From 4d03009ff65f84fed73809d54667bac83cfda94d Mon Sep 17 00:00:00 2001
From: s411752 <agnlac@st.amu.edu.pl>
Date: Wed, 6 Jun 2018 16:41:25 +0200
Subject: [PATCH] laboratoria4

---
 labs06/task02.py | 62 +++++++++++++++++++++++++++++++++++++++---------
 1 file changed, 51 insertions(+), 11 deletions(-)

diff --git a/labs06/task02.py b/labs06/task02.py
index 9d96016..43258fc 100755
--- a/labs06/task02.py
+++ b/labs06/task02.py
@@ -1,14 +1,22 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 
+import pandas as pd
+from matplotlib import pyplot as plt
+from sklearn import linear_model
+
 def wczytaj_dane():
-    pass
+    data = pd.read_csv("J:/PycharmProjects/Python2018/labs06/mieszkania.csv")
+    return pd.DataFrame(data)
 
 def most_common_room_number(dane):
-    pass
+    dane2 = dane['Rooms'].value_counts().head(1)
+    pokoje = int(dane2.index[0])
+    return pokoje
 
 def cheapest_flats(dane, n):
-    pass
+    result = dane.sort_values('Expected').head(n)
+    return result
 
 def find_borough(desc):
     dzielnice = ['Stare Miasto',
@@ -19,23 +27,55 @@ def find_borough(desc):
                  'Winogrady',
                  'Miłostowo',
                  'Dębiec']
-    pass
-
+    for i in range(0,len(dzielnice)):
+        if dzielnice[i] in desc:
+            result = dzielnice[i]
+            break
+        else:
+            result = 'Inne'
+    return result
 
 def add_borough(dane):
-    pass
+    dane['Borough'] = dane['Location'].apply(find_borough)
+    return dane
 
 def write_plot(dane, filename):
-    pass
+    dane['Borough'].value_counts().plot(kind='barh')
+    plt.savefig('J:/PycharmProjects/Python2018/labs06/'+filename)
+    return 0
 
 def mean_price(dane, room_number):
-    pass
+    dane2 = dane[dane.Rooms == room_number]
+    srednia = round(dane2.Expected.mean(),2)
+    return srednia
 
 def find_13(dane):
-    pass
+    dane2 = dane[dane.Floor == 13]
+    lista_dzielnic = dane2['Borough'].unique()
+    return lista_dzielnic
 
 def find_best_flats(dane):
-    pass
+    dane2 = dane[(dane['Borough']=='Winogrady') & (dane['Rooms']==3) & (dane['Floor']==1)]
+    return dane2
+
+def reg_lin(dane, metraz, pokoje):
+    reg = linear_model.LinearRegression()
+    reg.fit(dane[['SqrMeters', 'Rooms']], dane['Expected'])
+    result = reg.predict(pd.DataFrame([(metraz, pokoje)], columns=['var1', 'var2']))
+    return result
+
+"""
+dane = wczytaj_dane()
+print(most_common_room_number(dane))
+print(cheapest_flats(dane, 2))
+print(find_borough('Winogrady i Jeżyce'))
+add_borough(dane)
+write_plot(dane, 'wykres')
+print(mean_price(dane, 3))
+print(find_13(dane))
+print(find_best_flats(dane).shape[0])
+print(reg_lin(dane, 60, 3))
+"""
 
 def main():
     dane = wczytaj_dane()
@@ -45,7 +85,7 @@ 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)))