1. Components
  2. Dataframe

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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 in 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

Parameters
🔗 
value: pd.DataFrame | Styler | np.ndarray | pl.DataFrame | list | list[list] | dict | str | Callable | None
default = None

Default value to display in the DataFrame. Supports pandas, numpy, polars, and list of lists. 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. These are used to set the column headers of the dataframe if the value does not have headers. If None, no headers are shown.

🔗 
row_count: int | None
default = None

The number of rows to initially display in the dataframe. If None, the number of rows is determined automatically based on the `value`.

🔗 
row_limits: tuple[int | None, int | None] | None
default = None

A tuple of two integers specifying the minimum and maximum number of rows that can be created in the dataframe via the UI. If the first element is None, there is no minimum number of rows. If the second element is None, there is no maximum number of rows. Only applies if `interactive` is True.

🔗 
col_count: None
default = None

This parameter is deprecated. Please use `column_count` instead.

🔗 
column_count: int | None
default = None

The number of columns to initially display in the dataframe. If None, the number of columns is determined automatically based on the `value`.

🔗 
column_limits: tuple[int | None, int | None] | None
default = None

A tuple of two integers specifying the minimum and maximum number of columns that can be created in the dataframe via the UI. If the first element is None, there is no minimum number of columns. If the second element is None, there is no maximum number of columns. Only applies if `interactive` is True.

🔗 
datatype: Literal['str', 'number', 'bool', 'date', 'markdown', 'html', 'image', 'auto'] | list[Literal['str', 'number', 'bool', 'date', 'markdown', 'html']]
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". Boolean columns will display as checkboxes. If the datatype "auto" is used, the column datatypes are automatically selected based on the value input if possible.

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

🔗 
label: str | I18nData | 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: Timer | float | None
default = None

Continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.

🔗 
inputs: Component | list[Component] | set[Component] | None
default = None

Components that are used as inputs to calculate `value` if `value` is a function (has no effect otherwise). `value` is recalculated any time the inputs change.

🔗 
max_height: int | str
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 | Literal['hidden']
default = True

If False, component will be hidden. If "hidden", component will be visually hidden and not take up space in the layout but still exist in the DOM

🔗 
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 | tuple[int | str, ...] | None
default = None

in a gr.render, Components with the same key across re-renders are treated as the same component, not a new component. Properties set in 'preserved_by_key' are not reset across a re-render.

🔗 
preserved_by_key: list[str] | str | None
default = "value"

A list of parameters from this component's constructor. Inside a gr.render() function, if a component is re-rendered with the same key, these (and only these) parameters will be preserved in the UI (if they have been changed by the user or an event listener) instead of re-rendered based on the values provided during constructor.

🔗 
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%". The percentage width is calculated based on the viewport width of the table. If not provided, the column widths will be automatically determined based on the content of the cells.

🔗 
buttons: list[Literal['fullscreen', 'copy']] | None
default = None

A list of buttons to show in the top right corner of the component. Valid options are "fullscreen" and "copy". The "fullscreen" button allows the user to view the table in fullscreen mode. The "copy" button allows the user to copy the table data to the clipboard. By default, all buttons are shown.

🔗 
show_row_numbers: bool
default = False

If True, will display row numbers in a separate column.

🔗 
max_chars: int | None
default = None

Maximum number of characters to display in each cell before truncating (single-clicking a cell value will still reveal the full content). If None, no truncation is applied.

🔗 
pinned_columns: int | None
default = None

If provided, will pin the specified number of columns from the left.

🔗 
static_columns: list[int] | None
default = None

List of column indices (int) that should not be editable. Only applies when interactive=True. When specified, col_count is automatically set to "fixed" and columns cannot be inserted or deleted.

Shortcuts

ClassInterface String ShortcutInitialization

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

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.

ListenerDescription

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

Dataframe.edit(fn, ···)

This listener is triggered when the user edits the Dataframe (e.g. image) using the built-in editor.

Event Parameters

Parameters
🔗 
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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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
default = None

defines how the endpoint appears in the API docs. Can be a string or None. 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.

🔗 
api_description: str | None | Literal[False]
default = None

Description of the API endpoint. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given description. If None, the function's docstring will be used as the API endpoint description. If False, then no description will be displayed in the API docs.

🔗 
scroll_to_output: bool
default = False

If True, will scroll to output component on completion

🔗 
show_progress: Literal['full', 'minimal', 'hidden']
default = "full"

how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all

🔗 
show_progress_on: Component | list[Component] | None
default = None

Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components.

🔗 
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.

🔗 
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 | Literal[True] | 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.

🔗 
api_visibility: Literal['public', 'private', 'undocumented']
default = "public"

controls the visibility and accessibility of this endpoint. Can be "public" (shown in API docs and callable by clients), "private" (hidden from API docs and not callable by clients), or "undocumented" (hidden from API docs but callable by clients and via gr.load). If fn is None, api_visibility will automatically be set to "private".

🔗 
time_limit: int | None
default = None
🔗 
stream_every: float
default = 0.5
🔗 
key: int | str | tuple[int | str, ...] | None
default = None

A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical.

🔗 
validator: Callable | None
default = None

Optional validation function to run before the main function. If provided, this function will be executed first with queue=False, and only if it completes successfully will the main function be called. The validator receives the same inputs as the main function and should return a `gr.validate()` for each input value.

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