897 karma · joined August 31, 2019
[1] https://pydantic.dev [2] https://jcristharif.com/msgspec/
The only big thing to learn with Julia is the broadcasting syntax, other than that, Julia has a good macro system, so sometimes you see syntax that doesn't make sense without knowing what the macro does.
[1] https://en.wikipedia.org/wiki/List_of_indigenous_peoples_of_...
I find Python is fast enough for small to medium datasets. I've normally worked with data that needs to be loaded each morning or sometimes hourly, so whether the transformation takes 1 minute or 10 minutes it doesn't matter. The better way is of course to dump the data into a data warehouse as soon as possible and then use SQL for everything, so I only use Python for things that SQL isn't suited for, like making HTTP requests.
I'm not sure why using a static language would make translating data types difficult, but I add as many typehints as possible to my Python so I rarely do anything with dynamic types. I guess they're saying for small tasks where you're working with lots of types, when using a static language most of your code will be type definitions, so a dynamic language will let you focus on writing the transformation code.
[1] https://forgejo.org/ [2] https://codeberg.org/forgejo/forgejo/issues/59