Row Selection

Location and formatter functions (e.g. loc.body() and fmt_number()) can be applied to specific rows, using the rows= argument.

Rows may be specified using any of the following:

The following sections will use a subset of the exibble data, to demonstrate these options.

from great_tables import GT, exibble, loc, style

lil_exibble = exibble[["num", "char", "currency"]].head(3)
gt_ex = GT(lil_exibble)

Using integers

Use a single integer, or a list of integers, to select rows by position.

gt_ex.fmt_currency("currency", rows=0, decimals=1)
num char currency
0.1111 apricot $50.0
2.222 banana 17.95
33.33 coconut 1.39

Notice that a dollar sign ($) was only added to the first row (index 0 in python).

Indexing works the same as selecting items from a python list. This negative integers select relative to the final row.

gt_ex.fmt_currency("currency", rows=[0, -1], decimals=1)
num char currency
0.1111 apricot $50.0
2.222 banana 17.95
33.33 coconut $1.4

Using polars expressions

The rows= argument accepts polars expressions, which return a boolean Series, indicating which rows to operate on.

For example, the code below only formats the num column, but only when currency is less than 40.

import polars as pl

gt_polars = GT(pl.from_pandas(lil_exibble))

gt_polars.fmt_integer("num", rows=pl.col("currency") < 40)
num char currency
0.1111 apricot 49.95
2 banana 17.95
33 coconut 1.39

Here’s a more realistic example, which highlights the row with the highest value for currency.

import polars.selectors as cs

gt_polars.tab_style(
    style.fill("yellow"),
    loc.body(
        columns=cs.all(),
        rows=pl.col("currency") == pl.col("currency").max()
    )
)
num char currency
0.1111 apricot 49.95
2.222 banana 17.95
33.33 coconut 1.39

Using a function

Since libraries like pandas don’t have lazy expressions, the rows= argument also accepts a function for selecting rows. The function should take a DataFrame and return a boolean series.

Here’s the same example as the previous polars section, but with pandas data, and a lamba for selecting rows.

gt_ex.fmt_integer("num", rows=lambda D: D["currency"] < 40)
num char currency
0.1111 apricot 49.95
2 banana 17.95
33 coconut 1.39

Here’s the styling example from the previous polars section.

import polars.selectors as cs

gt_ex.tab_style(
    style.fill("yellow"),
    loc.body(
        columns=lambda colname: True,
        rows=lambda D: D["currency"] == D["currency"].max()
    )
)
num char currency
0.1111 apricot 49.95
2.222 banana 17.95
33.33 coconut 1.39