shiny-plotly

Render plotly figures in Shiny for Python with plain plotly.js, without the shinywidgets layer.

An independent project, not affiliated with or endorsed by Posit or Plotly.

from shiny_plotly import output_plotly, render_plotly

# UI
output_plotly("sales")


# server
@render_plotly
def sales():
    return go.Figure(go.Bar(x=months, y=totals))

That is the whole API surface for the common case. The figure travels as plotly's own JSON over Shiny's websocket; a small output binding draws it with Plotly.newPlot the first time and Plotly.react on every re-render, into one graph div it keeps. No ipywidgets, no kernel comm, no anywidget. Every render replaces the figure, which is how most dashboards already use @render_widget; zoom and pan survive it when the figure sets layout.uirevision.

Why

shinywidgets renders a plotly figure by wrapping it in a FigureWidget and shipping it through the ipywidgets comm protocol. That machinery earns its keep when the app mutates a figure in place (fig.data[0].y = ...) and wants the browser to patch it. Most Shiny apps do not do that; they rebuild the figure inside a reactive function and let Shiny re-render the output. For those apps the widget layer is overhead:

• extra dependencies (ipywidgets, anywidget, shinywidgets) and their JavaScript bundles on every page;
• a second rendering path next to Shiny's own, with its own quirks around sizing and full screen;
• figures held as widget state on the server for the life of the session.

shiny-plotly sends the figure as plotly JSON and draws it with plotly.js directly, through a Shiny output binding. The plotly.js bundle is served straight from the installed plotly wheel, keyed by its version, pre-compressed and with an immutable cache lifetime, so nothing is copied or vendored and a browser fetches it once.

Measured on the same app (a slider and one fillable card with a line chart; bench/), shiny 1.7.0, plotly 6.9.0, shinywidgets 0.8.1, shiny-plotly 0.2.0, headless Chromium, 2026-08-19:

Both need plotly.js in the browser. shiny-plotly serves plotly.min.js compressed (4.9 MB raw) with Cache-Control: immutable, so a browser fetches it once per plotly version; shinywidgets sends plotly's widget bundle as part of the FigureWidget state over the websocket, and a re-render creates a new FigureWidget, so that cost is paid on every visit and every re-render. The round-trip numbers come from a loaded laptop and are a range across runs, not a constant. shinywidgets does things this package does not (arbitrary in-place FigureWidget mutation, any ipywidget), which the table does not measure; the common in-place updates, appending points and changing trace or layout attributes, are covered by extend_traces, restyle and relayout below. make bench reproduces it; bench/results.json holds the raw numbers.

Install

uv add shiny-plotly
# or
pip install shiny-plotly

Requires Python 3.10+, shiny>=1.0, plotly>=5.0.

Use

Core

import random
from itertools import accumulate

import plotly.graph_objects as go
from shiny import App, ui

from shiny_plotly import output_plotly, render_plotly

app_ui = ui.page_fillable(
    ui.input_slider("n", "Points", 10, 500, 100),
    ui.card(
        ui.card_header("Fills the card; try full screen"),
        output_plotly("walk"),
        full_screen=True,
    ),
)


def server(input, output, session):
    @render_plotly
    def walk():
        rng = random.Random(input.n())
        y = list(accumulate(rng.gauss(0, 1) for _ in range(input.n())))
        return go.Figure(go.Scatter(y=y, mode="lines"))


app = App(app_ui, server)

Anything that is a plotly.graph_objects.Figure works, including what plotly.express builds (install plotly[express] for that).

Express

import random
from itertools import accumulate

import plotly.graph_objects as go
from shiny.express import input, ui

from shiny_plotly import render_plotly

ui.page_opts(fillable=True)

with ui.sidebar():
    ui.input_slider("n", "Points", 10, 500, 100)

with ui.card(full_screen=True):

    @render_plotly
    def walk():
        rng = random.Random(input.n())
        y = list(accumulate(rng.gauss(0, 1) for _ in range(input.n())))
        return go.Figure(go.Scatter(y=y, mode="lines"))

The decorator creates its own output placeholder in Express, just like @render_widget does.

Options

@render_plotly(
    height="300px",  # fixed height; default None fills the container
    width="100%",
    figurewidget_margins=True,  # the l16/t32/r16/b16 margins shinywidgets applies
    config={"displaylogo": False},
    events=("click", "selected"),  # arrive as input.sales_click, input.sales_selected
    max_event_points=10_000,  # above it an event carries the count and range, not the points
    post_script=MORE_JS,  # JavaScript run once, when the graph is first drawn
)
def sales(): ...

None from the render function empties the output. The function may be sync or async. It may also return fig.to_dict() instead of a Figure. Anything plotly's own encoder serializes is fine as trace data: numpy arrays, pandas columns, datetimes.

Re-renders, zoom and pan

Each output_plotly holds one plotly graph div. The first figure is drawn with Plotly.newPlot; every later one goes through Plotly.react, which diffs the new figure into the graph that is already there. So the DOM node, the event handlers (from events= or post_script) and plotly's per-graph state all survive a re-render.

Whether the user's zoom and pan survive is plotly's uirevision rule, the same one shinywidgets users rely on for in-place updates: set layout.uirevision to any value and keep it the same across renders to preserve the view, change it to reset the view, leave it unset to reset on every render.

@render_plotly
def prices():
    return px.line(frame(), x="date", y="close").update_layout(uirevision="prices")

Migrating from shinywidgets

Three things change on purpose:

• Margins. shinywidgets sets tight margins (l=16, t=32, r=16, b=16) on every FigureWidget; plotly's own defaults are 80/100/80/80. shiny-plotly uses plotly's defaults unless you pass figurewidget_margins=True, which fills in only the sides your figure leaves unset. Set margins explicitly on the figure if you want something else.
• In-place mutation. A FigureWidget you keep on the server and mutate (fig.data[0].y = ..., fig.add_trace(...) after render) is exactly what shinywidgets is for. shiny-plotly has no channel for that; return a new figure from the render function and let Shiny re-render. If your app depends on in-place widget updates, stay on shinywidgets for those outputs. Both packages can coexist in one app.
• Zoom across re-renders. A mutated FigureWidget keeps the user's zoom because nothing replaces the figure. Here a re-render is a new figure, so plotly's uirevision decides: set layout.uirevision (see above) to keep the view.

Sizing

The rules mirror output_widget:

• height=None (default): the plot fills its container. Inside ui.card(full_screen=True), a fillable page or a sidebar layout it grows and shrinks with the card, from a 400px basis. Outside a fill layout it is 400px tall.
• height="300px" (on the decorator or on output_plotly): the plot is exactly that tall and opts out of filling.

Plotly alone re-measures a graph only on window resize. shiny-plotly ships a small helper script (shiny-plotly.js, loaded with every output) that observes each graph's container with a ResizeObserver, so a card that changes size without a window resize, for example when a sibling output renders below it, or when a sidebar collapses, re-lays the graph out. The same helper purges a graph once it leaves the document, which releases the window listener and layout state plotly would otherwise keep.

Dark mode

Plotly does not follow Bootstrap's color mode by itself. Give the dark mode switch an id, read it in the render function to pick the template, and make the figure's backgrounds transparent so the card's own background shows through in both modes:

app_ui = ui.page_fillable(
    ui.input_dark_mode(id="mode"),
    ui.card(output_plotly("sales")),
)


@render_plotly
def sales():
    template = "plotly_dark" if input.mode() == "dark" else "plotly"
    fig = px.bar(df, x="month", y="total", template=template)
    return fig.update_layout(paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)")

Flipping the switch re-renders the figure through Plotly.react, the same as any other re-render. input.mode() is "light" or "dark" and follows the user's system preference when the switch is not given an initial mode.

Events back to Shiny

events= names the plotly events to forward; each arrives as input.<id>_<event>, namespaced like the output inside a module. Four are available: click, hover, selected and relayout.

@render_plotly(events=("click", "selected"))
def scatter(): ...


@render.text
def click_info():
    if not input.scatter_click.is_set():
        return "Click a point."
    pt = input.scatter_click()["points"][0]
    return f"trace {pt['curveNumber']}, point {pt['pointNumber']}: x={pt['x']}, y={pt['y']}"

What arrives is plotly's own event data, cut to what serializes, the same way Dash cuts it:

Each point carries plotly's scalar fields for that trace type (curveNumber, pointNumber, pointIndex, x, y, z, text, label, value, lat, lon, ...) plus customdata (as a plain list, also when it was a numpy array), bbox and pointNumbers when present. input.<id>_<event>() raises a silent exception until the event has fired once, so check is_set() when the output should show something before that.

Dense traces

A point is about 100 bytes of JSON, so a box over a dense trace builds a large message, and a large enough one ends the session: uvicorn closes a websocket on a message above 16 MB by default. max_event_points (default 10 000) is the most points one event carries. Above it the points stay in the browser and the value says so, with the selection's geometry intact:

@render_plotly(events="selected")
def scatter(): ...


@render.text
def picked():
    sel = input.scatter_selected()
    if sel is None:
        return "Nothing selected."
    if sel["points"] is not None:
        return f"{len(sel['points'])} points"
    # More than max_event_points: {"points": None, "point_count": 120000, "range": {...}}.
    # The data is here, so membership is a filter on the box the user dragged.
    (x0, x1), (y0, y1) = sel["range"]["x"], sel["range"]["y"]
    inside = df[df.x.between(x0, x1) & df.y.between(y0, y1)]
    return f"{sel['point_count']} points, {len(inside)} rows"

The value is never silently cut: points is a full list or None, and point_count is there when it is None. A lasso carries lassoPoints (the polygon's x and y lists) instead of range. max_event_points=None lifts the cap. Measured with make bench-events (one Scattergl trace, every point box-selected with a real mouse, headless Chromium and the server on the same laptop, 2026-08-19):

click and hover carry one point per trace under the pointer, so the cap matters for selected; hover is also debounced (100 ms), so a pointer sweeping across a dense trace sends one event when it rests, not one per point.

For anything else, post_script runs once, after the first figure is drawn, with {plot_id} replaced by the graph div's id. Re-renders go through Plotly.react into the same graph div, so handlers attached either way stay attached and are never stacked.

LEGEND_TO_INPUT = """
document.getElementById('{plot_id}').on('plotly_legendclick', function (ev) {
    Shiny.setInputValue('legend', ev.curveNumber, {priority: 'event'});
    return true;  // let plotly toggle the trace as usual
});
"""


@render_plotly(post_script=LEGEND_TO_INPUT)
def scatter(): ...

Live updates without a re-render

A re-render sends the whole figure. For a stream of points, a colour change or a new title, send just the change: extend_traces, restyle and relayout call the plotly.js functions of the same names on the graph an output holds. All three are coroutines, so the effect that calls them is async def.

from shiny_plotly import extend_traces, relayout, restyle


@render_plotly
def prices():
    return go.Figure(go.Scatter(x=[], y=[], mode="lines"))  # the seed; the stream fills it


@reactive.effect
async def _stream():
    reactive.invalidate_later(1)
    t, v = latest_sample()
    await extend_traces("prices", {"x": [[t]], "y": [[v]]}, max_points=500)


@reactive.effect
@reactive.event(input.highlight)
async def _highlight():
    await restyle("prices", {"line.color": "crimson"}, indices=0)
    await relayout("prices", {"title.text": "highlighted"})

• extend_traces(id, data, indices=None, *, max_points=None): data maps an array attribute to one sequence of new values per trace, in the order of indices ({"x": [[t]], "y": [[v]]} appends one point to one trace; {"y": [[1], [2]]} with indices=[0, 1] one point to each of two). indices (an int or a list) defaults to every trace; max_points drops the oldest points past that many, for a rolling window.
• restyle(id, update, indices=None): update maps attribute paths to values; {"marker.color": "red"} applies to every trace in indices, a list value applies per trace ({"opacity": [0.5, 1]} with indices=[0, 1]).
• relayout(id, update): layout attribute paths, {"title.text": "Live"}, {"xaxis.range": [0, 10]}, {"xaxis.autorange": True}. With events="relayout" on the output, the result comes back as input.<id>_relayout, the same as a user's zoom.

The values go through plotly's encoder, so numpy arrays, pandas columns and datetimes work. The id is namespaced inside a module, like the output. An update reaches the figure that is drawn at that moment; one sent while the output has no figure (its first render is still running, it sits in a hidden tab, it shows an error or was emptied by None) is held and applied, in order, right after the output's next draw. A re-render replaces the figure, updates included, with what the render function returns: the server stays the source of truth, and a figure that should keep its streamed points across a re-render builds them in from server-side state.

Lower level

• fig_to_ui(fig, div_id=None, *, height, width, figurewidget_margins, config, post_script) returns a TagList holding the plotly.js dependency, the helper dependency and a <div class="shiny-plotly"> that draws the figure with Plotly.newPlot (plotly's own to_html fragment). Use it from a plain @render.ui that composes a figure with other UI, or from any htmltools context. Each render draws a fresh graph; an output that is only a figure is better served by render_plotly.
• plotly_js() is the HTMLDependency for plotly.js, served from the installed plotly wheel at /lib/plotly-<version>/plotly.min.js. Every output_plotly and every fig_to_ui fragment carries it, so it is optional; add it to the page UI when the first figure is inserted later (ui.insert_ui, a @render.ui that starts empty) and the bundle should load with the page.
• shiny_plotly_js() is the helper's dependency. Every output and fragment carries it too.
• FIGUREWIDGET_MARGINS is the {"l": 16, "t": 32, "r": 16, "b": 16} mapping.
• enable_compressed_plotly_js(app) turns on compressed, immutable serving of plotly.js for a shiny.App before its first session (see below).
• extend_traces, restyle and relayout take an optional session= when called outside the current session's context.

render_plotly needs output_plotly; it is an output binding, not a render.ui, so ui.output_ui(id) does not draw it.

plotly.js on the wire

Shiny serves HTML dependencies from a plain static mount: no compression, no Cache-Control. plotly.min.js is 4.9 MB, so once the first session of a process has rendered a figure, shiny-plotly adds a route in front of that mount for the bundle's exact path (/lib/plotly-<version>/plotly.min.js) that serves it pre-compressed (brotli when the brotli package is installed, gzip otherwise; 1.2 MB or 1.5 MB on the wire) with Cache-Control: public, max-age=31536000, immutable, Vary: Accept-Encoding and an ETag per encoding. The URL is keyed by the plotly version, so a browser fetches each version once. Compression runs once per process, in a background thread; until it has finished the route serves the raw file with the same headers.

uv add "shiny-plotly[brotli]"  # optional: brotli instead of gzip

Two things to know. The page load that starts the very first session of a process has already asked for the bundle before the route exists, so that one visitor gets the raw file from Shiny's mount; everyone after gets the compressed one. A Core app can close that gap by enabling the route as soon as the App exists:

from shiny_plotly import enable_compressed_plotly_js

app = App(app_ui, server)
enable_compressed_plotly_js(app)

And if a reverse proxy in front of the app does its own compression and caching, or you want Shiny's static serving untouched for any reason, set SHINY_PLOTLY_NO_COMPRESS=1 in the app's environment; enable_compressed_plotly_js then returns False and adds nothing.

Examples

uv run --with shiny-plotly shiny run examples/core_app.py
uv run --with shiny-plotly shiny run examples/express_app.py

Development

make sync        # uv sync --all-groups
make browsers    # playwright install chromium, once
make check       # lint, typecheck, unit + e2e tests, browser tests, wheel check, floor check
make bench       # the shinywidgets comparison above, on this machine
make bench-events  # what a selection over a dense trace costs, capped and uncapped

make test runs the unit tests and the in-process Shiny end-to-end tests over a real websocket, including the compressed bundle route. make test-browser drives the package in headless Chromium: fill sizing, resize without a window event, the graph div surviving a re-render, uirevision keeping a dragged zoom, purge once an output leaves the page, full screen, events= click, hover, selection and relayout inputs (attached once, also inside a module, a selection above max_event_points arriving as count and range), extend_traces, restyle and relayout applied in place (rolling window, one trace or all, held until the first draw, reset by a re-render, inside a module, dropped with a warning for an unknown output), post_script click wiring (once, not stacked), the dark mode recipe, error and None rendering, on-demand loading of plotly.js and the compressed, cached bundle as a fresh visitor sees it. make check-wheel installs the built wheel into a throwaway venv and runs the suite against it, so the published artifact is what was tested. make check-floor installs the package with plotly, shiny and htmltools at the oldest versions pyproject.toml allows and runs the whole suite again, browser tests included, so the declared lower bounds are tested on every push rather than assumed.

License

MIT. See LICENSE.