# bitwise_and print('bitwise_and of two arrays: ') print(np.bitwise_and(even, odd)) # bitwise_or print('bitwise_or of two arrays: ') print(np.bitwise_or(even, odd)) # bitwise_xor print('bitwise_xor of two arrays: ') print(np.bitwise_xor(even, odd)) # invert or not print(...
numpy.setdiff1d 【冰糖Python】numpy 差集、异或集、并集、交集 setdiff1d() setxor1d() union1d() intersect1d() How to find intersection between two Numpy arrays? NumPy Set Operations – A Detailed Guide! 2. 概率图的交并差 2.1 概念说明 区别于正经统计意义上的事件概率:极速统计教程之八 | 概率和...
print(np.logical_xor(True, False)) # True print(np.logical_xor([True, True, False, False], [True, False, True, False])) # [False True True False] print(np.logical_xor(x < 1, x > 3)) # [ True False False False True] print(np.logical_xor(0, np.eye(2))) # [[ True F...
Compute the truth value of x1 OR x2 element-wise. 4)numpy.logical_xor 不同为True相同为False numpy.logical_xor(x1, x2, *args, **kwargs) Compute the truth value of x1 XOR x2, element-wise. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. ...
numpy.logical_xor(x1, x2, args, kwargs) Compute the truth value of x1 XOR x2, element-wise. 1.2数组内容 numpy.isnan numpy.isnan(x, args, kwargs) Test element-wise for NaN and return result as a boolean array. 1.1真值测试
setxor1d(a1, a2) 相当于 set(a1) + set(a2) - intersect1d(a1, a2) 数学运算 numpy重载了常见的数学运算符号,因此我们可以将加、减、乘、除、平方、开方等方法运用于单个或者两个数组, 或者运用于一个数组与一个标量之间。 a = np.array((1, 2, 3)) b = np.array((1.5, 2, 3)) # 如果 ...
np.unique(a)#去重并自动排序np.intersect1d(a,b)#交集np.union1d(a,b)#并集np.setdiff1d(a,b)#差集np.setxor1d(a,b)#Find the set exclusive-or of two arrays.np.square(a)#平方np.sin(a) np.sqrt(a)#开根号np.log10(a) np.exp(a)#np.abs(a) ...
Open Compiler import numpy as np # Define two arrays a = np.array([1, 2, 3, 4]) b = np.array([4, 3, 2, 1]) # Perform bitwise XOR operation result = np.bitwise_xor(a, b) print(result) OutputFollowing is the output of the bitwise_xor() Function applied on two arrays −...
setxor1d(ar1, ar2, assume_unique=False)Find the set exclusive-or of two arrays. 【例】集合的对称差,即两个集合的交集的补集。简言之,就是两个数组中各自独自拥有的元素的集合。 AI检测代码解析 import numpy as np a = np.array([1, 2, 3, 2, 4, 1]) ...
add() Return element-wise string concatenation for two arrays of str or unicode. numpy.core.add(x1, x2) multiply() Return (a * i), that is string multiple concatenation, element-wise. numpy.core.multiply(a, i) mod() Compute the bit-wise XOR of two arrays element-wise. numpy.core....