- nlcpy.median(a, axis=None, out=None, overwrite_input=False, keepdims=False)
Computes the median along the specified axis.
Returns the median of the array elements.
Input array or scalar that can be converted to an array.
- axisint, None, optional
Axis along which the medians are computed. The default is to compute the median along a flattened version of the array.
- outndarray, 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_inputbool, optional
If True, then allow use of memory of input array a for calculations. The input array will be modified by the call to 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 is False. If overwrite_input is True and a is not already a
ndarray, an error will be raised.
- keepdimsbool, optional
If this is set to True, the axis 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 new array holding the result. If the input contains integers or floats smaller than
float64, then the output data-type is
nlcpy.float64. Otherwise, the data-type of the output is the same as that of the input. If out is specified, that array is returned instead.
Computes the arithmetic mean along the specified axis.
Given a vector
N, the median of V is the middle value of a sorted copy of
V, V_sorted- i.e.,
Nis odd, and the average of the two middle values of
If axis is neither a scalar nor None : NotImplementedError occurs.
For complex numbers, NotImplementedError occurs.
>>> import nlcpy as vp >>> a = vp.array([[10, 7, 4], [3, 2, 1]]) >>> a array([[10, 7, 4], [ 3, 2, 1]]) >>> vp.median(a) array(3.5) >>> vp.median(a, axis=0) array([6.5, 4.5, 2.5]) >>> vp.median(a, axis=1) array([7., 2.]) >>> m = vp.median(a, axis=0) >>> out = vp.zeros_like(m) >>> vp.median(a, axis=0, out=m) array([6.5, 4.5, 2.5]) >>> m array([6.5, 4.5, 2.5]) >>> b = a.copy() >>> vp.median(b, axis=1, overwrite_input=True) array([7., 2.]) >>> assert not vp.all(a==b) >>> b = a.copy() >>> vp.median(b, axis=None, overwrite_input=True) array(3.5) >>> assert not vp.all(a==b)