python配列のスライス

配列のスライス[;,i][i,:j,:k]
説明する

“.” 各次元を区切る

":"各次元内のスライス

1.2 D配列

import numpy as np

a = np.array([[1,2,3],[4,5,6],[7,8,9]])   #
print(a)
"""
array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])
"""

>>> b = a[:,0]  #      0   ，  0   
>>> b
array([1, 4, 7])
>>> b.shape
(3,)
>>> b.ndim
1

>>> c = a[:,1]   #      1   ，  0   
>>> c
array([2, 5, 8])
>>> c.shape
(3,)
>>> c.ndim
1

>>> d = a[:,1:]   #       ，    ，         ，   0 
>>> d
array([[2, 3],
       [5, 6],
       [8, 9]])
>>> d.shape
(3, 2)
>>> d.ndim
2

>>> e = a[1,:]  #         1         ，         
>>> e
array([4, 5, 6])
>>> e.shape
(3,)
>>> e.ndim
1

>>> f = a[:2,1:]   #       0    1 ，      1       
>>> f
array([[2, 3],
       [5, 6]])
>>> f.shape
(2, 2)
>>> f.ndim
2

2.3 D配列
3 D配列の理解:ページ、行、列

import numpy as np

#         
>>> a = [1,2,3]
>>> b = [4,5,6]
>>> c = [7,8,9]
>>> d = np.array([[a,b,c],[a,b,c]])
>>> d
array([[[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]],

       [[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]]])
>>> d.shape
(2, 3, 3)
>>> d.ndim
3

>>> x = d[1,1,2] #          1，       1，       2.
>>> x
6
>>> type(x)
<class 'numpy.int32'>
>>> x.ndim
0
>>> x.shape
()

>>> y = d[:,1,2]  #
>>> y
array([6, 6])
>>> y.shape
(2,)
>>> y.ndim
1

>>> z = d[:,1:,2]
>>> z
array([[6, 9],
       [6, 9]])
>>> z.shape
(2, 2)
>>> z.ndim
2

>>> o = d[:,1:,:2]
>>> o
array([[[4, 5],
        [7, 8]],

       [[4, 5],
        [7, 8]]])
>>> o.shape
(2, 2, 2)
>>> o.ndim
3

reshape()関数を使用した3 D配列の作成

>>> x = np.arange(27)
>>> x
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26])
>>> type(a)
<class 'list'>
>>> x = np.reshape(x,(3,3,3))
>>> x.shape
(3, 3, 3)
>>> x.ndim
3
>>> x
array([[[ 0,  1,  2],
        [ 3,  4,  5],
        [ 6,  7,  8]],

       [[ 9, 10, 11],
        [12, 13, 14],
        [15, 16, 17]],

       [[18, 19, 20],
        [21, 22, 23],
        [24, 25, 26]]])