numpy.average

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

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. New in version 1.7.0. 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 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 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. 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.