yup, the idea was to have minimal pandas.DataFrame-like storage (which I was using for a long time),

but without irritating problems with its row indexing and some other problems like interaction with matplotlib.

A dict of arrays?

that's what I've started from and implemented, but at some point I decided that I'm reinventing the wheel and numpy has something already. In principle, I can ignore this 'column-oriented' storage requirement, but potentially it may turn out to be quite slow-ish if dtype's size is large.

Suggestions are welcome.

Another strange question:

in general, it is considered that once numpy.array is created, it's shape not changed.

But if i want to keep the same recarray and change it's dtype and/or shape, is there a way to do this?

22 февр. 2017 г., в 3:53, Nathaniel Smith <njs@pobox.com> написал(а):

On Feb 21, 2017 3:24 PM, "Alex Rogozhnikov" <alex.rogozhnikov@yandex.ru> wrote:
Ah, got it. Thanks, Chris!
I thought recarray can be only one-dimensional (like tables with named columns).

Maybe it's better to ask directly what I was looking for:
something that works like a table with named columns (but no labelling for rows), and keeps data (of different dtypes) in a column-by-column way (and this is numpy, not pandas).

Is there such a magic thing?

Well, that's what pandas is for...

A dict of arrays?

-n
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