import dataframely as dy
import polars as pl
class HouseSchema(dy.Schema):
    zip_code = dy.String(nullable=False, min_length=3)
    num_bedrooms = dy.UInt8(nullable=False)
    num_bathrooms = dy.UInt8(nullable=False)
    price = dy.Float64(nullable=False)
    @dy.rule()
    def reasonable_bathroom_to_bedroom_ratio() -> pl.Expr:
        ratio = pl.col("num_bathrooms") / pl.col("num_bedrooms")
        return (ratio >= 1 / 3) & (ratio <= 3)
    @dy.rule(group_by=["zip_code"])
    def minimum_zip_code_count() -> pl.Expr:
        return pl.len() >= 2

import polars as pl
df = pl.DataFrame({
    "zip_code": ["01234", "01234", "1", "213", "123", "213"],
    "num_bedrooms": [2, 2, 1, None, None, 2],
    "num_bathrooms": [1, 2, 1, 1, 0, 8],
    "price": [100_000, 110_000, 50_000, 80_000, 60_000, 160_000]
})
# Validate the data and cast columns to expected types
validated_df: dy.DataFrame[HouseSchema] = HouseSchema.validate(df, cast=True)