homework

labs06/task02.py

@@ -1,14 +1,20 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
+import pandas as pd
+from statistics import mode
+import matplotlib.pyplot as plt
 
 def wczytaj_dane():
-    pass
+    data = pd.read_csv('mieszkania.csv')
+    return data
 
 def most_common_room_number(dane):
-    pass
+    return mode(dane.Rooms)
+
 
 def cheapest_flats(dane, n):
-    pass
+    sorted = dane.Expected.sort()
+    return sorted.head(n)
 
 def find_borough(desc):
     dzielnice = ['Stare Miasto',
@@ -19,23 +25,35 @@ def find_borough(desc):
                  'Winogrady',
                  'Miłostowo',
                  'Dębiec']
-    pass
+    for dzielnica in dzielnice:
+        list = desc.split(' ')
+        for element in list:
+            if len(element) > 2 and element == dzielnica:
+                return dzielnica
+                break
+    return "Inne"
+
 
 
 def add_borough(dane):
-    pass
+    dane['Borough'] = dane['Location'].apply(find_borough)
+    return dane
 
 def write_plot(dane, filename):
-    pass
+    plotdata = pd.Series(dane.Location.value_counts())
+    plotdata.plot(x='Location', y='Liczba ogłoszeń', kind='bar')
+    plt.savefig(filename)
+
 
 def mean_price(dane, room_number):
-    pass
+    mean_price = dane.Expected[(dane['Rooms'] == room_number)]
+    return mean_price.mean()
 
 def find_13(dane):
-    pass
+    return dane.Location[(dane['Floor'] == 13)].unique()
 
 def find_best_flats(dane):
-    pass
+    return dane[(dane['Location'] == 'Winogrady') & (dane['Rooms'] == 3) & (dane['Floor'] == 1)]
 
 def main():
     dane = wczytaj_dane()
@@ -45,7 +63,7 @@ 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)))