forked from tdwojak/Python2018
87 lines
2.1 KiB
Python
87 lines
2.1 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import pandas as pd
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def wczytaj_dane():
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data = pd.read_csv('mieszkania.csv', sep = ',')
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return data
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def most_common_room_number(dane):
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x= dane['Rooms'].value_counts()
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return(x.index.values[0])
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def cheapest_flats(dane, n):
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cheap=dane.sort_values('Expected')
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return (cheap.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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if dzielnica in desc:
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return dzielnica
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return 'Inne'
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def add_borough(dane):
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values = []
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for i in dane['Location']:
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values.append(find_borough(i))
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tempSeries = pd.Series(values)
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dane['Borough'] = tempSeries.values
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def write_plot(dane, filename):
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add_borough(dane)
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plot = dane['Borough'].value_counts()
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wykres = plot.plot(kind='bar', alpha=0.5, title='Liczba mieszkań z podziałem na dzielnice', fontsize=7, figsize=(8,8))
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wykres.set_ylabel('Liczba ogloszeń')
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wykres.set_xlabel('Dzielnice')
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plik = wykres.get_figure()
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plik.savefig(filename)
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def mean_price(dane, room_number):
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price = dane.loc[dane['Rooms'] == room_number]
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return price['Expected'].mean()
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def find_13(dane):
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x = dane.loc[dane['Floor'] == 13]
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return list(set(x['Borough'].tolist()))
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def find_best_flats(dane):
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the_best = dane.loc[dane['Borough'] == 'Winogrady' & dane['Rooms'] == 3 & dane['Floor'] == 1]
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return the_best
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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 najładniejsza 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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write_plot(dane, 'ogloszenia.png')
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if __name__ == "__main__":
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main() |