def METHOD_NAME(self, ratio_range: Tuple[float, float] = (0.01, 0.1)):
    """Add condition - Whether the ratio of rarer data type (strings or numbers) is not in the "danger zone".
    The "danger zone" represents the following logic - if the rarer data type is, for example, 30% of the data,
    than the column is presumably supposed to contain both numbers and string values. If the rarer data type is,
    for example, less than 1% of the data, than it's presumably a contamination, but a negligible one. In the range
    between, there is a real chance that the rarer data type may represent a problem to model training and
    inference.
    Parameters
    ----------
    ratio_range : Tuple[float, float] , default: (0.01 , 0.1)
        The range between which the ratio of rarer data type in the column is
        considered a problem.
    """
    def condition(result):
        no_mix_columns = []
        failing_columns = []
        for col, ratios in result.items():
            # Columns without a mix contains empty dict for ratios
            if not ratios:
                no_mix_columns.append(col)
                continue
            rarer_ratio = min(ratios['strings'], ratios['numbers'])
            if ratio_range[0] < rarer_ratio < ratio_range[1]:
                failing_columns.append(col)
        if failing_columns:
            details = f'Found {len(failing_columns)} out of {len(result)} columns with non-negligible quantities ' \
                      f'of samples with a different data type from the majority of samples: {failing_columns}'
            return ConditionResult(ConditionCategory.WARN, details)
        details = f'{len(result)} columns passed: found {len(result) - len(no_mix_columns)} columns with ' \
                  f'negligible types mix, and {len(no_mix_columns)} columns without any types mix'
        return ConditionResult(ConditionCategory.PASS, details)
    name = f'Rare data types in column are either more than {format_percent(ratio_range[1])} or less ' \
           f'than {format_percent(ratio_range[0])} of the data'
    return self.add_condition(name, condition)