GT.opt_all_caps

GT.opt_all_caps(
    self
    all_caps=True
    locations=['column_labels', 'stub', 'row_group']
)

Option to use all caps in select table locations.

Sometimes an all-capitalized look is suitable for a table. By using opt_all_caps(), we can transform characters in the column labels, the stub, and in all row groups in this way (and there’s control over which of these locations are transformed). This method serves as a convenient shortcut for tab_options(<location>_text_transform="uppercase", <location>_font_size="80%", <location>_font_weight="bolder") (for all locations selected).

Parameters

all_caps : bool = True: Indicates whether the text transformation to all caps should be performed (True, the default) or reset to default values (False) for the locations targeted.
locations : str | list[str] = ['column_labels', 'stub', 'row_group']: Which locations should undergo this text transformation? By default it includes all of the "column_labels", the "stub", and the "row_group" locations. However, we could just choose one or two of those.

Returns

: GT: The GT object is returned. This is the same object that the method is called on so that we can facilitate method chaining.

Examples

Using select columns from the exibble dataset, let’s create a table with a number of components added. Following that, we’ll ensure that all text in the column labels, the stub, and in all row groups is transformed to all caps using the opt_all_caps() method.

from great_tables import GT, exibble, md

(
  GT(
    exibble[["num", "char", "currency", "row", "group"]],
    rowname_col="row",
    groupname_col="group"
  )
  .tab_header(
    title=md("Data listing from **exibble**"),
    subtitle=md("`exibble` is a **Great Tables** dataset.")
  )
  .fmt_number(columns="num")
  .fmt_currency(columns="currency")
  .tab_source_note(source_note="This is only a subset of the dataset.")
  .opt_all_caps()
)

	num	char	currency
Data listing from exibble
`exibble` is a Great Tables dataset.
grp_a
row_1	0.11	apricot	$49.95
row_2	2.22	banana	$17.95
row_3	33.33	coconut	$1.39
row_4	444.40	durian	$65,100.00
grp_b
row_5	5,550.00		$1,325.81
row_6		fig	$13.26
row_7	777,000.00	grapefruit
row_8	8,880,000.00	honeydew	$0.44
This is only a subset of the dataset.