Say we have an array:
a = np.array([
[11, 12, 13],
[21, 22, 23],
[31, 32, 33],
[41, 42, 43]
])
a[[1, 3], [0, 2]] = 0
So we want to set zeros to 0th and 2nd element at both 1st and 3rd rows. But what we get is:
[[11 12 13]
[ 0 22 23]
[31 32 33]
[41 42 0]]
Why not:
[[11 12 13]
[ 0 22 0]
[31 32 33]
[0 42 0]]
?
In
import numpy as np
a = np.array([
[11, 12, 13],
[21, 22, 23],
[31, 32, 33],
[41, 42, 43]
])
a[[1, 3], [0, 2]] = 0
a
the last statement is equivalent to (edited)
a[1, 0] = 0
a[3, 2] = 0
It gives the following:
a = np.array([
[11, 12, 13],
[21, 22, 23],
[31, 32, 33],
[41, 42, 43]
])
a[1, 0] = 0
a[3, 2] = 0
a
# array([[11, 12, 13],
# [ 0, 22, 23],
# [31, 32, 33],
# [41, 42, 0]])
In numpy's documentation, this is referenced as "advanced indexing"
Advanced indexing is triggered when the selection object, obj, is a non-tuple sequence object, an
ndarray(of data type integer or bool), or a tuple with at least one sequence object or ndarray (of data type integer or bool).
In your case, your selection object is a tuple with at least one sequence object.