Look for error keyword arguments and return the actual errorbar data
        or return the error DataFrame/dict

        Error bars can be specified in several ways:
            Series: the user provides a pandas.Series object of the same
                    length as the data
            ndarray: provides a np.ndarray of the same length as the data
            DataFrame/dict: error values are paired with keys matching the
                    key in the plotted DataFrame
            str: the name of the column within the plotted DataFrame

def _parse_errorbars(self, label, err):
        """
        Look for error keyword arguments and return the actual errorbar data
        or return the error DataFrame/dict

        Error bars can be specified in several ways:
            Series: the user provides a pandas.Series object of the same
                    length as the data
            ndarray: provides a np.ndarray of the same length as the data
            DataFrame/dict: error values are paired with keys matching the
                    key in the plotted DataFrame
            str: the name of the column within the plotted DataFrame
        """

        if err is None:
            return None

        def match_labels(data, e):
            e = e.reindex(data.index)
            return e

        # key-matched DataFrame
        if isinstance(err, ABCDataFrame):

            err = match_labels(self.data, err)
        # key-matched dict
        elif isinstance(err, dict):
            pass

        # Series of error values
        elif isinstance(err, ABCSeries):
            # broadcast error series across data
            err = match_labels(self.data, err)
            err = np.atleast_2d(err)
            err = np.tile(err, (self.nseries, 1))

        # errors are a column in the dataframe
        elif isinstance(err, str):
            evalues = self.data[err].values
            self.data = self.data[self.data.columns.drop(err)]
            err = np.atleast_2d(evalues)
            err = np.tile(err, (self.nseries, 1))

        elif is_list_like(err):
            if is_iterator(err):
                err = np.atleast_2d(list(err))
            else:
                # raw error values
                err = np.atleast_2d(err)

            err_shape = err.shape

            # asymmetrical error bars
            if err.ndim == 3:
                if (err_shape[0] != self.nseries) or \
                        (err_shape[1] != 2) or \
                        (err_shape[2] != len(self.data)):
                    msg = "Asymmetrical error bars should be provided " + \
                        "with the shape (%u, 2, %u)" % \
                        (self.nseries, len(self.data))
                    raise ValueError(msg)

            # broadcast errors to each data series
            if len(err) == 1:
                err = np.tile(err, (self.nseries, 1))

        elif is_number(err):
            err = np.tile([err], (self.nseries, len(self.data)))

        else:
            msg = "No valid {label} detected".format(label=label)
            raise ValueError(msg)

        return err