Components
Dataframe

New to Gradio? Start here: Getting Started

To install Gradio from main, run the following command:

pip install https://gradio-builds.s3.amazonaws.com/02798ec170be7c9e8756dec24ef29c7f46fe2060/gradio-4.41.0-py3-none-any.whl

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

←

ColorPicker

Dataset

→

Dataframe

gradio.Dataframe(···)

import gradio as gr with gr.Blocks() as demo: gr.Dataframe(value= [ [0, 1, True], [1, 0, False] ] , interactive=True) demo.launch()

Description

This component displays a table of value spreadsheet-like component. Can be used to display data as an output component, or as an input to collect data from the user.

Behavior

As input component: Passes the uploaded spreadsheet data as a `pandas.DataFrame`, `numpy.array`, `polars.DataFrame`, or native 2D Python `list[list]` depending on `type`

Your function should accept one of these types:

def predict(
	value: pd.DataFrame | np.ndarray | pl.DataFrame | list[list]
)
	...

As output component: Expects data any of these formats: `pandas.DataFrame`, `pandas.Styler`, `numpy.array`, `polars.DataFrame`, `list[list]`, `list`, or a `dict` with keys 'data' (and optionally 'headers'), or `str` path to a csv, which is rendered as the spreadsheet.

Your function should return one of these types:

def predict(···) -> pd.DataFrame | Styler | np.ndarray | pl.DataFrame | list | list[list] | dict | str | None
	...	
	return value

Initialization

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.Dataframe`	"dataframe"	Uses default values
`gradio.Numpy`	"numpy"	Uses type="numpy"
`gradio.Matrix`	"matrix"	Uses type="array"
`gradio.List`	"list"	Uses type="array", col_count=1

Demos

import gradio as gr def filter_records(records, gender): return records[records["gender"] == gender] demo = gr.Interface( filter_records, [ gr.Dataframe( headers=["name", "age", "gender"], datatype=["str", "number", "str"], row_count=5, col_count=(3, "fixed"), ), gr.Dropdown(["M", "F", "O"]), ], "dataframe", description="Enter gender as 'M', 'F', or 'O' for other.", ) if __name__ == "__main__": demo.launch()

import gradio as gr

def filter_records(records, gender):
    return records[records["gender"] == gender]

demo = gr.Interface(
    filter_records,
    [
        gr.Dataframe(
            headers=["name", "age", "gender"],
            datatype=["str", "number", "str"],
            row_count=5,
            col_count=(3, "fixed"),
        ),
        gr.Dropdown(["M", "F", "O"]),
    ],
    "dataframe",
    description="Enter gender as 'M', 'F', or 'O' for other.",
)

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

import numpy as np import gradio as gr def transpose(matrix): return matrix.T demo = gr.Interface( transpose, gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3), "numpy", examples=[ [np.zeros((3, 3)).tolist()], [np.ones((2, 2)).tolist()], [np.random.randint(0, 10, (3, 10)).tolist()], [np.random.randint(0, 10, (10, 3)).tolist()], [np.random.randint(0, 10, (10, 10)).tolist()], ], cache_examples=False ) if __name__ == "__main__": demo.launch()

import numpy as np

import gradio as gr

def transpose(matrix):
    return matrix.T

demo = gr.Interface(
    transpose,
    gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
    "numpy",
    examples=[
        [np.zeros((3, 3)).tolist()],
        [np.ones((2, 2)).tolist()],
        [np.random.randint(0, 10, (3, 10)).tolist()],
        [np.random.randint(0, 10, (10, 3)).tolist()],
        [np.random.randint(0, 10, (10, 10)).tolist()],
    ],
    cache_examples=False
)

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

import gradio as gr def tax_calculator(income, marital_status, assets): tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)] total_deductible = sum(assets["Cost"]) taxable_income = income - total_deductible total_tax = 0 for bracket, rate in tax_brackets: if taxable_income > bracket: total_tax += (taxable_income - bracket) * rate / 100 if marital_status == "Married": total_tax *= 0.75 elif marital_status == "Divorced": total_tax *= 0.8 return round(total_tax) demo = gr.Interface( tax_calculator, [ "number", gr.Radio(["Single", "Married", "Divorced"]), gr.Dataframe( headers=["Item", "Cost"], datatype=["str", "number"], label="Assets Purchased this Year", ), ], "number", examples=[ [10000, "Married", [["Suit", 5000], ["Laptop", 800], ["Car", 1800]]], [80000, "Single", [["Suit", 800], ["Watch", 1800], ["Car", 800]]], ], ) demo.launch()

import gradio as gr

def tax_calculator(income, marital_status, assets):
    tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]
    total_deductible = sum(assets["Cost"])
    taxable_income = income - total_deductible

    total_tax = 0
    for bracket, rate in tax_brackets:
        if taxable_income > bracket:
            total_tax += (taxable_income - bracket) * rate / 100

    if marital_status == "Married":
        total_tax *= 0.75
    elif marital_status == "Divorced":
        total_tax *= 0.8

    return round(total_tax)

demo = gr.Interface(
    tax_calculator,
    [
        "number",
        gr.Radio(["Single", "Married", "Divorced"]),
        gr.Dataframe(
            headers=["Item", "Cost"],
            datatype=["str", "number"],
            label="Assets Purchased this Year",
        ),
    ],
    "number",
    examples=[
        [10000, "Married", [["Suit", 5000], ["Laptop", 800], ["Car", 1800]]],
        [80000, "Single", [["Suit", 800], ["Watch", 1800], ["Car", 800]]],
    ],
)

demo.launch()

import gradio as gr def sort_records(records): return records.sort("Quantity") demo = gr.Interface( sort_records, gr.Dataframe( headers=["Item", "Quantity"], datatype=["str", "number"], row_count=3, col_count=(2, "fixed"), type="polars" ), "dataframe", description="Sort by Quantity" ) if __name__ == "__main__": demo.launch()

import gradio as gr

def sort_records(records):
    return records.sort("Quantity")

demo = gr.Interface(
    sort_records,
    gr.Dataframe(
        headers=["Item", "Quantity"],
        datatype=["str", "number"],
        row_count=3,
        col_count=(2, "fixed"),
        type="polars"
    ),
    "dataframe",
    description="Sort by Quantity"
)

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

Listener	Description
`Dataframe.change(fn, ···)`	Triggered when the value of the Dataframe 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.
`Dataframe.input(fn, ···)`	This listener is triggered when the user changes the value of the Dataframe.
`Dataframe.select(fn, ···)`	Event listener for when the user selects or deselects the Dataframe. Uses event data gradio.SelectData to carry `value` referring to the label of the Dataframe, and `selected` to refer to state of the Dataframe. See EventData documentation on how to use this event data

Event Parameters

←

ColorPicker

Dataset

→

Dataframe

Description

This component displays a table of value spreadsheet-like component. Can be used to display data as an output component, or as an input to collect data from the user.

Behavior

As input component: Passes the uploaded spreadsheet data as a pandas.DataFrame, numpy.array, polars.DataFrame, or native 2D Python list[list] depending on type

Your function should accept one of these types:

As output component: Expects data any of these formats: pandas.DataFrame, pandas.Styler, numpy.array, polars.DataFrame, list[list], list, or a dict with keys 'data' (and optionally 'headers'), or str path to a csv, which is rendered as the spreadsheet.

Your function should return one of these types:

Initialization

Shortcuts

Demos

Event Listeners

Description

Supported Event Listeners

Event Parameters

As input component: Passes the uploaded spreadsheet data as a `pandas.DataFrame`, `numpy.array`, `polars.DataFrame`, or native 2D Python `list[list]` depending on `type`

As output component: Expects data any of these formats: `pandas.DataFrame`, `pandas.Styler`, `numpy.array`, `polars.DataFrame`, `list[list]`, `list`, or a `dict` with keys 'data' (and optionally 'headers'), or `str` path to a csv, which is rendered as the spreadsheet.