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Can someone help me with conditionally selecting elements in a numpy array? I am trying to return elements that are greater than a threshold. My current solution is:

sampleArr = np.array([ 0.725, 0.39, 0.99 ])
condition = (sampleArr > 0.5)`
extracted = np.extract(condition, sampleArr) #returns [0.725 0.99]

However, this seems roundabout and I suspect there's a way to do it in one line?

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2 Answers 2

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13

You can index directly like:

sampleArr[sampleArr > 0.5]

Test Code:

sampleArr = np.array([0.725, 0.39, 0.99])

condition = (sampleArr > 0.5)
extracted = np.extract(condition, sampleArr)  # returns [0.725 0.99]

print(sampleArr[sampleArr > 0.5])
print(sampleArr[condition])
print(extracted)

Results:

[ 0.725  0.99 ]
[ 0.725  0.99 ]
[ 0.725  0.99 ]
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3 Comments

why 2d array select becomes 1d array？
@CSQGB I do not see any 2d arrays here.
what about multiple conditions?
2

You can actually just do boolean indexing like this:

extracted = sampleArr[sampleArr > 0.5]
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3 Comments

You have the same answer :P
why 2d array select becomes 1d array？z=numpy.random.randint(1,9,(4,4)) z[z>4] array([ 5, 6, 7, 8, 9, 10, 11, 12]) . Not 2d array
@CSQGB Not a real explanation but this can never work since it would generally have to result in rows/columns of uneven length. You can use np.argwhereto find positions of elements satisfying the condition or use the z>4 mask and loop through it when required.

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