Extend Numpy Array In A Way Compatible With Builtin Arrays

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

Solution 1:

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)

Solution 2:

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 lists to be input as well, the first thing I would do is convert lists to numpy.arrays.

Like this:

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