NumPy教程-NumPy 数组迭代
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点击关注 👉约 4 分钟考虑以下示例。
示例
import numpy as npa = 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=' ')
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
迭代的顺序不遵循特殊的排序，如行优先或列优先。但是，它的意图是匹配数组的内存布局。
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让我们迭代上面示例中数组的转置。
示例
import numpy as npa = 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.Tprint(at)print("\nIterating over the transposed array:")for x in np.nditer(at):print(x, end=' ')
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
迭代顺序
正如我们所知，有两种方式将值存储到 numpy 数组中：
F 风格顺序
C 风格顺序
让我们看一个示例，演示 numpy 迭代器如何处理特定的顺序（F 或 C）。
示例
import numpy as npa = 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.Tprint(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=' ')d = at.copy(order='F')print(d)print("Iterating over the F-style array:\n")for x in np.nditer(d):print(x, end=' ')
Iterating over the transposed array1 2 3 4 2 4 5 6 10 20 39 3Sorting the transposed array in C-style:Iterating over the C-style array:1 2 10 2 4 20 3 5 39 4 6 3Iterating 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指定顺序 'C' 或 'F'。考虑以下示例。
示例
import numpy as npa = 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.Tprint(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=' ')
Iterating over the transposed array1 2 3 4 2 4 5 6 10 20 39 3Sorting 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，我们不能修改数组元素，因为与迭代器对象关联的 op-flag 设置为 readonly。
然而，我们可以将此标志设置为 readwrite 或 write only，以修改数组值。考虑以下示例。
示例
import numpy as npa = 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 * xprint(x, end=' ')
Printing the original array:[[ 1 2 3 4][ 2 4 5 6][10 20 39 3]]Iterating over the modified array3 6 9 12 6 12 15 18 30 60 117 9
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