##!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
def wczytaj_dane():
    mieszkania = pd.read_csv('mieszkania.csv',  # ścieżka  do pliku
                            sep=',',  # separator
                            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("Expected")[:n]

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

    return "Inne"


def add_borough(dane):
    newcol=dane["Location"].apply(find_borough)
    dane["Borough"]=newcol
    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 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()