[weberc2]

OP probably is overstating a bit, but it is hard to efficiently parallelize computation in Python. For example, if you have a large Python object graph that you need to compute over, you can't easily parallize the computation without paying some significant serialization cost. You can probably alleviate that by carefully choosing algorithms that minimize the amount of serialization per worker process, but at the end of the day, all of this is still quite a lot harder than using shared memory and goroutines. And not to mention Go is 1-2 orders of magnitude faster than Python in single-threaded execution... Python is great for lots of things, but efficient parallel programming in Python is _hard_, even if there are a handful of cases where it's not so hard.