Thu 24 Jun 2021

Python Data Viz Libraries Compared

I'm teaching a course about the essential tools of Data Science. Among those, I'm going to cover how to use some of the most popular data visualization libraries in Python: pandas (yes, that's not a typo!), matplotlib, seaborn, and

I thought it be useful for my students to have cheat sheet with some popular graphs made with each of these tools. So I wrote this cheat sheet.

In the next sections, you'll learn how to set up your local environment, read the data, and get the code to make the following types of graphs:

  • Line plot
  • Grouped bars plot
  • Stacked bars plot
  • Area chart
  • Pie/Donut chart
  • Histogram
  • Scatter plot
  • Boxplot

Let me know what you think!

Source: Python Data Viz Libraries Compared: 8 Popular Graphs Made with pandas, matplotlib, seaborn, and, an article by Dylan Castillo.

“Weak declaration”

PPK looks at aspect-ratio, a CSS property for layout that, for the most part, does exactly what you would think it does. It’s getting more interesting as it’s behind a flag in Firefox and Safari now, so we’ll have universal support pretty darn soon. I liked how he called it a “weak declaration” which I’m fairly sure isn’t an official term but a good way to think about it.

Source: "Weak declaration", an article by Chris Coyier.