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: |
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 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 New in version 1.9.0. |
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Returns: |
median : ndarray A new array holding the result. If the input contains integers or floats smaller than |
See also
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.
>>> 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)
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https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.median.html