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numpy.ma.average — NumPy v2.3 Manual

ma.average(a, axis=None, weights=None, returned=False, *, keepdims=<no value>)[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.

axisNone or int or tuple of ints, optional

Axis or axes along which to average a. The default, axis=None, will average over all of the elements of the input array. If axis is a tuple of ints, averaging is performed on all of the axes specified in the tuple instead of a single axis or all the axes as before.

weightsarray_like, optional

An array of weights associated with the values in a. Each value in a contributes to the average according to its associated weight. The array of weights must be the same shape as a if no axis is specified, otherwise the weights must have dimensions and shape consistent with a along the specified axis. If weights=None, then all data in a are assumed to have a weight equal to one. The calculation is:

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

where the sum is over all included elements. The only constraint on the values of 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.

keepdimsbool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original a. Note: keepdims will not work with instances of numpy.matrix or other classes whose methods do not support keepdims.

New in version 1.23.0.

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.

Raises:

ZeroDivisionError

When all weights along axis are zero. See numpy.ma.average for a version robust to this type of error.

TypeError

When weights does not have the same shape as a, and axis=None.

ValueError

When weights does not have dimensions and shape consistent with a along specified axis.

Examples

>>> import numpy as np
>>> 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)
>>> data = np.arange(8).reshape((2, 2, 2))
>>> data
array([[[0, 1],
        [2, 3]],
       [[4, 5],
        [6, 7]]])
>>> np.ma.average(data, axis=(0, 1), weights=[[1./4, 3./4], [1., 1./2]])
masked_array(data=[3.4, 4.4],
         mask=[False, False],
   fill_value=1e+20)
>>> np.ma.average(data, axis=0, weights=[[1./4, 3./4], [1., 1./2]])
Traceback (most recent call last):
    ...
ValueError: Shape of weights must be consistent
with shape of a along specified axis.

>>> 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)

With keepdims=True, the following result has shape (3, 1).

>>> np.ma.average(x, axis=1, keepdims=True)
masked_array(
  data=[[0.5],
        [2.5],
        [4.5]],
  mask=False,
  fill_value=1e+20)

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