xarray.core.resample.DatasetResample.quantile

xarray.core.resample.DatasetResample.quantile

DatasetResample.quantile(q, dim=None, interpolation='linear', keep_attrs=None, skipna=True)[source]

Compute the qth quantile over each array in the groups and concatenate them together into a new array.

Parameters
  • q (float or sequence of float) – Quantile to compute, which must be between 0 and 1 inclusive.

  • dim (..., str or sequence of str, optional) – Dimension(s) over which to apply quantile. Defaults to the grouped dimension.

  • interpolation ({"linear", "lower", "higher", "midpoint", "nearest"}, default: "linear") – This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points i < j:

    • linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.

    • lower: i.

    • higher: j.

    • nearest: i or j, whichever is nearest.

    • midpoint: (i + j) / 2.

  • skipna (bool, optional) – Whether to skip missing values when aggregating.

Returns

quantiles (Variable) – If q is a single quantile, then the result is a scalar. If multiple percentiles are given, first axis of the result corresponds to the quantile. In either case a quantile dimension is added to the return array. The other dimensions are the dimensions that remain after the reduction of the array.

See also

numpy.nanquantile, numpy.quantile, pandas.Series.quantile, Dataset.quantile, DataArray.quantile

Examples

>>> da = xr.DataArray(
...     [[1.3, 8.4, 0.7, 6.9], [0.7, 4.2, 9.4, 1.5], [6.5, 7.3, 2.6, 1.9]],
...     coords={"x": [0, 0, 1], "y": [1, 1, 2, 2]},
...     dims=("x", "y"),
... )
>>> ds = xr.Dataset({"a": da})
>>> da.groupby("x").quantile(0)
<xarray.DataArray (x: 2, y: 4)>
array([[0.7, 4.2, 0.7, 1.5],
       [6.5, 7.3, 2.6, 1.9]])
Coordinates:
  * y         (y) int64 1 1 2 2
    quantile  float64 0.0
  * x         (x) int64 0 1
>>> ds.groupby("y").quantile(0, dim=...)
<xarray.Dataset>
Dimensions:   (y: 2)
Coordinates:
    quantile  float64 0.0
  * y         (y) int64 1 2
Data variables:
    a         (y) float64 0.7 0.7
>>> da.groupby("x").quantile([0, 0.5, 1])
<xarray.DataArray (x: 2, y: 4, quantile: 3)>
array([[[0.7 , 1.  , 1.3 ],
        [4.2 , 6.3 , 8.4 ],
        [0.7 , 5.05, 9.4 ],
        [1.5 , 4.2 , 6.9 ]],

       [[6.5 , 6.5 , 6.5 ],
        [7.3 , 7.3 , 7.3 ],
        [2.6 , 2.6 , 2.6 ],
        [1.9 , 1.9 , 1.9 ]]])
Coordinates:
  * y         (y) int64 1 1 2 2
  * quantile  (quantile) float64 0.0 0.5 1.0
  * x         (x) int64 0 1
>>> ds.groupby("y").quantile([0, 0.5, 1], dim=...)
<xarray.Dataset>
Dimensions:   (y: 2, quantile: 3)
Coordinates:
  * quantile  (quantile) float64 0.0 0.5 1.0
  * y         (y) int64 1 2
Data variables:
    a         (y, quantile) float64 0.7 5.35 8.4 0.7 2.25 9.4