Matplotlib Pie Chart

Case Study (Tele Company) : Churn Analysis

- Churn analysis is the evaluation of a company's customer loss rate 

import pandas as pd

url= "https://datascienceschools.github.io/tele_customers.csv"

df = pd.read_csv(url)

df.head()

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	...	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	...	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

5 rows × 21 columns

Number of customers

- who continued their subscriptions -> Churn : No
- who discontinued their subscriptions -> Churn : Yes

df['Churn'].value_counts()   

No     5174
Yes    1869
Name: Churn, dtype: int64

Data Visualizations

- import matplotlib.pyplot as plt -> for plotting the graph 

- import seaborn as sns -> for plotting interactive graph

- from pylab import rcParams -> for customizing Matplotlib plots 

- Data to plot

   labels = df['Churn'].value_counts(sort = True).index -> Labeling each slice

   sizes = df['Churn'].value_counts(sort = True) -> Size of each slice

   colors = ["lightblue","pink"] -> Color of each slice

   explode = (0.05,0) -> This will explode first slice

- rcParams['figure.figsize'] = 6,6  -> Changing size of figure
- plt.axis('equal') -> Making a square plot with equal axes


- Plot 

plt.pie(sizes, explode=explode, labels=labels, colors=colors, 
               autopct='%1.1f%%', shadow=True, startangle=90,)
plt.title('Customer Churn Breakdown')
plt.show()

- Legend

patches, texts = plt.pie(sizes, explode=explode, colors=colors,shadow=True, startangle=90,)
plt.legend(patches, labels, loc="best")

Simple Pie Chart

import matplotlib.pyplot as plt 
import seaborn as sns 
from pylab import rcParams


labels = df['Churn'].value_counts(sort = True).index
sizes = df['Churn'].value_counts(sort = True)
colors = ["lightblue","pink"]
 
plt.pie(sizes, labels=labels, colors=colors)
plt.title('Customer Churn Breakdown')
plt.show()

Adding rcParams

- Changing size of figure

import matplotlib.pyplot as plt 
import seaborn as sns 
from pylab import rcParams

labels = df['Churn'].value_counts(sort = True).index
sizes = df['Churn'].value_counts(sort = True)
colors = ["lightblue","pink"]
 
rcParams['figure.figsize'] = 8,8

plt.pie(sizes, labels=labels, colors=colors)
plt.title('Customer Churn Breakdown')
plt.show()

Adding plt.axis('equal')

- Making a square plot with equal axes

import matplotlib.pyplot as plt 
import seaborn as sns 

labels = df['Churn'].value_counts(sort = True).index
sizes = df['Churn'].value_counts(sort = True)
colors = ["lightblue","pink"]
 
plt.pie(sizes, labels=labels, colors=colors)
plt.title('Customer Churn Breakdown')
plt.axis('equal')
plt.show()

Add Percentage

- plt.pie -> Add autopct='%1.1f%%'

import matplotlib.pyplot as plt 
import seaborn as sns 
from pylab import rcParams


labels = df['Churn'].value_counts(sort = True).index
sizes = df['Churn'].value_counts(sort = True)
colors = ["lightblue","pink"]
 
rcParams['figure.figsize'] = 6,6

plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%')
plt.title('Customer Churn Breakdown')
plt.show()

Explode First Slice

- explode = (0.1,0) 
- plt.pie -> Add explode = explode

import matplotlib.pyplot as plt 
import seaborn as sns 
from pylab import rcParams


labels = df['Churn'].value_counts(sort = True).index
sizes = df['Churn'].value_counts(sort = True)
colors = ["lightblue","pink"]
explode = (0.1,0) 
 
rcParams['figure.figsize'] = 6,6

plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%')
plt.title('Customer Churn Breakdown')
plt.show()

Add Shadow

- plt.pie -> Add shadow=True

import matplotlib.pyplot as plt 
import seaborn as sns 
from pylab import rcParams


labels = df['Churn'].value_counts(sort = True).index
sizes = df['Churn'].value_counts(sort = True)
colors = ["lightblue","pink"]
explode = (0.1,0) 
 
rcParams['figure.figsize'] = 6,6

plt.pie(sizes, explode=explode, labels=labels, colors=colors,
               autopct='%1.1f%%',shadow=True)
plt.title('Customer Churn Breakdown')
plt.show()

Specify Angel

- plt.pie -> Add startangle = 90

import matplotlib.pyplot as plt 
import seaborn as sns 
from pylab import rcParams


labels = df['Churn'].value_counts(sort = True).index
sizes = df['Churn'].value_counts(sort = True)
colors = ["lightblue","pink"]
explode = (0.1,0) 
 
rcParams['figure.figsize'] = 6,6

plt.pie(sizes, explode=explode, labels=labels, colors=colors,  
               autopct='%1.1f%%', shadow=True, startangle=90)
plt.title('Customer Churn Breakdown')
plt.show()

Adding Legend

- patches, texts = plt.pie(sizes, explode=explode, colors=colors,shadow=True, startangle=90,)

- plt.legend(patches, labels, loc="best")

import matplotlib.pyplot as plt 
import seaborn as sns 
from pylab import rcParams


#labels = df['Churn'].value_counts(sort = True).index
sizes = df['Churn'].value_counts(sort = True)
colors = ["lightblue","pink"]
explode = (0.1,0) 
 
rcParams['figure.figsize'] = 6,6

patches, texts = plt.pie(sizes, explode=explode, colors=colors, shadow=True, startangle=90)
plt.legend(patches, labels, loc="best")

plt.title('Customer Churn Breakdown')
plt.show()