Great Tables 3 in Python: Data Color and Polishing

The Great Tables package is ideal for crafting beautiful, high-quality tables for reports, publications, and data presentations. Effective tables typically share three key characteristics:

1. structuring that aids in the reading of the table.

2. well-formatted values, fitting expectations for the field of study.

3. styling that reduces time to insight and improves aesthetics.

Key Skills Acquired:

1. Adding Explanatory Footnotes and Source Notes

• Usedtab_source_note() to append footnotes or cite data sources directly within the table.

• Provided helpful context for interpretation and ensured reproducibility.

2. Adjusting Column Widths with (cols_width)

• Resized specific columns to optimize space usage, especially for wide or dense datasets.

• Improved layout and prevented content overflow.

3. Formatting Table Values with fmt_*() Functions

• Applied formatting to numeric values, dates, percentages, and more to enhance clarity and alignment with standards in scientific or business reporting.

4. Styling Enhancements Using data_color(), cols_align() and tab_options()

• Applied conditional coloring (data_color()) to emphasize data ranges or categories.

• Aligned columns (cols_align()) for consistency and readability.

• Customized general table aesthetics using tab_options() to refine spacing, borders, and fonts.

For a step-by-step guide with practical examples, visit this site.