numpy.ma.average¶

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

Return the weighted average of array over the given axis.

Parameters

aarray_like

Data to be averaged. Masked entries are not taken into account in the computation.

axisint, optional

Axis along which to average a. If None, averaging is done over the flattened array.

weightsarray_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. The 1-D calculation is:

avg = sum(a * weights) / sum(weights)

The only constraint on weights is that sum(weights) must not be 0.

returnedbool, 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 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)
>>> x
masked_array(
  data=[[0., 1.],
        [2., 3.],
        [4., 5.]],
  mask=False,
  fill_value=1e+20)
>>> avg, sumweights = np.ma.average(x, axis=0, weights=[1, 2, 3],
...                                 returned=True)
>>> avg
masked_array(data=[2.6666666666666665, 3.6666666666666665],
             mask=[False, False],
       fill_value=1e+20)

numpy.ma.anomalies

numpy.ma.conjugate