In [1]:

    

import pandas as pd


    

In [2]:

    

df = pd.read_csv('data/src/titanic_train.csv')


    

In [3]:

    

df.drop(labels=['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)


    

In [4]:

    

df_single = df.set_index('PassengerId')


    

In [5]:

    

print(df_single.head())


    

    




             Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked
PassengerId                                                                
1                   0       3    male  22.0      1      0   7.2500        S
2                   1       1  female  38.0      1      0  71.2833        C
3                   1       3  female  26.0      0      0   7.9250        S
4                   1       1  female  35.0      1      0  53.1000        S
5                   0       3    male  35.0      0      0   8.0500        S

In [6]:

    

df_multi = df.set_index(['Sex', 'Pclass', 'Embarked', 'PassengerId']).sort_index()


    

In [7]:

    

print(df_multi.head())


    

    




                                    Survived   Age  SibSp  Parch      Fare
Sex    Pclass Embarked PassengerId                                        
female 1      C        2                   1  38.0      1      0   71.2833
                       32                  1   NaN      1      0  146.5208
                       53                  1  49.0      1      0   76.7292
                       178                 0  50.0      0      0   28.7125
                       195                 1  44.0      0      0   27.7208

In [8]:

    

print(df_multi.tail())


    

    




                                  Survived   Age  SibSp  Parch    Fare
Sex  Pclass Embarked PassengerId                                      
male 3      S        877                 0  20.0      0      0  9.8458
                     878                 0  19.0      0      0  7.8958
                     879                 0   NaN      0      0  7.8958
                     882                 0  33.0      0      0  7.8958
                     885                 0  25.0      0      0  7.0500

In [9]:

    

print(df_multi.mean())


    

    




Survived     0.383838
Age         29.699118
SibSp        0.523008
Parch        0.381594
Fare        32.204208
dtype: float64

In [10]:

    

print(df_single.mean())


    

    




Survived     0.383838
Pclass       2.308642
Age         29.699118
SibSp        0.523008
Parch        0.381594
Fare        32.204208
dtype: float64

In [11]:

    

print(df_multi.max())


    

    




Survived      1.0000
Age          80.0000
SibSp         8.0000
Parch         6.0000
Fare        512.3292
dtype: float64

In [12]:

    

print(df_single.max())


    

    




Survived          1
Pclass            3
Sex            male
Age              80
SibSp             8
Parch             6
Fare        512.329
dtype: object

In [13]:

    

print(df_multi.mean(level='Sex'))


    

    




        Survived        Age     SibSp     Parch       Fare
Sex                                                       
female  0.742038  27.915709  0.694268  0.649682  44.479818
male    0.188908  30.726645  0.429809  0.235702  25.523893

In [14]:

    

print(df_multi.mean(level=0))


    

    




        Survived        Age     SibSp     Parch       Fare
Sex                                                       
female  0.742038  27.915709  0.694268  0.649682  44.479818
male    0.188908  30.726645  0.429809  0.235702  25.523893

In [15]:

    

print(df_multi.mean(level=1))


    

    




        Survived        Age     SibSp     Parch       Fare
Pclass                                                    
1       0.629630  38.233441  0.416667  0.356481  84.154687
2       0.472826  29.877630  0.402174  0.380435  20.662183
3       0.242363  25.140620  0.615071  0.393075  13.675550

In [16]:

    

print(df_multi.mean(level=2))


    

    




          Survived        Age     SibSp     Parch       Fare
Embarked                                                    
C         0.553571  30.814769  0.386905  0.363095  59.954144
Q         0.389610  28.089286  0.428571  0.168831  13.276030
S         0.336957  29.445397  0.571429  0.413043  27.079812

In [17]:

    

print(df_multi.mean(level=['Sex', 'Pclass']))


    

    




               Survived        Age     SibSp     Parch        Fare
Sex    Pclass                                                     
female 1       0.968085  34.611765  0.553191  0.457447  106.125798
       2       0.921053  28.722973  0.486842  0.605263   21.970121
       3       0.500000  21.750000  0.895833  0.798611   16.118810
male   1       0.368852  41.281386  0.311475  0.278689   67.226127
       2       0.157407  30.740707  0.342593  0.222222   19.741782
       3       0.135447  26.507589  0.498559  0.224784   12.661633

In [18]:

    

print(df_multi.mean(level=[0, 1, 2]))


    

    




                        Survived        Age     SibSp     Parch        Fare
Sex    Pclass Embarked                                                     
female 1      C         0.976744  36.052632  0.511628  0.302326  115.640309
              Q         1.000000  33.000000  1.000000  0.000000   90.000000
              S         0.958333  32.704545  0.604167  0.625000   99.026910
       2      C         1.000000  19.142857  0.714286  0.571429   25.268457
              Q         1.000000  30.000000  0.000000  0.000000   12.350000
              S         0.910448  29.719697  0.477612  0.626866   21.912687
       3      C         0.652174  14.062500  0.565217  0.826087   14.694926
              Q         0.727273  22.850000  0.212121  0.242424   10.307833
              S         0.375000  23.223684  1.238636  1.000000   18.670077
male   1      C         0.404762  40.111111  0.238095  0.333333   93.536707
              Q         0.000000  44.000000  2.000000  0.000000   90.000000
              S         0.354430  41.897188  0.329114  0.253165   52.949947
       2      C         0.200000  25.937500  0.500000  0.500000   25.421250
              Q         0.000000  57.000000  0.000000  0.000000   12.350000
              S         0.154639  30.875889  0.329897  0.195876   19.232474
       3      C         0.232558  25.016800  0.232558  0.139535    9.352237
              Q         0.076923  28.142857  0.589744  0.128205   11.924251
              S         0.128302  26.574766  0.528302  0.252830   13.307149

In [19]:

    

print(df_single.groupby(by='Sex').mean())


    

    




        Survived    Pclass        Age     SibSp     Parch       Fare
Sex                                                                 
female  0.742038  2.159236  27.915709  0.694268  0.649682  44.479818
male    0.188908  2.389948  30.726645  0.429809  0.235702  25.523893

In [20]:

    

print(df_single.groupby(by=['Sex', 'Pclass', 'Embarked']).mean())


    

    




                        Survived        Age     SibSp     Parch        Fare
Sex    Pclass Embarked                                                     
female 1      C         0.976744  36.052632  0.511628  0.302326  115.640309
              Q         1.000000  33.000000  1.000000  0.000000   90.000000
              S         0.958333  32.704545  0.604167  0.625000   99.026910
       2      C         1.000000  19.142857  0.714286  0.571429   25.268457
              Q         1.000000  30.000000  0.000000  0.000000   12.350000
              S         0.910448  29.719697  0.477612  0.626866   21.912687
       3      C         0.652174  14.062500  0.565217  0.826087   14.694926
              Q         0.727273  22.850000  0.212121  0.242424   10.307833
              S         0.375000  23.223684  1.238636  1.000000   18.670077
male   1      C         0.404762  40.111111  0.238095  0.333333   93.536707
              Q         0.000000  44.000000  2.000000  0.000000   90.000000
              S         0.354430  41.897188  0.329114  0.253165   52.949947
       2      C         0.200000  25.937500  0.500000  0.500000   25.421250
              Q         0.000000  57.000000  0.000000  0.000000   12.350000
              S         0.154639  30.875889  0.329897  0.195876   19.232474
       3      C         0.232558  25.016800  0.232558  0.139535    9.352237
              Q         0.076923  28.142857  0.589744  0.128205   11.924251
              S         0.128302  26.574766  0.528302  0.252830   13.307149

In [21]:

    

print(df_multi.groupby(level='Sex').size())


    

    




Sex
female    314
male      577
dtype: int64

In [22]:

    

print(df_multi.groupby(level=2).size())


    

    




Embarked
C    168
Q     77
S    644
dtype: int64

In [23]:

    

print(df_multi.groupby(level=[0, 1, 2]).size())


    

    




Sex     Pclass  Embarked
female  1       C            43
                Q             1
                S            48
        2       C             7
                Q             2
                S            67
        3       C            23
                Q            33
                S            88
male    1       C            42
                Q             1
                S            79
        2       C            10
                Q             1
                S            97
        3       C            43
                Q            39
                S           265
dtype: int64

In [24]:

    

print(df_single.groupby(by=['Sex', 'Pclass', 'Embarked']).size())


    

    




Sex     Pclass  Embarked
female  1       C            43
                Q             1
                S            48
        2       C             7
                Q             2
                S            67
        3       C            23
                Q            33
                S            88
male    1       C            42
                Q             1
                S            79
        2       C            10
                Q             1
                S            97
        3       C            43
                Q            39
                S           265
dtype: int64