Python2017/labs06/task02.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np

def wczytaj_dane():
    rooms_data = pd.read_csv('mieszkania.csv',  # ścieżka  do pliku
                            sep=',',  # separator
                            encoding='utf-8',  # kodowanie
                            index_col='Id')  # ustawienie indeksu na kolumnę Date
    return rooms_data

def most_common_room_number(dane):
    d=dane['Rooms']
    d= d.value_counts()
    j=0
    """
    for i in d:
        print d.index[j] , i
        j +=1
    """
    d = d.index[0]
    return d


def cheapest_flats(dane, n):
    SortDane = dane.sort_values('Expected',ascending=True)
    PriceCheapest = SortDane['Expected']
    PriceCheapest = PriceCheapest.head(n)
    return PriceCheapest

def find_borough(desc):
    dzielnice = ['Stare Miasto',
                 'Wilda',
                 'Jeżyce',
                 'Rataje',
                 'Piątkowo',
                 'Winogrady',
                 'Miłostowo',
                 'Dębiec']
    for district in dzielnice:
        if district in desc: return district
    return 'Inne'


def add_borough(dane):
    pass

def write_plot(dane, filename):
    pass

def mean_price(dane, room_number):
    AVGdane = dane[dane.Rooms == room_number]
    AVGdane = round(AVGdane.Expected.mean(),2)
    return AVGdane


def find_13(dane):
    pass

def find_best_flats(dane):
    pass

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)))

    roomCheapest = cheapest_flats(dane, 5)
    #print roomCheapest

    

if __name__ == "__main__":
    main()