- numpy.average(a, axis=None, weights=None, returned=False)[source]¶
Compute the weighted average along the specified axis.
a : array_like
Array containing data to be averaged. If a is not an array, a conversion is attempted.
axis : int, optional
Axis along which to average a. If None, averaging is done over the flattened array.
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.
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.
When all weights along axis are zero. See numpy.ma.average for a version robust to this type of error.
When the length of 1D weights is not the same as the shape of a along axis.
- average for masked arrays – useful if your data contains “missing” values
>>> 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.