Returns a new :class:`DataFrame` omitting rows with null values.
        :func:`DataFrame.dropna` and :func:`DataFrameNaFunctions.drop` are aliases of each other.

        :param how: 'any' or 'all'.
            If 'any', drop a row if it contains any nulls.
            If 'all', drop a row only if all its values are null.
        :param thresh: int, default None
            If specified, drop rows that have less than `thresh` non-null values.
            This overwrites the `how` parameter.
        :param subset: optional list of column names to consider.

        >>> df4.na.drop().show()
        +---+------+-----+
        |age|height| name|
        +---+------+-----+
        | 10|    80|Alice|
        +---+------+-----+

def dropna(self, how='any', thresh=None, subset=None):
        """Returns a new :class:`DataFrame` omitting rows with null values.
        :func:`DataFrame.dropna` and :func:`DataFrameNaFunctions.drop` are aliases of each other.

        :param how: 'any' or 'all'.
            If 'any', drop a row if it contains any nulls.
            If 'all', drop a row only if all its values are null.
        :param thresh: int, default None
            If specified, drop rows that have less than `thresh` non-null values.
            This overwrites the `how` parameter.
        :param subset: optional list of column names to consider.

        >>> df4.na.drop().show()
        +---+------+-----+
        |age|height| name|
        +---+------+-----+
        | 10|    80|Alice|
        +---+------+-----+
        """
        if how is not None and how not in ['any', 'all']:
            raise ValueError("how ('" + how + "') should be 'any' or 'all'")

        if subset is None:
            subset = self.columns
        elif isinstance(subset, basestring):
            subset = [subset]
        elif not isinstance(subset, (list, tuple)):
            raise ValueError("subset should be a list or tuple of column names")

        if thresh is None:
            thresh = len(subset) if how == 'any' else 1

        return DataFrame(self._jdf.na().drop(thresh, self._jseq(subset)), self.sql_ctx)