np.ndarray: The element-wise sum of the input arrays. """returnnp.add(x,y) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 在这个示例中,我们定义了一个名为add的函数,它接受两个NumPy数组作为输入,并返回一个NumPy数组。在注释中,我们清楚地描述了函数的功能、输入参数和返回值类
maintaining the masks result_array = np.ma.add(masked_array1, masked_array2) # Print the original masked arrays and the resulting array print("Masked Array 1:") print(masked_array1) print("\nMasked Array 2:") print(masked_array2) print("\nResulting Array after Element-wise Addition:"...
[3,4]] ) >>> A*B # elementwise product array([[2, 0], [0, 4]]) >>> A.dot(B) # matrix product array([[5, 4], [3, 4]]) >>> np.dot(A, B) # another matrix product array([[5, 4], [3, 4]]) 有一些操作,如 += 和 *=,其输出结果会改变一个已存在的数组,而不...
numpy.isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False) * Returns a boolean array where two arrays are element-wise equal within a tolerance. numpy.allclose numpy.allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False) * Returns True if two arrays are element-wise ...
>>> A = np.array([[1, 1], ... [0, 1]]) >>> B = np.array([[2, 0], ... [3, 4]]) >>> A * B # elementwise product array([[2, 0], [0, 4]]) >>> A @ B # matrix product array([[5, 4], [3, 4]]) >>> A.dot(B) # another matrix product array([[...
>>> A*B# elementwise productarray([[2,0], [0,4]]) >>> dot(A,B)# matrix productarray([[5,4], [3,4]]) 有些操作符像+=和*=被用来更改已存在数组而不创建一个新的数组。 >>> a = ones((2,3), dtype=int) >>> b = random.random((2,3)) ...
Input 4 creates the mask by performing a vectorized Boolean computation, taking each element and checking to see if it divides evenly by four. This returns a mask array of the same shape with the element-wise results of the computation. Input 6 uses this mask to index into the original num...
Using the add() function: [ 3 7 11 15] In the above example, first we created two arrays named:first_arrayandsecond_array. Then, we used the+operator and theadd()function to perform element-wise addition respectively. As we can see, both+andadd()give the same result. ...
arctan2(x1, x2[, out]) Element-wise arc tangent of x1/x2 choosing the quadrant correctly. degrees(x[, out]) 弧度求角度 radians(x[, out]) 角度求弧度 unwrap(p[, discont, axis]) Unwrap by changing deltas between values to 2*pi complement. deg2rad(x[, out]) 角度求弧度 rad2deg(x...
[3, 4]]) >>> A * B # elementwise product array([[2, 0], [0, 4]]) >>> A @ B # matrix product array([[5, 4], [3, 4]]) >>> A.dot(B) # another matrix product array([[5, 4], [3, 4]]) 一些操作,例如+=和*=,会就地修改现有数组,而不是创建新数组。 >>> rg...