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Practice

Introduction to Seaborn

Seaborn is a Python visualization library built on top of Matplotlib. It simplifies the process of creating clear, attractive, and informative statistical graphics.

Compared to writing raw Matplotlib code, Seaborn allows you to create polished charts with just a few lines.

Think of Matplotlib as the engine that powers visualization — and Seaborn as the designer that adds style, structure, and readability.


Why Use Seaborn?​

Here are some reasons why Seaborn is popular among data analysts and scientists:

  • Beautiful by default – Charts look polished and professional without extra formatting.
  • Concise syntax – Complex visualizations often require only one line of code.
  • Statistical focus – Includes built-in tools for distributions, regressions, and category comparisons.
  • Seamless with Pandas – Works directly with DataFrames, making exploratory analysis fast and intuitive.

Example: Creating a Simple Seaborn Plot​

Basic Seaborn Scatter Plot
import seaborn as sns
import matplotlib.pyplot as plt

# Example dataset
tips = sns.load_dataset("tips")

# Create a scatter plot of total bill vs tip
sns.scatterplot(data=tips, x="total_bill", y="tip")

plt.show()

With just one command, Seaborn automatically adds axis labels, a styled grid, and a clean color palette — no extra setup needed.

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