polars.Expr.qcut#

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

Bin continuous values into discrete categories based on their quantiles.

Warning

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

Parameters:

quantiles: Either a list of quantile probabilities between 0 and 1 or a positive integer determining the number of bins with uniform probability.
labels: Names of the categories. The number of labels must be equal to the number of categories.
left_closed: Set the intervals to be left-closed instead of right-closed.
allow_duplicates: If set to True, duplicates in the resulting quantiles are dropped, rather than raising a DuplicateError. This can happen even with unique probabilities, depending on the data.
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

cut

Examples

Divide a column into three categories according to pre-defined quantile probabilities.

>>> df = pl.DataFrame({"foo": [-2, -1, 0, 1, 2]})
>>> df.with_columns(
...     pl.col("foo").qcut([0.25, 0.75], labels=["a", "b", "c"]).alias("qcut")
... )
shape: (5, 2)
┌─────┬──────┐
│ foo ┆ qcut │
│ --- ┆ ---  │
│ i64 ┆ cat  │
╞═════╪══════╡
│ -2  ┆ a    │
│ -1  ┆ a    │
│ 0   ┆ b    │
│ 1   ┆ b    │
│ 2   ┆ c    │
└─────┴──────┘

Divide a column into two categories using uniform quantile probabilities.

>>> df.with_columns(
...     pl.col("foo")
...     .qcut(2, labels=["low", "high"], left_closed=True)
...     .alias("qcut")
... )
shape: (5, 2)
┌─────┬──────┐
│ foo ┆ qcut │
│ --- ┆ ---  │
│ i64 ┆ cat  │
╞═════╪══════╡
│ -2  ┆ low  │
│ -1  ┆ low  │
│ 0   ┆ high │
│ 1   ┆ high │
│ 2   ┆ high │
└─────┴──────┘

Add both the category and the breakpoint.

>>> df.with_columns(
...     pl.col("foo").qcut([0.25, 0.75], include_breaks=True).alias("qcut")
... ).unnest("qcut")
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]   │
└─────┴──────┴────────────┘