diff --git a/labs06/task02.py b/labs06/task02.py
index 9d96016..5640f1b 100755
--- a/labs06/task02.py
+++ b/labs06/task02.py
@@ -1,54 +1,77 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-
-def wczytaj_dane():
-    pass
-
-def most_common_room_number(dane):
-    pass
-
-def cheapest_flats(dane, n):
-    pass
-
-def find_borough(desc):
-    dzielnice = ['Stare Miasto',
-                 'Wilda',
-                 'Jeżyce',
-                 'Rataje',
-                 'Piątkowo',
-                 'Winogrady',
-                 'Miłostowo',
-                 'Dębiec']
-    pass
+import pandas as pd
+import matplotlib.pyplot as plt
 
 
-def add_borough(dane):
-    pass
+def wczytaj_dane ( ):
+    return pd.read_csv ( 'mieszkania.csv' )
 
-def write_plot(dane, filename):
-    pass
 
-def mean_price(dane, room_number):
-    pass
+def most_common_room_number ( dane ):
+    return dane[ 'Rooms' ].value_counts ( )
 
-def find_13(dane):
-    pass
 
-def find_best_flats(dane):
-    pass
+def cheapest_flats ( dane , n ):
+    cheapest = dane[ [ 'Expected' , 'Location' ] ].sort_values ( by=[ "Expected" ] , ascending=False )
+    return cheapest[ :n ]
 
-def main():
-    dane = wczytaj_dane()
-    print(dane[:5])
 
-    print("Najpopularniejsza liczba pokoi w mieszkaniu to: {}"
-          .format(most_common_room_number(dane)))
+def find_borough ( desc ):
+    dzielnice = [ 'Stare Miasto' , 'Wilda' , 'Jeżyce' , 'Rataje' , 'Piątkowo' , 'Winogrady' , 'Miłostowo' , 'Dębiec' ]
 
-    print("{} to najłądniejsza dzielnica w Poznaniu."
-          .format(find_borough("Grunwald i Jeżyce"))))
+    descSplit = desc.split ( ' ' )
+    dzielnica = [ ds for ds in descSplit if ds in dzielnice ]
+    return dzielnica[ 0 ] if len ( dzielnica ) > 0  else 'Inne'
+
+
+def add_borough ( dane ):
+    dane[ 'Borough' ] = dane.apply ( lambda row: find_borough ( row[ 'Location' ] ) , axis=1 )
+    return dane
+
+
+def write_plot ( dane , filename ):
+    dane2 = add_borough ( dane )
+    do_plota = dane2[ 'Borough' ].value_counts ( )
+    do_plota.plot ( kind='bar' )
+    # plt.show()
+    plt.savefig ( filename )
+
+
+def mean_price ( dane , room_number ):
+    pokoje = dane[ dane[ 'Rooms' ] == room_number ]
+    return pokoje[ 'Expected' ].mean ( )
+
+
+def find_13 ( dane ):
+    dane2 = add_borough ( dane )
+    pietro = dane2[ dane2[ 'Floor' ] == 13 ]
+    return ' '.join ( set ( pietro[ 'Borough' ].tolist ( ) ) )
+
+
+def find_best_flats ( dane ):
+    dane2 = add_borough ( dane )
+    isWinogrady = dane2[ 'Borough' ] == 'Winogrady'
+    isPierwsze = dane2[ 'Floor' ] == 1
+    isTrzypokojowe = dane2[ 'Rooms' ] == 3
+    best = dane2[ isWinogrady & isPierwsze & isTrzypokojowe ]
+    return best
+
+
+def main ( ):
+    dane = wczytaj_dane ( )
+    print(dane[ :5 ])
+
+    print("Najpopularniejsza liczba pokoi w mieszkaniu to: {}".format ( most_common_room_number ( dane ) ))
+
+    print("{} to najłądniejsza dzielnica w Poznaniu.".format ( find_borough ( "Grunwald i Jeżyce" ) ))
+
+    print("Średnia cena mieszkania 3-pokojowego, to: {:.2f}".format ( mean_price ( dane , 3 ) ))
+
+    print("Dzielnice z ofertami na 13 pietrze: {}".format ( find_13 ( dane ) ))
+
+    print("Najlepsze oferty: {}".format ( find_best_flats ( dane ) ))
 
-    print("Średnia cena mieszkania 3-pokojowego, to: {}"
-          .format(mean_price(dane, 3)))
 
 if __name__ == "__main__":
-    main()
+    main ( )
\ No newline at end of file