def test_setitem_scalar(self, val, missing_value):
arr = LabelArray(self.strs, missing_value=missing_value)
if not arr.has_label(val):
self.assertTrue(
(val == 'not in the array')
or (val is None and missing_value is not None)
)
for slicer in [(0, 0), (0, 1), 1]:
with self.assertRaises(ValueError):
arr[slicer] = val
return
arr[0, 0] = val
self.assertEqual(arr[0, 0], val)
arr[0, 1] = val
self.assertEqual(arr[0, 1], val)
arr[1] = val
if val == missing_value:
self.assertTrue(arr.is_missing()[1].all())
else:
self.assertTrue((arr[1] == val).all())
self.assertTrue((arr[1].as_string_array() == val).all())
arr[:, -1] = val
if val == missing_value:
self.assertTrue(arr.is_missing()[:, -1].all())
else:
self.assertTrue((arr[:, -1] == val).all())
self.assertTrue((arr[:, -1].as_string_array() == val).all())
arr[:] = val
if val == missing_value:
self.assertTrue(arr.is_missing().all())
else:
self.assertFalse(arr.is_missing().any())
self.assertTrue((arr == val).all())
def test_setitem_scalar(self, val, missing_value):
arr = LabelArray(self.strs, missing_value=missing_value)
if not arr.has_label(val):
self.assertTrue(
(val == 'not in the array')
or (val is None and missing_value is not None)
)
for slicer in [(0, 0), (0, 1), 1]:
with self.assertRaises(ValueError):
arr[slicer] = val
return
arr[0, 0] = val
self.assertEqual(arr[0, 0], val)
arr[0, 1] = val
self.assertEqual(arr[0, 1], val)
arr[1] = val
if val == missing_value:
self.assertTrue(arr.is_missing()[1].all())
else:
self.assertTrue((arr[1] == val).all())
self.assertTrue((arr[1].as_string_array() == val).all())
arr[:, -1] = val
if val == missing_value:
self.assertTrue(arr.is_missing()[:, -1].all())
else:
self.assertTrue((arr[:, -1] == val).all())
self.assertTrue((arr[:, -1].as_string_array() == val).all())
arr[:] = val
if val == missing_value:
self.assertTrue(arr.is_missing().all())
else:
self.assertFalse(arr.is_missing().any())
self.assertTrue((arr == val).all())
def test_setitem_scalar(self, val, missing_value):
arr = LabelArray(self.strs, missing_value=missing_value)
if not arr.has_label(val):
assert (val == "not in the array") or (
val is None and missing_value is not None
)
for slicer in [(0, 0), (0, 1), 1]:
with pytest.raises(ValueError):
arr[slicer] = val
return
arr[0, 0] = val
assert arr[0, 0] == val
arr[0, 1] = val
assert arr[0, 1] == val
arr[1] = val
if val == missing_value:
assert arr.is_missing()[1].all()
else:
assert (arr[1] == val).all()
assert (arr[1].as_string_array() == val).all()
arr[:, -1] = val
if val == missing_value:
assert arr.is_missing()[:, -1].all()
else:
assert (arr[:, -1] == val).all()
assert (arr[:, -1].as_string_array() == val).all()
arr[:] = val
if val == missing_value:
assert arr.is_missing().all()
else:
assert not arr.is_missing().any()
assert (arr == val).all()
def test_compare_to_str_array(self, missing_value):
strs = self.strs
shape = strs.shape
arr = LabelArray(strs, missing_value=missing_value)
if missing_value is None:
# As of numpy 1.9.2, object array != None returns just False
# instead of an array, with a deprecation warning saying the
# behavior will change in the future. Work around that by just
# using the ufunc.
notmissing = np.not_equal(strs, missing_value)
else:
notmissing = (strs != missing_value)
check_arrays(arr.not_missing(), notmissing)
check_arrays(arr.is_missing(), ~notmissing)
# The arrays are equal everywhere, but comparisons against the
# missing_value should always produce False
check_arrays(strs == arr, notmissing)
check_arrays(strs != arr, np.zeros_like(strs, dtype=bool))
def broadcastable_row(value, dtype):
return np.full((shape[0], 1), value, dtype=strs.dtype)
def broadcastable_col(value, dtype):
return np.full((1, shape[1]), value, dtype=strs.dtype)
# Test comparison between arr and a like-shap 2D array, a column
# vector, and a row vector.
for comparator, dtype, value in product((eq, ne),
(bytes, unicode, object),
set(self.rowvalues)):
check_arrays(
comparator(arr, np.full_like(strs, value)),
comparator(strs, value) & notmissing,
)
check_arrays(
comparator(arr, broadcastable_row(value, dtype=dtype)),
comparator(strs, value) & notmissing,
)
check_arrays(
comparator(arr, broadcastable_col(value, dtype=dtype)),
comparator(strs, value) & notmissing,
)
def test_compare_to_str_array(self, missing_value):
strs = self.strs
shape = strs.shape
arr = LabelArray(strs, missing_value=missing_value)
if missing_value is None:
# As of numpy 1.9.2, object array != None returns just False
# instead of an array, with a deprecation warning saying the
# behavior will change in the future. Work around that by just
# using the ufunc.
notmissing = np.not_equal(strs, missing_value)
else:
notmissing = (strs != missing_value)
check_arrays(arr.not_missing(), notmissing)
check_arrays(arr.is_missing(), ~notmissing)
# The arrays are equal everywhere, but comparisons against the
# missing_value should always produce False
check_arrays(strs == arr, notmissing)
check_arrays(strs != arr, np.zeros_like(strs, dtype=bool))
def broadcastable_row(value, dtype):
return np.full((shape[0], 1), value, dtype=strs.dtype)
def broadcastable_col(value, dtype):
return np.full((1, shape[1]), value, dtype=strs.dtype)
# Test comparison between arr and a like-shap 2D array, a column
# vector, and a row vector.
for comparator, dtype, value in product((eq, ne),
(bytes, unicode, object),
set(self.rowvalues)):
check_arrays(
comparator(arr, np.full_like(strs, value)),
comparator(strs, value) & notmissing,
)
check_arrays(
comparator(arr, broadcastable_row(value, dtype=dtype)),
comparator(strs, value) & notmissing,
)
check_arrays(
comparator(arr, broadcastable_col(value, dtype=dtype)),
comparator(strs, value) & notmissing,
)
