Gradients

Gradient methods map numeric values to colours along a colormap. See Colors & Colormaps for the full list of available cmap values.

gradient(in_col, scope="column", cmap="viridis", color=False, exclude=None)

Apply a sequential colour gradient. Values are normalised to [0, 1] then mapped to the colormap.

in_col str | list | Expr — Column(s) to apply to
scope "column" | "table", default "column" — Normalise per column or across all selected columns
cmap str, default "viridis" — Colormap name (see Colors & Colormaps)
color bool, default False — If True, also colour the text using the colormap
exclude str | list | None, default None — Columns to exclude

Example:

df.style().gradient("Revenue", cmap="viridis").show()

Company	Revenue	Profit	Growth	Employees_k
Apple	383.3	97.0	7.8	161
Microsoft	211.9	72.4	6.9	221
Google	307.4	73.8	8.7	182
Amazon	574.8	30.4	11.8	1541
Meta	134.9	39.1	16.4	67

gradient_divergent(in_col, center, scope="column", cmap="RdBu", color=False, exclude=None)

Apply a divergent colour gradient that splits at center. Values below center use the left half of the colormap; values above use the right half.

in_col str | list | Expr — Column(s) to apply to
center float — The neutral midpoint value
scope "column" | "table", default "column" — Normalise per column or across all selected
cmap str, default "RdBu" — Divergent colormap name
color bool, default False — Also colour text using the colormap
exclude str | list | None, default None — Columns to exclude

Example:

df.style().gradient_divergent("Cost_per_1M", center=15.0, cmap="vik").show()

Model	MMLU	HumanEval	GPQA	Context_k	Cost_per_1M
GPT-4	86.4	67.0	50.1	128	30.0
Claude-3.5 Sonnet	88.7	92.0	59.4	200	3.0
Gemini-1.5 Pro	85.9	71.9	46.2	2048	7.0
Llama-3-70B	82.0	81.7	42.7	8	0.9
Mixtral-8x7B	70.6	40.2	34.3	32	0.7

gradient_between(in_col, low, high, scope="column", cmap="viridis", color=False, exclude=None)

Apply a gradient only within a specified range. Values outside [low, high] receive no colour.

in_col str | list | Expr — Column(s) to apply to
low float — Lower bound of the colour range
high float — Upper bound of the colour range
scope "column" | "table", default "column" — Normalise per column or globally
cmap str, default "viridis" — Colormap name
color bool, default False — Also colour text
exclude str | list | None, default None — Columns to exclude

Example:

df.style().gradient_between("Revenue", low=100.0, high=400.0, cmap="lapaz").show()

Company	Revenue	Profit	Growth	Employees_k
Apple	383.3	97.0	7.8	161
Microsoft	211.9	72.4	6.9	221
Google	307.4	73.8	8.7	182
Amazon	574.8	30.4	11.8	1541
Meta	134.9	39.1	16.4	67

heat_map(in_col=pl.all(), cmap="viridis", color=False, exclude=None)

Apply a gradient across all (or selected) numeric columns simultaneously with a shared scale. Convenience wrapper around gradient(scope="table").

in_col str | list | Expr, default pl.all() — Column(s) to apply to (defaults to all numeric)
cmap str, default "viridis" — Colormap name
color bool, default False — Also colour text
exclude str | list | None, default None — Columns to exclude from pl.all()

Example:

df.select(["Revenue", "Profit", "Growth"]).style().heat_map().show()