labs04 z1

labs06/task02.py

@@ -1,23 +1,27 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 import pandas as pd
+import sys
+import numpy as np
 
 def wczytaj_dane():
     my_data = pd.read_csv('mieszkania.csv',
                           encoding='utf-8',
-                          index_col='Id')
+                          index_col='Id',
+                          sep = ',')
     return my_data
     #print(my_data)
 
 def most_common_room_number(dane):
-    room_num = dane['Rooms']
-    room_num_rank = room_num.value_counts()
-    room_num_list = list(room_num_rank)
-    return type(room_num_rank)
+    rooms = dane['Rooms']
+    rooms_max = rooms.value_counts().index[0]
+    return rooms_max
     #return ( room_num_list[room_num_list.index(max(room_num_list))] )
     #pass
 def cheapest_flats(dane, n):
-    pass
+    expected = dane['Expected']
+    n_cheapest = expected.sort_values().head(n)
+    return n_cheapest
 
 def find_borough(desc):
     dzielnice = ['Stare Miasto',
@@ -28,23 +32,27 @@ def find_borough(desc):
                  'Winogrady',
                  'Miłostowo',
                  'Dębiec']
-    pass
-
+    for dzielnica in dzielnice:
+        if dzielnica in desc:
+            return dzielnica
+    return 'Inne'
 
 def add_borough(dane):
-    pass
+    dane['Borough'] = dane.apply(lambda row: find_borough(row['Location']))
 
 def write_plot(dane, filename):
-    pass
+    dane['Borough'].value_counts().plot.bar().get_figure().savefig(filename)
 
 def mean_price(dane, room_number):
-    pass
+    mean_value = dane.loc[dane['Rooms'] == room_number]['Expected'].mean()
+    return mean_value
 
 def find_13(dane):
-    pass
+    return dane.loc[dane['Floor'] == 13]['Borough'].unique()
 
 def find_best_flats(dane):
-    pass
+    best_flats = dane.loc[(df['Borough'] == 'Winogrady') & (dane['Rooms'] == 3) & (dane['Floor'] == 1)]
+    return best_flats
 
 def main():
     dane = wczytaj_dane()