That's not so obvious, the abstractions for multi-dimensional indexing are better than (I think) you're imagining.
Here's a nice recent example: a new reverse!(A; dims) function, which is fast and works on arrays with any number of dimensions. And is completely indifferent to where the indices start:
Maybe less readable than I remembered, sorry! But these `CartesianIndex` things are how you index multi-dimensional arrays.
If by indexing you only mean adding offsets to pointers with your bare hands, this is possible but discouraged. Because you'll produce a buggier and less general version of exactly what already exists. In this case, reversing an N-dimensional array, along any M of its axes, with arbitrary offsets -- there are a lot of ways to get that wrong.
# one dimensional
a = [1, 2, 3]
# prints the number 1 @ index 0
print(a[0])
# multi-dimensional
b = [[1, 2, 3], [4, 5, 6]]
# prints the number 4 @ index (1, 0)
print(b[1][0])
OK, I thought you were advocating zero based on complicated index calculations for some algorithm, with mod & div, which is what I was trying to say could be abstracted away. The literal syntax `a=[1,2,3]` makes a one-dimensional `Array{Int,1}` whose first element is indeed `a[1]`. (`b` is an array of arrays, while `c = [1 2 3; 4 5 6]` is an honest 2-dimensional array.)
Here's a nice recent example: a new reverse!(A; dims) function, which is fast and works on arrays with any number of dimensions. And is completely indifferent to where the indices start:
https://github.com/JuliaLang/julia/pull/37367/files#diff-eb9...