def test_isna(self):
expected = np.array([False, True, False, False])
# Test numeric
x = np.array([[1, 2], [np.nan, np.nan], [3, np.nan], [5, 6]])
s = TensorArray(x)
result = s.isna()
npt.assert_equal(result, expected)
# Test object
d = {"a": 1}
x = np.array([[d, d], None, [d, None], [d, d]], dtype=object)
s = TensorArray(x)
result = s.isna()
npt.assert_equal(result, expected)
# Test str
x = np.array([["foo", "foo"], ["", ""], ["bar", ""], ["baz", "baz"]])
s = TensorArray(x)
result = s.isna()
npt.assert_equal(result, expected)
def test_create_from_scalar_list(self):
x = [1, 2, 3, 4, 5]
s = TensorArray(x)
self.assertTupleEqual(s.numpy_shape, (len(x),))
expected = np.array(x)
npt.assert_array_equal(s.to_numpy(), expected)
# Now with TensorElement values
e = [TensorElement(np.array(i)) for i in x]
s = pd.array(e, dtype=TensorDtype())
npt.assert_array_equal(s.to_numpy(), expected)
# Now with list of 1d tensors
x = [np.array([i]) for i in x]
s = pd.array(x, dtype=TensorDtype())
self.assertTupleEqual(s.to_numpy().shape, (len(x), 1))
npt.assert_array_equal(s.to_numpy(), np.array([[e] for e in expected]))
# Pandas will create list of copies of the tensor element for the given indices
s = pd.Series(np.nan, index=[0, 1, 2], dtype=TensorDtype())
self.assertEqual(len(s), 3)
self.assertTupleEqual(s.to_numpy().shape, (3,))
result = s.isna()
self.assertTrue(np.all(result.to_numpy()))