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Python2017/labs06/task02.py

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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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import statistics
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def wczytaj_dane():
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raw_data = pd.read_csv('mieszkania.csv',sep=',')
data = pd.DataFrame(raw_data)
return data
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def most_common_room_number(dane):
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rooms=dane['Rooms']
return(int(statistics.mode(rooms)))
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def cheapest_flats(dane, n):
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cheapest=pd.DataFrame(dane['Expected'])
cheapest.sort=cheapest.sort_values(by=['Expected'])
return cheapest.sort[:n]
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def find_borough(desc):
dzielnice = ['Stare Miasto',
'Wilda',
'Jeżyce',
'Rataje',
'Piątkowo',
'Winogrady',
'Miłostowo',
'Dębiec']
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for dzielnica in dzielnice:
if desc.find(dzielnica)!=-1:
return dzielnica
return 'Inne'
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def add_borough(dane):
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borough_list=[]
for data in dane['Location']:
borough_list.append(find_borough(data))
borough_series = pd.Series(borough_list,name='Borough')
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)
plot_data = data.groupby(dane['Borough']).size()
plt = plot_data.plot.bar()
fig = plt.get_figure()
fig.savefig('{}.png'.format(filename))
return 0
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def mean_price(dane, room_number):
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data=dane.loc[dane['Rooms'] == room_number]
return round(statistics.mean(data['Expected']),2)
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def find_13(dane):
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data = add_borough(dane)
boroughs = data.loc[data['Floor'] == 13]
return set(boroughs['Borough'])
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def find_best_flats(dane):
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data = add_borough(dane)
best_flats = data.loc[(data['Borough'] == 'Winogrady') & (data['Floor'] == 1) & (data['Rooms'] == 3)]
return best_flats
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def main():
dane = wczytaj_dane()
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."
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.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()