jax.numpy.average(a, axis=None, weights=None, returned=False)[source]ΒΆ

Compute the weighted average along the specified axis.

LAX-backend implementation of average(). Original docstring below.

  • 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.

  • 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. The 1-D calculation is:

  • 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.


retval, [sum_of_weights] – 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.

Return type

array_type or double

  • 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



average for masked arrays – useful if your data contains β€œmissing” values


Returns the type that results from applying the numpy type promotion rules to the arguments.


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