forked from tdwojak/Python2018
65 lines
1.6 KiB
Python
Executable File
65 lines
1.6 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import pandas as pd
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import matplotlib.pyplot as plt
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def wczytaj_dane():
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df = pd.read_csv("./mieszkania.csv", sep=',', header=0)
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return df
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def most_common_room_number(dane):
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return dane['Rooms'].value_counts().idxmax()
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def cheapest_flats(dane, n):
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return dane.sort_values(by='Expected').head(n)
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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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return next((desc for i in dzielnice if desc in i), 'Inne')
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def add_borough(dane):
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dane['Borough'] = dane['Location'].apply(find_borough)
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def write_plot(dane, filename):
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dane['Borough'].value_counts().plot(x='Borough', y='Quantity of adwerts', kind='bar')
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plt.savefig('./'+filename)
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def mean_price(dane, room_number):
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return dane[dane["Rooms"] == room_number]["Expected"].mean()
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def find_13(dane):
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return dane[dane["Floor"] == 13]["Borough"].unique()
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def find_best_flats(dane):
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return dane[(dane["Borough"] == "Winogrady") & (dane["Floor"] == 1) & (dane["Rooms"] == 3)]
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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: {}".format(most_common_room_number(dane)))
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print("{} to najłądniejsza dzielnica w Poznaniu.".format(find_borough("Grunwald i Jeżyce")))
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print("Średnia cena mieszkania 3-pokojowego, to: {}".format(mean_price(dane, 3)))
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if __name__ == "__main__":
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main()
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