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Introduction to Seaborn

  • This module introduces Seaborn, a powerful data visualization library in Python. You will learn what Seaborn is, why it is preferred over Matplotlib, how to install and set it up, explore built-in datasets, and understand the basic plot structure.

  • What is Seaborn?

    Seaborn is a Python data visualization library built on top of Matplotlib.

    It is mainly used for:

    • Statistical data visualization

    • Attractive and modern charts

    • Easy integration with Pandas DataFrames

    Seaborn makes complex visualizations easier with fewer lines of code.

    Why Seaborn over Matplotlib?

    Feature

    Matplotlib

    Seaborn

    Styling

    Basic

    Beautiful default themes

    Statistical plots

    Manual coding

    Built-in statistical support

    DataFrames support

    Limited

    Direct support

    Code length

    More

    Less

    Advantages of Seaborn:

    • Better default design

    • Easy statistical plots (boxplot, violinplot, heatmap)

    • Automatic legends

    • Built-in themes & color palettes

    In short:
     Matplotlib = Foundation
     Seaborn = Stylish & Statistical Upgrade

    Installation & Setup

    Install Seaborn

pip install seaborn
  • Import in Python

import seaborn as sns
import matplotlib.pyplot as plt

  • Built-in Datasets

    Seaborn provides ready-made datasets for practice.

    Example datasets:

    • tips

    • iris

    • titanic

    • flights

    Example:

Loading and Previewing the "Tips" Dataset

This code uses the Seaborn library to load a built-in sample dataset called "tips" and displays the first five rows. 

import seaborn as sns
df = sns.load_dataset("tips")
print(df.head())

  • Basic Plot Structure

    General Structure:

Basic Seaborn Plot Structure (Template Code)

This code shows the general structure of creating a plot using Seaborn with a dataset. 

import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset("tips")
sns.plot_type(x="column1", y="column2", data=df)
plt.show()
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