mxnet.np.percentile

percentile(a, q, axis=None, out=None, overwrite_input=None, interpolation='linear', keepdims=False)

Compute the q-th percentile of the data along the specified axis. Returns the q-th percentile(s) of the array elements.

Parameters

  • a (array_like) – Input array

  • q (array_like) – Percentile or sequence of percentiles to compute.

  • 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.

  • out (ndarray, optional) – 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 (Not supported yet)) – 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'}) – 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 – Output array.

Return type

scalar or ndarray

Examples

>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10,  7,  4],
    [ 3,  2,  1]])
>>> np.percentile(a, np.array(50))
array(3.5)
>>> np.percentile(a, np.array(50), axis=0)
array([6.5, 4.5, 2.5])
>>> np.percentile(a, np.array(50), axis=1)
array([7.,  2.])
>>> np.percentile(a, np.array(50), axis=1, keepdims=True)
array([[7.],
    [2.]])

>>> m = np.percentile(a, np.array(50), axis=0)
>>> out = np.zeros_like(m)
>>> np.percentile(a, np.array(50), axis=0, out=out)
array([6.5, 4.5, 2.5])
>>> m
array([6.5, 4.5, 2.5])