We have a soft spot for long, thorough guides here at TDS—but we also appreciate focused posts targeting specific challenges and pain points that data scientists face in their day-to-day work.
To celebrate these highly useful and practical articles, this week’s Variable turns the spotlight on recent highlights from our Tips & Tricks column: they offer actionable, tried-and-true advice that can help you save time and effort and produce better results in your projects. Whether or not you’ve already enjoyed your share of treats this week (happy belated Halloween to those who celebrate!), we hope these tricks will inspire you to find a new approach or tool to experiment with.
- 6 Common Index-Related Operations You Should Know about Pandas
Given Pandas’ ubiquity in data-science workflows, it’s never a bad idea to gain a deeper understanding of its features and expand your knowledge of effective ways to handle dataframes. Yong Cui’s new post zooms in on index-related operations and breaks them down using simple, real-life use cases as examples.
- How to Use Color in Data Visualizations
If you’ve been treating color choices in your charts and plots as an afterthought, Michal Szudejko’s compendium of tips on the proper use of color is sure to make you reconsider your approach. From accessibility to palette options, you’ll learn how small tweaks can make your visualizations clearer and help them become stronger storytelling tools.
- Unleashing the Power of the Julia SuperType
For the growing number of Julia aficionados out there, Emma Boudreau’s hands-on resource on abstraction and how to incorporate it effectively into your code is a must-read—it offers a detailed overview of the ways you can start creating our own supertypes with minimal effort.