PANDAS STYLING
What is styling and why care?
The basic idea behind styling is that a user will want to modify the way the data is presented but still preserve the underlying format for further manipulation.
The most straightforward styling example is using a currency symbol when working with currency values. For instance, if your data contains the value 25.00, you do not immediately know if the value is in dollars, pounds, euros or some other currency. If the number is $25 then the meaning is clear.
Percentages are another useful example where formatting the output makes it simpler to understand the underlying analysis. For instance, which is quicker to understand: .05 or 5%? Using the percentage sign makes it very clear how to interpret the data.
The key item to keep in mind is that styling presents the data so a human can read it but keeps the data in the same pandas data type so you can perform your normal pandas math, date or string functions.
Pandas styling also includes more advanced tools to add colors or other visual elements to the output. The pandas documentation has some really good examples but it may be a bit overwhelming if you are just getting started. The rest of this article will go through examples of using styling to improve the readability of your final analysis.
Styling the data
Let’s get started by looking at some data. For this example we will use some 2018 sales data for a fictitious organization. We will pretend to be an analyst looking for high level sales trends for 2018. All of the data and example notebook are on github. PLease note that the styling does not seem to render properly in github but if you choose to download the notebooks it should look fine.
👇CLICK BELOW EXAMPLE PROGRAMS👇
