Am Neumarkt 😱

#visualization Plot Overview for Matplotlib Users / Observable / Observable https://observablehq.com/@observablehq/plot-overview-for-matplotlib-users

#visualization Please click on the link and watch the animation. It's 3D. ------ "The clever people at @NASA have created this deceptively simple yet highly effective data visualisation showing monthly global temperatures between 1880-2021".: nextfuckinglevel https://www.reddit.com/r/nextfuckinglevel/comments/tejc0l/the_clever_people_at_nasa_have_created_this/?utm_source=share&utm_medium=ios_app&utm_name=iossmf

#visualization Seaborn is getting a new interface. Would be great if the author defines a dunder method _ _ add _ _ () instead of using .add() method. Using dunder add, we can simply use + on layers. Nevertheless, we can all move away from plotnine when the migration is done. https://seaborn.pydata.org/nextgen/

#visualization Beautiful, elegant, and informative. It reminds me of the Netflix movie chromatic storytelling visualization. Full image: https://zenodo.org/record/5828349 Other discussions: https://www.reddit.com/r/dataisbeautiful/comments/s6vh8k/dutch_astronomer_cees_bassa_took_a_photo_of_the/

#visualization Pu X, Kay M. A probabilistic grammar of graphics. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM; 2020. doi:10.1145/3313831.3376466 Available at: https://dl.acm.org/doi/10.1145/3313831.3376466 A very good read if you are visualizing probability densities a lot. The paper began with a common mistake people make when visualizing densities. Then they proposed a systematic grammar of graphics for probabilities. They also provide a package (quite preliminary, see here https://github.com/MUCollective/pgog ).

#visualization#fun https://www.githubwrapped.com/

#DS#visualization https://percival.ink/ A new lightweight language for data analysis and visualization. It looks promising. I hate jupyter notebooks and I don't use them on most of my projects. One of the reasons is low reproducibility due to its non-reative nature. You changed some old cells and forgot to run a cell below, you may read wrong results. This new language is reactive. If old cells are changed, related results are also updated.

#visualization Hmmm my plate is way off the planetary heath diet recommendation. Source: https://www.nature.com/articles/d41586-021-03612-1

#visualization An interactive Visual Vocabulary: https://ft-interactive.github.io/visual-vocabulary/

#visualization Nicolas P. Rougier released his book on scientific visualization. He made some aesthetically pleasing figures. And the book is free. https://github.com/rougier/scientific-visualization-book

#DS#Visualization Okay, I'll tell you the reason I wrote this post. It is because xkcd made [this](https://xkcd.com/2537/). --- Choosing proper colormaps for our visualizations is important. It's almost like shooting a photo using your phone. Some phones capture details in every corner, while some phones give us overexposed photos and we get no details in the bright regions. A proper colormap should make sure we see the details we need to see. To address the importance of colormaps, we use the two examples shown on the website of colorcet[^colorcet]. The two colormaps, hot, and fire, can be found in matplotlib and colorcet, respectively. I can not post multiple images in one message, please see the full post for the comparisons of the two colormaps. Really, it is amazing. Find the link below: https://github.com/kausalflow/community/discussions/20 It is clear that "hot" brings in some overexposure. The other colormap, "fire", is a so-called perceptually uniform colormap. More experiments are performed in colorcet. Glasbey et al showed some examples of inspecting different properties using different colormaps[^Glasbey2007]. One of the methods to make sure the colormap shows enough details is to use perceptually uniform colrmaps[^Kovesi2015]. Kovesi provides a method to validate if a color map has uniform perceptual contrast[^Kovesi2015]. --- References and links mentioned in this post: [^colorcet]: Anaconda. colorcet 1.0.0 documentation. [cited 12 Nov 2021]. Available: https://colorcet.holoviz.org/ [^colorcet-github]: holoviz. colorcet/index.ipynb at master · holoviz/colorcet. In: GitHub [Internet]. [cited 12 Nov 2021]. Available: https://github.com/holoviz/colorcet/blob/master/examples/index.ipynb [^Kovesi2015]: Kovesi P. Good Colour Maps: How to Design Them. arXiv [cs.GR]. 2015. Available: http://arxiv.org/abs/1509.03700 [^Glasbey2007]: Glasbey C, van der Heijden G, Toh VFK, Gray A. Colour displays for categorical images. Color Research & Application. 2007. pp. 304–309. doi:10.1002/col.20327 [^matplotlib-colormaps]: Choosing Colormaps in Matplotlib — Matplotlib 3.4.3 documentation. [cited 12 Nov 2021]. Available: https://matplotlib.org/stable/tutorials/colors/colormaps.html

#visualization "Fail" When visualizing data, the units being used have to be specified for any values shown. But the style of the charts is attractive. :) By chungischef Available at: https://www.reddit.com/r/dataisbeautiful/comments/q958if/recreation_of_a_classic_population_density_map/