def keypairs(cls, data, columns=None, use_index=False, name=None):
        """This will format the data as Key: Value pairs, rather than the
        idx/col/val style. This is useful for some transforms, and to
        key choropleth map data

        Standard Data Types:
            List: [0, 10, 20, 30, 40]
            Paired Tuples: ((0, 1), (0, 2), (0, 3))
            Dict: {'A': 10, 'B': 20, 'C': 30, 'D': 40, 'E': 50}

        Plus Pandas DataFrame and Series, and Numpy ndarray

        Parameters
        ----------
        data:
            List, Tuple, Dict, Pandas Series/DataFrame, Numpy ndarray
        columns: list, default None
            If passing Pandas DataFrame, you must pass at least one column
            name.If one column is passed, x-values will default to the index
            values.If two column names are passed, x-values are columns[0],
            y-values columns[1].
        use_index: boolean, default False
            Use the DataFrame index for your x-values

        """
        if not name:
            name = 'table'
        cls.raw_data = data

        # Tuples
        if isinstance(data, tuple):
            values = [{"x": x[0], "y": x[1]} for x in data]

        # Lists
        elif isinstance(data, list):
            values = [{"x": x, "y": y}
                      for x, y in zip(range(len(data) + 1), data)]

        # Dicts
        elif isinstance(data, dict) or isinstance(data, pd.Series):
            values = [{"x": x, "y": y} for x, y in sorted(data.items())]

        # Dataframes
        elif isinstance(data, pd.DataFrame):
            if len(columns) > 1 and use_index:
                raise ValueError('If using index as x-axis, len(columns)'
                                 'cannot be > 1')
            if use_index or len(columns) == 1:
                values = [{"x": cls.serialize(x[0]),
                           "y": cls.serialize(x[1][columns[0]])}
                          for x in data.iterrows()]
            else:

                values = [{"x": cls.serialize(x[1][columns[0]]),
                           "y": cls.serialize(x[1][columns[1]])}
                          for x in data.iterrows()]

        # NumPy arrays
        elif isinstance(data, np.ndarray):
            values = cls._numpy_to_values(data)
        else:
            raise TypeError('unknown data type %s' % type(data))

        return cls(name, values=values)