class_docstr: Class for applying imputation on tabular data
  
  Args:
      params: OperationParameters with the arguments
class_docstr_tok: ["Class","for","applying","imputation","on","tabular","data","Args",":","params",":","OperationParameters","with","the","arguments"]
docstr: Find indices of features with only two unique values in column
  
  Notes:
      All features in table are numerical
docstr_tok: ["Find","indices","of","features","with","only","two","unique","values","in","column","Notes",":","All","features","in","table","are","numerical"]
code: def _find_binary_features(self, numerical_features: np.array):
      """Find indices of features with only two unique values in column
  
      Notes:
          All features in table are numerical
      """
  
      df = pd.DataFrame(numerical_features)
  
      # Calculate unique values per column (excluding nans)
      for column_id, col in enumerate(df):
          unique_values = df[col].dropna().unique()
          if len(unique_values) == 2:
              # Current numerical column has only two values
              column_info = {
                  column_id: {
                      "min": min(unique_values),
                      "max": max(unique_values),
                  }
              }
              self.ids_binary_integer_features.update(column_info)
  
imports: import random
  from typing import Optional
  import numpy
  import pandas
  from sklearn.decomposition import FastICA, KernelPCA, PCA
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import MinMaxScaler, PolynomialFeatures, StandardScaler
  from fedot.core.data.data import InputData, OutputData, data_type_is_table
  from fedot.core.data.data_preprocessing import convert_into_column, data_has_categorical_features, divide_data_categorical_numerical, find_categorical_columns, replace_inf_with_nans
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import DataOperationImplementation, EncodedInvariantImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.preprocessing.data_types import TYPE_TO_ID
label_desc: Use image node_id 6 for calling the ImputationImplementation obj's underlying member method code with example usage: obj._find_binary_features(numerical_features) without return types