import urllib2
from scipy import stats
from pandas import Series, DataFrame
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
path = 'http://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data'
raw_csv = urllib2.urlopen(path)
feature_names = ('buying', 'maint', 'doors', 'persons', "log_boot", "safety")
target_name = 'eval'
all_names = feature_names + (target_name,)
df = pd.read_csv(raw_csv, names=all_names)
dfb
buying | maint | doors | persons | log_boot | safety | eval | |
---|---|---|---|---|---|---|---|
0 | vhigh | vhigh | 2 | 2 | small | low | unacc |
1 | vhigh | vhigh | 2 | 2 | small | med | unacc |
2 | vhigh | vhigh | 2 | 2 | small | high | unacc |
3 | vhigh | vhigh | 2 | 2 | med | low | unacc |
4 | vhigh | vhigh | 2 | 2 | med | med | unacc |
5 | vhigh | vhigh | 2 | 2 | med | high | unacc |
6 | vhigh | vhigh | 2 | 2 | big | low | unacc |
7 | vhigh | vhigh | 2 | 2 | big | med | unacc |
8 | vhigh | vhigh | 2 | 2 | big | high | unacc |
9 | vhigh | vhigh | 2 | 4 | small | low | unacc |
10 | vhigh | vhigh | 2 | 4 | small | med | unacc |
11 | vhigh | vhigh | 2 | 4 | small | high | unacc |
12 | vhigh | vhigh | 2 | 4 | med | low | unacc |
13 | vhigh | vhigh | 2 | 4 | med | med | unacc |
14 | vhigh | vhigh | 2 | 4 | med | high | unacc |
15 | vhigh | vhigh | 2 | 4 | big | low | unacc |
16 | vhigh | vhigh | 2 | 4 | big | med | unacc |
17 | vhigh | vhigh | 2 | 4 | big | high | unacc |
18 | vhigh | vhigh | 2 | more | small | low | unacc |
19 | vhigh | vhigh | 2 | more | small | med | unacc |
20 | vhigh | vhigh | 2 | more | small | high | unacc |
21 | vhigh | vhigh | 2 | more | med | low | unacc |
22 | vhigh | vhigh | 2 | more | med | med | unacc |
23 | vhigh | vhigh | 2 | more | med | high | unacc |
24 | vhigh | vhigh | 2 | more | big | low | unacc |
25 | vhigh | vhigh | 2 | more | big | med | unacc |
26 | vhigh | vhigh | 2 | more | big | high | unacc |
27 | vhigh | vhigh | 3 | 2 | small | low | unacc |
28 | vhigh | vhigh | 3 | 2 | small | med | unacc |
29 | vhigh | vhigh | 3 | 2 | small | high | unacc |
... | ... | ... | ... | ... | ... | ... | ... |
1698 | low | low | 4 | more | big | low | unacc |
1699 | low | low | 4 | more | big | med | good |
1700 | low | low | 4 | more | big | high | vgood |
1701 | low | low | 5more | 2 | small | low | unacc |
1702 | low | low | 5more | 2 | small | med | unacc |
1703 | low | low | 5more | 2 | small | high | unacc |
1704 | low | low | 5more | 2 | med | low | unacc |
1705 | low | low | 5more | 2 | med | med | unacc |
1706 | low | low | 5more | 2 | med | high | unacc |
1707 | low | low | 5more | 2 | big | low | unacc |
1708 | low | low | 5more | 2 | big | med | unacc |
1709 | low | low | 5more | 2 | big | high | unacc |
1710 | low | low | 5more | 4 | small | low | unacc |
1711 | low | low | 5more | 4 | small | med | acc |
1712 | low | low | 5more | 4 | small | high | good |
1713 | low | low | 5more | 4 | med | low | unacc |
1714 | low | low | 5more | 4 | med | med | good |
1715 | low | low | 5more | 4 | med | high | vgood |
1716 | low | low | 5more | 4 | big | low | unacc |
1717 | low | low | 5more | 4 | big | med | good |
1718 | low | low | 5more | 4 | big | high | vgood |
1719 | low | low | 5more | more | small | low | unacc |
1720 | low | low | 5more | more | small | med | acc |
1721 | low | low | 5more | more | small | high | good |
1722 | low | low | 5more | more | med | low | unacc |
1723 | low | low | 5more | more | med | med | good |
1724 | low | low | 5more | more | med | high | vgood |
1725 | low | low | 5more | more | big | low | unacc |
1726 | low | low | 5more | more | big | med | good |
1727 | low | low | 5more | more | big | high | vgood |
1728 rows × 7 columns