For example, if we have a numpy array A, and we want a numpy array B with the same elements.
What is the difference between the following (see below) methods? When is additional memory allocated, and when is it not?
B = AB[:] = A (same as B[:]=A[:]?)numpy.copy(B, A)All three versions do different things:
B = A
This binds a new name B to the existing object already named A. Afterwards they refer to the same object, so if you modify one in place, you'll see the change through the other one too.
B[:] = A (same as B[:]=A[:]?)
This copies the values from A into an existing array B. The two arrays must have the same shape for this to work. B[:] = A[:] does the same thing (but B = A[:] would do something more like 1).
numpy.copy(B, A)
This is not legal syntax. You probably meant B = numpy.copy(A). This is almost the same as 2, but it creates a new array, rather than reusing the B array. If there were no other references to the previous B value, the end result would be the same as 2, but it will use more memory temporarily during the copy.
Or maybe you meant numpy.copyto(B, A), which is legal, and is equivalent to 2?
but B = A[:] would do something more like 1 ? According to this stackoverflow.com/a/2612815 new_list = old_list[:] is also a copy.some_array[:] will create a new array object, but that new object will be a view of the same memory as the original array, which won't have been copied. That's why I said it's more like B = A. It takes only O(1) space and time, rather than the O(n) of each a real copy would need.
B = A.copy(). However this form doesn't preserve order by default, you need B = A.copy(order='k').B=A creates a referenceB[:]=A makes a copynumpy.copy(B,A) makes a copythe last two need additional memory.
To make a deep copy you need to use B = copy.deepcopy(A)
B[:] = A does not make a deep copy of arrays of object-type, e.g. A = np.array([[1,2,3],[4,5]]); B = np.array([None,None], dtype='O'). Now try B[:] = A; B[0][0]=99, this will change the first element in both A and B! To my knowledge, there is no other way to guarantee a deep copy, even of a numpy-array, than copy.deepcopyThis is the only working answer for me:
B=numpy.array(A)
