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 _check_mixed_percentage(self, column_data: pd.Series) -> dict:
      total_rows = column_data.count()
  
      numbers_in_col = set()
      strings_in_col = set()
  
      def is_float(x) -> bool:
          try:
              float(x)
              if len(numbers_in_col) < 3:
                  numbers_in_col.add(x)
              return True
          except ValueError:
              if len(strings_in_col) < 3:
                  strings_in_col.add(x)
              return False
  
      nums = sum(column_data.apply(is_float))
      if nums in (total_rows, 0):
          return {}
  
      # Then we've got a mix
      nums_pct = nums / total_rows
      strs_pct = (np.abs(nums - total_rows)) / total_rows
  
      return {
          "strings": strs_pct,
          "numbers": nums_pct,
          "strings_examples": strings_in_col,
          "numbers_examples": numbers_in_col,
      }
  
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 4 for calling the MixedDataTypes obj's underlying member method code with example usage: obj._check_mixed_percentage(column_data) and returns: dict, dict, True, False