74 lines
1.8 KiB
Plaintext
74 lines
1.8 KiB
Plaintext
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import pandas as pd
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from statistics import mode
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import matplotlib.pyplot as plt
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def wczytaj_dane():
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data = pd.read_csv('mieszkania.csv')
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return data
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def most_common_room_number(dane):
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return mode(dane.Rooms)
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def cheapest_flats(dane, n):
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sorted = dane.Expected.sort()
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return sorted.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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for dzielnica in dzielnice:
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list = desc.split(' ')
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for element in list:
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if len(element) > 2 and element == dzielnica:
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return dzielnica
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break
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return "Inne"
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def add_borough(dane):
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dane['Borough'] = dane['Location'].apply(find_borough)
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return dane
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def write_plot(dane, filename):
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plotdata = pd.Series(dane.Location.value_counts())
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plotdata.plot(x='Location', y='Liczba ogłoszeń', kind='bar')
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plt.savefig(filename)
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def mean_price(dane, room_number):
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mean_price = dane.Expected[(dane['Rooms'] == room_number)]
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return mean_price.mean()
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def find_13(dane):
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return dane.Location[(dane['Floor'] == 13)].unique()
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def find_best_flats(dane):
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return dane[(dane['Location'] == 'Winogrady') & (dane['Rooms'] == 3) & (dane['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 najladniejsza dzielnica w Poznaniu."
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.format(find_borough("Grunwald i Jeżyce")))
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print("Srednia 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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