class_docstr: Detect columns which contain a mix of numerical and string values.
  
  Parameters
  ----------
  columns : Union[Hashable, List[Hashable]] , default: None
      Columns to check, if none are given checks all columns
      except ignored ones.
  ignore_columns : Union[Hashable, List[Hashable]] , default: None
      Columns to ignore, if none given checks based on columns
      variable.
  n_top_columns : int , optional
      amount of columns to show ordered by feature importance (date, index, label are first)
  n_samples : int , default: 10_000_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
code: def _get_data_mix(self, column_data: pd.Series) -> dict:
      if is_string_column(column_data):
          return self._check_mixed_percentage(column_data)
      return {}
  
file_path: deepchecks/deepchecks/tabular/checks/data_integrity/mixed_data_types.py
imports: from typing import List, Tuple, Union
  import numpy
  import pandas
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.tabular.utils.feature_importance import N_TOP_MESSAGE, column_importance_sorter_df
  from deepchecks.utils.dataframes import select_from_dataframe
  from deepchecks.utils.strings import format_list, format_number, format_percent, is_string_column, truncate_string
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 3 for calling the MixedDataTypes obj's underlying member method code with example usage: obj._get_data_mix(column_data) and returns: dict, self