Titanic Dataset

In this project, we will be working with the titanic dataset. A set of data manipulation and visualization techniques will be used.



In [1]:

    
import seaborn as sns 
import matplotlib.pyplot as plt
%matplotlib inline



In [2]:

    
sns.set_style('whitegrid')



In [3]:

    
titanic = sns.load_dataset('titanic')



In [4]:

    
titanic.head()









    Out[4]:






  
    
      
      survived
      pclass
      sex
      age
      sibsp
      parch
      fare
      embarked
      class
      who
      adult_male
      deck
      embark_town
      alive
      alone
    
  
  
    
      0
      0
      3
      male
      22.0
      1
      0
      7.2500
      S
      Third
      man
      True
      NaN
      Southampton
      no
      False
    
    
      1
      1
      1
      female
      38.0
      1
      0
      71.2833
      C
      First
      woman
      False
      C
      Cherbourg
      yes
      False
    
    
      2
      1
      3
      female
      26.0
      0
      0
      7.9250
      S
      Third
      woman
      False
      NaN
      Southampton
      yes
      True
    
    
      3
      1
      1
      female
      35.0
      1
      0
      53.1000
      S
      First
      woman
      False
      C
      Southampton
      yes
      False
    
    
      4
      0
      3
      male
      35.0
      0
      0
      8.0500
      S
      Third
      man
      True
      NaN
      Southampton
      no
      True

Jointplot comparing fare and age



In [6]:

    
sns.jointplot(x='fare',y='age',data=titanic)









    Out[6]:





<seaborn.axisgrid.JointGrid at 0xae5efd0>

Plot the fare column as distribution



In [8]:

    
sns.distplot(titanic['fare'],bins=30,kde=False,color='red')









    Out[8]:





<matplotlib.axes._subplots.AxesSubplot at 0xbc0e828>

Displaying passenger and age over a boxplot



In [10]:

    
sns.boxplot(x='class',y='age',data=titanic,palette='rainbow')









    Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0xbd0ef98>



In [12]:

    
sns.swarmplot(x='class',y='age',data=titanic,palette='Set2')









    Out[12]:





<matplotlib.axes._subplots.AxesSubplot at 0xbdf7cf8>

A simple count plot displying the number of passanger by sex



In [14]:

    
sns.countplot(x='sex',data=titanic)









    Out[14]:





<matplotlib.axes._subplots.AxesSubplot at 0xbe1a5f8>

A heatmap showing the correlations for the entire dataset



In [16]:

    
sns.heatmap(titanic.corr(),cmap='coolwarm')
plt.title('titanic.corr()')









    Out[16]:





<matplotlib.text.Text at 0xc0f7ac8>

Two histograms ploted using FacetGrid based on age and sex



In [18]:

    
g = sns.FacetGrid(data=titanic,col='sex')
g.map(plt.hist,'age')









    Out[18]:





<seaborn.axisgrid.FacetGrid at 0xc151860>

	survived	pclass	sex	age	sibsp	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True