8000 BUG: np.ma.mean and var should return scalar if no mask by ahaldane · Pull Request #8142 · numpy/numpy · GitHub
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BUG: np.ma.mean and var should return scalar if no mask #8142

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Merged
merged 1 commit into from
Oct 14, 2016
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BUG: np.ma.mean and var should return scalar if no mask #8142

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12 changes: 9 additions & 3 deletions numpy/ma/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5057,7 +5057,7 @@ def mean(self, axis=None, dtype=None, out=None, keepdims=np._NoValue):

if self._mask is nomask:
result = super(MaskedArray, self).mean(axis=axis,
dtype=dtype, **kwargs)
dtype=dtype, **kwargs)[()]
else:
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I assume the super mean returns a 0-d array scalar for relevant cases. I note the following odd behavior of ordinary arrays that is new to me.

>>> np.float64(1)
1.0
>>> np.float64(1)[()]
array(1.0)
>>> np.float64(1)[()][()]
1.0

That is, the [()] construct flips the result back and forth between arrays and scalars.

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@eric-wieser eric-wieser Feb 28, 2017 •
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For anyone coming here in future, the above is no longer (1.11.1) the case. [()] does not promote scalars to 0d-arrays

dsum = self.sum(axis=axis, dtype=dtype, **kwargs)
cnt = self.count(axis=axis, **kwargs)
Expand Down Expand Up @@ -5134,8 +5134,14 @@ def var(self, axis=None, dtype=None, out=None, ddof=0,

# Easy case: nomask, business as usual
if self._mask is nomask:
return self._data.var(axis=axis, dtype=dtype, out=out,
ddof=ddof, **kwargs)
ret = super(MaskedArray, self).var(axis=axis, dtype=dtype, out=out,
ddof=ddof, **kwargs)[()]
if out is not None:
if isinstance(out, MaskedArray):
out.__setmask__(nomask)
return out
return ret

# Some data are masked, yay!
cnt = self.count(axis=axis, **kwargs) - ddof
danom = self - self.mean(axis, dtype, keepdims=True)
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16 changes: 16 additions & 0 deletions numpy/ma/tests/test_core.py
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Expand Up @@ -3340,6 +3340,22 @@ def test_dot_shape_mismatch(self):
assert_almost_equal(z.filled(0), [[1, 0], [15, 16]])
assert_almost_equal(z.mask, [[0, 1], [0, 0]])

def test_varmean_nomask(self):
# gh-5769
foo = array([1,2,3,4], dtype='f8')
bar = array([1,2,3,4], dtype='f8')
assert_equal(type(foo.mean()), np.float64)
assert_equal(type(foo.var()), np.float64)
assert((foo.mean() == bar.mean()) is np.bool_(True))

# check array type is preserved and out works
foo = array(np.arange(16).reshape((4,4)), dtype='f8')
bar = empty(4, dtype='f4')
assert_equal(type(foo.mean(axis=1)), MaskedArray)
assert_equal(type(foo.var(axis=1)), MaskedArray)
assert_(foo.mean(axis=1, out=bar) is bar)
assert_(foo.var(axis=1, out=bar) is bar)

def test_varstd(self):
# Tests var & std on MaskedArrays.
(x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
Expand Down
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