md() or html() to style the text as Markdown or to retain HTML elements in the footnote text.
`locations: Loc | None | list[Loc | None] = None`
The cell or set of cells to be associated with the footnote. Supplying any of the location classes from the `loc` module is a useful way to target the location cells that are associated with the footnote text. These location classes are: [loc.title](loc.title.md#great_tables.loc.title), [loc.stubhead](loc.stubhead.md#great_tables.loc.stubhead), [loc.spanner_labels](loc.spanner_labels.md#great_tables.loc.spanner_labels), [loc.column_labels](loc.column_labels.md#great_tables.loc.column_labels), [loc.row_groups](loc.row_groups.md#great_tables.loc.row_groups), [loc.stub](loc.stub.md#great_tables.loc.stub), [loc.body](loc.body.md#great_tables.loc.body), etc. Additionally, we can enclose several location calls within a `list()` if we wish to link the footnote text to different types of locations (e.g., body cells, row group labels, the table title, etc.).
`placement: PlacementOptions = ``"auto"`
Where to affix footnote marks to the table content. Two options for this are `"left"` or `"right"`, where the placement is either to the absolute left or right of the cell content. By default, however, this option is set to `"auto"` whereby **Great Tables** will choose a preferred left-or-right placement depending on the alignment of the cell content.
## 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
This example table will be based on the [towny](data.towny.md#great_tables.data.towny) dataset. We have a header part, with a title and a subtitle. We can choose which of these could be associated with a footnote and in this case it is the `"subtitle"`. This table has a stub with row labels and some of those labels are associated with a footnote. So long as row labels are unique, they can be easily used as row identifiers in [loc.stub()](loc.stub.md#great_tables.loc.stub). The third footnote is placed on the `"Density"` column label. Here, changing the order of the [tab_footnote()](GT.tab_footnote.md#great_tables.GT.tab_footnote) calls has no effect on the final table rendering.
``` python
import polars as pl
from great_tables import GT, loc, md
from great_tables.data import towny
towny_mini = (
pl.from_pandas(towny)
.filter(pl.col("csd_type") == "city")
.select(["name", "density_2021", "population_2021"])
.top_k(10, by="population_2021")
.sort("population_2021", descending=True)
)
(
GT(towny_mini, rowname_col="name")
.tab_header(
title=md("The 10 Largest Municipalities in `towny`"),
subtitle="Population values taken from the 2021 census."
)
.fmt_integer()
.cols_label(
density_2021="Density",
population_2021="Population"
)
.tab_footnote(
footnote="Part of the Greater Toronto Area.",
locations=loc.stub(rows=[
"Toronto", "Mississauga", "Brampton", "Markham", "Vaughan"
])
)
.tab_footnote(
footnote=md("Density is in terms of persons per {{km^2}}."),
locations=loc.column_labels(columns="density_2021")
)
.tab_footnote(
footnote="Census results made public on February 9, 2022.",
locations=loc.subtitle()
)
.tab_source_note(
source_note=md("Data taken from the `towny` dataset.")
)
.opt_footnote_marks(marks="letters")
)
```
| The 10 Largest Municipalities in [towny](data.towny.md#great_tables.data.towny) | ||
|---|---|---|
| Population values taken from the 2021 census.a | ||
| Densityb | Population | |
| Torontoc | 4,428 | 2,794,356 |
| Ottawa | 365 | 1,017,449 |
| Mississaugac | 2,453 | 717,961 |
| Bramptonc | 2,469 | 656,480 |
| Hamilton | 509 | 569,353 |
| London | 1,004 | 422,324 |
| Markhamc | 1,605 | 338,503 |
| Vaughanc | 1,186 | 323,103 |
| Kitchener | 1,878 | 256,885 |
| Windsor | 1,573 | 229,660 |
| Data taken from the [towny](data.towny.md#great_tables.data.towny) dataset. | ||
| a Census results made public on February 9, 2022. | ||
| b Density is in terms of persons per km2. | ||
| c Part of the Greater Toronto Area. | ||