forked from tdwojak/Python2017
63 lines
1.7 KiB
Python
Executable File
63 lines
1.7 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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mieszkania = pd.read_csv('mieszkania.csv',sep=',',encoding='UTF-8',usecols=[0,1,2,3,4,5,6])
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return mieszkania
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def most_common_room_number(dane):
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return dane.mode(numeric_only =True)["Rooms"][0]
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def cheapest_flats(dane, n):
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return dane.sort_values("Expected")[: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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inputList=desc.split(' ')
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for i in inputList:
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if i in dzielnice:
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return i
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break
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return "Inne"
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def add_borough(dane):
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newcol=dane["Location"].apply(find_borough)
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dane["Borough"]=newcol
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return dane
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def write_plot(dane, filename):
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bar=dane["Borough"].value_counts().plot(kind="bar", figsize=(6,6))
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fig=bar.get_figure()
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fig.savefig(filename)
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def mean_price(dane, room_number):
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return dane[dane["Rooms"]==room_number]["Expected"].mean()
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def find_13(dane):
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return dane[dane["Floor"]==13]["Borough"].unique()
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def find_best_flats(dane):
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return dane[(dane["Borough"]=="Winogrady") & (dane["Floor"]==1) & (dane["Rooms"]==3)]
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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() |