panel.widgets.tables module#

class panel.widgets.tables.BaseTable(value=None, **params)[source]#

Bases: ReactiveData, Widget

Attributes:

current_view: Returns the current view of the table after filtering and sorting are applied.
indexes
selected_dataframe: Returns a DataFrame of the currently selected rows.

Methods

`add_filter`(filter[, column])	Adds a filter to the table which can be a static value or dynamic parameter based object which will automatically update the table when changed..
`patch`(patch_value[, as_index])	Efficiently patches (updates) the existing value with the patch_value.
`remove_filter`(filter)	Removes a filter which was previously added.
`stream`(stream_value[, rollover, reset_index])	Streams (appends) the stream_value provided to the existing value in an efficient manner.

Parameter Definitions

Parameters inherited from:

panel.viewable.Layoutable: align, aspect_ratio, css_classes, design, min_width, min_height, max_width, max_height, styles, stylesheets, tags, width_policy, height_policy, sizing_mode, visible

panel.viewable.Viewable: loading

panel.widgets.base.Widget: height, margin, width, disabled

value = Parameter(allow_None=True, allow_refs=True, label='Value'): The widget value which the widget type resolves to when used as a reactive param reference.
selection = List(bounds=(0, None), default=[], item_type=<class 'int'>, label='Selection'): The currently selected rows of the table.
aggregators = Dict(allow_refs=True, class_=<class 'dict'>, default={}, label='Aggregators', nested_refs=True): A dictionary mapping from index name to an aggregator to be used for hierarchical multi-indexes (valid aggregators include ‘min’, ‘max’, ‘mean’ and ‘sum’). If separate aggregators for different columns are required the dictionary may be nested as {index_name: {column_name: aggregator}}
editors = Dict(allow_refs=True, class_=<class 'dict'>, default={}, label='Editors', nested_refs=True): Bokeh CellEditor to use for a particular column (overrides the default chosen based on the type).
formatters = Dict(allow_refs=True, class_=<class 'dict'>, default={}, label='Formatters', nested_refs=True): Bokeh CellFormatter to use for a particular column (overrides the default chosen based on the type).
hierarchical = Boolean(allow_refs=True, constant=True, default=False, label='Hierarchical'): Whether to generate a hierarchical index.
row_height = Integer(default=40, inclusive_bounds=(True, True), label='Row height'): The height of each table row.
show_index = Boolean(allow_refs=True, default=True, label='Show index'): Whether to show the index column.
sorters = List(allow_refs=True, bounds=(0, None), default=[], label='Sorters'): A list of sorters to apply during pagination.
text_align = ClassSelector(allow_refs=True, class_=(<class 'dict'>, <class 'str'>), default={}, label='Text align', nested_refs=True): A mapping from column name to alignment or a fixed column alignment, which should be one of ‘left’, ‘center’, ‘right’.
titles = Dict(allow_refs=True, class_=<class 'dict'>, default={}, label='Titles', nested_refs=True): A mapping from column name to a title to override the name with.
widths = ClassSelector(allow_refs=True, class_=(<class 'dict'>, <class 'int'>), default={}, label='Widths', nested_refs=True): A mapping from column name to column width or a fixed column width.

add_filter(filter: Any, column: str | None = None)[source]#

Adds a filter to the table which can be a static value or dynamic parameter based object which will automatically update the table when changed..

When a static value, widget or parameter is supplied the filtering will follow a few well defined behaviors:

scalar: Filters by checking for equality

tuple: A tuple will be interpreted as range.

list: A list will be interpreted as a set of discrete
scalars and the filter will check if the values in the column match any of the items in the list.

Parameters:

filter: Widget, param.Parameter or FunctionType: The value by which to filter the DataFrame along the declared column, or a function accepting the DataFrame to be filtered and returning a filtered copy of the DataFrame.
column: str or None: Column to which the filter will be applied, if the filter is a constant value, widget or parameter.

Raises:

ValueError: If the filter type is not supported or no column: was declared.

property current_view#: Returns the current view of the table after filtering and sorting are applied.

patch(patch_value, as_index=True)[source]#

Efficiently patches (updates) the existing value with the patch_value.

Parameters:

patch_value: (pd.DataFrame | pd.Series | Dict): The value(s) to patch the existing value with.
as_index: boolean: Whether to treat the patch index as DataFrame indexes (True) or as simple integer index.

Raises:

ValueError: Raised if the patch_value is not a supported type.

Examples

Patch a DataFrame with a Dictionary row. >>> value = pd.DataFrame({“x”: [1, 2], “y”: [“a”, “b”]}) >>> tabulator = Tabulator(value=value) >>> patch_value = {“x”: [(0, 3)]} >>> tabulator.patch(patch_value) >>> tabulator.value.to_dict(“list”) {‘x’: [3, 2], ‘y’: [‘a’, ‘b’]}

Patch a Dataframe with a Dictionary of Columns. >>> value = pd.DataFrame({“x”: [1, 2], “y”: [“a”, “b”]}) >>> tabulator = Tabulator(value=value) >>> patch_value = {“x”: [(slice(2), (3,4))], “y”: [(1,’d’)]} >>> tabulator.patch(patch_value) >>> tabulator.value.to_dict(“list”) {‘x’: [3, 4], ‘y’: [‘a’, ‘d’]}

Patch a DataFrame with a Series. Please note the index is used in the update. >>> value = pd.DataFrame({“x”: [1, 2], “y”: [“a”, “b”]}) >>> tabulator = Tabulator(value=value) >>> patch_value = pd.Series({“index”: 1, “x”: 4, “y”: “d”}) >>> tabulator.patch(patch_value) >>> tabulator.value.to_dict(“list”) {‘x’: [1, 4], ‘y’: [‘a’, ‘d’]}

Patch a Dataframe with a Dataframe. Please note the index is used in the update. >>> value = pd.DataFrame({“x”: [1, 2], “y”: [“a”, “b”]}) >>> tabulator = Tabulator(value=value) >>> patch_value = pd.DataFrame({“x”: [3, 4], “y”: [“c”, “d”]}) >>> tabulator.patch(patch_value) >>> tabulator.value.to_dict(“list”) {‘x’: [3, 4], ‘y’: [‘c’, ‘d’]}

remove_filter(filter)[source]#: Removes a filter which was previously added.

property selected_dataframe#: Returns a DataFrame of the currently selected rows.

stream(stream_value, rollover=None, reset_index=True)[source]#

Streams (appends) the stream_value provided to the existing value in an efficient manner.

Parameters:

stream_value: (pd.DataFrame | pd.Series | Dict): The new value(s) to append to the existing value.
rollover: int: A maximum column size, above which data from the start of the column begins to be discarded. If None, then columns will continue to grow unbounded.
reset_index: (bool, default=True): If True and the stream_value is a DataFrame, then its index is reset. Helps to keep the index unique and named index

Raises:

ValueError: Raised if the stream_value is not a supported type.

Examples

Stream a Series to a DataFrame >>> value = pd.DataFrame({“x”: [1, 2], “y”: [“a”, “b”]}) >>> tabulator = Tabulator(value=value) >>> stream_value = pd.Series({“x”: 4, “y”: “d”}) >>> tabulator.stream(stream_value) >>> tabulator.value.to_dict(“list”) {‘x’: [1, 2, 4], ‘y’: [‘a’, ‘b’, ‘d’]}

Stream a Dataframe to a Dataframe >>> value = pd.DataFrame({“x”: [1, 2], “y”: [“a”, “b”]}) >>> tabulator = Tabulator(value=value) >>> stream_value = pd.DataFrame({“x”: [3, 4], “y”: [“c”, “d”]}) >>> tabulator.stream(stream_value) >>> tabulator.value.to_dict(“list”) {‘x’: [1, 2, 3, 4], ‘y’: [‘a’, ‘b’, ‘c’, ‘d’]}

Stream a Dictionary row to a DataFrame >>> value = pd.DataFrame({“x”: [1, 2], “y”: [“a”, “b”]}) >>> tabulator = Tabulator(value=value) >>> stream_value = {“x”: 4, “y”: “d”} >>> tabulator.stream(stream_value) >>> tabulator.value.to_dict(“list”) {‘x’: [1, 2, 4], ‘y’: [‘a’, ‘b’, ‘d’]}

Stream a Dictionary of Columns to a Dataframe >>> value = pd.DataFrame({“x”: [1, 2], “y”: [“a”, “b”]}) >>> tabulator = Tabulator(value=value) >>> stream_value = {“x”: [3, 4], “y”: [“c”, “d”]} >>> tabulator.stream(stream_value) >>> tabulator.value.to_dict(“list”) {‘x’: [1, 2, 3, 4], ‘y’: [‘a’, ‘b’, ‘c’, ‘d’]}

class panel.widgets.tables.ColumnSpec[source]#: Bases: TypedDict

class panel.widgets.tables.DataFrame(value=None, **params)[source]#

Bases: BaseTable

The DataFrame widget allows displaying and editing a pandas DataFrame.

Note that editing is not possible for multi-indexed DataFrames, in which case you will need to reduce the DataFrame to a single index.

Also note that the DataFrame widget will eventually be replaced with the Tabulator widget, and so new code should be written to use Tabulator instead.

Reference: https://panel.holoviz.org/reference/widgets/DataFrame.html

Example:

>>> DataFrame(df, name='DataFrame')

Parameter Definitions

Parameters inherited from:

panel.viewable.Layoutable: align, aspect_ratio, css_classes, design, min_width, min_height, max_width, max_height, styles, stylesheets, tags, width_policy, height_policy, sizing_mode, visible

panel.viewable.Viewable: loading

panel.widgets.base.Widget: height, margin, width, disabled

panel.widgets.tables.BaseTable: value, selection, aggregators, editors, formatters, hierarchical, row_height, show_index, sorters, text_align, titles, widths

auto_edit = Boolean(default=False, label='Auto edit'): Whether clicking on a table cell automatically starts edit mode.
autosize_mode = Selector(default='force_fit', label='Autosize mode', names={}, objects=['none', 'fit_columns', 'fit_viewport', 'force_fit']): Determines the column autosizing mode, as one of the following options: "fit_columns" Compute column widths based on cell contents while ensuring the table fits into the available viewport. This results in no horizontal scrollbar showing up, but data can get unreadable if there is not enough space available. "fit_viewport" Adjust the viewport size after computing column widths based on cell contents. "force_fit" Fit columns into available space dividing the table width across the columns equally (equivalent to fit_columns=True). This results in no horizontal scrollbar showing up, but data can get unreadable if there is not enough space available. "none" Do not automatically compute column widths.
fit_columns = Boolean(allow_None=True, label='Fit columns'): Whether columns should expand to the available width. This results in no horizontal scrollbar showing up, but data can get unreadable if there is no enough space available.
frozen_columns = Integer(allow_None=True, inclusive_bounds=(True, True), label='Frozen columns'): Integer indicating the number of columns to freeze. If set, the first N columns will be frozen, which prevents them from scrolling out of frame.
frozen_rows = Integer(allow_None=True, inclusive_bounds=(True, True), label='Frozen rows'): Integer indicating the number of rows to freeze. If set, the first N rows will be frozen, which prevents them from scrolling out of frame; if set to a negative value the last N rows will be frozen.
reorderable = Boolean(default=True, label='Reorderable'): Allows the reordering of a table’s columns. To reorder a column, click and drag a table’s header to the desired location in the table. The columns on either side will remain in their previous order.
sortable = Boolean(default=True, label='Sortable'): Allows to sort table’s contents. By default natural order is preserved. To sort a column, click on its header. Clicking one more time changes sort direction. Use Ctrl + click to return to natural order. Use Shift + click to sort multiple columns simultaneously.

class panel.widgets.tables.GroupSpec[source]#: Bases: TypedDict

class panel.widgets.tables.Tabulator(value=None, **params)[source]#

Bases: BaseTable

The Tabulator widget wraps the [Tabulator js](http://tabulator.info/) table to provide a full-featured, very powerful interactive table.

Reference: https://panel.holoviz.org/reference/widgets/Tabulator.html

Example:

>>> Tabulator(df, theme='site', pagination='remote', page_size=25)

Attributes:

current_view: Returns the current view of the table after filtering and sorting are applied.

Methods

`download`([filename])	Triggers downloading of the table as a CSV or JSON.
`download_menu`([text_kwargs, button_kwargs])	Returns a menu containing a TextInput and Button widget to set the filename and trigger a client-side download of the data.
`on_click`(callback[, column])	Register a callback to be executed when any cell is clicked.
`on_edit`(callback)	Register a callback to be executed when a cell is edited.
`stream`(stream_value[, rollover, ...])	Streams (appends) the stream_value provided to the existing value in an efficient manner.

Parameter Definitions

Parameters inherited from:

panel.viewable.Layoutable: align, aspect_ratio, css_classes, design, min_width, min_height, max_width, max_height, styles, stylesheets, tags, width_policy, height_policy, sizing_mode, visible

panel.viewable.Viewable: loading

panel.widgets.base.Widget: height, margin, width, disabled

panel.widgets.tables.BaseTable: value, aggregators, editors, formatters, hierarchical, show_index, sorters, text_align, titles, widths

selection = _ListValidateWithCallable(allow_refs=True, bounds=(0, None), default=[], item_type=<class 'int'>, label='Selection'): The currently selected rows of the table. It validates its values against ‘selectable_rows’ if used.
row_height = Integer(allow_refs=True, default=30, inclusive_bounds=(True, True), label='Row height'): The height of each table row.
buttons = Dict(allow_refs=True, class_=<class 'dict'>, default={}, label='Buttons', nested_refs=True): Dictionary mapping from column name to a HTML element to use as the button icon.
expanded = List(allow_refs=True, bounds=(0, None), default=[], label='Expanded', nested_refs=True): List of expanded rows, only applicable if a row_content function has been defined.
embed_content = Boolean(allow_refs=True, default=False, label='Embed content'): Whether to embed the row_content or render it dynamically when a row is expanded.
filters = List(allow_refs=True, bounds=(0, None), default=[], label='Filters'): List of client-side filters declared as dictionaries containing ‘field’, ‘type’ and ‘value’ keys.
frozen_columns = ClassSelector(allow_refs=True, class_=(<class 'list'>, <class 'dict'>), default=[], label='Frozen columns', nested_refs=True): One of: - List indicating the columns to freeze. The column(s) may be selected by name or index. - Dict indicating columns to freeze as keys and their freeze location as values, freeze location is either ‘right’ or ‘left’.
frozen_rows = List(allow_refs=True, bounds=(0, None), default=[], label='Frozen rows', nested_refs=True): List indicating the rows to freeze. If set, the first N rows will be frozen, which prevents them from scrolling out of frame; if set to a negative value the last N rows will be frozen.
groups = Dict(allow_refs=True, class_=<class 'dict'>, default={}, label='Groups', nested_refs=True): Dictionary mapping defining the groups.
groupby = List(allow_refs=True, bounds=(0, None), default=[], label='Groupby', nested_refs=True): Groups rows in the table by one or more columns.
header_align = ClassSelector(allow_refs=True, class_=(<class 'dict'>, <class 'str'>), default={}, label='Header align', nested_refs=True): A mapping from column name to alignment or a fixed column alignment, which should be one of ‘left’, ‘center’, ‘right’.
header_filters = ClassSelector(allow_None=True, allow_refs=True, class_=(<class 'bool'>, <class 'dict'>), label='Header filters', nested_refs=True): Whether to enable filters in the header or dictionary configuring filters for each column.
header_tooltips = Dict(allow_refs=True, class_=<class 'dict'>, default={}, label='Header tooltips'): Dictionary mapping from column name to a tooltip to show when hovering over the column header.
hidden_columns = List(allow_refs=True, bounds=(0, None), default=[], label='Hidden columns', nested_refs=True): List of columns to hide.

layout = Selector(allow_refs=True, default='fit_data_table', label='Layout', names={}, objects=['fit_data', 'fit_data_fill', 'fit_data_stretch', 'fit_data_table', 'fit_columns'])

initial_page_size = Integer(allow_refs=True, bounds=(1, None), default=20, inclusive_bounds=(True, True), label='Initial page size'): Initial page size if page_size is None and therefore automatically set.

pagination = Selector(allow_None=True, allow_refs=True, label='Pagination', names={}, objects=['local', 'remote'])

page = Integer(allow_refs=True, default=1, inclusive_bounds=(True, True), label='Page'): Currently selected page (indexed starting at 1), if pagination is enabled.
page_size = Integer(allow_None=True, allow_refs=True, bounds=(1, None), inclusive_bounds=(True, True), label='Page size'): Number of rows to render per page, if pagination is enabled.
row_content = Callable(allow_None=True, allow_refs=True, label='Row content'): A function which is given the DataFrame row and should return a Panel object to render as additional detail below the row.
selectable = ClassSelector(allow_refs=True, class_=(<class 'bool'>, <class 'str'>, <class 'int'>), default=True, label='Selectable'): Defines the selection mode of the Tabulator. - True Selects rows on click. To select multiple use Ctrl-select, to select a range use Shift-select - False Disables selection - ‘checkbox’ Adds a column of checkboxes to toggle selections - ‘checkbox-single’ Same as ‘checkbox’ but header does not allow select/deselect all - ‘toggle’ Selection toggles when clicked - int The maximum number of selectable rows.
selectable_rows = Callable(allow_None=True, allow_refs=True, label='Selectable rows'): A function which given a DataFrame should return a list of rows by integer index, which are selectable.
sortable = ClassSelector(allow_refs=True, class_=(<class 'bool'>, <class 'dict'>), default=True, label='Sortable'): Whether the columns in the table should be sortable. Can either be specified as a simple boolean toggling the behavior on and off or as a dictionary specifying the option per column.
theme = Selector(allow_refs=True, default='simple', label='Theme', names={}, objects=['default', 'site', 'simple', 'midnight', 'modern', 'bootstrap', 'bootstrap4', 'materialize', 'bulma', 'semantic-ui', 'fast', 'bootstrap5']): Tabulator CSS theme to apply to table.
theme_classes = List(allow_refs=True, bounds=(0, None), class_=<class 'str'>, default=[], item_type=<class 'str'>, label='Theme classes', nested_refs=True): List of extra CSS classes to apply to the Tabulator element to customize the theme.
title_formatters = Dict(allow_refs=True, class_=<class 'dict'>, default={}, label='Title formatters', nested_refs=True): Tabulator formatter specification to use for a particular column header title.

property current_view: pd.DataFrame#: Returns the current view of the table after filtering and sorting are applied.

download(filename: str = 'table.csv')[source]#

Triggers downloading of the table as a CSV or JSON.

Parameters:

filename: str: The filename to save the table as.

download_menu(text_kwargs={}, button_kwargs={})[source]#

Returns a menu containing a TextInput and Button widget to set the filename and trigger a client-side download of the data.

Parameters:

text_kwargs: dict: Keyword arguments passed to the TextInput constructor
button_kwargs: dict: Keyword arguments passed to the Button constructor

Returns:

filename: TextInput: The TextInput widget setting a filename.
button: Button: The Button that triggers a download.

on_click(callback: Callable[[CellClickEvent], None], column: str | None = None)[source]#

Register a callback to be executed when any cell is clicked. The callback is given a CellClickEvent declaring the column and row of the cell that was clicked.

Parameters:

callback: (callable): The callback to run on edit events.
column: (str): Optional argument restricting the callback to a specific column.

on_edit(callback: Callable[[TableEditEvent], None])[source]#

Register a callback to be executed when a cell is edited. Whenever a cell is edited on_edit callbacks are called with a TableEditEvent as the first argument containing the column, row and value of the edited cell.

Parameters:

callback: (callable): The callback to run on edit events.

stream(stream_value, rollover=None, reset_index=True, follow=True)[source]#

Streams (appends) the stream_value provided to the existing value in an efficient manner.

Parameters:

stream_value: (pd.DataFrame | pd.Series | Dict): The new value(s) to append to the existing value.
rollover: int: A maximum column size, above which data from the start of the column begins to be discarded. If None, then columns will continue to grow unbounded.
reset_index: (bool, default=True): If True and the stream_value is a DataFrame, then its index is reset. Helps to keep the index unique and named index

Raises:

ValueError: Raised if the stream_value is not a supported type.

Examples

Stream a Series to a DataFrame >>> value = pd.DataFrame({“x”: [1, 2], “y”: [“a”, “b”]}) >>> tabulator = Tabulator(value=value) >>> stream_value = pd.Series({“x”: 4, “y”: “d”}) >>> tabulator.stream(stream_value) >>> tabulator.value.to_dict(“list”) {‘x’: [1, 2, 4], ‘y’: [‘a’, ‘b’, ‘d’]}

Stream a Dataframe to a Dataframe >>> value = pd.DataFrame({“x”: [1, 2], “y”: [“a”, “b”]}) >>> tabulator = Tabulator(value=value) >>> stream_value = pd.DataFrame({“x”: [3, 4], “y”: [“c”, “d”]}) >>> tabulator.stream(stream_value) >>> tabulator.value.to_dict(“list”) {‘x’: [1, 2, 3, 4], ‘y’: [‘a’, ‘b’, ‘c’, ‘d’]}

Stream a Dictionary row to a DataFrame >>> value = pd.DataFrame({“x”: [1, 2], “y”: [“a”, “b”]}) >>> tabulator = Tabulator(value=value) >>> stream_value = {“x”: 4, “y”: “d”} >>> tabulator.stream(stream_value) >>> tabulator.value.to_dict(“list”) {‘x’: [1, 2, 4], ‘y’: [‘a’, ‘b’, ‘d’]}

Stream a Dictionary of Columns to a Dataframe >>> value = pd.DataFrame({“x”: [1, 2], “y”: [“a”, “b”]}) >>> tabulator = Tabulator(value=value) >>> stream_value = {“x”: [3, 4], “y”: [“c”, “d”]} >>> tabulator.stream(stream_value) >>> tabulator.value.to_dict(“list”) {‘x’: [1, 2, 3, 4], ‘y’: [‘a’, ‘b’, ‘c’, ‘d’]}