beginner/파이썬 기초
NumPy_잘라내기
johh
2019. 2. 11. 13:22
In [1]:
import numpy as np
Iris 데이터 불러오기¶
In [2]:
f = open('iris.csv')
'''작업내용'''
f.close()
In [3]:
data = []
f = open('iris.csv')
f.readline() # 첫 줄은 분류 목록이므로
for line in f:
I = line.strip().split(',')
I[0] = float(I[0])
I[1] = float(I[1])
I[2] = float(I[2])
I[3] = float(I[3])
data.append(I)
if I[4] == 'Iris-setosa':
I[4] = 0
elif I[4] == 'Iris-versicolor':
I[4] = 1
else:
I[4] = 2
f.close()
iris = np.array(data)
print(iris)
[[5.1 3.5 1.4 0.2 0. ] [4.9 3. 1.4 0.2 0. ] [4.7 3.2 1.3 0.2 0. ] [4.6 3.1 1.5 0.2 0. ] [5. 3.6 1.4 0.2 0. ] [5.4 3.9 1.7 0.4 0. ] [4.6 3.4 1.4 0.3 0. ] [5. 3.4 1.5 0.2 0. ] [4.4 2.9 1.4 0.2 0. ] [4.9 3.1 1.5 0.1 0. ] [5.4 3.7 1.5 0.2 0. ] [4.8 3.4 1.6 0.2 0. ] [4.8 3. 1.4 0.1 0. ] [4.3 3. 1.1 0.1 0. ] [5.8 4. 1.2 0.2 0. ] [5.7 4.4 1.5 0.4 0. ] [5.4 3.9 1.3 0.4 0. ] [5.1 3.5 1.4 0.3 0. ] [5.7 3.8 1.7 0.3 0. ] [5.1 3.8 1.5 0.3 0. ] [5.4 3.4 1.7 0.2 0. ] [5.1 3.7 1.5 0.4 0. ] [4.6 3.6 1. 0.2 0. ] [5.1 3.3 1.7 0.5 0. ] [4.8 3.4 1.9 0.2 0. ] [5. 3. 1.6 0.2 0. ] [5. 3.4 1.6 0.4 0. ] [5.2 3.5 1.5 0.2 0. ] [5.2 3.4 1.4 0.2 0. ] [4.7 3.2 1.6 0.2 0. ] [4.8 3.1 1.6 0.2 0. ] [5.4 3.4 1.5 0.4 0. ] [5.2 4.1 1.5 0.1 0. ] [5.5 4.2 1.4 0.2 0. ] [4.9 3.1 1.5 0.1 0. ] [5. 3.2 1.2 0.2 0. ] [5.5 3.5 1.3 0.2 0. ] [4.9 3.1 1.5 0.1 0. ] [4.4 3. 1.3 0.2 0. ] [5.1 3.4 1.5 0.2 0. ] [5. 3.5 1.3 0.3 0. ] [4.5 2.3 1.3 0.3 0. ] [4.4 3.2 1.3 0.2 0. ] [5. 3.5 1.6 0.6 0. ] [5.1 3.8 1.9 0.4 0. ] [4.8 3. 1.4 0.3 0. ] [5.1 3.8 1.6 0.2 0. ] [4.6 3.2 1.4 0.2 0. ] [5.3 3.7 1.5 0.2 0. ] [5. 3.3 1.4 0.2 0. ] [7. 3.2 4.7 1.4 1. ] [6.4 3.2 4.5 1.5 1. ] [6.9 3.1 4.9 1.5 1. ] [5.5 2.3 4. 1.3 1. ] [6.5 2.8 4.6 1.5 1. ] [5.7 2.8 4.5 1.3 1. ] [6.3 3.3 4.7 1.6 1. ] [4.9 2.4 3.3 1. 1. ] [6.6 2.9 4.6 1.3 1. ] [5.2 2.7 3.9 1.4 1. ] [5. 2. 3.5 1. 1. ] [5.9 3. 4.2 1.5 1. ] [6. 2.2 4. 1. 1. ] [6.1 2.9 4.7 1.4 1. ] [5.6 2.9 3.6 1.3 1. ] [6.7 3.1 4.4 1.4 1. ] [5.6 3. 4.5 1.5 1. ] [5.8 2.7 4.1 1. 1. ] [6.2 2.2 4.5 1.5 1. ] [5.6 2.5 3.9 1.1 1. ] [5.9 3.2 4.8 1.8 1. ] [6.1 2.8 4. 1.3 1. ] [6.3 2.5 4.9 1.5 1. ] [6.1 2.8 4.7 1.2 1. ] [6.4 2.9 4.3 1.3 1. ] [6.6 3. 4.4 1.4 1. ] [6.8 2.8 4.8 1.4 1. ] [6.7 3. 5. 1.7 1. ] [6. 2.9 4.5 1.5 1. ] [5.7 2.6 3.5 1. 1. ] [5.5 2.4 3.8 1.1 1. ] [5.5 2.4 3.7 1. 1. ] [5.8 2.7 3.9 1.2 1. ] [6. 2.7 5.1 1.6 1. ] [5.4 3. 4.5 1.5 1. ] [6. 3.4 4.5 1.6 1. ] [6.7 3.1 4.7 1.5 1. ] [6.3 2.3 4.4 1.3 1. ] [5.6 3. 4.1 1.3 1. ] [5.5 2.5 4. 1.3 1. ] [5.5 2.6 4.4 1.2 1. ] [6.1 3. 4.6 1.4 1. ] [5.8 2.6 4. 1.2 1. ] [5. 2.3 3.3 1. 1. ] [5.6 2.7 4.2 1.3 1. ] [5.7 3. 4.2 1.2 1. ] [5.7 2.9 4.2 1.3 1. ] [6.2 2.9 4.3 1.3 1. ] [5.1 2.5 3. 1.1 1. ] [5.7 2.8 4.1 1.3 1. ] [6.3 3.3 6. 2.5 2. ] [5.8 2.7 5.1 1.9 2. ] [7.1 3. 5.9 2.1 2. ] [6.3 2.9 5.6 1.8 2. ] [6.5 3. 5.8 2.2 2. ] [7.6 3. 6.6 2.1 2. ] [4.9 2.5 4.5 1.7 2. ] [7.3 2.9 6.3 1.8 2. ] [6.7 2.5 5.8 1.8 2. ] [7.2 3.6 6.1 2.5 2. ] [6.5 3.2 5.1 2. 2. ] [6.4 2.7 5.3 1.9 2. ] [6.8 3. 5.5 2.1 2. ] [5.7 2.5 5. 2. 2. ] [5.8 2.8 5.1 2.4 2. ] [6.4 3.2 5.3 2.3 2. ] [6.5 3. 5.5 1.8 2. ] [7.7 3.8 6.7 2.2 2. ] [7.7 2.6 6.9 2.3 2. ] [6. 2.2 5. 1.5 2. ] [6.9 3.2 5.7 2.3 2. ] [5.6 2.8 4.9 2. 2. ] [7.7 2.8 6.7 2. 2. ] [6.3 2.7 4.9 1.8 2. ] [6.7 3.3 5.7 2.1 2. ] [7.2 3.2 6. 1.8 2. ] [6.2 2.8 4.8 1.8 2. ] [6.1 3. 4.9 1.8 2. ] [6.4 2.8 5.6 2.1 2. ] [7.2 3. 5.8 1.6 2. ] [7.4 2.8 6.1 1.9 2. ] [7.9 3.8 6.4 2. 2. ] [6.4 2.8 5.6 2.2 2. ] [6.3 2.8 5.1 1.5 2. ] [6.1 2.6 5.6 1.4 2. ] [7.7 3. 6.1 2.3 2. ] [6.3 3.4 5.6 2.4 2. ] [6.4 3.1 5.5 1.8 2. ] [6. 3. 4.8 1.8 2. ] [6.9 3.1 5.4 2.1 2. ] [6.7 3.1 5.6 2.4 2. ] [6.9 3.1 5.1 2.3 2. ] [5.8 2.7 5.1 1.9 2. ] [6.8 3.2 5.9 2.3 2. ] [6.7 3.3 5.7 2.5 2. ] [6.7 3. 5.2 2.3 2. ] [6.3 2.5 5. 1.9 2. ] [6.5 3. 5.2 2. 2. ] [6.2 3.4 5.4 2.3 2. ] [5.9 3. 5.1 1.8 2. ]]
In [4]:
iris.shape
Out[4]:
(150, 5)
In [5]:
iris[0]
Out[5]:
array([5.1, 3.5, 1.4, 0.2, 0. ])
In [6]:
iris[:,4]
Out[6]:
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.])
In [7]:
f = open('iris.csv')
line = f.readline()
features = line.strip().split(',')[:4]
labels = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']
data = []
for line in f:
I = line.strip().split(',')
I[:4] = [float(i) for i in I[:4]]
I[4] = labels.index(I[4])
data.append(I)
f.close()
iris = np.array(data)
display : print와 유사한데 아래로 쭉 써준다
In [8]:
display(type(iris), iris.shape, len(iris), iris[0])
numpy.ndarray
(150, 5)
150
array([5.1, 3.5, 1.4, 0.2, 0. ])
데이터를 가공해서 더 잘 다루기 위해 list를 NumPy로 바꾼다.
행(샘플, 레코드)읽기¶
In [9]:
iris[0]
Out[9]:
array([5.1, 3.5, 1.4, 0.2, 0. ])
In [10]:
iris[1]
Out[10]:
array([4.9, 3. , 1.4, 0.2, 0. ])
In [11]:
iris[149]
Out[11]:
array([5.9, 3. , 5.1, 1.8, 2. ])
In [12]:
iris[-1]
Out[12]:
array([5.9, 3. , 5.1, 1.8, 2. ])
In [13]:
iris[150] # 0부터 시작하니 마지막 줄은 149이다.
--------------------------------------------------------------------------- IndexError Traceback (most recent call last) <ipython-input-13-ba6032c5562e> in <module>() ----> 1 iris[150] # 0부터 시작하니 마지막 줄은 149이다. IndexError: index 150 is out of bounds for axis 0 with size 150
In [ ]:
iris[0:5]
In [14]:
iris[50:55]
Out[14]:
array([[7. , 3.2, 4.7, 1.4, 1. ],
[6.4, 3.2, 4.5, 1.5, 1. ],
[6.9, 3.1, 4.9, 1.5, 1. ],
[5.5, 2.3, 4. , 1.3, 1. ],
[6.5, 2.8, 4.6, 1.5, 1. ]])
한 항목 읽기¶
In [15]:
iris[0,0]
Out[15]:
5.1
In [16]:
iris[50,3]
Out[16]:
1.4
In [17]:
iris[0,4], iris[0,-1], iris[-1,0], iris[-1,-1]
Out[17]:
(0.0, 0.0, 5.9, 2.0)
열(칼럼,속성)읽기¶
In [18]:
iris[:,0]
Out[18]:
array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,
4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,
5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,
5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. , 7. , 6.4,
6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5. , 5.9, 6. , 6.1, 5.6,
6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7,
6. , 5.7, 5.5, 5.5, 5.8, 6. , 5.4, 6. , 6.7, 6.3, 5.6, 5.5, 5.5,
6.1, 5.8, 5. , 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3,
6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5,
7.7, 7.7, 6. , 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2,
7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6. , 6.9, 6.7, 6.9, 5.8,
6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9])
In [19]:
iris[:,-1]
Out[19]:
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.])
In [20]:
X = iris[:,:4]
X
Out[20]:
array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2],
[5. , 3.6, 1.4, 0.2],
[5.4, 3.9, 1.7, 0.4],
[4.6, 3.4, 1.4, 0.3],
[5. , 3.4, 1.5, 0.2],
[4.4, 2.9, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1],
[5.4, 3.7, 1.5, 0.2],
[4.8, 3.4, 1.6, 0.2],
[4.8, 3. , 1.4, 0.1],
[4.3, 3. , 1.1, 0.1],
[5.8, 4. , 1.2, 0.2],
[5.7, 4.4, 1.5, 0.4],
[5.4, 3.9, 1.3, 0.4],
[5.1, 3.5, 1.4, 0.3],
[5.7, 3.8, 1.7, 0.3],
[5.1, 3.8, 1.5, 0.3],
[5.4, 3.4, 1.7, 0.2],
[5.1, 3.7, 1.5, 0.4],
[4.6, 3.6, 1. , 0.2],
[5.1, 3.3, 1.7, 0.5],
[4.8, 3.4, 1.9, 0.2],
[5. , 3. , 1.6, 0.2],
[5. , 3.4, 1.6, 0.4],
[5.2, 3.5, 1.5, 0.2],
[5.2, 3.4, 1.4, 0.2],
[4.7, 3.2, 1.6, 0.2],
[4.8, 3.1, 1.6, 0.2],
[5.4, 3.4, 1.5, 0.4],
[5.2, 4.1, 1.5, 0.1],
[5.5, 4.2, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1],
[5. , 3.2, 1.2, 0.2],
[5.5, 3.5, 1.3, 0.2],
[4.9, 3.1, 1.5, 0.1],
[4.4, 3. , 1.3, 0.2],
[5.1, 3.4, 1.5, 0.2],
[5. , 3.5, 1.3, 0.3],
[4.5, 2.3, 1.3, 0.3],
[4.4, 3.2, 1.3, 0.2],
[5. , 3.5, 1.6, 0.6],
[5.1, 3.8, 1.9, 0.4],
[4.8, 3. , 1.4, 0.3],
[5.1, 3.8, 1.6, 0.2],
[4.6, 3.2, 1.4, 0.2],
[5.3, 3.7, 1.5, 0.2],
[5. , 3.3, 1.4, 0.2],
[7. , 3.2, 4.7, 1.4],
[6.4, 3.2, 4.5, 1.5],
[6.9, 3.1, 4.9, 1.5],
[5.5, 2.3, 4. , 1.3],
[6.5, 2.8, 4.6, 1.5],
[5.7, 2.8, 4.5, 1.3],
[6.3, 3.3, 4.7, 1.6],
[4.9, 2.4, 3.3, 1. ],
[6.6, 2.9, 4.6, 1.3],
[5.2, 2.7, 3.9, 1.4],
[5. , 2. , 3.5, 1. ],
[5.9, 3. , 4.2, 1.5],
[6. , 2.2, 4. , 1. ],
[6.1, 2.9, 4.7, 1.4],
[5.6, 2.9, 3.6, 1.3],
[6.7, 3.1, 4.4, 1.4],
[5.6, 3. , 4.5, 1.5],
[5.8, 2.7, 4.1, 1. ],
[6.2, 2.2, 4.5, 1.5],
[5.6, 2.5, 3.9, 1.1],
[5.9, 3.2, 4.8, 1.8],
[6.1, 2.8, 4. , 1.3],
[6.3, 2.5, 4.9, 1.5],
[6.1, 2.8, 4.7, 1.2],
[6.4, 2.9, 4.3, 1.3],
[6.6, 3. , 4.4, 1.4],
[6.8, 2.8, 4.8, 1.4],
[6.7, 3. , 5. , 1.7],
[6. , 2.9, 4.5, 1.5],
[5.7, 2.6, 3.5, 1. ],
[5.5, 2.4, 3.8, 1.1],
[5.5, 2.4, 3.7, 1. ],
[5.8, 2.7, 3.9, 1.2],
[6. , 2.7, 5.1, 1.6],
[5.4, 3. , 4.5, 1.5],
[6. , 3.4, 4.5, 1.6],
[6.7, 3.1, 4.7, 1.5],
[6.3, 2.3, 4.4, 1.3],
[5.6, 3. , 4.1, 1.3],
[5.5, 2.5, 4. , 1.3],
[5.5, 2.6, 4.4, 1.2],
[6.1, 3. , 4.6, 1.4],
[5.8, 2.6, 4. , 1.2],
[5. , 2.3, 3.3, 1. ],
[5.6, 2.7, 4.2, 1.3],
[5.7, 3. , 4.2, 1.2],
[5.7, 2.9, 4.2, 1.3],
[6.2, 2.9, 4.3, 1.3],
[5.1, 2.5, 3. , 1.1],
[5.7, 2.8, 4.1, 1.3],
[6.3, 3.3, 6. , 2.5],
[5.8, 2.7, 5.1, 1.9],
[7.1, 3. , 5.9, 2.1],
[6.3, 2.9, 5.6, 1.8],
[6.5, 3. , 5.8, 2.2],
[7.6, 3. , 6.6, 2.1],
[4.9, 2.5, 4.5, 1.7],
[7.3, 2.9, 6.3, 1.8],
[6.7, 2.5, 5.8, 1.8],
[7.2, 3.6, 6.1, 2.5],
[6.5, 3.2, 5.1, 2. ],
[6.4, 2.7, 5.3, 1.9],
[6.8, 3. , 5.5, 2.1],
[5.7, 2.5, 5. , 2. ],
[5.8, 2.8, 5.1, 2.4],
[6.4, 3.2, 5.3, 2.3],
[6.5, 3. , 5.5, 1.8],
[7.7, 3.8, 6.7, 2.2],
[7.7, 2.6, 6.9, 2.3],
[6. , 2.2, 5. , 1.5],
[6.9, 3.2, 5.7, 2.3],
[5.6, 2.8, 4.9, 2. ],
[7.7, 2.8, 6.7, 2. ],
[6.3, 2.7, 4.9, 1.8],
[6.7, 3.3, 5.7, 2.1],
[7.2, 3.2, 6. , 1.8],
[6.2, 2.8, 4.8, 1.8],
[6.1, 3. , 4.9, 1.8],
[6.4, 2.8, 5.6, 2.1],
[7.2, 3. , 5.8, 1.6],
[7.4, 2.8, 6.1, 1.9],
[7.9, 3.8, 6.4, 2. ],
[6.4, 2.8, 5.6, 2.2],
[6.3, 2.8, 5.1, 1.5],
[6.1, 2.6, 5.6, 1.4],
[7.7, 3. , 6.1, 2.3],
[6.3, 3.4, 5.6, 2.4],
[6.4, 3.1, 5.5, 1.8],
[6. , 3. , 4.8, 1.8],
[6.9, 3.1, 5.4, 2.1],
[6.7, 3.1, 5.6, 2.4],
[6.9, 3.1, 5.1, 2.3],
[5.8, 2.7, 5.1, 1.9],
[6.8, 3.2, 5.9, 2.3],
[6.7, 3.3, 5.7, 2.5],
[6.7, 3. , 5.2, 2.3],
[6.3, 2.5, 5. , 1.9],
[6.5, 3. , 5.2, 2. ],
[6.2, 3.4, 5.4, 2.3],
[5.9, 3. , 5.1, 1.8]])
In [21]:
X.shape
Out[21]:
(150, 4)
In [22]:
y = iris[:,4]
y
Out[22]:
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.])
In [23]:
y.shape
Out[23]:
(150,)
데이타와 레이블(target)을 분리
In [24]:
# iris[행번호, 열번호]
그래프 그려보기¶
In [25]:
import matplotlib.pyplot as plt
In [26]:
plt.plot(iris[:,0])
Out[26]:
[<matplotlib.lines.Line2D at 0xece15bbba8>]
In [27]:
plt.plot(iris[:,-1])
Out[27]:
[<matplotlib.lines.Line2D at 0xece165d198>]
In [28]:
plt.plot(iris[:,1])
Out[28]:
[<matplotlib.lines.Line2D at 0xece130b550>]
In [29]:
plt.plot(iris[:,0])
plt.plot(iris[:,1])
plt.plot(iris[:,2])
plt.plot(iris[:,3])
plt.title('Iris features', fontsize = 15)
plt.legend(['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth'])
Out[29]:
<matplotlib.legend.Legend at 0xece163b0f0>
In [30]:
plt.scatter(iris[:,0], iris[:,1], c=iris[:,-1]) #c는 컬러
plt.colorbar()
plt.xlabel('SepalLength')
plt.ylabel('SepalWidth') #속성 4개를 가지고 6종류의 산점도를 그릴수 있다.
Out[30]:
Text(0,0.5,'SepalWidth')
In [31]:
plt.plot(iris[:50,:4].T, 'r-', alpha=0.1)
plt.plot(iris[50:100,:4].T, 'g-',alpha=0.1)
plt.plot(iris[100:,:4].T, 'b-', alpha=0.1)
plt.xticks(range(4),['SepalLength','SepalWidth', 'PetalLength', 'PetalWidth'])
pass
In [32]:
iris[:,0]
Out[32]:
array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,
4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,
5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,
5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. , 7. , 6.4,
6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5. , 5.9, 6. , 6.1, 5.6,
6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7,
6. , 5.7, 5.5, 5.5, 5.8, 6. , 5.4, 6. , 6.7, 6.3, 5.6, 5.5, 5.5,
6.1, 5.8, 5. , 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3,
6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5,
7.7, 7.7, 6. , 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2,
7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6. , 6.9, 6.7, 6.9, 5.8,
6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9])
In [33]:
iris[:,0]
Out[33]:
array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,
4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,
5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,
5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. , 7. , 6.4,
6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5. , 5.9, 6. , 6.1, 5.6,
6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7,
6. , 5.7, 5.5, 5.5, 5.8, 6. , 5.4, 6. , 6.7, 6.3, 5.6, 5.5, 5.5,
6.1, 5.8, 5. , 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3,
6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5,
7.7, 7.7, 6. , 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2,
7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6. , 6.9, 6.7, 6.9, 5.8,
6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9])
In [34]:
data
Out[34]:
[[5.1, 3.5, 1.4, 0.2, 0], [4.9, 3.0, 1.4, 0.2, 0], [4.7, 3.2, 1.3, 0.2, 0], [4.6, 3.1, 1.5, 0.2, 0], [5.0, 3.6, 1.4, 0.2, 0], [5.4, 3.9, 1.7, 0.4, 0], [4.6, 3.4, 1.4, 0.3, 0], [5.0, 3.4, 1.5, 0.2, 0], [4.4, 2.9, 1.4, 0.2, 0], [4.9, 3.1, 1.5, 0.1, 0], [5.4, 3.7, 1.5, 0.2, 0], [4.8, 3.4, 1.6, 0.2, 0], [4.8, 3.0, 1.4, 0.1, 0], [4.3, 3.0, 1.1, 0.1, 0], [5.8, 4.0, 1.2, 0.2, 0], [5.7, 4.4, 1.5, 0.4, 0], [5.4, 3.9, 1.3, 0.4, 0], [5.1, 3.5, 1.4, 0.3, 0], [5.7, 3.8, 1.7, 0.3, 0], [5.1, 3.8, 1.5, 0.3, 0], [5.4, 3.4, 1.7, 0.2, 0], [5.1, 3.7, 1.5, 0.4, 0], [4.6, 3.6, 1.0, 0.2, 0], [5.1, 3.3, 1.7, 0.5, 0], [4.8, 3.4, 1.9, 0.2, 0], [5.0, 3.0, 1.6, 0.2, 0], [5.0, 3.4, 1.6, 0.4, 0], [5.2, 3.5, 1.5, 0.2, 0], [5.2, 3.4, 1.4, 0.2, 0], [4.7, 3.2, 1.6, 0.2, 0], [4.8, 3.1, 1.6, 0.2, 0], [5.4, 3.4, 1.5, 0.4, 0], [5.2, 4.1, 1.5, 0.1, 0], [5.5, 4.2, 1.4, 0.2, 0], [4.9, 3.1, 1.5, 0.1, 0], [5.0, 3.2, 1.2, 0.2, 0], [5.5, 3.5, 1.3, 0.2, 0], [4.9, 3.1, 1.5, 0.1, 0], [4.4, 3.0, 1.3, 0.2, 0], [5.1, 3.4, 1.5, 0.2, 0], [5.0, 3.5, 1.3, 0.3, 0], [4.5, 2.3, 1.3, 0.3, 0], [4.4, 3.2, 1.3, 0.2, 0], [5.0, 3.5, 1.6, 0.6, 0], [5.1, 3.8, 1.9, 0.4, 0], [4.8, 3.0, 1.4, 0.3, 0], [5.1, 3.8, 1.6, 0.2, 0], [4.6, 3.2, 1.4, 0.2, 0], [5.3, 3.7, 1.5, 0.2, 0], [5.0, 3.3, 1.4, 0.2, 0], [7.0, 3.2, 4.7, 1.4, 1], [6.4, 3.2, 4.5, 1.5, 1], [6.9, 3.1, 4.9, 1.5, 1], [5.5, 2.3, 4.0, 1.3, 1], [6.5, 2.8, 4.6, 1.5, 1], [5.7, 2.8, 4.5, 1.3, 1], [6.3, 3.3, 4.7, 1.6, 1], [4.9, 2.4, 3.3, 1.0, 1], [6.6, 2.9, 4.6, 1.3, 1], [5.2, 2.7, 3.9, 1.4, 1], [5.0, 2.0, 3.5, 1.0, 1], [5.9, 3.0, 4.2, 1.5, 1], [6.0, 2.2, 4.0, 1.0, 1], [6.1, 2.9, 4.7, 1.4, 1], [5.6, 2.9, 3.6, 1.3, 1], [6.7, 3.1, 4.4, 1.4, 1], [5.6, 3.0, 4.5, 1.5, 1], [5.8, 2.7, 4.1, 1.0, 1], [6.2, 2.2, 4.5, 1.5, 1], [5.6, 2.5, 3.9, 1.1, 1], [5.9, 3.2, 4.8, 1.8, 1], [6.1, 2.8, 4.0, 1.3, 1], [6.3, 2.5, 4.9, 1.5, 1], [6.1, 2.8, 4.7, 1.2, 1], [6.4, 2.9, 4.3, 1.3, 1], [6.6, 3.0, 4.4, 1.4, 1], [6.8, 2.8, 4.8, 1.4, 1], [6.7, 3.0, 5.0, 1.7, 1], [6.0, 2.9, 4.5, 1.5, 1], [5.7, 2.6, 3.5, 1.0, 1], [5.5, 2.4, 3.8, 1.1, 1], [5.5, 2.4, 3.7, 1.0, 1], [5.8, 2.7, 3.9, 1.2, 1], [6.0, 2.7, 5.1, 1.6, 1], [5.4, 3.0, 4.5, 1.5, 1], [6.0, 3.4, 4.5, 1.6, 1], [6.7, 3.1, 4.7, 1.5, 1], [6.3, 2.3, 4.4, 1.3, 1], [5.6, 3.0, 4.1, 1.3, 1], [5.5, 2.5, 4.0, 1.3, 1], [5.5, 2.6, 4.4, 1.2, 1], [6.1, 3.0, 4.6, 1.4, 1], [5.8, 2.6, 4.0, 1.2, 1], [5.0, 2.3, 3.3, 1.0, 1], [5.6, 2.7, 4.2, 1.3, 1], [5.7, 3.0, 4.2, 1.2, 1], [5.7, 2.9, 4.2, 1.3, 1], [6.2, 2.9, 4.3, 1.3, 1], [5.1, 2.5, 3.0, 1.1, 1], [5.7, 2.8, 4.1, 1.3, 1], [6.3, 3.3, 6.0, 2.5, 2], [5.8, 2.7, 5.1, 1.9, 2], [7.1, 3.0, 5.9, 2.1, 2], [6.3, 2.9, 5.6, 1.8, 2], [6.5, 3.0, 5.8, 2.2, 2], [7.6, 3.0, 6.6, 2.1, 2], [4.9, 2.5, 4.5, 1.7, 2], [7.3, 2.9, 6.3, 1.8, 2], [6.7, 2.5, 5.8, 1.8, 2], [7.2, 3.6, 6.1, 2.5, 2], [6.5, 3.2, 5.1, 2.0, 2], [6.4, 2.7, 5.3, 1.9, 2], [6.8, 3.0, 5.5, 2.1, 2], [5.7, 2.5, 5.0, 2.0, 2], [5.8, 2.8, 5.1, 2.4, 2], [6.4, 3.2, 5.3, 2.3, 2], [6.5, 3.0, 5.5, 1.8, 2], [7.7, 3.8, 6.7, 2.2, 2], [7.7, 2.6, 6.9, 2.3, 2], [6.0, 2.2, 5.0, 1.5, 2], [6.9, 3.2, 5.7, 2.3, 2], [5.6, 2.8, 4.9, 2.0, 2], [7.7, 2.8, 6.7, 2.0, 2], [6.3, 2.7, 4.9, 1.8, 2], [6.7, 3.3, 5.7, 2.1, 2], [7.2, 3.2, 6.0, 1.8, 2], [6.2, 2.8, 4.8, 1.8, 2], [6.1, 3.0, 4.9, 1.8, 2], [6.4, 2.8, 5.6, 2.1, 2], [7.2, 3.0, 5.8, 1.6, 2], [7.4, 2.8, 6.1, 1.9, 2], [7.9, 3.8, 6.4, 2.0, 2], [6.4, 2.8, 5.6, 2.2, 2], [6.3, 2.8, 5.1, 1.5, 2], [6.1, 2.6, 5.6, 1.4, 2], [7.7, 3.0, 6.1, 2.3, 2], [6.3, 3.4, 5.6, 2.4, 2], [6.4, 3.1, 5.5, 1.8, 2], [6.0, 3.0, 4.8, 1.8, 2], [6.9, 3.1, 5.4, 2.1, 2], [6.7, 3.1, 5.6, 2.4, 2], [6.9, 3.1, 5.1, 2.3, 2], [5.8, 2.7, 5.1, 1.9, 2], [6.8, 3.2, 5.9, 2.3, 2], [6.7, 3.3, 5.7, 2.5, 2], [6.7, 3.0, 5.2, 2.3, 2], [6.3, 2.5, 5.0, 1.9, 2], [6.5, 3.0, 5.2, 2.0, 2], [6.2, 3.4, 5.4, 2.3, 2], [5.9, 3.0, 5.1, 1.8, 2]]
In [35]:
slen2=[]
for a in data:
slen2.append(a[0])
slen2
Out[35]:
[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
In [36]:
type(data)
Out[36]:
list
In [37]:
slen1=[]
for i in data:
slen1.append(i[:4])
slen1
Out[37]:
[[5.1, 3.5, 1.4, 0.2], [4.9, 3.0, 1.4, 0.2], [4.7, 3.2, 1.3, 0.2], [4.6, 3.1, 1.5, 0.2], [5.0, 3.6, 1.4, 0.2], [5.4, 3.9, 1.7, 0.4], [4.6, 3.4, 1.4, 0.3], [5.0, 3.4, 1.5, 0.2], [4.4, 2.9, 1.4, 0.2], [4.9, 3.1, 1.5, 0.1], [5.4, 3.7, 1.5, 0.2], [4.8, 3.4, 1.6, 0.2], [4.8, 3.0, 1.4, 0.1], [4.3, 3.0, 1.1, 0.1], [5.8, 4.0, 1.2, 0.2], [5.7, 4.4, 1.5, 0.4], [5.4, 3.9, 1.3, 0.4], [5.1, 3.5, 1.4, 0.3], [5.7, 3.8, 1.7, 0.3], [5.1, 3.8, 1.5, 0.3], [5.4, 3.4, 1.7, 0.2], [5.1, 3.7, 1.5, 0.4], [4.6, 3.6, 1.0, 0.2], [5.1, 3.3, 1.7, 0.5], [4.8, 3.4, 1.9, 0.2], [5.0, 3.0, 1.6, 0.2], [5.0, 3.4, 1.6, 0.4], [5.2, 3.5, 1.5, 0.2], [5.2, 3.4, 1.4, 0.2], [4.7, 3.2, 1.6, 0.2], [4.8, 3.1, 1.6, 0.2], [5.4, 3.4, 1.5, 0.4], [5.2, 4.1, 1.5, 0.1], [5.5, 4.2, 1.4, 0.2], [4.9, 3.1, 1.5, 0.1], [5.0, 3.2, 1.2, 0.2], [5.5, 3.5, 1.3, 0.2], [4.9, 3.1, 1.5, 0.1], [4.4, 3.0, 1.3, 0.2], [5.1, 3.4, 1.5, 0.2], [5.0, 3.5, 1.3, 0.3], [4.5, 2.3, 1.3, 0.3], [4.4, 3.2, 1.3, 0.2], [5.0, 3.5, 1.6, 0.6], [5.1, 3.8, 1.9, 0.4], [4.8, 3.0, 1.4, 0.3], [5.1, 3.8, 1.6, 0.2], [4.6, 3.2, 1.4, 0.2], [5.3, 3.7, 1.5, 0.2], [5.0, 3.3, 1.4, 0.2], [7.0, 3.2, 4.7, 1.4], [6.4, 3.2, 4.5, 1.5], [6.9, 3.1, 4.9, 1.5], [5.5, 2.3, 4.0, 1.3], [6.5, 2.8, 4.6, 1.5], [5.7, 2.8, 4.5, 1.3], [6.3, 3.3, 4.7, 1.6], [4.9, 2.4, 3.3, 1.0], [6.6, 2.9, 4.6, 1.3], [5.2, 2.7, 3.9, 1.4], [5.0, 2.0, 3.5, 1.0], [5.9, 3.0, 4.2, 1.5], [6.0, 2.2, 4.0, 1.0], [6.1, 2.9, 4.7, 1.4], [5.6, 2.9, 3.6, 1.3], [6.7, 3.1, 4.4, 1.4], [5.6, 3.0, 4.5, 1.5], [5.8, 2.7, 4.1, 1.0], [6.2, 2.2, 4.5, 1.5], [5.6, 2.5, 3.9, 1.1], [5.9, 3.2, 4.8, 1.8], [6.1, 2.8, 4.0, 1.3], [6.3, 2.5, 4.9, 1.5], [6.1, 2.8, 4.7, 1.2], [6.4, 2.9, 4.3, 1.3], [6.6, 3.0, 4.4, 1.4], [6.8, 2.8, 4.8, 1.4], [6.7, 3.0, 5.0, 1.7], [6.0, 2.9, 4.5, 1.5], [5.7, 2.6, 3.5, 1.0], [5.5, 2.4, 3.8, 1.1], [5.5, 2.4, 3.7, 1.0], [5.8, 2.7, 3.9, 1.2], [6.0, 2.7, 5.1, 1.6], [5.4, 3.0, 4.5, 1.5], [6.0, 3.4, 4.5, 1.6], [6.7, 3.1, 4.7, 1.5], [6.3, 2.3, 4.4, 1.3], [5.6, 3.0, 4.1, 1.3], [5.5, 2.5, 4.0, 1.3], [5.5, 2.6, 4.4, 1.2], [6.1, 3.0, 4.6, 1.4], [5.8, 2.6, 4.0, 1.2], [5.0, 2.3, 3.3, 1.0], [5.6, 2.7, 4.2, 1.3], [5.7, 3.0, 4.2, 1.2], [5.7, 2.9, 4.2, 1.3], [6.2, 2.9, 4.3, 1.3], [5.1, 2.5, 3.0, 1.1], [5.7, 2.8, 4.1, 1.3], [6.3, 3.3, 6.0, 2.5], [5.8, 2.7, 5.1, 1.9], [7.1, 3.0, 5.9, 2.1], [6.3, 2.9, 5.6, 1.8], [6.5, 3.0, 5.8, 2.2], [7.6, 3.0, 6.6, 2.1], [4.9, 2.5, 4.5, 1.7], [7.3, 2.9, 6.3, 1.8], [6.7, 2.5, 5.8, 1.8], [7.2, 3.6, 6.1, 2.5], [6.5, 3.2, 5.1, 2.0], [6.4, 2.7, 5.3, 1.9], [6.8, 3.0, 5.5, 2.1], [5.7, 2.5, 5.0, 2.0], [5.8, 2.8, 5.1, 2.4], [6.4, 3.2, 5.3, 2.3], [6.5, 3.0, 5.5, 1.8], [7.7, 3.8, 6.7, 2.2], [7.7, 2.6, 6.9, 2.3], [6.0, 2.2, 5.0, 1.5], [6.9, 3.2, 5.7, 2.3], [5.6, 2.8, 4.9, 2.0], [7.7, 2.8, 6.7, 2.0], [6.3, 2.7, 4.9, 1.8], [6.7, 3.3, 5.7, 2.1], [7.2, 3.2, 6.0, 1.8], [6.2, 2.8, 4.8, 1.8], [6.1, 3.0, 4.9, 1.8], [6.4, 2.8, 5.6, 2.1], [7.2, 3.0, 5.8, 1.6], [7.4, 2.8, 6.1, 1.9], [7.9, 3.8, 6.4, 2.0], [6.4, 2.8, 5.6, 2.2], [6.3, 2.8, 5.1, 1.5], [6.1, 2.6, 5.6, 1.4], [7.7, 3.0, 6.1, 2.3], [6.3, 3.4, 5.6, 2.4], [6.4, 3.1, 5.5, 1.8], [6.0, 3.0, 4.8, 1.8], [6.9, 3.1, 5.4, 2.1], [6.7, 3.1, 5.6, 2.4], [6.9, 3.1, 5.1, 2.3], [5.8, 2.7, 5.1, 1.9], [6.8, 3.2, 5.9, 2.3], [6.7, 3.3, 5.7, 2.5], [6.7, 3.0, 5.2, 2.3], [6.3, 2.5, 5.0, 1.9], [6.5, 3.0, 5.2, 2.0], [6.2, 3.4, 5.4, 2.3], [5.9, 3.0, 5.1, 1.8]]
In [38]:
X = iris[:,:4]
y = iris[:,4]
In [39]:
X
Out[39]:
array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2],
[5. , 3.6, 1.4, 0.2],
[5.4, 3.9, 1.7, 0.4],
[4.6, 3.4, 1.4, 0.3],
[5. , 3.4, 1.5, 0.2],
[4.4, 2.9, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1],
[5.4, 3.7, 1.5, 0.2],
[4.8, 3.4, 1.6, 0.2],
[4.8, 3. , 1.4, 0.1],
[4.3, 3. , 1.1, 0.1],
[5.8, 4. , 1.2, 0.2],
[5.7, 4.4, 1.5, 0.4],
[5.4, 3.9, 1.3, 0.4],
[5.1, 3.5, 1.4, 0.3],
[5.7, 3.8, 1.7, 0.3],
[5.1, 3.8, 1.5, 0.3],
[5.4, 3.4, 1.7, 0.2],
[5.1, 3.7, 1.5, 0.4],
[4.6, 3.6, 1. , 0.2],
[5.1, 3.3, 1.7, 0.5],
[4.8, 3.4, 1.9, 0.2],
[5. , 3. , 1.6, 0.2],
[5. , 3.4, 1.6, 0.4],
[5.2, 3.5, 1.5, 0.2],
[5.2, 3.4, 1.4, 0.2],
[4.7, 3.2, 1.6, 0.2],
[4.8, 3.1, 1.6, 0.2],
[5.4, 3.4, 1.5, 0.4],
[5.2, 4.1, 1.5, 0.1],
[5.5, 4.2, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1],
[5. , 3.2, 1.2, 0.2],
[5.5, 3.5, 1.3, 0.2],
[4.9, 3.1, 1.5, 0.1],
[4.4, 3. , 1.3, 0.2],
[5.1, 3.4, 1.5, 0.2],
[5. , 3.5, 1.3, 0.3],
[4.5, 2.3, 1.3, 0.3],
[4.4, 3.2, 1.3, 0.2],
[5. , 3.5, 1.6, 0.6],
[5.1, 3.8, 1.9, 0.4],
[4.8, 3. , 1.4, 0.3],
[5.1, 3.8, 1.6, 0.2],
[4.6, 3.2, 1.4, 0.2],
[5.3, 3.7, 1.5, 0.2],
[5. , 3.3, 1.4, 0.2],
[7. , 3.2, 4.7, 1.4],
[6.4, 3.2, 4.5, 1.5],
[6.9, 3.1, 4.9, 1.5],
[5.5, 2.3, 4. , 1.3],
[6.5, 2.8, 4.6, 1.5],
[5.7, 2.8, 4.5, 1.3],
[6.3, 3.3, 4.7, 1.6],
[4.9, 2.4, 3.3, 1. ],
[6.6, 2.9, 4.6, 1.3],
[5.2, 2.7, 3.9, 1.4],
[5. , 2. , 3.5, 1. ],
[5.9, 3. , 4.2, 1.5],
[6. , 2.2, 4. , 1. ],
[6.1, 2.9, 4.7, 1.4],
[5.6, 2.9, 3.6, 1.3],
[6.7, 3.1, 4.4, 1.4],
[5.6, 3. , 4.5, 1.5],
[5.8, 2.7, 4.1, 1. ],
[6.2, 2.2, 4.5, 1.5],
[5.6, 2.5, 3.9, 1.1],
[5.9, 3.2, 4.8, 1.8],
[6.1, 2.8, 4. , 1.3],
[6.3, 2.5, 4.9, 1.5],
[6.1, 2.8, 4.7, 1.2],
[6.4, 2.9, 4.3, 1.3],
[6.6, 3. , 4.4, 1.4],
[6.8, 2.8, 4.8, 1.4],
[6.7, 3. , 5. , 1.7],
[6. , 2.9, 4.5, 1.5],
[5.7, 2.6, 3.5, 1. ],
[5.5, 2.4, 3.8, 1.1],
[5.5, 2.4, 3.7, 1. ],
[5.8, 2.7, 3.9, 1.2],
[6. , 2.7, 5.1, 1.6],
[5.4, 3. , 4.5, 1.5],
[6. , 3.4, 4.5, 1.6],
[6.7, 3.1, 4.7, 1.5],
[6.3, 2.3, 4.4, 1.3],
[5.6, 3. , 4.1, 1.3],
[5.5, 2.5, 4. , 1.3],
[5.5, 2.6, 4.4, 1.2],
[6.1, 3. , 4.6, 1.4],
[5.8, 2.6, 4. , 1.2],
[5. , 2.3, 3.3, 1. ],
[5.6, 2.7, 4.2, 1.3],
[5.7, 3. , 4.2, 1.2],
[5.7, 2.9, 4.2, 1.3],
[6.2, 2.9, 4.3, 1.3],
[5.1, 2.5, 3. , 1.1],
[5.7, 2.8, 4.1, 1.3],
[6.3, 3.3, 6. , 2.5],
[5.8, 2.7, 5.1, 1.9],
[7.1, 3. , 5.9, 2.1],
[6.3, 2.9, 5.6, 1.8],
[6.5, 3. , 5.8, 2.2],
[7.6, 3. , 6.6, 2.1],
[4.9, 2.5, 4.5, 1.7],
[7.3, 2.9, 6.3, 1.8],
[6.7, 2.5, 5.8, 1.8],
[7.2, 3.6, 6.1, 2.5],
[6.5, 3.2, 5.1, 2. ],
[6.4, 2.7, 5.3, 1.9],
[6.8, 3. , 5.5, 2.1],
[5.7, 2.5, 5. , 2. ],
[5.8, 2.8, 5.1, 2.4],
[6.4, 3.2, 5.3, 2.3],
[6.5, 3. , 5.5, 1.8],
[7.7, 3.8, 6.7, 2.2],
[7.7, 2.6, 6.9, 2.3],
[6. , 2.2, 5. , 1.5],
[6.9, 3.2, 5.7, 2.3],
[5.6, 2.8, 4.9, 2. ],
[7.7, 2.8, 6.7, 2. ],
[6.3, 2.7, 4.9, 1.8],
[6.7, 3.3, 5.7, 2.1],
[7.2, 3.2, 6. , 1.8],
[6.2, 2.8, 4.8, 1.8],
[6.1, 3. , 4.9, 1.8],
[6.4, 2.8, 5.6, 2.1],
[7.2, 3. , 5.8, 1.6],
[7.4, 2.8, 6.1, 1.9],
[7.9, 3.8, 6.4, 2. ],
[6.4, 2.8, 5.6, 2.2],
[6.3, 2.8, 5.1, 1.5],
[6.1, 2.6, 5.6, 1.4],
[7.7, 3. , 6.1, 2.3],
[6.3, 3.4, 5.6, 2.4],
[6.4, 3.1, 5.5, 1.8],
[6. , 3. , 4.8, 1.8],
[6.9, 3.1, 5.4, 2.1],
[6.7, 3.1, 5.6, 2.4],
[6.9, 3.1, 5.1, 2.3],
[5.8, 2.7, 5.1, 1.9],
[6.8, 3.2, 5.9, 2.3],
[6.7, 3.3, 5.7, 2.5],
[6.7, 3. , 5.2, 2.3],
[6.3, 2.5, 5. , 1.9],
[6.5, 3. , 5.2, 2. ],
[6.2, 3.4, 5.4, 2.3],
[5.9, 3. , 5.1, 1.8]])
In [40]:
y
Out[40]:
array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.])
원하는 속성 뽑아내기¶
In [41]:
iris[:5]
Out[41]:
array([[5.1, 3.5, 1.4, 0.2, 0. ],
[4.9, 3. , 1.4, 0.2, 0. ],
[4.7, 3.2, 1.3, 0.2, 0. ],
[4.6, 3.1, 1.5, 0.2, 0. ],
[5. , 3.6, 1.4, 0.2, 0. ]])
In [42]:
iris[[0,2,4]]
Out[42]:
array([[5.1, 3.5, 1.4, 0.2, 0. ],
[4.7, 3.2, 1.3, 0.2, 0. ],
[5. , 3.6, 1.4, 0.2, 0. ]])
In [43]:
iris[[4,3,2,1,0]]
Out[43]:
array([[5. , 3.6, 1.4, 0.2, 0. ],
[4.6, 3.1, 1.5, 0.2, 0. ],
[4.7, 3.2, 1.3, 0.2, 0. ],
[4.9, 3. , 1.4, 0.2, 0. ],
[5.1, 3.5, 1.4, 0.2, 0. ]])
In [44]:
iris[[0,50,100]]
Out[44]:
array([[5.1, 3.5, 1.4, 0.2, 0. ],
[7. , 3.2, 4.7, 1.4, 1. ],
[6.3, 3.3, 6. , 2.5, 2. ]])
In [45]:
iris[:,[0,2,4]]
Out[45]:
array([[5.1, 1.4, 0. ],
[4.9, 1.4, 0. ],
[4.7, 1.3, 0. ],
[4.6, 1.5, 0. ],
[5. , 1.4, 0. ],
[5.4, 1.7, 0. ],
[4.6, 1.4, 0. ],
[5. , 1.5, 0. ],
[4.4, 1.4, 0. ],
[4.9, 1.5, 0. ],
[5.4, 1.5, 0. ],
[4.8, 1.6, 0. ],
[4.8, 1.4, 0. ],
[4.3, 1.1, 0. ],
[5.8, 1.2, 0. ],
[5.7, 1.5, 0. ],
[5.4, 1.3, 0. ],
[5.1, 1.4, 0. ],
[5.7, 1.7, 0. ],
[5.1, 1.5, 0. ],
[5.4, 1.7, 0. ],
[5.1, 1.5, 0. ],
[4.6, 1. , 0. ],
[5.1, 1.7, 0. ],
[4.8, 1.9, 0. ],
[5. , 1.6, 0. ],
[5. , 1.6, 0. ],
[5.2, 1.5, 0. ],
[5.2, 1.4, 0. ],
[4.7, 1.6, 0. ],
[4.8, 1.6, 0. ],
[5.4, 1.5, 0. ],
[5.2, 1.5, 0. ],
[5.5, 1.4, 0. ],
[4.9, 1.5, 0. ],
[5. , 1.2, 0. ],
[5.5, 1.3, 0. ],
[4.9, 1.5, 0. ],
[4.4, 1.3, 0. ],
[5.1, 1.5, 0. ],
[5. , 1.3, 0. ],
[4.5, 1.3, 0. ],
[4.4, 1.3, 0. ],
[5. , 1.6, 0. ],
[5.1, 1.9, 0. ],
[4.8, 1.4, 0. ],
[5.1, 1.6, 0. ],
[4.6, 1.4, 0. ],
[5.3, 1.5, 0. ],
[5. , 1.4, 0. ],
[7. , 4.7, 1. ],
[6.4, 4.5, 1. ],
[6.9, 4.9, 1. ],
[5.5, 4. , 1. ],
[6.5, 4.6, 1. ],
[5.7, 4.5, 1. ],
[6.3, 4.7, 1. ],
[4.9, 3.3, 1. ],
[6.6, 4.6, 1. ],
[5.2, 3.9, 1. ],
[5. , 3.5, 1. ],
[5.9, 4.2, 1. ],
[6. , 4. , 1. ],
[6.1, 4.7, 1. ],
[5.6, 3.6, 1. ],
[6.7, 4.4, 1. ],
[5.6, 4.5, 1. ],
[5.8, 4.1, 1. ],
[6.2, 4.5, 1. ],
[5.6, 3.9, 1. ],
[5.9, 4.8, 1. ],
[6.1, 4. , 1. ],
[6.3, 4.9, 1. ],
[6.1, 4.7, 1. ],
[6.4, 4.3, 1. ],
[6.6, 4.4, 1. ],
[6.8, 4.8, 1. ],
[6.7, 5. , 1. ],
[6. , 4.5, 1. ],
[5.7, 3.5, 1. ],
[5.5, 3.8, 1. ],
[5.5, 3.7, 1. ],
[5.8, 3.9, 1. ],
[6. , 5.1, 1. ],
[5.4, 4.5, 1. ],
[6. , 4.5, 1. ],
[6.7, 4.7, 1. ],
[6.3, 4.4, 1. ],
[5.6, 4.1, 1. ],
[5.5, 4. , 1. ],
[5.5, 4.4, 1. ],
[6.1, 4.6, 1. ],
[5.8, 4. , 1. ],
[5. , 3.3, 1. ],
[5.6, 4.2, 1. ],
[5.7, 4.2, 1. ],
[5.7, 4.2, 1. ],
[6.2, 4.3, 1. ],
[5.1, 3. , 1. ],
[5.7, 4.1, 1. ],
[6.3, 6. , 2. ],
[5.8, 5.1, 2. ],
[7.1, 5.9, 2. ],
[6.3, 5.6, 2. ],
[6.5, 5.8, 2. ],
[7.6, 6.6, 2. ],
[4.9, 4.5, 2. ],
[7.3, 6.3, 2. ],
[6.7, 5.8, 2. ],
[7.2, 6.1, 2. ],
[6.5, 5.1, 2. ],
[6.4, 5.3, 2. ],
[6.8, 5.5, 2. ],
[5.7, 5. , 2. ],
[5.8, 5.1, 2. ],
[6.4, 5.3, 2. ],
[6.5, 5.5, 2. ],
[7.7, 6.7, 2. ],
[7.7, 6.9, 2. ],
[6. , 5. , 2. ],
[6.9, 5.7, 2. ],
[5.6, 4.9, 2. ],
[7.7, 6.7, 2. ],
[6.3, 4.9, 2. ],
[6.7, 5.7, 2. ],
[7.2, 6. , 2. ],
[6.2, 4.8, 2. ],
[6.1, 4.9, 2. ],
[6.4, 5.6, 2. ],
[7.2, 5.8, 2. ],
[7.4, 6.1, 2. ],
[7.9, 6.4, 2. ],
[6.4, 5.6, 2. ],
[6.3, 5.1, 2. ],
[6.1, 5.6, 2. ],
[7.7, 6.1, 2. ],
[6.3, 5.6, 2. ],
[6.4, 5.5, 2. ],
[6. , 4.8, 2. ],
[6.9, 5.4, 2. ],
[6.7, 5.6, 2. ],
[6.9, 5.1, 2. ],
[5.8, 5.1, 2. ],
[6.8, 5.9, 2. ],
[6.7, 5.7, 2. ],
[6.7, 5.2, 2. ],
[6.3, 5. , 2. ],
[6.5, 5.2, 2. ],
[6.2, 5.4, 2. ],
[5.9, 5.1, 2. ]])
조건식 적용¶
In [46]:
iris2 = iris[[0,50,100]]
iris2
Out[46]:
array([[5.1, 3.5, 1.4, 0.2, 0. ],
[7. , 3.2, 4.7, 1.4, 1. ],
[6.3, 3.3, 6. , 2.5, 2. ]])
In [47]:
iris2[[True,False,True]]
Out[47]:
array([[5.1, 3.5, 1.4, 0.2, 0. ],
[6.3, 3.3, 6. , 2.5, 2. ]])
In [48]:
iris2[[0,2]]
Out[48]:
array([[5.1, 3.5, 1.4, 0.2, 0. ],
[6.3, 3.3, 6. , 2.5, 2. ]])
In [49]:
iris2[:,[0,2]]
Out[49]:
array([[5.1, 1.4],
[7. , 4.7],
[6.3, 6. ]])
In [50]:
iris2[:,[True,False,True,False,False]]
Out[50]:
array([[5.1, 1.4],
[7. , 4.7],
[6.3, 6. ]])
- 인덱스 이용
- 리스트 이용
- 조건식 이용 : 갯수 일치
versicolor와 virginica 만 골라내 보자¶
In [54]:
a = np.array([1,2,3,4,5])
a
Out[54]:
array([1, 2, 3, 4, 5])
In [56]:
a>3 # 부등식도 항목 하나하나 적용
Out[56]:
array([False, False, False, True, True])
In [57]:
a == 3
Out[57]:
array([False, False, True, False, False])
In [51]:
iris2
Out[51]:
array([[5.1, 3.5, 1.4, 0.2, 0. ],
[7. , 3.2, 4.7, 1.4, 1. ],
[6.3, 3.3, 6. , 2.5, 2. ]])
In [59]:
t = iris2[:,-1] # t는 target을 의미
t
Out[59]:
array([0., 1., 2.])
In [61]:
t < 1
Out[61]:
array([ True, False, False])
In [63]:
iris2[t<1] # = iris2[[True, False, False]]
Out[63]:
array([[5.1, 3.5, 1.4, 0.2, 0. ]])
In [64]:
iris2[iris2[:,-1]==0]
Out[64]:
array([[5.1, 3.5, 1.4, 0.2, 0. ]])
In [68]:
setosa = iris[iris[:,-1]==0]
setosa
Out[68]:
array([[5.1, 3.5, 1.4, 0.2, 0. ],
[4.9, 3. , 1.4, 0.2, 0. ],
[4.7, 3.2, 1.3, 0.2, 0. ],
[4.6, 3.1, 1.5, 0.2, 0. ],
[5. , 3.6, 1.4, 0.2, 0. ],
[5.4, 3.9, 1.7, 0.4, 0. ],
[4.6, 3.4, 1.4, 0.3, 0. ],
[5. , 3.4, 1.5, 0.2, 0. ],
[4.4, 2.9, 1.4, 0.2, 0. ],
[4.9, 3.1, 1.5, 0.1, 0. ],
[5.4, 3.7, 1.5, 0.2, 0. ],
[4.8, 3.4, 1.6, 0.2, 0. ],
[4.8, 3. , 1.4, 0.1, 0. ],
[4.3, 3. , 1.1, 0.1, 0. ],
[5.8, 4. , 1.2, 0.2, 0. ],
[5.7, 4.4, 1.5, 0.4, 0. ],
[5.4, 3.9, 1.3, 0.4, 0. ],
[5.1, 3.5, 1.4, 0.3, 0. ],
[5.7, 3.8, 1.7, 0.3, 0. ],
[5.1, 3.8, 1.5, 0.3, 0. ],
[5.4, 3.4, 1.7, 0.2, 0. ],
[5.1, 3.7, 1.5, 0.4, 0. ],
[4.6, 3.6, 1. , 0.2, 0. ],
[5.1, 3.3, 1.7, 0.5, 0. ],
[4.8, 3.4, 1.9, 0.2, 0. ],
[5. , 3. , 1.6, 0.2, 0. ],
[5. , 3.4, 1.6, 0.4, 0. ],
[5.2, 3.5, 1.5, 0.2, 0. ],
[5.2, 3.4, 1.4, 0.2, 0. ],
[4.7, 3.2, 1.6, 0.2, 0. ],
[4.8, 3.1, 1.6, 0.2, 0. ],
[5.4, 3.4, 1.5, 0.4, 0. ],
[5.2, 4.1, 1.5, 0.1, 0. ],
[5.5, 4.2, 1.4, 0.2, 0. ],
[4.9, 3.1, 1.5, 0.1, 0. ],
[5. , 3.2, 1.2, 0.2, 0. ],
[5.5, 3.5, 1.3, 0.2, 0. ],
[4.9, 3.1, 1.5, 0.1, 0. ],
[4.4, 3. , 1.3, 0.2, 0. ],
[5.1, 3.4, 1.5, 0.2, 0. ],
[5. , 3.5, 1.3, 0.3, 0. ],
[4.5, 2.3, 1.3, 0.3, 0. ],
[4.4, 3.2, 1.3, 0.2, 0. ],
[5. , 3.5, 1.6, 0.6, 0. ],
[5.1, 3.8, 1.9, 0.4, 0. ],
[4.8, 3. , 1.4, 0.3, 0. ],
[5.1, 3.8, 1.6, 0.2, 0. ],
[4.6, 3.2, 1.4, 0.2, 0. ],
[5.3, 3.7, 1.5, 0.2, 0. ],
[5. , 3.3, 1.4, 0.2, 0. ]])
In [67]:
setosa.shape
Out[67]:
(50, 5)
In [70]:
versicolor = iris[iris[:,-1]==1]
versicolor
Out[70]:
array([[7. , 3.2, 4.7, 1.4, 1. ],
[6.4, 3.2, 4.5, 1.5, 1. ],
[6.9, 3.1, 4.9, 1.5, 1. ],
[5.5, 2.3, 4. , 1.3, 1. ],
[6.5, 2.8, 4.6, 1.5, 1. ],
[5.7, 2.8, 4.5, 1.3, 1. ],
[6.3, 3.3, 4.7, 1.6, 1. ],
[4.9, 2.4, 3.3, 1. , 1. ],
[6.6, 2.9, 4.6, 1.3, 1. ],
[5.2, 2.7, 3.9, 1.4, 1. ],
[5. , 2. , 3.5, 1. , 1. ],
[5.9, 3. , 4.2, 1.5, 1. ],
[6. , 2.2, 4. , 1. , 1. ],
[6.1, 2.9, 4.7, 1.4, 1. ],
[5.6, 2.9, 3.6, 1.3, 1. ],
[6.7, 3.1, 4.4, 1.4, 1. ],
[5.6, 3. , 4.5, 1.5, 1. ],
[5.8, 2.7, 4.1, 1. , 1. ],
[6.2, 2.2, 4.5, 1.5, 1. ],
[5.6, 2.5, 3.9, 1.1, 1. ],
[5.9, 3.2, 4.8, 1.8, 1. ],
[6.1, 2.8, 4. , 1.3, 1. ],
[6.3, 2.5, 4.9, 1.5, 1. ],
[6.1, 2.8, 4.7, 1.2, 1. ],
[6.4, 2.9, 4.3, 1.3, 1. ],
[6.6, 3. , 4.4, 1.4, 1. ],
[6.8, 2.8, 4.8, 1.4, 1. ],
[6.7, 3. , 5. , 1.7, 1. ],
[6. , 2.9, 4.5, 1.5, 1. ],
[5.7, 2.6, 3.5, 1. , 1. ],
[5.5, 2.4, 3.8, 1.1, 1. ],
[5.5, 2.4, 3.7, 1. , 1. ],
[5.8, 2.7, 3.9, 1.2, 1. ],
[6. , 2.7, 5.1, 1.6, 1. ],
[5.4, 3. , 4.5, 1.5, 1. ],
[6. , 3.4, 4.5, 1.6, 1. ],
[6.7, 3.1, 4.7, 1.5, 1. ],
[6.3, 2.3, 4.4, 1.3, 1. ],
[5.6, 3. , 4.1, 1.3, 1. ],
[5.5, 2.5, 4. , 1.3, 1. ],
[5.5, 2.6, 4.4, 1.2, 1. ],
[6.1, 3. , 4.6, 1.4, 1. ],
[5.8, 2.6, 4. , 1.2, 1. ],
[5. , 2.3, 3.3, 1. , 1. ],
[5.6, 2.7, 4.2, 1.3, 1. ],
[5.7, 3. , 4.2, 1.2, 1. ],
[5.7, 2.9, 4.2, 1.3, 1. ],
[6.2, 2.9, 4.3, 1.3, 1. ],
[5.1, 2.5, 3. , 1.1, 1. ],
[5.7, 2.8, 4.1, 1.3, 1. ]])
In [71]:
versicolor.shape
Out[71]:
(50, 5)
In [72]:
virginica = iris[iris[:,-1]==2]
virginica
Out[72]:
array([[6.3, 3.3, 6. , 2.5, 2. ],
[5.8, 2.7, 5.1, 1.9, 2. ],
[7.1, 3. , 5.9, 2.1, 2. ],
[6.3, 2.9, 5.6, 1.8, 2. ],
[6.5, 3. , 5.8, 2.2, 2. ],
[7.6, 3. , 6.6, 2.1, 2. ],
[4.9, 2.5, 4.5, 1.7, 2. ],
[7.3, 2.9, 6.3, 1.8, 2. ],
[6.7, 2.5, 5.8, 1.8, 2. ],
[7.2, 3.6, 6.1, 2.5, 2. ],
[6.5, 3.2, 5.1, 2. , 2. ],
[6.4, 2.7, 5.3, 1.9, 2. ],
[6.8, 3. , 5.5, 2.1, 2. ],
[5.7, 2.5, 5. , 2. , 2. ],
[5.8, 2.8, 5.1, 2.4, 2. ],
[6.4, 3.2, 5.3, 2.3, 2. ],
[6.5, 3. , 5.5, 1.8, 2. ],
[7.7, 3.8, 6.7, 2.2, 2. ],
[7.7, 2.6, 6.9, 2.3, 2. ],
[6. , 2.2, 5. , 1.5, 2. ],
[6.9, 3.2, 5.7, 2.3, 2. ],
[5.6, 2.8, 4.9, 2. , 2. ],
[7.7, 2.8, 6.7, 2. , 2. ],
[6.3, 2.7, 4.9, 1.8, 2. ],
[6.7, 3.3, 5.7, 2.1, 2. ],
[7.2, 3.2, 6. , 1.8, 2. ],
[6.2, 2.8, 4.8, 1.8, 2. ],
[6.1, 3. , 4.9, 1.8, 2. ],
[6.4, 2.8, 5.6, 2.1, 2. ],
[7.2, 3. , 5.8, 1.6, 2. ],
[7.4, 2.8, 6.1, 1.9, 2. ],
[7.9, 3.8, 6.4, 2. , 2. ],
[6.4, 2.8, 5.6, 2.2, 2. ],
[6.3, 2.8, 5.1, 1.5, 2. ],
[6.1, 2.6, 5.6, 1.4, 2. ],
[7.7, 3. , 6.1, 2.3, 2. ],
[6.3, 3.4, 5.6, 2.4, 2. ],
[6.4, 3.1, 5.5, 1.8, 2. ],
[6. , 3. , 4.8, 1.8, 2. ],
[6.9, 3.1, 5.4, 2.1, 2. ],
[6.7, 3.1, 5.6, 2.4, 2. ],
[6.9, 3.1, 5.1, 2.3, 2. ],
[5.8, 2.7, 5.1, 1.9, 2. ],
[6.8, 3.2, 5.9, 2.3, 2. ],
[6.7, 3.3, 5.7, 2.5, 2. ],
[6.7, 3. , 5.2, 2.3, 2. ],
[6.3, 2.5, 5. , 1.9, 2. ],
[6.5, 3. , 5.2, 2. , 2. ],
[6.2, 3.4, 5.4, 2.3, 2. ],
[5.9, 3. , 5.1, 1.8, 2. ]])
In [73]:
virginica.shape
Out[73]:
(50, 5)
In [77]:
X = iris[:, :4]
y = iris[:, 4]
setosa1 = X[y==0]
versicolor1 = X[y==1]
virginica1 = X[y==2]
In [78]:
setosa1
Out[78]:
array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2],
[5. , 3.6, 1.4, 0.2],
[5.4, 3.9, 1.7, 0.4],
[4.6, 3.4, 1.4, 0.3],
[5. , 3.4, 1.5, 0.2],
[4.4, 2.9, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1],
[5.4, 3.7, 1.5, 0.2],
[4.8, 3.4, 1.6, 0.2],
[4.8, 3. , 1.4, 0.1],
[4.3, 3. , 1.1, 0.1],
[5.8, 4. , 1.2, 0.2],
[5.7, 4.4, 1.5, 0.4],
[5.4, 3.9, 1.3, 0.4],
[5.1, 3.5, 1.4, 0.3],
[5.7, 3.8, 1.7, 0.3],
[5.1, 3.8, 1.5, 0.3],
[5.4, 3.4, 1.7, 0.2],
[5.1, 3.7, 1.5, 0.4],
[4.6, 3.6, 1. , 0.2],
[5.1, 3.3, 1.7, 0.5],
[4.8, 3.4, 1.9, 0.2],
[5. , 3. , 1.6, 0.2],
[5. , 3.4, 1.6, 0.4],
[5.2, 3.5, 1.5, 0.2],
[5.2, 3.4, 1.4, 0.2],
[4.7, 3.2, 1.6, 0.2],
[4.8, 3.1, 1.6, 0.2],
[5.4, 3.4, 1.5, 0.4],
[5.2, 4.1, 1.5, 0.1],
[5.5, 4.2, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1],
[5. , 3.2, 1.2, 0.2],
[5.5, 3.5, 1.3, 0.2],
[4.9, 3.1, 1.5, 0.1],
[4.4, 3. , 1.3, 0.2],
[5.1, 3.4, 1.5, 0.2],
[5. , 3.5, 1.3, 0.3],
[4.5, 2.3, 1.3, 0.3],
[4.4, 3.2, 1.3, 0.2],
[5. , 3.5, 1.6, 0.6],
[5.1, 3.8, 1.9, 0.4],
[4.8, 3. , 1.4, 0.3],
[5.1, 3.8, 1.6, 0.2],
[4.6, 3.2, 1.4, 0.2],
[5.3, 3.7, 1.5, 0.2],
[5. , 3.3, 1.4, 0.2]])
In [79]:
versicolor1
Out[79]:
array([[7. , 3.2, 4.7, 1.4],
[6.4, 3.2, 4.5, 1.5],
[6.9, 3.1, 4.9, 1.5],
[5.5, 2.3, 4. , 1.3],
[6.5, 2.8, 4.6, 1.5],
[5.7, 2.8, 4.5, 1.3],
[6.3, 3.3, 4.7, 1.6],
[4.9, 2.4, 3.3, 1. ],
[6.6, 2.9, 4.6, 1.3],
[5.2, 2.7, 3.9, 1.4],
[5. , 2. , 3.5, 1. ],
[5.9, 3. , 4.2, 1.5],
[6. , 2.2, 4. , 1. ],
[6.1, 2.9, 4.7, 1.4],
[5.6, 2.9, 3.6, 1.3],
[6.7, 3.1, 4.4, 1.4],
[5.6, 3. , 4.5, 1.5],
[5.8, 2.7, 4.1, 1. ],
[6.2, 2.2, 4.5, 1.5],
[5.6, 2.5, 3.9, 1.1],
[5.9, 3.2, 4.8, 1.8],
[6.1, 2.8, 4. , 1.3],
[6.3, 2.5, 4.9, 1.5],
[6.1, 2.8, 4.7, 1.2],
[6.4, 2.9, 4.3, 1.3],
[6.6, 3. , 4.4, 1.4],
[6.8, 2.8, 4.8, 1.4],
[6.7, 3. , 5. , 1.7],
[6. , 2.9, 4.5, 1.5],
[5.7, 2.6, 3.5, 1. ],
[5.5, 2.4, 3.8, 1.1],
[5.5, 2.4, 3.7, 1. ],
[5.8, 2.7, 3.9, 1.2],
[6. , 2.7, 5.1, 1.6],
[5.4, 3. , 4.5, 1.5],
[6. , 3.4, 4.5, 1.6],
[6.7, 3.1, 4.7, 1.5],
[6.3, 2.3, 4.4, 1.3],
[5.6, 3. , 4.1, 1.3],
[5.5, 2.5, 4. , 1.3],
[5.5, 2.6, 4.4, 1.2],
[6.1, 3. , 4.6, 1.4],
[5.8, 2.6, 4. , 1.2],
[5. , 2.3, 3.3, 1. ],
[5.6, 2.7, 4.2, 1.3],
[5.7, 3. , 4.2, 1.2],
[5.7, 2.9, 4.2, 1.3],
[6.2, 2.9, 4.3, 1.3],
[5.1, 2.5, 3. , 1.1],
[5.7, 2.8, 4.1, 1.3]])
In [81]:
virginica1
Out[81]:
array([[6.3, 3.3, 6. , 2.5],
[5.8, 2.7, 5.1, 1.9],
[7.1, 3. , 5.9, 2.1],
[6.3, 2.9, 5.6, 1.8],
[6.5, 3. , 5.8, 2.2],
[7.6, 3. , 6.6, 2.1],
[4.9, 2.5, 4.5, 1.7],
[7.3, 2.9, 6.3, 1.8],
[6.7, 2.5, 5.8, 1.8],
[7.2, 3.6, 6.1, 2.5],
[6.5, 3.2, 5.1, 2. ],
[6.4, 2.7, 5.3, 1.9],
[6.8, 3. , 5.5, 2.1],
[5.7, 2.5, 5. , 2. ],
[5.8, 2.8, 5.1, 2.4],
[6.4, 3.2, 5.3, 2.3],
[6.5, 3. , 5.5, 1.8],
[7.7, 3.8, 6.7, 2.2],
[7.7, 2.6, 6.9, 2.3],
[6. , 2.2, 5. , 1.5],
[6.9, 3.2, 5.7, 2.3],
[5.6, 2.8, 4.9, 2. ],
[7.7, 2.8, 6.7, 2. ],
[6.3, 2.7, 4.9, 1.8],
[6.7, 3.3, 5.7, 2.1],
[7.2, 3.2, 6. , 1.8],
[6.2, 2.8, 4.8, 1.8],
[6.1, 3. , 4.9, 1.8],
[6.4, 2.8, 5.6, 2.1],
[7.2, 3. , 5.8, 1.6],
[7.4, 2.8, 6.1, 1.9],
[7.9, 3.8, 6.4, 2. ],
[6.4, 2.8, 5.6, 2.2],
[6.3, 2.8, 5.1, 1.5],
[6.1, 2.6, 5.6, 1.4],
[7.7, 3. , 6.1, 2.3],
[6.3, 3.4, 5.6, 2.4],
[6.4, 3.1, 5.5, 1.8],
[6. , 3. , 4.8, 1.8],
[6.9, 3.1, 5.4, 2.1],
[6.7, 3.1, 5.6, 2.4],
[6.9, 3.1, 5.1, 2.3],
[5.8, 2.7, 5.1, 1.9],
[6.8, 3.2, 5.9, 2.3],
[6.7, 3.3, 5.7, 2.5],
[6.7, 3. , 5.2, 2.3],
[6.3, 2.5, 5. , 1.9],
[6.5, 3. , 5.2, 2. ],
[6.2, 3.4, 5.4, 2.3],
[5.9, 3. , 5.1, 1.8]])