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

## Question

Asked By – Alexandre Holden Daly

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?

Now we will see solution for issue: numpy.where() detailed, step-by-step explanation / examples [closed]

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
``````

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])

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.

This question is answered By – Alexandre Holden Daly