Finding frequent items for columns, possibly with false positives. Using the
        frequent element count algorithm described in
        "https://doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou".
        :func:`DataFrame.freqItems` and :func:`DataFrameStatFunctions.freqItems` are aliases.

        .. note:: This function is meant for exploratory data analysis, as we make no
            guarantee about the backward compatibility of the schema of the resulting DataFrame.

        :param cols: Names of the columns to calculate frequent items for as a list or tuple of
            strings.
        :param support: The frequency with which to consider an item 'frequent'. Default is 1%.
            The support must be greater than 1e-4.

def freqItems(self, cols, support=None):
        """
        Finding frequent items for columns, possibly with false positives. Using the
        frequent element count algorithm described in
        "https://doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou".
        :func:`DataFrame.freqItems` and :func:`DataFrameStatFunctions.freqItems` are aliases.

        .. note:: This function is meant for exploratory data analysis, as we make no
            guarantee about the backward compatibility of the schema of the resulting DataFrame.

        :param cols: Names of the columns to calculate frequent items for as a list or tuple of
            strings.
        :param support: The frequency with which to consider an item 'frequent'. Default is 1%.
            The support must be greater than 1e-4.
        """
        if isinstance(cols, tuple):
            cols = list(cols)
        if not isinstance(cols, list):
            raise ValueError("cols must be a list or tuple of column names as strings.")
        if not support:
            support = 0.01
        return DataFrame(self._jdf.stat().freqItems(_to_seq(self._sc, cols), support), self.sql_ctx)