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In [1]:



    


!pip install plotly_express



    


















    






Collecting plotly_express
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Collecting plotly>=3.9.0 (from plotly_express)
  Downloading https://files.pythonhosted.org/packages/ff/75/3982bac5076d0ce6d23103c03840fcaec90c533409f9d82c19f54512a38a/plotly-3.10.0-py2.py3-none-any.whl (41.5MB)
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Installing collected packages: retrying, plotly, plotly-express
  Found existing installation: plotly 2.5.1
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In [2]:



    


import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly_express as px
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline



    











In [3]:



    


train_df= pd.read_csv('titanic_train.csv')
train_df.head()



    


















    
Out[3]:











  
    

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

  
  
    

      0
      1
      0
      3
      Braund, Mr. Owen Harris
      male
      22.0
      1
      0
      A/5 21171
      7.2500
      NaN
      S
    

    

      1
      2
      1
      1
      Cumings, Mrs. John Bradley (Florence Briggs Th...
      female
      38.0
      1
      0
      PC 17599
      71.2833
      C85
      C
    

    

      2
      3
      1
      3
      Heikkinen, Miss. Laina
      female
      26.0
      0
      0
      STON/O2. 3101282
      7.9250
      NaN
      S
    

    

      3
      4
      1
      1
      Futrelle, Mrs. Jacques Heath (Lily May Peel)
      female
      35.0
      1
      0
      113803
      53.1000
      C123
      S
    

    

      4
      5
      0
      3
      Allen, Mr. William Henry
      male
      35.0
      0
      0
      373450
      8.0500
      NaN
      S
    

  




















In [4]:



    


test_df = pd.read_csv('titanic_test.csv')
test_df.head()



    


















    
Out[4]:











  
    

      
      PassengerId
      Pclass
      Name
      Sex
      Age
      SibSp
      Parch
      Ticket
      Fare
      Cabin
      Embarked
    

  
  
    

      0
      892
      3
      Kelly, Mr. James
      male
      34.5
      0
      0
      330911
      7.8292
      NaN
      Q
    

    

      1
      893
      3
      Wilkes, Mrs. James (Ellen Needs)
      female
      47.0
      1
      0
      363272
      7.0000
      NaN
      S
    

    

      2
      894
      2
      Myles, Mr. Thomas Francis
      male
      62.0
      0
      0
      240276
      9.6875
      NaN
      Q
    

    

      3
      895
      3
      Wirz, Mr. Albert
      male
      27.0
      0
      0
      315154
      8.6625
      NaN
      S
    

    

      4
      896
      3
      Hirvonen, Mrs. Alexander (Helga E Lindqvist)
      female
      22.0
      1
      1
      3101298
      12.2875
      NaN
      S
    

  




















In [5]:



    


(train_df.shape, test_df.shape)



    


















    
Out[5]:








((891, 12), (418, 11))

















In [6]:



    


gender_sub_df = pd.read_csv('titanic_gender_submission.csv')
gender_sub_df.head()



    


















    
Out[6]:











  
    

      
      PassengerId
      Survived
    

  
  
    

      0
      892
      0
    

    

      1
      893
      1
    

    

      2
      894
      0
    

    

      3
      895
      0
    

    

      4
      896
      1
    

  




















In [7]:



    


gender_sub_df.shape



    


















    
Out[7]:








(418, 2)

Exploratory analysis

Let us look at the data type of the columns present in this data set.





In [8]:



    


# datatype of columns
train_df.info()



    


















    






<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB

Visualize missing data





In [9]:



    


# find missing data
sns.heatmap(train_df.isnull(), yticklabels=False, cbar=False, cmap='viridis')



    


















    
Out[9]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f2e9c5432b0>

Age and Cabin do have some missing data, we will impute for these later.





In [10]:



    


import warnings
warnings.filterwarnings("ignore")

Visualize distributions and correlations





In [11]:



    


sns.pairplot(train_df[['Survived','Pclass','Age','Fare']], dropna=True)



    


















    
Out[11]:








<seaborn.axisgrid.PairGrid at 0x7f2e9c8322b0>
  • Does age show any correlation with Fare? Do older people pay higher?
  • Does passenger class show any correlation with Fare? Does first class cost more?
  • Do older passenger prefer better class?
  • Does the passenger class / fare / age / gender determine whether or not you survived?

Visualize distribution of class vs Age





In [9]:



    


plot1 = px.histogram(train_df, x='Age', color='Pclass')
plot1