# col_numeric()
Create a color-mapping function for continuous numeric values.
Usage
``` python
col_numeric(
palette=None,
domain=None,
na_color=None,
reverse=False,
truncate=False,
)
```
The [col_numeric()](col_numeric.md#great_tables.col_numeric) helper returns a function that linearly maps numeric values onto a color palette (interpolating between the palette's colors). The returned function is designed to be passed to the `fn=` argument of data_color(), but it can be called on any list of values.
## Parameters
`palette: str | list[str] | None = None`
The colors to interpolate between. This can be a list of colors (as hexadecimal values or color names) or the name of a ColorBrewer or viridis palette (see data_color() for the available names). If `None`, then a default palette will be used.
`domain: list[int] | list[float] | None = None`
The range of values to map onto the palette, given as `[min, max]`. Values outside of this range receive the missing-value color (unless `truncate=True`). If `None`, then the domain is taken from the range of the (non-missing) values supplied to the returned function each time it is called.
`na_color: str | None = None`
The color to use for missing values and values outside of the domain. If `None`, then the returned function gives `None` for those values, which lets [data_color()](GT.data_color.md#great_tables.GT.data_color) apply its own `na_color=` color.
`reverse: bool = ``False`
Should the order of the palette colors be reversed?
`truncate: bool = ``False`
If `True`, then values outside of the domain are treated as the nearest end of the domain, so they receive the first or last color of the palette. If `False` (the default), then they receive the missing-value color.
## Returns
`Callable[[list[Any]], list[str | None]]`
A function that takes a list of numeric values and returns a list of hexadecimal colors.
## Examples
A diverging palette with a domain centered on zero colors negative values increasingly red and positive values increasingly green, the further they are from zero:
``` python
import pandas as pd
from great_tables import GT, col_numeric
df = pd.DataFrame(
{
"region": ["North", "South", "East", "West", "Central"],
"change": [12.5, -8.1, 0.0, 3.2, -1.0],
}
)
GT(df).data_color(
columns="change",
fn=col_numeric(palette=["#D7191C", "white", "#1A9641"], domain=[-15, 15]),
)
```
| region | change |
|---------|--------|
| North | 12.5 |
| South | -8.1 |
| East | 0.0 |
| West | 3.2 |
| Central | -1.0 |
Because [col_numeric()](col_numeric.md#great_tables.col_numeric) returns an ordinary function, it can be composed with other logic. Here, the domain is made symmetric around zero based on the largest absolute value in the column:
``` python
def red_white_green(vals):
m = max(abs(x) for x in vals if not pd.isna(x))
return col_numeric(palette=["#D7191C", "white", "#1A9641"], domain=[-m, m])(vals)
GT(df).data_color(columns="change", fn=red_white_green)
```
| region | change |
|---------|--------|
| North | 12.5 |
| South | -8.1 |
| East | 0.0 |
| West | 3.2 |
| Central | -1.0 |
With a narrower domain, `truncate=True` saturates the colors of values beyond its limits rather than leaving them uncolored. Here, anything above `5` is fully green and anything below `-5` is fully red:
``` python
GT(df).data_color(
columns="change",
fn=col_numeric(palette=["#D7191C", "white", "#1A9641"], domain=[-5, 5], truncate=True),
)
```
| region | change |
|---------|--------|
| North | 12.5 |
| South | -8.1 |
| East | 0.0 |
| West | 3.2 |
| Central | -1.0 |