How does NumPy handle memory fragmentation issues?

NumPy reduces fragmentation using contiguous allocation and views.

Fragmentation occurs when memory is split into non-contiguous blocks. NumPy minimizes this by allocating large contiguous buffers and using views instead of copies. However, repeated slicing and advanced indexing can still cause fragmentation.