Asked By – Alexandre Holden Daly
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.
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
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
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.