Just because a tool can be used for a given case does not mean it should be used. Python has a lot of drawbacks which make it a poor choice for use in producti

Just because a tool can be used for a given case does not mean it should be used. Python has a lot of drawbacks which make it a poor choice for use in production. Duck typing means many classes of errors which could be caught by a compiler will make it into production, necessitating much more rigorous testing process (i.e. developer time) Dependence on the system python interpreter and poor dependency management solutions make environment encapsulation at runtime a more serious problem in Python than other languages. Python performs poorly and uses lots of memory compared to other languages, making it an expensive choice to build an application on top of. Python's USP is that it has a shallow learning curve, which is why it has uptake in domains like science and ML which are populated by "non-programmers", but virtually every other property of the language makes it a poor choice for serious engineering work.