Add inverse to test_timedelta64_dtype_array_returned

pandas/tests/test_algos.py

@@ -28,6 +28,20 @@
 from pandas.util.testing import assert_almost_equal
 
 
+def assert_series_or_index_or_array_or_categorical_equal(left, right):
+ if isinstance(left, Series):
+ tm.assert_series_equal(left, right)
+ elif isinstance(left, Index):
+ tm.assert_index_equal(left, right)
+ elif isinstance(left, np.ndarray):
+ tm.assert_numpy_array_equal(left, right)
+ elif isinstance(left, Categorical):
+ tm.assert_categorical_equal(left, right)
+ else:
+ # will fail
+ assert isinstance(left, (Series, Index, np.ndarray, Categorical))
+
+
 class TestMatch(object):
 
  def test_ints(self):
@@ -395,6 +409,16 @@ def test_timedelta64_dtype_array_returned(self, box):
  result = algos.unique(obj)
  tm.assert_numpy_array_equal(result, expected)
 
+ # reuse result as expected outcome of return_inverse case
+ expected_uniques = result.copy()
+
+ result_uniques, result_inverse = algos.unique(obj, return_inverse=True)
+ tm.assert_numpy_array_equal(result_uniques, expected_uniques)
+
+ # reconstruction can only work if inverse is correct
+ reconstr = box(result_uniques[result_inverse])
+ assert_series_or_index_or_array_or_categorical_equal(reconstr, obj)
+
  def test_uint64_overflow(self):
  s = Series([1, 2, 2**63, 2**63], dtype=np.uint64)
  exp = np.array([1, 2, 2**63], dtype=np.uint64)