Python2017/labs06/task02.py

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


def wczytaj_dane():
    csv_data = pd.read_csv('mieszkania.csv', index_col='Id')
    return pd.DataFrame(csv_data)


def most_common_room_number(dane):

    dane_agg = dane["Rooms"].value_counts()
    return dane_agg.index.tolist()[0]


def cheapest_flats(dane, n):
    dane_cheapest = dane.sort_values(by=["Expected"])[:n]
    return dane_cheapest


def find_borough(desc):
    dzielnice = ['Stare Miasto',
                 'Wilda',
                 'Jeżyce',
                 'Rataje',
                 'Piątkowo',
                 'Winogrady',
                 'Miłostowo',
                 'Dębiec']
    first = ""
    found = False
    for dz in dzielnice:
        if dz in desc:
            first = dz
            found = True
            break

    if not found:
        return 'Inne'
    else:
        return first


def add_borough(dane):
    dane['Borough'] = dane['Location'].map(lambda loc: find_borough(loc))


def write_plot(dane, filename):

    dane['Borough'].value_counts().plot(kind='bar', figsize = (10, 10))
    plt.savefig(filename)


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


def find_13(dane):

    ff = dane[dane['Floor'] == 13]
    return ff['Borough'].unique()


def find_best_flats(dane):

    bf = dane[(dane['Rooms'] == 3) & (dane['Floor'] == 1) & (dane['Borough'] == 'Winogrady')]
    return bf


def main():

    dane = wczytaj_dane()
    add_borough(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)))

    print("Dzielnice z mieszkaniami na 13 piętrze, to: {}"
          .format(find_13(dane)))

    ile = 10
    print("Najtańsze oferty mieszkań, to: {}"
          .format(cheapest_flats(dane, ile)))

    write_plot(dane, 'mieszkania_plot.png')


if __name__ == "__main__":
    main()