gt_hulk_col_numeric

gt_hulk_col_numeric(
    gt,
    columns=None,
    palette='PRGn',
    domain=None,
    na_color=None,
    alpha=None,
    reverse=False,
    autocolor_text=True,
)

Apply a color gradient to numeric columns in a GT object.

The gt_hulk_col_numeric() function takes an existing GT object and applies a color gradient to the background of specified numeric columns, based on their values. This is useful for visually emphasizing the distribution or magnitude of numeric data within a table.

Parameters

gt : GT: An existing GT object.
columns : SelectExpr = None: The columns to target. Can be a single column name or a list of column names. If None, the color gradient is applied to all columns.
palette : str | list[str] = 'PRGn': The color palette to use for the gradient. Can be a string referencing a palette name or a list of color hex codes. Defaults to the "PRGn" palette from the ColorBrewer package.
domain : list[int] | list[float] | None = None: The range of values to map to the color palette. Should be a list of two values (min and max). If None, the domain is inferred from the data.
na_color : str | None = None: The color to use for missing (NA) values. If None, a default color is used.
alpha : int | float | None = None: The alpha (transparency) value for the colors, as a float between 0 (fully transparent) and 1 (fully opaque).
reverse : bool = False: If True, reverses the color palette direction.
autocolor_text : bool = True: If True, automatically adjusts text color for readability against the background.

Returns

: GT: The modified GT object, allowing for method chaining.

Examples

from great_tables import GT
from great_tables.data import gtcars
import gt_extras as gte

gtcars_mini = gtcars.loc[0:8, ["model", "mfr", "year", "hp", "trq", "mpg_h"]]

gt = (
    GT(gtcars_mini, rowname_col="model")
    .tab_stubhead(label="Car")
)

gt.pipe(gte.gt_hulk_col_numeric, columns=["hp", "trq", "mpg_h"])

Car	mfr	year	hp	trq	mpg_h
GT	Ford	2017	647.0	550.0	18.0
458 Speciale	Ferrari	2015	597.0	398.0	17.0
458 Spider	Ferrari	2015	562.0	398.0	17.0
458 Italia	Ferrari	2014	562.0	398.0	17.0
488 GTB	Ferrari	2016	661.0	561.0	22.0
California	Ferrari	2015	553.0	557.0	23.0
GTC4Lusso	Ferrari	2017	680.0	514.0	17.0
FF	Ferrari	2015	652.0	504.0	16.0
F12Berlinetta	Ferrari	2015	731.0	509.0	16.0

A more involved setup.

from great_tables.data import towny

towny_mini = towny[
    [
        "name",
        "pop_change_1996_2001_pct",
        "pop_change_2001_2006_pct",
        "pop_change_2006_2011_pct",
        "pop_change_2011_2016_pct",
        "pop_change_2016_2021_pct",
    ]
].head(10)

gt = (
    GT(towny_mini, rowname_col="name")
    .tab_stubhead(label="Town")
    .tab_spanner(
        label="Population Change",
        columns=[1, 2, 3, 4, 5]
    )
    .cols_label(
        pop_change_1996_2001_pct="1996-2001",
        pop_change_2001_2006_pct="2001-2006",
        pop_change_2006_2011_pct="2006-2011",
        pop_change_2011_2016_pct="2011-2016",
        pop_change_2016_2021_pct="2016-2021",
    )
)

gt.pipe(gte.gt_hulk_col_numeric, columns=[1, 2, 3, 4, 5], domain = [-0.1, 0.23])

Town	Population Change
Town	1996-2001	2001-2006	2006-2011	2011-2016	2016-2021
Addington Highlands	-0.0111	0.0458	0.002	-0.0791	0.0932
Adelaide Metcalfe	0.0067	-0.0044	-0.0341	-0.0125	0.007
Adjala-Tosorontio	0.0773	0.0608	-0.0086	0.0351	0.0013
Admaston/Bromley	-0.0046	-0.0382	0.0471	0.032	0.0204
Ajax	0.1447	0.2226	0.2155	0.0919	0.0584
Alberton	-0.0691	0.0021	-0.0981	0.1215	-0.0155
Alfred and Plantagenet	0.0334	0.0071	0.0626	0.0526	0.0278
Algonquin Highlands	0.083	0.0816	0.1063	0.0755	0.1008
Alnwick/Haldimand	0.0575	0.1008	0.0283	0.0381	0.0879
Amaranth	0.0928	0.0199	0.0307	0.0293	0.0608