def factor_mark(field_name, markers, factors, start=0, end=None):
    ''' Create a ``DataSpec`` dict that applies a client-side
    ``CategoricalMarkerMapper`` transformation to a ``ColumnDataSource``
    column.

    .. note::
        This transform is primarily only useful with ``scatter``, which
        can be parameterized by glyph type.

    Args:
        field_name (str) : a field name to configure ``DataSpec`` with

        markers (seq[string]) : a list of markers to use to map to

        factors (seq) : a sequences of categorical factors corresponding to
            the palette

        start (int, optional) : a start slice index to apply when the column
            data has factors with multiple levels. (default: 0)

        end (int, optional) : an end slice index to apply when the column
            data has factors with multiple levels. (default: None)

    Returns:
        dict

    '''
    return field(field_name, CategoricalMarkerMapper(markers=markers,
                                                     factors=factors,
                                                     start=start,
                                                     end=end))
Create a ``DataSpec`` dict that applies a client-side
    ``CategoricalMarkerMapper`` transformation to a ``ColumnDataSource``
    column.

    .. note::
        This transform is primarily only useful with ``scatter``, which
        can be parameterized by glyph type.

    Args:
        field_name (str) : a field name to configure ``DataSpec`` with

        markers (seq[string]) : a list of markers to use to map to

        factors (seq) : a sequences of categorical factors corresponding to
            the palette

        start (int, optional) : a start slice index to apply when the column
            data has factors with multiple levels. (default: 0)

        end (int, optional) : an end slice index to apply when the column
            data has factors with multiple levels. (default: None)

    Returns:
        dict