[RyEgswuCsn]

Well there is no type stability or scoping rules to worry about in Python, so just for the sake of comparing the two, I was indeed thinking of 'naive' Julia code vs 'naive' Python code.

The thing with Python is that 'naive' Python code is already pretty close to 'unoptimised' Python code, so one can write naive Python code with numpy and still ends up with not-too-shabby performance, provided they chose an efficient algorithm, of course. In other words, there are not as many performance mistakes one can make with Python (perhaps because it can't get any worse). I imagine that's also why so many Python users who tried Julia were disappointed that direct translations of their Python program fail to perform as fast as advertised.

[timholy]

The point is, once you've gotten used to Julia you tend to write good code most of the time without even thinking about it. And that good code still "looks good," meaning it takes advantage of Julia's expressiveness and brevity. Understandably, newcomers make many more performance mistakes.

So there's often a huge difference between "unoptimized code" (something written by an experienced developer who's deliberately taking the easy way out) and "naive code" (something a newcomer might write). There can literally be orders-of-magnitude performance difference.

I agree that there isn't as much to learn about Python. But of course that's largely because of the gap in opportunities.