commited tasks

labs06/task02.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 import pandas as pd
+import numpy as np
 
 def wczytaj_dane():
     rooms_data = pd.read_csv('mieszkania.csv',  # ścieżka  do pliku
@@ -12,12 +13,21 @@ def wczytaj_dane():
 def most_common_room_number(dane):
     d=dane['Rooms']
     d= d.value_counts()
-    print d[]
+    j=0
-    pass
+    """
+    for i in d:
+        print d.index[j] , i
+        j +=1
+    """
+    d = d.index[0]
+    return d
 
 
 def cheapest_flats(dane, n):
-    pass
+    SortDane = dane.sort_values('Expected',ascending=True)
+    PriceCheapest = SortDane['Expected']
+    PriceCheapest = PriceCheapest.head(n)
+    return PriceCheapest
 
 def find_borough(desc):
     dzielnice = ['Stare Miasto',
@@ -28,7 +38,9 @@ def find_borough(desc):
                  'Winogrady',
                  'Miłostowo',
                  'Dębiec']
-    pass
+    for district in dzielnice:
+        if district in desc: return district
+    return 'Inne'
 
 
 def add_borough(dane):
@@ -38,7 +50,10 @@ def write_plot(dane, filename):
     pass
 
 def mean_price(dane, room_number):
-    pass
+    AVGdane = dane[dane.Rooms == room_number]
+    AVGdane = round(AVGdane.Expected.mean(),2)
+    return AVGdane
+
 
 def find_13(dane):
     pass
@@ -60,5 +75,10 @@ def main():
     print("Średnia cena mieszkania 3-pokojowego, to: {}"
           .format(mean_price(dane, 3)))
 
+    roomCheapest = cheapest_flats(dane, 5)
+    #print roomCheapest
+
+    
+
 if __name__ == "__main__":
     main()