Task02 - completed

labs06/task02.py

@@ -1,14 +1,15 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-
+import pandas as pd
 def wczytaj_dane():
-    pass
+    mieszkania = pd.read_csv('mieszkania.csv',sep=',',encoding='UTF-8',usecols=[0,1,2,3,4,5,6])
+    return mieszkania
 
 def most_common_room_number(dane):
-    pass
+    return dane.mode(numeric_only =True)["Rooms"][0]
 
 def cheapest_flats(dane, n):
-    pass
+    return dane.sort_values("Expected")[:n]
 
 def find_borough(desc):
     dzielnice = ['Stare Miasto',
@@ -19,23 +20,31 @@ def find_borough(desc):
                  'Winogrady',
                  'Miłostowo',
                  'Dębiec']
-    pass
-
+    inputList=desc.split(' ')
+    for i in inputList:
+        if i in dzielnice:
+            return i
+            break
+    return "Inne"
 
 def add_borough(dane):
-    pass
+    newcol=dane["Location"].apply(find_borough)
+    dane["Borough"]=newcol
+    return dane
 
 def write_plot(dane, filename):
-    pass
+    bar=dane["Borough"].value_counts().plot(kind="bar", figsize=(6,6))
+    fig=bar.get_figure()
+    fig.savefig(filename)
 
 def mean_price(dane, room_number):
-    pass
+    return dane[dane["Rooms"]==room_number]["Expected"].mean()
 
 def find_13(dane):
-    pass
+    return dane[dane["Floor"]==13]["Borough"].unique()
 
 def find_best_flats(dane):
-    pass
+    return dane[(dane["Borough"]=="Winogrady") & (dane["Floor"]==1) & (dane["Rooms"]==3)]
 
 def main():
     dane = wczytaj_dane()
@@ -45,7 +54,7 @@ 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)))