def median(a, axis=None, out=None, overwrite_input=False, keepdims=False):
r, k = _ureduce(a, func=_median, axis=axis, out=out,<EOL>overwrite_input=overwrite_input)<EOL>if keepdims:<EOL><INDENT>return r.reshape(k)<EOL><DEDENT>else:<EOL><INDENT>return r<EOL><DEDENT>
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 or sequence of int, optional
    Axis along which the medians are computed. The default (axis=None)
    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. Note
   that, if `overwrite_input` is True and the input 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 `arr`.

    .. versionadded:: 1.9.0


Returns
-------
median : ndarray
    A new array holding the result (unless `out` is specified, in which
    case that array is returned instead).  If the input contains
    integers, or floats of smaller precision than 64, then the output
    data-type is float64.  Otherwise, the output data-type is the same
    as that of the input.

See Also
--------
mean, percentile

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.  When N is even, it is the average of the two middle values of
``V_sorted``.

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)