forked from tdwojak/Python2017
72 lines
2.0 KiB
Python
Executable File
72 lines
2.0 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 sys
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import numpy as np
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def wczytaj_dane():
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my_data = pd.read_csv('mieszkania.csv',
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encoding='utf-8',
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index_col='Id',
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sep = ',')
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return my_data
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#print(my_data)
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def most_common_room_number(dane):
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rooms = dane['Rooms']
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rooms_max = rooms.value_counts().index[0]
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return rooms_max
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#return ( room_num_list[room_num_list.index(max(room_num_list))] )
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#pass
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def cheapest_flats(dane, n):
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expected = dane['Expected']
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n_cheapest = expected.sort_values().head(n)
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return n_cheapest
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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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dane['Borough'] = dane.apply(lambda row: find_borough(row['Location']))
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def write_plot(dane, filename):
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dane['Borough'].value_counts().plot.bar().get_figure().savefig(filename)
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def mean_price(dane, room_number):
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mean_value = dane.loc[dane['Rooms'] == room_number]['Expected'].mean()
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return mean_value
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def find_13(dane):
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return dane.loc[dane['Floor'] == 13]['Borough'].unique()
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def find_best_flats(dane):
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best_flats = dane.loc[(df['Borough'] == 'Winogrady') & (dane['Rooms'] == 3) & (dane['Floor'] == 1)]
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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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