numpy array indexing: list index and np.array index give different result

Question

I am trying to index an np.array using list and np.array indexes. But they give different result.

Here is an illustration:

import numpy         


        

Answer 1

If it's an array it's interpreted as shape of the final array containing the indices - but if it's an list it's the indices along the "dimensions" (multi-dimensional array indices).

So the first example (with an array) is equivalent to:

[[x[0], x[1],
 [x[1], x[2]]

But the second example (list) is interpreted as:

[x[0, 1], x[1, 2]]

But x[0, 1] gives a IndexError: too many indices for array because your x has only one dimension.

That's because lists are interpreted like it was a tuple, which is identical to passing them in "separately":

x[[0, 1], [1, 2]]
          ^^^^^^----- indices for the second dimension
  ^^^^^^------------- indices for the first dimension

Answer 2

From numpy indexing documentation:

ndarrays can be indexed using the standard Python x[obj] syntax, where x is the array and obj the selection.

...
Basic slicing occurs when obj is a slice object (constructed by start:stop:step notation inside of brackets), an integer, or a tuple of slice objects and integers. Ellipsis and newaxis objects can be interspersed with these as well. In order to remain backward compatible with a common usage in Numeric, basic slicing is also initiated if the selection object is any non-ndarray sequence (such as a list) containing slice objects, the Ellipsis object, or the newaxis object, but not for integer arrays or other embedded sequences. ...