Understanding Numpy Array Assignment: When is New Memory Allocated?

Numpy Array Assignment with Copy

Introduction

When dealing with Numpy arrays, it's crucial to understand how assignments affect the data. This article explores the differences between three assignment methods: B = A, B[:] = A, and numpy.copy(B, A), addressing when additional memory is allocated and when it isn't.

B = A

This assignment simply binds a new name (B) to an existing Numpy object (A). Both names refer to the same object, so any in-place modification to one will be reflected in the other. No additional memory is allocated.

B[:] = A (Equivalent to B[:]=A[:])

This operation copies the values from A into an existing array B. The shapes of B and A must match. No additional memory is allocated because the existing B array is reused.

numpy.copy(B, A)

This syntax is incorrect. The correct syntax is B = numpy.copy(A), which creates a new array containing the copy of A. The original B array is not reused, and thus, additional memory is allocated while copying the data.

numpy.copyto(B, A)

This assignment is equivalent to B[:] = A. It copies the values from A into B, overwriting its existing data. If there is sufficient space in B, no additional memory is allocated; otherwise, a new array is created and additional memory is allocated.

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