计算两个 NumPy 数组的逐元素按位异或
要计算两个数组的逐元素按位异或,请在 Python NumPy 中使用 **numpy.bitwise_xor()** 方法。计算输入数组中整数的底层二进制表示的按位异或。此 ufunc 实现 C/Python 运算符 ^。
第一个和第二个参数是数组,只处理整数和布尔类型。如果 x1.shape != x2.shape,则它们必须能够广播到一个公共形状。
where 参数是在输入上广播的条件。在条件为 True 的位置,out 数组将设置为 ufunc 结果。在其他位置,out 数组将保留其原始值。请注意,如果通过默认的 out=None 创建未初始化的 out 数组,则其中条件为 False 的位置将保持未初始化状态。
步骤
首先,导入所需的库:
import numpy as np
使用 array() 方法创建两个 NumPy 数组。我们插入了 int 类型的元素:
arr1 = np.array([[93, 43, 61], [82, 69, 29]]) arr2 = np.array([[29, 14, 56], [81, 55, 32]])
显示数组:
print("Array 1...
", arr1) print("
Array 2...
", arr2)
获取数组的类型:
print("
Our Array 1 type...
", arr1.dtype) print("
Our Array 2 type...
", arr2.dtype)
获取数组的维度:
print("
Our Array 1 Dimensions...
",arr1.ndim) print("
Our Array 2 Dimensions...
",arr2.ndim)
获取数组的形状:
print("
Our Array 1 Shape...
",arr1.shape) print("
Our Array 2 Shape...
",arr2.shape)
要计算两个数组的逐元素按位异或,请使用 numpy.bitwise_xor() 方法:
print("
Result (bit-wise XOR)...
",np.bitwise_xor(arr1, arr2))
示例
import numpy as np # Creating two numpy arrays using the array() method # We have inserted elements of int type arr1 = np.array([[93, 43, 61], [82, 69, 29]]) arr2 = np.array([[29, 14, 56], [81, 55, 32]]) # Display the arrays print("Array 1...", arr1) print("Array 2...", arr2) #Get the type of the arrays print("Our Array 1 type...", arr1.dtype) print("Our Array 2 type...", arr2.dtype) # Get the dimensions of the Arrays print("Our Array 1 Dimensions...",arr1.ndim) print("Our Array 2 Dimensions...",arr2.ndim) # Get the shape of the Arrays print("Our Array 1 Shape...",arr1.shape) print("Our Array 2 Shape...",arr2.shape) # To compute the bit-wise XOR of two arrays element-wise, use the numpy.bitwise_xor() method in Python Numpy print("Result (bit-wise XOR)...",np.bitwise_xor(arr1, arr2))
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输出
Array 1... [[93 43 61] [82 69 29]] Array 2... [[29 14 56] [81 55 32]] Our Array 1 type... int64 Our Array 2 type... int64 Our Array 1 Dimensions... 2 Our Array 2 Dimensions... 2 Our Array 1 Shape... (2, 3) Our Array 2 Shape... (2, 3) Result (bit-wise XOR)... [[ 64 37 5] [ 3 114 61]]
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