polars.int_range#

polars.int_range( start: int | IntoExprColumn = 0, end: int | IntoExprColumn | None = None, step: int = 1, *, dtype: PolarsIntegerType = Int64, eager: bool = False, ) → Expr | Series[source]#

Generate a range of integers.

Parameters:

start: Start of the range (inclusive). Defaults to 0.
end: End of the range (exclusive). If set to None (default), the value of start is used and start is set to 0.
step: Step size of the range.
dtype: Data type of the range.
eager: Evaluate immediately and return a Series. If set to False (default), return an expression instead.

Returns:

Expr or Series: Column of integer data type dtype.

See also

int_ranges: Generate a range of integers for each row of the input columns.

Examples

>>> pl.int_range(0, 3, eager=True)
shape: (3,)
Series: 'literal' [i64]
[
        0
        1
        2
]

end can be omitted for a shorter syntax.

>>> pl.int_range(3, eager=True)
shape: (3,)
Series: 'literal' [i64]
[
        0
        1
        2
]

Generate an index column by using int_range in conjunction with len().

>>> df = pl.DataFrame({"a": [1, 3, 5], "b": [2, 4, 6]})
>>> df.select(
...     pl.int_range(pl.len(), dtype=pl.UInt32).alias("index"),
...     pl.all(),
... )
shape: (3, 3)
┌───────┬─────┬─────┐
│ index ┆ a   ┆ b   │
│ ---   ┆ --- ┆ --- │
│ u32   ┆ i64 ┆ i64 │
╞═══════╪═════╪═════╡
│ 0     ┆ 1   ┆ 2   │
│ 1     ┆ 3   ┆ 4   │
│ 2     ┆ 5   ┆ 6   │
└───────┴─────┴─────┘