Chapter 14 Further Reading and Resources

Official Documentation

matplotlib User Guide https://matplotlib.org/stable/users/index.html

The authoritative reference for every matplotlib class and function. Most useful sections for business analysts: - "Tutorials → Introductory" — walks through the object-oriented interface in detail - "Tutorials → Intermediate → Artist tutorial" — explains how matplotlib draws every visual element - "API Reference → matplotlib.axes.Axes" — complete list of methods you can call on an Axes object, including all the formatting and annotation methods

matplotlib Gallery https://matplotlib.org/stable/gallery/index.html

Hundreds of example charts with source code. Indispensable for discovering what is possible. Search for "business", "finance", "annotate", "subplot", or "text" to find relevant examples. Each gallery page provides the complete Python code that produced it.

matplotlib cheatsheets https://matplotlib.org/cheatsheets/

Two official cheat sheets: one for beginners covering the core interface and one for intermediate users covering figure architecture and customization. Printable PDF format.

Books

Storytelling with Data — Cole Nussbaumer Knaflic Wiley, 2015

The most practical book on business data visualization. Knaflic does not teach code — she teaches how to choose the right chart type, eliminate clutter, focus attention, and tell a coherent data story. Read this alongside the technical matplotlib documentation. Chapter 2 ("Choosing an Effective Visual") is directly applicable to the chart selection decisions in this chapter.

ISBN: 978-1119002253

The Visual Display of Quantitative Information — Edward Tufte Graphics Press, 2001 (2nd edition)

The foundational academic text on information design. Tufte introduces the concept of "data-ink ratio" — maximizing the share of your chart's ink that represents data — which underlies the spine-removal and gridline-minimization practices in this chapter. Dense but influential.

ISBN: 978-0961392147

Python Data Science Handbook — Jake VanderPlas O'Reilly (also free online: https://jakevdp.github.io/PythonDataScienceHandbook/)

Chapter 4 covers matplotlib comprehensively, including the object-oriented interface, subplots, annotation, and 3D plots. The section "Customizing matplotlib: Configurations and Stylesheets" is useful for setting up consistent themes.

Fundamentals of Data Visualization — Claus O. Wilke O'Reilly (also free online: https://clauswilke.com/dataviz/)

A modern, chart-type-agnostic reference for visualization principles. Each chapter addresses a specific analytical question (showing distributions, comparing categories, visualizing time, etc.) and covers when to use which chart type. Excellent for the business analyst wanting a principled framework for chart selection.

Online Tutorials

Real Python — Python Plotting With matplotlib https://realpython.com/python-matplotlib-guide/

A thorough tutorial covering the full matplotlib API, including the artist hierarchy, common plot types, figure styling, and saving. One of the most comprehensive beginner-to-intermediate resources available.

Towards Data Science — matplotlib Basics for Business Charts https://towardsdatascience.com

Search for "matplotlib business charts" or "matplotlib dashboard tutorial" to find several high-quality step-by-step walkthroughs. Articles by authors like Will Koehrsen and Dario Radecic are particularly practical.

The Python Graph Gallery https://python-graph-gallery.com/matplotlib/

A comprehensive gallery of matplotlib charts organized by type (distribution, correlation, ranking, part-of-whole, evolution, etc.). Each entry includes Python code and variants. Useful for finding the right chart type and seeing implementation options.

Matplotlib Tutorial — Nicholas Rougier https://github.com/rougier/matplotlib-tutorial

A detailed GitHub-hosted tutorial with excellent visual diagrams explaining the Figure/Axes/Artist architecture. The section on figure anatomy is particularly helpful for understanding how all the components relate to each other.

Color Resources

ColorBrewer https://colorbrewer2.org

A web tool for selecting data visualization color palettes. Enter your number of categories and data type (sequential, diverging, or qualitative) and get colorblind-safe, print-friendly hex codes. All palettes are also available via the seaborn library.

IBM Design Color Palette https://www.ibm.com/design/language/color/

IBM's enterprise design system includes a carefully curated color palette tested for contrast, accessibility, and cross-media consistency. The palette works well for business charts.

Coloring for Colorblindness https://davidmathlogic.com/colorblind/

An interactive tool to simulate how your color choices appear under different types of color vision deficiency. Always check your multi-series charts here before publishing.

Advanced Topics to Explore Next

seaborn — A statistical visualization library built on matplotlib. seaborn handles styling, statistical summary charts (distribution plots, box plots, violin plots, heatmaps), and multi-panel grids with significantly less code. It is the natural next step after matplotlib. See Chapter 15.

plotly — A library for creating interactive charts that work in web browsers and Jupyter notebooks. Particularly useful for dashboards where users want to hover over data points, zoom, and filter. The free version supports most chart types; Plotly Dash adds full web application capability.

Bokeh — Another interactive visualization library. Better suited for large datasets (handles streaming data) but has a steeper learning curve than plotly.

matplotlib styles — matplotlib includes a set of built-in style sheets (plt.style.use("ggplot"), plt.style.use("seaborn-v0_8-whitegrid")) that apply pre-configured aesthetics. The "ggplot" style mimics R's ggplot2. Use plt.style.available to see all options.

matplotlib figure templates — For production reporting, define a reusable figure_template() function that applies your organization's brand colors, font settings, and standard formatting before drawing any chart. This ensures every output looks consistent.

Data Sources for Practice

Tableau Sample Data https://public.tableau.com/app/learn/sample-data

Tableau provides several free sample datasets specifically designed for business visualization practice: Superstore (retail sales), Airbnb (hospitality), Regional Sales (multi-region transactional), and Financial Performance. Download as CSV and practice building each chart type from this chapter.

Google Trends https://trends.google.com

Download time-series data for any search term or topic. Good for practicing line charts with real-world time series data.

Our World in Data https://ourworldindata.org

Free datasets on economics, health, education, and demographics. Clean, well-documented data with accompanying visualizations to compare your charts against.

SEC EDGAR Financial Statements https://efts.sec.gov/LATEST/search-index?q=%22annual+report%22&dateRange=custom

Public company financial statements in machine-readable formats. Revenue and cost data from real companies is excellent for practicing bar and line chart construction.