diff --git a/labs06/task02.py b/labs06/task02.py
index 9d96016..f4b0eb2 100755
--- a/labs06/task02.py
+++ b/labs06/task02.py
@@ -1,14 +1,25 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+import pandas as pd
+
 def wczytaj_dane():
-    pass
+
+    mieszkania = pd.read_csv('C:\Users\mlyska\Documents\python2017\labs06\mieszkania.csv', sep = ',')
+    mieszkania.fillna('', inplace=True)
+    mieszkania['Description'] = mieszkania['Description'] + mieszkania['Unnamed: 7'] + mieszkania['Unnamed: 8'] + \
+                                mieszkania['Unnamed: 9'] + mieszkania['Unnamed: 10'] + mieszkania['Unnamed: 11']
+    mieszkania.drop(['Unnamed: 7','Unnamed: 8','Unnamed: 9','Unnamed: 10','Unnamed: 11'], 1, inplace=True)
+    return mieszkania
 
 def most_common_room_number(dane):
-    pass
+    counter = dane['Rooms'].value_counts()
+    maxCount = counter.to_frame()
+    return maxCount.index.values[0]
 
 def cheapest_flats(dane, n):
-    pass
+    dane.sort_values('Expected')
+    return dane.head(n)
 
 def find_borough(desc):
     dzielnice = ['Stare Miasto',
@@ -19,23 +30,46 @@ def find_borough(desc):
                  'Winogrady',
                  'Miłostowo',
                  'Dębiec']
-    pass
+
+    for i in dzielnice:
+        if i in desc:
+            return i
+            break
+    if desc not in dzielnice:
+        return 'Inne'
+
 
 
 def add_borough(dane):
-    pass
+    values = []
+    for i in dane['Location']:
+        values.append(find_borough(i))
+
+    tempSeries = pd.Series(values)
+    dane['Borough'] = tempSeries.values
 
 def write_plot(dane, filename):
-    pass
+    add_borough(dane)
+    dataPlot = dane['Borough'].value_counts()
+
+    imgPlot = dataPlot.plot(kind='bar', alpha=0.5, title='Liczba ogloszen mieszkan z podzialem na dzielnice', fontsize=6, figsize=(8,7))
+    imgPlot.set_ylabel('Liczba ogloszen')
+    imgPlot.set_xlabel('Dzielnice')
+
+    filePlot = imgPlot.get_figure()
+    filePlot.savefig(filename)
 
 def mean_price(dane, room_number):
-    pass
+    x = dane.loc[dane['Rooms'] == room_number]
+    return x['Expected'].mean()
 
 def find_13(dane):
-    pass
+    x = dane.loc[dane['Floor'] == 13]
+    return list(set(x['Borough'].tolist()))
 
 def find_best_flats(dane):
-    pass
+    best = dane.loc[dane['Borough'] == 'Winogrady' & dane['Rooms'] == 3 & dane['Floor'] == 1]
+    return best
 
 def main():
     dane = wczytaj_dane()
@@ -45,10 +79,12 @@ def main():
           .format(most_common_room_number(dane)))
 
     print("{} to najłądniejsza dzielnica w Poznaniu."
-          .format(find_borough("Grunwald i Jeżyce"))))
+          .format(find_borough("Grunwald i Jeżyce")))
 
     print("Średnia cena mieszkania 3-pokojowego, to: {}"
           .format(mean_price(dane, 3)))
 
+    write_plot(dane, 'ogloszenia.png')
+
 if __name__ == "__main__":
     main()