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 : None or int or tuple of ints, optional 
Axis or axes along which to average a . The default, axis=None, will average over all of the elements of the input array. If axis is negative it counts from the last to the first axis. If axis is a tuple of ints, averaging is performed on all of the axes specified in the tuple instead of a single axis or all the axes as before. 
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 1D (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: 

retval, [sum_of_weights] : array_type or 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. sum_of_weights is of the same type as retval . The result dtype follows a genereal pattern. If weights is None, the result dtype will be that of a , or float64 if a is integral. Otherwise, if weights is not None and a is non integral, the result type will be the type of lowest precision capable of representing values of both a and weights . If a happens to be integral, the previous rules still applies but the result dtype will at least be float64 . 
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

numpy.result_type
 Returns the type that results from applying the numpy type promotion rules to the arguments.
Examples
>>> data = list(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.
>>> a = np.ones(5, dtype=np.float128)
>>> w = np.ones(5, dtype=np.complex64)
>>> avg = np.average(a, weights=w)
>>> print(avg.dtype)
complex256