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. 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
