[Numpy-discussion] Who will use numpy.ma?

[Bruce Southey]

Hi,
For any data collection in the real world, actual missing values occur very
frequently - almost a certainity. For various operations there is probably
no difference in what is really used, the main thing that comes to mind is
the ability to separate those values that are actually missing i.e.
unobserved from those that are obtained from mathematical functions like
division by zero. However, it has been some time since I looked at the
options so I am out-of-date. Perhaps the approach of the R language (
http://wwwr-project.org) may provide suitable approach to this.

A second aspect of the masked arrays that is very neat is to be able to
choose a masking value and it can be changed. This is a really feature that
you don't realize how great it really is unless you have it! . It is very
easy to identify and work with elements of the array that meet changing
criteria just by changing the mask rather than a series of complex boolean
operations and steps to get the same results.

Regards
Bruce


On 1/11/06, Sasha <ndarray at mac.com> wrote:
>
> MA is intended to be a drop-in replacement for Numeric arrays that can
> explicitely handle missing observations.  With the recent improvements
> to the array object in NumPy, the MA library has fallen behind.  There
> are more than 50 methods in the ndarray object that are not present in
> ma.array.
>
> I would like to hear from people who work with datasets with missing
> observations? Do you use MA? Do you think with the support for nan's
> and replaceable mathematical operations, should missing observations
> be handled in numpy using special values rather than an array of
> masks?
>
> Thanks.
>
> -- sasha
>
>
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