Python2017/labs06/task02.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os;
os.chdir('J:\PycharmProjects\Python2017\labs06')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
plt.rcParams['figure.figsize'] = (15, 5)

def wczytaj_dane():
    dane = pd.read_csv('mieszkania.csv', sep=',')
    dane.rename(columns={'Unnamed: 11': 'Borough'}, inplace=True)
    #type(dane)
    #print(dane.head())
    return(dane)
    pass


def most_common_room_number(dane):
    m = dane.groupby(['Rooms']).count()
    return(m['Id'].idxmax())
    pass


def cheapest_flats(dane, n):
    dane2 = dane.sort_values(by=['Expected'])
    return(dane2.head(n))
    pass


def find_borough(desc):
    dzielnice = ['Stare Miasto',
                 'Wilda',
                 'Jeżyce',
                 'Rataje',
                 'Piątkowo',
                 'Winogrady',
                 'Miłostowo',
                 'Dębiec']
    tekst = desc.split(" ")
    for i in range(len(tekst)):
        for j in range(len(dzielnice)):
            if tekst[i] == dzielnice[j]:
                return(dzielnice[j])
            else:
                j = j + 1
        i = i + 1
    return('Inne')
    pass


def add_borough(dane):
    for i in range(5000):#len(dane.axes[0])):
        dane['Borough'][i] = find_borough(dane['Location'][i])

    pass


def write_plot(dane, filename):
    my_plot = dane['Borough'].hist()
    fig = my_plot.get_figure()

    dir_name = 'J:/PycharmProjects/Python2017/labs06/'
    filename_suffix = 'pdf'
    fig.savefig(os.path.join(dir_name, filename + "." + filename_suffix))
    pass


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


def find_13(dane):
    df_filtered = dane[dane['Floor'] == 13]
    return(df_filtered['Borough'])
    pass


def find_best_flats(dane):
    df_filtered = dane[dane['Floor'] == 1]
    df_filtered2=df_filtered[df_filtered['Borough']=='Winogrady']
    df_filtered3 = df_filtered2[df_filtered2['Rooms'] == 3]
    pass


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()