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# numpy.average

`numpy.``average`(a, axis=None, weights=None, returned=False)

  Compute the weighted average along the specified axis.

  Parameters : | a : array_like Array containing data to be averaged. If a is not an array, a conversion is attempted. axis : int, optional Axis along which to average a. If None, averaging is done over the flattened array. weights : array_like, optional An array of weights associated with the values in a. Each value in a contributes to the average according to its associated weight. The weights array can either be 1-D (in which case its length must be the size of a along the given axis) or of the same shape as a. If weights=None, then all data in a are assumed to have a weight equal to one. returned : bool, optional Default is False. If True, the tuple (`average`, sum_of_weights) is returned, otherwise only the average is returned. If weights=None, sum_of_weights is equivalent to the number of elements over which the average is taken.
  Returns : | average, [sum_of_weights] : {array_type, double} Return the average along the specified axis. When returned is True, return a tuple with the average as the first element and the sum of the weights as the second element. The return type is Float if a is of integer type, otherwise it is of the same type as a. sum_of_weights is of the same type as `average`.
  Raises : | ZeroDivisionError When all weights along axis are zero. See `numpy.ma.average` for a version robust to this type of error. TypeError When the length of 1D weights is not the same as the shape of a along axis.

  See also

  `mean`

  `ma.average`
    average for masked arrays – useful if your data contains “missing” values

  Examples

      >>> data = range(1,5)
      >>> data
      [1, 2, 3, 4]
      >>> np.average(data)
      2.5
      >>> np.average(range(1,11), weights=range(10,0,-1))
      4.0

      >>> data = np.arange(6).reshape((3,2))
      >>> data
      array([[0, 1],
             [2, 3],
             [4, 5]])
      >>> np.average(data, axis=1, weights=[1./4, 3./4])
      array([ 0.75,  2.75,  4.75])
      >>> np.average(data, weights=[1./4, 3./4])
      Traceback (most recent call last):
      ...
      TypeError: Axis must be specified when shapes of a and weights differ.