I think that there's a lot of confusion going around about recarrays vs structured arrays.
[`recarray`]( https://github.com/numpy/numpy/blob/v1.13.0/numpy/core/records.py) are a wrapper around structured arrays that provide: * Attribute access to fields as `arr.field` in addition to the normal `arr['field']` * Automatic datatype-guessing for nested lists of tuples (which needs a little work, but seems like a justifiable feature) * An undocumented `field` method that behaves like the 1.14 indexing behavior (!) Meanwhile, `recfunctions` is a collection of functions that work on normal structured arrays - so is misleadingly named. The only link to recarrays is that most of the functions have a `asrecarray` parameter which applies `.view(recarray)` to the result. > deprecate recarrays Given how thin an abstraction they are over structured arrays, I don't think you mean this. Are you advocating for deprecating structured arrays entirely, or just deprecating recfunctions? Eric On Mon, 29 Jan 2018 at 09:39 Chris Barker <chris.bar...@noaa.gov> wrote: > On Sat, Jan 27, 2018 at 8:50 PM, Allan Haldane <allanhald...@gmail.com> > wrote: > >> On 01/26/2018 06:01 PM, josef.p...@gmail.com wrote: >> >>> I thought recarrays were pretty cool back in the day, but pandas is >>> a much better option. >>> >>> So I pretty much only use structured arrays for data exchange with C >>> code.... >>> >>> My impression is that this turns into a deprecate recarrays and >>> supporting recfunction issue. >>> >>> > >> *should* we have any dataframe-like functionality in numpy? > > >> >> We get requests every once in a while about how to sort rows, or about >> adding a "groupby" function. I myself have used recarrays in a >> dataframe-like way, when I wanted a quick multiple-array object that >> supported numpy indexing. So there is some demand to have minimal >> "dataframe-like" behavior in numpy itself. >> >> recarrays play part of this role currently, though imperfectly due to >> padding and cache issues. I think I'm comfortable with supporting some >> minor use of structured/recarrays as dataframe-like, with a warning in docs >> that the user should really look at pandas/xarray, and that structured >> arrays are primarily for data exchange. >> > > Well, I think we should either: > > deprecate recarrays -- i.e. explicitly not support DataFrame-like > functionality in numpy, keeping only the data-exchange functionality as > maintained. > > or > > Properly support it -- which doesn't mean re-implementing Pandas or > xarray, but it would mean addressing any bug-like issues like not dealing > properly with padding. > > Personally, I don't need/want it enough to contribute, but if someone > does, great. > > This reminds me a bit of the old numpy.Matrix issue -- it was ALMOST > there, but not quite, with issues, and there was essentially no overlap > between the people that wanted it and the people that had the time and > skills to really make it work. > > (If we want to dream, maybe one day we should make a minimal >> multiple-array container class. I imagine it would look pretty similar to >> recarray, but stored as a set of arrays instead of a structured array. But >> maybe recarrays are good enough, and let's not reimplement pandas either.) >> > > Exactly -- we really don't need to re-implement Pandas.... > > (except it's CSV reading capability :-) ) > > -CHB > > > -- > > Christopher Barker, Ph.D. > Oceanographer > > Emergency Response Division > NOAA/NOS/OR&R (206) 526-6959 voice > 7600 Sand Point Way NE (206) 526-6329 fax > Seattle, WA 98115 (206) 526-6317 main reception > > chris.bar...@noaa.gov > _______________________________________________ > 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
