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