[Python] Why do I get different results and types when accessing unmasked elements of a NumPy...

I'm working with NumPy MaskedArray. I've noticed that when I try to access and perform calculations on the unmasked elements, using data[~data.mask] versus data.data[~data.mask] yields different results in terms of precision and the type of the resulting array.

Here's a minimal reproducible example:

import numpy as np

data = np.array([1.23, 4.56, 7.89], dtype=np.float32)
no_value = np.float32(-9999)
factor = 100
# dtype = np.int16 # This line is not directly relevant to the issue, so commented out

data = np.ma.masked_equal(data, no_value)

# 1. Using data[~data.mask]
result1 = data[~data.mask] * factor
print(result1, type(result1), result1.dtype)

# 2. Using data.data[~data.mask]
result2 = data.data[~data.mask] * factor
print(result2, type(result2), result2.dtype)


Running this code produces the following output:

[123.00000191 455.99999428 788.99998665] <class 'numpy.ma.core.MaskedArray'> float32
[123. 456. 789. ] <class 'numpy.ndarray'> float32


The result of data[~data.mask] * factor remains a MaskedArray, and the values seem to include precision errors attributable to the original float32 dtype. On the other hand, the result of data.data[~data.mask] * factor is an ndarray, and the values are closer to what I would expect.

Why do these different access methods lead to differences in the type of the result (MaskedArray vs ndarray) and the precision of the floating-point arithmetic? Specifically, I'd like to understand why using data[~data.mask] returns a MaskedArray and why its internal calculation process might differ from directly referencing data.data.

I was hoping the results shown above would be the same

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