> Columns have names, except when they don't. Things (rows? individual cells?)
They are not a fit for every problem. Please don't try to use them for that. For normalized tabular data, it is a good fit.
> have types which is sometimes enforced and sometimes not. If you don't follow a recipe exactly, your dataframe will be subtly different and might not work.
Polars is really strict on types. A type is always determined by the input datatype and the operation applied. You can check the schema before you run the query. Polars will produce the same output datatype as defined by its logical plan.
> A lot of code mixes mutation in place and creating new dataframes, and so on
All polars methods are pure, e.g. they don't mutate the original.
They are not a fit for every problem. Please don't try to use them for that. For normalized tabular data, it is a good fit.
> have types which is sometimes enforced and sometimes not. If you don't follow a recipe exactly, your dataframe will be subtly different and might not work.
Polars is really strict on types. A type is always determined by the input datatype and the operation applied. You can check the schema before you run the query. Polars will produce the same output datatype as defined by its logical plan.
> A lot of code mixes mutation in place and creating new dataframes, and so on
All polars methods are pure, e.g. they don't mutate the original.