For masked arrays, isin() and in1d() produce wrong results. In particular, the result is masked at wrong places. The issue was discussed initially on stackoverflow and it seems that the involved sorting does not handle the masked value correctly.
Reproducing code example:
import numpy.ma as ma
a = ma.MaskedArray([[1,2,3],[4,5,6]], [[True,False,False],[False,False,False]])
ta = ma.array([1,4,5])
ma.isin(a, ta)
The output of ma.isin() is wrong (see below). I use the following workaround:
import numpy as np
def ma_isin(array, comparison):
return ma.MaskedArray(
data=np.isin(array, comparison),
mask=array.mask.copy())Output and expected results:
These are the results from the code above
>>> a
masked_array(
data=[[--, 2, 3],
[4, 5, 6]],
mask=[[ True, False, False],
[False, False, False]],
fill_value=999999)
>>> ta
masked_array(data=[1, 4, 5],
mask=False,
fill_value=999999)
>>> ma.isin(a, ta)
masked_array(
data=[[False, False, False],
[True, True, --]],
mask=[[False, False, False],
[False, False, True]],
fill_value=True)The issue is mainly that the last element in the result is masked, and probably also that the first element is not masked. I would expect the following output:
>>> ma_isin(a, ta)
masked_array(
data=[[--, False, False],
[True, True, False]],
mask=[[ True, False, False],
[False, False, False]],
fill_value=True)in1d() shows an analogue issue.
NumPy/Python version information:
1.21.2 3.9.4 (default, Apr 9 2021, 16:34:09)
[GCC 7.3.0]
For masked arrays,
isin()andin1d()produce wrong results. In particular, the result is masked at wrong places. The issue was discussed initially on stackoverflow and it seems that the involved sorting does not handle the masked value correctly.Reproducing code example:
The output of ma.isin() is wrong (see below). I use the following workaround:
Output and expected results:
These are the results from the code above
The issue is mainly that the last element in the result is masked, and probably also that the first element is not masked. I would expect the following output:
in1d()shows an analogue issue.NumPy/Python version information:
1.21.2 3.9.4 (default, Apr 9 2021, 16:34:09)
[GCC 7.3.0]