dpnp.median

dpnp.median(a, axis=None, out=None, overwrite_input=False, keepdims=False)[source]

Compute the median along the specified axis.

For full documentation refer to numpy.median.

Parameters:

a ({dpnp.ndarray, usm_ndarray}) -- Input array.
axis ({None, int, tuple or list of ints}, optional) -- Axis or axes along which the medians are computed. The default, axis=None, will compute the median along a flattened version of the array. If a sequence of axes, the array is first flattened along the given axes, then the median is computed along the resulting flattened axis. Default: None.
out ({None, dpnp.ndarray, usm_ndarray}, optional) -- Alternative output array in which to place the result. It must have the same shape as the expected output but the type (of the calculated values) will be cast if necessary. Default: None.
overwrite_input (bool, optional) -- If True, then allow use of memory of input array a for calculations. The input array will be modified by the call to dpnp.median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default: False.
keepdims (bool, optional) -- If True, the reduced axes (dimensions) are included in the result as singleton dimensions, so that the returned array remains compatible with the input array according to Array Broadcasting rules. Otherwise, if False, the reduced axes are not included in the returned array. Default: False.

Returns:

out -- A new array holding the result. If a has a floating-point data type, the returned array will have the same data type as a. If a has a boolean or integral data type, the returned array will have the default floating point data type for the device where input array a is allocated.

Return type:

dpnp.ndarray

See also

dpnp.mean: Compute the arithmetic mean along the specified axis.
dpnp.percentile: Compute the q-th percentile of the data along the specified axis.

Notes

Given a vector V of length N, the median of V is the middle value of a sorted copy of V, V_sorted - i.e., V_sorted[(N-1)/2], when N is odd, and the average of the two middle values of V_sorted when N is even.

Examples

>>> import dpnp as np
>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10,  7,  4],
       [ 3,  2,  1]])
>>> np.median(a)
array(3.5)

>>> np.median(a, axis=0)
array([6.5, 4.5, 2.5])
>>> np.median(a, axis=1)
array([7., 2.])
>>> np.median(a, axis=(0, 1))
array(3.5)

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

>>> b = a.copy()
>>> np.median(b, axis=1, overwrite_input=True)
array([7., 2.])
>>> assert not np.all(a==b)
>>> b = a.copy()
>>> np.median(b, axis=None, overwrite_input=True)
array(3.5)
>>> assert not np.all(a==b)