def column_map_expectation(cls, func):
        """For SqlAlchemy, this decorator allows individual column_map_expectations to simply return the filter
        that describes the expected condition on their data.

        The decorator will then use that filter to obtain unexpected elements, relevant counts, and return the formatted
        object.
        """
        if PY3:
            argspec = inspect.getfullargspec(func)[0][1:]
        else:
            argspec = inspect.getargspec(func)[0][1:]

        @cls.expectation(argspec)
        @wraps(func)
        def inner_wrapper(self, column, mostly=None, result_format=None, *args, **kwargs):
            if result_format is None:
                result_format = self.default_expectation_args["result_format"]

            result_format = parse_result_format(result_format)

            if result_format['result_format'] == 'COMPLETE':
                warnings.warn("Setting result format to COMPLETE for a SqlAlchemyDataset can be dangerous because it will not limit the number of returned results.")
                unexpected_count_limit = None
            else:
                unexpected_count_limit = result_format['partial_unexpected_count']

            expected_condition = func(self, column, *args, **kwargs)

            # FIXME Temporary Fix for counting missing values
            # Added to compensate when an ignore_values argument is added to the expectation
            ignore_values = [None]
            if func.__name__ in ['expect_column_values_to_not_be_null', 'expect_column_values_to_be_null']:
                ignore_values = []
                result_format['partial_unexpected_count'] = 0  # Optimization to avoid meaningless computation for these expectations

            count_query = sa.select([
                sa.func.count().label('element_count'),
                sa.func.sum(
                    sa.case([(sa.or_(
                        sa.column(column).in_(ignore_values),
                        # Below is necessary b/c sa.in_() uses `==` but None != None
                        # But we only consider this if None is actually in the list of ignore values
                        sa.column(column).is_(None) if None in ignore_values else False), 1)], else_=0)
                ).label('null_count'),
                sa.func.sum(
                    sa.case([
                        (
                            sa.and_(
                                sa.not_(expected_condition),
                                sa.case([
                                    (
                                        sa.column(column).is_(None),
                                        False
                                    )
                                ], else_=True) if None in ignore_values else True
                            ),
                            1
                        )
                    ], else_=0)
                ).label('unexpected_count')
            ]).select_from(self._table)

            count_results = dict(self.engine.execute(count_query).fetchone())

            # Handle case of empty table gracefully:
            if "element_count" not in count_results or count_results["element_count"] is None:
                count_results["element_count"] = 0
            if "null_count" not in count_results or count_results["null_count"] is None:
                count_results["null_count"] = 0
            if "unexpected_count" not in count_results or count_results["unexpected_count"] is None:
                count_results["unexpected_count"] = 0

            # Retrieve unexpected values
            unexpected_query_results = self.engine.execute(
                sa.select([sa.column(column)]).select_from(self._table).where(
                    sa.and_(sa.not_(expected_condition),
                            sa.or_(
                                # SA normally evaluates `== None` as `IS NONE`. However `sa.in_()`
                                # replaces `None` as `NULL` in the list and incorrectly uses `== NULL`
                                sa.case([
                                    (
                                        sa.column(column).is_(None),
                                        False
                                    )
                                ], else_=True) if None in ignore_values else False,
                                # Ignore any other values that are in the ignore list
                                sa.column(column).in_(ignore_values) == False))
                ).limit(unexpected_count_limit)
            )

            nonnull_count = count_results['element_count'] - \
                count_results['null_count']

            if "output_strftime_format" in kwargs:
                output_strftime_format = kwargs["output_strftime_format"]
                maybe_limited_unexpected_list = []
                for x in unexpected_query_results.fetchall():
                    if isinstance(x[column], string_types):
                        col = parse(x[column])
                    else:
                        col = x[column]
                    maybe_limited_unexpected_list.append(datetime.strftime(col, output_strftime_format))
            else:
                maybe_limited_unexpected_list = [x[column]
                                             for x in unexpected_query_results.fetchall()]

            success_count = nonnull_count - count_results['unexpected_count']
            success, percent_success = self._calc_map_expectation_success(
                success_count, nonnull_count, mostly)

            return_obj = self._format_map_output(
                result_format, success,
                count_results['element_count'], nonnull_count,
                count_results['unexpected_count'],
                maybe_limited_unexpected_list, None
            )

            if func.__name__ in ['expect_column_values_to_not_be_null', 'expect_column_values_to_be_null']:
                # These results are unnecessary for the above expectations
                del return_obj['result']['unexpected_percent_nonmissing']
                try:
                    del return_obj['result']['partial_unexpected_counts']
                except KeyError:
                    pass

            return return_obj

        inner_wrapper.__name__ = func.__name__
        inner_wrapper.__doc__ = func.__doc__

        return inner_wrapper
For SqlAlchemy, this decorator allows individual column_map_expectations to simply return the filter
        that describes the expected condition on their data.

        The decorator will then use that filter to obtain unexpected elements, relevant counts, and return the formatted
        object.