On 01/26/2018 06:01 PM, josef.pktd@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.
recfunctions and the associated function from matplotlib.mlab where explicitly designed for using structured dtypes as dataframe_like > (old question: does numpy have a sort_rows function now without detouring to structured dtype views?)
No, that's still the way to do it. *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. (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.) Allan
Josef
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