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

def wczytaj_dane():
    return pd.read_csv('mieszkania.csv')

def most_common_room_number(dane):
    return dane['Rooms'].mode()[0]

def cheapest_flats(dane, n):
    return dane.sort_values('Expected', axis=0, ascending=True)[:n]

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


def add_borough(dane):
    dane['Borough'] = dane['Location'].apply(find_borough)

def write_plot(dane, filename):
    # TODO
    pass

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)))