Project: Visualizing Data with Seaborn (Titanic survival data)

Questions to address:

  • What was the distribution of Ages for passengers for different classes, different survival outcomes and different genders?

Tools:

  • Seaborn kdeplot
  • Seaborn FacetGrid
  • jointplot


load defaults

In [1]:
import pandas as pd
import re
import numpy as np

from IPython.display import display
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
from matplotlib import rcParams

from functions import *

plt.rcParams.update({'axes.titlepad': 20, 'font.size': 12, 'axes.titlesize':20})

colors = [(0/255,107/255,164/255), (255/255, 128/255, 14/255), 'red', 'green']

Dataset: Titanic Survival Data

In [2]:
titanic = pd.read_csv('./data/train.csv')
#remove columns with too many missing values
titanic.drop(['PassengerId','Name','Ticket','Cabin'], axis = 1, inplace = True)
titanic.dropna(axis=0, how='any', inplace = True)

Variables available:

In [3]:
for element in titanic.columns:
    print("%s | " % element, end='')

Survived | Pclass | Sex | Age | SibSp | Parch | Fare | Embarked |

DF snipet:

In [4]:
display(titanic.iloc[:3])
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0 3 male 22.0 1 0 7.2500 S
1 1 1 female 38.0 1 0 71.2833 C
2 1 3 female 26.0 0 0 7.9250 S


Analysis:

Q1: What was the distribution the passenger ages?

In [6]:
import seaborn as sns

sns.set_style('white')
sns.kdeplot(titanic['Age'], shade=True)
plt.xlabel('Age')

sns.despine(left=True, bottom=True)
plt.show()
  • Mean Age ~35


Q2: What was the distribution of Ages for passengers in different classes?

In [7]:
grid = sns.FacetGrid(titanic, col='Pclass', height=6) 
grid.map(sns.kdeplot, 'Age', shade=True)
sns.despine(left=True, bottom=True)
plt.show()
  • Older passengers in higher class (Pclass=1)


Q3: What was the distribution of Ages for passengers in different classes that survived or not?

In [11]:
grid = sns.FacetGrid(titanic, col='Survived', row='Pclass', height=4)
grid.map(sns.kdeplot, 'Age', shade=True)
sns.despine(left=True, bottom=True)
plt.show()
  • For Pclass = 1, survived and not survived ages looked the same (slightly younger survived).
  • For Pclass = 2 and 3, older survived.


Q4: What was the distribution of Ages for passengers in different classes that survived or not separated by gender?

In [12]:
#we can add a condition just by setting the hue parameter
grid = sns.FacetGrid(titanic, col='Survived', row='Pclass', hue='Sex', height=3)
grid.map(sns.kdeplot, 'Age', shade=True)
grid.add_legend()
#grid = grid.map(sns.kdeplot, 'Age', shade=True).add_legend()
sns.despine(left=True, bottom=True)
plt.show()
  • In Pclass = 1, there were more younger women and these did not survived.
  • In Pclass = 2 Women older than Men survived.


Q5: Distribution of Age versus Fare price

In [20]:
sns.jointplot('Age', 'Fare', titanic.loc[titanic['Fare']<75,:], kind='kde', height=4)
sns.despine(left=True, bottom=True)
plt.show()
In [ ]: