quantile
- UnitsAwareDataArray.quantile(q: Any, dim: Optional[Union[Hashable, Sequence[Hashable]]] = None, interpolation: str = 'linear', keep_attrs: Optional[bool] = None, skipna: bool = True) xarray.core.dataarray.DataArray
Compute the qth quantile of the data along the specified dimension.
Returns the qth quantiles(s) of the array elements.
- Parameters
q (float or array-like of float) – Quantile to compute, which must be between 0 and 1 inclusive.
dim (hashable or sequence of hashable, optional) – Dimension(s) over which to apply quantile.
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
, wherefraction
is the fractional part of the index surrounded byi
andj
.lower:
i
.higher:
j
.nearest:
i
orj
, whichever is nearest.midpoint:
(i + j) / 2
.
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.
skipna (bool, optional) – Whether to skip missing values when aggregating.
- Returns
quantiles – 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 and a quantile dimension is added to the return array. The other dimensions are the dimensions that remain after the reduction of the array.
- Return type
DataArray
See also
numpy.nanquantile
,numpy.quantile
,pandas.Series.quantile
,Dataset.quantile
Examples
>>> da = xr.DataArray( ... data=[[0.7, 4.2, 9.4, 1.5], [6.5, 7.3, 2.6, 1.9]], ... coords={"x": [7, 9], "y": [1, 1.5, 2, 2.5]}, ... dims=("x", "y"), ... ) >>> da.quantile(0) # or da.quantile(0, dim=...) <xarray.DataArray ()> array(0.7) Coordinates: quantile float64 0.0 >>> da.quantile(0, dim="x") <xarray.DataArray (y: 4)> array([0.7, 4.2, 2.6, 1.5]) Coordinates: * y (y) float64 1.0 1.5 2.0 2.5 quantile float64 0.0 >>> da.quantile([0, 0.5, 1]) <xarray.DataArray (quantile: 3)> array([0.7, 3.4, 9.4]) Coordinates: * quantile (quantile) float64 0.0 0.5 1.0 >>> da.quantile([0, 0.5, 1], dim="x") <xarray.DataArray (quantile: 3, y: 4)> array([[0.7 , 4.2 , 2.6 , 1.5 ], [3.6 , 5.75, 6. , 1.7 ], [6.5 , 7.3 , 9.4 , 1.9 ]]) Coordinates: * y (y) float64 1.0 1.5 2.0 2.5 * quantile (quantile) float64 0.0 0.5 1.0