[Numpy-discussion] Setting custom dtypes and 1.14
ben.v.root at gmail.com
Mon Jan 29 15:44:56 EST 2018
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.
On Mon, Jan 29, 2018 at 3:24 PM, <josef.pktd at gmail.com> wrote:
> On Mon, Jan 29, 2018 at 2:55 PM, Stefan van der Walt <stefanv at berkeley.edu
> > wrote:
>> On Mon, 29 Jan 2018 14:10:56 -0500, josef.pktd at gmail.com wrote:
>>> Given that there is pandas, xarray, dask and more, numpy could as well
>>> any pretense of supporting dataframe_likes. Or, adjust the recfunctions
>>> we can still work dataframe_like with structured
>> 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
> 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.
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