Sun 06 Sep 2020

How to pick more beautiful colors for your data visualizations

Choosing good colors for your charts is hard. This article tries to make it easier.

I want you to feel more confident in your color choices. And if you have no sense for colors at all, here’s my attempt to help you find good ones anyway. We’ll talk about common color mistakes I see out there in the wild, and how to avoid them.

This is not the right article for you if you’re trying to find good gradients or shades. But if you need to find beautiful, distinctive colors for different categories (e.g. continents, industries, bird species) for your line charts, pie charts, stacked bar charts, etc., read on.

Source: How to pick more beautiful colors for your data visualizations, an article by Lisa Charlotte Rost.

Understanding Python Package Distribution Types

If you’ve done much Python development you’re probably familiar with importing dependencies using pip, or even easy_install, if you’ve been at this for awhile. Whether you were aware of it or not, these dependencies likely came from the public Python Package Index (PyPI) or perhaps an internal mirror of the PyPi repository that is hosted by your company.

What you may not have been aware of is how these dependencies are actually packaged, delivered, and installed, and the differences between the different distribution types available for Python.

Source: Understanding Python Package Distribution Types, an article by Andrew Scott.