How does NumPy handle internal efficiency of concatenation and stacking operations?

Concatenation and stacking typically allocate new memory and copy data from input arrays.

Unlike slicing or broadcasting, concatenation requires allocating a new contiguous memory block large enough to hold all input arrays. Data is then copied sequentially. This makes concatenation expensive for large arrays, especially in loops. Stacking adds an extra dimension but follows the same copy-based mechanism.