What is the difference between an iterable and an array_like object in Python programs which use Numpy?
Both iterable and array_like are often seen in Python documentation and they share some similar properties.
I understand that in this context an array_like object should support Numpy type operations like broadcasting, however Numpy arrays area also iterable. Is it correct to say that array_like is an extension (or super-set?) of iterable?
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.)
array-like and the first arg of numpy.array().
int type is array-like and can be passed to numpy.array(), but it is not iterable.
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