[Numpy-discussion] Setting custom dtypes and 1.14
josef.pktd at gmail.com
josef.pktd at gmail.com
Mon Jan 29 23:50:46 EST 2018
On Mon, Jan 29, 2018 at 10:44 PM, Allan Haldane <allanhaldane at gmail.com>
wrote:
> On 01/29/2018 05:59 PM, josef.pktd at gmail.com wrote:
>
>>
>>
>> On Mon, Jan 29, 2018 at 5:50 PM, <josef.pktd at gmail.com <mailto:
>> josef.pktd at gmail.com>> wrote:
>>
>>
>>
>> On Mon, Jan 29, 2018 at 4:11 PM, Allan Haldane
>> <allanhaldane at gmail.com <mailto:allanhaldane at gmail.com>> wrote:
>>
>> On 01/29/2018 04:02 PM, josef.pktd at gmail.com
>> <mailto:josef.pktd at gmail.com> wrote:
>> >
>> >
>> > On Mon, Jan 29, 2018 at 3:44 PM, Benjamin Root <
>> ben.v.root at gmail.com <mailto:ben.v.root at gmail.com>
>> > <mailto:ben.v.root at gmail.com <mailto:ben.v.root at gmail.com>>>
>> wrote:
>> >
>> > I <3 structured arrays. I love the fact that I can access
>> data by
>> > row and then by fieldname, or vice versa. There are times
>> when I
>> > need to pass just a column into a function, and there are
>> times when
>> > I need to process things row by row. Yes, pandas is nice if
>> you want
>> > the specialized indexing features, but it becomes a bear to
>> deal
>> > with if all you want is normal indexing, or even the
>> ability to
>> > easily loop over the dataset.
>> >
>> >
>> > I don't think there is a doubt that structured arrays, arrays
>> with
>> > structured dtypes, are a useful container. The question is
>> whether they
>> > should be more or the foundation for more.
>> >
>> > For example, computing a mean, or reduce operation, over
>> numeric element
>> > ("columns"). Before padded views it was possible to index by
>> selecting
>> > the relevant "columns" and view them as standard array. With
>> padded
>> > views that breaks and AFAICS, there is no way in numpy 1.14.0
>> to compute
>> > a mean of some "columns". (I don't have numpy 1.14 to try or
>> find a
>> > workaround, like maybe looping over all relevant columns.)
>> >
>> > Josef
>>
>> Just to clarify, structured types have always had padding bytes,
>> that
>> isn't new.
>>
>> What *is* new (which we are pushing to 1.15, I think) is that it
>> may be
>> somewhat more common to end up with padding than before, and
>> only if you
>> are specifically using multi-field indexing, which is a fairly
>> specialized case.
>>
>> I think recfunctions already account properly for padding bytes.
>> Except
>> for the bug in #8100, which we will fix, padding-bytes in
>> recarrays are
>> more or less invisible to a non-expert who only cares about
>> dataframe-like behavior.
>>
>> In other words, padding is no obstacle at all to computing a
>> mean over a
>> column, and single-field indexes in 1.15 behave identically as
>> before.
>> The only thing that will change in 1.15 is multi-field indexing,
>> and it
>> has never been possible to compute a mean (or any binary
>> operation) on
>> multiple fields.
>>
>>
>> from the example in the other thread
>> a[['b', 'c']].view(('f8', 2)).mean(0)
>>
>>
>> (from the statsmodels usecase:
>> read csv with genfromtext to get recarray or structured array
>> select/index the numeric columns
>> view them as standard array
>> do whatever we can do with standard numpy arrays
>> )
>>
>
> Oh ok, I misunderstood. I see your point: a mean over fields is more
> difficult than before.
>
> Or, to phrase it as a question:
>>
>> How do we get a standard array with homogeneous dtype from the
>> corresponding elements of a structured dtype in numpy 1.14.0?
>>
>> Josef
>>
>
> The answer may be that "numpy has never had a way to that",
> even if in a few special cases you might hack a workaround using views.
>
> That's what your example seems like to me. It uses an explicit view, which
> is an "expert" feature since views depend on the exact memory layout and
> binary representation of the array. Your example only works if the two
> fields have exactly the same dtype as each other and as the final dtype,
> and evidently breaks if there is byte padding for any reason.
>
> Pandas can do row means without these problems:
>
> >>> pd.DataFrame(np.ones(10, dtype='i8,f8')).mean(axis=0)
>
> Numpy is missing this functionality, so you or whoever wrote that example
> figured out a fragile workaround using views.
>
Once upon a time (*) this wasn't fragile but the only and recommended way.
Because dtypes were low level with clear memory layout and stayed that way,
it was easy to check item size or whatever and get different views on it.
e.g.
https://mail.scipy.org/pipermail/numpy-discussion/2008-December/039340.html
(*) pre-pandas, pre-stackoverflow on the mailing lists which was for me
roughly 2008 to 2012
but a late thread
https://mail.scipy.org/pipermail/numpy-discussion/2015-October/074014.html
"What is now the recommended way of converting structured dtypes/recarrays
to ndarrays?"
> I suggest that if we want to allow either means over fields, or conversion
> of a n-D structured array to an n+1-D regular ndarray, we should add a
> dedicated function to do so in numpy.lib.recfunctions
> which does not depend on the binary representation of the array.
>
>
I don't really want to defend an obsolete (?) usecase of structured dtypes.
However, I think there should be a decision about the future plans for
whether dataframe like usages of structure dtypes or through higher level
classes or functions are still supported, instead of removing slowly and
silently (*) the foundation for this use case, either support this usage or
say you will be dropping it.
(*) I didn't read the details of the release notes
And another footnote about obsolete:
Given that I'm the only one arguing about the dataframe_like usecase of
recarrays and structured dtypes, I think they are dead for this specific
usecase and only my inertia and conservativeness kept them alive in
statsmodels.
Josef
> Allan
>
>
> Josef
>>
>>
>> Allan
>>
>> >
>> > Cheers!
>> > Ben Root
>> >
>> > On Mon, Jan 29, 2018 at 3:24 PM, <josef.pktd at gmail.com
>> <mailto:josef.pktd at gmail.com>
>> > <mailto:josef.pktd at gmail.com <mailto:josef.pktd at gmail.com>>>
>> wrote:
>> >
>> >
>> >
>> > On Mon, Jan 29, 2018 at 2:55 PM, Stefan van der Walt
>> > <stefanv at berkeley.edu <mailto:stefanv at berkeley.edu>
>> <mailto:stefanv at berkeley.edu <mailto:stefanv at berkeley.edu>>>
>> wrote:
>> >
>> > On Mon, 29 Jan 2018 14:10:56 -0500,
>> josef.pktd at gmail.com <mailto:josef.pktd at gmail.com>
>> > <mailto:josef.pktd at gmail.com
>>
>> <mailto:josef.pktd at gmail.com>> wrote:
>> >
>> > Given that there is pandas, xarray, dask and
>> more, numpy
>> > could as well drop
>> > any pretense of supporting dataframe_likes.
>> Or, adjust
>> > the recfunctions so
>> > we can still work dataframe_like with
>> structured
>> > dtypes/recarrays/recfunctions.
>> >
>> >
>> > I haven't been following the duckarray discussion
>> carefully,
>> > but could
>> > this be an opportunity for a dataframe protocol,
>> so that we
>> > can have
>> > libraries ingest structured arrays, record
>> arrays, pandas
>> > dataframes,
>> > etc. without too much specialized code?
>> >
>> >
>> > AFAIU while not being in the data handling area,
>> pandas defines
>> > the interface and other libraries provide pandas
>> compatible
>> > interfaces or implementations.
>> >
>> > statsmodels currently still has recarray support and
>> usage. In
>> > some interfaces we support pandas, recarrays and
>> plain arrays,
>> > or anything where asarray works correctly.
>> >
>> > But recarrays became messy to support, one rewrite of
>> some
>> > functions last year converts recarrays to pandas,
>> does the
>> > manipulation and then converts back to recarrays.
>> > Also we need to adjust our recarray usage with new
>> numpy
>> > versions. But there is no real benefit because I
>> doubt that
>> > statsmodels still has any recarray/structured dtype
>> users. So,
>> > we only have to remove our own uses in the datasets
>> and unit tests.
>> >
>> > Josef
>> >
>> >
>> >
>> >
>> > Stéfan
>> >
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