Mailman 3 February 2020 - Pandas-dev

Domain and hosting
by Marc Garcia April 8, 2020

April 8, 2020

We've got a bit of a mess at the moment with the pandas domain and hosting. I'll try to leave things in a more reasonable way, but there are some decisions pending. My understanding from a thread in this list was that everybody was happy with using pandas.io, and we set up the domain for the new hosting, and also dev.pandas.io for the development version of the website (and the blog and anything published in our GitHub organization pages). >From the discussion in https://github.com/pandas-dev/pandas/issues/28528 seems like the preference is to keep the old pandas.pydata.org instead. Given that, I think we can get rid of the dev.pandas.io, and point pandas.pydata.org to the new server once it's ready. For the website, I think the agreement is to update it with the latest version from master. So, no dev.pandas.io. For the development (master) documentation, I think it can live in pandas.pydata.org/docs/dev/. The blog, I think the best is to have the posts as pages on the website, in a directory blog/, so we don't need to maintain separaterly, and it has the look and feel of the website. For the new hosting, I'll move everything (all old documentation versions) from the current server to the new one, and then set up that the website and the development docs are automatically updated. Tom, can you give me access to the current web server so I can fetch the data please? Once everything is working in the new server, we'll be able to see it in pandas.io, and when we're happy we can change the domain pandas.pydata.org to point to it, and disable pandas.io. Please let me know if there are objections to any of the above, otherwise I'll move forward. Cheers!

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Fwd: What could a pandas 2.0 look like?
by Tom Augspurger Feb. 26, 2020

Feb. 26, 2020

(Accidentally dropped the mailing list) On Mon, Feb 17, 2020 at 7:17 PM Brock Mendel <jbrockmendel(a)gmail.com> wrote: > > You have no problem with changing the behavior of NaT, or changing to > use pd.NA? > > If/when we get to a point where we propagate NAs in all other comparisons, > I would have no problem with editing `NaT.__richcmp__` to match that > convention. > What are the advantages of a NaT with NA-like comparison semantics over using NA (or NA[datetime])? 1. Retain dtype in array - scalar ops with a scalar NA 2. ... 3. Less disruptive than changing to NA My ... could include things like `isinstance(NaT, Timestamp)` being true and `NaT.<attr>` for Timestamp attributes. But those don't strike me as necessarily good things. They seem sometimes useful and sometimes harmful. The downside of changing NaT in comparison operations are 1. We're diverging from `np.NaT`. I don't know how problematic this actually is. 2. It's a special case. Should users need to know that datelikes use their own NA value because the underlying storage is able to store them "in-band" rather than as a mask? My gut reaction is "no, users shouldn't be exposed to this." 3. Changing NaT would leave just NaN with the "always unequal in comparisons" behavior. Thus far, I see three options going forward 1. Use NaN for floats, NaT for datelikes, NA for other. 1-a: Leave NaT with always unequal 1-b: Change NaT to have NA-like comparison behavior 2. Use NA everywhere (no NaN for float, no NaT for datelike 3. Implement a typed `NA<T>`, where we have an `NA` per dtype. Option 3 I think solves the array - scalar op issue. It's more complex for users though hopefully not too complex? My biggest worry is that it makes the implementation much more complex, though perhaps I'm being pessimistic. On balance, I'm not sure where I come down yet. Good news: we can take time to figure this out :) > On Mon, Feb 17, 2020 at 10:06 AM Tom Augspurger < > tom.augspurger88(a)gmail.com> wrote: > >> >> >> >> On Mon, Feb 17, 2020 at 11:58 AM Brock Mendel <jbrockmendel(a)gmail.com> >> wrote: >> >>> > or changing the behavior of NaT in comparisons to be like NA. >>> >>> Pending the kinks being worked out of pd.NA, I have no problem with that. >>> >> >> You have no problem with changing the behavior of NaT, or changing to use >> pd.NA? >> >> Is changing the defined behavior of NaT even an option? Is it defined in >> a spec >> like NaN, or did NumPy just choose that behavior? >> >> Assuming NaT had NA-like behavior in comparisons, what's remaining >> arguments for keeping NaT? >> Preserving dtypes in scalar - array ops? Anything else? >> >> On Mon, Feb 17, 2020 at 9:55 AM Tom Augspurger < >>> tom.augspurger88(a)gmail.com> wrote: >>> >>>> Is NaT defined to be unequal in all comparisons, just like NaN? I think >>>> the goal of propagating NA >>>> requires either using NA or changing the behavior of NaT in comparisons >>>> to be like NA. >>>> >>>> On Mon, Feb 17, 2020 at 11:50 AM Brock Mendel <jbrockmendel(a)gmail.com> >>>> wrote: >>>> >>>>> > I think consistently propagating NA in comparison operations is a >>>>> worthwhile goal. >>>>> >>>>> That's an argument for having a three-valued bool-dtype, not for >>>>> replacing all other NA-like values. >>>>> >>>>> On Mon, Feb 17, 2020 at 8:34 AM Tom Augspurger < >>>>> tom.augspurger88(a)gmail.com> wrote: >>>>> >>>>>> > 2) The "only one NA value is simpler" argument strikes me as a >>>>>> solution in search of a problem. >>>>>> >>>>>> I don't think that's correct. I think consistently propagating NA in >>>>>> comparison operations is a worthwhile goal. >>>>>> >>>>>> On Mon, Feb 17, 2020 at 10:25 AM Brock Mendel <jbrockmendel(a)gmail.com> >>>>>> wrote: >>>>>> >>>>>>> > It's not fully clear to me what you want to say with this, so a >>>>>>> more detailed clarification is welcome (I mean, I understand the sentence >>>>>>> and remember the discussion, but don't fully understand the point being >>>>>>> made in context, or in what direction you think more discussion is needed). >>>>>>> >>>>>>> I don't particularly think more discussion is needed, as this is a >>>>>>> rehash of #28095, where this horse has already been beaten to death. >>>>>>> >>>>>>> As Tom noted here >>>>>>> <https://github.com/pandas-dev/pandas/issues/28095#issuecomment-537501744>, >>>>>>> using pd.NA in places where we currently use NaT breaks the usual identity >>>>>>> (that we rely on A LOT) >>>>>>> >>>>>>> ```(array + array)[0].dtype <=> (array + array[0]).dtype``` >>>>>>> >>>>>>> (Yes, this holds only imperfectly for NaT because NaT serves as both >>>>>>> NotADatetime and NotATimedelta, and I'll refer you to the decomposing horse >>>>>>> in #28095.) >>>>>>> >>>>>>> Also from #28095: >>>>>>> >>>>>>> ```Series[timedelta64] * pd.NaT``` unambiguously raises, but >>>>>>> ```Series[timedelta64] * pd.NA``` could be timedelta64 >>>>>>> >>>>>>> > Assume we introduce a new "nullable datetime" dtype that uses a >>>>>>> mask to track NAs, and can still have NaT in the values. In practice, this >>>>>>> still means that we "replace NaT with NA" >>>>>>> >>>>>>> This strikes me as contradictory. >>>>>>> >>>>>>> > So do you mean: "in my opinion, we should not do this" (what I >>>>>>> just described above), because in practice that would mean breaking >>>>>>> arithmetic consistency? Or that if we want to start using NA for >>>>>>> datetimelike dtypes, you think "dtype-parametrized" NA values are necessary >>>>>>> (so you can distinguish NA[datetime] and NA[timedelta] ?) >>>>>>> >>>>>>> I think: >>>>>>> >>>>>>> 1) pd.NA solves an _actual_ problem which is that we used to use >>>>>>> np.nan in places (categorical, object) where np.nan was semantically >>>>>>> misleading. >>>>>>> a) What these have in common is that they are in general >>>>>>> non-arithmetic dtypes. >>>>>>> b) This is an improvement, and I'm glad you put in the effort to >>>>>>> make it happen. >>>>>>> c) Trying to shoe-horn pd.NA into cases where it is semantically >>>>>>> misleading based on the Highlander Principle is counter-productive. >>>>>>> >>>>>>> 2) The "only one NA value is simpler" argument strikes me as a >>>>>>> solution in search of a problem. >>>>>>> a) All the more so if you want this to supplement np.nan/pd.NaT >>>>>>> instead of replace them. >>>>>>> b) *the idea of replacing vs supplementing needs to be made much >>>>>>> more explicit/clear* >>>>>>> >>>>>>> 3) The "dtype-parametrized" NA did come up in #28095, but I never >>>>>>> advocated it. >>>>>>> a) I am open to separating out a NaTimedelta (xref #24983) from >>>>>>> pd.NaT, and don't particularly care what it is called. >>>>>>> >>>>>>> >>>>>>> On Mon, Feb 17, 2020 at 3:37 AM Joris Van den Bossche < >>>>>>> jorisvandenbossche(a)gmail.com> wrote: >>>>>>> >>>>>>>> > This would also imply creating a nullable float dtype and making >>>>>>>>> our datelikes use NA rather than NaT too. That seemed to be generally OK, >>>>>>>>> but wasn't discussed too much. >>>>>>>>> >>>>>>>>> My understanding of the discussion is that using a mask on top of >>>>>>>>> datetimelike arrays would not _replace_ NaT, but supplement it with >>>>>>>>> something semantically different. >>>>>>>>> >>>>>>>> >>>>>>>> Yes, if we see it similar as NaNs for floats (where NaN is a >>>>>>>> specific float value in the data array, while NAs are tracked in the mask >>>>>>>> array), then for datetimelike arrays we can do something similar. And the >>>>>>>> same discussions about to what extent to distinguish NaN and NA or whether >>>>>>>> we need to provide options that we are going to have for float dtypes, will >>>>>>>> also be relevant for datetimelike dtypes (but then for NaT and NA). >>>>>>>> >>>>>>>> But note that in practice, I *think* that the big majority of use >>>>>>>> cases will mostly use NA and not NaT in the data (eg when reading from >>>>>>>> files that have missing data). >>>>>>>> >>>>>>>> Replacing NaT with NA breaks arithmetic consistency, as has been >>>>>>>>> discussed ad nauseum. >>>>>>>>> >>>>>>>> >>>>>>>> It's not fully clear to me what you want to say with this, so a >>>>>>>> more detailed clarification is welcome (I mean, I understand the sentence >>>>>>>> and remember the discussion, but don't fully understand the point being >>>>>>>> made in context, or in what direction you think more discussion is needed). >>>>>>>> >>>>>>>> Assume we introduce a new "nullable datetime" dtype that uses a >>>>>>>> mask to track NAs, and can still have NaT in the values. In practice, this >>>>>>>> still means that we "replace NaT with NA" (because even though NaT is still >>>>>>>> possible, I think you would mostly get NAs as mentioned above; eg reading a >>>>>>>> file would now give NA instaed of NaT). >>>>>>>> So do you mean: "in my opinion, we should not do this" (what I just >>>>>>>> described above), because in practice that would mean breaking arithmetic >>>>>>>> consistency? Or that if we want to start using NA for datetimelike dtypes, >>>>>>>> you think "dtype-parametrized" NA values are necessary (so you can >>>>>>>> distinguish NA[datetime] and NA[timedelta] ?) >>>>>>>> >>>>>>>> Joris >>>>>>>> >>>>>>>> >>>>>>>> _______________________________________________ >>>>>>>> Pandas-dev mailing list >>>>>>>> Pandas-dev(a)python.org >>>>>>>> https://mail.python.org/mailman/listinfo/pandas-dev >>>>>>>> >>>>>>> _______________________________________________ >>>>>>> Pandas-dev mailing list >>>>>>> Pandas-dev(a)python.org >>>>>>> https://mail.python.org/mailman/listinfo/pandas-dev >>>>>>> >>>>>>

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What could a pandas 2.0 look like?
by Joris Van den Bossche Feb. 19, 2020

Feb. 19, 2020

pandas 1.0 is out, so time to start thinking about 2.0 ;) In principle, pandas 2.0 will just be one of the next releases when we decide we want to clean-up the deprecations / make a few changes that are hard to deprecate (following our new versioning policy). But nonetheless, I think it can still be interesting to think about it if it can also be something more than that, and have more specific goals in mind*. Last year I made the pd.NA proposal, which resulted in using that for the nullale integer, boolean and string dtypes. In the proposal, pd.NA was described as "can be used consistently across all data types". And for me, the aspirational end goal of this proposal is to *actually* have this for *all* dtypes, but we never really discussed this aspect explicitly. So, for me, a possible future pandas 2.0: - Uses all "nullable dtypes" by default (i.e. dtypes that use pd.NA as missing value indicator). That means we add a nullable version of all other dtypes (as we now already did for int, boolean, string). End goal: a single missing value indicator with the same behavior for all dtypes. - If we add such nullable dtypes using the extension dtypes/array mechanism (so it can first be opt-in in 1.X), this could "automatically" lead to a simplification of the internals / Block Manager (another aspirational goal that has been discussed before, but never became concrete). Because in such a case (all extension dtypes), we would only be using 1D blocks (simplifying the 1D / 2D thorny cases in internals). This simplifies the memory model, consolidation, etc Do you think this is a desirable goal? And realistic? Other aspirational goals? Best, Joris *Agreeing on goals doesn't mean it will happen, that's open source (or at least community-based open source). But I think it can still be useful to guide some efforts where possible or in trying to get traction for certain issues from contributors. And then we can still see if it gets done in 2.0, 3.0, 4.0 or never ;)

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Re: [Pandas-dev] What could a pandas 2.0 look like?
by Irv Lustig Feb. 11, 2020

Feb. 11, 2020

Joris: Another aspirational goal for pandas 2.0 would be to clean up the API so that index names and column names are treated equivalently throughout. I created a meta-issue for this 6+ months ago: https://github.com/pandas-dev/pandas/issues/27652 Dr-Irv > ---------------------------------------------------------------------- > > Message: 1 > Date: Mon, 10 Feb 2020 18:43:21 +0100 > From: Joris Van den Bossche <jorisvandenbossche(a)gmail.com> > To: pandas-dev <pandas-dev(a)python.org> > Subject: [Pandas-dev] What could a pandas 2.0 look like? > Message-ID: > < > CALQtMBZdrFD7iiNwOQeXs94tYxLqLb-otoJcSfvTX9meQnPcyw(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > pandas 1.0 is out, so time to start thinking about 2.0 ;) > > In principle, pandas 2.0 will just be one of the next releases when we > decide we want to clean-up the deprecations / make a few changes that are > hard to deprecate (following our new versioning policy). > But nonetheless, I think it can still be interesting to think about it if > it can also be something more than that, and have more specific goals in > mind*. > > Last year I made the pd.NA proposal, which resulted in using that for the > nullale integer, boolean and string dtypes. In the proposal, pd.NA was > described as "can be used consistently across all data types". And for me, > the aspirational end goal of this proposal is to *actually* have this for > *all* dtypes, but we never really discussed this aspect explicitly. > > So, for me, a possible future pandas 2.0: > > - Uses all "nullable dtypes" by default (i.e. dtypes that use pd.NA as > missing value indicator). That means we add a nullable version of all > other > dtypes (as we now already did for int, boolean, string). End goal: a > single > missing value indicator with the same behavior for all dtypes. > - If we add such nullable dtypes using the extension dtypes/array > mechanism (so it can first be opt-in in 1.X), this could "automatically" > lead to a simplification of the internals / Block Manager (another > aspirational goal that has been discussed before, but never became > concrete). Because in such a case (all extension dtypes), we would only > be > using 1D blocks (simplifying the 1D / 2D thorny cases in internals). > This > simplifies the memory model, consolidation, etc > > Do you think this is a desirable goal? And realistic? Other aspirational > goals? > > Best, > Joris > > *Agreeing on goals doesn't mean it will happen, that's open source (or at > least community-based open source). But I think it can still be useful to > guide some efforts where possible or in trying to get traction for certain > issues from contributors. And then we can still see if it gets done in 2.0, > 3.0, 4.0 or never ;) > >

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Monthly Dev Meeting - February 202
by Tom Augspurger Feb. 11, 2020

Feb. 11, 2020

Hi all, The next monthly dev call is tomorrow (Wednesday, February 12th) at 18:00 UTC. We invite all to come. https://dev.pandas.io/docs/development/meeting.html We'll use this Zoom link: https://zoom.us/j/942410248 The agenda is at https://docs.google.com/document/d/1tGbTiYORHiSPgVMXawiweGJlBw5dOkVJLY-lico… if you have topics to discuss. Tom

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SciPy 2020 Tutorials — Call for Papers
by Alexandre Chabot-Leclerc Feb. 2, 2020

Feb. 2, 2020

Dear pandas developers, The SciPy tutorial committee is interested in including a tutorial on advanced pandas this year and we would like to encourage you to submit for SciPy 2020. Tutorials will be presented July 6–7 and, as usual, the conference is in Austin. In recognition of the effort required to plan and prepare a high quality tutorial, we pay a stipend of $1,000 to each instructor (or team of instructors) for each half-day session they lead. You may find additional information at the link below, and the deadline for submission is February 11. https://www.scipy2020.scipy.org/tutorials You can find examples of successful submissions on GitHub: https://github.com/scipy-conference/scipy-conference Please feel free to email with any questions. Kind regards, Scipy 2020 Tutorial Chairs Serah Njambi Rono Alexandre Chabot-Leclerc Mike Hearne

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