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

import pandas as pd
import matplotlib.pyplot as plt

def wczytaj_dane():
    dane = pd.read_csv("./mieszkania.csv")
    return dane

def most_common_room_number(dane):
    return dane.Rooms.value_counts().idxmax()

def cheapest_flats(dane, n):
    return dane.sort('Expected').head(n)
     
def find_borough(desc):
    dzielnice = ['Stare Miasto',
                 'Wilda',
                 'Jeżyce',
                 'Rataje',
                 'Piątkowo',
                 'Winogrady',
                 'Miłostowo',
                 'Dębiec']
    for dzielnica in dzielnice:
        list = desc.split(' ')
        for element in list:
            if len(element) > 2 and element == dzielnica:
                return dzielnica
                break
    return "Inne"

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

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

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

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

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

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()