PDEP-15: Reject PDEP-10 #58623
PDEP-15: Reject PDEP-10 #58623
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Website preview of this PR available at: https://pandas.pydata.org/preview/pandas-dev/pandas/58623/ |
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Ugh, looks like the bullets aren't rendering correctly. |
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Did I miss an official vote on rejecting this? I am not sure yet that I would want to reject, and am still leaning towards keeping in spite of some negative feedback |
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Nope, just opening since I said I would in the discussion issue. We'll still need a formal vote - I'm just kicking off the discussion here. |
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| 2) Many of the benefits presented in this PDEP can be materialized even with payrrow as an optional dependency. | ||
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| For example, as detailed in PDEP-14, it is possible to create a new string data type with the same semantics |
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PDEP 14 does not change performance or memory savings if you do not have pyarrow installed
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added a note in parentheses at the end of that sentence.
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Did you push this up? I don't see anything in parentheses.
The way I am interpreting this now is "we don't need/care for pyarrow strings because we have always had a string data type using Python strings" - is that correct?
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I updated the PDEP-15 text, and forgot to remove the PDEP-10 changes.
I've removed the PDEP-10 changes now.
| The primary reasons for rejecting this PDEP are twofold: | ||
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| 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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I think what we could learn from this process is what caused this to change our minds? These issues were discussed leading up to the acceptance of PDEP-10.
The way this is written I think reads more as "we discovered this after the fact" instead of "we decided that X amount of negative feedback on these points was enough to revert". I think there is some value to future PDEPs to set expectations around the latter
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cc @pandas-dev/pandas-core @pandas-dev/pandas-triage xref #57073 (comment) for context |
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Thanks @lithomas1 for making the updates needed to formally reject PDEP-10.
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As discussed in dev meeting on 5/8/24, suggestion is to do a new PDEP that reverts PDEP-10, and keeps any parts we want to keep. |
I'm now leaning towards approving the rejection. My approval of the original PDEP was based solely on improvements to default inference for other dtypes. Despite some recent comments about this, no discussion/clarification has followed on this topic. I'd need to see some positive evidence that the original PDEP-10 authors still intend to support delivering the promised enhancements in this area. Now that the implications of using pd.NA as a default has been discussed in more depth, I suspect that any improved inference would need a couple of dtype variants. |
Yep, I'm planning on updating this current PR to do that, so if anyone has any objections or whatever, we can still discuss here. |
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Minor note - do we need to rename this PR? Right now PDEP-10 shows twice on the website
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Yeah, I'll probably change the name to PDEP-15 once I get around to moving this to a separate PDEP (probably tomorrow). I was travelling the past week, so didn't really have time then. |
lithomas1
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| The primary reasons for rejecting this PDEP are twofold: | ||
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| 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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Within the context of recent conversation I don't think this comment about AWS is true. AWS distributes an official pandas image for lambda which already includes pyarrow, pandas, and NumPy. This is all required by their own "AWS SDK on pandas" library.
The issue more finely scoped I think is that the default wheel installation via pip into a lambda image exceeds the 256 MB limit. Either using the official AWS provided image or using miniconda should not exceed the space limits
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| 2) Many of the benefits presented in this PDEP can be materialized even with payrrow as an optional dependency. | ||
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| For example, as detailed in PDEP-14, it is possible to create a new string data type with the same semantics |
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Did you push this up? I don't see anything in parentheses.
The way I am interpreting this now is "we don't need/care for pyarrow strings because we have always had a string data type using Python strings" - is that correct?
Dr-Irv
changed the title
| While both of these reasons are mentioned in the drawbacks section of this PDEP, at the time of the writing | ||
| of the PDEP, we underestimated the impact this would have on users, and also downstream developers. | ||
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| 2) Many of the benefits presented in this PDEP can be materialized even with payrrow as an optional dependency. |
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I personally don't find this point very convincing. Saying Many of the benefits but then following it up with one bullet point seems to miss the mark - what are the other many benefits that we don't need pyarrow for? Without pyarrow users are forgoing:
- High performance string operations
- Direct string creation from I/O routines (i.e. no intermediate copies)
- Zero copy data exchange through Arrow C Data Interface
- Performant, memory efficient, and consistent NA handling
On the larger roadmap of pandas this moves us away from tighter Arrow integration, which means we move further away from Arrow compute algorithms / joins and the larger ecosystem of tools that includes streaming, query optimizers, planners, data engines, etc...
I think this argument in its current form is saying "we don't need a car because we have a horse and buggy"
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I personally don't find this point very convincing. Saying
Many of the benefitsbut then following it up with one bullet point seems to miss the mark - what are the other many benefits that we don't need pyarrow for? Without pyarrow users are forgoing:
- High performance string operations
- Direct string creation from I/O routines (i.e. no intermediate copies)
- Zero copy data exchange through Arrow C Data Interface
- Performant, memory efficient, and consistent NA handling
On the larger roadmap of pandas this moves us away from tighter Arrow integration, which means we move further away from Arrow compute algorithms / joins and the larger ecosystem of tools that includes streaming, query optimizers, planners, data engines, etc...
I think this argument in its current form is saying "we don't need a car because we have a horse and buggy"
In PDEP-10, there are 3 benefits listed
- pyarrow strings (possible to provide users this benefit without making pyarrow required)
- Nested datatypes (can't have this without arrow, but this is a bit niche)
- Interopability (the alternative is the dataframe interchange protocol, which is more widely adopted at the moment. Not sure about the zero-copy stuff for that, though. I think it also might be possible to implement Arrow C Data interface support without taking on a hard dep on pyarrow)
- Also, the primary beneficiary of this is other dataframe libraries (as opposed to us).
So, IMO, this argument is accurate, in that most of the benefits in PDEP-10 can be made possible (for those user that have pyarrow installed) without making pyarrow required.
The future benefits of Arrow are very compelling, but decisions on making a dependency required should be based on immediate and not future benefits. Like I said before, it is easy to reconsider this decision in a years time if those future benefits are materialize.
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If you think points 1 and 3 are possible without pyarrow then the alternatives for that should be laid out in this PDEP, at least at a super high level. I'm assuming point 1 refers to the nanoarrow POC I was sharing; point 3 requires reimplementing the conversions that pyarrow already has. (I personally don't think building either of those from scratch is a good long term solution but it can at least be discussed)
For point 2 how do you know those are niche applications? Its easy to dismiss things that don't exist today as not worthwhile, but I get the feeling that there could be plenty of use cases for the aggregate types, since they have a natural fit with many of the Python containers.
On interoperability the long term prospects for the dataframe interchange protocol seem dubious, and we have even discussed moving that out of pandas (see #56732).
- Also, the primary beneficiary of this is other dataframe libraries (as opposed to us).
The Arrow interchange protocol can be used by any library that needs to work with Arrow data - there is no limit to it being used by other dataframe libraries. It provides a standardized API so that third parties don't need to hack into our internals, which is a direct benefit for us. It also works in two directions - we can be a consumer just as much as a producer.
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Nested datatypes (can't have this without arrow, but this is a bit niche)
Also wanted to point out that arrow has a decimal128 and decimal256 type which is especially useful for financial calculations where floating point inaccuracies cannot be tolerated, and the arrow decimal types are an extremely significant improvement over using object.
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Sure, will update and add a note in the PDEP when I get time again.
Co-authored-by: Irv Lustig <[email protected]>
Co-authored-by: Irv Lustig <[email protected]>
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This pull request is stale because it has been open for thirty days with no activity. Please update and respond to this comment if you're still interested in working on this. |
| arrays to those dtypes by default, without forcing a pyarrow requirement on users, | ||
| as there is no Python/numpy equivalent for these dtypes). | ||
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| - Interoperability |
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I think @MarcoGorelli point was to remove the Interoperability section entirely, but if that's not true then I don't understand the point this is trying to make in its current form.
The beneficiary of the Arrow C Data interface is not just other dataframe libraries - a decent listing can be found here:
For a direct benefit to pandas, it helps with I/O to boost performance, ensure proper data types, and reduce the amount of code burden. We have already seen this benefit with ADBC drivers, and from the link above, it looks like there is some near-term potential for it to help Excel I/O via fastexcel
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Sure, will take it out the next go.
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The fact that we don't require PyArrow might put is in a bind for downstream libraries that want interchange with pandas, but themselves probably aren't in a position to require PyArrow. In particular, this conversation is happening with seaborn:
mwaskom/seaborn#3782 (comment)
Somewhat unfortunately, this may mean that we are asked to put more maintenance work into the interchange protocol
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This pull request is stale because it has been open for thirty days with no activity. Please update and respond to this comment if you're still interested in working on this. |
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If there are no objections I'll open a vote on this shortly. While in general we've been assuming that we don't want to make pyarrow a required dependency as previously agreed, no formal vote has happened (other than in other PDEPs that assumed this one was already approved). While the discussion here could continue for longer, I think it had enough iterations to be ready for a vote. CC @pandas-dev/pandas-core |
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Just left a comment on the musllinux topic, which I think has since been addressed
I'm glad that the decision require PyArrow was postponed, and that in the ~2 years since the decision was taken it gave a chance for several issues to be addressed:
- pyodide support (pyarrow's supported now 🥳 https://pyodide.org/en/stable/console.html)
- musllinux support
- smaller conda package
- many, many bugs have been fixed. In Narwhals we parametrise tests over all pandas dtype backends, and since pandas 2.2, the pyarrow dtypes seem really solid
It's true that this increases the load on PyPI, and that's something I'd still feel uneasy about (Thomas is right that there are worse offenders - has anyone seen PySpark 4.0? https://pypi.org/project/pyspark/#pyspark-4.0.0.tar.gz), Having said that, I did give up my right to vote in #61163 , so I can't (and won't) vote on this.
At this point, it does seem harder to make the case against requiring PyArrow
| - While pyarrow has made great strides towards supporting most platforms that pandas is installable on | ||
| (e.g. the recent addition of pyodide support in pyarrow), we would still have to drop support for some | ||
| platforms like musllinux (the feature request is tracked [here](https://github.com/apache/arrow/issues/18036)) if pyarrow was to be required. |
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not sure if I'm reading apache/arrow#18036 (comment) correctly, but it looks like PyArrow v20 does indeed support musllinux? this was mentioned in #50511 (comment) too
Thinking some more I would now probably vote against rejecting PDEP-10. PDEP-10 states "NumPy object dtype will be avoided as much as possible." and gives a list of new dtypes that would be implemented in the future. Without these being included in another approved PDEP, rejecting PDEP-10 would be effectively removing this agreed upon direction from our roadmap which I don't think provides clarity to the community. If the issue is just the timing of the pyArrow requirement, then an update to the timelines in PDEP-10 may suffice? |
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This PDEP only rejects PDEP-10, and doesn't discuss how to move forward, which will have to be decided later (for example via PDEP-16 if this one is approved). So I think the sooner we vote on this, the sooner we can decide what exactly to do for pandas 3.0, and the sooner we can start working on it. I started the voting at #61596. The deadline for voting is 15 days, until June 22nd (I guess if everybody who can votes cast their vote before, we can move accept or reject the PDEP earlier). |
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in #61596 (comment) @rhshadrach wrote
I may be misunderstanding or our PDEP guidelines may need to be updated but these cases are for when we move to an early vote. The PDEP process states "A PDEP discussion will remain open for up to 60 days.". we have far exceeded this requirement and so we are not calling an early vote? also may need clarification but PDEP-1 also states
going stale is IMO a good reason to declare this PDEP invalid? We need better guidelines in PDEP-1 about when a PDEP can be closed automatically to eliminate the situation we find ourselves in here and other PDEPs such as PDEP-16. Stale PDEP do not provide clarity to the community on the direction of pandas and the intent of the development team. |
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I officially call the vote for this PDEP. No fully agree this is needed, as this PDEP exceeded the discussion period by almost a year, but that gives people 15 days to finish the discussion. Note that the PDEP author is not an active pandas maintainer, and I'm not aware of anyone who is planning to lead the discussion or update the PDEP with the feedback. So, while it feels just like a waste of time, I'll reopen the voting issue in 15 days. I'll also be sending an email as stated in PDEP-1, but note that may vote on the PDEP-1 is -1, so my intention here is not to get the PDEP accepted, but to make a decision regarding PDEP-10 (requiring PyArrow in pandas 3.0), which so far continues to be approved. |
In all cases there needs to be a notice that there will be a vote, even in the case of early voting.
I do not think this needs updating. We need to give proper notice that a vote will be taking place. Anyone with final thoughts on a PDEP needs time to voice them prior to the vote starting.
Agreed that deciding not to put a PDEP up for a vote does not require a notice. That is not what happened here. I don't think we need better guidelines - the guidelines are clear on what needs to happen in order to have a vote. If that doesn't happen, then there is no vote. The author of the PDEP can close it, or we can close one due to inactivity as in any other PR. It can always be reopened, and if another core member wants to take up a PDEP someone else has backed away from they are free to do so. |
I find it a paradox that we acted like PDEP-10 was already rejected immediately just because of some informal discussions, and now we need to wait one month to have confirmation if people really wanted it. Anyway, Marco brought some very good points, maybe we do benefit from some extra discussion time, we'll see. |
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This is a bit of a mess at this point. PDEP-10 states:
The feedback issue is here: #54466 We then received a lot of complaints, and we held a vote on Feb. 20, 2024, at #57424 to remove the So if we are to require One option we have is to release a 2.3.1 with the warning put back in so that people get the appropriate warning. We could also create a new feedback issue to see if the community still feels the requirement is onerous. After a month or two of collecting feedback, we can then have the vote on PDEP-15. Personally, I'm really uncomfortable requiring |
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That vote took 6 days to be decided based on the issue log. It was won by just one vote, with no previous time for discussion, and with an ad-hoc voting not in the governance, and not discusssed in advance. Everybody seems ok with it (including myself). Can't understand the double standards compared to adding 15 extra days delay here for something that people has been discussing and thinking about for more than two years. And that it just needs a final decision so things can be unblock so devs and users can act accordingly. Good point about the warning. Personally not too bothered about it. Most users won't even realize if pandas installed some extra depemdencies or not. And users know in major versions things change (particularly from 2 to 3, if we think of Python or Gnome). In any case, PDEP-15 is about rejecting the previous PDEP-10. Whether it's accepted or not, another discussion will be required to see how to move forward. |
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Going through the feedback issue, the concerns voiced by users that still remain are:
I too am concerned about making PyArrow a hard dependency. Some of that is based on not understanding how many users we will leave without an upgrade path, and some of it is based on what maintenance PyArrow itself will receive (I've heard the situation there is not great, but don't have a good understanding).
Assuming we go forward with this, I'm on board with adding back the warning if we also add a config to disable it. Many complained about the inability to silence the warning globally. Being able to silence it from the system environment is also a need-to-have in my opinion. |
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Thanks @rhshadrach. In my mind this is not a binary issue. There are three options:
Surely welcome suggestions from others for alternative options. Now my rejection of this PDEP in the "invalid" vote is based on not wanting to remove some previously agreed roadmap items. in #61596 (comment) I said I may vote differently if the motivation for rejecting PDEP-10 was to keep PyArrow an optional dependency indefinitely. I am not adverse to that but would want to clarify the pandas roadmap for default inference of some new dtypes before agreeing to that. I would probably want to see some object fallback versions of these dtypes for default inference when PyArrow is not installed. |
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I agree with you @simonjayhawkins, but except for the 3 point. I think this PDEP leaves the door open to adding PyArrow in the future. Rejecting this in my opinion means PDEP-10 is what we want. Otherwise we'll wait one month for the outcome here, and then more than another extra month to vote on a change to PDEP-10, and we may keep the uncertainty until 2026. I think this uncertainty is harming both pandas development and users. Regarding the warning, the one who are in favor kf adding back the warning, can you clarify what's the goal? I think the previous warning had the goal of gathering feedback from users that don't follow pandas development. We got it, and it was useful. I don't think the new feedback is particularly useful, in that in my opinion we know the implications. Or would you just have the warning as a message so users know what's coming? I think we may be the first project I'm aware of that does this. Python broke everything in version 3 and I don't remember any warning in tbe interpreter. There are better ways to communicate with users. |
Yes, if we decide to require pyarrow, we need to put the warning back. Not necessarily to get feedback, but just so people know this will be happening.
Not sure what "this" is, but we have always created warnings when we do things like this. What's odd in this case is that we had a warning, and removed it, but now there is no visibility to the community if we decide to reject PDEP-15 and require
With respect to (2), if that is decision, then I think we also need to decide whether we put the warning back in prior to 3.0. So maybe (2) gets split into 2 options - one with the warning and one without. I like the options, and maybe we need to just vote on those 3 (or 4) options as opposed to a approve/reject on PDEP-15. Not sure what threshold to use when we have multiple options. |
Sorry I wasn't clear. "Doing this" I meant showing a warning to users of one version because we will add an extra dependency to next version. pandas didn't even add an extra dependency, so there wasn't the opportunity. And I don't of any other Python library (or any software at all) that has shown a warning. No idea how common is to add dependencies that significantly invrease the installation size, or that don't work in all previously supported architectures (is this still the case?). So, while we can do whatever we want, I diaagree we must show a warning because that's a standard practice, it's not. And I personally don't find it useful, most users won't care about the new dependecy, and if you care because you can't upgrade pandas, in my opinion it's better to know when you try to upgrade, that when you are using a previous version and it's not a problem. |
What I struggle with in this conversation is that we did decide to require pyarrow through the initial vote, but we have implicitly backtracked on that in implementation I am guessing we all must have a different interpretation over the scope of PDEP-10. I think part of the team interpreted it as "pyarrow is required now, let's figure out the implementation details and any problems as we go," whereas the other part of the team may be coming from the mindset of "PDEP-10 did not clarify the scope of these implementation details or address challenge X, so we should revisit it." There are probably other interpretations therein, but I think we collectively need to clarify how far-reaching the scope is of a PDEP like this; otherwise I think the value of our PDEP process is questionable. I am particularly frustrated with PDEP-10 and some of the text in PDEP-15 because the goals we used to subvert PDEP-10 have never been goals of the project since I have been involved. Taking package size as an example, adding pyarrow of course increases the size versus the current state, but it would still be smaller than what pandas distributed for much of its history, when it alone was 100MB+. Of course its always nicer to have a smaller package, but I don't recall it being a huge issue when it was reduced however many years ago. We use Cython currently without much concern even though it creates bloated packages, so its hard to understand what we are aiming for. These things feel like moving targets, and if the idea is a PDEP needs to address them in detail before being valid, then I worry that the PDEP process won't be suitable to clarify larger enhancements to the project, as that is an impossible ask |
While I fully agree that breaking changes in program behavior merit explicit warnings, the shift of a dependency from optional to required isn’t the same kind of risk. This change is fundamentally an infrastructure and packaging decision, not one that alters how pandas behaves once installed.
In summary, while it’s crucial to warn users about genuine breaking changes, issuing a warning for a dependency’s transition from optional to required misplaces the focus. Instead, we should concentrate on improving our installation guidance and rely on established packaging practices—keeping the runtime warnings for actual behavioral changes that directly affect the user’s code. |
I see your point. But we did put in PDEP-10 that we would include a warning, and we did that in Now if we can address all of those issues brought up a year ago by better documentation of how to deal with the pyarrow requirement, then let's make sure that documentation gets written. |
our project roadmap states
We use the roadmap to solicit project funding and so that the community has an idea of the future direction of pandas. So I think that we need to be able to approve PDEPs which are presented as no more than a concept, without significant discussion on implementation details, naming or agreed timelines. |
IIUC from prior discussion the issue is only expected to affect a small proportion of the pandas userbase? And presumably mainly commercial users that in theory have the pockets to fund any work needed? So do you see the need for documentation as a blocker? Affected users can stay on pandas 2.x until the documentation is written? |
after further discussions here and elsewhere, I accept that providing object fallback versions so that PyArrow can remain an optional dependency for now and still use pyarrow backed arrays when pyArrow is installed is a maintenance/development burden and, at this time, the project does not have the resources. PDEP-14 has shown that the work involved is not trivial and our release cadence has suffered as a result. With this is mind, I will not change my vote but accept that rejecting this PDEP does accept making pyArrow as required dependency in the 3.0 release. So I am happy to withdraw option 3 unless others favor that approach. |
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AFAICT this PDEP has had no announcements on the pandas-dev list. In your comment #61596 (comment) you seemed to miss out the first part of the paragraph from the PDEP. PDEP-1 states
So the official discussion period was never started? Closing the vote for a procedural irregularity is IMO unjustified. |
doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.Just something quick and dirty I threw up that we should talk about tomorrow at the dev call.