#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from statistics import mode
import matplotlib.pyplot as plt

def wczytaj_dane():
    data = pd.read_csv('mieszkania.csv')
    return data

def most_common_room_number(dane):
    return mode(dane.Rooms)


def cheapest_flats(dane, n):
    sorted = dane.Expected.sort()
    return sorted.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):
    plotdata = pd.Series(dane.Location.value_counts())
    plotdata.plot(x='Location', y='Liczba ogłoszeń', kind='bar')
    plt.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()