## Issue

I wonder what could be the easiest way to create a bi-dimensional array, that has for each row the indices to another multi-dimensional array.

For example, let’s say I have a cube 4×4, the “indices matrix” would be the following:

```
np.concatenate([
np.expand_dims(curr.ravel(),axis=0).T
for curr
in np.meshgrid(
np.arange(4),
np.arange(4),
np.arange(4)
)
],axis=1)
```

with the following result:

```
array([[0, 0, 0],
[0, 0, 1],
[0, 0, 2],
[0, 0, 3],
[1, 0, 0],
[1, 0, 1],
...
[2, 3, 2],
[2, 3, 3],
[3, 3, 0],
[3, 3, 1],
[3, 3, 2],
[3, 3, 3]])
```

Besides the fact that it seems that the second column should be in place of the first, is there a more “numpythonic” way to create the same or similar matrix in a more compact way?

It would be nice if existed a function that just takes an arbitrary multi-dimensional array and returns it’s index table.

## Solution

You could use `np.indices`

:

```
>>> a = np.random.random((4,4,4))
>>> np.indices(a.shape).reshape((a.ndim, -1)).T
array([[0, 0, 0],
[0, 0, 1],
[0, 0, 2],
[0, 0, 3],
[0, 1, 0],
[0, 1, 1],
[...]
[3, 3, 2],
[3, 3, 3]])
```

There are also other utilities like `np.ndindex`

, depending on your use case. (FWIW I don’t think getting the coordinates in the form you’re looking for is going to be as helpful as you might think it is, but YMMV.)

Answered By – DSM

Answer Checked By – Katrina (BugsFixing Volunteer)