Compute the quantile of the data along the specified axis, ignoring NaNs.
JAX implementation of numpy.nanquantile().
a (ArrayLike) – N-dimensional array input.
q (ArrayLike) – scalar or 1-dimensional array specifying the desired quantiles. q should contain floating-point values between 0.0 and 1.0.
axis (int | tuple[int, ...] | None) – optional axis or tuple of axes along which to compute the quantile
out (None) – not implemented by JAX; will error if not None
overwrite_input (bool) – not implemented by JAX; will error if not False
method (str) – specify the interpolation method to use. Options are one of ["linear", "lower", "higher", "midpoint", "nearest"]. default is linear.
keepdims (bool) – if True, then the returned array will have the same number of dimensions as the input. Default is False.
interpolation (DeprecatedArg | str) – deprecated alias of the method argument. Will result in a DeprecationWarning if used.
An array containing the specified quantiles along the specified axes.
Examples
Computing the median and quartiles of a 1D array:
>>> x = jnp.array([0, 1, 2, jnp.nan, 3, 4, 5, 6]) >>> q = jnp.array([0.25, 0.5, 0.75])
Because of the NaN value, jax.numpy.quantile() returns all NaNs, while nanquantile() ignores them:
>>> jnp.quantile(x, q) Array([nan, nan, nan], dtype=float32) >>> jnp.nanquantile(x, q) Array([1.5, 3. , 4.5], dtype=float32)
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