I have an array A :
A = [[1, 2 ,3 ,4],
[5, 6 ,7 ,8],
[9, 10 ,11 ,12],]
and I want to get the 2nd row in the 3rd element (i.e. '7') :
I can do it by:
A[1,2]
For the general dimension number I want to have something generic. Given index list B=[1,2] I want to have something like MATLAB indexing:
A[B] or A[*B]
The first gives 2 rows and the second results in an error. How can I do this?
edit: type(A)=type(B)=np.array
You don't need to unpack collection with "*" because numpy supports tuple indexing. You problem is that there is difference between indexing by list and by tuple. So,
import numpy as np
A = np.array([
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12]
])
print("indexing by list:\n", A[[0, 1]], '\n')
# Output:
# indexing by list:
# [[1 2 3 4]
# [5 6 7 8]]
print("indexing by tuple:\n", A[(0, 1)])
# Output:
# indexing by tuple:
# 2
Hope, it helps. For the further reading about numpy indexing.
As an alternative, suppose you are interested in "array indexing", i.e., getting an array of values back of length L, where each value in the array is from a location in A. You would like to indicate these locations via an index of some kind.
Suppose A is N-dimensional. Array indexing solves this problem by letting you pass a tuple of N lists of length L. The ith list gives the indices corresponding to the ith dimension in A. In the simplest case, the index lists can be length-one (L=1):
>>> A[[1], [2]]
array([7])
But the index lists could be longer (L=5):
>>> A[[1,1,1,1,0], [2,2,2,2,0]]
array([7, 7, 7, 7, 1])
I emphasize that this is a tuple of lists; you can also go:
>>> first_axis, second_axis = [1,1,1,1,0], [2,2,2,2,0]
>>> A[(first_axis,second_axis)]
array([7, 7, 7, 7, 1])
You can also pass a non-tuple sequence (a list of lists rather than a tuple of lists) A[[first_axis,second_axis]] but in NumPy version 1.21.2, a FutureWarning is given noting that this is deprecated.
Another example here: https://stackoverflow.com/a/23435869/4901005
TypeError: list indices must be integers or slices, not tupleerror. Or is it, for example, numpy's array?