polars.Expr.is_between#

Check if this expression is between the given start and end values.

Parameters:

start: Lower bound value (can be an expression or literal).
end: Upper bound value (can be an expression or literal).
closed{‘both’, ‘left’, ‘right’, ‘none’}: Define which sides of the interval are closed (inclusive).

Returns:

Expr that evaluates to a Boolean Series.

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      │
└─────┴────────────┘