IndexVariable.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.

  • 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.

  • 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. The “method” argument was previously called “interpolation”, renamed in accordance with numpy version 1.22.0.

  • keep_attrs (bool, optional) – If True, the variable’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).


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 and a quantile dimension is added to the return array. The other dimensions are the dimensions that remain after the reduction of the array.