forked from tdwojak/Python2017
89 lines
2.1 KiB
Python
Executable File
89 lines
2.1 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 statistics
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def wczytaj_dane():
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raw_data = pd.read_csv('mieszkania.csv',sep=',')
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data = pd.DataFrame(raw_data)
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return data
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def most_common_room_number(dane):
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rooms=dane['Rooms']
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return(int(statistics.mode(rooms)))
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def cheapest_flats(dane, n):
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cheapest=pd.DataFrame(dane['Expected'])
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cheapest.sort=cheapest.sort_values(by=['Expected'])
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return cheapest.sort[: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 desc.find(dzielnica)!=-1:
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return dzielnica
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return 'Inne'
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def add_borough(dane):
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borough_list=[]
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for data in dane['Location']:
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borough_list.append(find_borough(data))
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borough_series = pd.Series(borough_list,name='Borough')
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dane['Borough']=borough_series
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return dane
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def write_plot(dane, filename):
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data = add_borough(dane)
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plot_data = data.groupby(dane['Borough']).size()
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plt = plot_data.plot.bar()
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fig = plt.get_figure()
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fig.savefig('{}.png'.format(filename))
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return 0
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def mean_price(dane, room_number):
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data=dane.loc[dane['Rooms'] == room_number]
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return round(statistics.mean(data['Expected']),2)
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def find_13(dane):
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data = add_borough(dane)
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boroughs = data.loc[data['Floor'] == 13]
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return set(boroughs['Borough'])
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def find_best_flats(dane):
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data = add_borough(dane)
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best_flats = data.loc[(data['Borough'] == 'Winogrady') & (data['Floor'] == 1) & (data['Rooms'] == 3)]
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return best_flats
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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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