avg = sum(a * weights) / sum(weights)
The only constraint on weights is that sum(weights) must not be 0.
returnedbool, 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.
keepdimsbool, 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 a.
Note: keepdims will not work with instances of numpy.matrix
or other classes whose methods do not support keepdims.
New in version 1.23.0.
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.
>>> np.average(data)  
>>> np.average(np.arange(1, 11), weights=np.arange(10, 0, -1))  
>>> data = np.arange(6).reshape((3, 2))  
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
With keepdims=True, the following result has shape (3, 1).
>>> np.average(data, axis=1, keepdims=True)  
array([[0.5],
       [2.5],
       [4.5]])