numpy.ma

Extended Summary

Arrays sometimes contain invalid or missing data. When doing operations on such arrays, we wish to suppress invalid values, which is the purpose masked arrays fulfill (an example of typical use is given below).

For example, examine the following array:

>>> x = np.array([2, 1, 3, np.nan, 5, 2, 3, np.nan])

When we try to calculate the mean of the data, the result is undetermined:

>>> np.mean(x)
nan

The mean is calculated using roughly np.sum(x)/len(x), but since any number added to NaN ^[1] produces NaN, this doesn't work. Enter masked arrays:

>>> m = np.ma.masked_array(x, np.isnan(x))
>>> m
masked_array(data=[2.0, 1.0, 3.0, --, 5.0, 2.0, 3.0, --],
             mask=[False, False, False, True, False, False, False, True],
      fill_value=1e+20)

Here, we construct a masked array that suppress all NaN values. We may now proceed to calculate the mean of the other values:

>>> np.mean(m)
2.6666666666666665