Exploring Titanic Dataset

Questions:

What does the data look like?
What possible factors could be correlated to (not) surviving the disaster?



In [1]:

    
# Import magic
%matplotlib inline

# More imports
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

# Set up Seaborn
sns.set() # matplotlib defaults









    



/Users/graysonricketts/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
  warnings.warn(self.msg_depr % (key, alt_key))



In [2]:

    
# Load and show CSV data
titanic_data = pd.read_csv('titanic_data.csv')
titanic_data.head()









    Out[2]:






  
    
      
      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

Observations

Tickets vary and most likely don't affect survivorship, or at least the ticket #'s don't. There are other variables attached to the ticket (price, location, class) that most likely have a clearer affect on survivorship.
What do the columns mean?
What do the embarked codes mean?

variable source

survival Survival (0 = No; 1 = Yes) pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd) name Name sex Sex age Age sibsp Number of Siblings/Spouses Aboard parch Number of Parents/Children Aboard ticket Ticket Number fare Passenger Fare cabin Cabin embarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)



In [27]:

    
titanic_data.shape









    Out[27]:





(891, 12)



In [28]:

    
titanic_data.columns









    Out[28]:





Index([u'PassengerId', u'Survived', u'Pclass', u'Name', u'Sex', u'Age',
       u'SibSp', u'Parch', u'Ticket', u'Fare', u'Cabin', u'Embarked'],
      dtype='object')

Cabin seems to have a lot of NaN spaces. How much information is there?



In [25]:

    
titanic_data[pd.notnull(titanic_data['Cabin'])].shape









    Out[25]:





(204, 12)

What is the distribution of class, port, gender?



In [97]:

    
first = second = third = 0
cher = queens = shamp = some = 0
male = female = 0

for index, row in titanic_data.iterrows():# Determine classes
    if row['Pclass'] == 1:
        first += 1
    elif row['Pclass'] == 2:
        second += 2
    else:
        third += 1
    
    if row['Embarked'] == 'C':
        cher += 1
    elif row['Embarked'] == 'Q':
        queens += 1
    elif row['Embarked'] == 'S':
        shamp +=1
    else:
        some += 1
        
    if row['Sex'] == 'male':
        male += 1
    else:
        female += 1
    
print '''\tFirst: %d
        Second: %d
        Third: %d \n
        Cherbourg: %d
        Queenstown: %d
        Southampton: %d 
        nill: %d \n
        Male: %d
        Female: %d''' % (first, second, third, cher, queens, shamp, some, male, female)









    



	First: 216
        Second: 368
        Third: 491 

        Cherbourg: 168
        Queenstown: 77
        Southampton: 644 
        nill: 2 

        Male: 577
        Female: 314

IMPORTANT: Age has NaN



In [61]:

    
titanic_data[pd.notnull(titanic_data['Age'])].shape









    Out[61]:





(714, 12)



In [44]:

    
titanic_data['Age'].describe()









    Out[44]:





count    714.000000
mean      29.699118
std       14.526497
min        0.420000
25%       20.125000
50%       28.000000
75%       38.000000
max       80.000000
Name: Age, dtype: float64



In [70]:

    
# Plot of non-null age
sns.distplot(titanic_data[pd.notnull(titanic_data['Age'])]['Age'], kde=False)









    Out[70]:





<matplotlib.axes._subplots.AxesSubplot at 0x10a7b19d0>

Possible Question

How did married couples fair? e.g. number both died, just one?



In [82]:

    
married = titanic_data[((titanic_data['SibSp'] > 0) & (titanic_data['Age'] > 18))]
married.shape









    Out[82]:





(165, 12)



In [95]:

    
sns.distplot(married[pd.notnull(married['Age'])]['Age'], kde=False, color='r')

not_married = titanic_data[((titanic_data['SibSp'] == 0) & (titanic_data['Age'] > 18))]
sns.distplot(not_married[pd.notnull(not_married['Age'])]['Age'], kde=False)









    Out[95]:





<matplotlib.axes._subplots.AxesSubplot at 0x10bbfe490>

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35	0	373450	8.0500	NaN	S