[SciPy-user] NaN's in numpy (and Scipy)
Pierre GM
pgmdevlist at gmail.com
Sat Jan 6 15:25:56 CST 2007
On Saturday 06 January 2007 16:08, Alan Jackson wrote:
> Missing data. Basically want to handle missing data as painlessly as
> possible without having to add a bunch of stuff every time a calculation is
> done.
Alan,
Could you be a bit more specific about you mean by this "bunch of stuffs" ?
What are your complaints about the current implementation of masked arrays ?
Assuming you have Nan in your data, you can get a masked array as easily as
that:
>>> import numpy as N
>>> import numpy.core.ma as MA
>>> x = N.array([1,2,N.nan,4])
>>> X = MA.masked_array(x, mask=N.isnan(x)
>>> X
array(data =
[ 1.00000000e+00 2.00000000e+00 1.00000000e+20 4.00000000e+00],
mask =
[False False True False],
fill_value=1e+20)
And then you can play with X.
A few months ago, I ran into some problems while trying to subclass
MaskedArray. I rewrote most of numpy.core.ma to solve my particular issues.
This new implementation is available on the scipy SVN server, in the
sandbox/maskedarray folder. I'd be glad if you could give it a try, so that I
could improve it.
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