A Highly Overlooked Approach To Analysing Pandas DataFrames
A guide to visually appealing DataFrames.
Instead of previewing raw DataFrames, styling can make data analysis much easier and faster. Here's how.
Jupyter is a web-based IDE. Anything you print is rendered using HTML and CSS.
This means you can style your output in many different ways.
To style Pandas DataFrames, use its Styling API (𝗱𝗳.𝘀𝘁𝘆𝗹𝗲). As a result, the DataFrame is rendered with the specified styling.
Read more here: Documentation.
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