From looking at most of these examples it's clear that Python will be able to perform really well with projects like numba, Cython and PyPy in the picture. My i

From looking at most of these examples it's clear that Python will be able to perform really well with projects like numba, Cython and PyPy in the picture. My impressions after using Cython are that Cython can for the most part even outperform Julia. Couple that with the fact that with Julia there will probably be a few steps before you squeeze the maximum performance possible of out your algorithm, and it makes Cython a no brainier for a developer that is already using Python. That is until you consider that most of these examples do not showcase (what is in my humble opinion) Julia's true strength - homoiconicity and an opt in extensible type system that is part of the language. It's trivial to make a new Class in Python and set up behavior that one would be interested in. One could easily implement decorators, metaclasses, descriptors, properties and other dunder methods to customize class behavior to the heart's desire. But Cython (as far as I understand) does not support these Python features. If an existing library implements an interface using these Python features, it's not obvious to me how it would be possible to use Cython to improve performance. Julia on the other hand has no such restrictions. I would say it is desired to use macros and metaprogramming in everyday programming. I've been a Python user for over 6 years now. I've only looked at Julia for a few months now and I already can see concepts and ideas in Julia that have no analogue in Python. But most if not all Python programming features translate to Julia one way or the other. I love Python, but I can't help but feel that the lack of a opt in type system and more powerful metaprogramming constructs is hurting it in these comparisons.