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.
Cheers! Ben Root On Mon, Jan 29, 2018 at 3:24 PM, <josef.p...@gmail.com> wrote: > > > On Mon, Jan 29, 2018 at 2:55 PM, Stefan van der Walt <stef...@berkeley.edu > > wrote: > >> On Mon, 29 Jan 2018 14:10:56 -0500, 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 >> 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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