polars.arange¶
- polars.arange(low: Union[int, polars.internals.expr.Expr, polars.internals.series.Series], high: Union[int, polars.internals.expr.Expr, polars.internals.series.Series], step: int = 1, *, eager: Literal[False]) polars.internals.expr.Expr¶
- polars.arange(low: Union[int, polars.internals.expr.Expr, polars.internals.series.Series], high: Union[int, polars.internals.expr.Expr, polars.internals.series.Series], step: int = 1, *, eager: Literal[True]) polars.internals.series.Series
- polars.arange(low: Union[int, polars.internals.expr.Expr, polars.internals.series.Series], high: Union[int, polars.internals.expr.Expr, polars.internals.series.Series], step: int = 1, *, eager: bool = 'False') Union[polars.internals.expr.Expr, polars.internals.series.Series]
Create a range expression. This can be used in a select, with_column etc. Be sure that the range size is equal to the DataFrame you are collecting.
- Parameters
- low
Lower bound of range.
- high
Upper bound of range.
- step
Step size of the range
- eager
If eager evaluation is True, a Series is returned instead of an Expr
Examples
>>> df.lazy().filter(pl.col("foo") < pl.arange(0, 100)).collect()