I am reading the numpy.where(condition[, x, y]) documentation, but I can not understand the small example:
>>> x = np.arange(9.).reshape(3, 3)
>>> np.where( x > 5 )
Out: (array([2, 2, 2]), array([0, 1, 2]))
Can some one explain how the result comes?
The first array (array([2, 2, 2])) is the index of rows and the second (array([0, 1, 2])) is the columns of those values that are more than 5.
You can use zip to get the exact index of values :
>>> zip(*np.where( x > 5 ))
[(2, 0), (2, 1), (2, 2)]
Or use np.dstack :
>>> np.dstack(np.where( x > 5 ))
array([[[2, 0],
[2, 1],
[2, 2]]])
np.where to zip function.It's printing out the coordinates to your condition
import numpy as np
x = np.arange(9.).reshape(3, 3)
print x
print np.where( x > 5 )
where print x prints:
[[ 0. 1. 2.]
[ 3. 4. 5.]
[ 6. 7. 8.]]
and np.where( x > 5 ) prints the index location of all elements greater than 5
(array([2, 2, 2]), array([0, 1, 2]))
where 2,0 == 6 and 2,1 == 7 and 2,2 == 8
