polars.Expr.cast#

Expr.cast( dtype: PolarsDataType | type[Any], *, strict: bool = True, wrap_numerical: bool = False, ) → Self[source]#

Cast between data types.

Parameters:

dtype: DataType to cast to.
strict: If True invalid casts generate exceptions instead of nulls.
wrap_numerical: If True numeric casts wrap overflowing values instead of marking the cast as invalid.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3],
...         "b": ["4", "5", "6"],
...     }
... )
>>> df.with_columns(
...     pl.col("a").cast(pl.Float64),
...     pl.col("b").cast(pl.Int32),
... )
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ f64 ┆ i32 │
╞═════╪═════╡
│ 1.0 ┆ 4   │
│ 2.0 ┆ 5   │
│ 3.0 ┆ 6   │
└─────┴─────┘