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Best practice in data visualization (slides) (here slides with notes). Slides accompanying the tutorial, together with some written notes.
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Exercise 1: Mastering matplotlib (notebook). In this introduction, we'll see how to make a figure and play with the different settings such as to improve the rendering. We learn how to use style sheets, and how to fine tune your plot. We also learn how to make a figure composed by several subplots.
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Exercise 2: Which visualization should I use? (notebook). In this exercise you are given a dataset and you're asked to think, decide and implement a data visualization that will best answer a research question.
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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.
Interactive visualizations: