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Data Visualization with Matplotlib and Seaborn

Chapter 1: Introduction to Data Visualization

What is Data Visualization?

Why Visualize Data in AI and Engineering?

Introduction to Matplotlib

Introduction to Seaborn

Relationship between Matplotlib and Seaborn

Setting Up Your Python Environment

Importing Libraries

Your First Simple Plot

Quiz for Chapter 1

Chapter 2: Fundamentals of Matplotlib Plotting

Anatomy of a Matplotlib Plot: Figure and Axes

Creating a Basic Plotting Script

Line Plots: Plotting Trends

Scatter Plots: Showing Relationships

Adding Titles and Labels

Changing Colors and Line Styles

Setting Axis Limits

Practice: Creating Simple Line and Scatter Plots

Quiz for Chapter 2

Chapter 3: Basic Matplotlib Plot Types

Bar Charts for Comparisons

Creating Vertical and Horizontal Bar Charts

Histograms for Distributions

Understanding Bins in Histograms

Pie Charts for Proportions

Hands-on Practical: Plotting Different Data Types

Quiz for Chapter 3

Chapter 4: Introduction to Seaborn for Enhanced Visuals

Seaborn's Role: High-Level Interface

Setting Seaborn Styles and Contexts

Seaborn Color Palettes

Creating Scatter Plots with Seaborn (scatterplot)

Creating Line Plots with Seaborn (lineplot)

Understanding Seaborn's Data Input Requirements

Practice: Recreating Plots with Seaborn

Quiz for Chapter 4

Chapter 5: Visualizing Data Distributions with Seaborn

Histograms and Kernel Density Estimates (histplot, kdeplot)

Visualizing Joint Distributions (jointplot)

Box Plots for Summary Statistics (boxplot)

Violin Plots: Combining Box Plots and KDE (violinplot)

Understanding Distribution Plot Choices

Hands-on Practical: Plotting Distributions

Quiz for Chapter 5

Chapter 6: Plotting Categorical Data using Seaborn

Understanding Categorical Data

Bar Plots for Aggregate Statistics (barplot)

Count Plots for Frequencies (countplot)

Box Plots for Categorical Variables (boxplot)

Strip Plots and Swarm Plots for Individual Points (stripplot, swarmplot)

Point Plots for Trends (pointplot)

Hands-on Practical: Visualizing Categorical Features

Quiz for Chapter 6

Chapter 7: Customizing and Saving Visualizations

Adding Text and Annotations to Plots

Managing Legends

Adjusting Figure Size and Layout

Using Matplotlib Object-Oriented API for Finer Control

Applying Seaborn Themes

Saving Plots to Files (PNG, JPG, PDF, SVG)

Choosing the Right File Format

Practice: Polishing Your Plots

Quiz for Chapter 7

Chapter 8: Plotting with Pandas DataFrames

Brief Introduction to Pandas Series and DataFrames

Loading Data into a DataFrame

Basic Plotting Directly from DataFrames

Using Matplotlib with DataFrame Columns

Using Seaborn with DataFrames

Filtering and Preparing Data for Plotting

Hands-on Practical: Visualizing Data from a File

Quiz for Chapter 8

Quiz
Beta

Chapter: Plotting Categorical Data using Seaborn

Test your understanding and practice the concepts from this chapter

Quiz Instructions

This quiz contains 12 questions to help you practice.
You need to score at least 70% to pass.
Attempts: Unlimited.
Your highest score will be kept.
Please attempt this quiz without assistance; however, feel free to refer to the chapter notes or use a code interpreter if needed.
This assessment is for your learning and understanding, It is not formally accredited (yet).

Question Format

The questions are designed to be engaging, focusing on understanding, application, and interpretation rather than rote memorization. Expect scenario-based problems that test your ability to apply what you've learned.

Attempts

Best scores and quiz attempts will appear.

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