ndarray 的各种索引和切片

import numpy as np

一维数组索引和切片

arr = np.arange(10)
print(arr[5])
print(arr[3:8])
print(arr[:])

5
[3 4 5 6 7]
[0 1 2 3 4 5 6 7 8 9]

arr[2:5] = 12
print(arr)

[ 0  1 12 12 12  5  6  7  8  9]

二维数组

arr = np.array([[1,2,3],[3,4,5],[6,7,8]])
print(arr)
print()
print(arr[2])
print()
print(arr[0][0])
print()
print(arr[0,0])

[[1 2 3]
 [3 4 5]
 [6 7 8]]

[6 7 8]

1

1

多维数组

arr = np.array([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]]])
print(arr)
print(arr.shape)
print(arr[0])
print(arr[0,0])
print(arr[0][0][0])

[[[ 1  2  3]
  [ 4  5  6]]

 [[ 7  8  9]
  [10 11 12]]]
(2, 2, 3)
[[1 2 3]
 [4 5 6]]
[1 2 3]
1

数组的copy

arr = np.array([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]]])
old_values = arr[0].copy()
values = arr[0]
arr[0] = 2
print(old_values)

[[1 2 3]
 [4 5 6]]

print(arr)

[[[ 2  2  2]
  [ 2  2  2]]

 [[ 7  8  9]
  [10 11 12]]]

arr[0] = old_values
print(arr)

[[[ 1  2  3]
  [ 4  5  6]]

 [[ 7  8  9]
  [10 11 12]]]

切片和索引

arr = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(arr[:2])    #打印真正的1，2行

[[1 2 3]
 [4 5 6]]

print(arr[:2,1:])  #12行，23列

[[2 3]
 [5 6]]

print(arr[:,:1])  #第一列

[[1]
 [4]
 [7]]

arr[:,1:2] = 0
print(arr)

[[1 0 3]
 [4 0 6]
 [7 0 9]]

布尔数组

arr = np.random.randn(7,4)
str_arr = np.array(['a','c','b','a','d','e','c'])
print(str_arr == 'a')
print(str_arr == 'b')

[ True False False  True False False False]
[False False  True False False False False]

利用布尔数组作为索引取值

print(arr)

[[ 0.21610951  0.95843796  0.17142308  1.7343341 ]
 [ 1.87321275  0.13157334  1.26847987  0.10746907]
 [ 0.26667955 -1.26403189  1.13455612 -1.13179328]
 [ 0.31517531 -0.19478254  0.43399553 -0.98840003]
 [ 0.7935211  -0.07746844  0.00948928 -0.06383993]
 [ 0.68035449 -0.05042796 -0.54446476 -1.02342964]
 [-1.00130873  0.9020401  -1.07449253  0.37424268]]

print(arr[str_arr == 'a'])

[[ 0.21610951  0.95843796  0.17142308  1.7343341 ]
 [ 0.31517531 -0.19478254  0.43399553 -0.98840003]]

print(arr[str_arr == 'b'])

[[ 0.26667955 -1.26403189  1.13455612 -1.13179328]]

print(arr[str_arr == 'a',:2])

[[ 0.21610951  0.95843796]
 [ 0.31517531 -0.19478254]]

print(arr[str_arr == 'a',:1])

[[ 0.21610951]
 [ 0.31517531]]

print(arr[~(str_arr == 'a')])

[[ 1.87321275  0.13157334  1.26847987  0.10746907]
 [ 0.26667955 -1.26403189  1.13455612 -1.13179328]
 [ 0.7935211  -0.07746844  0.00948928 -0.06383993]
 [ 0.68035449 -0.05042796 -0.54446476 -1.02342964]
 [-1.00130873  0.9020401  -1.07449253  0.37424268]]

mask_arr = (str_arr == 'a')|(str_arr == 'b')
print(mask_arr)

[ True False  True  True False False False]

print(arr[mask_arr])

[[ 0.21610951  0.95843796  0.17142308  1.7343341 ]
 [ 0.26667955 -1.26403189  1.13455612 -1.13179328]
 [ 0.31517531 -0.19478254  0.43399553 -0.98840003]]

arr[str_arr == 'b'] = 0
print(arr)

[[ 0.21610951  0.95843796  0.17142308  1.7343341 ]
 [ 1.87321275  0.13157334  1.26847987  0.10746907]
 [ 0.          0.          0.          0.        ]
 [ 0.31517531 -0.19478254  0.43399553 -0.98840003]
 [ 0.7935211  -0.07746844  0.00948928 -0.06383993]
 [ 0.68035449 -0.05042796 -0.54446476 -1.02342964]
 [-1.00130873  0.9020401  -1.07449253  0.37424268]]

花式索引

arr = np.empty((8,4))

for i in range(8):
    arr[i] = i

print(arr)

[[ 0.  0.  0.  0.]
 [ 1.  1.  1.  1.]
 [ 2.  2.  2.  2.]
 [ 3.  3.  3.  3.]
 [ 4.  4.  4.  4.]
 [ 5.  5.  5.  5.]
 [ 6.  6.  6.  6.]
 [ 7.  7.  7.  7.]]

print(arr[[4,2,5,7]])

[[ 4.  4.  4.  4.]
 [ 2.  2.  2.  2.]
 [ 5.  5.  5.  5.]
 [ 7.  7.  7.  7.]]

print(arr[[-3,-7,-5]])

[[ 5.  5.  5.  5.]
 [ 1.  1.  1.  1.]
 [ 3.  3.  3.  3.]]

print(arr[[1,5,7,2],[0,3,1,2]])

[ 1.  5.  7.  2.]

选取区域

print(arr[[1,5,7,2]][:,[0,3,1,2]])

[[ 1.  1.  1.  1.]
 [ 5.  5.  5.  5.]
 [ 7.  7.  7.  7.]
 [ 2.  2.  2.  2.]]

arr[np.ix_([1,5,7,2],[0,3,1,2])]

array([[ 1.,  1.,  1.,  1.],
       [ 5.,  5.,  5.,  5.],
       [ 7.,  7.,  7.,  7.],
       [ 2.,  2.,  2.,  2.]])