Numpy mask in a list

Question

What is the easiest way to save elements from a list using np.where or similar?

A short example:

l1 = [-144.92170726320364, 697.7739312692029, -2.0, -2.0]

I want to keep the elements which are different from -2.0. I tried:

l2 = [l1[index] for index in len(l1) if l1[index] != -2.0]

As well as:

l3 = np.where(l1 != -2.0)

In first case I received error. In the second case I obtained (array([0]),).

Thanks for the help.

Cory Kramer · Accepted Answer · 2017-09-27 11:55:54Z

3

If you change your list into a numpy.array

>>> import numpy as np
>>> l1 = np.array([-144.92170726320364, 697.7739312692029, -2.0, -2.0])

you can do the comparison against the scalar

>>> l1 != -2.0
array([ True,  True, False, False], dtype=bool)

Then use that expression to index into your original array

>>> l1[l1 != -2.0]
array([-144.92170726,  697.77393127])

answered Sep 27, 2017 at 11:55

Cory Kramer

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Comments

ddg · Accepted Answer · 2017-09-27 11:57:21Z

2

Normal Python array

l2 = [i for i in l1 if i!=-2.0]

Numpy array

l1 = np.array(l1)
l2 = l1[l1 != -2.0]

answered Sep 27, 2017 at 11:57

ddg

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Yorian · Accepted Answer · 2017-09-27 11:56:45Z

2

try this:

[l for l in l1 if l != -2.0]

answered Sep 27, 2017 at 11:56

Yorian

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