dash_table.DataTable

Access this documentation in your Python terminal with:
```python

help(dash_table.DataTable)
```

active_cell (dict; optional): The row and column indices and IDs of the currently active cell. row_id is only returned if the data rows have an id key. active_cell has the following type: dict containing keys ‘row’, ‘column’, ‘row_id’, ‘column_id’. Those keys have the following types:

  • row (number; optional)
  • column (number; optional)
  • row_id (string | number; optional)
  • column_id (string; optional)

columns (dict; optional): Columns describes various aspects about each individual column. name and id are the only required parameters. columns has the following type: list of dicts containing keys ‘clearable’, ‘deletable’, ‘editable’, ‘hideable’, ‘renamable’, ‘selectable’, ‘format’, ‘id’, ‘name’, ‘presentation’, ‘on_change’, ‘sort_as_null’, ‘validation’, ‘type’. Those keys have the following types:

  • clearable (a value equal to: ‘first’, ‘last’ | boolean | list of booleans; optional): If true, the user can clear the column by clicking on the clear action button on the column. If there are multiple header rows, true will display the action button on each row. If last, the clear action button will only appear on the last header row. If first it will only appear on the first header row. These are respectively shortcut equivalents to [false, ..., false, true] and [true, false, ..., false]. If there are merged, multi-header columns then you can choose which column header row to display the clear action button in by supplying an array of booleans. For example, [true, false] will display the clear action button on the first row, but not the second row. If the clear action button appears on a merged column, then clicking on that button will clear all of the merged columns associated with it. Unlike column.deletable, this action does not remove the column(s) from the table. It only removed the associated entries from data.
  • deletable (a value equal to: ‘first’, ‘last’ | boolean | list of booleans; optional): If true, the user can remove the column by clicking on the delete action button on the column. If there are multiple header rows, true will display the action button on each row. If last, the delete action button will only appear on the last header row. If first it will only appear on the first header row. These are respectively shortcut equivalents to [false, ..., false, true] and [true, false, ..., false]. If there are merged, multi-header columns then you can choose which column header row to display the delete action button in by supplying an array of booleans. For example, [true, false] will display the delete action button on the first row, but not the second row. If the delete action button appears on a merged column, then clicking on that button will remove all of the merged columns associated with it.
  • editable (boolean; optional): There are two editable flags in the table. This is the column-level editable flag and there is also the table-level editable flag. These flags determine whether the contents of the table are editable or not. If the column-level editable flag is set it overrides the table-level editable flag for that column.
  • hideable (a value equal to: ‘first’, ‘last’ | boolean | list of booleans; optional): If true, the user can hide the column by clicking on the hide action button on the column. If there are multiple header rows, true will display the action button on each row. If last, the hide action button will only appear on the last header row. If first it will only appear on the first header row. These are respectively shortcut equivalents to [false, ..., false, true] and [true, false, ..., false]. If there are merged, multi-header columns then you can choose which column header row to display the hide action button in by supplying an array of booleans. For example, [true, false] will display the hide action button on the first row, but not the second row. If the hide action button appears on a merged column, then clicking on that button will hide all of the merged columns associated with it.
  • renamable (a value equal to: ‘first’, ‘last’ | boolean | list of booleans; optional): If true, the user can rename the column by clicking on the rename action button on the column. If there are multiple header rows, true will display the action button on each row. If last, the rename action button will only appear on the last header row. If first it will only appear on the first header row. These are respectively shortcut equivalents to [false, ..., false, true] and [true, false, ..., false]. If there are merged, multi-header columns then you can choose which column header row to display the rename action button in by supplying an array of booleans. For example, [true, false] will display the rename action button on the first row, but not the second row. If the rename action button appears on a merged column, then clicking on that button will rename all of the merged columns associated with it.
  • selectable (a value equal to: ‘first’, ‘last’ | boolean | list of booleans; optional): If true, the user can select the column by clicking on the checkbox or radio button in the column. If there are multiple header rows, true will display the input on each row. If last, the input will only appear on the last header row. If first it will only appear on the first header row. These are respectively shortcut equivalents to [false, ..., false, true] and [true, false, ..., false]. If there are merged, multi-header columns then you can choose which column header row to display the input in by supplying an array of booleans. For example, [true, false] will display the selectable input on the first row, but now on the second row. If the selectable input appears on a merged columns, then clicking on that input will select all of the merged columns associated with it. The table-level prop column_selectable is used to determine the type of column selection to use.
  • format (dict; optional): The formatting applied to the column’s data. This prop is derived from the d3-format library specification. Apart from being structured slightly differently (under a single prop), the usage is the same. ‘locale’: represents localization specific formatting information. When left unspecified, will use the default value provided by d3-format. The keys are as follows: ‘symbol’: (default: [‘$’, ‘’]) a list of two strings representing the prefix and suffix symbols. Typically used for currency, and implemented using d3’s currency format, but you can use this for other symbols such as measurement units; ‘decimal’: (default: ‘.’) the string used for the decimal separator; ‘group’: (default: ‘,’) the string used for the groups separator; ‘grouping’: (default: [3]) a list of integers representing the grouping pattern. ‘numerals’: a list of ten strings used as replacements for numbers 0-9; ‘percent’: (default: ‘%’) the string used for the percentage symbol; ‘separate_4digits’: (default: True) separate integers with 4-digits or less. ‘nully’: a value that will be used in place of the nully value during formatting. If the value type matches the column type, it will be formatted normally. ‘prefix’: a number representing the SI unit to use during formatting. See dash_table.Format.Prefix enumeration for the list of valid values ‘specifier’: (default: ‘’) represents the rules to apply when formatting the number. dash_table.FormatTemplate contains helper functions to rapidly use certain typical number formats. format has the following type: dict containing keys ‘locale’, ‘nully’, ‘prefix’, ‘specifier’. Those keys have the following types: - locale (dict; optional): locale has the following type: dict containing keys ‘symbol’, ‘decimal’, ‘group’, ‘grouping’, ‘numerals’, ‘percent’, ‘separate_4digits’. Those keys have the following types: - symbol (list of strings; optional) - decimal (string; optional) - group (string; optional) - grouping (list of numbers; optional) - numerals (list of strings; optional) - percent (string; optional) - separate_4digits (boolean; optional) - nully (boolean | number | string | dict | list; optional) - prefix (number; optional) - specifier (string; optional)
  • id (string; required): The id of the column. The column id is used to match cells in data with particular columns. The id is not visible in the table.
  • name (string | list of strings; required): The name of the column, as it appears in the column header. If name is a list of strings, then the columns will render with multiple headers rows.
  • presentation (a value equal to: ‘input’, ‘dropdown’, ‘markdown’; optional): The presentation to use to display the value. Defaults to ‘input’ for [‘datetime’, ‘numeric’, ‘text’, ‘any’].
  • on_change (dict; optional): The on_change behavior of the column for user-initiated modifications. ‘action’ (default ‘coerce’): none: do not validate data; coerce: check if the data corresponds to the destination type and attempts to coerce it into the destination type if not; validate: check if the data corresponds to the destination type (no coercion). ‘failure’ (default ‘reject’): what to do with the value if the action fails: accept: use the invalid value; default: replace the provided value with validation.default; reject: do not modify the existing value. on_change has the following type: dict containing keys ‘action’, ‘failure’. Those keys have the following types: - action (a value equal to: ‘coerce’, ‘none’, ‘validate’; optional) - failure (a value equal to: ‘accept’, ‘default’, ‘reject’; optional)
  • sort_as_null (list of string | number | booleans; optional): An array of string, number and boolean values that are treated as null (i.e. ignored and always displayed last) when sorting. This value overrides the table-level sort_as_null.
  • validation (dict; optional): The validation options. ‘allow_null’: Allow the use of nully values. (undefined, null, NaN) (default: false) ‘default’: The default value to apply with on_change.failure = ‘default’. (default: null) ‘allow_YY’: datetime columns only, allow 2-digit years (default: false). If true, we interpret years as ranging from now-70 to now+29 - in 2019 this is 1949 to 2048 but in 2020 it will be different. If used with action: 'coerce', will convert user input to a 4-digit year. validation has the following type: dict containing keys ‘allow_null’, ‘default’, ‘allow_YY’. Those keys have the following types: - allow_null (boolean; optional) - default (boolean | number | string | dict | list; optional) - allow_YY (boolean; optional)
  • type (a value equal to: ‘any’, ‘numeric’, ‘text’, ‘datetime’; optional): The data-type of the column’s data. ‘numeric’: represents both floats and ints. ‘text’: represents a string. ‘datetime’: a string representing a date or date-time, in the form: ‘YYYY-MM-DD HH:MM:SS.ssssss’ or some truncation thereof. Years must have 4 digits, unless you use validation.allow_YY: true. Also accepts ‘T’ or ‘t’ between date and time, and allows timezone info at the end. To convert these strings to Python datetime objects, use dateutil.parser.isoparse. In R use parse_iso_8601 from the parsedate library. WARNING: these parsers do not work with 2-digit years, if you use validation.allow_YY: true and do not coerce to 4-digit years. And parsers that do work with 2-digit years may make a different guess about the century than we make on the front end. ‘any’: represents any type of data. Defaults to ‘any’ if undefined. NOTE: This feature has not been fully implemented. In the future, it’s data types will impact things like text formatting options in the cell (e.g. display 2 decimals for a number), filtering options and behavior, and editing behavior. Stay tuned by following https://github.com/plotly/dash-table/issues/166

css (dict; optional): The css property is a way to embed CSS selectors and rules onto the page. We recommend starting with the style_* properties before using this css property. Example: [ {“selector”: “.dash-spreadsheet”, “rule”: ‘font-family: “monospace”’} ]. css has the following type: list of dicts containing keys ‘selector’, ‘rule’. Those keys have the following types:

  • selector (string; required)
  • rule (string; required)

column_selectable (a value equal to: ‘single’, ‘multi’, false; default False): If single, then the uer can select a single column or group of merged columns via the radio button that will appear in the header rows. If multi, then the user can select multiple columns or groups of merged columns via the checkbox that will appear in the header rows. If false, then the user will not be able to select columns and no input will appear in the header rows. When a column is selected, its id will be contained in selected_columns and derived_viewport_selected_columns.

cell_selectable (boolean; default True): If True (default), then it is possible to click and navigate table cells.

data (list of dicts; optional): The contents of the table. The keys of each item in data should match the column IDs. Each item can also have an ‘id’ key, whose value is its row ID. If there is a column with ID=’id’ this will display the row ID, otherwise it is just used to reference the row for selections, filtering, etc. Example: [ {‘column-1’: 4.5, ‘column-2’: ‘montreal’, ‘column-3’: ‘canada’}, {‘column-1’: 8, ‘column-2’: ‘boston’, ‘column-3’: ‘america’} ]

data_previous (list of dicts; optional): The previous state of data. data_previous has the same structure as data and it will be updated whenever data changes, either through a callback or by editing the table. This is a read-only property: setting this property will not have any impact on the table.

data_timestamp (number; optional): The unix timestamp when the data was last edited. Use this property with other timestamp properties (such as n_clicks_timestamp in dash_html_components) to determine which property has changed within a callback.

dropdown (dict; optional): dropdown specifies dropdown options for different columns. Each entry refers to the column ID. The clearable property defines whether the value can be deleted. The options property refers to the options of the dropdown. dropdown has the following type: dict with strings as keys and values of type dict containing keys ‘clearable’, ‘options’. Those keys have the following types:

  • clearable (boolean; optional)
  • options (dict; required): options has the following type: list of dicts containing keys ‘label’, ‘value’. Those keys have the following types: - label (string; required) - value (number | string | boolean; required)

dropdown_conditional (dict; optional): dropdown_conditional specifies dropdown options in various columns and cells. This property allows you to specify different dropdowns depending on certain conditions. For example, you may render different “city” dropdowns in a row depending on the current value in the “state” column. dropdown_conditional has the following type: list of dicts containing keys ‘clearable’, ‘if’, ‘options’. Those keys have the following types:

  • clearable (boolean; optional)
  • if (dict; optional): if has the following type: dict containing keys ‘column_id’, ‘filter_query’. Those keys have the following types: - column_id (string; optional) - filter_query (string; optional)
  • options (dict; required): options has the following type: list of dicts containing keys ‘label’, ‘value’. Those keys have the following types: - label (string; required) - value (number | string | boolean; required)

dropdown_data (dict; optional): dropdown_data specifies dropdown options on a row-by-row, column-by-column basis. Each item in the array corresponds to the corresponding dropdowns for the data item at the same index. Each entry in the item refers to the Column ID. dropdown_data has the following type: list of dicts with strings as keys and values of type dict containing keys ‘clearable’, ‘options’. Those keys have the following types:

  • clearable (boolean; optional)
  • options (dict; required): options has the following type: list of dicts containing keys ‘label’, ‘value’. Those keys have the following types: - label (string; required) - value (number | string | boolean; required)

derived_filter_query_structure (dict; optional): This property represents the current structure of filter_query as a tree structure. Each node of the query structure has: type (string; required): ‘open-block’, ‘logical-operator’, ‘relational-operator’, ‘unary-operator’, or ‘expression’; subType (string; optional): ‘open-block’: ‘()’, ‘logical-operator’: ‘&&’, ‘||’, ‘relational-operator’: ‘=’, ‘>=’, ‘>’, ‘<=’, ‘<’, ‘!=’, ‘contains’, ‘unary-operator’: ‘!’, ‘is bool’, ‘is even’, ‘is nil’, ‘is num’, ‘is object’, ‘is odd’, ‘is prime’, ‘is str’, ‘expression’: ‘value’, ‘field’; value (any): ‘expression, value’: passed value, ‘expression, field’: the field/prop name. block (nested query structure; optional). left (nested query structure; optional). right (nested query structure; optional). If the query is invalid or empty, the derived_filter_query_structure will be null.

derived_viewport_data (list of dicts; optional): This property represents the current state of data on the current page. This property will be updated on paging, sorting, and filtering.

derived_viewport_indices (list of numbers; optional): derived_viewport_indices indicates the order in which the original rows appear after being filtered, sorted, and/or paged. derived_viewport_indices contains indices for the current page, while derived_virtual_indices contains indices across all pages.

derived_viewport_row_ids (list of string | numbers; optional): derived_viewport_row_ids lists row IDs in the order they appear after being filtered, sorted, and/or paged. derived_viewport_row_ids contains IDs for the current page, while derived_virtual_row_ids contains IDs across all pages.

derived_viewport_selected_columns (list of strings; optional): derived_viewport_selected_columns contains the ids of the selected_columns that are not currently hidden.

derived_viewport_selected_rows (list of numbers; optional): derived_viewport_selected_rows represents the indices of the selected_rows from the perspective of the derived_viewport_indices.

derived_viewport_selected_row_ids (list of string | numbers; optional): derived_viewport_selected_row_ids represents the IDs of the selected_rows on the currently visible page.

derived_virtual_data (list of dicts; optional): This property represents the visible state of data across all pages after the front-end sorting and filtering as been applied.

derived_virtual_indices (list of numbers; optional): derived_virtual_indices indicates the order in which the original rows appear after being filtered and sorted. derived_viewport_indices contains indices for the current page, while derived_virtual_indices contains indices across all pages.

derived_virtual_row_ids (list of string | numbers; optional): derived_virtual_row_ids indicates the row IDs in the order in which they appear after being filtered and sorted. derived_viewport_row_ids contains IDs for the current page, while derived_virtual_row_ids contains IDs across all pages.

derived_virtual_selected_rows (list of numbers; optional): derived_virtual_selected_rows represents the indices of the selected_rows from the perspective of the derived_virtual_indices.

derived_virtual_selected_row_ids (list of string | numbers; optional): derived_virtual_selected_row_ids represents the IDs of the selected_rows as they appear after filtering and sorting, across all pages.

editable (boolean; default False): If True, then the data in all of the cells is editable. When editable is True, particular columns can be made uneditable by setting editable to False inside the columns property. If False, then the data in all of the cells is uneditable. When editable is False, particular columns can be made editable by setting editable to True inside the columns property.

end_cell (dict; optional): When selecting multiple cells (via clicking on a cell and then shift-clicking on another cell), end_cell represents the row / column coordinates and IDs of the cell in one of the corners of the region. start_cell represents the coordinates of the other corner. end_cell has the following type: dict containing keys ‘row’, ‘column’, ‘row_id’, ‘column_id’. Those keys have the following types:

  • row (number; optional)
  • column (number; optional)
  • row_id (string | number; optional)
  • column_id (string; optional)

export_columns (a value equal to: ‘all’, ‘visible’; default 'visible'): Denotes the columns that will be used in the export data file. If all, all columns will be used (visible + hidden). If visible, only the visible columns will be used. Defaults to visible.

export_format (a value equal to: ‘csv’, ‘xlsx’, ‘none’; default 'none'): Denotes the type of the export data file, Defaults to 'none'

export_headers (a value equal to: ‘none’, ‘ids’, ‘names’, ‘display’; optional): Denotes the format of the headers in the export data file. If 'none', there will be no header. If 'display', then the header of the data file will be be how it is currently displayed. Note that 'display' is only supported for 'xlsx' export_format and will behave like 'names' for 'csv' export format. If 'ids' or 'names', then the headers of data file will be the column id or the column names, respectively

fill_width (boolean; default True): fill_width toggles between a set of CSS for two common behaviors: True: The table container’s width will grow to fill the available space; False: The table container’s width will equal the width of its content.

fixed_columns (dict; default { headers: false, data: 0 }): fixed_columns will “fix” the set of columns so that they remain visible when scrolling horizontally across the unfixed columns. fixed_columns fixes columns from left-to-right. If headers is False, no columns are fixed. If headers is True, all operation columns (see row_deletable and row_selectable) are fixed. Additional data columns can be fixed by assigning a number to data. Defaults to { headers: False }. Note that fixing columns introduces some changes to the underlying markup of the table and may impact the way that your columns are rendered or sized. View the documentation examples to learn more. fixed_columns has the following type: dict containing keys ‘headers’, ‘data’. Those keys have the following types:

  • headers (a value equal to: false; optional)
  • data (a value equal to: 0; optional) | dict containing keys ‘headers’, ‘data’. Those keys have the following types: - headers (a value equal to: true; required) - data (number; optional)

fixed_rows (dict; default { headers: false, data: 0 }): fixed_rows will “fix” the set of rows so that they remain visible when scrolling vertically down the table. fixed_rows fixes rows from top-to-bottom, starting from the headers. If headers is False, no rows are fixed. If headers is True, all header and filter rows (see filter_action) are fixed. Additional data rows can be fixed by assigning a number to data. Defaults to { headers: False }. Note that fixing rows introduces some changes to the underlying markup of the table and may impact the way that your columns are rendered or sized. View the documentation examples to learn more. fixed_rows has the following type: dict containing keys ‘headers’, ‘data’. Those keys have the following types:

  • headers (a value equal to: false; optional)
  • data (a value equal to: 0; optional) | dict containing keys ‘headers’, ‘data’. Those keys have the following types: - headers (a value equal to: true; required) - data (number; optional)

filter_query (string; default ''): If filter_action is enabled, then the current filtering string is represented in this filter_query property.

filter_action (dict; default 'none'): The filter_action property controls the behavior of the filtering UI. If 'none', then the filtering UI is not displayed. If 'native', then the filtering UI is displayed and the filtering logic is handled by the table. That is, it is performed on the data that exists in the data property. If 'custom', then the filtering UI is displayed but it is the responsibility of the developer to program the filtering through a callback (where filter_query or derived_filter_query_structure would be the input and data would be the output). filter_action has the following type: a value equal to: ‘custom’, ‘native’, ‘none’ | dict containing keys ‘type’, ‘operator’. Those keys have the following types: - type (a value equal to: ‘custom’, ‘native’; required) - operator (a value equal to: ‘and’, ‘or’; optional)

hidden_columns (list of strings; optional): List of columns ids of the columns that are currently hidden. See the associated nested prop columns.hideable.

include_headers_on_copy_paste (boolean; default False): If true, headers are included when copying from the table to different tabs and elsewhere. Note that headers are ignored when copying from the table onto itself and between two tables within the same tab.

id (string; optional): The ID of the table.

is_focused (boolean; optional): If True, then the active_cell is in a focused state.

locale_format (dict; optional): The localization specific formatting information applied to all columns in the table. This prop is derived from the d3.formatLocale data structure specification. When left unspecified, each individual nested prop will default to a pre-determined value. ‘symbol’: (default: [‘$’, ‘’]) a list of two strings representing the prefix and suffix symbols. Typically used for currency, and implemented using d3’s currency format, but you can use this for other symbols such as measurement units. ‘decimal’: (default: ‘.’) the string used for the decimal separator. ‘group’: (default: ‘,’) the string used for the groups separator. ‘grouping’: (default: [3]) a list of integers representing the grouping pattern. ‘numerals’: a list of ten strings used as replacements for numbers 0-9. ‘percent’: (default: ‘%’) the string used for the percentage symbol. ‘separate_4digits’: (default: True) separate integers with 4-digits or less. locale_format has the following type: dict containing keys ‘symbol’, ‘decimal’, ‘group’, ‘grouping’, ‘numerals’, ‘percent’, ‘separate_4digits’. Those keys have the following types:

  • symbol (list of strings; optional)
  • decimal (string; optional)
  • group (string; optional)
  • grouping (list of numbers; optional)
  • numerals (list of strings; optional)
  • percent (string; optional)
  • separate_4digits (boolean; optional)

loading_state (dict; optional): Object that holds the loading state object coming from dash-renderer. loading_state has the following type: dict containing keys ‘is_loading’, ‘prop_name’, ‘component_name’. Those keys have the following types:

  • is_loading (boolean; optional): Determines if the component is loading or not
  • prop_name (string; optional): Holds which property is loading
  • component_name (string; optional): Holds the name of the component that is loading

markdown_options (dict; default { link_target: '_blank' }): The markdown_options property allows customization of the markdown cells behavior. ‘link_target’: (default: ‘_blank’) the link’s behavior (_blank opens the link in a new tab, _parent opens the link in the parent frame, _self opens the link in the current tab, and _top opens the link in the top frame) or a string. markdown_options has the following type: dict containing keys ‘link_target’. Those keys have the following types:

  • link_target (string | a value equal to: ‘_blank’, ‘_parent’, ‘_self’, ‘_top’; required)

merge_duplicate_headers (boolean; optional): If True, then column headers that have neighbors with duplicate names will be merged into a single cell. This will be applied for single column headers and multi-column headers.

page_action (a value equal to: ‘custom’, ‘native’, ‘none’; default 'native'): page_action refers to a mode of the table where not all of the rows are displayed at once: only a subset are displayed (a “page”) and the next subset of rows can viewed by clicking “Next” or “Previous” buttons at the bottom of the page. Pagination is used to improve performance: instead of rendering all of the rows at once (which can be expensive), we only display a subset of them. With pagination, we can either page through data that exists in the table (e.g. page through 10,000 rows in data 100 rows at a time) or we can update the data on-the-fly with callbacks when the user clicks on the “Previous” or “Next” buttons. These modes can be toggled with this page_action parameter: 'native': all data is passed to the table up-front, paging logic is handled by the table; 'custom': data is passed to the table one page at a time, paging logic is handled via callbacks; none: disables paging, render all of the data at once.

page_current (number; default 0): page_current represents which page the user is on. Use this property to index through data in your callbacks with backend paging.

page_count (number; optional): page_count represents the number of the pages in the paginated table. This is really only useful when performing backend pagination, since the front end is able to use the full size of the table to calculate the number of pages.

page_size (number; default 250): page_size represents the number of rows that will be displayed on a particular page when page_action is 'custom' or 'native'

persistence (boolean | string | number; optional): Used to allow user interactions in this component to be persisted when the component - or the page - is refreshed. If persisted is truthy and hasn’t changed from its previous value, any persisted_props that the user has changed while using the app will keep those changes, as long as the new prop value also matches what was given originally. Used in conjunction with persistence_type and persisted_props.

persisted_props (list of a value equal to: ‘columns.name’, ‘data’, ‘filter_query’, ‘hidden_columns’, ‘selected_columns’, ‘selected_rows’, ‘sort_by’s; default [ 'columns.name', // data is not included by default 'filter_query', 'hidden_columns', 'selected_columns', 'selected_rows', 'sort_by' ]): Properties whose user interactions will persist after refreshing the component or the page.

persistence_type (a value equal to: ‘local’, ‘session’, ‘memory’; default 'local'): Where persisted user changes will be stored: memory: only kept in memory, reset on page refresh. local: window.localStorage, data is kept after the browser quit. session: window.sessionStorage, data is cleared once the browser quit.

row_deletable (boolean; optional): If True, then a x will appear next to each row and the user can delete the row.

row_selectable (a value equal to: ‘single’, ‘multi’, false; default False): If single, then the user can select a single row via a radio button that will appear next to each row. If multi, then the user can select multiple rows via a checkbox that will appear next to each row. If false, then the user will not be able to select rows and no additional UI elements will appear. When a row is selected, its index will be contained in selected_rows.

selected_cells (dict; optional): selected_cells represents the set of cells that are selected, as an array of objects, each item similar to active_cell. Multiple cells can be selected by holding down shift and clicking on a different cell or holding down shift and navigating with the arrow keys. selected_cells has the following type: list of dicts containing keys ‘row’, ‘column’, ‘row_id’, ‘column_id’. Those keys have the following types:

  • row (number; optional)
  • column (number; optional)
  • row_id (string | number; optional)
  • column_id (string; optional)

selected_rows (list of numbers; optional): selected_rows contains the indices of rows that are selected via the UI elements that appear when row_selectable is 'single' or 'multi'.

selected_columns (list of strings; optional): selected_columns contains the ids of columns that are selected via the UI elements that appear when column_selectable is 'single' or 'multi'.

selected_row_ids (list of string | numbers; optional): selected_row_ids contains the ids of rows that are selected via the UI elements that appear when row_selectable is 'single' or 'multi'.

start_cell (dict; optional): When selecting multiple cells (via clicking on a cell and then shift-clicking on another cell), start_cell represents the [row, column] coordinates of the cell in one of the corners of the region. end_cell represents the coordinates of the other corner. start_cell has the following type: dict containing keys ‘row’, ‘column’, ‘row_id’, ‘column_id’. Those keys have the following types:

  • row (number; optional)
  • column (number; optional)
  • row_id (string | number; optional)
  • column_id (string; optional)

style_as_list_view (boolean; default False): If True, then the table will be styled like a list view and not have borders between the columns.

sort_action (a value equal to: ‘custom’, ‘native’, ‘none’; default 'none'): The sort_action property enables data to be sorted on a per-column basis. If 'none', then the sorting UI is not displayed. If 'native', then the sorting UI is displayed and the sorting logic is hanled by the table. That is, it is performed on the data that exists in the data property. If 'custom', the the sorting UI is displayed but it is the responsibility of the developer to program the sorting through a callback (where sort_by would be the input and data would be the output). Clicking on the sort arrows will update the sort_by property.

sort_mode (a value equal to: ‘single’, ‘multi’; default 'single'): Sorting can be performed across multiple columns (e.g. sort by country, sort within each country, sort by year) or by a single column. NOTE - With multi-column sort, it’s currently not possible to determine the order in which the columns were sorted through the UI. See https://github.com/plotly/dash-table/issues/170

sort_by (dict; optional): sort_by describes the current state of the sorting UI. That is, if the user clicked on the sort arrow of a column, then this property will be updated with the column ID and the direction (asc or desc) of the sort. For multi-column sorting, this will be a list of sorting parameters, in the order in which they were clicked. sort_by has the following type: list of dicts containing keys ‘column_id’, ‘direction’. Those keys have the following types:

  • column_id (string; required)
  • direction (a value equal to: ‘asc’, ‘desc’; required)

sort_as_null (list of string | number | booleans; optional): An array of string, number and boolean values that are treated as null (i.e. ignored and always displayed last) when sorting. This value will be used by columns without sort_as_null. Defaults to [].

style_table (dict; optional): CSS styles to be applied to the outer table container. This is commonly used for setting properties like the width or the height of the table.

style_cell (dict; optional): CSS styles to be applied to each individual cell of the table. This includes the header cells, the data cells, and the filter cells.

style_data (dict; optional): CSS styles to be applied to each individual data cell. That is, unlike style_cell, it excludes the header and filter cells.

style_filter (dict; optional): CSS styles to be applied to the filter cells. Note that this may change in the future as we build out a more complex filtering UI.

style_header (dict; optional): CSS styles to be applied to each individual header cell. That is, unlike style_cell, it excludes the data and filter cells.

style_cell_conditional (dict; optional): Conditional CSS styles for the cells. This can be used to apply styles to cells on a per-column basis. style_cell_conditional has the following type: list of dicts containing keys ‘if’. Those keys have the following types:

  • if (dict; optional): if has the following type: dict containing keys ‘column_id’, ‘column_type’. Those keys have the following types: - column_id (string | list of strings; optional) - column_type (a value equal to: ‘any’, ‘numeric’, ‘text’, ‘datetime’; optional)

style_data_conditional (dict; optional): Conditional CSS styles for the data cells. This can be used to apply styles to data cells on a per-column basis. style_data_conditional has the following type: list of dicts containing keys ‘if’. Those keys have the following types:

  • if (dict; optional): if has the following type: dict containing keys ‘column_id’, ‘column_type’, ‘filter_query’, ‘state’, ‘row_index’, ‘column_editable’. Those keys have the following types: - column_id (string | list of strings; optional) - column_type (a value equal to: ‘any’, ‘numeric’, ‘text’, ‘datetime’; optional) - filter_query (string; optional) - state (a value equal to: ‘active’, ‘selected’; optional) - row_index (number | a value equal to: ‘odd’, ‘even’ | list of numbers; optional) - column_editable (boolean; optional)

style_filter_conditional (dict; optional): Conditional CSS styles for the filter cells. This can be used to apply styles to filter cells on a per-column basis. style_filter_conditional has the following type: list of dicts containing keys ‘if’. Those keys have the following types:

  • if (dict; optional): if has the following type: dict containing keys ‘column_id’, ‘column_type’, ‘column_editable’. Those keys have the following types: - column_id (string | list of strings; optional) - column_type (a value equal to: ‘any’, ‘numeric’, ‘text’, ‘datetime’; optional) - column_editable (boolean; optional)

style_header_conditional (dict; optional): Conditional CSS styles for the header cells. This can be used to apply styles to header cells on a per-column basis. style_header_conditional has the following type: list of dicts containing keys ‘if’. Those keys have the following types:

  • if (dict; optional): if has the following type: dict containing keys ‘column_id’, ‘column_type’, ‘header_index’, ‘column_editable’. Those keys have the following types: - column_id (string | list of strings; optional) - column_type (a value equal to: ‘any’, ‘numeric’, ‘text’, ‘datetime’; optional) - header_index (number | list of numbers | a value equal to: ‘odd’, ‘even’; optional) - column_editable (boolean; optional)

tooltip (dict; optional): tooltip represents the tooltip shown for different columns. The property name refers to the column ID. The type refers to the type of tooltip syntax used for the tooltip generation. Can either be markdown or text. Defaults to text. The value refers to the syntax-based content of the tooltip. This value is required. The delay represents the delay in milliseconds before the tooltip is shown when hovering a cell. This overrides the table’s tooltip_delay property. If set to null, the tooltip will be shown immediately. The duration represents the duration in milliseconds during which the tooltip is shown when hovering a cell. This overrides the table’s tooltip_duration property. If set to null, the tooltip will not disappear. Alternatively, the value of the property can also be a plain string. The text syntax will be used in that case. tooltip has the following type: dict with strings as keys and values of type dict containing keys ‘delay’, ‘duration’, ‘type’, ‘value’. Those keys have the following types:

  • delay (number; optional)
  • duration (number; optional)
  • type (a value equal to: ‘text’, ‘markdown’; optional)
  • value (string; required) | string

tooltip_conditional (dict; optional): tooltip_conditional represents the tooltip shown for different columns and cells. This property allows you to specify different tooltips for depending on certain conditions. For example, you may have different tooltips in the same column based on the value of a certain data property. Priority is from first to last defined conditional tooltip in the list. Higher priority (more specific) conditional tooltips should be put at the beginning of the list. The if refers to the condition that needs to be fulfilled in order for the associated tooltip configuration to be used. If multiple conditions are defined, all conditions must be met for the tooltip to be used by a cell. The if nested property column_id refers to the column ID that must be matched. The if nested property row_index refers to the index of the row in the source data. The if nested property filter_query refers to the query that must evaluate to True. The type refers to the type of tooltip syntax used for the tooltip generation. Can either be markdown or text. Defaults to text. The value refers to the syntax-based content of the tooltip. This value is required. The delay represents the delay in milliseconds before the tooltip is shown when hovering a cell. This overrides the table’s tooltip_delay property. If set to null, the tooltip will be shown immediately. The duration represents the duration in milliseconds during which the tooltip is shown when hovering a cell. This overrides the table’s tooltip_duration property. If set to null, the tooltip will not disappear. tooltip_conditional has the following type: list of dicts containing keys ‘delay’, ‘duration’, ‘if’, ‘type’, ‘value’. Those keys have the following types:

  • delay (number; optional)
  • duration (number; optional)
  • if (dict; required): if has the following type: dict containing keys ‘column_id’, ‘filter_query’, ‘row_index’. Those keys have the following types: - column_id (string; optional) - filter_query (string; optional) - row_index (number | a value equal to: ‘odd’, ‘even’; optional)
  • type (a value equal to: ‘text’, ‘markdown’; optional)
  • value (string; required)

tooltip_data (dict; optional): tooltip_data represents the tooltip shown for different columns and cells. The property name refers to the column ID. Each property contains a list of tooltips mapped to the source data row index. The type refers to the type of tooltip syntax used for the tooltip generation. Can either be markdown or text. Defaults to text. The value refers to the syntax-based content of the tooltip. This value is required. The delay represents the delay in milliseconds before the tooltip is shown when hovering a cell. This overrides the table’s tooltip_delay property. If set to null, the tooltip will be shown immediately. The duration represents the duration in milliseconds during which the tooltip is shown when hovering a cell. This overrides the table’s tooltip_duration property. If set to null, the tooltip will not disappear. Alternatively, the value of the property can also be a plain string. The text syntax will be used in that case. tooltip_data has the following type: list of dicts with strings as keys and values of type string | dict containing keys ‘delay’, ‘duration’, ‘type’, ‘value’. Those keys have the following types:

  • delay (number; optional)
  • duration (number; optional)
  • type (a value equal to: ‘text’, ‘markdown’; optional)
  • value (string; required)

tooltip_delay (number; default 350): tooltip_delay represents the table-wide delay in milliseconds before the tooltip is shown when hovering a cell. If set to null, the tooltip will be shown immediately. Defaults to 350.

tooltip_duration (number; default 2000): tooltip_duration represents the table-wide duration in milliseconds during which the tooltip will be displayed when hovering a cell. If set to null, the tooltip will not disappear. Defaults to 2000.

virtualization (boolean; default False): This property tells the table to use virtualization when rendering. Assumptions are that: the width of the columns is fixed; the height of the rows is always the same; and runtime styling changes will not affect width and height vs. first rendering