# numpy.ma.average¶

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

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 to average a. If None, averaging is done over 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 and floats smaller than float64, or the input data-type, otherwise. If returned, sum_of_weights is always float64.

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]
```

#### Previous topic

numpy.ma.anomalies

#### Next topic

numpy.ma.conjugate