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A Checklist for Data Wrangling
“Cheatsheet” for everyday data scientists
It was before the Stack Overflow era, so not much help was available online. Some people would print out cheatsheets of different kinds and hang on the walls around their workstations. Having a couple of pages of frequently used codes in front of the desk was an efficient way of correcting syntax errors.
Help is now at the fingertips only few clicks away. But an old-fashioned cheatsheet is still a valuable time-saving tool. It’s even more the case if you have to juggle between multiple programming languages.
Data scientists spend most of their time on data wrangling, so being efficient is a valuable skill to have. So the purpose of this article is to show how to build a “cheatsheet” for data wrangling following a typical analytics workflow. I am not going to write down all the codes needed every step of the way, rather I’ll focus on how to compile a cheatsheet that serves your purpose, so you can spend more time coding, less time searching for the right syntax.
1 ) Setup
Soon after firing up the IDE/text editor, you may very well be staring at the blank screen. Loading in the dataset(s) is probably the first thing coming to your mind. So let’s start there and import a couple of libraries.