xarray.DataArray.quantile#

DataArray.quantile(q, dim=None, method='linear', keep_attrs=None, skipna=None, interpolation=None)[source]#

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.

  • 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 numpy.quantile() or 1 for details. Methods marked with an asterix require numpy version 1.22 or newer. 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.

  • 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 have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).

Returns

quantiles (DataArray) – 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.

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

References

1(1,2)

R. J. Hyndman and Y. Fan, “Sample quantiles in statistical packages,” The American Statistician, 50(4), pp. 361-365, 1996