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Answer: When operating on NumPy arrays, it is possible to access the internal data buffer directly using a view without copying data around. This ensures good performance but can also cause unwanted problems if the user is not aware of how this works. (S1)

Views

• It is possible to access the array differently by just changing certain metadata like stride and dtype without changing the data buffer; these new arrays are called views. (S1)
• The data buffer remains the same, so any changes made to a view reflects in the original copy. (S1)
• A view can be forced through the ndarray.view method. (S1)
• Views are created when elements can be addressed with offsets and strides in the original array. (S1)

Copies

• When a new array is created by duplicating the data buffer as well as the metadata, it is called a copy. (S1)
• Changes made to the copy do not reflect on the original array. (S1)
• Making a copy is slower and memory-consuming but sometimes necessary. (S1)
• The copy method makes a complete copy of the array and its data. (S2)
• Sometimes copy should be called after slicing if the original array is not required anymore. (S2)

No copy at all

• Simple assignments make no copy of objects or their data. (S2)
• Python passes mutable objects as references, so function calls make no copy. (S2)

Claims

• id: C1 claim: Changes made to a view reflect in the original. source: S1 evidence: "to a view reflects in the original copy."
• id: C2 claim: Changes made to a copy do not reflect on the original. source: S1 evidence: "do not reflect on the original array."
• id: C3 claim: Simple assignments make no copy. source: S2 evidence: "Simple assignments make no copy of objects or their data."
• id: C4 claim: The copy method makes a complete copy. source: S2 evidence: "The copy method makes a complete copy of the array and its data."

Sources

• [S1] Copies and views — NumPy v2.5 Manual. NumPy docs. https://numpy.org/doc/stable/user/basics.copies.html . License: BSD-3-Clause (numpy/numpy LICENSE.txt; Copyright (c) 2005-2026, NumPy Developers; https://github.com/numpy/numpy/blob/main/LICENSE.txt). Accessed 2026-11-04.
• [S2] NumPy quickstart — NumPy v2.5 Manual. NumPy docs. https://numpy.org/doc/stable/user/quickstart.html . License: BSD-3-Clause (numpy/numpy LICENSE.txt; Copyright (c) 2005-2026, NumPy Developers; https://github.com/numpy/numpy/blob/main/LICENSE.txt). Accessed 2026-11-04.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted).

@misc{iil_note_vh418676,
  author = {{iinventedlight}},
  title = {{NumPy copies versus views}},
  howpublished = {iinventedlight Library},
  year = {2026},
  date = {2026-11-04},
  url = {https://iinventedlight.com/library/notes/numpy-copies-views/},
  note = {Permanent ID iil:note:vh418676. License: CC-BY-4.0.},
}

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