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Wed, 22 Feb 2017 07:37:48 -0800

2017-02-22 16:30 GMT+01:00 Kiko <kikocorre...@gmail.com>:

>
>
> 2017-02-22 16:23 GMT+01:00 Alex Rogozhnikov <alex.rogozhni...@yandex.ru>:
>
>> 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).
>>
>
?You can disable compression for bcolz by default too:

http://bcolz.blosc.org/en/latest/defaults.html#list-of-default-values?



>
>> 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?
>>
>
> http://www.lfd.uci.edu/~gohlke/pythonlibs/#bcolz
> Check if the link solves the issue with installing.
>

?Yeah.  Also, there are binaries for conda:

http://bcolz.blosc.org/en/latest/install.html#installing-from-conda-forge?



>
>> ?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.
>>
>
?Well, if copying is not a problem for you, then you can just create a new
numpy container and do the copy by yourself.?

Francesc


>
>> Cheers,
>> Alex.
>>
>>
>>
>>
>> 22 ֧ӧ. 2017 .,  17:03, Francesc Alted <fal...@gmail.com> ߧѧڧѧ():
>>
>> Hi Alex,
>>
>> 2017-02-22 12:45 GMT+01:00 Alex Rogozhnikov <alex.rogozhni...@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 ֧ӧ. 2017 .,  3:53, Nathaniel Smith <n...@pobox.com> ߧѧڧѧ():
>>>
>>> On Feb 21, 2017 3:24 PM, "Alex Rogozhnikov" <alex.rogozhni...@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
>>> _______________________________________________
>>> NumPy-Discussion mailing list
>>> NumPy-Discussion@scipy.org
>>> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>>>
>>>
>>>
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>>>
>>>
>>
>>
>> --
>> Francesc Alted
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-- 
Francesc Alted
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