Operation in Numpy multi-dimensional array

Question

I have two multi-dimensional numpy array. I would like to convert the entry in the second array to NaN, if the corresponding element in first is zero. Below is example to manually mimic the same: (Can this be done programmatically)

import numpy as np    
a = np.random.rand(4,5)
a[0][0] = 0
a[1][0] = 0
a[1][1] = 0

b = np.random.rand(4,5)
b[0][0] = np.nan
b[1][0] = np.nan
b[1][1] = np.nan

Can we use masking here?

Loop over the first array, assign elements of the 2nd array based on the elements you are iterating over. for i in range(len(a)): for j in range(len(a[i])): if (a[i][j] == 0) b[i][j] = np.nan — Vince
– Vince, Commented May 18, 2020 at 6:52
@VinceEmigh That's bad advice for numpy arrays. If you must use loops consider lists instead. Numpy shines with vector operations, but fails (performance wise, and badly) with iterative algorithms. — kabanus
– kabanus, Commented May 18, 2020 at 7:00

kabanus · Accepted Answer · 2020-05-18 06:59:27Z

3

Write it like you say it:

b[a==0] = np.nan

answered May 18, 2020 at 6:59

kabanus

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3 Comments

Vince Over a year ago

Very nice, didn't know you could inline the condition like that in Python 👌

kabanus Over a year ago

@VinceEmigh Yup, numpy indexing is awesome. The manual on it is quite extensive.

amzon-ex Over a year ago

@VinceEmigh It's very powerful, check numpy.org/devdocs/user/basics.indexing.html

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