ENH: propagating attrs always uses deepcopy
Always using a deepcopy prevents shared state and thus unintentional modification of the attrs of other objects. IMHO this safety has a higher priority than the slight performance cost of the deepcopy. The implementation now skips the copying if *attrs* are not used (i.e. an empty dict). This check takes only ~20ns. Thus, the attrs copy mechanism has no performance impact if attrs are not used.



- [x] Closes #54134
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

Importing stdlib `copy` at the top of `generic.py` issues the a number of pylint warnings of the type:

> pandas/core/generic.py:289:8: W0621: Redefining name 'copy' from outer scope (line 5) (redefined-outer-name)

What is the preferred way in the project to handle this? > What is the preferred way in the project to handle this?

Probably using `from copy import deepcopy` should be fine Just a pre-commit check failing otherwise looks good Thanks @timhoffm  This patch may have induced a performance regression. If it was a necessary behavior change, this may have been expected and everything is okay.

Please check the links below. If any ASVs are parameterized, the combinations of parameters that a regression has been detected for appear as subbullets.

 - [finalize.Finalize.time_finalize_micro](https://asv-runner.github.io/asv-collection/pandas/#finalize.Finalize.time_finalize_micro)
   - [series=<class 'pandas.core.series.Series'>](https://asv-runner.github.io/asv-collection/pandas/#finalize.Finalize.time_finalize_micro?p-series=%3Cclass%20%27pandas.core.series.Series%27%3E)
   - [series=<class 'pandas.core.frame.DataFrame'>](https://asv-runner.github.io/asv-collection/pandas/#finalize.Finalize.time_finalize_micro?p-series=%3Cclass%20%27pandas.core.frame.DataFrame%27%3E)

Subsequent benchmarks may have skipped some commits. The link below lists the commits that are between the two benchmark runs where the regression was identified.

https://github.com/pandas-dev/pandas/compare/6a83910674a2be1f95d75cfd935fdff22b09bd33...364c9cb65acf47119dc251b25226ab415cc9a100
 I believe the detected changes come from this PR.

The test uses `obj.attrs`, which this PR changes to deepcopy in finalize. finalize performance should not decrease if `attrs` are not used (in fact, it might be marginally faster), but I don't see whether you have an ASV test for that as well.

Note also that the tests use `attrs` with 1000 entries, which makes the deepcopy cost quite prominent. I expect that typical attrs is a more like a bit of metadata with << 1000 entries.

The use of `deepcopy` is expected to be a bit slower. I'd argue that it's a necessary safety measure to prevent multiple objects to share the same attrs value objects. It might otherwise be quite surprising that a user changes attrs down the road of operations, and by that change the attrs of an input object. Such a coupling is even more surprising nowadays that dataframe values themselves can be considered independent because of copy-on-write.
IMHO, if the user does not want to afford the cost of the safety net, they should not use `attrs` but mange that state themselves outside of pandas. - Or if there is really interest make shallow copy available as an opt-in.