numpy.median#

numpy.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:
aarray_like

Input array or object that can be converted to an array.

axis{int, sequence of int, None}, optional

Axis or axes along which the medians are computed. The default,axis=None, will compute the median along a flattened version ofthe array. If a sequence of axes, the array is first flattenedalong the given axes, then the median is computed along theresulting flattened axis.

outndarray, optional

Alternative output array in which to place the result. It musthave 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 arraya 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. Ifoverwrite_input isTrue anda is not already anndarray, an error will be raised.

keepdimsbool, 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 originalarr.

Returns:
medianndarray

A new array holding the result. If the input contains integersor floats smaller thanfloat64, then the output data-type isnp.float64. Otherwise, the data-type of the output is thesame as that of the input. Ifout is specified, that array isreturned instead.

Notes

Given a vectorV of lengthN, the median ofV is themiddle value of a sorted copy ofV,V_sorted - ie.,V_sorted[(N-1)/2], whenN is odd, and the average of thetwo middle values ofV_sorted whenN is even.

Examples

>>>importnumpyasnp>>>a=np.array([[10,7,4],[3,2,1]])>>>aarray([[10,  7,  4],       [ 3,  2,  1]])>>>np.median(a)np.float64(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))np.float64(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])>>>marray([6.5,  4.5,  2.5])>>>b=a.copy()>>>np.median(b,axis=1,overwrite_input=True)array([7.,  2.])>>>assertnotnp.all(a==b)>>>b=a.copy()>>>np.median(b,axis=None,overwrite_input=True)np.float64(3.5)>>>assertnotnp.all(a==b)
On this page