Alex, Can you please post some code showing exactly what you are trying to do and any issues you are having, particularly the "irritating problems with its row indexing and some other problems" you quote above? On Wed, Feb 22, 2017 at 10:34 AM, Robert McLeod <robbmcleod@gmail.com> wrote:
Just as a note, Appveyor supports uploading modules to "public websites":
https://packaging.python.org/appveyor/
The main issue I would see from this, is the PyPi has my password stored on my machine in a plain text file. I'm not sure whether there's a way to provide Appveyor with a SSH key instead.
On Wed, Feb 22, 2017 at 4:23 PM, Alex Rogozhnikov < alex.rogozhnikov@yandex.ru> wrote:
Hi Francesc, thanks a lot for you reply and for your impressive job on bcolz!
Bcolz seems to make stress on compression, which is not of much interest for me, but the *ctable*, and chunked operations look very appropriate to me now. (Of course, I'll need to test it much before I can say this for sure, that's current impression).
The strongest concern with bcolz so far is that it seems to be completely non-trivial to install on windows systems, while pip provides binaries for most (or all?) OS for numpy. I didn't build pip binary wheels myself, but is it hard / impossible to cook pip-installabel binaries?
You can change shapes of numpy arrays, but that usually involves copies of the whole container.
sure, but this is ok for me, as I plan to organize column editing in 'batches', so this should require seldom copying. It would be nice to see an example to understand how deep I need to go inside numpy.
Cheers, Alex.
22 февр. 2017 г., в 17:03, Francesc Alted <faltet@gmail.com> написал(а):
Hi Alex,
2017-02-22 12:45 GMT+01:00 Alex Rogozhnikov <alex.rogozhnikov@yandex.ru>:
Hi Nathaniel,
pandas
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.
You may want to try bcolz:
https://github.com/Blosc/bcolz
bcolz is a columnar storage, basically as you require, but data is compressed by default even when stored in-memory (although you can disable compression if you want to).
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?
You can change shapes of numpy arrays, but that usually involves copies of the whole container. With bcolz you can change length and add/del columns without copies. If your containers are large, it is better to inform bcolz on its final estimated size. See:
http://bcolz.blosc.org/en/latest/opt-tips.html
Francesc
Thanks, Alex.
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?
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