polars.Expr.cut#

Expr.cut( breaks: Sequence[float], *, labels: Sequence[str] | None = None, left_closed: bool = False, include_breaks: bool = False, ) → Self[source]#

Bin continuous values into discrete categories.

Parameters:

breaks: List of unique cut points.
labels: Names of the categories. The number of labels must be equal to the number of cut points plus one.
left_closed: Set the intervals to be left-closed instead of right-closed.
include_breaks: Include a column with the right endpoint of the bin each observation falls in. This will change the data type of the output from a Categorical to a Struct.

Returns:

Expr: Expression of data type Categorical if include_breaks is set to False (default), otherwise an expression of data type Struct.

See also

qcut

Examples

Divide a column into three categories.

>>> df = pl.DataFrame({"foo": [-2, -1, 0, 1, 2]})
>>> df.with_columns(
...     pl.col("foo").cut([-1, 1], labels=["a", "b", "c"]).alias("cut")
... )
shape: (5, 2)
┌─────┬─────┐
│ foo ┆ cut │
│ --- ┆ --- │
│ i64 ┆ cat │
╞═════╪═════╡
│ -2  ┆ a   │
│ -1  ┆ a   │
│ 0   ┆ b   │
│ 1   ┆ b   │
│ 2   ┆ c   │
└─────┴─────┘

Add both the category and the breakpoint.

>>> df.with_columns(
...     pl.col("foo").cut([-1, 1], include_breaks=True).alias("cut")
... ).unnest("cut")
shape: (5, 3)
┌─────┬──────┬────────────┐
│ foo ┆ brk  ┆ foo_bin    │
│ --- ┆ ---  ┆ ---        │
│ i64 ┆ f64  ┆ cat        │
╞═════╪══════╪════════════╡
│ -2  ┆ -1.0 ┆ (-inf, -1] │
│ -1  ┆ -1.0 ┆ (-inf, -1] │
│ 0   ┆ 1.0  ┆ (-1, 1]    │
│ 1   ┆ 1.0  ┆ (-1, 1]    │
│ 2   ┆ inf  ┆ (1, inf]   │
└─────┴──────┴────────────┘