numpy 1D array to numpy arrays

Question

I'd like to read a 1D numpy array in Python and generate two other numpy arrays:

first one is the input, if there is no 'nan' values. Otherwise, input with 'nan' values replaced by '0'
second one is a mask, 1='input value is not 'nan'', and '0'='input value is nan''

For example:

a = numpy.array([1,2,numpy.nan,4])

would give

[1,2,0,4]
[1,1,0,1]

What's the most efficient way to do this in python ?

Thanks

Have a look at numpy.ma.masked_invalid(). It kind of gives you both of these. — Sven Marnach
– Sven Marnach, Commented Oct 24, 2013 at 13:23
It looks good, although the array is masked with '--'. There does not seem to be any configuration for the mask value. Cheers — Carmellose
– Carmellose, Commented Oct 24, 2013 at 14:06

falsetru · Accepted Answer · 2013-10-24 14:08:27Z

1

To replace nan to 0, use numpy.nan_to_num:

>>> a = numpy.array([1,2,numpy.nan,4])
>>> numpy.nan_to_num(a)
array([ 1.,  2.,  0.,  4.])

Use numpy.isnan to convert nan to True, non-nan numbers to False. Then substract them from 1.

>>> numpy.isnan(a)
array([False, False,  True, False], dtype=bool)
>>> 1 - numpy.isnan(a)
array([ 1.,  1.,  0.,  1.])

edited Oct 24, 2013 at 14:08

answered Oct 24, 2013 at 13:21

falsetru

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2 Comments

Carmellose Over a year ago

Thanks, that's exactly what I'm looking for :) Vikash's solution also does it fine. For the last command, I managed to do it also with the ~ sign instead of substracting one.

falsetru Over a year ago

@carmellose, I slightly updated the answer code. You don't need to use astype if use 1 - numpy.isnan(..).

VIKASH JAISWAL · Accepted Answer · 2013-10-24 13:21:16Z

0

for first one:

numpy.nan_to_num(a)

second one:

numpy.invert(numpy.isnan(a)).astype(int)

answered Oct 24, 2013 at 13:21

VIKASH JAISWAL

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Comments

irenemeanspeace · Accepted Answer · 2013-10-24 14:01:00Z

0

To convert NaNs to zeros, use:

numpy.nan_to_num(a)

To set 1 for non-NaNs and 0 for NaNs, try:

numpy.isfinite(a)*1

answered Oct 24, 2013 at 14:01

irenemeanspeace

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