Understanding Long Format (Tidy) Data

We have a moderately complex dataset that we’ll be working with. It consists of four CSV files containing information on almost all the countries and regions in the world. We have more than sixty metrics spanning more than forty years, which means that there are quite a lot of options and combinations to choose from.

But before going through the process of preparing our dataset, let’s demonstrate our end goal with a simple example so we have an idea of where we’re heading. It will also hopefully show why we’re investing the time to make those changes.

Plotly Express example chart

Plotly Express ships with a few datasets for practicing and testing certain features whenever we want to do so. They fall under the data module of plotly.express, and calling them as functions returns the respective dataset. Let’s take a look at the famous Gapminder dataset.

Displaying the dataset

Running the widget below displays sample rows of the gapminder DataFrame.