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labs04 z1

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s45148 2018-01-06 00:11:57 +01:00
parent ad880b3674
commit e69a13c79a
1 changed files with 21 additions and 13 deletions
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34
labs06/task02.py
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@ -1,23 +1,27 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
import sys
import numpy as np
def wczytaj_dane():
my_data = pd.read_csv('mieszkania.csv',
encoding='utf-8',
index_col='Id')
index_col='Id',
sep = ',')
return my_data
#print(my_data)
def most_common_room_number(dane):
room_num = dane['Rooms']
room_num_rank = room_num.value_counts()
room_num_list = list(room_num_rank)
return type(room_num_rank)
rooms = dane['Rooms']
rooms_max = rooms.value_counts().index[0]
return rooms_max
#return ( room_num_list[room_num_list.index(max(room_num_list))] )
#pass
def cheapest_flats(dane, n):
pass
expected = dane['Expected']
n_cheapest = expected.sort_values().head(n)
return n_cheapest
def find_borough(desc):
dzielnice = ['Stare Miasto',
@ -28,23 +32,27 @@ def find_borough(desc):
'Winogrady',
'Miłostowo',
'Dębiec']
pass
for dzielnica in dzielnice:
if dzielnica in desc:
return dzielnica
return 'Inne'
def add_borough(dane):
pass
dane['Borough'] = dane.apply(lambda row: find_borough(row['Location']))
def write_plot(dane, filename):
pass
dane['Borough'].value_counts().plot.bar().get_figure().savefig(filename)
def mean_price(dane, room_number):
pass
mean_value = dane.loc[dane['Rooms'] == room_number]['Expected'].mean()
return mean_value
def find_13(dane):
pass
return dane.loc[dane['Floor'] == 13]['Borough'].unique()
def find_best_flats(dane):
pass
best_flats = dane.loc[(df['Borough'] == 'Winogrady') & (dane['Rooms'] == 3) & (dane['Floor'] == 1)]
return best_flats
def main():
dane = wczytaj_dane()