Pandas Styler: Improve the Presentation of your DataFrames

When working with data in Python, Pandas DataFrames are our main tool. However, their default presentation in consoles or notebooks can be very basic. This is where the Pandas Styler object becomes a fundamental ally to transform simple DataFrames into visually attractive and informative tables.

What is Pandas Styler?¶

The Styler object allows you to apply conditional formatting and CSS styles directly to your Pandas DataFrames. With it, you can highlight patterns, emphasize important values, and improve readability without having to export the data to another tool.

Key Features¶

• Conditional Formatting: Highlight cells, rows, or columns based on conditions defined by you (e.g., values above a threshold, duplicates, etc.).
• CSS Styles: Apply CSS styles such as background colors, fonts, borders, or alignments to customize the appearance.
• Predefined Styles: Includes methods like background_gradient() to apply color gradients that visualize the distribution of values.
• Excel Integration: Maintains styles when exporting DataFrames to Excel files, facilitating professional reports.

Conclusion¶

The Pandas Styler object is a simple but powerful tool to take your data presentation to the next level. With a few lines of code, you can make your DataFrames not only a data source but also an effective communication tool.

Original article: Stylin' with Pandas | Practical Business Python