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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 usenumpy.fromiter()instead. Nonetheless, a generator expression isn’t an “array-like” in the terminology of the NumPy documentation.)