jax.numpy.nanpercentileΒΆ
-
jax.numpy.
nanpercentile
(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False)[source]ΒΆ Compute the qth percentile of the data along the specified axis, while ignoring nan values.
LAX-backend implementation of
nanpercentile()
. Original docstring below.Returns the qth percentile(s) of the array elements.
New in version 1.9.0.
- Parameters
a (array_like) β Input array or object that can be converted to an array, containing nan values to be ignored.
q (array_like of float) β Percentile or sequence of percentiles to compute, which must be between 0 and 100 inclusive.
axis ({int, tuple of int, None}, optional) β Axis or axes along which the percentiles are computed. The default is to compute the percentile(s) along a flattened version of the array.
out (ndarray, optional) β Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary.
overwrite_input (bool, optional) β If True, then allow the input array a to be modified by intermediate calculations, to save memory. In this case, the contents of the input a after this function completes is undefined.
interpolation ({'linear', 'lower', 'higher', 'midpoint', 'nearest'}) β This optional parameter specifies the interpolation method to use when the desired percentile lies between two data points
i < j
:keepdims (bool, optional) β 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 β 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 isfloat64
. 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
See also
nanmedian()
equivalent to
nanpercentile(..., 50)
percentile()
,median()
,mean()
nanquantile()
equivalent to nanpercentile, but with q in the range [0, 1].
Notes
Given a vector
V
of lengthN
, theq
-th percentile ofV
is the valueq/100
of the way from the minimum to the maximum in a sorted copy ofV
. The values and distances of the two nearest neighbors as well as the interpolation parameter will determine the percentile if the normalized ranking does not match the location ofq
exactly. This function is the same as the median ifq=50
, the same as the minimum ifq=0
and the same as the maximum ifq=100
.Examples
>>> a = np.array([[10., 7., 4.], [3., 2., 1.]]) >>> a[0][1] = np.nan >>> a array([[10., nan, 4.], [ 3., 2., 1.]]) >>> np.percentile(a, 50) nan >>> np.nanpercentile(a, 50) 3.0 >>> np.nanpercentile(a, 50, axis=0) array([6.5, 2. , 2.5]) >>> np.nanpercentile(a, 50, axis=1, keepdims=True) array([[7.], [2.]]) >>> m = np.nanpercentile(a, 50, axis=0) >>> out = np.zeros_like(m) >>> np.nanpercentile(a, 50, axis=0, out=out) array([6.5, 2. , 2.5]) >>> m array([6.5, 2. , 2.5])
>>> b = a.copy() >>> np.nanpercentile(b, 50, axis=1, overwrite_input=True) array([7., 2.]) >>> assert not np.all(a==b)