average(a, axis=None, weights=None, returned=False)

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. The 1-D calculation is:

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

The only constraint on weights is that :None:None:`sum(weights)` must not be 0.

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 :None:None:`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 :None:None:`np.float64` if a is of integer type and floats smaller than float64 , or the input data-type, otherwise. If returned, :None:None:`sum_of_weights` is always float64 .

Return the weighted average of array over the given axis.

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)

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.ma.core.mean dask.array.ma.average dask.array.routines.average numpy.average numpy.ma.core.MaskedArray.mean

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