What type of PR is this? (check all applicable)

• 💾 Refactor
• ✨ Feature
• 🐛 Bug Fix
• 🔧 Optimization
• 📝 Documentation
• ✅ Test
• 🐳 Other

Related issues

• Related issue [Enh]: Support polars.Expr.rank #1323

Checklist

• Code follows style guide (ruff)
• Tests added
• Documented the changes

If you have comments or can explain your changes, please do so below.

This PR provides initial support for rank method. I will start it as a draft due to a bunch of shortcomings:

• pandas:
  • there is a (nasty) trick to make it work with nulls/nans and nullable dtypes (related: to BUG: rank does not respect na_option='keep' for numpy nullable integer dtypes, which should be fixed in pandas 3) - I will comment the trick in the code
  • support in group by context seems quite hard, or at least I could not find a clear way to achieve it due to the inability to forward rank arguments. The API to rank on a grouped object is df.groupby(keys)[cols].rank(*args, **kwargs), and this does not even return an aggregated value. Maybe we could support it if that's the only expression passed in the context, yet we need to figure out how to pass the arguments along. This is relevant since over is implemented as a group_by under the hood.
• pyarrow:
  • does not support polars default method (namely, "average"), therefore if rank is called without specifying another method, it will end up raising an error
  • does not implement rank in a group by context
  • I am using pyarrow.compute.rank which is available but not exposed/documented (?)
• dask: it just does not support ranking
• polars:
  • group by context always returns an aggregate, in this case a list of ranks - which is fairly useless as it is a list of increasing/descreasing range until the size of the group