From a numpy array
a=np.arange(100).reshape(10,10)
I want to obtain an array
[[99, 90, 91],
[9, 0, 1],
[19, 10, 11]]
I tried
a[[-1,0,1],[-1,0,1]]
but this instead gives array([99, 0, 11]). How can I solve this problem?
-
its called numpy slicing... just google it you will find everything you need to know. To answer your question quickly you probably want to something like a[3:7,5:9]some_name.py– some_name.py2021-07-02 11:25:33 +00:00Commented Jul 2, 2021 at 11:25
Add a comment
|
3 Answers
Roll your array over two axis and slice 3x3:
>>> np.roll(a, shift=1, axis=[0,1])[:3, :3]
array([[99, 90, 91],
[ 9, 0, 1],
[19, 10, 11]])
Comments
Split the slicing into two seperate operations
arr[ [ -1,0,1] ][ :, [ -1,0,1]]
# array([[99., 90., 91.],
# [ 9., 0., 1.],
# [19., 10., 11.]])
Equivalent to:
temp = arr[ [ -1,0,1] ] # Extract the rows
temp[ :, [ -1,0,1]] # Extract the columns from those rows
# array([[99., 90., 91.],
# [ 9., 0., 1.],
# [19., 10., 11.]])
Comments
a[[-1,0,1],[-1,0,1]] This is wrong, this means you want an elements from row -1, column -1 i.e (99) and row 0, column 0 i.e (0) and row 1, column 1 i.e (11) this is the reason you are getting array([99, 0, 11])
Your Answer:
a[ [[-1],[0],[1]], [-1,0,1] ]: This means, we want every element from column -1, 0, 1 from row [-1], [0], [1].
