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
Stacking Plots
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
Understanding Seaborn's Data Input Requirements
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- Tidy Data, Wickham, H., 2014 Journal of Statistical Software, Vol. 59 DOI: 10.18637/jss.v059.i10 - This foundational paper introduces the concept of tidy data, explaining its principles and benefits for statistical analysis and visualization.
- Data structures supported by seaborn, Michael Waskom and the Seaborn development team, 2024 - The official user guide section detailing how Seaborn expects data to be structured, primarily emphasizing Pandas DataFrames and long-form (tidy) data.
- User Guide: Data structures, The Pandas development team, 2024 - The official documentation introducing Pandas DataFrames, explaining their structure and fundamental operations, which is helpful for understanding Seaborn's preferred input.
- Python Data Science Handbook: Essential Tools for Working with Data, Jake VanderPlas, 2016 (O'Reilly Media) - A widely respected book providing a detailed introduction to Python's data science stack, including a section dedicated to Seaborn, discussing its data input conventions within the broader context of data visualization.