jax.numpy.quantile(a, q, axis=None, out=None, overwrite_input=False, method='linear', keepdims=False, *, interpolation=Deprecated)[source]#

Compute the q-th quantile of the data along the specified axis.

LAX-backend implementation of numpy.quantile().

Original docstring below.

Added in version 1.15.0.

  • a (array_like of real numbers) – Input array or object that can be converted to an array.

  • q (array_like of float) – Probability or sequence of probabilities for the quantiles to compute. Values must be between 0 and 1 inclusive.

  • axis ({int, tuple of int, None}, optional) – Axis or axes along which the quantiles are computed. The default is to compute the quantile(s) along a flattened version of the array.

  • method (str, optional) –

    This parameter specifies the method to use for estimating the quantile. There are many different methods, some unique to NumPy. See the notes for explanation. 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 discontinuous. NumPy further defines the following discontinuous variations of the default ‘linear’ (7.) option:

    • ’lower’

    • ’higher’,

    • ’midpoint’

    • ’nearest’

    Changed in version 1.22.0: This argument was previously called “interpolation” and only offered the “linear” default and last four options.

  • keepdims (bool, optional) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original array a.

  • interpolation (str, optional) –

    Deprecated name for the method keyword argument.

    Deprecated since version 1.22.0.

  • out (None)

  • overwrite_input (bool)


quantile – If q is a single probability and axis=None, then the result is a scalar. If multiple probabilies levels are given, first axis of the result corresponds to the quantiles. The other axes are the axes that remain after the reduction of a. If the input contains integers or floats smaller than float64, the output data-type is float64. Otherwise, the output data-type is the same as that of the input. If out is specified, that array is returned instead.

Return type:

scalar or ndarray