polars.Expr.to_physical

Expr.to_physical() → Expr

Cast to physical representation of the logical dtype.

• polars.datatypes.Date() -> polars.datatypes.Int32()

• polars.datatypes.Datetime() -> polars.datatypes.Int64()

• polars.datatypes.Time() -> polars.datatypes.Int64()

• polars.datatypes.Duration() -> polars.datatypes.Int64()

• polars.datatypes.Categorical() -> polars.datatypes.UInt32()

• List(inner) -> List(physical of inner)

• Array(inner) -> Struct(physical of inner)

• Struct(fields) -> Array(physical of fields)

Other data types will be left unchanged.

Warning

The physical representations are an implementation detail and not guaranteed to be stable.

Examples

Replicating the pandas pd.factorize function.

>>> pl.DataFrame({"vals": ["a", "x", None, "a"]}).with_columns(
...     pl.col("vals").cast(pl.Categorical),
...     pl.col("vals")
...     .cast(pl.Categorical)
...     .to_physical()
...     .alias("vals_physical"),
... )
shape: (4, 2)
┌──────┬───────────────┐
│ vals ┆ vals_physical │
│ ---  ┆ ---           │
│ cat  ┆ u32           │
╞══════╪═══════════════╡
│ a    ┆ 0             │
│ x    ┆ 1             │
│ null ┆ null          │
│ a    ┆ 0             │
└──────┴───────────────┘