Dataset.quantile(self, q, dim=None, interpolation='linear', numeric_only=False, keep_attrs=None)

Compute the qth quantile of the data along the specified dimension.

Returns the qth quantiles(s) of the array elements for each variable in the Dataset.

  • q (float in range of [0,1] or array-like of floats) – 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.

  • interpolation ({'linear', 'lower', 'higher', 'midpoint', 'nearest'}) –

    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.

  • 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.

  • numeric_only (bool, optional) – If True, only apply func to variables with a numeric dtype.


quantiles – If q is a single quantile, then the result is a scalar for each variable in data_vars. If multiple percentiles are given, first axis of the result corresponds to the quantile and a quantile dimension is added to the return Dataset. The other dimensions are the dimensions that remain after the reduction of the array.

Return type


See also

numpy.nanpercentile(), pandas.Series.quantile(), DataArray.quantile()