polars.LazyFrame.cast#
- LazyFrame.cast(
- dtypes: Mapping[ColumnNameOrSelector | PolarsDataType, PolarsDataType | PythonDataType] | PolarsDataType,
- *,
- strict: bool = True,
Cast LazyFrame column(s) to the specified dtype(s).
- Parameters:
- dtypes
Mapping of column names (or selector) to dtypes, or a single dtype to which all columns will be cast.
- strict
Throw an error if a cast could not be done (for instance, due to an overflow).
Examples
>>> from datetime import date >>> lf = pl.LazyFrame( ... { ... "foo": [1, 2, 3], ... "bar": [6.0, 7.0, 8.0], ... "ham": [date(2020, 1, 2), date(2021, 3, 4), date(2022, 5, 6)], ... } ... )
Cast specific frame columns to the specified dtypes:
>>> lf.cast({"foo": pl.Float32, "bar": pl.UInt8}).collect() shape: (3, 3) ┌─────┬─────┬────────────┐ │ foo ┆ bar ┆ ham │ │ --- ┆ --- ┆ --- │ │ f32 ┆ u8 ┆ date │ ╞═════╪═════╪════════════╡ │ 1.0 ┆ 6 ┆ 2020-01-02 │ │ 2.0 ┆ 7 ┆ 2021-03-04 │ │ 3.0 ┆ 8 ┆ 2022-05-06 │ └─────┴─────┴────────────┘
Cast all frame columns matching one dtype (or dtype group) to another dtype:
>>> lf.cast({pl.Date: pl.Datetime}).collect() shape: (3, 3) ┌─────┬─────┬─────────────────────┐ │ foo ┆ bar ┆ ham │ │ --- ┆ --- ┆ --- │ │ i64 ┆ f64 ┆ datetime[μs] │ ╞═════╪═════╪═════════════════════╡ │ 1 ┆ 6.0 ┆ 2020-01-02 00:00:00 │ │ 2 ┆ 7.0 ┆ 2021-03-04 00:00:00 │ │ 3 ┆ 8.0 ┆ 2022-05-06 00:00:00 │ └─────┴─────┴─────────────────────┘
Use selectors to define the columns being cast:
>>> import polars.selectors as cs >>> lf.cast({cs.numeric(): pl.UInt32, cs.temporal(): pl.String}).collect() shape: (3, 3) ┌─────┬─────┬────────────┐ │ foo ┆ bar ┆ ham │ │ --- ┆ --- ┆ --- │ │ u32 ┆ u32 ┆ str │ ╞═════╪═════╪════════════╡ │ 1 ┆ 6 ┆ 2020-01-02 │ │ 2 ┆ 7 ┆ 2021-03-04 │ │ 3 ┆ 8 ┆ 2022-05-06 │ └─────┴─────┴────────────┘
Cast all frame columns to the specified dtype:
>>> lf.cast(pl.String).collect().to_dict(as_series=False) {'foo': ['1', '2', '3'], 'bar': ['6.0', '7.0', '8.0'], 'ham': ['2020-01-02', '2021-03-04', '2022-05-06']}