Class DataTable

dash/dash_table/DataTable.py:24–1803

A DataTable component. Dash DataTable is an interactive table component designed for viewing, editing, and exploring large datasets. DataTable is rendered with standard, semantic HTML markup, which makes it accessible, responsive, and easy to style. This component was wr

class DataTable(Component):
    """A DataTable component.
    Dash DataTable is an interactive table component designed for
    viewing, editing, and exploring large datasets.
    DataTable is rendered with standard, semantic HTML <table/> markup,
    which makes it accessible, responsive, and easy to style. This
    component was written from scratch in React.js specifically for the
    Dash community. Its API was designed to be ergonomic and its behavior
    is completely customizable through its properties.

    Keyword arguments:

    - data (list of dicts with strings as keys and values of type string | number | boolean; 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'} ].

    - 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:

        - 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.

        - type (a value equal to: 'any', 'numeric', 'text', 'datetime'; optional):
            The data-type provides support for per column typing and
            allows for data validation and coercion. '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.

        - presentation (a value equal to: 'input', 'dropdown', 'markdown'; optional):
            The `presentation` to use to display data. Markdown can be
            used on columns with type 'text'.  See 'dropdown' for more