numpy.ma.average

numpy.ma.average(a, axis=None, weights=None, returned=False)

Return the weighted average of array over the given axis.

Parameters:

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.

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.

Returns:

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.

Examples

>>> 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]