code: def test_condition_pass_fail_ignore_column():
      # Arrange
      data = {"col1": ["1", "bar", "cat"], "col2": [6, 66, 666.66]}
      dataframe = pd.DataFrame(data=data)
      check = MixedDataTypes(
          ignore_columns=["col2"]
      ).add_condition_rare_type_ratio_not_in_range((0.01, 0.4))
      # Act
      result = check.conditions_decision(check.run(dataframe))
      # Assert
      assert_that(
          result,
          has_items(
              equal_condition_result(
                  is_pass=False,
                  name="Rare data types in column are either more than 40% or"
                  " less than 1% of the data",
                  details="Found 1 out of 1 columns with non-negligible quantities of samples with a "
                  "different data type from the majority of samples: ['col1']",
                  category=ConditionCategory.WARN,
              )
          ),
      )
  
imports: import numpy
  import pandas
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_entries, has_entry, has_items, has_length, raises
  from deepchecks.core import ConditionCategory
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.tabular.checks.data_integrity.mixed_data_types import MixedDataTypes
  from deepchecks.tabular.dataset import Dataset
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 12 for calling a global function with example usage: test_condition_pass_fail_ignore_column() without return types