numpy.ma.median¶

numpy.ma.median(a,axis=None,out=None,overwrite_input=False,keepdims=False)[source]¶

Compute the median along the specified axis.

Returns the median of the array elements.

Parameters:

a:array_like Input array or object that can be converted to an array. axis:int, optional Axis along which the medians are computed. The default (None) isto compute the median along a flattened version of the array. out:ndarray, optional Alternative output array in which to place the result. It musthave the same shape and buffer length as the expected outputbut the type will be cast if necessary. overwrite_input:bool, optional If True, then allow use of memory of input array (a) forcalculations. The input array will be modified by the call tomedian. This will save memory when you do not need to preservethe contents of the input array. Treat the input as undefined,but it will probably be fully or partially sorted. Default isFalse. Note that, ifoverwrite_input is True, and the inputis not already anndarray, an error will be raised. keepdims:bool, optional If this is set to True, the axes which are reduced are leftin the result as dimensions with size one. With this option,the result will broadcast correctly against the input array. New in version 1.10.0.

Returns:

median:ndarray A new array holding the result is returned unless out isspecified, in which case a reference to out is returned.Return data-type isfloat64 for integers and floats smaller thanfloat64, or the input data-type, otherwise.

See also

mean

Notes

Given a vectorV withN non masked values, the median ofVis the middle value of a sorted copy ofV (Vs) - i.e.Vs[(N-1)/2], whenN is odd, or{Vs[N/2-1]+Vs[N/2]}/2whenN is even.

Examples

>>>x=np.ma.array(np.arange(8),mask=[0]*4+[1]*4)>>>np.ma.median(x)1.5

>>>x=np.ma.array(np.arange(10).reshape(2,5),mask=[0]*6+[1]*4)>>>np.ma.median(x)2.5>>>np.ma.median(x,axis=-1,overwrite_input=True)masked_array(data = [ 2.  5.],             mask = False,       fill_value = 1e+20)