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Introduction
Real world Python

Data Visualization

Explore the basics of data visualization in Python, focusing on using Matplotlib and Seaborn libraries.

By Kevin McAleer,    2 Minutes

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Introduction

Data visualization is an important part of data analysis. Python offers multiple libraries for creating static, animated, and interactive visualizations, including Matplotlib and Seaborn. In this lesson, we’ll explore the basics of these two libraries.

Learning Objectives

  • Understand what data visualization is and its importance.
  • Learn how to create basic plots using Matplotlib.
  • Learn how to create statistical plots using Seaborn.

What is Data Visualization?

Data visualization is the graphical representation of data and information. It uses visual elements like charts, graphs, and maps to provide an easy way to understand trends, outliers, and patterns in data.

Introduction to Matplotlib

Matplotlib is a plotting library for Python. It provides an object-oriented API for embedding plots into applications.

import matplotlib.pyplot as plt

# Create a simple line plot
plt.plot([1, 2, 3, 4])
plt.ylabel('Some Numbers')
plt.show()

Introduction to Seaborn

Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for creating attractive graphics.

import seaborn as sns

# Load an example dataset
tips = sns.load_dataset("tips")

# Create a simple histogram
sns.histplot(data=tips, x="total_bill")

Summary

In this lesson, you’ve learned about the basics of data visualization in Python. We’ve covered the importance of data visualization and explored two Python libraries, Matplotlib and Seaborn, for creating static visualizations. Data visualization is a powerful tool for understanding data and communicating results.

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