jax.numpy.percentile¶

jax.numpy.percentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False)[source]¶

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

LAX-backend implementation of percentile().

Original docstring below.

Returns the q-th percentile(s) of the array elements.

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

  • q (array_like of float) – Percentile or sequence of percentiles to compute, which must be between 0 and 100 inclusive.

  • axis ({int, tuple of int, None}, optional) –

    Axis or axes along which the percentiles are computed. The default is to compute the percentile(s) along a flattened version of the array.

    Changed in version 1.9.0: A tuple of axes is supported

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

    This optional parameter specifies the interpolation method to use when the desired percentile 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.

  • 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.

Returns

percentile – If q is a single percentile and axis=None, then the result is a scalar. If multiple percentiles are given, first axis of the result corresponds to the percentiles. 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