DataFrame Table Styles

Introduction

One feature that often goes unnoticed is the customization of the appearance of DataFrames and Series objects, which allows us to present them in Jupyter Notebooks and web applications in a visually appealing way. The basic idea behind styling is that a user modifies the way the output is presented while the underlying data structure is preserved.

It is the Styler object that enable us to turn DataFrames into interactive and stylized visual displays. At a fundamental level, the Styler object provides a wrapper around DataFrames and contains methods for us to add styling in an intuitive way.

Note: While the Styler works for both DataFrames and Series objects, this lesson will focus on the styling of DataFrames. The Styler object is returned with the attribute DataFrame.style.

The styles defined by these methods are translated into corresponding CSS formatting instructions and applied to the rendered HTML table, thereby providing the Styler object with highly flexible and customizable designs.

The options for stylization cover a wide array of parameters, spanning properties like colors, fonts, borders, and backgrounds. Given that the styles aren’t dynamically updated if further changes to the DataFrame are made, it’s important that we make these style customizations after the data has been processed and finalized.

We’ll be working with a retail shop customer dataset for this lesson, as shown below: