dash_table.DataTable

DataTable Properties

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

help(dash.dash_table.DataTable)
```

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which has typeahead support for Dash Component Properties.
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.

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

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 is a dict with keys:

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

column_selectable (a value equal to: ‘single’, ‘multi’ or false; default False):
If single, then the user 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.

columns (list of dicts; optional):
Columns describes various aspects about each individual column. name
and id are the only required parameters.

columns is a list of dicts with keys:

css (list of dicts; 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 is a list of dicts with keys:

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.

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 None.

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

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_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 strings | 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_row_ids (list of strings | numbers; optional):
derived_virtual_selected_row_ids represents the IDs of the
selected_rows as they appear after filtering and sorting, 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.

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 is a dict with strings as keys and values of type dict with
keys:

dropdown_conditional (list of dicts; 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 is a list of dicts with keys:

dropdown_data (list of dicts; 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 is a list of dicts with strings as keys and values of
type dict with keys:

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 is a dict with keys:

export_columns (a value equal to: ‘all’ or ‘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’ or ‘none’; default 'none'):
Denotes the type of the export data file, Defaults to 'none'.

export_headers (a value equal to: ‘none’, ‘ids’, ‘names’ or ‘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.

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 is an a value equal to: ‘custom’, ‘native’ or ‘none’ |
dict with keys:

filter_options (dict; optional):
There are two filter_options props in the table. This is the
table-level filter_options prop and there is also the column-level
filter_options prop. These props determine whether the applicable
filter relational operators will default to sensitive or
insensitive comparison. If the column-level filter_options prop is
set it overrides the table-level filter_options prop for that column.

filter_options is a dict with keys:

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

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.
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 is a dict with keys:

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. 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 is a dict with keys:

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.

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

loading_state (dict; optional):
Object that holds the loading state object coming from dash-renderer.

loading_state is a dict with keys:

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.

locale_format is a dict with keys:

markdown_options (dict; default { link_target: '_blank', html: False}):
The markdown_options property allows customization of the markdown
cells behavior.

markdown_options is a dict with keys:

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’ or ‘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_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_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_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'.

persisted_props (list of values equal to: ‘columns.name’, ‘data’, ‘filter_query’, ‘hidden_columns’, ‘selected_columns’, ‘selected_rows’ or ‘sort_by’; default [ 'columns.name', 'filter_query', 'hidden_columns', 'selected_columns', 'selected_rows', 'sort_by']):
Properties whose user interactions will persist after refreshing the
component or the page.

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.

persistence_type (a value equal to: ‘local’, ‘session’ or ‘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’ or 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 (list of dicts; 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 is a list of dicts with keys:

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 strings | 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'.

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'.

sort_action (a value equal to: ‘custom’, ‘native’ or ‘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
handled 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_as_null (list of strings | numbers | booleans; optional):
An array of string, number and boolean values that are treated as
None (i.e. ignored and always displayed last) when sorting. This
value will be used by columns without sort_as_None. Defaults to [].

sort_by (list of dicts; 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 is a list of dicts with keys:

sort_mode (a value equal to: ‘single’ or ‘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.

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 is a dict with keys:

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.

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_cell_conditional (list of dicts; optional):
Conditional CSS styles for the cells. This can be used to apply styles
to cells on a per-column basis.

style_cell_conditional is a list of dicts with keys:

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_data_conditional (list of dicts; 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 is a list of dicts with keys:

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_filter_conditional (list of dicts; 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 is a list of dicts with keys:

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_header_conditional (list of dicts; 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 is a list of dicts with keys:

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.

tooltip (dict; optional):
tooltip is the column based tooltip configuration applied to all
rows. The key is the column id and the value is a tooltip
configuration. Example: {i: {‘value’: i, ‘use_with: ‘both’} for i in
df.columns}.

tooltip is a dict with strings as keys and values of type string |
dict with keys:

tooltip_conditional (list of dicts; optional):
tooltip_conditional represents the tooltip shown for different
columns and cells. This property allows you to specify different
tooltips 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.

tooltip_conditional is a list of dicts with keys:

tooltip_data (list of dicts; optional):
tooltip_data represents the tooltip shown for different columns and
cells. A list of dicts for which each key is a column id and the value
is a tooltip configuration.

tooltip_data is a list of dicts with strings as keys and values of
type string | dict with keys:

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 None, 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 None, the tooltip will not disappear. Defaults to 2000.

tooltip_header (dict; optional):
tooltip_header represents the tooltip shown for each header column
and optionally each header row. Example to show long column names in a
tooltip: {i: i for i in df.columns}. Example to show different column
names in a tooltip: {‘Rep’: ‘Republican’, ‘Dem’: ‘Democrat’}. If the
table has multiple rows of headers, then use a list as the value of
the tooltip_header items.

tooltip_header is a dict with strings as keys and values of type
string | dict with keys:

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.