4 ms·
In data science, types change all the times. E.g. when dealing with tabular data, the columns (which might be used as type parameters) change frequently. In Jul
by snicker7 6y ago
In data science, types change all the times. E.g. when dealing with tabular data, the columns (which might be used as type parameters) change frequently. In Julia, for example, see DataFrames.jl vs. TypedTables.jl for some trade-offs.
In general, type annotations are useful if you know the business requirements beforehand. Data science is exploratory, where type annotations can be a form of premature abstraction. Languages with traits (e.g. Scala) might be a decent compromise.
- rowanG077 6y agoBut now you are talking about type annotations and not static typing. You can have static typing without any type annotations
- wk_end 6y agoIt sounds like the structural typing in Typescript might help a lot here - you don’t need to formally define any types for a record, the language just infers and tracks fields/types for you. I don’t know what the current state of data science is for TS though.