numpy.where() detailed, step-by-step explanation / examples [closed]

Question

I have trouble properly understanding numpy.where() despite reading the doc, this post and this other post.

Can someone provide step-by-step commented examples with 1D and 2D arrays?

Answer 1

After fiddling around for a while, I figured things out, and am posting them here hoping it will help others.

Intuitively, np.where is like asking "tell me where in this array, entries satisfy a given condition".

>>> a = np.arange(5,10) >>> np.where(a < 8)       # tell me where in a, entries are < 8 (array([0, 1, 2]),)       # answer: entries indexed by 0, 1, 2 

It can also be used to get entries in array that satisfy the condition:

>>> a[np.where(a < 8)]  array([5, 6, 7])          # selects from a entries 0, 1, 2 

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When a is a 2d array, np.where() returns an array of row idx's, and an array of col idx's:

>>> a = np.arange(4,10).reshape(2,3) array([[4, 5, 6],        [7, 8, 9]]) >>> np.where(a > 8) (array(1), array(2)) 

As in the 1d case, we can use np.where() to get entries in the 2d array that satisfy the condition:

>>> a[np.where(a > 8)] # selects from a entries 0, 1, 2 

array([9])

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Note, when a is 1d, np.where() still returns an array of row idx's and an array of col idx's, but columns are of length 1, so latter is empty array.