ui.output_table
ui.output_table(id, **kwargs)
Create a output container for a table.
Parameters
id: str
-
An output id.
**kwargs: TagAttrValue = {}
-
Additional attributes to add to the container.
Returns
Type | Description |
---|---|
Tag |
See Also
Examples
#| standalone: true
#| components: [editor, viewer]
#| layout: vertical
#| viewerHeight: 400
## file: app.py
import pathlib
import pandas as pd
from shiny import App, Inputs, Outputs, Session, render, ui
dir = pathlib.Path(__file__).parent
mtcars = pd.read_csv(dir / "mtcars.csv")
app_ui = ui.page_fluid(
ui.input_checkbox("highlight", "Highlight min/max values"),
ui.output_table("result"),
# Legend
ui.panel_conditional(
"input.highlight",
ui.panel_absolute(
"Yellow is maximum, grey is minimum",
bottom="6px",
right="6px",
class_="p-1 bg-light border",
),
),
class_="p-3",
)
def server(input: Inputs, output: Outputs, session: Session):
@render.table
def result():
if not input.highlight():
# If we're not highlighting values, we can simply
# return the pandas data frame as-is; @render.table
# will call .to_html() on it.
return mtcars
else:
# We need to use the pandas Styler API. The default
# formatting options for Styler are not the same as
# DataFrame.to_html(), so we set a few options to
# make them match.
return (
mtcars.style.set_table_attributes(
'class="dataframe shiny-table table w-auto"'
)
.hide(axis="index")
.format(
{
"mpg": "{0:0.1f}",
"disp": "{0:0.1f}",
"drat": "{0:0.2f}",
"wt": "{0:0.3f}",
"qsec": "{0:0.2f}",
}
)
.set_table_styles(
[dict(selector="th", props=[("text-align", "right")])]
)
.highlight_min(color="silver")
.highlight_max(color="yellow")
)
app = App(app_ui, server)
## file: mtcars.csv
mpg,cyl,disp,hp,drat,wt,qsec,vs,am,gear,carb
21,6,160,110,3.9,2.62,16.46,0,1,4,4
21,6,160,110,3.9,2.875,17.02,0,1,4,4
22.8,4,108,93,3.85,2.32,18.61,1,1,4,1
21.4,6,258,110,3.08,3.215,19.44,1,0,3,1
18.7,8,360,175,3.15,3.44,17.02,0,0,3,2
18.1,6,225,105,2.76,3.46,20.22,1,0,3,1
14.3,8,360,245,3.21,3.57,15.84,0,0,3,4
24.4,4,146.7,62,3.69,3.19,20,1,0,4,2
22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2
19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4
17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4
16.4,8,275.8,180,3.07,4.07,17.4,0,0,3,3
17.3,8,275.8,180,3.07,3.73,17.6,0,0,3,3
15.2,8,275.8,180,3.07,3.78,18,0,0,3,3
10.4,8,472,205,2.93,5.25,17.98,0,0,3,4
10.4,8,460,215,3,5.424,17.82,0,0,3,4
14.7,8,440,230,3.23,5.345,17.42,0,0,3,4
32.4,4,78.7,66,4.08,2.2,19.47,1,1,4,1
30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2
33.9,4,71.1,65,4.22,1.835,19.9,1,1,4,1
21.5,4,120.1,97,3.7,2.465,20.01,1,0,3,1
15.5,8,318,150,2.76,3.52,16.87,0,0,3,2
15.2,8,304,150,3.15,3.435,17.3,0,0,3,2
13.3,8,350,245,3.73,3.84,15.41,0,0,3,4
19.2,8,400,175,3.08,3.845,17.05,0,0,3,2
27.3,4,79,66,4.08,1.935,18.9,1,1,4,1
26,4,120.3,91,4.43,2.14,16.7,0,1,5,2
30.4,4,95.1,113,3.77,1.513,16.9,1,1,5,2
15.8,8,351,264,4.22,3.17,14.5,0,1,5,4
19.7,6,145,175,3.62,2.77,15.5,0,1,5,6
15,8,301,335,3.54,3.57,14.6,0,1,5,8
21.4,4,121,109,4.11,2.78,18.6,1,1,4,2