forked from tdwojak/Python2017
79 lines
2.5 KiB
Python
Executable File
79 lines
2.5 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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def wczytaj_dane():
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return pd.read_csv('mieszkania.csv')
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def most_common_room_number(dane):
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return dane['Rooms'].mode()[0]
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def cheapest_flats(dane, n):
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return dane.sort_values(by=[u'Expected'], na_position='last').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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histogram = {districtName: desc.find(districtName) for districtName in dzielnice if desc.find(districtName) != -1}
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return 'Inne' if not histogram else min(histogram, key=histogram.get)
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def add_borough(dane):
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dane['Borough'] = dane.apply( lambda row: find_borough( row['Location']), axis = 1)
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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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return dane.loc[ dane['Rooms'] == room_number ].mean()[1]
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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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return dane.loc[(dane['Borough'] == 'Winogrady') & (dane['Rooms'] == 3) & (dane['Floor'] == 1)]
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def predict(dane, rooms, sqrMeters):
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from sklearn import linear_model
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import numpy as np
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data = dane
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df = pd.DataFrame(data, columns=np.array(['Rooms','SqrMeters']))
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target = pd.DataFrame(data, columns=["Expected"])
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X = df
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y = target["Expected"]
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lm = linear_model.LinearRegression()
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model = lm.fit(X, y)
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inData = pd.DataFrame.from_records([(rooms, sqrMeters)], columns=['Rooms', 'SqrMeters'])
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return lm.predict(inData)[0]
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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("7 najtańszych mieszkań to: ")
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print(cheapest_flats(dane, 7))
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print("{} to najłądniejsza dzielnica w Poznaniu.".format(find_borough("Grunwald i Jeżyce")))
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add_borough(dane)
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write_plot(dane, 'tmp_borough_hist.png')
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for i in dane['Rooms'].unique():
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print("Średnia cena mieszkania {}-pokojowego, to: {}".format(i, mean_price(dane, i)))
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print('Dzielnice z mieszkaniami na 13 piętrze: {}'.format(find_13(dane)))
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print('"Najlepsze" mieszkania: ')
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print(find_best_flats(dane))
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print('Predicted price(actual 146000): ', predict(dane,1,31.21))
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if __name__ == "__main__":
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main() |