Terminology: Python and Numpy - `iterable` versus `array_like`

Question

What is the difference between an iterable and an array_like object in Python programs which use Numpy?

Both iterable

Answer 1

The term "array-like" is indeed only used in NumPy and refers to anything that can be passed as first parameter to numpy.array() to create an array.

The term "iterable" is standard python terminology and refers to anything that can be iterated over (for example using for x in iterable).

Most array-like objects are iterable, with the exception of scalar types.

Many iterables are not array-like -- for example you can't construct a NumPy array from a generator expression using numpy.array(). (You would have to use numpy.fromiter() instead. Nonetheless, a generator expression isn't an "array-like" in the terminology of the NumPy documentation.)

Answer 2

While the first part of the Sven's answer is correct, I would like to add that array-like objects should not necessarily be iterable.

For example, in my particular situation I was interested in using numpy.rint() function that accepts array-like objects with scalars of type int. They are not iterable, but they are accepted. You can also pass ints to numpy.array(), so they are array-like.

Here is the confirmation from the "NumPy-Discussion" mailing list: https://mail.scipy.org/pipermail/numpy-discussion/2016-November/076224.html