express.render.table

express.render.table(
    self
    _fn=None
    *
    index=False
    classes='table shiny-table w-auto'
    border=0
    **kwargs
)

Reactively render a pandas DataFrame object (or similar) as a basic HTML table.

Consider using data_frame instead of this renderer, as it provides high performance virtual scrolling, built-in filtering and sorting, and a better default appearance. This renderer may still be helpful if you use pandas styling features that are not currently supported by data_frame.

Parameters

index : bool = False

Whether to print index (row) labels. (Ignored for pandas Styler objects; call style.hide(axis="index") from user code instead.)

classes : str = 'table shiny-table w-auto'

CSS classes (space separated) to apply to the resulting table. By default, we use table shiny-table w-auto which is designed to look reasonable with Bootstrap 5. (Ignored for pandas Styler objects; call style.set_table_attributes('class="dataframe table shiny-table w-auto"') from user code instead.)

****kwargs** : object = {}

Additional keyword arguments passed to pandas.DataFrame.to_html() or pandas.io.formats.style.Styler.to_html().

Returns

:

A decorator for a function that returns any of the following: 1. A pandas DataFrame object. 2. A pandas Styler object. 3. Any object that has a .to_pandas() method (e.g., a Polars data frame or Arrow table).

Tip

The name of the decorated function (or @output(id=...)) should match the id of a output_table container (see output_table for example usage).

See Also

• output_table for the corresponding UI component to this render function.

Examples

#| standalone: true
#| components: [editor, viewer]
#| layout: vertical
#| viewerHeight: 400

## file: app.py
import pathlib

import pandas as pd

from shiny.express import input, render, ui

dir = pathlib.Path(__file__).parent
mtcars = pd.read_csv(dir / "mtcars.csv")


ui.input_checkbox("highlight", "Highlight min/max values")


@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")
        )


# Legend
with ui.panel_conditional("input.highlight"):
    with ui.panel_absolute(bottom="6px", right="6px", class_="p-1 bg-light border"):
        "Yellow is maximum, grey is minimum"


## 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