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

def wczytaj_dane():
    my_data = pd.read_csv('mieszkania.csv',
                          encoding='utf-8',
                          index_col='Id',
                          sep = ',')
    return my_data
    #print(my_data)

def most_common_room_number(dane):
    rooms = dane['Rooms']
    rooms_max = rooms.value_counts().index[0]
    return rooms_max
    #return ( room_num_list[room_num_list.index(max(room_num_list))] )
    #pass
def cheapest_flats(dane, n):
    expected = dane['Expected']
    n_cheapest = expected.sort_values().head(n)
    return n_cheapest

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.apply(lambda row: find_borough(row['Location']))

def write_plot(dane, filename):
    dane['Borough'].value_counts().plot.bar().get_figure().savefig(filename)

def mean_price(dane, room_number):
    mean_value = dane.loc[dane['Rooms'] == room_number]['Expected'].mean()
    return mean_value

def find_13(dane):
    return dane.loc[dane['Floor'] == 13]['Borough'].unique()

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

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()