- numpy.ma.average(a, axis=None, weights=None, returned=False)¶
Return the weighted average of array over the given axis.
a : array_like
Data to be averaged. Masked entries are not taken into account in the computation.
axis : int, optional
Axis along which the variance is computed. The default is to compute the variance of the flattened array.
weights : array_like, optional
The importance that each element has in the computation of the average. 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. If weights is complex, the imaginary parts are ignored.
returned : bool, optional
Flag indicating whether a tuple (result, sum of weights) should be returned as output (True), or just the result (False). Default is False.
average, [sum_of_weights] : (tuple of) scalar or MaskedArray
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 np.float64 if a is of integer type, otherwise it is of the same type as a. If returned, sum_of_weights is of the same type as average.
>>> a = np.ma.array([1., 2., 3., 4.], mask=[False, False, True, True]) >>> np.ma.average(a, weights=[3, 1, 0, 0]) 1.25
>>> x = np.ma.arange(6.).reshape(3, 2) >>> print x [[ 0. 1.] [ 2. 3.] [ 4. 5.]] >>> avg, sumweights = np.ma.average(x, axis=0, weights=[1, 2, 3], ... returned=True) >>> print avg [2.66666666667 3.66666666667]