Painful (polars-lazy): - Very scant documentation. Many things missing docs, many documented items missing examples. - Using a custom function in a map. You hav

Painful (polars-lazy): - Very scant documentation. Many things missing docs, many documented items missing examples. - Using a custom function in a map. You have to do this `Ok(series.type()?.into_iter().map(...).collect::<ChunkedArray<_>>()).into_series()` dance every time, and it's annoying. And you have to specify the output type even though it should be inferable from the function body. - There is no good way to "get-or-null" a column, i.e., treat an absent column as all null. You have to try to select the column, check if that errored, and if so insert a column of nulls (casted to the correct type) before proceeding. - Perhaps because it uses Arrow under the hood, there is no such thing as a non-nullable column. You just always have to handle nulls. - A bit verbose due to the lack of Into<Expr> implementations for most basic types. E.G., `lit(false)` instead of `false` because `bool` is not `Into<Expr>`. - It's a bit confusing at times which features you need for which abilities, and things will panic at runtime if you don't have the necessary features enabled (e.g., the strptime function has a tz_aware:bool parameter that panics if it's true but you don't have the time zone feature enabled). Ultimately it's a young library and so I guess it comes with the territory. It's still fantastic and having used it I'd be hard pressed to go back to pandas even despite all the flaws.