#!/usr/bin/env python
# -*- coding: utf-8 -*-

def wczytaj_dane():
    import pandas as pd
    dane = pd.read_csv("labs06/mieszkania.csv")
    dane.head()


def most_common_room_number(dane):
    dane['Rooms'].value_counts().idxmax()



def cheapest_flats(dane, n):
    p = dane.sort_values(['Expected'], ascending=[0])
    p.head(7)

def find_borough(desc):
    dzielnice = ['Stare Miasto',
                 'Wilda',
                 'Jeżyce',
                 'Rataje',
                 'Piątkowo',
                 'Winogrady',
                 'Miłostowo',
                 'Dębiec']
    pass


def add_borough(dane):
    pass

def write_plot(dane, filename):
    pass

def mean_price(dane, room_number):
    p1 = dane[dane['Rooms'] == 2]
    p2 = p1['Expected']
    p2.mean()

def find_13(dane):
    p1 = dane[dane['Floor'] == 13]
    p1.Location.unique()

def find_best_flats(dane):
    p_index = dane['Location'].str.contains('Winogrady')
    p = dane[p_index]
    p[(p['Rooms'] == 3) & (p['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 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()