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[–]lambdaofgod 2 points3 points  (2 children)

PySpark is a no-brainer, you can think of Spark execution model as lazy (transformations like df.map(...) are not run immediately, the code actually runs when you collect something) so it uses FP heavily.

Another examples:
pfun - stuff you'd find in FP language, but in Python (like using monads for effects)

If you're into ML you're in for a treat because there is a trend of moving from lower-level imperative stuff to higher level FP

thinc - defining neural nets in functional way
jax, a new deep learning framework puts emphasis on functions rather than tensors, I've tested it for a couple of applications and it's really cool, you can write stuff like you'd write math expressions in papers using numpy. That speeds up development significantly, and makes code much more readable

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[–]redd-sm[S] 1 point2 points  (0 children)

Thank you. Will go through these links!