Telling Effective Stories With Your Python Visualizations

The Real Python Podcast

How do you make compelling visualizations that best convey the story of your data? What methods can you employ within popular Python tools to improve your plots and graphs? This week on the show, Matt Harrison returns to discuss his new book “Effective Visualization: Exploiting Matplotlib & Pandas.”

As a data scientist and instructor, Matt has been teaching the concepts of managing tabular data and making visualizations for over 20 years. Matt shares his methodology for taking a basic plot and then telling a compelling story with it. We discuss why you should limit your plot types to a few that your audience is familiar with.

We cover the resources built into pandas and Matplotlib and some of the libraries’ limitations. Matt talks about the professionally produced plots that inspired him and the process of recreating them. He also answers questions about finding data sources to practice these techniques with.

This episode is sponsored by Postman.

Course Spotlight: Using plt.scatter() to Visualize Data in Python

In this course, you’ll learn how to create scatter plots in Python, which are a key part of many data visualization applications. You’ll get an introduction to plt.scatter(), a versatile function in the Matplotlib module for creating scatter plots.

Topics:

• 00:00:00 – Introduction
• 00:02:57 – XGBoost book and interview
• 00:04:00 – Effective Visualization – Exploiting Matplotlib & pandas
• 00:04:27 – Why focus on pandas?
• 00:06:01 – Plotting inside of pandas
• 00:08:41 – How did you get involved in visualizations?
• 00:13:54 – Why write this book?
• 00:16:17 – Sponsor: Postman
• 00:17:09 – What are the plots you appreciate?
• 00:22:41 – Creating a methodology for plotting
• 00:24:24 – Color to spell out the story
• 00:27:50 – Limited and simple types of visualizations
• 00:31:34 – Explaining the story
• 00:37:19 – highlight-text library for matplotlib
• 00:39:02 – Video Course Spotlight
• 00:40:11 – Who is the audience?
• 00:43:19 – Why not include interactivity?
• 00:45:38 – Listing the references for the data
• 00:49:12 – Deciding on the examples and recipes
• 00:54:45 – Using existing visualizations as inspiration
• 00:55:41 – Matplotlib style sheets
• 00:57:54 – Finding sources of data to work with
• 01:04:17 – How to purchase the book
• 01:05:07 – What are you excited about in the world of Python?
• 01:06:33 – What do you want to learn next?
• 01:07:36 – How can people follow your work online?
• 01:08:04 – Thanks and goodbye

Show Links:

• Effective Visualization – Exploiting Matplotlib & Pandas
• Matplotlib — Visualization with Python
• Episode #169: Improving Classification Models With XGBoost
• Episode #214: Build Captivating Display Tables in Python With Great Tables
• pandas documentation
• highlight-text · PyPI
• Style sheets — Matplotlib 3.10.0 documentation
• Kaggle: Your Machine Learning and Data Science Community
• nytimes/data-training: Files from the NYT data training program, available for public use.
• Astral: Next-gen Python tooling
• Episode #238: Charlie Marsh: Accelerating Python Tooling With Ruff and uv
• Polars — DataFrames for the new era
• CircuitPython
• Effective Visualization: Exploiting Matplotlib & Pandas - Amazon
• Matt Harrison (@dunder-matt.bsky.social) — Bluesky

Level up your Python skills with our expert-led courses:

• Plot With pandas: Python Data Visualization Basics
• Using plt.scatter() to Visualize Data in Python
• Exploring Astrophysics in Python With pandas and Matplotlib

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