Polarise
Style your data to explore. Style your results to present.
A Polars-native DataFrame styling tool for HTML visualization
- Fast, expressive styling with a clean, chainable API
- Turn Polars DataFrames into clear, expressive HTML views
- Style using native Polars expressions
- Built for data inspection, debugging, and exploration
- Ready for reports, presentations, and sharing
Raw
Explore
Showcase
Quickstart
pip install polarise
import polars as pl
import polarise
df = pl.DataFrame({
"date": ["2024-01-01", "2024-01-02", "2024-01-03", "2024-01-04"],
"region": ["EU", "EU", "US", "US"],
"sales": [120, 85, 210, 250],
"profit": [20, -15, 45, 55]
})
(
df.style()
.highlight_when(
in_col="date",
when=pl.col("profit") < 0,
then_fill="alert_orange"
)
.gradient("sales", cmap="greens")
.bar("profit", fill_pos="alert_green", fill_neg="alert_orange")
.fashion_zebra()
.show()
)
| date | region | sales | profit |
|---|---|---|---|
| 2024-01-01 | EU | 120 | 20 |
| 2024-01-02 | EU | 85 | -15 |
| 2024-01-03 | US | 210 | 45 |
| 2024-01-04 | US | 250 | 55 |
Where Polarise fits
Polarise is inspired by the styling capabilities of pandas, but built for a Polars workflow.
While Great Tables provides a rich and highly customizable system for building publication-quality tables, it comes with a more structured and declarative approach.
Polarise takes a different path:
- Lightweight and fast
- Fully aligned with Polars expressions
- Designed for quick inspection and clean presentation
It started as a simple tool to explore Polars DataFrames visually, and grew into a practical way to produce clear, styled HTML tables for reports and sharing — with optional LaTeX export for simple use cases.
At a glance
| Feature | pandas Styler | Great Tables | Polarise |
|---|---|---|---|
| Ecosystem | pandas | Polars | Polars |
| Philosophy | Flexible, built-in | Rich, declarative | Lightweight, expressive |
| Best for | General styling | Publication workflows | Inspection & quick presentation |
| Syntax | pandas-based | Table grammar | Polars expressions |
| Complexity | Medium | High | Low |
| Speed (iteration) | Medium | Slower | Fast |