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Best practice in data visualization (slides). Slides accompanying the tutorial.
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Exercise 1: Mastering matplotlib (notebook). Here we go beyond matplotlib's defaults and fine tune the details so to make a figure publication-ready.
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Exercise 2: Which visualization should I use? (notebook). You are given a dataset and you're asked to decide and implement a data visualization that will best answer a research question. Applying what was learned in the previous exercise, you should come up with a figure that is publication-ready.
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Exercise 3: Working with images (notebook). Here you will learn how to visualize data as images.
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Scales & projections (notebook). Tutorial on different type of scales (log scale, symlog scale, logit scale) and projections (polar, 3D, geographic).
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Animation (notebook). Animation with matplotlib can be created very easily using the animation framework. This notebook shows how to create an animation and save it as a movie.
At the implementation level (code, galleries and how-tos):
- Seaborn, a python library for statistical data visualization. Very recommended as a next step in your learning journey.
- Matplotlib Cheatsheets, Nicolas P. Rougier (2020)
- Scientific Visualization – Python & Matplotlib, open-source book from Nicolas P. Rougier (2021)
- Python Graph Gallery, Yan Holtz (2017)
- Matplotlib Gallery, Matplotlib team
At the conceptual level :
- Ten simple rules for better figures, Nicolas P. Rougier, Michael Droettboom, Philip E. Bourne (2014)
- Fundamentals of Data Visualization, book by Claus O. Wilke (2019)
- Chart Suggestions - a though-starter by A. Abelas.
- Data Visualization Catalogue
- Edward Tufte's series of books: The Visual Display of Quantitative Information (1983), Envisioning Information (1990), Beautiful Evidence (2006), etc.
- Let my dataset change your mindset, Ted Talk by Hans Rosling.
Interactive visualizations: