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# numpy.nanmedian

`numpy.``nanmedian`(a, axis=None, out=None, overwrite_input=False, keepdims=False)

  Compute the median along the specified axis, while ignoring NaNs.

  Returns the median of the array elements.

  New in version 1.9.0.

  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 (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.
  Returns: | median : ndarray A new array holding the result. 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`, `median`, `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([[10.0, 7, 4], [3, 2, 1]])
      >>> a[0, 1] = np.nan
      >>> a
      array([[ 10.,  nan,   4.],
         [  3.,   2.,   1.]])
      >>> np.median(a)
      nan
      >>> np.nanmedian(a)
      3.0
      >>> np.nanmedian(a, axis=0)
      array([ 6.5,  2.,  2.5])
      >>> np.median(a, axis=1)
      array([ 7.,  2.])
      >>> b = a.copy()
      >>> np.nanmedian(b, axis=1, overwrite_input=True)
      array([ 7.,  2.])
      >>> assert not np.all(a==b)
      >>> b = a.copy()
      >>> np.nanmedian(b, axis=None, overwrite_input=True)
      3.0
      >>> assert not np.all(a==b)