done

lib/python3.11/site-packages/pandas/tests/indexes/object/test_astype.py

@@ -0,0 +1,15 @@
+import pytest
+
+from pandas import (
+    Index,
+    NaT,
+)
+
+
+def test_astype_invalid_nas_to_tdt64_raises():
+    # GH#45722 don't cast np.datetime64 NaTs to timedelta64 NaT
+    idx = Index([NaT.asm8] * 2, dtype=object)
+
+    msg = r"Invalid type for timedelta scalar: <class 'numpy.datetime64'>"
+    with pytest.raises(TypeError, match=msg):
+        idx.astype("m8[ns]")

lib/python3.11/site-packages/pandas/tests/indexes/object/test_indexing.py

@@ -0,0 +1,159 @@
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from pandas._libs.missing import is_matching_na
+
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestGetIndexer:
+    @pytest.mark.parametrize(
+        "method,expected",
+        [
+            ("pad", np.array([-1, 0, 1, 1], dtype=np.intp)),
+            ("backfill", np.array([0, 0, 1, -1], dtype=np.intp)),
+        ],
+    )
+    def test_get_indexer_strings(self, method, expected):
+        expected = np.array(expected, dtype=np.intp)
+        index = Index(["b", "c"], dtype=object)
+        actual = index.get_indexer(["a", "b", "c", "d"], method=method)
+
+        tm.assert_numpy_array_equal(actual, expected)
+
+    def test_get_indexer_strings_raises(self):
+        index = Index(["b", "c"], dtype=object)
+
+        msg = "|".join(
+            [
+                "operation 'sub' not supported for dtype 'str'",
+                r"unsupported operand type\(s\) for -: 'str' and 'str'",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            index.get_indexer(["a", "b", "c", "d"], method="nearest")
+
+        with pytest.raises(TypeError, match=msg):
+            index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2)
+
+        with pytest.raises(TypeError, match=msg):
+            index.get_indexer(
+                ["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2]
+            )
+
+    def test_get_indexer_with_NA_values(
+        self, unique_nulls_fixture, unique_nulls_fixture2
+    ):
+        # GH#22332
+        # check pairwise, that no pair of na values
+        # is mangled
+        if unique_nulls_fixture is unique_nulls_fixture2:
+            return  # skip it, values are not unique
+        arr = np.array([unique_nulls_fixture, unique_nulls_fixture2], dtype=object)
+        index = Index(arr, dtype=object)
+        result = index.get_indexer(
+            Index(
+                [unique_nulls_fixture, unique_nulls_fixture2, "Unknown"], dtype=object
+            )
+        )
+        expected = np.array([0, 1, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_infer_string_missing_values(self):
+        # ensure the passed list is not cast to string but to object so that
+        # the None value is matched in the index
+        # https://github.com/pandas-dev/pandas/issues/55834
+        idx = Index(["a", "b", None], dtype="object")
+        result = idx.get_indexer([None, "x"])
+        expected = np.array([2, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestGetIndexerNonUnique:
+    def test_get_indexer_non_unique_nas(self, nulls_fixture):
+        # even though this isn't non-unique, this should still work
+        index = Index(["a", "b", nulls_fixture], dtype=object)
+        indexer, missing = index.get_indexer_non_unique([nulls_fixture])
+
+        expected_indexer = np.array([2], dtype=np.intp)
+        expected_missing = np.array([], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected_indexer)
+        tm.assert_numpy_array_equal(missing, expected_missing)
+
+        # actually non-unique
+        index = Index(["a", nulls_fixture, "b", nulls_fixture], dtype=object)
+        indexer, missing = index.get_indexer_non_unique([nulls_fixture])
+
+        expected_indexer = np.array([1, 3], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected_indexer)
+        tm.assert_numpy_array_equal(missing, expected_missing)
+
+        # matching-but-not-identical nans
+        if is_matching_na(nulls_fixture, float("NaN")):
+            index = Index(["a", float("NaN"), "b", float("NaN")], dtype=object)
+            match_but_not_identical = True
+        elif is_matching_na(nulls_fixture, Decimal("NaN")):
+            index = Index(["a", Decimal("NaN"), "b", Decimal("NaN")], dtype=object)
+            match_but_not_identical = True
+        else:
+            match_but_not_identical = False
+
+        if match_but_not_identical:
+            indexer, missing = index.get_indexer_non_unique([nulls_fixture])
+
+            expected_indexer = np.array([1, 3], dtype=np.intp)
+            tm.assert_numpy_array_equal(indexer, expected_indexer)
+            tm.assert_numpy_array_equal(missing, expected_missing)
+
+    @pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning")
+    def test_get_indexer_non_unique_np_nats(self, np_nat_fixture, np_nat_fixture2):
+        expected_missing = np.array([], dtype=np.intp)
+        # matching-but-not-identical nats
+        if is_matching_na(np_nat_fixture, np_nat_fixture2):
+            # ensure nats are different objects
+            index = Index(
+                np.array(
+                    ["2021-10-02", np_nat_fixture.copy(), np_nat_fixture2.copy()],
+                    dtype=object,
+                ),
+                dtype=object,
+            )
+            # pass as index to prevent target from being casted to DatetimeIndex
+            indexer, missing = index.get_indexer_non_unique(
+                Index([np_nat_fixture], dtype=object)
+            )
+            expected_indexer = np.array([1, 2], dtype=np.intp)
+            tm.assert_numpy_array_equal(indexer, expected_indexer)
+            tm.assert_numpy_array_equal(missing, expected_missing)
+        # dt64nat vs td64nat
+        else:
+            try:
+                np_nat_fixture == np_nat_fixture2
+            except (TypeError, OverflowError):
+                # Numpy will raise on uncomparable types, like
+                # np.datetime64('NaT', 'Y') and np.datetime64('NaT', 'ps')
+                # https://github.com/numpy/numpy/issues/22762
+                return
+            index = Index(
+                np.array(
+                    [
+                        "2021-10-02",
+                        np_nat_fixture,
+                        np_nat_fixture2,
+                        np_nat_fixture,
+                        np_nat_fixture2,
+                    ],
+                    dtype=object,
+                ),
+                dtype=object,
+            )
+            # pass as index to prevent target from being casted to DatetimeIndex
+            indexer, missing = index.get_indexer_non_unique(
+                Index([np_nat_fixture], dtype=object)
+            )
+            expected_indexer = np.array([1, 3], dtype=np.intp)
+            tm.assert_numpy_array_equal(indexer, expected_indexer)
+            tm.assert_numpy_array_equal(missing, expected_missing)