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Conclusion: to copy data from a numpy array to another use one of the built-in numpy functions numpy. array(src) or numpy. copyto(dst, src) wherever possible.
The copy owns the data and any changes made to the copy will not affect original array, and any changes made to the original array will not affect the copy. The view does not own the data and any changes made to the view will affect the original array, and any changes made to the original array will affect the view.
The array. copy() method in numpy is used to make and change an original array, and returns the two arrays.
Create a Copy of 2D Arrays Using the NumPy copy() Function NumPy offers the copy() function. The copy() function can be implemented as shown below. In the example code, we created a copy of the original array and changed the copy. However, we have retained the original copy of the array, printed in the last line.
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?
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)
This is the only working answer for me:
B=numpy.array(A)
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