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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.


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


Parameter Description

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.


list[str] | None

default: None

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


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'


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'


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".


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.


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. Only applies to columns whose datatype is "markdown".


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.


bool | None

default: None

if True, will display label.


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.



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.


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.



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.


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.



default: True

If False, component will be hidden.


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.


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.



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.


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.



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.



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."


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.


Class Interface String Shortcut Initialization



Uses default values



Uses type="numpy"



Uses type="array"



Uses type="array", col_count=1


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


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, ยทยทยท)

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, ยทยทยท)

This listener is triggered when the user changes the value of the Dataframe., ยทยทยท)

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

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.


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.


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.


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.



default: False

If True, will scroll to output component on completion


Literal[('full', 'minimal', 'hidden')]

default: "full"

If True, will show progress animation while pending



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.



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.



default: 4

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



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).



default: True

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


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.


float | None

default: None

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


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.


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.


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).


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.



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.