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LinePlot

gradio.LinePlot(ยทยทยท)

Description

Create a line plot.

Behavior

As input: this component does *not* accept input.

As output: expects a pandas dataframe with the data to plot.

Initialization

Parameter Description
value

pd.DataFrame | Callable | None

default: None

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

x

str | None

default: None

Column corresponding to the x axis.

y

str | None

default: None

Column corresponding to the y axis.

color

str | None

default: None

The column to determine the point color. If the column contains numeric data, gradio will interpolate the column data so that small values correspond to light colors and large values correspond to dark values.

stroke_dash

str | None

default: None

The column to determine the symbol used to draw the line, e.g. dashed lines, dashed lines with points.

overlay_point

bool | None

default: None

Whether to draw a point on the line for each (x, y) coordinate pair.

title

str | None

default: None

The title to display on top of the chart.

tooltip

list[str] | str | None

default: None

The column (or list of columns) to display on the tooltip when a user hovers a point on the plot.

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.

x_label_angle

float | None

default: None

The angle for the x axis labels. Positive values are clockwise, and negative values are counter-clockwise.

y_label_angle

float | None

default: None

The angle for the y axis labels. Positive values are clockwise, and negative values are counter-clockwise.

color_legend_title

str | None

default: None

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

stroke_dash_legend_title

str | None

default: None

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

color_legend_position

Literal[('left', 'right', 'top', 'bottom', 'top-left', 'top-right', 'bottom-left', 'bottom-right', 'none')] | None

default: None

The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.

stroke_dash_legend_position

Literal[('left', 'right', 'top', 'bottom', 'top-left', 'top-right', 'bottom-left', 'bottom-right', 'none')] | None

default: None

The position of the stoke_dash legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.

height

int | None

default: None

The height of the plot in pixels.

width

int | None

default: None

The width of the plot in pixels.

x_lim

list[int] | None

default: None

A tuple or list containing the limits for the x-axis, specified as [x_min, x_max].

y_lim

list[int] | None

default: None

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

caption

str | None

default: None

The (optional) caption to display below the plot.

interactive

bool | None

default: True

Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.

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

scale

int | None

default: None

min_width

int

default: 160

every

float | None

default: None

If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.

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.

Shortcuts

Class Interface String Shortcut Initialization

gradio.LinePlot

"lineplot"

Uses default values

Demos

import gradio as gr
from vega_datasets import data

stocks = data.stocks()
gapminder = data.gapminder()
gapminder = gapminder.loc[
    gapminder.country.isin(["Argentina", "Australia", "Afghanistan"])
]
climate = data.climate()
seattle_weather = data.seattle_weather()

## Or generate your own fake data, here's an example for stocks:
#
# import pandas as pd
# import random
#
# stocks = pd.DataFrame(
#     {
#         "symbol": [
#             random.choice(
#                 [
#                     "MSFT",
#                     "AAPL",
#                     "AMZN",
#                     "IBM",
#                     "GOOG",
#                 ]
#             )
#             for _ in range(120)
#         ],
#         "date": [
#             pd.Timestamp(year=2000 + i, month=j, day=1)
#             for i in range(10)
#             for j in range(1, 13)
#         ],
#         "price": [random.randint(10, 200) for _ in range(120)],
#     }
# )


def line_plot_fn(dataset):
    if dataset == "stocks":
        return gr.LinePlot.update(
            stocks,
            x="date",
            y="price",
            color="symbol",
            color_legend_position="bottom",
            title="Stock Prices",
            tooltip=["date", "price", "symbol"],
            height=300,
            width=500,
        )
    elif dataset == "climate":
        return gr.LinePlot.update(
            climate,
            x="DATE",
            y="HLY-TEMP-NORMAL",
            y_lim=[250, 500],
            title="Climate",
            tooltip=["DATE", "HLY-TEMP-NORMAL"],
            height=300,
            width=500,
        )
    elif dataset == "seattle_weather":
        return gr.LinePlot.update(
            seattle_weather,
            x="date",
            y="temp_min",
            tooltip=["weather", "date"],
            overlay_point=True,
            title="Seattle Weather",
            height=300,
            width=500,
        )
    elif dataset == "gapminder":
        return gr.LinePlot.update(
            gapminder,
            x="year",
            y="life_expect",
            color="country",
            title="Life expectancy for countries",
            stroke_dash="cluster",
            x_lim=[1950, 2010],
            tooltip=["country", "life_expect"],
            stroke_dash_legend_title="Country Cluster",
            height=300,
            width=500,
        )


with gr.Blocks() as line_plot:
    with gr.Row():
        with gr.Column():
            dataset = gr.Dropdown(
                choices=["stocks", "climate", "seattle_weather", "gapminder"],
                value="stocks",
            )
        with gr.Column():
            plot = gr.LinePlot()
    dataset.change(line_plot_fn, inputs=dataset, outputs=plot)
    line_plot.load(fn=line_plot_fn, inputs=dataset, outputs=plot)


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

Event Listeners

Description

Event listeners allow you to capture and 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 LinePlot component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Arguments table below.

Listener Description

gradio.LinePlot.change(fn, ยทยทยท)

This listener is triggered when the component's value changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.

gradio.LinePlot.clear(fn, ยทยทยท)

This listener is triggered when the user clears the component (e.g. image or audio) using the X button for the component.

Event Arguments

Parameter Description
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 | Sequence[Component] | set[Component] | 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 | Sequence[Component] | 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 False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.

status_tracker

None

default: None

Deprecated and has no effect.

scroll_to_output

bool

default: False

If True, will scroll to output component on completion

show_progress

Literal[('full', 'minimal', 'hidden')] | None

default: None

If True, will show progress animation while pending

queue

bool | None

default: None

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.

every

float | None

default: None

Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.