Introducing Gradio ClientsJoin us on Thursday, 9am PST

Livestream
Gradio logo

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/ecbc363ee094a73535f53a612c2f6bd1b2154d83/gradio-4.34.0-py3-none-any.whl

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

← Colorpicker
Dataset →

Dataframe

gradio.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:
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

Parameter Description
value

pd.DataFrame | Styler | np.ndarray | pl.DataFrame | list | list[list] | dict | str | Callable | None

default: None

Default value to display in the DataFrame. If a Styler is provided, it will be used to set the displayed value in the DataFrame (e.g. to set precision of numbers) if the interactive is False. If a Callable function is provided, the function will be called whenever the app loads to set the initial value of the component.
headers

list[str] | None

default: None

List of str header names. If None, no headers are shown.
row_count

int | tuple[int, str]

default: (1, 'dynamic')

Limit number of rows for input and decide whether user can create new rows. The first element of the tuple is an int, the row count; the second should be 'fixed' or 'dynamic', the new row behaviour. If an int is passed the rows default to 'dynamic'
col_count

int | tuple[int, str] | None

default: None

Limit number of columns for input and decide whether user can create new columns. The first element of the tuple is an int, the number of columns; the second should be 'fixed' or 'dynamic', the new column behaviour. If an int is passed the columns default to 'dynamic'
datatype

str | list[str]

default: "str"

Datatype of values in sheet. Can be provided per column as a list of strings, or for the entire sheet as a single string. Valid datatypes are "str", "number", "bool", "date", and "markdown".
type

Literal[('pandas', 'numpy', 'array', 'polars')]

default: "pandas"

Type of value to be returned by component. "pandas" for pandas dataframe, "numpy" for numpy array, "polars" for polars dataframe, or "array" for a Python list of lists.
latex_delimiters

list[dict[str, str | bool]] | None

default: None

A list of dicts of the form "left": open delimiter (str), "right": close delimiter (str), "display": whether to display in newline (bool) that will be used to render LaTeX expressions. If not provided, latex_delimiters is set to [ "left": "$$", "right": "$$", "display": True ], so only expressions enclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass in an empty list to disable LaTeX rendering. For more information, see the [KaTeX documentation](https://katex.org/docs/autorender.html). Only applies to columns whose datatype is "markdown".
label

str | None

default: None

The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a gr.Interface, the label will be the name of the parameter this component is assigned to.
show_label

bool | None

default: None

if True, will display label.
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. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
height

int

default: 500

The maximum height of the dataframe, specified in pixels if a number is passed, or in CSS units if a string is passed. If more rows are created than can fit in the height, a scrollbar will appear.
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.
interactive

bool | None

default: None

if True, will allow users to edit the dataframe; if False, can only be used to display data. If not provided, this is inferred based on whether the component is used as an input or output.
visible

bool

default: True

If False, component will be hidden.
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.
wrap

bool

default: False

If True, the text in table cells will wrap when appropriate. If False and the column_width parameter is not set, the column widths will expand based on the cell contents and the table may need to be horizontally scrolled. If column_width is set, then any overflow text will be hidden.
line_breaks

bool

default: True

If True (default), will enable Github-flavored Markdown line breaks in chatbot messages. If False, single new lines will be ignored. Only applies for columns of type "markdown."
column_widths

list[str | int] | None

default: None

An optional list representing the width of each column. The elements of the list should be in the format "100px" (ints are also accepted and converted to pixel values) or "10%". If not provided, the column widths will be automatically determined based on the content of the cells. Setting this parameter will cause the browser to try to fit the table within the page width.

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()

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 Dataframe component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Arguments 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 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 | list[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

Block | list[Block] | list[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 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"

If True, will show progress animation while pending
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.
every

float | None

default: None

Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds.
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.
← Colorpicker
Dataset →