From c5b58c514b83d392f5c2661488712beeec90f670 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andrea=20G=C3=B3ralczyk?= <andgor@st.amu.edu.pl>
Date: Sat, 23 Jun 2018 16:42:09 +0000
Subject: [PATCH] Dodaj 'labs06/task02.py'

---
 labs06/task02.py | 87 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 87 insertions(+)
 create mode 100644 labs06/task02.py

diff --git a/labs06/task02.py b/labs06/task02.py
new file mode 100644
index 0000000..6c5797c
--- /dev/null
+++ b/labs06/task02.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+
+def wczytaj_dane():
+
+    data = pd.read_csv('mieszkania.csv', sep = ',')
+    return data
+
+def most_common_room_number(dane):
+
+    x= dane['Rooms'].value_counts()  
+    return(x.index.values[0])
+
+def cheapest_flats(dane, n):
+
+    cheap=dane.sort_values('Expected') 
+    return (cheap.head(n))
+
+def find_borough(desc):
+
+    dzielnice = ['Stare Miasto',
+                 'Wilda',
+                 'Jeżyce',
+                 'Rataje',
+                 'Piątkowo',
+                 'Winogrady',
+                 'Miłostowo',
+                 'Dębiec']
+    for dzielnica in dzielnice:
+        if dzielnica in desc:
+            return dzielnica
+    return 'Inne'
+
+def add_borough(dane):
+
+    values = []
+    for i in dane['Location']:
+        values.append(find_borough(i))
+
+    tempSeries = pd.Series(values)
+    dane['Borough'] = tempSeries.values
+
+def write_plot(dane, filename):
+
+    add_borough(dane)
+    plot = dane['Borough'].value_counts()
+
+    wykres = plot.plot(kind='bar', alpha=0.5, title='Liczba mieszkań z podziałem na dzielnice', fontsize=7, figsize=(8,8))
+    wykres.set_ylabel('Liczba ogloszeń')
+    wykres.set_xlabel('Dzielnice')
+    plik = wykres.get_figure()
+    plik.savefig(filename)
+
+def mean_price(dane, room_number):
+
+    price = dane.loc[dane['Rooms'] == room_number]
+    return price['Expected'].mean()
+
+def find_13(dane):
+
+    x = dane.loc[dane['Floor'] == 13]
+    return list(set(x['Borough'].tolist()))
+
+def find_best_flats(dane):
+
+    the_best = dane.loc[dane['Borough'] == 'Winogrady' & dane['Rooms'] == 3 & dane['Floor'] == 1]
+    return the_best
+
+def main():
+    dane = wczytaj_dane()
+    print(dane[:5])
+
+    print("Najpopularniejsza liczba pokoi w mieszkaniu to: {}"
+          .format(most_common_room_number(dane)))
+
+    print("{} to najładniejsza dzielnica w Poznaniu."
+          .format(find_borough("Grunwald i Jeżyce")))
+
+    print("Średnia cena mieszkania 3-pokojowego, to: {}"
+          .format(mean_price(dane, 3)))
+
+    write_plot(dane, 'ogloszenia.png')
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file