diff --git a/src/main/df_loader.py b/src/main/df_loader.py
index 0c8fd0a..48a6942 100644
--- a/src/main/df_loader.py
+++ b/src/main/df_loader.py
@@ -2,7 +2,6 @@ from datetime import date
 
 import numpy as np
 import pandas as pd
-from numpy import ndarray
 from pandas import DataFrame
 
 from src.main.constants import Constants as const
@@ -49,27 +48,33 @@ class DfLoader:
     def __prepare_dataset_status(self) -> None:
         is_univer_mask = ((self.__df['age'] >= const.university_gr_age()) | (self.__df['age'] == const.empty_age())) & \
                          ((self.__df['universities'].str.len() > 0) | (self.__df['occupation_type'] == 'university'))
-        self.__df['is_university'] = np.where(is_univer_mask, True, False)
+        self.__df['is_university'] = np.where(is_univer_mask, 1, 0)
 
         is_work_mask = ((self.__df['age'] > const.school_gr_age()) | (self.__df['age'] == const.empty_age())) & \
-                       ((self.__df['is_university']) | (self.__df['occupation_type'] == 'work')) | \
+                       ((self.__df['is_university'] == 1) | (self.__df['occupation_type'] == 'work')) | \
                        (self.__df['age'] > const.university_gr_age())
-        self.__df['is_work'] = np.where(is_work_mask, True, False)
+        self.__df['is_work'] = np.where(is_work_mask, 1, 0)
 
         is_student_mask = ((self.__df['occupation_type'] == 'university') &
                            ((self.__df['age'] >= const.school_gr_age()) &
                             (self.__df['age'] <= const.university_gr_age())))
-        self.__df['is_student'] = np.where(is_student_mask, True, False)
+        self.__df['is_student'] = np.where(is_student_mask, 1, 0)
 
         is_schoolboy_mask = ((self.__df['age'] < const.school_gr_age()) & (self.__df['age'] != const.empty_age())) | \
                             ((self.__df['age'] == const.empty_age()) & (self.__df['occupation_type'] == 'school'))
-        self.__df['is_schoolboy'] = np.where(is_schoolboy_mask, True, False)
+        self.__df['is_schoolboy'] = np.where(is_schoolboy_mask, 1, 0)
 
     def __prepare_dataset_location(self) -> None:
         self.__geocache.update_geo_cache(self.__df['city'].unique().tolist())
         self.__df['location'] = self.__df['city'] \
             .apply(lambda val: '' if Utils.is_empty_str(val) else self.__geocache.get_location(val))
+        self.__df['location-la'] = self.__df.loc[:, 'location'] \
+            .apply(lambda val: 0 if Utils.is_empty_collection(val) else val[0])
+        self.__df['location-lo'] = self.__df.loc[:, 'location'] \
+            .apply(lambda val: 0 if Utils.is_empty_collection(val) else val[1])
 
-    def get_clustering_data(self) -> ndarray:
-        columns: [] = ['location', 'sex', 'age', 'is_university', 'is_work', 'is_student', 'is_schoolboy']
-        return self.__df[columns].to_numpy()
+    def get_clustering_data(self) -> DataFrame:
+        columns: [] = ['location-la', 'location-lo',
+                       'sex', 'age', 'is_university', 'is_work', 'is_student', 'is_schoolboy']
+        df = self.__df
+        return df[columns]