zad2domowe

labs06/zad2domowe

@@ -0,0 +1,72 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import pandas as pd
+from statistics import mode
+import matplotlib.pyplot as plt
+
+def wczytaj_dane():
+    data = pd.read_csv('mieszkania.csv')
+    return data
+
+def most_common_room_number(dane):
+    return mode(dane.Rooms)
+
+
+def cheapest_flats(dane, n):
+    sorted = dane.Expected.sort()
+    return sorted.head(n)
+
+def find_borough(desc):
+    dzielnice = ['Stare Miasto',
+                 'Wilda',
+                 'Jezyce',
+                 'Rataje',
+                 'Piatkowo',
+                 'Winogrady',
+                 'Milostowo',
+                 'Debiec']
+    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):
+    dane['Borough'] = dane['Location'].apply(find_borough)
+    return dane
+
+def write_plot(dane, filename):
+    plotdata = pd.Series(dane.Location.value_counts())
+    plotdata.plot(x='Location', y='Liczba ogloszen', kind='bar')
+    plt.savefig(filename)
+
+
+def mean_price(dane, room_number):
+    mean_price = dane.Expected[(dane['Rooms'] == room_number)]
+    return mean_price.mean()
+
+def find_13(dane):
+    return dane.Location[(dane['Floor'] == 13)].unique()
+
+def find_best_flats(dane):
+    return dane[(dane['Location'] == 'Winogrady') & (dane['Rooms'] == 3) & (dane['Floor'] == 1)]
+
+def main():
+    dane = wczytaj_dane()
+    print(dane[:5])
+
+    print("Najpopularniejsza liczba pokoi w mieszkaniu to: {}"
+          .format(most_common_room_number(dane)))
+
+    print("{} to najadniejsza dzielnica w Poznaniu."
+          .format(find_borough("Jeżyce i Wilda")))
+
+    print("Średnia cena mieszkania 3-pokojowego, to: {}"
+          .format(mean_price(dane, 3)))
+
+if __name__ == "__main__":
+    main()