forked from tdwojak/Python2017
63 lines
1.8 KiB
Python
Executable File
63 lines
1.8 KiB
Python
Executable File
##!/usr/bin/env python
|
|
# -*- coding: utf-8 -*-
|
|
import pandas as pd
|
|
def wczytaj_dane():
|
|
mieszkania = pd.read_csv('mieszkania.csv', # ścieżka do pliku
|
|
sep=',', # separator
|
|
encoding='UTF-8',
|
|
usecols=[0,1,2,3,4,5,6])
|
|
return mieszkania
|
|
|
|
def most_common_room_number(dane):
|
|
return dane.mode(numeric_only =True)["Rooms"][0]
|
|
|
|
def cheapest_flats(dane, n):
|
|
return dane.sort_values("Expected")[:n]
|
|
|
|
def find_borough(desc):
|
|
dzielnice = ['Stare Miasto',
|
|
'Wilda',
|
|
'Jeżyce',
|
|
'Rataje',
|
|
'Piątkowo',
|
|
'Winogrady',
|
|
'Miłostowo',
|
|
'Dębiec']
|
|
inputList=desc.split(' ')
|
|
for i in inputList:
|
|
if i in dzielnice:
|
|
return i
|
|
|
|
return "Inne"
|
|
|
|
|
|
def add_borough(dane):
|
|
newcol=dane["Location"].apply(find_borough)
|
|
dane["Borough"]=newcol
|
|
return dane
|
|
|
|
def write_plot(dane, filename):
|
|
bar=dane["Borough"].value_counts().plot(kind="bar", figsize=(6,6))
|
|
fig=bar.get_figure()
|
|
fig.savefig(filename)
|
|
|
|
def mean_price(dane, room_number):
|
|
return dane[dane["Rooms"]==room_number]["Expected"].mean()
|
|
|
|
def find_13(dane):
|
|
return dane[dane["Floor"]==13]["Borough"].unique()
|
|
|
|
def find_best_flats(dane):
|
|
return dane[(dane["Borough"]=="Winogrady") & (dane["Floor"]==1) & (dane["Rooms"]==3)]
|
|
|
|
def main():
|
|
dane = wczytaj_dane()
|
|
print(dane[:5])
|
|
print("Najpopularniejsza liczba pokoi w mieszkaniu to: {}"
|
|
.format(most_common_room_number(dane)))
|
|
print("{} to najłądniejsza dzielnica w Poznaniu."
|
|
.format(find_borough("Grunwald i Jeżyce")))
|
|
print("Średnia cena mieszkania 3-pokojowego, to: {}"
|
|
.format(mean_price(dane, 3)))
|
|
if __name__ == "__main__":
|
|
main() |