Chaining Pandas Operations: Strengths and Limitations | by Marcin Kozak

PYTHON PROGRAMMING

Learn when it’s worth chaining Pandas operations in pipes.

The title of this article stresses the strengths and limitations of chaining Pandas operations — but to be honest, I will write about fun.

Why fun? Is it at all important when we have data to analyze?

I don’t know what works for you, but for me fun in work is important. During my 20+ years of experience in data science, I’ve found that the more enjoyment I derive from coding, the more satisfied I am from completing the task. And I do mean the process of pursuing the task, not only just completing it. Of course, achieving results matters, probably the most. But trust me, if you dislike the tools you’re using, all you’ll want is to finish the job as quickly as possible. This can lead to mistakes, as you might work hastily and overlook important details in the data. And that’s something you want to avoid.

I transitioned to Python from R, and analyzing data with R is a lot of fun — thanks to the dplyr syntax. I’ve always enjoyed it, and I still do. However, when I switched to Python, I found myself preferring it over R. I’ve never really enjoyed programming in R (note the distinction between analyzing data and programming), while…