I think the comment about types takes a rather narrow view of Python use cases. Sure, if you’re some lone sysadmin using Python as an alternative to bash to implement some questionable ETL pipeline comprising a bunch of random scripts running off a single machine somewhere then I agree. You probably are not the target audience for type annotations.
But even moderately complex libraries and applications are such a huge pain to develop, read, and maintain without some of the tooling that leverages type annotations. This is especially true in the ML / AI / Data Science world where a lot of the people implementing models have dubious
coding practices.
Pure dynamic typing paradigms simply have not delivered on their promises over the last 30 years. There are definitely some areas where they make sense, but I doubt we will see a massive readoption until our tooling becomes sufficiently intelligent. Imagine, for example, a probabilistic type inference based on both the structural aspects of the code and previous runs over actual data.
People in the data science world with dubious coding practices should keep their data in commonly used types such as Pandas tables. The last thing we should want from them is encoding their data with ill-conceived class/type systems. Not sure if that is what you mean.
I'd say Python's large scale adoption is exactly a good example of dynamic typing delivering on its promises.
But even moderately complex libraries and applications are such a huge pain to develop, read, and maintain without some of the tooling that leverages type annotations. This is especially true in the ML / AI / Data Science world where a lot of the people implementing models have dubious coding practices.
Pure dynamic typing paradigms simply have not delivered on their promises over the last 30 years. There are definitely some areas where they make sense, but I doubt we will see a massive readoption until our tooling becomes sufficiently intelligent. Imagine, for example, a probabilistic type inference based on both the structural aspects of the code and previous runs over actual data.