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I have a numpy array with some floats and some nans:

a = [ 8.08970226  nan  nan  8.30043545  nan  nan   nan  nan]

I want to convert it to an array (for printing in Latex) to the mixed form:

a = ['8.08970226', '--', '--', '8.30043545', '--', '--', '--', '--']

The method I've worked out, which is not elegant, is:

a=a.astype('|S10')
a[a=='nan']='--'
a=list(a)

Is there a more elegant way to do the job? (I could probably stop at the second line for my Latex requirement.)

Advice apreciated

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1 Answer 1

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Using numpy masked arrays

>>> import numpy as np
>>> a = np.array([ 8.08970226,  np.NAN,  np.NAN,  8.30043545,  np.NAN,  np.NAN,   np.NAN,  np.NAN])
>>> np.ma.fix_invalid(a)
masked_array(data = [8.08970226 -- -- 8.30043545 -- -- -- --],
             mask = [False  True  True False  True  True  True  True],
       fill_value = 1e+20)

>>> print _
[8.08970226 -- -- 8.30043545 -- -- -- --]

or since you need it as that particular list:

>>> np.ma.fix_invalid(a).astype('|S10').tolist(fill_value='--')
['8.08970226', '--', '--', '8.30043545', '--', '--', '--', '--']
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5 Comments

Thanks. That works, but I need to intersperse ' &' elements for a Latex table, and that adds complexity.
@dcnicholls I change it to save to a list, it's longer than your code but this seems like a job for masked array
That's a nice one-liner. Thanks
You can also just use np.ma.fix_invalid.
@tiago that's much better, updated and community wikied since that's a much better way of doing it

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