NumPy数组迭代实例详解 _PythonNumPy

本文概述

迭代顺序
数组值修改

NumPy提供了一个迭代器对象, 即nditer, 可以使用python标准的Iterator接口在给定数组上进行迭代。
考虑以下示例。
例子

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=' ')

输出

Printing array:[[ 1234] [ 2456] [10 20 393]]Iterating over the array:1 2 3 4 2 4 5 6 10 20 39 3

迭代顺序不遵循任何特殊的顺序, 例如行优先或列顺序。但是, 它旨在匹配阵列的内存布局。
让我们遍历上面示例中给出的数组的转置。
例子

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)#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:[[ 1234] [ 2456] [10 20 393]]Printing the transpose of the array:[[ 12 10] [ 24 20] [ 35 39] [ 463]]1 2 3 4 2 4 5 6 10 20 39 3

迭代顺序众所周知, 有两种方法将值存储到numpy数组中：

F风格订单
C风格订单

让我们看一下numpy迭代器如何处理特定订单(F或C)的示例。
例子

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")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:[[ 1234] [ 2456] [10 20 393]]Printing the transpose of the array:[[ 12 10] [ 24 20] [ 35 39] [ 463]]Iterating over the transposed array1 2 3 4 2 4 5 6 10 20 39 3 Sorting the transposed array in C-style:[[ 12 10] [ 24 20] [ 35 39] [ 463]]Iterating over the C-style array:1 2 10 2 4 20 3 5 39 4 6 3 [[ 12 10] [ 24 20] [ 35 39] [ 463]]Iterating over the F-style array:1 2 3 4 2 4 5 6 10 20 39 3

在定义Iterator对象本身时, 我们可以提及顺序” C” 或” F” 。考虑以下示例。
例子

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=' ')

输出

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

数组值修改由于与Iterator对象关联的op-flag设置为readonly, 因此我们无法在迭代期间修改数组元素。
但是, 我们可以将此标志设置为读写或仅写以修改数组值。考虑以下示例。
例子

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 * x; print(x, end = ' ')

【NumPy数组迭代实例详解】输出