done

labs05/task00.py

@@ -1,10 +1,12 @@
 #!/usr/bin/env python2
 # -*- coding: utf-8 -*-
 
+"""
+Napisz funkcję ``suma``, która przyjmnie jeden argument: listę liczb i zwróci ich sumę.
+"""
+
 def suma(liczby):
-    assert len(liczby) > 0
+    return sum(liczby)
-    a=reduce(lambda a, b: a+b, liczby)
-    return a
 
 def main():
     print(suma([1, 2, 3, 4]))

labs05/task01.py

@@ -1,7 +1,14 @@
-import task00, sys
+from task00 import suma
+import sys
 
 def main():
-    print('%f' % (task00.suma(map(lambda x: float(x), sys.argv[1:]))))
+    arguments=sys.argv[1:]
+    float_arguments=[]
+    for arg in arguments:
+        float_arguments.append(float(arg))
+
+    print(suma(float_arguments))
+
 
 if __name__ == '__main__':
     main()

labs05/task02.py

@@ -1,12 +1,18 @@
-import task00, sys
+from task00 import suma
+import sys
 
 def main():
-    def a_to_f(a):
+    arg_list = sys.argv[1:]
+    float_arg_list = []
+    for arg in arg_list:
         try:
-            return float(a)
+            float_arg=float(arg)
+            float_arg_list.append(float_arg)
         except:
-            return 0
+            float_arg_list.append(0)
-    print('%f' % (task00.suma(map(a_to_f, sys.argv[1:]))))
+
+    print(suma(float_arg_list))
+
 
 if __name__ == '__main__':
     main()

labs05/task04.py

@@ -1,12 +1,5 @@
-#!/usr/bin/env python
+import sys
-# -*- coding: utf-8 -*-
+import argparse
-
-"""
-Implementacja narzedzia ``wc`` z linuksa (word counter).
-Zwraca liczbę słów, znaków i linii.
-"""
-
-import sys, argparse
 
 
 def count_lines(text):
@@ -35,16 +28,22 @@ def main():
     parser = argparse.ArgumentParser()
     parser.add_argument('-l', help="number of lines", action="store_true")
     parser.add_argument('-w', help="number of words", action="store_true")
-    parser.add_argument('-c', help="number of characters", action="store_true")
+    parser.add_argument('-c', help="number of chars", action="store_true")
-    parser.add_argument('fn', nargs='?', type=file, default=sys.stdin, help='optional filename to read from')
+    parser.add_argument('-filename', type=argparse.FileType('r'), default='-', help='filename to read from')
     args = parser.parse_args()
-    lines, words, chars = wc(args.fn.read())
+
-    if( args.l or args.w or args.c ):
+    if args.filename:
-        if(args.l):
+        lines, words, chars = wc(args.filename.read())
-            print(lines)
+    elif not sys.stdin.isatty():
-        if(args.w):
+        lines, words, chars = wc(args.stdin.read())
-            print(words)
+    else:
-        if(args.c):
+        pass
+
+    if(args.l):
+        print(lines)
+    elif(args.w):
+        print(words)
+    elif(args.c):
             print(chars)
     else:
         print(lines, words, chars)
@@ -52,3 +51,4 @@ def main():
 
 if __name__ == "__main__":
     main()
+

labs06/task02.py

@@ -1,18 +1,22 @@
-##!/usr/bin/env python
+#!/usr/bin/env python
 # -*- coding: utf-8 -*-
+
 import pandas as pd
+import statistics
+
 def wczytaj_dane():
-    mieszkania = pd.read_csv('mieszkania.csv',  # ścieżka  do pliku
+    raw_data = pd.read_csv('mieszkania.csv',sep=',')
-                            sep=',',  # separator
+    data = pd.DataFrame(raw_data)
-                            encoding='UTF-8',
+    return data
-                            usecols=[0,1,2,3,4,5,6])
-    return mieszkania
 
 def most_common_room_number(dane):
-    return dane.mode(numeric_only =True)["Rooms"][0]
+    rooms=dane['Rooms']
+    return(int(statistics.mode(rooms)))
 
 def cheapest_flats(dane, n):
-    return dane.sort_values("Expected")[:n]
+    cheapest=pd.DataFrame(dane['Expected'])
+    cheapest.sort=cheapest.sort_values(by=['Expected'])
+    return cheapest.sort[:n]
 
 def find_borough(desc):
     dzielnice = ['Stare Miasto',
@@ -23,41 +27,62 @@ def find_borough(desc):
                  'Winogrady',
                  'Miłostowo',
                  'Dębiec']
-    inputList=desc.split(' ')
+    for dzielnica in dzielnice:
-    for i in inputList:
+        if desc.find(dzielnica)!=-1:
-        if i in dzielnice:
+            return dzielnica
-            return i
+    return 'Inne'
-
-    return "Inne"
 
 
 def add_borough(dane):
-    newcol=dane["Location"].apply(find_borough)
+    borough_list=[]
-    dane["Borough"]=newcol
+    for data in dane['Location']:
+        borough_list.append(find_borough(data))
+    borough_series = pd.Series(borough_list,name='Borough')
+    dane['Borough']=borough_series
     return dane
 
+
 def write_plot(dane, filename):
-    bar=dane["Borough"].value_counts().plot(kind="bar", figsize=(6,6))
+    data = add_borough(dane)
-    fig=bar.get_figure()
+    plot_data = data.groupby(dane['Borough']).size()
-    fig.savefig(filename)
+    plt = plot_data.plot.bar()
+    fig = plt.get_figure()
+    fig.savefig('{}.png'.format(filename))
+    return 0
+
+
 
 def mean_price(dane, room_number):
-    return dane[dane["Rooms"]==room_number]["Expected"].mean()
+    data=dane.loc[dane['Rooms'] == room_number]
+    return round(statistics.mean(data['Expected']),2)
 
 def find_13(dane):
-    return dane[dane["Floor"]==13]["Borough"].unique()
+    data = add_borough(dane)
+    boroughs = data.loc[data['Floor'] == 13]
+    return set(boroughs['Borough'])
+
 
 def find_best_flats(dane):
-    return dane[(dane["Borough"]=="Winogrady") & (dane["Floor"]==1) & (dane["Rooms"]==3)]
+    data = add_borough(dane)
+    best_flats = data.loc[(data['Borough'] == 'Winogrady') & (data['Floor'] == 1) & (data['Rooms'] == 3)]
+    return 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("Średnia cena mieszkania 3-pokojowego, to: {}"
           .format(mean_price(dane, 3)))
+
+
+
 if __name__ == "__main__":
-    main()
+    main()