polars.Expr.is_between

Expr.is_between(

lower_bound: IntoExpr,

upper_bound: IntoExpr,

closed: ClosedInterval = 'both',

) → Self

Check if this expression is between the given lower and upper bounds.

Parameters:

lower_bound

Lower bound value. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.

upper_bound

Upper bound value. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.

closed{‘both’, ‘left’, ‘right’, ‘none’}

Define which sides of the interval are closed (inclusive).

Returns:

Expr

Expression of data type Boolean.

Notes

If the value of the lower_bound is greater than that of the upper_bound then the result will be False, as no value can satisfy the condition.

Examples

>>> df = pl.DataFrame({"num": [1, 2, 3, 4, 5]})
>>> df.with_columns(pl.col("num").is_between(2, 4).alias("is_between"))
shape: (5, 2)
┌─────┬────────────┐
│ num ┆ is_between │
│ --- ┆ ---        │
│ i64 ┆ bool       │
╞═════╪════════════╡
│ 1   ┆ false      │
│ 2   ┆ true       │
│ 3   ┆ true       │
│ 4   ┆ true       │
│ 5   ┆ false      │
└─────┴────────────┘

Use the closed argument to include or exclude the values at the bounds:

>>> df.with_columns(
...     pl.col("num").is_between(2, 4, closed="left").alias("is_between")
... )
shape: (5, 2)
┌─────┬────────────┐
│ num ┆ is_between │
│ --- ┆ ---        │
│ i64 ┆ bool       │
╞═════╪════════════╡
│ 1   ┆ false      │
│ 2   ┆ true       │
│ 3   ┆ true       │
│ 4   ┆ false      │
│ 5   ┆ false      │
└─────┴────────────┘

You can also use strings as well as numeric/temporal values (note: ensure that string literals are wrapped with lit so as not to conflate them with column names):

>>> df = pl.DataFrame({"a": ["a", "b", "c", "d", "e"]})
>>> df.with_columns(
...     pl.col("a")
...     .is_between(pl.lit("a"), pl.lit("c"), closed="both")
...     .alias("is_between")
... )
shape: (5, 2)
┌─────┬────────────┐
│ a   ┆ is_between │
│ --- ┆ ---        │
│ str ┆ bool       │
╞═════╪════════════╡
│ a   ┆ true       │
│ b   ┆ true       │
│ c   ┆ true       │
│ d   ┆ false      │
│ e   ┆ false      │
└─────┴────────────┘

Use column expressions as lower/upper bounds, comparing to a literal value:

>>> df = pl.DataFrame({"a": [1, 2, 3, 4, 5], "b": [5, 4, 3, 2, 1]})
>>> df.with_columns(
...     pl.lit(3).is_between(pl.col("a"), pl.col("b")).alias("between_ab")
... )
shape: (5, 3)
┌─────┬─────┬────────────┐
│ a   ┆ b   ┆ between_ab │
│ --- ┆ --- ┆ ---        │
│ i64 ┆ i64 ┆ bool       │
╞═════╪═════╪════════════╡
│ 1   ┆ 5   ┆ true       │
│ 2   ┆ 4   ┆ true       │
│ 3   ┆ 3   ┆ true       │
│ 4   ┆ 2   ┆ false      │
│ 5   ┆ 1   ┆ false      │
└─────┴─────┴────────────┘