class_docstr: Class for categories features preprocessing: converting binary string features into integers. 
class_docstr_tok: ["Class","for","categories","features","preprocessing",":","converting","binary","string","features","into","integers","."]
docstr: Applies already fitted encoders to all binary features inplace
  
  Args:
      data: numpy array with all features
docstr_tok: ["Applies","already","fitted","encoders","to","all","binary","features","inplace","Args",":","data",":","numpy","array","with","all","features"]
code: def _apply_encoder(self, data: np.ndarray):
      """
      Applies already fitted encoders to all binary features inplace
  
      Args:
          data: numpy array with all features
      """
      binary_columns = data[:, self.binary_ids_to_convert]
      for column_id, column in zip(
          self.binary_ids_to_convert, binary_columns.T
      ):
          encoder = self.binary_encoders[column_id]
          nan_idxs = np.flatnonzero(pd.isna(column))
          column[nan_idxs] = FEDOT_STR_NAN
          # Extend encoder classes if the column contains categories not previously encountered
          encoder.classes_ = np.unique(
              np.concatenate((encoder.classes_, column))
          )
  
          converted = encoder.transform(column)
          if len(nan_idxs):
              # Column has nans in its structure - replace them after conversion
              converted = converted.astype(float)
              converted[nan_idxs] = np.nan
          data[:, column_id] = converted
  
file_path: FEDOT/fedot/preprocessing/categorical.py
imports: from copy import deepcopy
  import numpy
  import pandas
  from sklearn.preprocessing import LabelEncoder
  from fedot.core.data.data import InputData
  from fedot.core.data.data_preprocessing import find_categorical_columns
  from fedot.preprocessing.data_types import FEDOT_STR_NAN, TYPE_TO_ID
label_desc: Use image node_id 6 for calling the BinaryCategoricalPreprocessor obj's underlying member method code with example usage: obj._apply_encoder(data) without return types