以与内置数组兼容的方式扩展numpy数组

Question

I am trying to write code that would not depend on whether the user uses np.array or a builtin array. I am trying to avoid checking object types, etc. The only problem that I have is in extending arrays. For example, if I have two Python arrays, a and b , to extend the first by the second, I can either do a = a + b , or a.extend(b) . On the other hand, if a is a numpy array, I need to use np.append or something else.

Is there a quick way to extend arrays independently of whether they are np arrays or Python arrays?

Answer 1

Even if your function is flexible on input, your output should be of specific type. So I would just convert to desired output type.

For example, if my functions is working with numpy.array and returns a numpy.array , but I want to allow list s to be input as well, the first thing I would do is convert list s to numpy.array s.

Like this:

def my_func(a, b):
    a = np.asarray(a)
    b = np.asarray(b)
    # do my stuff here

Answer 2

NumPy's append() works on lists too!

>>> np.append([1,2,3], [1,2,3])
array([1, 2, 3, 1, 2, 3])

If you want to automatically make the result be the same type as the input, try this:

mytype = type(a)
arr = np.append(a, b)
result = mytype(arr)