From 49fe26305c12b5a52fed3bd88285c5dca44105d5 Mon Sep 17 00:00:00 2001
From: "wagner.agnieszka" <wagner.agnieszka@gmail.com>
Date: Fri, 22 Jun 2018 22:38:06 +0200
Subject: [PATCH] passed

---
 labs06/task02.py | 73 ++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 58 insertions(+), 15 deletions(-)

diff --git a/labs06/task02.py b/labs06/task02.py
index 9eb863f..5210c0d 100755
--- a/labs06/task02.py
+++ b/labs06/task02.py
@@ -1,14 +1,15 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
+import pandas as pd
+import matplotlib.pyplot as plt
 
 def wczytaj_dane():
-    import pandas as pd
-    dane = pd.read_csv("labs06/mieszkania.csv")
-    dane.head()
-
+    dane = pd.read_csv("mieszkania.csv")
+    print(dane.head())
+    return(dane)
 
 def most_common_room_number(dane):
-    dane['Rooms'].value_counts().idxmax()
+    return(dane['Rooms'].value_counts().idxmax())
 
 
 
@@ -24,20 +25,61 @@ def find_borough(desc):
                  'Piątkowo',
                  'Winogrady',
                  'Miłostowo',
-                 'Dębiec']
-    pass
+                 'Dębiec',
+                 'Grunwald',
+                 'Nowe Miasto']
+
+
+    check = 0
+    for dzielnica in dzielnice:
+        if dzielnica in desc:
+            check = 1
+            save_dzielnica = dzielnica
+    if check == 1:
+        return(save_dzielnica)
+    else:
+        return("Inne")
+
 
 
 def add_borough(dane):
-    pass
+    dzielnice = ['Stare Miasto',
+                     'Wilda',
+                     'Jeżyce',
+                     'Rataje',
+                     'Piątkowo',
+                     'Winogrady',
+                     'Miłostowo',
+                     'Dębiec',
+                     'Grunwald',
+                     'Nowe Miasto']
+    Borough = []
+    column = dane['Location']
+    for item in column:
+        check = 0
+        for dzielnica in dzielnice:
+            if dzielnica in item:
+                check = 1
+                save_dzielnica = dzielnica
+        if check == 1:
+            Borough.append(save_dzielnica)
+        else:
+            Borough.append("Inne")
+
+    Borough = pd.DataFrame(Borough)
+
+    dane = pd.concat([dane.reset_index(drop=True), Borough], axis=1)
+    print(dane)
 
 def write_plot(dane, filename):
-    pass
+    dane.groupby('Borough')['Id'].nunique().plot(kind='bar')
+    plt.show()
+    plt.savefig('output.png')
 
 def mean_price(dane, room_number):
-    p1 = dane[dane['Rooms'] == 2]
+    p1 = dane[dane['Rooms'] == room_number]
     p2 = p1['Expected']
-    p2.mean()
+    return(p2.mean())
 
 def find_13(dane):
     p1 = dane[dane['Floor'] == 13]
@@ -46,17 +88,18 @@ def find_13(dane):
 def find_best_flats(dane):
     p_index = dane['Location'].str.contains('Winogrady')
     p = dane[p_index]
-    p[(p['Rooms'] == 3) & (p['Floor'] == 1)]
+    best_flats = p[(p['Rooms'] == 3) & (p['Floor'] == 1)]
+    print(best_flats)
 
 def main():
     dane = wczytaj_dane()
-    print(dane[:5])
+
 
     print("Najpopularniejsza liczba pokoi w mieszkaniu to: {}"
           .format(most_common_room_number(dane)))
 
-    print("{} to najłądniejsza dzielnica w Poznaniu."
-          .format(find_borough("Grunwald i Jeżyce"))))
+    print("{} to najładniejsza dzielnica w Poznaniu."
+          .format(find_borough("Grunwald i Jeżyce")))
 
     print("Średnia cena mieszkania 3-pokojowego, to: {}"
           .format(mean_price(dane, 3)))