polars.Series.cut#

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

Bin continuous values into discrete categories.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

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:

Series: Series of data type Categorical if include_breaks is set to False (default), otherwise a Series of data type Struct.

See also

qcut

Examples

Divide the column into three categories.

>>> s = pl.Series("foo", [-2, -1, 0, 1, 2])
>>> s.cut([-1, 1], labels=["a", "b", "c"])
shape: (5,)
Series: 'foo' [cat]
[
        "a"
        "a"
        "b"
        "b"
        "c"
]

Create a DataFrame with the breakpoint and category for each value.

>>> cut = s.cut([-1, 1], include_breaks=True).alias("cut")
>>> s.to_frame().with_columns(cut).unnest("cut")
shape: (5, 3)
┌─────┬────────────┬────────────┐
│ foo ┆ breakpoint ┆ category   │
│ --- ┆ ---        ┆ ---        │
│ 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]   │
└─────┴────────────┴────────────┘