极客笔记NumPy 数组迭代
NumPy提供了一个迭代器对象，即nditer，可以使用python标准迭代器接口来迭代给定的数组。
考虑以下示例。
示例
import numpy as np
a = np.array([[1,2,3,4],[2,4,5,6],[10,20,39,3]])
print("Printing array:")
print(a);
print("Iterating over the array:")
for x in np.nditer(a):
print(x,end=' ')
输出:
Printing array:
[[ 1 2 3 4]
[ 2 4 5 6]
[10 20 39 3]]
Iterating over the array:
1 2 3 4 2 4 5 6 10 20 39 3
迭代的顺序不遵循任何特定的顺序，如行优先或列优先。然而，它旨在与数组的存储布局相匹配。
让我们迭代上述示例中给定数组的转置。
示例
import numpy as np
a = np.array([[1,2,3,4],[2,4,5,6],[10,20,39,3]])
print("Printing the array:")
print(a)
print("Printing the transpose of the array:")
at = a.T
print(at)
#this will be same as previous
for x in np.nditer(at):
print(print("Iterating over the array:")
for x in np.nditer(a):
print(x,end=' ')
输出：
Printing the array:
[[ 1 2 3 4]
[ 2 4 5 6]
[10 20 39 3]]
Printing the transpose of the array:
[[ 1 2 10]
[ 2 4 20]
[ 3 5 39]
[ 4 6 3]]
1 2 3 4 2 4 5 6 10 20 39 3
迭代顺序
如我们所知，有两种将值存储到numpy数组中的方式：
- F-style顺序
- C-style顺序
让我们来看看numpy迭代器如何处理特定的顺序（F或C）的示例。
示例
import numpy as np
a = np.array([[1,2,3,4],[2,4,5,6],[10,20,39,3]])
print("\nPrinting the array:\n")
print(a)
print("\nPrinting the transpose of the array:\n")
at = a.T
print(at)
print("\nIterating over the transposed array\n")
for x in np.nditer(at):
print(x, end= ' ')
print("\nSorting the transposed array in C-style:\n")
c = at.copy(order = 'C')
print(c)
print("\nIterating over the C-style array:\n")
for x in np.nditer(c):
print(x,end=' ')
d = at.copy(order = 'F')
print(d)
print("Iterating over the F-style array:\n")
for x in np.nditer(d):
print(x,end=' ')
输出：
Printing the array:
[[ 1 2 3 4]
[ 2 4 5 6]
[10 20 39 3]]
Printing the transpose of the array:
[[ 1 2 10]
[ 2 4 20]
[ 3 5 39]
[ 4 6 3]]
Iterating over the transposed array
1 2 3 4 2 4 5 6 10 20 39 3
Sorting the transposed array in C-style:
[[ 1 2 10]
[ 2 4 20]
[ 3 5 39]
[ 4 6 3]]
Iterating over the C-style array:
1 2 10 2 4 20 3 5 39 4 6 3 [[ 1 2 10]
[ 2 4 20]
[ 3 5 39]
[ 4 6 3]]
Iterating over the F-style array:
1 2 3 4 2 4 5 6 10 20 39 3
我们可以在定义迭代器对象本身时提及订单’C’或’F’。考虑以下示例。
示例
import numpy as np
a = np.array([[1,2,3,4],[2,4,5,6],[10,20,39,3]])
print("\nPrinting the array:\n")
print(a)
print("\nPrinting the transpose of the array:\n")
at = a.T
print(at)
print("\nIterating over the transposed array\n")
for x in np.nditer(at):
print(x, end= ' ')
print("\nSorting the transposed array in C-style:\n")
print("\nIterating over the C-style array:\n")
for x in np.nditer(at, order = 'C'):
print(x,end=' ')
输出：
Iterating over the transposed array
1 2 3 4 2 4 5 6 10 20 39 3
Sorting the transposed array in C-style:
Iterating over the C-style array:
1 2 10 2 4 20 3 5 39 4 6 3
数组值的修改
在迭代过程中，我们无法修改数组元素，因为与迭代器对象关联的op-flag设置为只读。
然而，我们可以将此标志设置为读写或仅写入以修改数组的值。考虑以下示例。
示例
import numpy as np
a = np.array([[1,2,3,4],[2,4,5,6],[10,20,39,3]])
print("\nPrinting the original array:\n")
print(a)
print("\nIterating over the modified array\n")
for x in np.nditer(a, op_flags = ['readwrite']):
x[...] = 3 * x;
print(x,end = ' ')
输出：
Printing the original array:
[[ 1 2 3 4]
[ 2 4 5 6]
[10 20 39 3]]
Iterating over the modified array
3 6 9 12 6 12 15 18 30 60 117 9