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

def wczytaj_dane():
    mieszkania = pd.read_csv('mieszkania.csv',
                            sep=',',
                            encoding='UTF-8',
                            usecols=[0,1,2,3,4,5,6])
    return mieszkania

def most_common_room_number(dane):
    return dane.mode(numeric_only=True)["Rooms"][0]

def cheapest_flats(dane, n):
    return dane.sort_values(by=['Expected'], ascending=False).head(n)

def find_borough(desc):
    dzielnice = ['Stare Miasto',
                 'Wilda',
                 'Jeżyce',
                 'Rataje',
                 'Piątkowo',
                 'Winogrady',
                 'Miłostowo',
                 'Dębiec']
    for i in dzielnice:
        if desc.find(i) + 1:
            return (i)
    return ('Inne')


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

def write_plot(dane, filename):
    bar = dane["Borough"].value_counts().plot(kind="bar", figsize=(6, 6))
    fig = bar.get_figure()
    fig.savefig(filename)

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

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

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

def main():
    dane = wczytaj_dane()
    print(dane[:5])

    print("Najpopularniejsza liczba pokoi w mieszkaniu to: {}"
          .format(most_common_room_number(dane)))

    print("{} to najładniejsza 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()