On Mon, Jan 29, 2018 at 4:11 PM, Allan Haldane <allanhald...@gmail.com> wrote:
> On 01/29/2018 04:02 PM, josef.p...@gmail.com wrote: > > > > > > On Mon, Jan 29, 2018 at 3:44 PM, Benjamin Root <ben.v.r...@gmail.com > > <mailto:ben.v.r...@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 ) Josef > > Allan > > > > > Cheers! > > Ben Root > > > > On Mon, Jan 29, 2018 at 3:24 PM, <josef.p...@gmail.com > > <mailto:josef.p...@gmail.com>> wrote: > > > > > > > > On Mon, Jan 29, 2018 at 2:55 PM, Stefan van der Walt > > <stef...@berkeley.edu <mailto:stef...@berkeley.edu>> wrote: > > > > On Mon, 29 Jan 2018 14:10:56 -0500, josef.p...@gmail.com > > <mailto:josef.p...@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 > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org <mailto:NumPy-Discussion@ > python.org> > > https://mail.python.org/mailman/listinfo/numpy-discussion > > <https://mail.python.org/mailman/listinfo/numpy-discussion> > > > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org <mailto:NumPy-Discussion@python.org> > > https://mail.python.org/mailman/listinfo/numpy-discussion > > <https://mail.python.org/mailman/listinfo/numpy-discussion> > > > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org <mailto:NumPy-Discussion@python.org> > > https://mail.python.org/mailman/listinfo/numpy-discussion > > <https://mail.python.org/mailman/listinfo/numpy-discussion> > > > > > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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