# Premade Themes Building a complete table theme from scratch with [tab_options()](../reference/GT.tab_options.md#great_tables.GT.tab_options) requires setting many individual options. For common styling needs, **Great Tables** offers a collection of `opt_*()` convenience methods that bundle these options into simple, one-line calls. This page demonstrates how to use premade themes and the various convenience methods available for quick table customization. Premade themes are ideal for getting a polished look quickly, especially during exploratory work or when building dashboards where visual consistency matters more than pixel-perfect custom styling. For publication or brand-specific tables, you may want to start with a premade theme and then customize it further with [tab_options()](../reference/GT.tab_options.md#great_tables.GT.tab_options) to match your exact requirements. Great Tables provides convenience methods starting with `opt_` (e.g. [opt_row_striping()](../reference/GT.opt_row_striping.md#great_tables.GT.opt_row_striping)), as a shortcut for various styles that can be set via [tab_options()](../reference/GT.tab_options.md#great_tables.GT.tab_options). There are two important kinds of `GT.opt_*()` methods: - [opt_stylize()](../reference/GT.opt_stylize.md#great_tables.GT.opt_stylize): allows setting one of six premade table themes. - The remaining `opt_*()` methods: shortcuts for common uses of [tab_options()](../reference/GT.tab_options.md#great_tables.GT.tab_options). We'll use the basic GT object below for most examples, since it marks some of the table parts. ``` python from great_tables import GT, exibble, md lil_exibble = exibble.head(5)[["num", "char", "row", "group"]] gt_ex = ( GT(lil_exibble, rowname_col="row", groupname_col="group") .tab_header( title=md("Data listing from exibble"), subtitle=md("This is a **Great Tables** dataset."), ) .tab_stubhead(label="row") .fmt_number(columns="num") .fmt_currency(columns="currency") .tab_source_note(source_note="This is only a portion of the dataset.") .opt_vertical_padding(scale=0.5) ) gt_ex ```
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
# [opt_stylize()](../reference/GT.opt_stylize.md#great_tables.GT.opt_stylize): premade themes Below are the first two premade styles. The first uses `color="blue"`, and the second uses `color="red"`. ``` python gt_ex.opt_stylize(style=1, color="blue") ```
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
``` python gt_ex.opt_stylize(style=2, color="red") ```
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
Notice that first table (blue) emphasizes the row labels with a solid background color. The second table (red) emphasizes the column labels, and uses solid lines to separate the body cell values. See [opt_stylize()](../reference/GT.opt_stylize.md#great_tables.GT.opt_stylize) for all available color options. There are six styles available, each emphasizing different table parts. The `style=` values are numbered from `1` to `6`: 1
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
2
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
3
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
4
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
5
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
6
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
When choosing a style, consider your audience and context. Styles 1 and 2 emphasize structure through color, making table regions visually distinct. Styles 3 and 4 use lines and shading for a more traditional look. Styles 5 and 6 are more minimal, relying on subtle separation. Heavier styling works well for standalone presentations, while lighter styling is better suited for tables embedded in text-heavy reports where the table should complement rather than dominate the page. # `opt_*()` convenience methods This section shows the different `opt_*()` methods available. They serve as convenience methods for common [tab_options()](../reference/GT.tab_options.md#great_tables.GT.tab_options) tasks. ## Align table header ``` python gt_ex.opt_align_table_header(align="left") ```
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
The title and subtitle are now left-aligned rather than centered, which works well for tables embedded in text-heavy documents. ## Make text ALL CAPS ``` python gt_ex.opt_all_caps() ```
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
Column labels and row group labels are rendered in uppercase, giving the table a more formal, structured appearance. ## Reduce or expand padding ``` python gt_ex.opt_vertical_padding(scale=0.3) ```
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
Reducing vertical padding creates a more compact table that fits more data into less vertical space. ``` python gt_ex.opt_horizontal_padding(scale=3) ```
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
Increasing horizontal padding adds breathing room between columns, which improves readability when columns contain long values. ## Set table outline ``` python gt_ex.opt_table_outline() ```
Data listing from exibble
This is a Great Tables dataset.
row num char
grp_a
row_1 0.11 apricot
row_2 2.22 banana
row_3 33.33 coconut
row_4 444.40 durian
grp_b
row_5 5,550.00 None
This is only a portion of the dataset.
The `opt_*()` methods give you quick access to common styling patterns without needing to remember the specific [tab_options()](../reference/GT.tab_options.md#great_tables.GT.tab_options) argument names. For full control, you can always drop down to [tab_options()](../reference/GT.tab_options.md#great_tables.GT.tab_options) directly, but these convenience methods cover the most frequent customization needs in just a single method call.