A Definition and An Analogy

A Definition

Wrangling is a weird word. Let’s check the definition. This is exactly what I did when I first heard the term and was perplexed just as you may be right now.

So wrangling means to round up, herd, or take charge of livestock, like horses or sheep. Let's focus in on the sheep example.

A shepherd's main goals are to get their sheep to their pastures to let them graze, guide them to market to shear them, and put them in the barn to sleep. Before any of that though, they must be rounded up in a nice and organized group. The consequences if they're not? These tasks take longer. If they're all scattered, some could also run off and get lost. A wolf could even sneak into the pack and feast on a few of them.

An Analogy

The same idea of organizing before acting is true for those who are shepherds of data. We need to wrangle our data for good outcomes, otherwise there could be consequences. If we analyze, visualize, or model our data before we wrangle it, our consequences could be making mistakes, missing out on cool insights, and wasting time. So best practices say wrangle. Always.

The development of Python and its libraries have made wrangling easier. In this course, you'll learn how to wrangle data like modern day data professionals.