diff --git a/labs06/task02.py b/labs06/task02.py
index 9d96016..7020153 100755
--- a/labs06/task02.py
+++ b/labs06/task02.py
@@ -1,15 +1,35 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
+import os;
+os.chdir('J:\PycharmProjects\Python2017\labs06')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.style.use('ggplot')
+plt.rcParams['figure.figsize'] = (15, 5)
 
 def wczytaj_dane():
+    dane = pd.read_csv('mieszkania.csv', sep=',')
+    dane.rename(columns={'Unnamed: 11': 'Borough'}, inplace=True)
+    #type(dane)
+    #print(dane.head())
+    return(dane)
     pass
 
+
 def most_common_room_number(dane):
+    m = dane.groupby(['Rooms']).count()
+    return(m['Id'].idxmax())
     pass
 
+
 def cheapest_flats(dane, n):
+    dane2 = dane.sort_values(by=['Expected'])
+    return(dane2.head(n))
     pass
 
+
 def find_borough(desc):
     dzielnice = ['Stare Miasto',
                  'Wilda',
@@ -19,24 +39,54 @@ def find_borough(desc):
                  'Winogrady',
                  'Miłostowo',
                  'Dębiec']
+    tekst = desc.split(" ")
+    for i in range(len(tekst)):
+        for j in range(len(dzielnice)):
+            if tekst[i] == dzielnice[j]:
+                return(dzielnice[j])
+            else:
+                j = j + 1
+        i = i + 1
+    return('Inne')
     pass
 
 
 def add_borough(dane):
+    for i in range(5000):#len(dane.axes[0])):
+        dane['Borough'][i] = find_borough(dane['Location'][i])
+
     pass
 
+
 def write_plot(dane, filename):
+    my_plot = dane['Borough'].hist()
+    fig = my_plot.get_figure()
+
+    dir_name = 'J:/PycharmProjects/Python2017/labs06/'
+    filename_suffix = 'pdf'
+    fig.savefig(os.path.join(dir_name, filename + "." + filename_suffix))
     pass
 
+
 def mean_price(dane, room_number):
+    m = dane.groupby(['Rooms']).mean()
+    return(m['Expected'][room_number])
     pass
 
+
 def find_13(dane):
+    df_filtered = dane[dane['Floor'] == 13]
+    return(df_filtered['Borough'])
     pass
 
+
 def find_best_flats(dane):
+    df_filtered = dane[dane['Floor'] == 1]
+    df_filtered2=df_filtered[df_filtered['Borough']=='Winogrady']
+    df_filtered3 = df_filtered2[df_filtered2['Rooms'] == 3]
     pass
 
+
 def main():
     dane = wczytaj_dane()
     print(dane[:5])
@@ -45,7 +95,7 @@ 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)))