[SciPy-User] peer review of scientific software

zetah otrov@hush...
Sun Jun 2 15:06:01 CDT 2013


Charles R Harris wrote:
>> If we speak about errors, I think that most of it, like taught in
>> Numerical analysis course, are due to human factor not understanding data
>> types and also variety of data sources representing data differently.
>> Trivial example that sql and netcdf databases represent same data in
>> different format. Similarly for other data sources which in turn can be
>> just plain text dumps. If that is handled correctly and user is familiar
>> with the tool used, there shouldn't be any surprises.
>>
>
>At least when no one checks ;) The errors that the gods of analysis gift to
>us are often hidden away and are easy to overlook. They also tend to creep
>in when one is overconfident. It's all part of the devine sense of humor.

Probably true. I know this comes from experience that I have not enough


>I confess to my shame that I have never learned to use a spreadsheet for
>any but the simplest things. It's just so darn complicated ;)

That's fine, maybe it's just a legacy habit no one wants to break or preference toward familiar data manipulation environment.

For myself, even with all that numpy broadcasting magics, I'd spend much more time slicing data in Python then doing it as I currently prefer, as more abstractions I'd have to use for same outcome. Viewing the values at the same time while calculating feels more natural to me and provides instant "validation" to say. But if I want real validation I can make validation scenario.

Earlier my only annoyance with pivoted data was that I couldn't do more then trivial calculations on values in pivoted view, unless using programmatic approach. Now that's possible (with DAX), and I can't imagine what else could make data manipulation more intuitive to me.

There are many aspects on this subject, and please do continue if I stepped in too carelessly :)

Cheers



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