Introducing Gradio Clients

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  1. Components
  2. LinePlot

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/7e61de7189376dbc10042a791ae0025b8789f3ec/gradio-4.42.0-py3-none-any.whl

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

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LinePlot

gradio.LinePlot(···)
import gradio as gr import pandas as pd import numpy as np simple = pd.DataFrame(np.array( [ [1, 28], [2, 55], [3, 43], [4, 91], [5, 81], [6, 53], [7, 19], [8, 87], [9, 52], ] ), columns=["week", "price"]) with gr.Blocks() as demo: gr.LinePlot( value=simple, x="week", y="price", title="Stock Price Chart", container=True, width=400 ) demo.launch() pandas numpy

Description

Creates a line 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

Shortcuts

Class Interface String Shortcut Initialization

gradio.LinePlot

"lineplot"

 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 line_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.LinePlot(
        temp_sensor_data,
        x="time",
        y="temperature",
    )
    temp_by_time_location = gr.LinePlot(
        temp_sensor_data,
        x="time",
        y="temperature",
        color="location",
    )

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

    def rescale(select: gr.SelectData):
        return select.index
    rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])

    for trigger in [apply_btn.click, rescale_evt.then]:
        trigger(
            lambda start, end: [gr.LinePlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs
        )

    price_by_cuisine = gr.LinePlot(
        food_rating_data,
        x="cuisine",
        y="price",
    )
    with gr.Row():
        price_by_rating = gr.LinePlot(
            food_rating_data,
            x="rating",
            y="price",
        )
        price_by_rating_color = gr.LinePlot(
            food_rating_data,
            x="rating",
            y="price",
            color="cuisine",
            color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
        )

if __name__ == "__main__":
    line_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 LinePlot component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Parameters table below.

Listener Description

LinePlot.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

LinePlot.double_click(fn, ···)

Triggered when the NativePlot is double clicked.

Event Parameters

Parameters
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