 6.946 0.03499 1.56391 1.21963 0.91894 0.61583 0.37798 0.28034 1.96961
    Malta 37 6.302 0.04206 1.20740 1.30203 0.88721 0.60365 0.13586 0.51752 1.64880
    Netherlands 7 7.378 0.02799 1.32944 1.28017 0.89284 0.61576 0.31814 0.47610 2.46570
    North Cyprus 66 5.695 0.05635 1.20806 1.07008 0.92356 0.49027 0.14280 0.26169 1.59888
    Norway 4 7.522 0.03880 1.45900 1.33095 0.88521 0.66973 0.36503 0.34699 2.46531
    Portugal 88 5.102 0.04802 1.15991 1.13935 0.87519 0.51469 0.01078 0.13719 1.26462
    Spain 36 6.329 0.03468 1.23011 1.31379 0.95562 0.45951 0.06398 0.18227 2.12367
    Sweden 8 7.364 0.03157 1.33171 1.28907 0.91087 0.65980 0.43844 0.36262 2.37119
    Switzerland 1 7.587 0.03411 1.39651 1.34951 0.94143 0.66557 0.41978 0.29678 2.51738
    United Kingdom 21 6.867 0.01866 1.26637 1.28548 0.90943 0.59625 0.32067 0.51912 1.96994

    158 rows × 10 columns

     

    7. 数据清洗

     
     
     
     
     
     
    log_data = pd.read_csv('log.csv')
    log_data
     
     
     
     timeuservideoplayback positionpausedvolume
    0 1469974424 cheryl intro.html 5 False 10.0
    1 1469974454 cheryl intro.html 6 NaN NaN
    2 1469974544 cheryl intro.html 9 NaN NaN
    3 1469974574 cheryl intro.html 10 NaN NaN
    4 1469977514 bob intro.html 1 NaN NaN
    5 1469977544 bob intro.html 1 NaN NaN
    6 1469977574 bob intro.html 1 NaN NaN
    7 1469977604 bob intro.html 1 NaN NaN
    8 1469974604 cheryl intro.html 11 NaN NaN
    9 1469974694 cheryl intro.html 14 NaN NaN
    10 1469974724 cheryl intro.html 15 NaN NaN
    11 1469974454 sue advanced.html 24 NaN NaN
    12 1469974524 sue advanced.html 25 NaN NaN
    13 1469974424 sue advanced.html 23 False 10.0
    14 1469974554 sue advanced.html 26 NaN NaN
    15 1469974624 sue advanced.html 27 NaN NaN
    16 1469974654 sue advanced.html 28 NaN 5.0
    17 1469974724 sue advanced.html 29 NaN NaN
    18 1469974484 cheryl intro.html 7 NaN NaN
    19 1469974514 cheryl intro.html 8 NaN NaN
    20 1469974754 sue advanced.html 30 NaN NaN
    21 1469974824 sue advanced.html 31 NaN NaN
    22 1469974854 sue advanced.html 32 NaN NaN
    23 1469974924 sue advanced.html 33 NaN NaN
    24 1469977424 bob intro.html 1 True 10.0
    25 1469977454 bob intro.html 1 NaN NaN
    26 1469977484 bob intro.html 1 NaN NaN
    27 1469977634 bob intro.html 1 NaN NaN
    28 1469977664 bob intro.html 1 NaN NaN
    29 1469974634 cheryl intro.html 12 NaN NaN
    30 1469974664 cheryl intro.html 13 NaN NaN
    31 1469977694 bob intro.html 1 NaN NaN
    32 1469977724 bob intro.html 1 NaN NaN
     
     
     
     
     
     
    log_data.set_index(['time', 'user'], inplace=True)
    log_data.sort_index(inplace=True)
    log_data
     
     
     
      videoplayback positionpausedvolume
    timeuser    
    1469974424cheryl intro.html 5 False 10.0
    sue advanced.html 23 False 10.0
    1469974454cheryl intro.html 6 NaN NaN
    sue advanced.html 24 NaN NaN
    1469974484cheryl intro.html 7 NaN NaN
    1469974514cheryl intro.html 8 NaN NaN
    1469974524sue advanced.html 25 NaN NaN
    1469974544cheryl intro.html 9 NaN NaN
    1469974554sue advanced.html 26 NaN NaN
    1469974574cheryl intro.html 10 NaN NaN
    1469974604cheryl intro.html 11 NaN NaN
    1469974624sue advanced.html 27 NaN NaN
    1469974634cheryl intro.html 12 NaN NaN
    1469974654sue advanced.html 28 NaN 5.0
    1469974664cheryl intro.html 13 NaN NaN
    1469974694cheryl intro.html 14 NaN NaN
    1469974724cheryl intro.html 15 NaN NaN
    sue advanced.html 29 NaN NaN
    1469974754sue advanced.html 30 NaN NaN
    1469974824sue advanced.html 31 NaN NaN
    1469974854sue advanced.html 32 NaN NaN
    1469974924sue advanced.html 33 NaN NaN
    1469977424bob intro.html 1 True 10.0
    1469977454bob intro.html 1 NaN NaN
    1469977484bob intro.html 1 NaN NaN
    1469977514bob intro.html 1 NaN NaN
    1469977544bob intro.html 1 NaN NaN
    1469977574bob intro.html 1 NaN NaN
    1469977604bob intro.html 1 NaN NaN
    1469977634bob intro.html 1 NaN NaN
    1469977664bob intro.html 1 NaN NaN
    1469977694bob intro.html 1 NaN NaN
    1469977724bob intro.html 1 NaN NaN
     
     
     
     
     
     
    log_data.fillna(0)
     
     
     
      videoplayback positionpausedvolume
    timeuser    
    1469974424cheryl intro.html 5 False 10.0
    sue advanced.html 23 False 10.0
    1469974454cheryl intro.html 6 0 0.0
    sue advanced.html 24 0 0.0
    1469974484cheryl intro.html 7 0 0.0
    1469974514cheryl intro.html 8 0 0.0
    1469974524sue advanced.html 25 0 0.0
    1469974544cheryl intro.html 9 0 0.0
    1469974554sue advanced.html 26 0 0.0
    1469974574cheryl intro.html 10 0 0.0
    1469974604cheryl intro.html 11 0 0.0
    1469974624sue advanced.html 27 0 0.0
    1469974634cheryl intro.html 12 0 0.0
    1469974654sue advanced.html 28 0 5.0
    1469974664cheryl intro.html 13 0 0.0
    1469974694cheryl intro.html 14 0 0.0
    1469974724cheryl intro.html 15 0 0.0
    sue advanced.html 29 0 0.0
    1469974754sue advanced.html 30 0 0.0
    1469974824sue advanced.html 31 0 0.0
    1469974854sue advanced.html 32 0 0.0
    1469974924sue advanced.html 33 0 0.0
    1469977424bob intro.html 1 True 10.0
    1469977454bob intro.html 1 0 0.0
    1469977484bob intro.html 1 0 0.0
    1469977514bob intro.html 1 0 0.0
    1469977544bob intro.html 1 0 0.0
    1469977574bob intro.html 1 0 0.0
    1469977604bob intro.html 1 0 0.0
    1469977634bob intro.html 1 0 0.0
    1469977664bob intro.html 1 0 0.0
    1469977694bob intro.html 1 0 0.0
    1469977724bob intro.html 1 0 0.0
     
     
     
     
     
     
    log_data.dropna()
     
     
     
      videoplayback positionpausedvolume
    timeuser    
    1469974424cheryl intro.html 5 False 10.0
    sue advanced.html 23 False 10.0
    1469977424bob intro.html 1 True 10.0
     
     
     
     
     
     
    log_data.ffill()
     
     
     
      videoplayback positionpausedvolume
    timeuser    
    1469974424cheryl intro.html 5 False 10.0
    sue advanced.html 23 False 10.0
    1469974454cheryl intro.html 6 False 10.0
    sue advanced.html 24 False 10.0
    1469974484cheryl intro.html 7 False 10.0
    1469974514cheryl intro.html 8 False 10.0
    1469974524sue advanced.html 25 False 10.0
    1469974544cheryl intro.html 9 False 10.0
    1469974554sue advanced.html 26 False 10.0
    1469974574cheryl intro.html 10 False 10.0
    1469974604cheryl intro.html 11 False 10.0
    1469974624sue advanced.html 27 False 10.0
    1469974634cheryl intro.html 12 False 10.0
    1469974654sue advanced.html 28 False 5.0
    1469974664cheryl intro.html 13 False 5.0
    1469974694cheryl intro.html 14 False 5.0
    1469974724cheryl intro.html 15 False 5.0
    sue advanced.html 29 False 5.0
    1469974754sue advanced.html 30 False 5.0
    1469974824sue advanced.html 31 False 5.0
    1469974854sue advanced.html 32 False 5.0
    1469974924sue advanced.html 33 False 5.0
    1469977424bob intro.html 1 True 10.0
    1469977454bob intro.html 1 True 10.0
    1469977484bob intro.html 1 True 10.0
    1469977514bob intro.html 1 True 10.0
    1469977544bob intro.html 1 True 10.0
    1469977574bob intro.html 1 True 10.0
    1469977604bob intro.html 1 True 10.0
    1469977634bob intro.html 1 True 10.0
    1469977664bob intro.html 1 True 10.0
    1469977694bob intro.html 1 True 10.0
    1469977724bob intro.html 1 True 10.0
     
     
     
     
     
     
    log_data.bfill()
     
     
     
      videoplayback positionpausedvolume
    timeuser    
    1469974424cheryl intro.html 5 False 10.0
    sue advanced.html 23 False 10.0
    1469974454cheryl intro.html 6 True 5.0
    sue advanced.html 24 True 5.0
    1469974484cheryl intro.html 7 True 5.0
    1469974514cheryl intro.html 8 True 5.0
    1469974524sue advanced.html 25 True 5.0
    1469974544cheryl intro.html 9 True 5.0
    1469974554sue advanced.html 26 True 5.0
    1469974574cheryl intro.html 10 True 5.0
    1469974604cheryl intro.html 11 True 5.0
    1469974624sue advanced.html 27 True 5.0
    1469974634cheryl intro.html 12 True 5.0
    1469974654sue advanced.html 28 True 5.0
    1469974664cheryl intro.html 13 True 10.0
    1469974694cheryl intro.html 14 True 10.0
    1469974724cheryl intro.html 15 True 10.0
    sue advanced.html 29 True 10.0
    1469974754sue advanced.html 30 True 10.0
    1469974824sue advanced.html 31 True 10.0
    1469974854sue advanced.html 32 True 10.0
    1469974924sue advanced.html 33 True 10.0
    1469977424bob intro.html 1 True 10.0
    1469977454bob intro.html 1 NaN NaN
    1469977484bob intro.html 1 NaN NaN
    1469977514bob intro.html 1 NaN NaN
    1469977544bob intro.html 1 NaN NaN
    1469977574bob intro.html 1 NaN NaN
    1469977604bob intro.html 1 NaN NaN
    1469977634bob intro.html 1 NaN NaN
    1469977664bob intro.html 1 NaN NaN
    1469977694bob intro.html 1 NaN NaN
    1469977724bob intro.html 1 NaN NaN
     
     
     
     
     
     
    ​
     
     
     
     
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  • 原文地址：https://www.cnblogs.com/crawer-1/p/7837209.html
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