numpy.ma.apply_along_axis

ma.apply_along_axis(func1d, axis, arr, *args, **kwargs)

Apply a function to 1-D slices of a masked array along an axis.

This function is the equivalent of numpy.apply_along_axis that returns a masked array. See numpy.apply_along_axis for the full documentation.

See also

numpy.apply_along_axis

Equivalent function for ndarrays.

Examples

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>>> import numpy as np

Sum each column, skipping masked values:

>>> a = np.ma.array([[1, 2, 3],
...                  [4, 5, 6]], mask=[[0, 1, 0],
...                                    [0, 0, 1]])
>>> np.ma.apply_along_axis(np.ma.sum, 0, a)
masked_array(data=[5, 5, 3],
             mask=False,
       fill_value=999999)

Compute the mean of each row, ignoring masked values:

>>> np.ma.apply_along_axis(np.ma.mean, 1, a)
masked_array(data=[2. , 4.5],
             mask=False,
       fill_value=1e+20)

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