polars.Series.qcut

Series.qcut(
    quantiles: Sequence[float] | int,
    *,
    labels: Sequence[str_] | None = None,
    left_closed: bool = False,
    allow_duplicates: bool = False,
    include_breaks: bool = False,
) → Series

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 cut points plus one.

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:

Series

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

See also

cut

Examples

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

>>> s = pl.Series("foo", [-2, -1, 0, 1, 2])
>>> s.qcut([0.25, 0.75], labels=["a", "b", "c"])
shape: (5,)
Series: 'foo' [cat]
[
    "a"
    "a"
    "b"
    "b"
    "c"
]

Divide a column into two categories using uniform quantile probabilities.

>>> s.qcut(2, labels=["low", "high"], left_closed=True)
shape: (5,)
Series: 'foo' [cat]
[
    "low"
    "low"
    "high"
    "high"
    "high"
]

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

>>> cut = s.qcut([0.25, 0.75], 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]   │
└─────┴────────────┴────────────┘