xarray.core.resample.DatasetResample.quantile#
- DatasetResample.quantile(q, dim=None, *, method='linear', keep_attrs=None, skipna=None, interpolation=None)[source]#
Compute the qth quantile over each array in the groups and concatenate them together into a new array.
- Parameters
q (
float
or sequence offloat
) – Quantile to compute, which must be between 0 and 1 inclusive.dim (
str
orIterable
ofHashable
, optional) – Dimension(s) over which to apply quantile. Defaults to the grouped dimension.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:“inverted_cdf”
“averaged_inverted_cdf”
“closest_observation”
“interpolated_inverted_cdf”
“hazen”
“weibull”
“linear” (default)
“median_unbiased”
“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
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
orNone
, default:None
) – If True, the dataarray’s attributes (attrs) will be copied from the original object to the new one. If False, the new object will be returned without attributes.skipna (
bool
orNone
, default:None
) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).
- 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)> Size: 64B array([[0.7, 4.2, 0.7, 1.5], [6.5, 7.3, 2.6, 1.9]]) Coordinates: * y (y) int64 32B 1 1 2 2 quantile float64 8B 0.0 * x (x) int64 16B 0 1 >>> ds.groupby("y").quantile(0, dim=...) <xarray.Dataset> Size: 40B Dimensions: (y: 2) Coordinates: quantile float64 8B 0.0 * y (y) int64 16B 1 2 Data variables: a (y) float64 16B 0.7 0.7 >>> da.groupby("x").quantile([0, 0.5, 1]) <xarray.DataArray (x: 2, y: 4, quantile: 3)> Size: 192B 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 32B 1 1 2 2 * quantile (quantile) float64 24B 0.0 0.5 1.0 * x (x) int64 16B 0 1 >>> ds.groupby("y").quantile([0, 0.5, 1], dim=...) <xarray.Dataset> Size: 88B Dimensions: (y: 2, quantile: 3) Coordinates: * quantile (quantile) float64 24B 0.0 0.5 1.0 * y (y) int64 16B 1 2 Data variables: a (y, quantile) float64 48B 0.7 5.35 8.4 0.7 2.25 9.4
References