feat: Custom namespace registration

[Excidion]

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

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

Related issues

• Related issue [Enh]: Registering extensions via narwhals to downstream dataframe libraries #267
• Enabler for alteryx/featuretools/issues/1967

Checklist

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

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

Adds ability to register new namespaces like so (I was surprised narwhals didn't have ceil and floor yet):

import narwhals as nw
import pandas as pd

@nw.api.register_expr_namespace("rounding")
class Rounding:
    def __init__(self, expr: nw.Expr) -> None:
        self._expr = expr

    def ceil(self, dtype=nw.Int64) -> nw.Expr:
        return (self._expr + 1).cast(dtype=dtype)   # i know this doesn't actually work for all cases

    def floor(self, dtype=nw.Int64) -> nw.Expr:
        return self._expr.cast(dtype=dtype)


df = nw.from_native(pd.DataFrame([1.4, 24.3, 55.7, 64.001], columns=["n"]))
print(df.select(
    nw.col("n"),
    nw.col("n").rounding.floor().alias("floor"),
    nw.col("n").rounding.ceil().alias("ceil"),
).to_native().to_markdown())
"""
|    |      n |   floor |   ceil |
|---:|-------:|--------:|-------:|
|  0 |  1.4   |       1 |      2 |
|  1 | 24.3   |      24 |     25 |
|  2 | 55     |      55 |     56 |
|  3 | 64.001 |      64 |     65 |
"""

Heavily "inspired" by the polars implementation. Still needs tests and also updates to the docstrings. Just wanted to know if this is even going in the right direction before proceeding?

Checklist:

• Tested for Expr
• Tested for Series ?
• Tested for DataFrame ?
• Tested for LazyFrame ?

This is my first time contributing to FOSS, so please forgive if I missed anything. :)

[MarcoGorelli]

thanks for the pr!

it's not clear to me why this is needed - could you clarify why this is more desirable than just

import narwhals as nw
import pandas as pd

def ceil(expr, dtype=nw.Int64) -> nw.Expr:
    return (expr + 1).cast(dtype=dtype)

def floor(expr, dtype=nw.Int64) -> nw.Expr:
    return expr.cast(dtype=dtype)


df = nw.from_native(pd.DataFrame([1.4, 24.3, 55.7, 64.001], columns=["n"]))
print(df.select(
    nw.col("n"),
    floor(nw.col("n")).alias("floor"),
    ceil(nw.col("n")).alias("ceil"),
).to_native().to_markdown())

please?