Data Visualisation : Conditional Plots

In [18]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
warnings.filterwarnings('ignore')

Data¶

In [2]:

titanic = pd.read_csv("titanic.csv")

In [3]:

print(titanic.head())

   PassengerId  Survived  Pclass  \
0            1         0       3   
1            2         1       1   
2            3         1       3   
3            4         1       1   
4            5         0       3   

                                                Name     Sex   Age  SibSp  \
0                            Braund, Mr. Owen Harris    male  22.0      1   
1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   
2                             Heikkinen, Miss. Laina  female  26.0      0   
3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   
4                           Allen, Mr. William Henry    male  35.0      0   

   Parch            Ticket     Fare Cabin Embarked  
0      0         A/5 21171   7.2500   NaN        S  
1      0          PC 17599  71.2833   C85        C  
2      0  STON/O2. 3101282   7.9250   NaN        S  
3      0            113803  53.1000  C123        S  
4      0            373450   8.0500   NaN        S  

In [4]:

titanic.shape

Out[4]:

(891, 12)

In [11]:

cols = ['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
titanic = titanic[cols].dropna()

Removed unwanted columns¶

In [12]:

titanic.head()

Out[12]:

	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
3	1	1	female	35.0	1	0	53.1000	S
4	0	3	male	35.0	0	0	8.0500	S

Visualizing using seaborn¶

Histogram¶

In [19]:

sns.distplot(titanic["Fare"])

Out[19]:

<matplotlib.axes._subplots.AxesSubplot at 0x119948c88>

In [20]:

sns.distplot(titanic["Age"])

Out[20]:

<matplotlib.axes._subplots.AxesSubplot at 0x119991320>

In [16]:

plt.boxplot(titanic["Age"])

Out[16]:

{'boxes': [<matplotlib.lines.Line2D at 0x1198a3ef0>],
 'caps': [<matplotlib.lines.Line2D at 0x1198b9a90>,
  <matplotlib.lines.Line2D at 0x119a3ee10>],
 'fliers': [<matplotlib.lines.Line2D at 0x11ce79b00>],
 'means': [],
 'medians': [<matplotlib.lines.Line2D at 0x119a3e860>],
 'whiskers': [<matplotlib.lines.Line2D at 0x1198a30b8>,
  <matplotlib.lines.Line2D at 0x1198b9e10>]}

Generating a kernal density plot¶

In [22]:

sns.kdeplot(titanic["Age"], shade= True)
plt.xlabel("Age")

Out[22]:

<matplotlib.text.Text at 0x11d3d5b70>

Conditional distribution using single condition¶

In [31]:

g = sns.FacetGrid(titanic, col = "Survived", size = 5)
g.map(sns.kdeplot, "Age")

Out[31]:

<seaborn.axisgrid.FacetGrid at 0x1209bd400>

In [32]:

g = sns.FacetGrid(titanic, col = "Pclass",size = 5)
g.map(sns.kdeplot,"Age")

Out[32]:

<seaborn.axisgrid.FacetGrid at 0x120bef0f0>

Creating a Conditional Plot using two conditions¶

Tip : use row parameter

In [33]:

g = sns.FacetGrid(titanic, row = "Pclass",col = "Survived", size = 5)
g.map(sns.kdeplot, "Age")

Out[33]:

<seaborn.axisgrid.FacetGrid at 0x120c02780>

Creating a Conditional Plot using three conditions¶

Tip: use hue

In [39]:

g = sns.FacetGrid(titanic, row = "Pclass", col = "Survived", hue = "Sex", size = 5)
g.map(sns.kdeplot, "Age", shade = True)
g.add_legend()

Out[39]:

<seaborn.axisgrid.FacetGrid at 0x11d7c7b70>

In [ ]: