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# numpy.average

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

  Return the weighted average of array over the specified axis.

  Parameters: | a : array_like Data to be averaged. axis : int, optional Axis along which to average a. If None, averaging is done over the entire array irrespective of its shape. weights : array_like, optional The importance that each datum has in the computation of the average. 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. Note that 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

  `ma.average`
    average for masked arrays

  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

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