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, break_point_label: str = 'break_point', category_label: str = 'category', as_series: Literal[True] = True, ) → Series[source]#

Bin continuous values into discrete categories based on their quantiles.

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.
break_point_label: Name of the breakpoint column. Only used if include_breaks is set to True.

Deprecated since version 0.19.0: This parameter will be removed. Use Series.struct.rename_fields to rename the field instead.
category_label: Name of the category column. Only used if include_breaks is set to True.

Deprecated since version 0.19.0: This parameter will be removed. Use Series.struct.rename_fields to rename the field instead.
as_series: If set to False, return a DataFrame containing the original values, the breakpoints, and the categories.

Deprecated since version 0.19.0: This parameter will be removed. The same behavior can be achieved by setting include_breaks=True, unnesting the resulting struct Series, and adding the result to the original Series.

Returns:

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

Warning

This functionality is experimental and may change without it being considered a breaking change.

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 ┆ break_point ┆ 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]   │
└─────┴─────────────┴────────────┘