How do you use the ellipsis slicing syntax in Python?

The ellipsis is used in numpy to slice higher-dimensional data structures.

It's designed to mean at this point, insert as many full slices (:) to extend the multi-dimensional slice to all dimensions.

Example:

>>> from numpy import arange
>>> a = arange(16).reshape(2,2,2,2)

Now, you have a 4-dimensional matrix of order 2x2x2x2. To select all first elements in the 4th dimension, you can use the ellipsis notation

>>> a[..., 0].flatten()
array([ 0,  2,  4,  6,  8, 10, 12, 14])

which is equivalent to

>>> a[:,:,:,0].flatten()
array([ 0,  2,  4,  6,  8, 10, 12, 14])

In your own implementations, you're free to ignore the contract mentioned above and use it for whatever you see fit.

Ellipsis, or ... is not a hidden feature, it's just a constant. It's quite different to, say, javascript ES6 where it's a part of the language syntax. No builtin class or Python language constuct makes use of it.

So the syntax for it depends entirely on you, or someone else, having written code to understand it.

Numpy uses it, as stated in the documentation. Some examples here.

In your own class, you'd use it like this:

>>> class TestEllipsis(object):
...     def __getitem__(self, item):
...         if item is Ellipsis:
...             return "Returning all items"
...         else:
...             return "return %r items" % item
... 
>>> x = TestEllipsis()
>>> print x[2]
return 2 items
>>> print x[...]
Returning all items

Of course, there is the python documentation, and language reference. But those aren't very helpful.

This is another use for Ellipsis, which has nothing to do with slices: I often use it in intra-thread communication with queues, as a mark that signals "Done"; it's there, it's an object, it's a singleton, and its name means "lack of", and it's not the overused None (which could be put in a queue as part of normal data flow). YMMV.

As stated in other answers, it can be used for creating slices. Useful when you do not want to write many full slices notations (:), or when you are just not sure on what is dimensionality of the array being manipulated.

What I thought important to highlight, and that was missing on the other answers, is that it can be used even when there is no more dimensions to be filled.

Example:

>>> from numpy import arange
>>> a = arange(4).reshape(2,2)

This will result in error:

>>> a[:,0,:]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IndexError: too many indices for array

This will work:

a[...,0,:]
array([0, 1])