import polars as pl
from great_tables import GT, from_column, style, loc
from great_tables.data import airquality
air_head = airquality.head()
gt_air = GT(air_head)
gt_pl_air = GT(pl.from_pandas(air_head))Styling the Table Body
Visual styling helps draw attention to important values and makes patterns in your data easier to spot. Great Tables provides a flexible system for applying fills, borders, and text styles to cells in the table body. This page covers the fundamentals of cell-level styling, from targeting specific cells to using column values and expressions to drive dynamic styles.
Great Tables can add styles—like color, text properties, and borders—on many different parts of the displayed table. The following set of examples shows how to set styles on the body of table, where the data cells are located.
For the examples on this page, we’ll use the included airquality dataset to set up GT objects for both Pandas and Polars DataFrames.
Note
When using Great Tables with VS Code, the IDE suppresses some forms of table styling displayed in notebooks. For example, border styles might not appear. Use .show("browser") to see the styled GT table in a separate browser window.
Style basics
We use the tab_style() method in combination with loc.body() to set styles on cells of data in the table body. For example, the table-making code below applies a yellow background color to specific cells.
gt_air.tab_style(
style=style.fill(color="yellow"),
locations=loc.body(columns="Temp", rows=[1, 2])
)| Ozone | Solar_R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41.0 | 190.0 | 7.4 | 67 | 5 | 1 |
| 36.0 | 118.0 | 8.0 | 72 | 5 | 2 |
| 12.0 | 149.0 | 12.6 | 74 | 5 | 3 |
| 18.0 | 313.0 | 11.5 | 62 | 5 | 4 |
| 14.3 | 56 | 5 | 5 |
There are two important arguments to tab_style(): style= and locations=. We are calling a specific function for each of these:
- style.fill(): the type of style to apply. In this case the fill (or background color).
- loc.body(): the area we want to style. In this case, it’s the table body with specific columns and rows specified.
In addition to style.fill(), several other styling functions exist. We’ll look at styling borders and text in the following sections.
Customizing Borders
Let’s use style.borders() to place borders around targeted cells. In this next example, the table has a red dashed border above two rows.
gt_air.tab_style(
style=style.borders(sides="top", color="red", style="dashed", weight="3px"),
locations=loc.body(rows=[1, 2])
)| Ozone | Solar_R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41.0 | 190.0 | 7.4 | 67 | 5 | 1 |
| 36.0 | 118.0 | 8.0 | 72 | 5 | 2 |
| 12.0 | 149.0 | 12.6 | 74 | 5 | 3 |
| 18.0 | 313.0 | 11.5 | 62 | 5 | 4 |
| 14.3 | 56 | 5 | 5 |
The red dashed border appears above rows 1 and 2, providing a visual separator. You can control the side ("top", "bottom", "left", "right"), color, style, and weight of the border.
Customizing Text
We can style text with by using the style.text() function. This gives us many customization possibilities for any text we target. For example, the Solar_R column below has green, bolded text in a custom font.
gt_air.tab_style(
style=style.text(color="green", font="Times New Roman", weight="bold"),
locations=loc.body(columns="Solar_R")
)| Ozone | Solar_R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41.0 | 190.0 | 7.4 | 67 | 5 | 1 |
| 36.0 | 118.0 | 8.0 | 72 | 5 | 2 |
| 12.0 | 149.0 | 12.6 | 74 | 5 | 3 |
| 18.0 | 313.0 | 11.5 | 62 | 5 | 4 |
| 14.3 | 56 | 5 | 5 |
The Solar_R column text appears in green, bold, and in the Times New Roman font. The style.text() function supports additional options like size=, style= (italic), and decorate= (underline, line-through).
Column-based Styles
In addition to setting styles to specific values (e.g., a "yellow" background fill), you can also use parameter values from table columns to specify styles. The way to do this is to use the from_column() helper function to access those values.
df = pl.DataFrame({"x": [1, 2], "background": ["lightyellow", "lightblue"]})
(
GT(df)
.tab_style(
style=style.fill(color=from_column(column="background")),
locations=loc.body(columns="x")
)
)| x | background |
|---|---|
| 1 | lightyellow |
| 2 | lightblue |
Notice that in the code above, we used values from the background column to specify the fill color for each styled row.
In the next few sections, we’ll first show how this combines nicely with the cols_hide() method, then, we’ll demonstrate how to use Polars expressions to do everything much more simply.
Combining Styling with cols_hide()
One common approach is to specify a style from a column, and then hide that column in the final output. For example, we can add a background column to our airquality data:
color_map = {
True: "lightyellow",
False: "lightblue"
}
with_color = air_head.assign(
background=(air_head["Temp"] > 70).replace(color_map)
)
with_color| Ozone | Solar_R | Wind | Temp | Month | Day | background | |
|---|---|---|---|---|---|---|---|
| 0 | 41.0 | 190.0 | 7.4 | 67 | 5 | 1 | lightblue |
| 1 | 36.0 | 118.0 | 8.0 | 72 | 5 | 2 | lightyellow |
| 2 | 12.0 | 149.0 | 12.6 | 74 | 5 | 3 | lightyellow |
| 3 | 18.0 | 313.0 | 11.5 | 62 | 5 | 4 | lightblue |
| 4 | NaN | NaN | 14.3 | 56 | 5 | 5 | lightblue |
Notice that the dataset now has a background column set to either "lightyellow" or "lightblue", depending on whether Temp is above 70.
We can then use this background column to set the fill color of certain body cells, and then hide the background column since we don’t need that in our finalized display table:
(
GT(with_color)
.tab_style(
style=style.fill(color=from_column(column="background")),
locations=loc.body(columns="Temp")
)
.cols_hide(columns="background")
)| Ozone | Solar_R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41.0 | 190.0 | 7.4 | 67 | 5 | 1 |
| 36.0 | 118.0 | 8.0 | 72 | 5 | 2 |
| 12.0 | 149.0 | 12.6 | 74 | 5 | 3 |
| 18.0 | 313.0 | 11.5 | 62 | 5 | 4 |
| 14.3 | 56 | 5 | 5 |
Note the two methods used above:
- tab_style(): uses from_column() to set the color using the values of the
backgroundcolumn. - cols_hide(): prevents the
backgroundcolumn from being displayed in the output.
Using Polars expressions
Styles can also be specified using Polars expressions. For example, the code below uses the Temp column to set color to "lightyellow" or "lightblue".
# A Polars expression defines color based on `Temp`
temp_color = (
pl.when(pl.col("Temp") > 70)
.then(pl.lit("lightyellow"))
.otherwise(pl.lit("lightblue"))
)
gt_pl_air.tab_style(
style=style.fill(color=temp_color),
locations=loc.body("Temp")
)| Ozone | Solar_R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41.0 | 190.0 | 7.4 | 67 | 5 | 1 |
| 36.0 | 118.0 | 8.0 | 72 | 5 | 2 |
| 12.0 | 149.0 | 12.6 | 74 | 5 | 3 |
| 18.0 | 313.0 | 11.5 | 62 | 5 | 4 |
| None | None | 14.3 | 56 | 5 | 5 |
The Polars expression evaluates per row and produces a color string for each cell. This approach avoids creating and hiding an extra column, keeping the code concise.
Using functions
You can also use a function, that takes the DataFrame and returns a Series with a style value for each row.
This is shown below on a pandas DataFrame.
def map_color(df):
return (df["Temp"] > 70).map(
{True: "lightyellow", False: "lightblue"}
)
gt_air.tab_style(
style=style.fill(
color=map_color),
locations=loc.body("Temp")
)| Ozone | Solar_R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41.0 | 190.0 | 7.4 | 67 | 5 | 1 |
| 36.0 | 118.0 | 8.0 | 72 | 5 | 2 |
| 12.0 | 149.0 | 12.6 | 74 | 5 | 3 |
| 18.0 | 313.0 | 11.5 | 62 | 5 | 4 |
| 14.3 | 56 | 5 | 5 |
The function receives the full DataFrame and returns a Series of color values aligned with the rows. This pattern works well with Pandas when you want to derive styles from data logic without Polars expressions.
Specifying columns and rows
Using polars selectors
If you are using Polars, you can use column selectors and expressions for selecting specific columns and rows:
import polars.selectors as cs
gt_pl_air.tab_style(
style=style.fill(color="yellow"),
locations=loc.body(
columns=cs.starts_with("Te"),
rows=pl.col("Temp") > 70
)
)| Ozone | Solar_R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41.0 | 190.0 | 7.4 | 67 | 5 | 1 |
| 36.0 | 118.0 | 8.0 | 72 | 5 | 2 |
| 12.0 | 149.0 | 12.6 | 74 | 5 | 3 |
| 18.0 | 313.0 | 11.5 | 62 | 5 | 4 |
| None | None | 14.3 | 56 | 5 | 5 |
See Column Selection for details on selecting columns.
Using a function
For tools like pandas, you can use a function (or lambda) to select rows. The function should take a DataFrame, and output a boolean Series.
gt_air.tab_style(
style=style.fill(color="yellow"),
locations=loc.body(
columns=lambda col_name: col_name.startswith("Te"),
rows=lambda D: D["Temp"] > 70,
)
)| Ozone | Solar_R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41.0 | 190.0 | 7.4 | 67 | 5 | 1 |
| 36.0 | 118.0 | 8.0 | 72 | 5 | 2 |
| 12.0 | 149.0 | 12.6 | 74 | 5 | 3 |
| 18.0 | 313.0 | 11.5 | 62 | 5 | 4 |
| 14.3 | 56 | 5 | 5 |
The function-based row selection gives you full flexibility: any callable that takes a DataFrame and returns a boolean Series can serve as a row filter.
Multiple styles and locations
We can use a list within style= to apply multiple styles at once. For example, the code below sets fill and border styles on the same set of body cells.
gt_air.tab_style(
style=[style.fill(color="yellow"), style.borders(sides="all")],
locations=loc.body(columns="Temp", rows=[1, 2]),
)| Ozone | Solar_R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41.0 | 190.0 | 7.4 | 67 | 5 | 1 |
| 36.0 | 118.0 | 8.0 | 72 | 5 | 2 |
| 12.0 | 149.0 | 12.6 | 74 | 5 | 3 |
| 18.0 | 313.0 | 11.5 | 62 | 5 | 4 |
| 14.3 | 56 | 5 | 5 |
Note that you can also pass a list to locations=!
gt_air.tab_style(
style=style.fill(color="yellow"),
locations=[
loc.body(columns="Temp", rows=[1, 2]),
loc.body(columns="Ozone", rows=[0])
]
)| Ozone | Solar_R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41.0 | 190.0 | 7.4 | 67 | 5 | 1 |
| 36.0 | 118.0 | 8.0 | 72 | 5 | 2 |
| 12.0 | 149.0 | 12.6 | 74 | 5 | 3 |
| 18.0 | 313.0 | 11.5 | 62 | 5 | 4 |
| 14.3 | 56 | 5 | 5 |
You can also combine Polars selectors with a row filtering expression, in order to select a combination of columns and rows.
import polars.selectors as cs
gt_pl_air.tab_style(
style=style.fill(color="yellow"),
locations=loc.body(
columns=cs.exclude(["Month", "Day"]),
rows=pl.col("Temp") == pl.col("Temp").max()
)
)| Ozone | Solar_R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41.0 | 190.0 | 7.4 | 67 | 5 | 1 |
| 36.0 | 118.0 | 8.0 | 72 | 5 | 2 |
| 12.0 | 149.0 | 12.6 | 74 | 5 | 3 |
| 18.0 | 313.0 | 11.5 | 62 | 5 | 4 |
| None | None | 14.3 | 56 | 5 | 5 |
Lastly, you can use Polars selectors or expressions to conditionally select rows on a per-column basis.
import polars.selectors as cs
gt_pl_air.tab_style(
style=style.fill(color="yellow"),
locations=loc.body(mask=cs.all().eq(cs.all().max())),
)| Ozone | Solar_R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41.0 | 190.0 | 7.4 | 67 | 5 | 1 |
| 36.0 | 118.0 | 8.0 | 72 | 5 | 2 |
| 12.0 | 149.0 | 12.6 | 74 | 5 | 3 |
| 18.0 | 313.0 | 11.5 | 62 | 5 | 4 |
| None | None | 14.3 | 56 | 5 | 5 |
The mask= argument applies row/column logic jointly, highlighting only the cells where a column’s value equals its own maximum. This is a powerful way to perform per-column conditional formatting in a single statement.
Learning more
The combination of tab_style(), location specifiers, and the various style functions gives you precise control over the visual presentation of your table body. Whether you are highlighting outliers, applying conditional formatting, or using column-driven styles, these tools let you communicate data insights through visual cues that complement the numeric values themselves.
For further reference, consult the API documentation: