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

import pandas as pd
import matplotlib
import matplotlib.pyplot as plt

def wczytaj_dane():
    dane = pd.read_csv('mieszkania.csv',
                        sep=',',
                        encoding='utf-8')
    return dane

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

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

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

    for dzielnica in dzielnice:
        if desc.find(dzielnica)>=0:
            return dzielnica

    return 'Inne'


def add_borough(dane):
    borough = []
    for current_location in dane:
        borough.append(find_borough(current_location))
    return pd.Series(borough)

def write_plot(dane, filename):
    dane['Borough'].hist()
    plt.savefig(filename)

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

def find_13(dane):
    dane = dane[dane.Floor == 13]
    return list(dane.Borough)

def find_best_flats(dane):
    dane = dane[(dane.Borough=='Winogrady') & (dane.Rooms==3) & (dane.Floor == 1)]
    return dane

def main():
    dane = wczytaj_dane()
    print(dane[:5])

    print("Najpopularniejsza liczba pokoi w mieszkaniu to: {}"
          .format(most_common_room_number(dane)))

    najtansze = cheapest_flats(dane,10)

    print("{} to najłądniejsza dzielnica w Poznaniu."
          .format(find_borough("Grunwald i Jeżyce")))

    dzielnice = add_borough(dane['Location'])
    dane['Borough'] = dzielnice.values

    write_plot(dane,'wykres.png')

    print("Średnia cena mieszkania 3-pokojowego, to: {}"
          .format(mean_price(dane, 3)))

    find_13(dane)

    find_best_flats(dane)

if __name__ == "__main__":
    main()