Thinking Like a Programmer•Interactive notebook lesson•Free

matplotlib seaborn projects

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Matplotlib and Seaborn Complete Guide

This notebook teaches data visualization in Python using:

Matplotlib
Seaborn

It also includes two real-world mini projects:

Housing price visualization (housing.csv)
Telecom churn analysis (TelecomCustomerChurn.csv)

Libraries used:

pandas
numpy
matplotlib
seaborn

Codepython


import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style="whitegrid")

print("Libraries loaded")

Output

Libraries loaded

Basic Line Plot

Codepython


x = np.arange(0,10)
y = x**2

plt.figure()
plt.plot(x,y)
plt.title("Line Plot Example")
plt.xlabel("X")
plt.ylabel("Y")
plt.show()

Output

Scatter Plot

Codepython


x = np.arange(0,50)
y = np.arange(50,100)

plt.scatter(x,y)
plt.title("Scatter Plot")
plt.show()

Output

Bar Chart

Codepython


categories = ["A","B","C"]
values = [10,20,15]

plt.bar(categories,values)
plt.title("Bar Chart")
plt.show()

Output

Histogram

Codepython


data = np.random.randn(1000)

plt.hist(data,bins=30)
plt.title("Histogram")
plt.show()

Output

Multiple Plots

Codepython


x = np.linspace(0,10,100)

plt.subplot(1,2,1)
plt.plot(x,np.sin(x))

plt.subplot(1,2,2)
plt.plot(x,np.cos(x))

plt.show()

Output

Seaborn Visualizations

Distribution Plot

Codepython


data = np.random.randn(500)

sns.histplot(data,kde=True)
plt.show()

Output

Box Plot

Codepython


data = pd.DataFrame({
"category":["A","A","B","B","C","C"],
"value":[5,7,3,4,6,8]
})

sns.boxplot(x="category",y="value",data=data)
plt.show()

Output

Heatmap

Codepython


matrix = np.random.rand(5,5)

sns.heatmap(matrix,annot=True)
plt.show()

Output

Pairplot

Codepython


df = pd.DataFrame(np.random.rand(100,4),columns=["A","B","C","D"])
sns.pairplot(df)

Output

<seaborn.axisgrid.PairGrid at 0x7f52499dee30>

Project 1: Housing Data Visualization

Dataset: housing.csv

Goals:

Explore price distribution
Analyze correlations
Visualize relationships

Codepython


housing = pd.read_csv("../../datasets/housing.csv")
housing.head()

Output

   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \
0    -122.23     37.88                41.0        880.0           129.0   
1    -122.22     37.86                21.0       7099.0          1106.0   
2    -122.24     37.85                52.0       1467.0           190.0   
3    -122.25     37.85                52.0       1274.0           235.0   
4    -122.25     37.85                52.0       1627.0           280.0   

   population  households  median_income  median_house_value ocean_proximity  
0       322.0       126.0         8.3252            452600.0        NEAR BAY  
1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  
2       496.0       177.0         7.2574            352100.0        NEAR BAY  
3       558.0       219.0         5.6431            341300.0        NEAR BAY  
4       565.0       259.0         3.8462            342200.0        NEAR BAY

Codepython


plt.hist(housing.select_dtypes(include=np.number).iloc[:,0], bins=30)
plt.title("Distribution Example")
plt.show()

Output

Codepython


corr = housing.corr(numeric_only=True)

plt.figure(figsize=(10,8))
sns.heatmap(corr,cmap="coolwarm")
plt.title("Correlation Heatmap")
plt.show()

Output

Codepython


num_cols = housing.select_dtypes(include=np.number).columns[:4]

sns.pairplot(housing[num_cols])

Output

<seaborn.axisgrid.PairGrid at 0x7f523eb961d0>

Project 2: Telecom Customer Churn Visualization

Dataset: TelecomCustomerChurn.csv

Goals:

Understand churn distribution
Identify patterns in features

Codepython


churn = pd.read_csv("../../datasets/TelecomCustomerChurn.csv")
churn.head()

Output

   customerID  Gender  SeniorCitizen Partner Dependents  Tenure PhoneService  \
0  7590-VHVEG  Female              0     Yes         No       1           No   
1  5575-GNVDE    Male              0      No         No      34          Yes   
2  3668-QPYBK    Male              0      No         No       2          Yes   
3  7795-CFOCW    Male              0      No         No      45           No   
4  9237-HQITU  Female              0      No         No       2          Yes   

  MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \
0            No             DSL             No  ...               No   
1            No             DSL            Yes  ...              Yes   
2            No             DSL            Yes  ...               No   
3            No             DSL            Yes  ...              Yes   
4            No     Fiber optic             No  ...               No   

  TechSupport StreamingTV StreamingMovies  Contract PaperlessBilling  \
0          No          No              No   Monthly              Yes   
1          No          No              No  One year               No   
2          No          No              No   Monthly              Yes   
3         Yes          No              No  One year               No   
4          No          No              No   Monthly              Yes   

               PaymentMethod MonthlyCharges  TotalCharges Churn  
0                     Manual          29.85         29.85    No  
1                     Manual          56.95        1889.5    No  
2                     Manual          53.85        108.15   Yes  
3  Bank transfer (automatic)          42.30       1840.75    No  
4                     Manual          70.70        151.65   Yes  

[5 rows x 21 columns]

Codepython


sns.countplot(x="Churn", data=churn)
plt.title("Churn Distribution")
plt.show()

Output

Codepython


numeric_cols = churn.select_dtypes(include=np.number).columns

churn[numeric_cols].hist(figsize=(10,8))
plt.show()

Output

Codepython


plt.figure(figsize=(10,8))
sns.heatmap(churn.corr(numeric_only=True), cmap="viridis")
plt.title("Feature Correlations")
plt.show()

Output

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