Im trying to understand numpy where condition.
>>> import numpy as np
>>> x = np.arange(9.).reshape(3, 3)
>>> x
array([[ 0., 1., 2.],
[ 3., 4., 5.],
[ 6., 7., 8.]])
>>> np.where( x > 5 )
(array([2, 2, 2]), array([0, 1, 2]))
IN the above case, what does the output actually mean, array([0,1,2]) I actually see in the input what is array([2,2,2])
Th first array indicates the row number and the second array indicates the corresponding column number.
If the array is following:
array([[ 0., 1., 2.],
[ 3., 4., 5.],
[ 6., 7., 8.]])
Then the following
(array([2, 2, 2]), array([0, 1, 2]))
Can be interpreted as
array(2,0) => 6
array(2,1) => 7
array (2,2) => 8
You might also want to know where those values appear visually in your array. In such cases, you can return the array's value where the condition is True and a null value where they are false. In the example below, the value of x is returned at the position where x>5, otherwise assign -1.
x = np.arange(9.).reshape(3, 3)
np.where(x>5, x, -1)
array([[-1., -1., -1.],
[-1., -1., -1.],
[ 6., 7., 8.]])
Three elements found, located at (2,0),(2,1),(2,2)..
By the way, tryhelp(np.where()) will help you a lot.
