66 lines
1.4 KiB
Python
Executable File
66 lines
1.4 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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def wczytaj_dane():
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import pandas as pd
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dane = pd.read_csv("labs06/mieszkania.csv")
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dane.head()
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def most_common_room_number(dane):
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dane['Rooms'].value_counts().idxmax()
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def cheapest_flats(dane, n):
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p = dane.sort_values(['Expected'], ascending=[0])
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p.head(7)
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def find_borough(desc):
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dzielnice = ['Stare Miasto',
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'Wilda',
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'Jeżyce',
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'Rataje',
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'Piątkowo',
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'Winogrady',
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'Miłostowo',
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'Dębiec']
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pass
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def add_borough(dane):
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pass
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def write_plot(dane, filename):
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pass
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def mean_price(dane, room_number):
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p1 = dane[dane['Rooms'] == 2]
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p2 = p1['Expected']
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p2.mean()
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def find_13(dane):
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p1 = dane[dane['Floor'] == 13]
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p1.Location.unique()
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def find_best_flats(dane):
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p_index = dane['Location'].str.contains('Winogrady')
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p = dane[p_index]
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p[(p['Rooms'] == 3) & (p['Floor'] == 1)]
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def main():
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dane = wczytaj_dane()
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print(dane[:5])
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print("Najpopularniejsza liczba pokoi w mieszkaniu to: {}"
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.format(most_common_room_number(dane)))
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print("{} to najłądniejsza dzielnica w Poznaniu."
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.format(find_borough("Grunwald i Jeżyce"))))
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print("Średnia cena mieszkania 3-pokojowego, to: {}"
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.format(mean_price(dane, 3)))
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if __name__ == "__main__":
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main()
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