📊 How to Visualize Data in Python Using Matplotlib and Seaborn

Introduction

In the world of data science, visualization is key. It helps turn raw data into meaningful insights that are easy to understand. Python offers several powerful libraries for data visualization, with Matplotlib and Seaborn being the most popular and beginner-friendly.

In this guide, you'll learn how to visualize data using both libraries. We'll cover line plots, bar charts, scatter plots, and heatmaps — all with simple, hands-on examples.

Prerequisites

Make sure you have Python installed. Then install the required libraries:

pip install matplotlib seaborn

You should also have pandas and numpy:

pip install pandas numpy

1. Importing the Libraries

Start by importing the necessary tools:

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

2. Creating Sample Data

Let’s create a basic dataset for demonstration:

# Create a simple DataFrame
data = pd.DataFrame({
    'Year': [2020, 2021, 2022, 2023],
    'Sales': [250, 400, 550, 700]
})

3. Line Plot with Matplotlib

plt.plot(data['Year'], data['Sales'], marker='o')
plt.title('Yearly Sales')
plt.xlabel('Year')
plt.ylabel('Sales')
plt.grid(True)
plt.show()

Why it’s useful: Line plots are great for showing trends over time.

4. Bar Plot with Seaborn

sns.barplot(x='Year', y='Sales', data=data)
plt.title('Sales by Year')
plt.show()

Why Seaborn? Seaborn integrates tightly with Pandas and makes beautiful visualizations with less code.

5. Scatter Plot (Comparison)

Let’s create a more detailed dataset:

np.random.seed(0)
df = pd.DataFrame({
    'Hours_Studied': np.random.randint(1, 10, 50),
    'Score': np.random.randint(40, 100, 50)
})

sns.scatterplot(x='Hours_Studied', y='Score', data=df)
plt.title('Study Time vs Exam Score')
plt.show()

Use case: Scatter plots show correlation between two numeric variables.

6. Histogram with Seaborn

sns.histplot(df['Score'], bins=10, kde=True)
plt.title('Distribution of Scores')
plt.show()

Histograms help understand the frequency distribution of values.

7. Heatmap with Correlation

corr = df.corr()
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show()

Tip: Use heatmaps to visualize correlation between features in a dataset.

8. Styling and Themes

Seaborn offers built-in themes:

sns.set_style('darkgrid')

You can choose from 'white', 'dark', 'whitegrid', 'darkgrid', and 'ticks'.

Conclusion

Matplotlib and Seaborn are must-have tools in any Python programmer's toolkit. With just a few lines of code, you can create insightful, professional-looking visualizations that bring your data to life.

Start with simple plots and explore more advanced options as you grow. For best practice, always label your axes, add titles, and make plots readable.

Note: Try changing data or adding your own visualizations.

Check out our other Python guides to continue learning:

← Previous go to Home →

Learn Python

Menu

Data Visualization in Python: Create Stunning Charts with Matplotlib & Seaborn