In [94]:

    

import pandas as pd
import numpy as np

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [95]:

    

figsize(7, 4)


    

In [96]:

    

df = pd.read_csv('titanic.csv')


    

In [97]:

    

df.head(5)


    

    
Out[97]:






  

    

      

      
PassengerId
      
Survived
      
Pclass
      
Name
      
Sex
      
Age
      
SibSp
      
Parch
      
Ticket
      
Fare
      
Cabin
      
Embarked
    
  
  

    

      
0
      
 1
      
 0
      
 3
      
                           Braund, Mr. Owen Harris
      
   male
      
 22
      
 1
      
 0
      
        A/5 21171
      
  7.2500
      
  NaN
      
 S
    
    

      
1
      
 2
      
 1
      
 1
      
 Cumings, Mrs. John Bradley (Florence Briggs Th...
      
 female
      
 38
      
 1
      
 0
      
         PC 17599
      
 71.2833
      
  C85
      
 C
    
    

      
2
      
 3
      
 1
      
 3
      
                            Heikkinen, Miss. Laina
      
 female
      
 26
      
 0
      
 0
      
 STON/O2. 3101282
      
  7.9250
      
  NaN
      
 S
    
    

      
3
      
 4
      
 1
      
 1
      
      Futrelle, Mrs. Jacques Heath (Lily May Peel)
      
 female
      
 35
      
 1
      
 0
      
           113803
      
 53.1000
      
 C123
      
 S
    
    

      
4
      
 5
      
 0
      
 3
      
                          Allen, Mr. William Henry
      
   male
      
 35
      
 0
      
 0
      
           373450
      
  8.0500
      
  NaN
      
 S
    
  

In [98]:

    

df['Survived']


    

    
Out[98]:





0     0
1     1
2     1
3     1
4     0
5     0
6     0
7     0
8     1
9     1
10    1
11    1
12    0
13    0
14    0
...
876    0
877    0
878    0
879    1
880    1
881    0
882    0
883    0
884    0
885    0
886    0
887    1
888    0
889    1
890    0
Name: Survived, Length: 891, dtype: int64

In [99]:

    

len(df)


    

    
Out[99]:





891

In [100]:

    

df['Sex'].value_counts()


    

    
Out[100]:





male      577
female    314
dtype: int64

In [101]:

    

df['Survived'].head(10)


    

    
Out[101]:





0    0
1    1
2    1
3    1
4    0
5    0
6    0
7    0
8    1
9    1
Name: Survived, dtype: int64

In [102]:

    

df['Sex'].value_counts().plot(kind='bar')


    

    
Out[102]:





<matplotlib.axes.AxesSubplot at 0x10cca2050>






    

In [103]:

    

df_survived = df[ df['Survived'] == 1 ]


    

In [104]:

    

df_survived.head(5)


    

    
Out[104]:






  

    

      

      
PassengerId
      
Survived
      
Pclass
      
Name
      
Sex
      
Age
      
SibSp
      
Parch
      
Ticket
      
Fare
      
Cabin
      
Embarked
    
  
  

    

      
1
      
  2
      
 1
      
 1
      
 Cumings, Mrs. John Bradley (Florence Briggs Th...
      
 female
      
 38
      
 1
      
 0
      
         PC 17599
      
 71.2833
      
  C85
      
 C
    
    

      
2
      
  3
      
 1
      
 3
      
                            Heikkinen, Miss. Laina
      
 female
      
 26
      
 0
      
 0
      
 STON/O2. 3101282
      
  7.9250
      
  NaN
      
 S
    
    

      
3
      
  4
      
 1
      
 1
      
      Futrelle, Mrs. Jacques Heath (Lily May Peel)
      
 female
      
 35
      
 1
      
 0
      
           113803
      
 53.1000
      
 C123
      
 S
    
    

      
8
      
  9
      
 1
      
 3
      
 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)
      
 female
      
 27
      
 0
      
 2
      
           347742
      
 11.1333
      
  NaN
      
 S
    
    

      
9
      
 10
      
 1
      
 2
      
               Nasser, Mrs. Nicholas (Adele Achem)
      
 female
      
 14
      
 1
      
 0
      
           237736
      
 30.0708
      
  NaN
      
 C
    
  

In [105]:

    

df0 = df[ df['Age'] < 10 ]


    

In [106]:

    

len([1, 4, 5, 6, 6])


    

    
Out[106]:





5

In [107]:

    

(df_survived['Pclass'].value_counts() / df['Pclass'].value_counts().astype(float)).plot(kind='bar')


    

    
Out[107]:





<matplotlib.axes.AxesSubplot at 0x10cca9450>






    

In [108]:

    

(df_survived['Sex'].value_counts() / df['Sex'].value_counts().astype(float)).plot(kind='bar')


    

    
Out[108]:





<matplotlib.axes.AxesSubplot at 0x10ce8fd10>






    

In [109]:

    

df['Age'].value_counts().plot(kind='bar')


    

    
Out[109]:





<matplotlib.axes.AxesSubplot at 0x10ce46f10>






    

In [110]:

    

df['Age'].hist(bins=30)


    

    
Out[110]:





<matplotlib.axes.AxesSubplot at 0x10d61a410>






    

In [111]:

    

df['Age'].dropna().plot(kind='kde')
df_survived['Age'].dropna().plot(kind='kde')


    

    
Out[111]:





<matplotlib.axes.AxesSubplot at 0x10d698750>






    

In [112]:

    

df[ df['Sex'] == 'male']['Age'].dropna().plot(kind='kde')
df_survived[df_survived['Sex'] == 'male']['Age'].dropna().plot(kind='kde')


    

    
Out[112]:





<matplotlib.axes.AxesSubplot at 0x10dc09890>






    

In [112]:

    




    

In [112]: