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

import pandas as pd

def wczytaj_dane():

    mieszkania = pd.read_csv('C:\Users\mlyska\Documents\python2017\labs06\mieszkania.csv', sep = ',')
    mieszkania.fillna('', inplace=True)
    mieszkania['Description'] = mieszkania['Description'] + mieszkania['Unnamed: 7'] + mieszkania['Unnamed: 8'] + \
                                mieszkania['Unnamed: 9'] + mieszkania['Unnamed: 10'] + mieszkania['Unnamed: 11']
    mieszkania.drop(['Unnamed: 7','Unnamed: 8','Unnamed: 9','Unnamed: 10','Unnamed: 11'], 1, inplace=True)
    return mieszkania

def most_common_room_number(dane):
    counter = dane['Rooms'].value_counts()
    maxCount = counter.to_frame()
    return maxCount.index.values[0]

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

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

    for i in dzielnice:
        if i in desc:
            return i
            break
    if desc not in dzielnice:
        return 'Inne'



def add_borough(dane):
    values = []
    for i in dane['Location']:
        values.append(find_borough(i))

    tempSeries = pd.Series(values)
    dane['Borough'] = tempSeries.values

def write_plot(dane, filename):
    add_borough(dane)
    dataPlot = dane['Borough'].value_counts()

    imgPlot = dataPlot.plot(kind='bar', alpha=0.5, title='Liczba ogloszen mieszkan z podzialem na dzielnice', fontsize=6, figsize=(8,7))
    imgPlot.set_ylabel('Liczba ogloszen')
    imgPlot.set_xlabel('Dzielnice')

    filePlot = imgPlot.get_figure()
    filePlot.savefig(filename)

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

def find_13(dane):
    x = dane.loc[dane['Floor'] == 13]
    return list(set(x['Borough'].tolist()))

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

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)))

    write_plot(dane, 'ogloszenia.png')

if __name__ == "__main__":
    main()