SAS7BDAT parser: Fast byteswap
Speed up SAS7BDAT int/float reading.

This is order of magnitude faster than using `struct.unpack(fmt, data)` or `precompiled_unpacker = struct.Struct(fmt).unpack; ...; precompiled_unpacker(data)`.

Unfortunately Python does not expose a low-level interface to `struct` or a byteswapping interface. The byteswap implementation in this change is from `pyreadstat`.

Today this brings a modest 10-20% performance improvement. But together with the other changes I will be proposing it will be a major bottleneck.

- [ ] closes #xxxx (Replace xxxx with the Github issue number)
- [ ] [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
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.

One thing I realized is that we could also use NumPy's byteswapping, at the cost of around 10% performance relative to this impl. > One thing I realized is that we could also use NumPy's byteswapping, at the cost of around 10% performance relative to this impl.

How much of this could be replaced with that? > How much of this could be replaced with that?

Everything copied from readstat so ~ 50 lines of Cython code 
 ASV from #47405:

```
       before           after         ratio
     [e915b0a4]       [435a003c]
     <main>           <sas/byteswap~1>
-      81.9±0.7ms       73.7±0.3ms     0.90  io.sas.SAS.time_read_sas7bdat_2_chunked
-      78.7±0.7ms       69.5±0.6ms     0.88  io.sas.SAS.time_read_sas7bdat_2
``` Test failure seems unrelated  I'm trying an intrinsics-based version: Fewer SLOC and faster. Test failure is unrelated. @jreback merge? @jonashaag seems like you didn't add the release note as requested, do you mind adding it?

Does it make sense to add a unit test for the new functions?

Also if you can please merge master. @datapythonista release notes are already merged with #47404. Will merge main. Do you want me to add unit tests? > Do you want me to add unit tests?

Personally I think it should be useful. Probably just one parametrized to test the function for every type is enough. The release note in the other PR is not accurate now. The changes in this PR won't be available until pandas 1.6/2.0, while for what you mention the release note in the other PR mentions this issue/PR for 1.5. Probably no big deal, but if you want to open a small PR to update that note in the releases to only include the issues/PRs that have already been merged, that would leave things more accurate (ping me in that PR, so I backport it to 1.5). And then you can add another release note for 1.6 here. Or we merge the PRs noted in those release notes to 1.5? The PRs have been ready for review with no code changes for a long time. We already released pandas 1.5 release candidate, and we are only backporting regressions to it, not new features, performance improvements. It's also unclear to me this will be merged before the release.

I know this has been forgotten for a long time, sorry about that. But that's unfortunately part of how open source development works in a project like pandas. I added tests using Hypothesis and moved the byteswapping code to a module. The byteswapping code also be moved somewhere else outside the SAS stuff. Updated release notes. I took the liberty of including #47656 already. Thanks @jonashaag 