> so it’s usually not worth spending a ton of time optimizing single-threaded performance for a single experiment when I can just perform a big MapReduce.

Is this the scientific version of "rich people problems"?

But I have a problem with talking real life application and using that to claim

> it will be impossible for pure Python to beat pure Nim at raw performance

Because it maybe be true. I didn't try it but there are many things that can be optimized in the python code example but away from that. In real life application in scientific computing I don't think anyone wouldn't use numpy to deal with that which will make things much better. Also the power of python in data analysis and scientific computing is the ecosystem and community. This will be very hard to beat. And there are more mature alternatives like Julia.

Edit: The author code for reading the data reading is creating a new file object for each iteration. I would guess that in nim this would be a similar problem but I am not sure how it actually work or if has the same effect. But anyway you don't do this in real life application with python. Also it would be nice to use a list comprehension to count the occurrences of 'C' and 'G' in each line.