class_docstr: Class for detecting text data during its import.
class_docstr_tok: ["Class","for","detecting","text","data","during","its","import","."]
docstr: Column contains text if:
  1. it's not float or float compatible
  (e.g. ['1.2', '2.3', '3.4', ...] is float too)
  2. fraction of unique values (except nans) is more than 0.95
  
  :param column: pandas series with data
  :return: True if column contains text
docstr_tok: ["Column","contains","text","if",":","1.","it","'s","not","float","or","float","compatible","(","e.g",".","[","'","1.2","'",",","'","2.3","'",",","'","3.4","'",",","...","]","is","float","too",")","2.","fraction","of","unique","values","(","except","nans",")","is","more","than","0.95",":","param","column",":","pandas","series","with","data",":","return",":","True","if","column","contains","text"]
code: def _column_contains_text(self, column: pd.Series) -> bool:
      """
      Column contains text if:
      1. it's not float or float compatible
      (e.g. ['1.2', '2.3', '3.4', ...] is float too)
      2. fraction of unique values (except nans) is more than 0.95
  
      :param column: pandas series with data
      :return: True if column contains text
      """
      if column.dtype == object and not self._is_float_compatible(
          column
      ):
          unique_num = len(column.unique())
          nan_num = pd.isna(column).sum()
          return (
              unique_num / len(column) > FRACTION_OF_UNIQUE_VALUES
              if nan_num == 0
              else (unique_num - 1) / (len(column) - nan_num)
              > FRACTION_OF_UNIQUE_VALUES
          )
      return False
  
imports: from abc import abstractmethod
  from typing import List
  import numpy
  import pandas
  from fedot.core.constants import FRACTION_OF_UNIQUE_VALUES
  from fedot.utilities.custom_errors import AbstractMethodNotImplementError
label_desc: Use image node_id 5 for calling the TextDataDetector obj's underlying member method code with example usage: obj._column_contains_text(column) and returns: False, unknown