How to replace all negative numbers in an array with zero [duplicate]

Question

If I have an NxN numpy array and if there is a negative number as any position, is there a simple and quick way I can replace that number with a 0? something like

for item in array: if item <= 0: item = 0

array[array <= 0 ] = 0 should work also you say negative, shouldn't it be array[array < 0] = 0 rather than <=0? — EdChum
– EdChum, Commented Dec 7, 2016 at 14:44
@EdChum ... it doesn't make a difference, because you end up replacing 0 with 0. The only difference might be if the array has floats and some 0 have the "negative sign" while other have the positive sign, by assigning 0 you set all of them to the "positive sign", but I believe all the operations should work the same. — Bakuriu
– Bakuriu, Commented Dec 7, 2016 at 14:48
@Bakuriu true but there is a semantic difference here between <0 and <=0 with the negative statement irrespective of the end result, it's clear that <0 means negative whilst <=0 does not — EdChum
– EdChum, Commented Dec 7, 2016 at 14:50
whats wrong with the code provided? (never used numpy so i might be asking a stupid question) — WhatsThePoint
– WhatsThePoint, Commented Dec 7, 2016 at 14:50
@WhatsThePoint it's not vectorised and the most efficient method of setting the elements to 0, if you have a large array then you end up iterating over each row in this case whilst using a mask will set the entire array in a vectorised manner — EdChum
– EdChum, Commented Dec 7, 2016 at 14:52

EdChum · Accepted Answer · 2016-12-07 14:46:55Z

You can mask the entire array by using a boolean mask, this will be much more efficient than iterating like you currently are:

In [41]: array = np.random.randn(5,3) array Out[41]: array([[-1.09127791, 0.51328095, -0.0300959 ], [ 0.62483282, -0.78287353, 1.43825556], [ 0.09558515, -1.96982215, -0.58196525], [-1.23462258, 0.95510649, -0.76008193], [ 0.22431534, -0.36874234, -1.46494516]]) In [42]: array[array < 0] = 0 array Out[42]: array([[ 0. , 0.51328095, 0. ], [ 0.62483282, 0. , 1.43825556], [ 0.09558515, 0. , 0. ], [ 0. , 0.95510649, 0. ], [ 0.22431534, 0. , 0. ]])

Community · Accepted Answer · 2017-05-23 12:30:48Z

2

Based on this SO answer, you can use numpy's builtin indexing for this.

arr[arr < 0] = 0

edited May 23, 2017 at 12:30

CommunityBot

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answered Dec 7, 2016 at 14:50

lucasnadalutti

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3 Comments

EdChum Over a year ago

how is this answer different to mine?

lucasnadalutti Over a year ago

I hadn't seen your answer while posting mine. I won't remove mine since it adds something new which is a reference that contains explanation and benchmarking. Don't worry, though, I upvoted yours and I think it should be the selected answer for being the first one.

EdChum Over a year ago

I don't have an issue with duplicated answers so long as it adds something new, it would be better if you added the relevant parts to your answer rather than just a link, just an observation

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