Class BinGrouper

xarray/groupers.py:344–470

Grouper object for binning numeric data. Attributes ---------- bins : int, sequence of scalars, or IntervalIndex The criteria to bin by. * int : Defines the number of equal-width bins in the range of `x`. The range of `x` is extended by .1% on each side t

@dataclass
class BinGrouper(Grouper):
    """
    Grouper object for binning numeric data.

    Attributes
    ----------
    bins : int, sequence of scalars, or IntervalIndex
        The criteria to bin by.

        * int : Defines the number of equal-width bins in the range of `x`. The
          range of `x` is extended by .1% on each side to include the minimum
          and maximum values of `x`.
        * sequence of scalars : Defines the bin edges allowing for non-uniform
          width. No extension of the range of `x` is done.
        * IntervalIndex : Defines the exact bins to be used. Note that
          IntervalIndex for `bins` must be non-overlapping.

    right : bool, default True
        Indicates whether `bins` includes the rightmost edge or not. If
        ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]``
        indicate (1,2], (2,3], (3,4]. This argument is ignored when
        `bins` is an IntervalIndex.
    labels : array or False, default None
        Specifies the labels for the returned bins. Must be the same length as
        the resulting bins. If False, returns only integer indicators of the
        bins. This affects the type of the output container (see below).
        This argument is ignored when `bins` is an IntervalIndex. If True,
        raises an error.
    retbins : bool, default False
        Whether to return the bins or not. Useful when bins is provided
        as a scalar.
    precision : int, default 3
        The precision at which to store and display the bins labels.
    include_lowest : bool, default False
        Whether the first interval should be left-inclusive or not.
    duplicates : {"raise", "drop"}, default: "raise"
        If bin edges are not unique, raise ValueError or drop non-uniques.
    """

    bins: Bins
    # The rest are copied from pandas
    right: bool = True
    labels: Any = None
    precision: int = 3
    include_lowest: bool = False
    duplicates: Literal["raise", "drop"] = "raise"

    def reset(self) -> Self:
        return type(self)(
            bins=self.bins,
            right=self.right,
            labels=self.labels,
            precision=self.precision,
            include_lowest=self.include_lowest,
            duplicates=self.duplicates,
        )

    def __post_init__(self) -> None: