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PDEP-15: Reject PDEP-10 #58623

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PDEP-15: Reject PDEP-10 #58623

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PDEP-10: Change status to rejected
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4 changes: 4 additions & 0 deletions web/pandas/pdeps/0010-required-pyarrow-dependency.md
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[Patrick Hoefler](https://github.com/phofl)
- Revision: 1

# Note

This PDEP is superseded by PDEP-15.

## Abstract

This PDEP proposes that:
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# PDEP-15: Do not require PyArrow as a required dependency (for pandas 3.0)

- Created: 8 May 2024
- Status: Under Discussion
- Discussion: [#58623](https://github.com/pandas-dev/pandas/pull/58623)
[#52711](https://github.com/pandas-dev/pandas/pull/52711)
[#52509](https://github.com/pandas-dev/pandas/issues/52509)
[#54466](https://github.com/pandas-dev/pandas/issues/54466)
- Author: [Thomas Li](https://github.com/lithomas1)
- Revision: 1

## Abstract

This PDEP supersedes PDEP-10, which stipulated that PyArrow should become a required dependency
for pandas 3.0. After reviewing feedback posted
on the feedback issue [#54466](https://github.com/pandas-dev/pandas/issues/54466), we, the members of
the core team, have decided against moving forward with this PDEP for pandas 3.0.

The primary reasons for rejecting this PDEP are twofold:

1) Requiring pyarrow as a dependency causes installation problems.
- Pyarrow does not fit or has a hard time fitting in space-constrained environments
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@WillAyd WillAyd Aug 27, 2024 •
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I think this really exaggerates the problem on AWS. AWS has long distributed its own AWS SDK for pandas library (formerly called awswrangler) which uses pyarrow to better integrate with many of its services (ex: pyarrow is used for high performance data exports to/from AWS Redshift, rather than using a traditional ODBC driver)

The issue here is really just scoped to users that don't want to use the AWS Lambda Managed Layer, but instead want to build their environment from scratch, assumedly without the AWS SDK for pandas. Even then, it may not be a current issue given the drastic reductions in the binary size of pyarrow through both pip and conda

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I think it's only conda that's been drastically reduced - install pyarrow and pandas in a fresh venv and it already hits 295 MB. And the wheel size on PyPI is still ~40MB, so we'd be noticeably increasing the load on PyPI by making PyArrow required

My current stance is: if the desire was to make PyArrow dtypes the default for all dtypes, then ok, maybe that'd be justified. But probably not if it's just for the sake of strings, for which I think that

  1. recommending pip install pandas[pyarrow] in all instructions
  2. auto-inferring pyarrow strings if pyarrow is installed

should be enough

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Sounds good to me.

I'll add this to the PDEP.

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And the wheel size on PyPI is still ~40MB, so we'd be noticeably increasing the load on PyPI by making PyArrow required

Out of curiosity - is this something that the PyPi maintainers have mentioned as a problem?

If so, the plot twist is then that we should really be pushing AWS users towards using the pre-provided image, rather than building their own from scratch. I believe that would forgo hitting PyPi altogether, and even if it doesn't, it is still smaller than what people are building themselves (see #54466 (comment))

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If so, the plot twist is then that we should really be pushing AWS users towards using the pre-provided image, rather than building their own from scratch. I believe that would forgo hitting PyPi altogether, and even if it doesn't, it is still smaller than what people are building themselves (see #54466 (comment))

Wouldn't we then be asking our users to be dependent on the AWS people updating their pre-provided image whenever we created a new release of pandas or the arrow team did a new release of pyarrow ?

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Yea I think that is generally how lambda works, even with the overall Python version. It's not quite an "anything goes" type of execution environment

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There's this post from 2021, in which they write about the monthly PyPI bill being almost 2 million US dollars

https://dustingram.com/articles/2021/04/14/powering-the-python-package-index-in-2021/

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There's this post from 2021, in which they write about the monthly PyPI bill being almost 2 million US dollars

https://dustingram.com/articles/2021/04/14/powering-the-python-package-index-in-2021/

I'm not too worried about this - PyPI's bill is subsidized anyways. Of course, it is nice to reduce our load on PyPI, but I don't think we are close to being the worse offenders here (those would probably be something like tensorflow and pytorch), and it's important to keep in mind that a lot of people have pyarrow installed for whatever reason anyways.

I would mostly only be concerned about an increase in size in our own pandas package (since PyPI does limit the total size of all packages uploaded by a project, and raising the limit is a manual and annoying process)

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I've made another go at clarifying point1, incorporating the feedback here.

PTAL @WillAyd @MarcoGorelli

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Thanks for the feedback, but I still disagree with calling the AWS-provided layer a "workaround" - essentially it is the canonical approach to solve an issue that has existed for years with pandas and lambda functions.

A quick google search for something how "how to run pandas on aws lambda" yields a slew of conflicting results on how to get this to work. If the AWS-provided layer is a workaround, then what are we calling the proper approach?

https://stackoverflow.com/questions/36054976/pandas-aws-lambda
https://stackoverflow.com/questions/53824556/how-to-install-numpy-and-pandas-for-aws-lambdas
https://medium.com/swlh/how-to-add-python-pandas-layer-to-aws-lambda-bab5ea7ced4f
https://medium.com/@johnnymao/how-to-use-pandas-in-your-aws-lambda-function-c3ce29f6f189
https://medium.com/@shimo164/lambda-layer-to-use-numpy-and-pandas-in-aws-lambda-function-8a0e040faa18
https://www.youtube.com/watch?v=1UDEp90S9h8

such as AWS Lambda and WASM, due to its large size of around ~40 MB for a compiled wheel
(which is larger than pandas' own wheel sizes)
- Installation of pyarrow is not possible on some platforms. We provide support for some
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less widely used platforms such as Alpine Linux (and there is third party support for pandas in
pyodide, a WASM distribution of pandas), both of which pyarrow does not provide wheels for.

While both of these reasons are mentioned in the drawbacks section of PDEP-10, at the time of the writing
of the PDEP, we underestimated the impact this would have on users, and also downstream developers.

2) Many of the benefits presented in PDEP-10 can be materialized even with payrrow as an optional dependency.

For example, as detailed in PDEP-14, it is possible to create a new string data type with the same semantics
as our current default object string data type, but that allows users to experience faster performance and memory savings
compared to the object strings (if pyarrow is installed).

While we've decided to not move forward with requiring pyarrow in pandas 3.0, the rejection of PDEP-10
does not mean that we are abandoning pyarrow support and integration in pandas. We, as the core team, still believe
that adopting support for pyarrow arrays and data types in more of pandas will lead to greater interoperability with the
ecosystem and better performance for users. Furthermore, a lot of the drawbacks, such as the large installation size of pyarrow
and the lack of support for certain platforms, can be solved, and potential solutions have been proposed for them, allowing us
to potentially revisit this decision in the future.

However, at this point in time, it is clear that we are not ready to require pyarrow
as a dependency in pandas.
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