In [1]:

    

import numpy as np
import pandas as pd


    

In [2]:

    

df = pd.read_csv('data/src/titanic_train.csv', index_col=0).drop(['Name', 'Ticket', 'SibSp', 'Parch'], axis=1)


    

In [3]:

    

print(df.head())


    

    




             Survived  Pclass     Sex   Age     Fare Cabin Embarked
PassengerId                                                        
1                   0       3    male  22.0   7.2500   NaN        S
2                   1       1  female  38.0  71.2833   C85        C
3                   1       3  female  26.0   7.9250   NaN        S
4                   1       1  female  35.0  53.1000  C123        S
5                   0       3    male  35.0   8.0500   NaN        S

In [4]:

    

print(pd.pivot_table(df, index='Pclass', columns='Sex'))


    

    




              Age                   Fare             Survived          
Sex        female       male      female       male    female      male
Pclass                                                                 
1       34.611765  41.281386  106.125798  67.226127  0.968085  0.368852
2       28.722973  30.740707   21.970121  19.741782  0.921053  0.157407
3       21.750000  26.507589   16.118810  12.661633  0.500000  0.135447

In [5]:

    

print(type(pd.pivot_table(df, index='Pclass', columns='Sex')))


    

    




<class 'pandas.core.frame.DataFrame'>

In [6]:

    

print(pd.pivot_table(df, index='Pclass', columns='Sex', values='Age'))


    

    




Sex        female       male
Pclass                      
1       34.611765  41.281386
2       28.722973  30.740707
3       21.750000  26.507589

In [7]:

    

print(pd.pivot_table(df, index=['Sex', 'Pclass'], columns='Survived', values=['Age', 'Fare']))


    

    




                     Age                   Fare            
Survived               0          1           0           1
Sex    Pclass                                              
female 1       25.666667  34.939024  110.604167  105.978159
       2       36.000000  28.080882   18.250000   22.288989
       3       23.818182  19.329787   19.773093   12.464526
male   1       44.581967  36.248000   62.894910   74.637320
       2       33.369048  16.022000   19.488965   21.095100
       3       27.255814  22.274211   12.204469   15.579696

In [8]:

    

print(pd.pivot_table(df, index='Sex', columns='Pclass', values='Age', margins=True))


    

    




Pclass          1          2          3        All
Sex                                               
female  34.611765  28.722973  21.750000  27.915709
male    41.281386  30.740707  26.507589  30.726645
All     38.233441  29.877630  25.140620  29.699118

In [9]:

    

print(pd.pivot_table(df, index='Sex', columns='Pclass', values='Age',
                     margins=True, margins_name='Total'))


    

    




Pclass          1          2          3      Total
Sex                                               
female  34.611765  28.722973  21.750000  27.915709
male    41.281386  30.740707  26.507589  30.726645
Total   38.233441  29.877630  25.140620  29.699118

In [10]:

    

print(pd.pivot_table(df, index='Sex', columns='Pclass', values='Age',
                     margins=True, aggfunc=np.min))


    

    




Pclass     1     2     3   All
Sex                           
female  2.00  2.00  0.75  0.75
male    0.92  0.67  0.42  0.42
All     0.92  0.67  0.42  0.42

In [11]:

    

print(pd.pivot_table(df, index='Sex', columns='Pclass', values='Age',
                     margins=True, aggfunc=[np.min, np.max]))


    

    




        amin                    amax                  
Pclass     1     2     3   All     1     2     3   All
Sex                                                   
female  2.00  2.00  0.75  0.75  63.0  57.0  63.0  63.0
male    0.92  0.67  0.42  0.42  80.0  70.0  74.0  80.0
All     0.92  0.67  0.42  0.42  80.0  70.0  74.0  80.0

In [12]:

    

print(pd.pivot_table(df, index='Sex', columns='Pclass', values='Age',
                     margins=True, aggfunc=len))


    

    




Pclass      1      2      3    All
Sex                               
female   94.0   76.0  144.0  261.0
male    122.0  108.0  347.0  453.0
All     186.0  173.0  355.0  714.0

In [13]:

    

print(len(df))


    

    




891

In [14]:

    

print(df.isnull().sum())


    

    




Survived      0
Pclass        0
Sex           0
Age         177
Fare          0
Cabin       687
Embarked      2
dtype: int64

In [15]:

    

print(pd.pivot_table(df, index='Sex', columns='Pclass', values='Age',
                     margins=True, aggfunc=len, dropna=False))


    

    




Pclass      1      2      3    All
Sex                               
female   94.0   76.0  144.0  314.0
male    122.0  108.0  347.0  577.0
All     216.0  184.0  491.0  891.0