# GT.pipe()
Provide a structured way to chain a function for a GT object.
Usage
``` python
GT.pipe(
func,
*args,
**kwargs,
)
```
This function accepts a function that receives a GT object along with optional positional and keyword arguments, returning a GT object. This allows users to easily integrate a function into the chained API offered by **Great Tables**.
## Parameters
`func: Callable[Concatenate[``"GT", P], ``"GT"``]`
A function that receives a GT object along with optional positional and keyword arguments, returning a GT object.
`*args: P.args`
Optional positional arguments to be passed to the function.
`**kwargs: P.kwargs`
Optional keyword arguments to be passed to the function.
## Returns
`gt`
A GT object.
## Examples
Let's use the `name`, `land_area_km2`, and `density_2021` columns of the [towny](data.towny.md#great_tables.data.towny) dataset to create a table. First, we'll demonstrate using two consecutive calls to the `.tab_style()` method to highlight the maximum value of the `land_area_km2` column with `"lightgray"` and the maximum value of the `density_2021` column with `"lightblue"`.
``` python
import polars as pl
from great_tables import GT, loc, style
from great_tables.data import towny
towny_mini = pl.from_pandas(towny).head(10)
(
GT(
towny_mini[["name", "land_area_km2", "density_2021"]],
rowname_col="name",
)
.tab_style(
style=style.fill(color="lightgray"),
locations=loc.body(
columns="land_area_km2",
rows=pl.col("land_area_km2").eq(pl.col("land_area_km2").max()),
),
)
.tab_style(
style=style.fill(color="lightblue"),
locations=loc.body(
columns="density_2021",
rows=pl.col("density_2021").eq(pl.col("density_2021").max()),
),
)
)
```
| | land_area_km2 | density_2021 |
|------------------------|---------------|--------------|
| Addington Highlands | 1293.99 | 1.96 |
| Adelaide Metcalfe | 331.11 | 9.09 |
| Adjala-Tosorontio | 371.53 | 29.58 |
| Admaston/Bromley | 519.59 | 5.76 |
| Ajax | 66.64 | 1900.75 |
| Alberton | 116.6 | 8.18 |
| Alfred and Plantagenet | 391.79 | 25.39 |
| Algonquin Highlands | 999.69 | 2.59 |
| Alnwick/Haldimand | 398.25 | 18.76 |
| Amaranth | 265.02 | 16.33 |
Next, we'll demonstrate how to achieve the same result using the `.pipe()` method to programmatically style each column.
``` python
columns = ["land_area_km2", "density_2021"]
colors = ["lightgray", "lightblue"]
def tbl_style(gtbl: GT, columns: list[str], colors: list[str]) -> GT:
for column, color in zip(columns, colors):
gtbl = gtbl.tab_style(
style=style.fill(color=color),
locations=loc.body(columns=column, rows=pl.col(column).eq(pl.col(column).max())),
)
return gtbl
(
GT(
towny_mini[["name", "land_area_km2", "density_2021"]],
rowname_col="name",
).pipe(tbl_style, columns, colors)
)
```
| | land_area_km2 | density_2021 |
|------------------------|---------------|--------------|
| Addington Highlands | 1293.99 | 1.96 |
| Adelaide Metcalfe | 331.11 | 9.09 |
| Adjala-Tosorontio | 371.53 | 29.58 |
| Admaston/Bromley | 519.59 | 5.76 |
| Ajax | 66.64 | 1900.75 |
| Alberton | 116.6 | 8.18 |
| Alfred and Plantagenet | 391.79 | 25.39 |
| Algonquin Highlands | 999.69 | 2.59 |
| Alnwick/Haldimand | 398.25 | 18.76 |
| Amaranth | 265.02 | 16.33 |