[SciPy-User] Filtering record arrays by contents of columns using `ismember`-like syntax

[josef.pktd at gmail.com]

On Tue, May 24, 2011 at 4:47 PM, Skipper Seabold <jsseabold at gmail.com> wrote:
> On Tue, May 24, 2011 at 4:39 PM, Chris Rodgers
> <chris.rodgers at berkeley.edu> wrote:
>> Thanks to everyone for their comments!
>>
>> Concerning the speed of numpy.in1d: my guess is that in1d works best
>> for arrays of comparable size, and this method works best for the
>> special case when one array contains just a few values. I suppose it
>> might make sense for me to break this into two objects. The first
>> would replicate in1d for this use case. The second would supply the
>> syntactic simplification for filtering.
>>
>
> Might it make sense to just patch in1d to handle this case? I'm not so
> sure though.

http://projects.scipy.org/numpy/ticket/1603

Josef

>
>> Concerning the use of PyTables: I definitely agree that is the answer
>> for complex queries. I see this object as solving a narrow slice of
>> problems between the complex (PyTables) and the trivially simple
>> (explicit mask). For whatever reason a lot of my actual day-to-day
>> problems fall into that category. Probably because I'm porting this
>> code from Matlab and that's just the Matlab way of thinking about
>> things.
>>
>>
>>> Just create an appropriate ticket with code, docstring, examples and
>>> test cases. :-)
>>> At least then it would not get lost in the email archives.
>>
>> I'm happy to do that, though having never done this, I'm not sure
>> where is "appropriate" (scipy trac, numpy trac, Cookbook, etc...)
>
> Yeah, I was thinking about doing this myself. You might want to create
> a fork of numpy, implement a function or method, and then request a
> review. This is sure to need plenty of testing. If it's a function,
> where should it reside? I was thinking of numpy.lib.recfunctions, but
> it's not strictly for structured/record arrays. Any other ideas?
>
> You might find this helpful for getting started:
> http://docs.scipy.org/doc/numpy/dev/gitwash/index.html
>
> Skipper
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