I\'d like to use np.argwhere()
to obtain the values in an np.array
.
For example:
z = np.arange(9).reshape(3,3)
[[0 1 2]
[3 4
Why not simply use masking here:
z[z % 3 == 0]
For your sample matrix, this will generate:
>>> z[z % 3 == 0]
array([0, 3, 6])
If you pass a matrix with the same dimensions with booleans as indices, you get an array with the elements of that matrix where the boolean matrix is True
.
This will furthermore work more efficient, since you do the filtering at the numpy level (whereas list comprehension works at the Python interpreter level).