Introducing Gradio 5.0

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  1. Components
  2. ScatterPlot

New to Gradio? Start here: Getting Started

See the Release History

To install Gradio from main, run the following command:

pip install https://gradio-builds.s3.amazonaws.com/ac9bf5ec9208b92579f36ee94a247ae3e676c02f/gradio-5.6.0-py3-none-any.whl

*Note: Setting share=True in launch() will not work.

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ScatterPlot

gradio.ScatterPlot(···)
import gradio as gr import pandas as pd import numpy as np simple = pd.DataFrame(np.array( [ [1, 23, "USA", "Ford Mustang"], [2, 40, "USA", "Chrysler New Yorker Brougham"], [3, 32, "Japan", "Toyota Corolla"], [4, 32, "Europe", "Mercedes Benz"], [5, 15, "USA", "AMC Matador"], [6, 35, "Europe", "BMW X5"], [7, 28, "Japan", "Honda Civic"], [8, 15, "Japan", "Honda Accord"], [9, 41, "Europe", "Peugeot 208"], ] ), columns=["Age", "Miles Per Gallon", "Origin of Car", "Name"]) with gr.Blocks() as demo: gr.ScatterPlot( value=simple, x="Age", y="Miles Per Gallon", title="Car Data", container=True, width=400, color="Origin of Car", tooltip="Name" ) demo.launch() pandas numpy

Description

Creates a scatter plot component to display data from a pandas DataFrame.

Behavior

As input component: The data to display in a line plot.

Your function should accept one of these types:
def predict(
	value: AltairPlotData | None
)
	...

As output component: Expects a pandas DataFrame containing the data to display in the line plot. The DataFrame should contain at least two columns, one for the x-axis (corresponding to this component's x argument) and one for the y-axis (corresponding to y).

Your function should return one of these types:
def predict(···) -> pd.DataFrame | dict | None
	...	
	return value

Initialization

Parameters 
value: pd.DataFrame | Callable | None
default = None

The pandas dataframe containing the data to display in the plot.

x: str | None
default = None

Column corresponding to the x axis. Column can be numeric, datetime, or string/category.

y: str | None
default = None

Column corresponding to the y axis. Column must be numeric.

color: str | None
default = None

Column corresponding to series, visualized by color. Column must be string/category.

title: str | None
default = None

The title to display on top of the chart.

x_title: str | None
default = None

The title given to the x axis. By default, uses the value of the x parameter.

y_title: str | None
default = None

The title given to the y axis. By default, uses the value of the y parameter.

color_title: str | None
default = None

The title given to the color legend. By default, uses the value of color parameter.

x_bin: str | float | None
default = None

Grouping used to cluster x values. If x column is numeric, should be number to bin the x values. If x column is datetime, should be string such as "1h", "15m", "10s", using "s", "m", "h", "d" suffixes.

y_aggregate: Literal['sum', 'mean', 'median', 'min', 'max', 'count'] | None
default = None

Aggregation function used to aggregate y values, used if x_bin is provided or x is a string/category. Must be one of "sum", "mean", "median", "min", "max".

color_map: dict[str, str] | None
default = None

Mapping of series to color names or codes. For example, {"success": "green", "fail": "#FF8888"}.

x_lim: list[float] | None
default = None

A tuple or list containing the limits for the x-axis, specified as [x_min, x_max]. If x column is datetime type, x_lim should be timestamps.

y_lim: list[float] | None
default = None

A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].

x_label_angle: float
default = 0

The angle of the x-axis labels in degrees offset clockwise.

y_label_angle: float
default = 0

The angle of the y-axis labels in degrees offset clockwise.

x_axis_labels_visible: bool
default = True

Whether the x-axis labels should be visible. Can be hidden when many x-axis labels are present.

caption: str | None
default = None

The (optional) caption to display below the plot.

sort: Literal['x', 'y', '-x', '-y'] | list[str] | None
default = None

The sorting order of the x values, if x column is type string/category. Can be "x", "y", "-x", "-y", or list of strings that represent the order of the categories.

tooltip: Literal['axis', 'none', 'all'] | list[str]
default = "axis"

The tooltip to display when hovering on a point. "axis" shows the values for the axis columns, "all" shows all column values, and "none" shows no tooltips. Can also provide a list of strings representing columns to show in the tooltip, which will be displayed along with axis values.

height: int | None
default = None

The height of the plot in pixels.

label: str | None
default = None

The (optional) label to display on the top left corner of the plot.

show_label: bool | None
default = None

Whether the label should be displayed.

container: bool
default = True

If True, will place the component in a container - providing some extra padding around the border.

scale: int | None
default = None

relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.

min_width: int
default = 160

minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.

every: Timer | float | None
default = None

Continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.

inputs: Component | list[Component] | Set[Component] | None
default = None

Components that are used as inputs to calculate `value` if `value` is a function (has no effect otherwise). `value` is recalculated any time the inputs change.

visible: bool
default = True

Whether the plot should be visible.

elem_id: str | None
default = None

An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.

elem_classes: list[str] | str | None
default = None

An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

render: bool
default = True

If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.

key: int | str | None
default = None

if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.

Shortcuts

Class Interface String Shortcut Initialization

gradio.ScatterPlot

"scatterplot"

 Uses default values

Demos

import pandas as pd
from random import randint, random
import gradio as gr


temp_sensor_data = pd.DataFrame(
    {
        "time": pd.date_range("2021-01-01", end="2021-01-05", periods=200),
        "temperature": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],
        "humidity": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],
        "location": ["indoor", "outdoor"] * 100,
    }
)

food_rating_data = pd.DataFrame(
    {
        "cuisine": [["Italian", "Mexican", "Chinese"][i % 3] for i in range(100)],
        "rating": [random() * 4 + 0.5 * (i % 3) for i in range(100)],
        "price": [randint(10, 50) + 4 * (i % 3) for i in range(100)],
        "wait": [random() for i in range(100)],
    }
)

with gr.Blocks() as scatter_plots:
    with gr.Row():
        start = gr.DateTime("2021-01-01 00:00:00", label="Start")
        end = gr.DateTime("2021-01-05 00:00:00", label="End")
        apply_btn = gr.Button("Apply", scale=0)
    with gr.Row():
        group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by")
        aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation")

    temp_by_time = gr.ScatterPlot(
        temp_sensor_data,
        x="time",
        y="temperature",
    )
    temp_by_time_location = gr.ScatterPlot(
        temp_sensor_data,
        x="time",
        y="temperature",
        color="location",
    )

    time_graphs = [temp_by_time, temp_by_time_location]
    group_by.change(
        lambda group: [gr.ScatterPlot(x_bin=None if group == "None" else group)] * len(time_graphs),
        group_by,
        time_graphs
    )
    aggregate.change(
        lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs),
        aggregate,
        time_graphs
    )

    price_by_cuisine = gr.ScatterPlot(
        food_rating_data,
        x="cuisine",
        y="price",
    )
    with gr.Row():
        price_by_rating = gr.ScatterPlot(
            food_rating_data,
            x="rating",
            y="price",
            color="wait",
            show_actions_button=True,
        )
        price_by_rating_color = gr.ScatterPlot(
            food_rating_data,
            x="rating",
            y="price",
            color="cuisine",
        )

if __name__ == "__main__":
    scatter_plots.launch()

Event Listeners

Description

Event listeners allow you to respond to user interactions with the UI components you've defined in a Gradio Blocks app. When a user interacts with an element, such as changing a slider value or uploading an image, a function is called.

Supported Event Listeners

The ScatterPlot component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Parameters table below.

Listener Description

ScatterPlot.select(fn, ···)

Event listener for when the user selects or deselects the NativePlot. Uses event data gradio.SelectData to carry value referring to the label of the NativePlot, and selected to refer to state of the NativePlot. See EventData documentation on how to use this event data

ScatterPlot.double_click(fn, ···)

Triggered when the NativePlot is double clicked.

Event Parameters

Parameters 
fn: Callable | None | Literal['decorator']
default = "decorator"

the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.

inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default = None

List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.

outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default = None

List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.

api_name: str | None | Literal[False]
default = None

defines how the endpoint appears in the API docs. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given name. If None (default), the name of the function will be used as the API endpoint. If False, the endpoint will not be exposed in the API docs and downstream apps (including those that `gr.load` this app) will not be able to use this event.

scroll_to_output: bool
default = False

If True, will scroll to output component on completion

show_progress: Literal['full', 'minimal', 'hidden']
default = "full"

how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all

queue: bool
default = True

If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.

batch: bool
default = False

If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.

max_batch_size: int
default = 4

Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)

preprocess: bool
default = True

If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).

postprocess: bool
default = True

If False, will not run postprocessing of component data before returning 'fn' output to the browser.

cancels: dict[str, Any] | list[dict[str, Any]] | None
default = None

A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.

trigger_mode: Literal['once', 'multiple', 'always_last'] | None
default = None

If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.

js: str | None
default = None

Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.

concurrency_limit: int | None | Literal['default']
default = "default"

If set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).

concurrency_id: str | None
default = None

If set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.

show_api: bool
default = True

whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.

time_limit: int | None
default = None 
stream_every: float
default = 0.5 
like_user_message: bool
default = False
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