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forked from tdwojak/Python2018
Python2018/labs06/zad2domowe
2018-06-27 16:26:55 +00:00

72 lines
1.8 KiB
Python

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from statistics import mode
import matplotlib.pyplot as plt
def wczytaj_dane():
data = pd.read_csv('mieszkania.csv')
return data
def most_common_room_number(dane):
return mode(dane.Rooms)
def cheapest_flats(dane, n):
sorted = dane.Expected.sort()
return sorted.head(n)
def find_borough(desc):
dzielnice = ['Stare Miasto',
'Wilda',
'Jezyce',
'Rataje',
'Piatkowo',
'Winogrady',
'Milostowo',
'Debiec']
for dzielnica in dzielnice:
list = desc.split(' ')
for element in list:
if len(element) > 2 and element == dzielnica:
return dzielnica
break
return "Inne"
def add_borough(dane):
dane['Borough'] = dane['Location'].apply(find_borough)
return dane
def write_plot(dane, filename):
plotdata = pd.Series(dane.Location.value_counts())
plotdata.plot(x='Location', y='Liczba ogloszen', kind='bar')
plt.savefig(filename)
def mean_price(dane, room_number):
mean_price = dane.Expected[(dane['Rooms'] == room_number)]
return mean_price.mean()
def find_13(dane):
return dane.Location[(dane['Floor'] == 13)].unique()
def find_best_flats(dane):
return dane[(dane['Location'] == 'Winogrady') & (dane['Rooms'] == 3) & (dane['Floor'] == 1)]
def main():
dane = wczytaj_dane()
print(dane[:5])
print("Najpopularniejsza liczba pokoi w mieszkaniu to: {}"
.format(most_common_room_number(dane)))
print("{} to najadniejsza dzielnica w Poznaniu."
.format(find_borough("Jeżyce i Wilda")))
print("Średnia cena mieszkania 3-pokojowego, to: {}"
.format(mean_price(dane, 3)))
if __name__ == "__main__":
main()