How does NumPy handle internal memory fragmentation over repeated operations?

Repeated allocations and slicing can cause logical fragmentation, impacting cache performance.

NumPy reduces fragmentation by reusing buffers where possible, but repeated transformations like slicing, stacking, and concatenation can create multiple non-contiguous views and allocations. Over time, this leads to poor memory locality and reduced performance even if total memory usage appears stable.