## 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], `<span class="st">`"GT"``]`</span>  
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        |