# dask.array.ma.average

dask.array.ma.average(a, axis=None, weights=None, returned=False, keepdims=False)

  Return the weighted average of array over the given axis.

  This docstring was copied from numpy.ma.average.

  Some inconsistencies with the Dask version may exist.

  Parameters:
    a array_like

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

    axis None 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.

    weights array_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.

    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.

    keepdims bool, 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.

      Added 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)