Document

median(a, axis=None, out=None, overwrite_input=False, keepdims=False)

Returns the median of the array elements.

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.

Parameters

a : array_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 is to compute the median along a flattened version of the array. A sequence of axes is supported since version 1.9.0.
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: 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 :None:None:`overwrite_input` is True and a is not already an ndarray , an error will be raised.
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 :None:None:`arr`.

versionadded

Returns

median : ndarray: A new array holding the result. If the input contains integers or floats smaller than float64 , then the output data-type is np.float64 . Otherwise, the data-type of the output is the same as that of the input. If :None:None:`out` is specified, that array is returned instead.

Compute the median along the specified axis.

Examples

>>> a = np.array([[10, 7, 4], [3, 2, 1]])
... a
array([[10,  7,  4],
       [ 3,  2,  1]])

>>> np.median(a)
3.5

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

>>> np.median(a, axis=1)
array([7.,  2.])

>>> 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)
3.5

>>> assert not np.all(a==b)

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.quantile dask.array.reductions.median dask.array.reductions.nanmedian numpy.nanpercentile numpy.nanmedian numpy.percentile numpy.median

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them