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

import sys
reload(sys)
sys.setdefaultencoding("utf-8")

import pandas as pd


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):
    boroughArr = []
    for current_location in dane:
        findborough =  find_borough(current_location)
        #print current_location , findborough
        boroughArr.append(findborough)
    return pd.Series(boroughArr)

def write_plot(dane, filename):
    add_borough(dane)
    hist_data = dane['Borough'].value_counts()
    Hist_out = hist_data.plot(kind='bar', alpha=0.5, title="Appartments in Distrinct",fontsize=5, figsize=(7, 5))
    fig = Hist_out.get_figure()
    fig.savefig(filename)

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


def find_13(dane):
    add_borough(dane)
    List_13 = []
    WhatLookFor = dane['Borough'].loc[dane['Floor'] == 13]
    for j in WhatLookFor:
        List_13.append(j)
    return List_13

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

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

    district = add_borough(dane['Location'])
    dane['Borough'] = district.values
    #print(dane[:5])

    write_plot(dane, 'plot.png')

    list_13 = find_13(dane)
    print list_13

    TheBestFlat = find_best_flats(dane)
    print TheBestFlat['Location']


if __name__ == "__main__":
    main()