Method quantile

xarray/core/dataset.py:8184–8361

Compute the qth quantile of the data along the specified dimension. Returns the qth quantiles(s) of the array elements for each variable in the Dataset. Parameters ---------- q : float or array-like of float Quantile to compute, which must be bet

(
        self,
        q: ArrayLike,
        dim: Dims = None,
        *,
        method: QuantileMethods = "linear",
        numeric_only: bool = False,
        keep_attrs: bool | None = None,
        skipna: bool | None = None,
        interpolation: QuantileMethods | None = None,
    )

        return aligned_self.isel(indices)

    def quantile(
        self,
        q: ArrayLike,
        dim: Dims = None,
        *,
        method: QuantileMethods = "linear",
        numeric_only: bool = False,
        keep_attrs: bool | None = None,
        skipna: bool | None = None,
        interpolation: QuantileMethods | None = None,
    ) -> Self:
        """Compute the qth quantile of the data along the specified dimension.

        Returns the qth quantiles(s) of the array elements for each variable
        in the Dataset.

        Parameters
        ----------
        q : float or array-like of float
            Quantile to compute, which must be between 0 and 1 inclusive.
        dim : str or Iterable of Hashable, optional
            Dimension(s) over which to apply quantile.
        method : str, default: "linear"
            This optional parameter specifies the interpolation method to use when the
            desired quantile lies between two data points. The options sorted by their R
            type as summarized in the H&F paper [1]_ are:

                1. "inverted_cdf"
                2. "averaged_inverted_cdf"
                3. "closest_observation"
                4. "interpolated_inverted_cdf"
                5. "hazen"
                6. "weibull"
                7. "linear"  (default)
                8. "median_unbiased"
                9. "normal_unbiased"

            The first three methods are discontiuous.  The following discontinuous
            variations of the default "linear" (7.) option are also available:

                * "lower"
                * "higher"
                * "midpoint"
                * "nearest"

            See :py:func:`numpy.quantile` or [1]_ for details. The "method" argument
            was previously called "interpolation", renamed in accordance with numpy
            version 1.22.0.

        keep_attrs : bool, optional
            If True, the dataset's attributes (`attrs`) will be copied from
            the original object to the new one.  If False (default), the new
            object will be returned without attributes.
        numeric_only : bool, optional
            If True, only apply ``func`` to variables with a numeric dtype.
        skipna : bool, optional
            If True, skip missing values (as marked by NaN). By default, only
            skips missing values for float dtypes; other dtypes either do not