https://github.com/haydeeperuyero/2023-mexico-data-visualization

https://github.com/haydeeperuyero/2023-mexico-data-visualization

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https://github.com/HaydeePeruyero/2023-mexico-data-visualization/blob/main/

# Best practices in data visualization - ASPP 2023 LATAM

## Content

* **[Best practice in data visualization (slides)](slides.pdf)** [(here slides with notes)](slides-notes.pdf). Slides accompanying the tutorial, together with some written notes.

* **[Exercise 1: Mastering matplotlib](exercise-1.ipynb)** (**notebook**). Here we go beyond matplotlib's defaults and fine tune the details so to  make a figure *publication-ready*.

* **[Exercise 2: Which visualization should I use?](exercise-2.ipynb)** (**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*.

* **[Exercise 3: Working with images](exercise-3.ipynb)** (**notebook**). Here you will learn how to visualize data as images.

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## Extra-Material (from ASPP-2021)

* **[Scales & projections](https://github.com/ASPP/2021-bordeaux-dataviz/blob/master/03-scale-projection.ipynb)**
  (**notebook**). Tutorial on different type of scales (log scale, symlog scale, logit scale) and projections
  (polar, 3D, geographic).

* **[Animation](https://github.com/ASPP/2021-bordeaux-dataviz/blob/master/04-animation.ipynb)** (**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.


## Further Resources

At the implementation level (code, galleries and how-tos):
- [**Seaborn**](https://seaborn.pydata.org/), a python library for statistical data visualization. Very recommended as a next step in your learning journey.
- [**Matplotlib Cheatsheets**](https://matplotlib.org/cheatsheets/), Nicolas P. Rougier (2020)
- [**Scientific Visualization  Python & Matplotlib**](https://github.com/rougier/scientific-visualization-book), open-source book from Nicolas P. Rougier (2021)
- [**Python Graph Gallery**](https://python-graph-gallery.com/), Yan Holtz (2017)
- [**Matplotlib Gallery**](https://matplotlib.org/stable/gallery/index.html), Matplotlib team


At the conceptual level :

- [**Ten simple rules for better figures**](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833), Nicolas P. Rougier, Michael Droettboom, Philip E. Bourne (2014)
- [**Fundamentals of Data Visualization**](https://serialmentor.com/dataviz/), book by Claus O. Wilke (2019)
- [**Chart Suggestions - a though-starter**](data/Abelas-Chart-Selection-Diagram.jpg) by A. Abelas.
- [**Data Visualization Catalogue**](https://datavizcatalogue.com/)
- **Edward Tufte**'s series of books: [The Visual Display of Quantitative Information (1983)](https://www.edwardtufte.com/tufte/books_vdqi), [Envisioning Information (1990)](https://www.edwardtufte.com/tufte/books_ei), [Beautiful Evidence (2006)](https://www.edwardtufte.com/tufte/books_be), etc.
- [**Let my dataset change your mindset**](https://www.ted.com/talks/hans_rosling_let_my_dataset_change_your_mindset?referrer=playlist-the_best_hans_rosling_talks_yo&autoplay=true), Ted Talk by [Hans Rosling](https://en.wikipedia.org/wiki/Hans_Rosling).


Interactive visualizations:

- [**Widgets in Jupyter notebook**](https://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html)
- [**Plotly**](https://plotly.com/python/)

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