On 01/29/2018 04:02 PM, josef.pktd@gmail.com wrote:

On Mon, Jan 29, 2018 at 3:44 PM, Benjamin Root

mailto:ben.v.root@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,

mailto:josef.pktd@gmail.com> wrote: On Mon, Jan 29, 2018 at 2:55 PM, Stefan van der Walt

mailto:stefanv@berkeley.edu> wrote: On Mon, 29 Jan 2018 14:10:56 -0500, josef.pktd@gmail.com mailto: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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