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.pktd@gmail.com> wrote:


On Mon, Jan 29, 2018 at 2:55 PM, Stefan van der Walt <stefanv@berkeley.edu> wrote:
On Mon, 29 Jan 2018 14:10:56 -0500, josef.pktd@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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