Why does hstack () copy data, but hsplit () creates a view on it?

Question

In NumPy, why hstack()copy data from arrays that are stacked:

A, B = np.array([1,2]), np.array([3,4])
C = np.hstack((A,B))
A[0]=99

gives for C:

array([1, 2, 3, 4])

whereas it hsplit()creates a data view:

a = np.array(((1,2),(3,4)))
b, c = np.hsplit(a,2)
a[0][0]=99

gives for b:

array([[99],
       [ 3]])

I mean - what is the reason for the implementation of this behavior (which I find inconsistent and difficult to remember): I agree that this happens because it is encoded in this way ...

Answer 1

ndarray , , . , , . , , ( - ).

C , :

http://scipy-lectures.imtqy.com/advanced/advanced_numpy/index.html#life-of-ndarray

Answer 2

NumPy , , .

hsplit . ( ). , NumPy .

hstack . , . , NumPy .