def test_create_series(self):
x = np.array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]] * 100)
a = TensorArray(x)
s1 = pd.Series(a)
s2 = pd.Series(a, dtype=TensorDtype())
s3 = pd.Series(a, dtype=TensorDtype(), copy=True)
self.assertEqual(len(x), len(s1))
npt.assert_array_equal(x, s1.to_numpy())
pdt.assert_series_equal(s1, s2)
pdt.assert_series_equal(s1, s3)
def test_arith_series_with_array(self, data, all_arithmetic_operators):
""" Override because creates Series from list of TensorElements as dtype=object."""
# ndarray & other series
op_name = all_arithmetic_operators
s = pd.Series(data)
self.check_opname(
s, op_name, pd.Series([s.iloc[0]] * len(s), dtype=TensorDtype()), exc=self.series_array_exc
)
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()))
def test_compare_array(self, data, all_compare_operators):
""" Override to change scalar value to something usable."""
op_name = all_compare_operators
s = pd.Series(data[1:])
other = pd.Series([data[0]] * len(s), dtype=TensorDtype())
self._compare_other(s, data, op_name, other)
def dtype():
return TensorDtype()
