Add files using upload-large-folder tool

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/conftest.py

@@ -0,0 +1,41 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Series,
+    array,
+)
+
+
+@pytest.fixture(params=[None, False])
+def sort(request):
+    """
+    Valid values for the 'sort' parameter used in the Index
+    setops methods (intersection, union, etc.)
+
+    Caution:
+        Don't confuse this one with the "sort" fixture used
+        for DataFrame.append or concat. That one has
+        parameters [True, False].
+
+        We can't combine them as sort=True is not permitted
+        in the Index setops methods.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["D", "3D", "-3D", "h", "2h", "-2h", "min", "2min", "s", "-3s"])
+def freq_sample(request):
+    """
+    Valid values for 'freq' parameter used to create date_range and
+    timedelta_range..
+    """
+    return request.param
+
+
+@pytest.fixture(params=[list, tuple, np.array, array, Series])
+def listlike_box(request):
+    """
+    Types that may be passed as the indexer to searchsorted.
+    """
+    return request.param

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_astype.py

@@ -0,0 +1,95 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    to_datetime,
+    to_timedelta,
+)
+import pandas._testing as tm
+
+
+class TestAstype:
+    def test_astype_float64_to_uint64(self):
+        # GH#45309 used to incorrectly return Index with int64 dtype
+        idx = Index([0.0, 5.0, 10.0, 15.0, 20.0], dtype=np.float64)
+        result = idx.astype("u8")
+        expected = Index([0, 5, 10, 15, 20], dtype=np.uint64)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        idx_with_negatives = idx - 10
+        with pytest.raises(ValueError, match="losslessly"):
+            idx_with_negatives.astype(np.uint64)
+
+    def test_astype_float64_to_object(self):
+        float_index = Index([0.0, 2.5, 5.0, 7.5, 10.0], dtype=np.float64)
+        result = float_index.astype(object)
+        assert result.equals(float_index)
+        assert float_index.equals(result)
+        assert isinstance(result, Index) and result.dtype == object
+
+    def test_astype_float64_mixed_to_object(self):
+        # mixed int-float
+        idx = Index([1.5, 2, 3, 4, 5], dtype=np.float64)
+        idx.name = "foo"
+        result = idx.astype(object)
+        assert result.equals(idx)
+        assert idx.equals(result)
+        assert isinstance(result, Index) and result.dtype == object
+
+    @pytest.mark.parametrize("dtype", ["int16", "int32", "int64"])
+    def test_astype_float64_to_int_dtype(self, dtype):
+        # GH#12881
+        # a float astype int
+        idx = Index([0, 1, 2], dtype=np.float64)
+        result = idx.astype(dtype)
+        expected = Index([0, 1, 2], dtype=dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        idx = Index([0, 1.1, 2], dtype=np.float64)
+        result = idx.astype(dtype)
+        expected = Index([0, 1, 2], dtype=dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    @pytest.mark.parametrize("dtype", ["float32", "float64"])
+    def test_astype_float64_to_float_dtype(self, dtype):
+        # GH#12881
+        # a float astype int
+        idx = Index([0, 1, 2], dtype=np.float64)
+        result = idx.astype(dtype)
+        assert isinstance(result, Index) and result.dtype == dtype
+
+    @pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
+    def test_astype_float_to_datetimelike(self, dtype):
+        # GH#49660 pre-2.0 Index.astype from floating to M8/m8/Period raised,
+        #  inconsistent with Series.astype
+        idx = Index([0, 1.1, 2], dtype=np.float64)
+
+        result = idx.astype(dtype)
+        if dtype[0] == "M":
+            expected = to_datetime(idx.values)
+        else:
+            expected = to_timedelta(idx.values)
+        tm.assert_index_equal(result, expected)
+
+        # check that we match Series behavior
+        result = idx.to_series().set_axis(range(3)).astype(dtype)
+        expected = expected.to_series().set_axis(range(3))
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [int, "int16", "int32", "int64"])
+    @pytest.mark.parametrize("non_finite", [np.inf, np.nan])
+    def test_cannot_cast_inf_to_int(self, non_finite, dtype):
+        # GH#13149
+        idx = Index([1, 2, non_finite], dtype=np.float64)
+
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(ValueError, match=msg):
+            idx.astype(dtype)
+
+    def test_astype_from_object(self):
+        index = Index([1.0, np.nan, 0.2], dtype="object")
+        result = index.astype(float)
+        expected = Index([1.0, np.nan, 0.2], dtype=np.float64)
+        assert result.dtype == expected.dtype
+        tm.assert_index_equal(result, expected)

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_indexing.py

@@ -0,0 +1,611 @@
+import numpy as np
+import pytest
+
+from pandas.errors import InvalidIndexError
+
+from pandas import (
+    NA,
+    Index,
+    RangeIndex,
+    Series,
+    Timestamp,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    ArrowExtensionArray,
+    FloatingArray,
+)
+
+
+@pytest.fixture
+def index_large():
+    # large values used in Index[uint64] tests where no compat needed with Int64/Float64
+    large = [2**63, 2**63 + 10, 2**63 + 15, 2**63 + 20, 2**63 + 25]
+    return Index(large, dtype=np.uint64)
+
+
+class TestGetLoc:
+    def test_get_loc(self):
+        index = Index([0, 1, 2])
+        assert index.get_loc(1) == 1
+
+    def test_get_loc_raises_bad_label(self):
+        index = Index([0, 1, 2])
+        with pytest.raises(InvalidIndexError, match=r"\[1, 2\]"):
+            index.get_loc([1, 2])
+
+    def test_get_loc_float64(self):
+        idx = Index([0.0, 1.0, 2.0], dtype=np.float64)
+
+        with pytest.raises(KeyError, match="^'foo'$"):
+            idx.get_loc("foo")
+        with pytest.raises(KeyError, match=r"^1\.5$"):
+            idx.get_loc(1.5)
+        with pytest.raises(KeyError, match="^True$"):
+            idx.get_loc(True)
+        with pytest.raises(KeyError, match="^False$"):
+            idx.get_loc(False)
+
+    def test_get_loc_na(self):
+        idx = Index([np.nan, 1, 2], dtype=np.float64)
+        assert idx.get_loc(1) == 1
+        assert idx.get_loc(np.nan) == 0
+
+        idx = Index([np.nan, 1, np.nan], dtype=np.float64)
+        assert idx.get_loc(1) == 1
+
+        # representable by slice [0:2:2]
+        msg = "'Cannot get left slice bound for non-unique label: nan'"
+        with pytest.raises(KeyError, match=msg):
+            idx.slice_locs(np.nan)
+        # not representable by slice
+        idx = Index([np.nan, 1, np.nan, np.nan], dtype=np.float64)
+        assert idx.get_loc(1) == 1
+        msg = "'Cannot get left slice bound for non-unique label: nan"
+        with pytest.raises(KeyError, match=msg):
+            idx.slice_locs(np.nan)
+
+    def test_get_loc_missing_nan(self):
+        # GH#8569
+        idx = Index([1, 2], dtype=np.float64)
+        assert idx.get_loc(1) == 0
+        with pytest.raises(KeyError, match=r"^3$"):
+            idx.get_loc(3)
+        with pytest.raises(KeyError, match="^nan$"):
+            idx.get_loc(np.nan)
+        with pytest.raises(InvalidIndexError, match=r"\[nan\]"):
+            # listlike/non-hashable raises TypeError
+            idx.get_loc([np.nan])
+
+    @pytest.mark.parametrize("vals", [[1], [1.0], [Timestamp("2019-12-31")], ["test"]])
+    def test_get_loc_float_index_nan_with_method(self, vals):
+        # GH#39382
+        idx = Index(vals)
+        with pytest.raises(KeyError, match="nan"):
+            idx.get_loc(np.nan)
+
+    @pytest.mark.parametrize("dtype", ["f8", "i8", "u8"])
+    def test_get_loc_numericindex_none_raises(self, dtype):
+        # case that goes through searchsorted and key is non-comparable to values
+        arr = np.arange(10**7, dtype=dtype)
+        idx = Index(arr)
+        with pytest.raises(KeyError, match="None"):
+            idx.get_loc(None)
+
+    def test_get_loc_overflows(self):
+        # unique but non-monotonic goes through IndexEngine.mapping.get_item
+        idx = Index([0, 2, 1])
+
+        val = np.iinfo(np.int64).max + 1
+
+        with pytest.raises(KeyError, match=str(val)):
+            idx.get_loc(val)
+        with pytest.raises(KeyError, match=str(val)):
+            idx._engine.get_loc(val)
+
+
+class TestGetIndexer:
+    def test_get_indexer(self):
+        index1 = Index([1, 2, 3, 4, 5])
+        index2 = Index([2, 4, 6])
+
+        r1 = index1.get_indexer(index2)
+        e1 = np.array([1, 3, -1], dtype=np.intp)
+        tm.assert_almost_equal(r1, e1)
+
+    @pytest.mark.parametrize("reverse", [True, False])
+    @pytest.mark.parametrize(
+        "expected,method",
+        [
+            (np.array([-1, 0, 0, 1, 1], dtype=np.intp), "pad"),
+            (np.array([-1, 0, 0, 1, 1], dtype=np.intp), "ffill"),
+            (np.array([0, 0, 1, 1, 2], dtype=np.intp), "backfill"),
+            (np.array([0, 0, 1, 1, 2], dtype=np.intp), "bfill"),
+        ],
+    )
+    def test_get_indexer_methods(self, reverse, expected, method):
+        index1 = Index([1, 2, 3, 4, 5])
+        index2 = Index([2, 4, 6])
+
+        if reverse:
+            index1 = index1[::-1]
+            expected = expected[::-1]
+
+        result = index2.get_indexer(index1, method=method)
+        tm.assert_almost_equal(result, expected)
+
+    def test_get_indexer_invalid(self):
+        # GH10411
+        index = Index(np.arange(10))
+
+        with pytest.raises(ValueError, match="tolerance argument"):
+            index.get_indexer([1, 0], tolerance=1)
+
+        with pytest.raises(ValueError, match="limit argument"):
+            index.get_indexer([1, 0], limit=1)
+
+    @pytest.mark.parametrize(
+        "method, tolerance, indexer, expected",
+        [
+            ("pad", None, [0, 5, 9], [0, 5, 9]),
+            ("backfill", None, [0, 5, 9], [0, 5, 9]),
+            ("nearest", None, [0, 5, 9], [0, 5, 9]),
+            ("pad", 0, [0, 5, 9], [0, 5, 9]),
+            ("backfill", 0, [0, 5, 9], [0, 5, 9]),
+            ("nearest", 0, [0, 5, 9], [0, 5, 9]),
+            ("pad", None, [0.2, 1.8, 8.5], [0, 1, 8]),
+            ("backfill", None, [0.2, 1.8, 8.5], [1, 2, 9]),
+            ("nearest", None, [0.2, 1.8, 8.5], [0, 2, 9]),
+            ("pad", 1, [0.2, 1.8, 8.5], [0, 1, 8]),
+            ("backfill", 1, [0.2, 1.8, 8.5], [1, 2, 9]),
+            ("nearest", 1, [0.2, 1.8, 8.5], [0, 2, 9]),
+            ("pad", 0.2, [0.2, 1.8, 8.5], [0, -1, -1]),
+            ("backfill", 0.2, [0.2, 1.8, 8.5], [-1, 2, -1]),
+            ("nearest", 0.2, [0.2, 1.8, 8.5], [0, 2, -1]),
+        ],
+    )
+    def test_get_indexer_nearest(self, method, tolerance, indexer, expected):
+        index = Index(np.arange(10))
+
+        actual = index.get_indexer(indexer, method=method, tolerance=tolerance)
+        tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp))
+
+    @pytest.mark.parametrize("listtype", [list, tuple, Series, np.array])
+    @pytest.mark.parametrize(
+        "tolerance, expected",
+        list(
+            zip(
+                [[0.3, 0.3, 0.1], [0.2, 0.1, 0.1], [0.1, 0.5, 0.5]],
+                [[0, 2, -1], [0, -1, -1], [-1, 2, 9]],
+            )
+        ),
+    )
+    def test_get_indexer_nearest_listlike_tolerance(
+        self, tolerance, expected, listtype
+    ):
+        index = Index(np.arange(10))
+
+        actual = index.get_indexer(
+            [0.2, 1.8, 8.5], method="nearest", tolerance=listtype(tolerance)
+        )
+        tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp))
+
+    def test_get_indexer_nearest_error(self):
+        index = Index(np.arange(10))
+        with pytest.raises(ValueError, match="limit argument"):
+            index.get_indexer([1, 0], method="nearest", limit=1)
+
+        with pytest.raises(ValueError, match="tolerance size must match"):
+            index.get_indexer([1, 0], method="nearest", tolerance=[1, 2, 3])
+
+    @pytest.mark.parametrize(
+        "method,expected",
+        [("pad", [8, 7, 0]), ("backfill", [9, 8, 1]), ("nearest", [9, 7, 0])],
+    )
+    def test_get_indexer_nearest_decreasing(self, method, expected):
+        index = Index(np.arange(10))[::-1]
+
+        actual = index.get_indexer([0, 5, 9], method=method)
+        tm.assert_numpy_array_equal(actual, np.array([9, 4, 0], dtype=np.intp))
+
+        actual = index.get_indexer([0.2, 1.8, 8.5], method=method)
+        tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp))
+
+    @pytest.mark.parametrize("idx_dtype", ["int64", "float64", "uint64", "range"])
+    @pytest.mark.parametrize("method", ["get_indexer", "get_indexer_non_unique"])
+    def test_get_indexer_numeric_index_boolean_target(self, method, idx_dtype):
+        # GH 16877
+
+        if idx_dtype == "range":
+            numeric_index = RangeIndex(4)
+        else:
+            numeric_index = Index(np.arange(4, dtype=idx_dtype))
+
+        other = Index([True, False, True])
+
+        result = getattr(numeric_index, method)(other)
+        expected = np.array([-1, -1, -1], dtype=np.intp)
+        if method == "get_indexer":
+            tm.assert_numpy_array_equal(result, expected)
+        else:
+            missing = np.arange(3, dtype=np.intp)
+            tm.assert_numpy_array_equal(result[0], expected)
+            tm.assert_numpy_array_equal(result[1], missing)
+
+    @pytest.mark.parametrize("method", ["pad", "backfill", "nearest"])
+    def test_get_indexer_with_method_numeric_vs_bool(self, method):
+        left = Index([1, 2, 3])
+        right = Index([True, False])
+
+        with pytest.raises(TypeError, match="Cannot compare"):
+            left.get_indexer(right, method=method)
+
+        with pytest.raises(TypeError, match="Cannot compare"):
+            right.get_indexer(left, method=method)
+
+    def test_get_indexer_numeric_vs_bool(self):
+        left = Index([1, 2, 3])
+        right = Index([True, False])
+
+        res = left.get_indexer(right)
+        expected = -1 * np.ones(len(right), dtype=np.intp)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = right.get_indexer(left)
+        expected = -1 * np.ones(len(left), dtype=np.intp)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = left.get_indexer_non_unique(right)[0]
+        expected = -1 * np.ones(len(right), dtype=np.intp)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = right.get_indexer_non_unique(left)[0]
+        expected = -1 * np.ones(len(left), dtype=np.intp)
+        tm.assert_numpy_array_equal(res, expected)
+
+    def test_get_indexer_float64(self):
+        idx = Index([0.0, 1.0, 2.0], dtype=np.float64)
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(idx), np.array([0, 1, 2], dtype=np.intp)
+        )
+
+        target = [-0.1, 0.5, 1.1]
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "pad"), np.array([-1, 0, 1], dtype=np.intp)
+        )
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "backfill"), np.array([0, 1, 2], dtype=np.intp)
+        )
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "nearest"), np.array([0, 1, 1], dtype=np.intp)
+        )
+
+    def test_get_indexer_nan(self):
+        # GH#7820
+        result = Index([1, 2, np.nan], dtype=np.float64).get_indexer([np.nan])
+        expected = np.array([2], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_int64(self):
+        index = Index(range(0, 20, 2), dtype=np.int64)
+        target = Index(np.arange(10), dtype=np.int64)
+        indexer = index.get_indexer(target)
+        expected = np.array([0, -1, 1, -1, 2, -1, 3, -1, 4, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected)
+
+        target = Index(np.arange(10), dtype=np.int64)
+        indexer = index.get_indexer(target, method="pad")
+        expected = np.array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected)
+
+        target = Index(np.arange(10), dtype=np.int64)
+        indexer = index.get_indexer(target, method="backfill")
+        expected = np.array([0, 1, 1, 2, 2, 3, 3, 4, 4, 5], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected)
+
+    def test_get_indexer_uint64(self, index_large):
+        target = Index(np.arange(10).astype("uint64") * 5 + 2**63)
+        indexer = index_large.get_indexer(target)
+        expected = np.array([0, -1, 1, 2, 3, 4, -1, -1, -1, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected)
+
+        target = Index(np.arange(10).astype("uint64") * 5 + 2**63)
+        indexer = index_large.get_indexer(target, method="pad")
+        expected = np.array([0, 0, 1, 2, 3, 4, 4, 4, 4, 4], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected)
+
+        target = Index(np.arange(10).astype("uint64") * 5 + 2**63)
+        indexer = index_large.get_indexer(target, method="backfill")
+        expected = np.array([0, 1, 1, 2, 3, 4, -1, -1, -1, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, expected)
+
+    @pytest.mark.parametrize("val, val2", [(4, 5), (4, 4), (4, NA), (NA, NA)])
+    def test_get_loc_masked(self, val, val2, any_numeric_ea_and_arrow_dtype):
+        # GH#39133
+        idx = Index([1, 2, 3, val, val2], dtype=any_numeric_ea_and_arrow_dtype)
+        result = idx.get_loc(2)
+        assert result == 1
+
+        with pytest.raises(KeyError, match="9"):
+            idx.get_loc(9)
+
+    def test_get_loc_masked_na(self, any_numeric_ea_and_arrow_dtype):
+        # GH#39133
+        idx = Index([1, 2, NA], dtype=any_numeric_ea_and_arrow_dtype)
+        result = idx.get_loc(NA)
+        assert result == 2
+
+        idx = Index([1, 2, NA, NA], dtype=any_numeric_ea_and_arrow_dtype)
+        result = idx.get_loc(NA)
+        tm.assert_numpy_array_equal(result, np.array([False, False, True, True]))
+
+        idx = Index([1, 2, 3], dtype=any_numeric_ea_and_arrow_dtype)
+        with pytest.raises(KeyError, match="NA"):
+            idx.get_loc(NA)
+
+    def test_get_loc_masked_na_and_nan(self):
+        # GH#39133
+        idx = Index(
+            FloatingArray(
+                np.array([1, 2, 1, np.nan]), mask=np.array([False, False, True, False])
+            )
+        )
+        result = idx.get_loc(NA)
+        assert result == 2
+        result = idx.get_loc(np.nan)
+        assert result == 3
+
+        idx = Index(
+            FloatingArray(np.array([1, 2, 1.0]), mask=np.array([False, False, True]))
+        )
+        result = idx.get_loc(NA)
+        assert result == 2
+        with pytest.raises(KeyError, match="nan"):
+            idx.get_loc(np.nan)
+
+        idx = Index(
+            FloatingArray(
+                np.array([1, 2, np.nan]), mask=np.array([False, False, False])
+            )
+        )
+        result = idx.get_loc(np.nan)
+        assert result == 2
+        with pytest.raises(KeyError, match="NA"):
+            idx.get_loc(NA)
+
+    @pytest.mark.parametrize("val", [4, 2])
+    def test_get_indexer_masked_na(self, any_numeric_ea_and_arrow_dtype, val):
+        # GH#39133
+        idx = Index([1, 2, NA, 3, val], dtype=any_numeric_ea_and_arrow_dtype)
+        result = idx.get_indexer_for([1, NA, 5])
+        expected = np.array([0, 2, -1])
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
+    @pytest.mark.parametrize("dtype", ["boolean", "bool[pyarrow]"])
+    def test_get_indexer_masked_na_boolean(self, dtype):
+        # GH#39133
+        if dtype == "bool[pyarrow]":
+            pytest.importorskip("pyarrow")
+        idx = Index([True, False, NA], dtype=dtype)
+        result = idx.get_loc(False)
+        assert result == 1
+        result = idx.get_loc(NA)
+        assert result == 2
+
+    def test_get_indexer_arrow_dictionary_target(self):
+        pa = pytest.importorskip("pyarrow")
+        target = Index(
+            ArrowExtensionArray(
+                pa.array([1, 2], type=pa.dictionary(pa.int8(), pa.int8()))
+            )
+        )
+        idx = Index([1])
+
+        result = idx.get_indexer(target)
+        expected = np.array([0, -1], dtype=np.int64)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result_1, result_2 = idx.get_indexer_non_unique(target)
+        expected_1, expected_2 = np.array([0, -1], dtype=np.int64), np.array(
+            [1], dtype=np.int64
+        )
+        tm.assert_numpy_array_equal(result_1, expected_1)
+        tm.assert_numpy_array_equal(result_2, expected_2)
+
+
+class TestWhere:
+    @pytest.mark.parametrize(
+        "index",
+        [
+            Index(np.arange(5, dtype="float64")),
+            Index(range(0, 20, 2), dtype=np.int64),
+            Index(np.arange(5, dtype="uint64")),
+        ],
+    )
+    def test_where(self, listlike_box, index):
+        cond = [True] * len(index)
+        expected = index
+        result = index.where(listlike_box(cond))
+
+        cond = [False] + [True] * (len(index) - 1)
+        expected = Index([index._na_value] + index[1:].tolist(), dtype=np.float64)
+        result = index.where(listlike_box(cond))
+        tm.assert_index_equal(result, expected)
+
+    def test_where_uint64(self):
+        idx = Index([0, 6, 2], dtype=np.uint64)
+        mask = np.array([False, True, False])
+        other = np.array([1], dtype=np.int64)
+
+        expected = Index([1, 6, 1], dtype=np.uint64)
+
+        result = idx.where(mask, other)
+        tm.assert_index_equal(result, expected)
+
+        result = idx.putmask(~mask, other)
+        tm.assert_index_equal(result, expected)
+
+    def test_where_infers_type_instead_of_trying_to_convert_string_to_float(self):
+        # GH 32413
+        index = Index([1, np.nan])
+        cond = index.notna()
+        other = Index(["a", "b"], dtype="string")
+
+        expected = Index([1.0, "b"])
+        result = index.where(cond, other)
+
+        tm.assert_index_equal(result, expected)
+
+
+class TestTake:
+    @pytest.mark.parametrize("idx_dtype", [np.float64, np.int64, np.uint64])
+    def test_take_preserve_name(self, idx_dtype):
+        index = Index([1, 2, 3, 4], dtype=idx_dtype, name="foo")
+        taken = index.take([3, 0, 1])
+        assert index.name == taken.name
+
+    def test_take_fill_value_float64(self):
+        # GH 12631
+        idx = Index([1.0, 2.0, 3.0], name="xxx", dtype=np.float64)
+        result = idx.take(np.array([1, 0, -1]))
+        expected = Index([2.0, 1.0, 3.0], dtype=np.float64, name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # fill_value
+        result = idx.take(np.array([1, 0, -1]), fill_value=True)
+        expected = Index([2.0, 1.0, np.nan], dtype=np.float64, name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = Index([2.0, 1.0, 3.0], dtype=np.float64, name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        msg = (
+            "When allow_fill=True and fill_value is not None, "
+            "all indices must be >= -1"
+        )
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -2]), fill_value=True)
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -5]), fill_value=True)
+
+        msg = "index -5 is out of bounds for (axis 0 with )?size 3"
+        with pytest.raises(IndexError, match=msg):
+            idx.take(np.array([1, -5]))
+
+    @pytest.mark.parametrize("dtype", [np.int64, np.uint64])
+    def test_take_fill_value_ints(self, dtype):
+        # see gh-12631
+        idx = Index([1, 2, 3], dtype=dtype, name="xxx")
+        result = idx.take(np.array([1, 0, -1]))
+        expected = Index([2, 1, 3], dtype=dtype, name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        name = type(idx).__name__
+        msg = f"Unable to fill values because {name} cannot contain NA"
+
+        # fill_value=True
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -1]), fill_value=True)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = Index([2, 1, 3], dtype=dtype, name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -2]), fill_value=True)
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -5]), fill_value=True)
+
+        msg = "index -5 is out of bounds for (axis 0 with )?size 3"
+        with pytest.raises(IndexError, match=msg):
+            idx.take(np.array([1, -5]))
+
+
+class TestContains:
+    @pytest.mark.parametrize("dtype", [np.float64, np.int64, np.uint64])
+    def test_contains_none(self, dtype):
+        # GH#35788 should return False, not raise TypeError
+        index = Index([0, 1, 2, 3, 4], dtype=dtype)
+        assert None not in index
+
+    def test_contains_float64_nans(self):
+        index = Index([1.0, 2.0, np.nan], dtype=np.float64)
+        assert np.nan in index
+
+    def test_contains_float64_not_nans(self):
+        index = Index([1.0, 2.0, np.nan], dtype=np.float64)
+        assert 1.0 in index
+
+
+class TestSliceLocs:
+    @pytest.mark.parametrize("dtype", [int, float])
+    def test_slice_locs(self, dtype):
+        index = Index(np.array([0, 1, 2, 5, 6, 7, 9, 10], dtype=dtype))
+        n = len(index)
+
+        assert index.slice_locs(start=2) == (2, n)
+        assert index.slice_locs(start=3) == (3, n)
+        assert index.slice_locs(3, 8) == (3, 6)
+        assert index.slice_locs(5, 10) == (3, n)
+        assert index.slice_locs(end=8) == (0, 6)
+        assert index.slice_locs(end=9) == (0, 7)
+
+        # reversed
+        index2 = index[::-1]
+        assert index2.slice_locs(8, 2) == (2, 6)
+        assert index2.slice_locs(7, 3) == (2, 5)
+
+    @pytest.mark.parametrize("dtype", [int, float])
+    def test_slice_locs_float_locs(self, dtype):
+        index = Index(np.array([0, 1, 2, 5, 6, 7, 9, 10], dtype=dtype))
+        n = len(index)
+        assert index.slice_locs(5.0, 10.0) == (3, n)
+        assert index.slice_locs(4.5, 10.5) == (3, 8)
+
+        index2 = index[::-1]
+        assert index2.slice_locs(8.5, 1.5) == (2, 6)
+        assert index2.slice_locs(10.5, -1) == (0, n)
+
+    @pytest.mark.parametrize("dtype", [int, float])
+    def test_slice_locs_dup_numeric(self, dtype):
+        index = Index(np.array([10, 12, 12, 14], dtype=dtype))
+        assert index.slice_locs(12, 12) == (1, 3)
+        assert index.slice_locs(11, 13) == (1, 3)
+
+        index2 = index[::-1]
+        assert index2.slice_locs(12, 12) == (1, 3)
+        assert index2.slice_locs(13, 11) == (1, 3)
+
+    def test_slice_locs_na(self):
+        index = Index([np.nan, 1, 2])
+        assert index.slice_locs(1) == (1, 3)
+        assert index.slice_locs(np.nan) == (0, 3)
+
+        index = Index([0, np.nan, np.nan, 1, 2])
+        assert index.slice_locs(np.nan) == (1, 5)
+
+    def test_slice_locs_na_raises(self):
+        index = Index([np.nan, 1, 2])
+        with pytest.raises(KeyError, match=""):
+            index.slice_locs(start=1.5)
+
+        with pytest.raises(KeyError, match=""):
+            index.slice_locs(end=1.5)
+
+
+class TestGetSliceBounds:
+    @pytest.mark.parametrize("side, expected", [("left", 4), ("right", 5)])
+    def test_get_slice_bounds_within(self, side, expected):
+        index = Index(range(6))
+        result = index.get_slice_bound(4, side=side)
+        assert result == expected
+
+    @pytest.mark.parametrize("side", ["left", "right"])
+    @pytest.mark.parametrize("bound, expected", [(-1, 0), (10, 6)])
+    def test_get_slice_bounds_outside(self, side, expected, bound):
+        index = Index(range(6))
+        result = index.get_slice_bound(bound, side=side)
+        assert result == expected

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_join.py

@@ -0,0 +1,380 @@
+import numpy as np
+import pytest
+
+import pandas._testing as tm
+from pandas.core.indexes.api import Index
+
+
+class TestJoinInt64Index:
+    def test_join_non_unique(self):
+        left = Index([4, 4, 3, 3])
+
+        joined, lidx, ridx = left.join(left, return_indexers=True)
+
+        exp_joined = Index([4, 4, 4, 4, 3, 3, 3, 3])
+        tm.assert_index_equal(joined, exp_joined)
+
+        exp_lidx = np.array([0, 0, 1, 1, 2, 2, 3, 3], dtype=np.intp)
+        tm.assert_numpy_array_equal(lidx, exp_lidx)
+
+        exp_ridx = np.array([0, 1, 0, 1, 2, 3, 2, 3], dtype=np.intp)
+        tm.assert_numpy_array_equal(ridx, exp_ridx)
+
+    def test_join_inner(self):
+        index = Index(range(0, 20, 2), dtype=np.int64)
+        other = Index([7, 12, 25, 1, 2, 5], dtype=np.int64)
+        other_mono = Index([1, 2, 5, 7, 12, 25], dtype=np.int64)
+
+        # not monotonic
+        res, lidx, ridx = index.join(other, how="inner", return_indexers=True)
+
+        # no guarantee of sortedness, so sort for comparison purposes
+        ind = res.argsort()
+        res = res.take(ind)
+        lidx = lidx.take(ind)
+        ridx = ridx.take(ind)
+
+        eres = Index([2, 12], dtype=np.int64)
+        elidx = np.array([1, 6], dtype=np.intp)
+        eridx = np.array([4, 1], dtype=np.intp)
+
+        assert isinstance(res, Index) and res.dtype == np.int64
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+        # monotonic
+        res, lidx, ridx = index.join(other_mono, how="inner", return_indexers=True)
+
+        res2 = index.intersection(other_mono)
+        tm.assert_index_equal(res, res2)
+
+        elidx = np.array([1, 6], dtype=np.intp)
+        eridx = np.array([1, 4], dtype=np.intp)
+        assert isinstance(res, Index) and res.dtype == np.int64
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+    def test_join_left(self):
+        index = Index(range(0, 20, 2), dtype=np.int64)
+        other = Index([7, 12, 25, 1, 2, 5], dtype=np.int64)
+        other_mono = Index([1, 2, 5, 7, 12, 25], dtype=np.int64)
+
+        # not monotonic
+        res, lidx, ridx = index.join(other, how="left", return_indexers=True)
+        eres = index
+        eridx = np.array([-1, 4, -1, -1, -1, -1, 1, -1, -1, -1], dtype=np.intp)
+
+        assert isinstance(res, Index) and res.dtype == np.int64
+        tm.assert_index_equal(res, eres)
+        assert lidx is None
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+        # monotonic
+        res, lidx, ridx = index.join(other_mono, how="left", return_indexers=True)
+        eridx = np.array([-1, 1, -1, -1, -1, -1, 4, -1, -1, -1], dtype=np.intp)
+        assert isinstance(res, Index) and res.dtype == np.int64
+        tm.assert_index_equal(res, eres)
+        assert lidx is None
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+        # non-unique
+        idx = Index([1, 1, 2, 5])
+        idx2 = Index([1, 2, 5, 7, 9])
+        res, lidx, ridx = idx2.join(idx, how="left", return_indexers=True)
+        eres = Index([1, 1, 2, 5, 7, 9])  # 1 is in idx2, so it should be x2
+        eridx = np.array([0, 1, 2, 3, -1, -1], dtype=np.intp)
+        elidx = np.array([0, 0, 1, 2, 3, 4], dtype=np.intp)
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+    def test_join_right(self):
+        index = Index(range(0, 20, 2), dtype=np.int64)
+        other = Index([7, 12, 25, 1, 2, 5], dtype=np.int64)
+        other_mono = Index([1, 2, 5, 7, 12, 25], dtype=np.int64)
+
+        # not monotonic
+        res, lidx, ridx = index.join(other, how="right", return_indexers=True)
+        eres = other
+        elidx = np.array([-1, 6, -1, -1, 1, -1], dtype=np.intp)
+
+        assert isinstance(other, Index) and other.dtype == np.int64
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        assert ridx is None
+
+        # monotonic
+        res, lidx, ridx = index.join(other_mono, how="right", return_indexers=True)
+        eres = other_mono
+        elidx = np.array([-1, 1, -1, -1, 6, -1], dtype=np.intp)
+        assert isinstance(other, Index) and other.dtype == np.int64
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        assert ridx is None
+
+        # non-unique
+        idx = Index([1, 1, 2, 5])
+        idx2 = Index([1, 2, 5, 7, 9])
+        res, lidx, ridx = idx.join(idx2, how="right", return_indexers=True)
+        eres = Index([1, 1, 2, 5, 7, 9])  # 1 is in idx2, so it should be x2
+        elidx = np.array([0, 1, 2, 3, -1, -1], dtype=np.intp)
+        eridx = np.array([0, 0, 1, 2, 3, 4], dtype=np.intp)
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+    def test_join_non_int_index(self):
+        index = Index(range(0, 20, 2), dtype=np.int64)
+        other = Index([3, 6, 7, 8, 10], dtype=object)
+
+        outer = index.join(other, how="outer")
+        outer2 = other.join(index, how="outer")
+        expected = Index([0, 2, 3, 4, 6, 7, 8, 10, 12, 14, 16, 18])
+        tm.assert_index_equal(outer, outer2)
+        tm.assert_index_equal(outer, expected)
+
+        inner = index.join(other, how="inner")
+        inner2 = other.join(index, how="inner")
+        expected = Index([6, 8, 10])
+        tm.assert_index_equal(inner, inner2)
+        tm.assert_index_equal(inner, expected)
+
+        left = index.join(other, how="left")
+        tm.assert_index_equal(left, index.astype(object))
+
+        left2 = other.join(index, how="left")
+        tm.assert_index_equal(left2, other)
+
+        right = index.join(other, how="right")
+        tm.assert_index_equal(right, other)
+
+        right2 = other.join(index, how="right")
+        tm.assert_index_equal(right2, index.astype(object))
+
+    def test_join_outer(self):
+        index = Index(range(0, 20, 2), dtype=np.int64)
+        other = Index([7, 12, 25, 1, 2, 5], dtype=np.int64)
+        other_mono = Index([1, 2, 5, 7, 12, 25], dtype=np.int64)
+
+        # not monotonic
+        # guarantee of sortedness
+        res, lidx, ridx = index.join(other, how="outer", return_indexers=True)
+        noidx_res = index.join(other, how="outer")
+        tm.assert_index_equal(res, noidx_res)
+
+        eres = Index([0, 1, 2, 4, 5, 6, 7, 8, 10, 12, 14, 16, 18, 25], dtype=np.int64)
+        elidx = np.array([0, -1, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, 9, -1], dtype=np.intp)
+        eridx = np.array(
+            [-1, 3, 4, -1, 5, -1, 0, -1, -1, 1, -1, -1, -1, 2], dtype=np.intp
+        )
+
+        assert isinstance(res, Index) and res.dtype == np.int64
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+        # monotonic
+        res, lidx, ridx = index.join(other_mono, how="outer", return_indexers=True)
+        noidx_res = index.join(other_mono, how="outer")
+        tm.assert_index_equal(res, noidx_res)
+
+        elidx = np.array([0, -1, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, 9, -1], dtype=np.intp)
+        eridx = np.array(
+            [-1, 0, 1, -1, 2, -1, 3, -1, -1, 4, -1, -1, -1, 5], dtype=np.intp
+        )
+        assert isinstance(res, Index) and res.dtype == np.int64
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+
+class TestJoinUInt64Index:
+    @pytest.fixture
+    def index_large(self):
+        # large values used in TestUInt64Index where no compat needed with int64/float64
+        large = [2**63, 2**63 + 10, 2**63 + 15, 2**63 + 20, 2**63 + 25]
+        return Index(large, dtype=np.uint64)
+
+    def test_join_inner(self, index_large):
+        other = Index(2**63 + np.array([7, 12, 25, 1, 2, 10], dtype="uint64"))
+        other_mono = Index(2**63 + np.array([1, 2, 7, 10, 12, 25], dtype="uint64"))
+
+        # not monotonic
+        res, lidx, ridx = index_large.join(other, how="inner", return_indexers=True)
+
+        # no guarantee of sortedness, so sort for comparison purposes
+        ind = res.argsort()
+        res = res.take(ind)
+        lidx = lidx.take(ind)
+        ridx = ridx.take(ind)
+
+        eres = Index(2**63 + np.array([10, 25], dtype="uint64"))
+        elidx = np.array([1, 4], dtype=np.intp)
+        eridx = np.array([5, 2], dtype=np.intp)
+
+        assert isinstance(res, Index) and res.dtype == np.uint64
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+        # monotonic
+        res, lidx, ridx = index_large.join(
+            other_mono, how="inner", return_indexers=True
+        )
+
+        res2 = index_large.intersection(other_mono)
+        tm.assert_index_equal(res, res2)
+
+        elidx = np.array([1, 4], dtype=np.intp)
+        eridx = np.array([3, 5], dtype=np.intp)
+
+        assert isinstance(res, Index) and res.dtype == np.uint64
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+    def test_join_left(self, index_large):
+        other = Index(2**63 + np.array([7, 12, 25, 1, 2, 10], dtype="uint64"))
+        other_mono = Index(2**63 + np.array([1, 2, 7, 10, 12, 25], dtype="uint64"))
+
+        # not monotonic
+        res, lidx, ridx = index_large.join(other, how="left", return_indexers=True)
+        eres = index_large
+        eridx = np.array([-1, 5, -1, -1, 2], dtype=np.intp)
+
+        assert isinstance(res, Index) and res.dtype == np.uint64
+        tm.assert_index_equal(res, eres)
+        assert lidx is None
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+        # monotonic
+        res, lidx, ridx = index_large.join(other_mono, how="left", return_indexers=True)
+        eridx = np.array([-1, 3, -1, -1, 5], dtype=np.intp)
+
+        assert isinstance(res, Index) and res.dtype == np.uint64
+        tm.assert_index_equal(res, eres)
+        assert lidx is None
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+        # non-unique
+        idx = Index(2**63 + np.array([1, 1, 2, 5], dtype="uint64"))
+        idx2 = Index(2**63 + np.array([1, 2, 5, 7, 9], dtype="uint64"))
+        res, lidx, ridx = idx2.join(idx, how="left", return_indexers=True)
+
+        # 1 is in idx2, so it should be x2
+        eres = Index(2**63 + np.array([1, 1, 2, 5, 7, 9], dtype="uint64"))
+        eridx = np.array([0, 1, 2, 3, -1, -1], dtype=np.intp)
+        elidx = np.array([0, 0, 1, 2, 3, 4], dtype=np.intp)
+
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+    def test_join_right(self, index_large):
+        other = Index(2**63 + np.array([7, 12, 25, 1, 2, 10], dtype="uint64"))
+        other_mono = Index(2**63 + np.array([1, 2, 7, 10, 12, 25], dtype="uint64"))
+
+        # not monotonic
+        res, lidx, ridx = index_large.join(other, how="right", return_indexers=True)
+        eres = other
+        elidx = np.array([-1, -1, 4, -1, -1, 1], dtype=np.intp)
+
+        tm.assert_numpy_array_equal(lidx, elidx)
+        assert isinstance(other, Index) and other.dtype == np.uint64
+        tm.assert_index_equal(res, eres)
+        assert ridx is None
+
+        # monotonic
+        res, lidx, ridx = index_large.join(
+            other_mono, how="right", return_indexers=True
+        )
+        eres = other_mono
+        elidx = np.array([-1, -1, -1, 1, -1, 4], dtype=np.intp)
+
+        assert isinstance(other, Index) and other.dtype == np.uint64
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_index_equal(res, eres)
+        assert ridx is None
+
+        # non-unique
+        idx = Index(2**63 + np.array([1, 1, 2, 5], dtype="uint64"))
+        idx2 = Index(2**63 + np.array([1, 2, 5, 7, 9], dtype="uint64"))
+        res, lidx, ridx = idx.join(idx2, how="right", return_indexers=True)
+
+        # 1 is in idx2, so it should be x2
+        eres = Index(2**63 + np.array([1, 1, 2, 5, 7, 9], dtype="uint64"))
+        elidx = np.array([0, 1, 2, 3, -1, -1], dtype=np.intp)
+        eridx = np.array([0, 0, 1, 2, 3, 4], dtype=np.intp)
+
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+    def test_join_non_int_index(self, index_large):
+        other = Index(
+            2**63 + np.array([1, 5, 7, 10, 20], dtype="uint64"), dtype=object
+        )
+
+        outer = index_large.join(other, how="outer")
+        outer2 = other.join(index_large, how="outer")
+        expected = Index(
+            2**63 + np.array([0, 1, 5, 7, 10, 15, 20, 25], dtype="uint64")
+        )
+        tm.assert_index_equal(outer, outer2)
+        tm.assert_index_equal(outer, expected)
+
+        inner = index_large.join(other, how="inner")
+        inner2 = other.join(index_large, how="inner")
+        expected = Index(2**63 + np.array([10, 20], dtype="uint64"))
+        tm.assert_index_equal(inner, inner2)
+        tm.assert_index_equal(inner, expected)
+
+        left = index_large.join(other, how="left")
+        tm.assert_index_equal(left, index_large.astype(object))
+
+        left2 = other.join(index_large, how="left")
+        tm.assert_index_equal(left2, other)
+
+        right = index_large.join(other, how="right")
+        tm.assert_index_equal(right, other)
+
+        right2 = other.join(index_large, how="right")
+        tm.assert_index_equal(right2, index_large.astype(object))
+
+    def test_join_outer(self, index_large):
+        other = Index(2**63 + np.array([7, 12, 25, 1, 2, 10], dtype="uint64"))
+        other_mono = Index(2**63 + np.array([1, 2, 7, 10, 12, 25], dtype="uint64"))
+
+        # not monotonic
+        # guarantee of sortedness
+        res, lidx, ridx = index_large.join(other, how="outer", return_indexers=True)
+        noidx_res = index_large.join(other, how="outer")
+        tm.assert_index_equal(res, noidx_res)
+
+        eres = Index(
+            2**63 + np.array([0, 1, 2, 7, 10, 12, 15, 20, 25], dtype="uint64")
+        )
+        elidx = np.array([0, -1, -1, -1, 1, -1, 2, 3, 4], dtype=np.intp)
+        eridx = np.array([-1, 3, 4, 0, 5, 1, -1, -1, 2], dtype=np.intp)
+
+        assert isinstance(res, Index) and res.dtype == np.uint64
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)
+
+        # monotonic
+        res, lidx, ridx = index_large.join(
+            other_mono, how="outer", return_indexers=True
+        )
+        noidx_res = index_large.join(other_mono, how="outer")
+        tm.assert_index_equal(res, noidx_res)
+
+        elidx = np.array([0, -1, -1, -1, 1, -1, 2, 3, 4], dtype=np.intp)
+        eridx = np.array([-1, 0, 1, 2, 3, 4, -1, -1, 5], dtype=np.intp)
+
+        assert isinstance(res, Index) and res.dtype == np.uint64
+        tm.assert_index_equal(res, eres)
+        tm.assert_numpy_array_equal(lidx, elidx)
+        tm.assert_numpy_array_equal(ridx, eridx)

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_numeric.py

@@ -0,0 +1,553 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestFloatNumericIndex:
+    @pytest.fixture(params=[np.float64, np.float32])
+    def dtype(self, request):
+        return request.param
+
+    @pytest.fixture
+    def simple_index(self, dtype):
+        values = np.arange(5, dtype=dtype)
+        return Index(values)
+
+    @pytest.fixture(
+        params=[
+            [1.5, 2, 3, 4, 5],
+            [0.0, 2.5, 5.0, 7.5, 10.0],
+            [5, 4, 3, 2, 1.5],
+            [10.0, 7.5, 5.0, 2.5, 0.0],
+        ],
+        ids=["mixed", "float", "mixed_dec", "float_dec"],
+    )
+    def index(self, request, dtype):
+        return Index(request.param, dtype=dtype)
+
+    @pytest.fixture
+    def mixed_index(self, dtype):
+        return Index([1.5, 2, 3, 4, 5], dtype=dtype)
+
+    @pytest.fixture
+    def float_index(self, dtype):
+        return Index([0.0, 2.5, 5.0, 7.5, 10.0], dtype=dtype)
+
+    def test_repr_roundtrip(self, index):
+        tm.assert_index_equal(eval(repr(index)), index, exact=True)
+
+    def check_coerce(self, a, b, is_float_index=True):
+        assert a.equals(b)
+        tm.assert_index_equal(a, b, exact=False)
+        if is_float_index:
+            assert isinstance(b, Index)
+        else:
+            assert type(b) is Index
+
+    def test_constructor_from_list_no_dtype(self):
+        index = Index([1.5, 2.5, 3.5])
+        assert index.dtype == np.float64
+
+    def test_constructor(self, dtype):
+        index_cls = Index
+
+        # explicit construction
+        index = index_cls([1, 2, 3, 4, 5], dtype=dtype)
+
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        expected = np.array([1, 2, 3, 4, 5], dtype=dtype)
+        tm.assert_numpy_array_equal(index.values, expected)
+
+        index = index_cls(np.array([1, 2, 3, 4, 5]), dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        index = index_cls([1.0, 2, 3, 4, 5], dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        index = index_cls(np.array([1.0, 2, 3, 4, 5]), dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        index = index_cls([1.0, 2, 3, 4, 5], dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        index = index_cls(np.array([1.0, 2, 3, 4, 5]), dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        # nan handling
+        result = index_cls([np.nan, np.nan], dtype=dtype)
+        assert pd.isna(result.values).all()
+
+        result = index_cls(np.array([np.nan]), dtype=dtype)
+        assert pd.isna(result.values).all()
+
+    def test_constructor_invalid(self):
+        index_cls = Index
+        cls_name = index_cls.__name__
+        # invalid
+        msg = (
+            rf"{cls_name}\(\.\.\.\) must be called with a collection of "
+            r"some kind, 0\.0 was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            index_cls(0.0)
+
+    def test_constructor_coerce(self, mixed_index, float_index):
+        self.check_coerce(mixed_index, Index([1.5, 2, 3, 4, 5]))
+        self.check_coerce(float_index, Index(np.arange(5) * 2.5))
+
+        result = Index(np.array(np.arange(5) * 2.5, dtype=object))
+        assert result.dtype == object  # as of 2.0 to match Series
+        self.check_coerce(float_index, result.astype("float64"))
+
+    def test_constructor_explicit(self, mixed_index, float_index):
+        # these don't auto convert
+        self.check_coerce(
+            float_index, Index((np.arange(5) * 2.5), dtype=object), is_float_index=False
+        )
+        self.check_coerce(
+            mixed_index, Index([1.5, 2, 3, 4, 5], dtype=object), is_float_index=False
+        )
+
+    def test_type_coercion_fail(self, any_int_numpy_dtype):
+        # see gh-15832
+        msg = "Trying to coerce float values to integers"
+        with pytest.raises(ValueError, match=msg):
+            Index([1, 2, 3.5], dtype=any_int_numpy_dtype)
+
+    def test_equals_numeric(self):
+        index_cls = Index
+
+        idx = index_cls([1.0, 2.0])
+        assert idx.equals(idx)
+        assert idx.identical(idx)
+
+        idx2 = index_cls([1.0, 2.0])
+        assert idx.equals(idx2)
+
+        idx = index_cls([1.0, np.nan])
+        assert idx.equals(idx)
+        assert idx.identical(idx)
+
+        idx2 = index_cls([1.0, np.nan])
+        assert idx.equals(idx2)
+
+    @pytest.mark.parametrize(
+        "other",
+        (
+            Index([1, 2], dtype=np.int64),
+            Index([1.0, 2.0], dtype=object),
+            Index([1, 2], dtype=object),
+        ),
+    )
+    def test_equals_numeric_other_index_type(self, other):
+        idx = Index([1.0, 2.0])
+        assert idx.equals(other)
+        assert other.equals(idx)
+
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            pd.date_range("2016-01-01", periods=3),
+            pd.timedelta_range("1 Day", periods=3),
+        ],
+    )
+    def test_lookups_datetimelike_values(self, vals, dtype):
+        # If we have datetime64 or timedelta64 values, make sure they are
+        #  wrapped correctly  GH#31163
+        ser = Series(vals, index=range(3, 6))
+        ser.index = ser.index.astype(dtype)
+
+        expected = vals[1]
+
+        result = ser[4.0]
+        assert isinstance(result, type(expected)) and result == expected
+        result = ser[4]
+        assert isinstance(result, type(expected)) and result == expected
+
+        result = ser.loc[4.0]
+        assert isinstance(result, type(expected)) and result == expected
+        result = ser.loc[4]
+        assert isinstance(result, type(expected)) and result == expected
+
+        result = ser.at[4.0]
+        assert isinstance(result, type(expected)) and result == expected
+        # GH#31329 .at[4] should cast to 4.0, matching .loc behavior
+        result = ser.at[4]
+        assert isinstance(result, type(expected)) and result == expected
+
+        result = ser.iloc[1]
+        assert isinstance(result, type(expected)) and result == expected
+
+        result = ser.iat[1]
+        assert isinstance(result, type(expected)) and result == expected
+
+    def test_doesnt_contain_all_the_things(self):
+        idx = Index([np.nan])
+        assert not idx.isin([0]).item()
+        assert not idx.isin([1]).item()
+        assert idx.isin([np.nan]).item()
+
+    def test_nan_multiple_containment(self):
+        index_cls = Index
+
+        idx = index_cls([1.0, np.nan])
+        tm.assert_numpy_array_equal(idx.isin([1.0]), np.array([True, False]))
+        tm.assert_numpy_array_equal(idx.isin([2.0, np.pi]), np.array([False, False]))
+        tm.assert_numpy_array_equal(idx.isin([np.nan]), np.array([False, True]))
+        tm.assert_numpy_array_equal(idx.isin([1.0, np.nan]), np.array([True, True]))
+        idx = index_cls([1.0, 2.0])
+        tm.assert_numpy_array_equal(idx.isin([np.nan]), np.array([False, False]))
+
+    def test_fillna_float64(self):
+        index_cls = Index
+        # GH 11343
+        idx = Index([1.0, np.nan, 3.0], dtype=float, name="x")
+        # can't downcast
+        exp = Index([1.0, 0.1, 3.0], name="x")
+        tm.assert_index_equal(idx.fillna(0.1), exp, exact=True)
+
+        # downcast
+        exp = index_cls([1.0, 2.0, 3.0], name="x")
+        tm.assert_index_equal(idx.fillna(2), exp)
+
+        # object
+        exp = Index([1.0, "obj", 3.0], name="x")
+        tm.assert_index_equal(idx.fillna("obj"), exp, exact=True)
+
+    def test_logical_compat(self, simple_index):
+        idx = simple_index
+        assert idx.all() == idx.values.all()
+        assert idx.any() == idx.values.any()
+
+        assert idx.all() == idx.to_series().all()
+        assert idx.any() == idx.to_series().any()
+
+
+class TestNumericInt:
+    @pytest.fixture(params=[np.int64, np.int32, np.int16, np.int8, np.uint64])
+    def dtype(self, request):
+        return request.param
+
+    @pytest.fixture
+    def simple_index(self, dtype):
+        return Index(range(0, 20, 2), dtype=dtype)
+
+    def test_is_monotonic(self):
+        index_cls = Index
+
+        index = index_cls([1, 2, 3, 4])
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_increasing is True
+        assert index._is_strictly_monotonic_increasing is True
+        assert index.is_monotonic_decreasing is False
+        assert index._is_strictly_monotonic_decreasing is False
+
+        index = index_cls([4, 3, 2, 1])
+        assert index.is_monotonic_increasing is False
+        assert index._is_strictly_monotonic_increasing is False
+        assert index._is_strictly_monotonic_decreasing is True
+
+        index = index_cls([1])
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_decreasing is True
+        assert index._is_strictly_monotonic_increasing is True
+        assert index._is_strictly_monotonic_decreasing is True
+
+    def test_is_strictly_monotonic(self):
+        index_cls = Index
+
+        index = index_cls([1, 1, 2, 3])
+        assert index.is_monotonic_increasing is True
+        assert index._is_strictly_monotonic_increasing is False
+
+        index = index_cls([3, 2, 1, 1])
+        assert index.is_monotonic_decreasing is True
+        assert index._is_strictly_monotonic_decreasing is False
+
+        index = index_cls([1, 1])
+        assert index.is_monotonic_increasing
+        assert index.is_monotonic_decreasing
+        assert not index._is_strictly_monotonic_increasing
+        assert not index._is_strictly_monotonic_decreasing
+
+    def test_logical_compat(self, simple_index):
+        idx = simple_index
+        assert idx.all() == idx.values.all()
+        assert idx.any() == idx.values.any()
+
+    def test_identical(self, simple_index, dtype):
+        index = simple_index
+
+        idx = Index(index.copy())
+        assert idx.identical(index)
+
+        same_values_different_type = Index(idx, dtype=object)
+        assert not idx.identical(same_values_different_type)
+
+        idx = index.astype(dtype=object)
+        idx = idx.rename("foo")
+        same_values = Index(idx, dtype=object)
+        assert same_values.identical(idx)
+
+        assert not idx.identical(index)
+        assert Index(same_values, name="foo", dtype=object).identical(idx)
+
+        assert not index.astype(dtype=object).identical(index.astype(dtype=dtype))
+
+    def test_cant_or_shouldnt_cast(self, dtype):
+        msg = r"invalid literal for int\(\) with base 10: 'foo'"
+
+        # can't
+        data = ["foo", "bar", "baz"]
+        with pytest.raises(ValueError, match=msg):
+            Index(data, dtype=dtype)
+
+    def test_view_index(self, simple_index):
+        index = simple_index
+        msg = "Passing a type in .*Index.view is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            index.view(Index)
+
+    def test_prevent_casting(self, simple_index):
+        index = simple_index
+        result = index.astype("O")
+        assert result.dtype == np.object_
+
+
+class TestIntNumericIndex:
+    @pytest.fixture(params=[np.int64, np.int32, np.int16, np.int8])
+    def dtype(self, request):
+        return request.param
+
+    def test_constructor_from_list_no_dtype(self):
+        index = Index([1, 2, 3])
+        assert index.dtype == np.int64
+
+    def test_constructor(self, dtype):
+        index_cls = Index
+
+        # scalar raise Exception
+        msg = (
+            rf"{index_cls.__name__}\(\.\.\.\) must be called with a collection of some "
+            "kind, 5 was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            index_cls(5)
+
+        # copy
+        # pass list, coerce fine
+        index = index_cls([-5, 0, 1, 2], dtype=dtype)
+        arr = index.values.copy()
+        new_index = index_cls(arr, copy=True)
+        tm.assert_index_equal(new_index, index, exact=True)
+        val = int(arr[0]) + 3000
+
+        # this should not change index
+        if dtype != np.int8:
+            # NEP 50 won't allow assignment that would overflow
+            arr[0] = val
+            assert new_index[0] != val
+
+        if dtype == np.int64:
+            # pass list, coerce fine
+            index = index_cls([-5, 0, 1, 2], dtype=dtype)
+            expected = Index([-5, 0, 1, 2], dtype=dtype)
+            tm.assert_index_equal(index, expected)
+
+            # from iterable
+            index = index_cls(iter([-5, 0, 1, 2]), dtype=dtype)
+            expected = index_cls([-5, 0, 1, 2], dtype=dtype)
+            tm.assert_index_equal(index, expected, exact=True)
+
+            # interpret list-like
+            expected = index_cls([5, 0], dtype=dtype)
+            for cls in [Index, index_cls]:
+                for idx in [
+                    cls([5, 0], dtype=dtype),
+                    cls(np.array([5, 0]), dtype=dtype),
+                    cls(Series([5, 0]), dtype=dtype),
+                ]:
+                    tm.assert_index_equal(idx, expected)
+
+    def test_constructor_corner(self, dtype):
+        index_cls = Index
+
+        arr = np.array([1, 2, 3, 4], dtype=object)
+
+        index = index_cls(arr, dtype=dtype)
+        assert index.values.dtype == index.dtype
+        if dtype == np.int64:
+            without_dtype = Index(arr)
+            # as of 2.0 we do not infer a dtype when we get an object-dtype
+            #  ndarray of numbers, matching Series behavior
+            assert without_dtype.dtype == object
+
+            tm.assert_index_equal(index, without_dtype.astype(np.int64))
+
+        # preventing casting
+        arr = np.array([1, "2", 3, "4"], dtype=object)
+        msg = "Trying to coerce float values to integers"
+        with pytest.raises(ValueError, match=msg):
+            index_cls(arr, dtype=dtype)
+
+    def test_constructor_coercion_signed_to_unsigned(
+        self,
+        any_unsigned_int_numpy_dtype,
+    ):
+        # see gh-15832
+        msg = "|".join(
+            [
+                "Trying to coerce negative values to unsigned integers",
+                "The elements provided in the data cannot all be casted",
+            ]
+        )
+        with pytest.raises(OverflowError, match=msg):
+            Index([-1], dtype=any_unsigned_int_numpy_dtype)
+
+    def test_constructor_np_signed(self, any_signed_int_numpy_dtype):
+        # GH#47475
+        scalar = np.dtype(any_signed_int_numpy_dtype).type(1)
+        result = Index([scalar])
+        expected = Index([1], dtype=any_signed_int_numpy_dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_constructor_np_unsigned(self, any_unsigned_int_numpy_dtype):
+        # GH#47475
+        scalar = np.dtype(any_unsigned_int_numpy_dtype).type(1)
+        result = Index([scalar])
+        expected = Index([1], dtype=any_unsigned_int_numpy_dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_coerce_list(self):
+        # coerce things
+        arr = Index([1, 2, 3, 4])
+        assert isinstance(arr, Index)
+
+        # but not if explicit dtype passed
+        arr = Index([1, 2, 3, 4], dtype=object)
+        assert type(arr) is Index
+
+
+class TestFloat16Index:
+    # float 16 indexes not supported
+    # GH 49535
+    def test_constructor(self):
+        index_cls = Index
+        dtype = np.float16
+
+        msg = "float16 indexes are not supported"
+
+        # explicit construction
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls([1, 2, 3, 4, 5], dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls(np.array([1, 2, 3, 4, 5]), dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls([1.0, 2, 3, 4, 5], dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls(np.array([1.0, 2, 3, 4, 5]), dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls([1.0, 2, 3, 4, 5], dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls(np.array([1.0, 2, 3, 4, 5]), dtype=dtype)
+
+        # nan handling
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls([np.nan, np.nan], dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls(np.array([np.nan]), dtype=dtype)
+
+
+@pytest.mark.parametrize(
+    "box",
+    [list, lambda x: np.array(x, dtype=object), lambda x: Index(x, dtype=object)],
+)
+def test_uint_index_does_not_convert_to_float64(box):
+    # https://github.com/pandas-dev/pandas/issues/28279
+    # https://github.com/pandas-dev/pandas/issues/28023
+    series = Series(
+        [0, 1, 2, 3, 4, 5],
+        index=[
+            7606741985629028552,
+            17876870360202815256,
+            17876870360202815256,
+            13106359306506049338,
+            8991270399732411471,
+            8991270399732411472,
+        ],
+    )
+
+    result = series.loc[box([7606741985629028552, 17876870360202815256])]
+
+    expected = Index(
+        [7606741985629028552, 17876870360202815256, 17876870360202815256],
+        dtype="uint64",
+    )
+    tm.assert_index_equal(result.index, expected)
+
+    tm.assert_equal(result, series.iloc[:3])
+
+
+def test_float64_index_equals():
+    # https://github.com/pandas-dev/pandas/issues/35217
+    float_index = Index([1.0, 2, 3])
+    string_index = Index(["1", "2", "3"])
+
+    result = float_index.equals(string_index)
+    assert result is False
+
+    result = string_index.equals(float_index)
+    assert result is False
+
+
+def test_map_dtype_inference_unsigned_to_signed():
+    # GH#44609 cases where we don't retain dtype
+    idx = Index([1, 2, 3], dtype=np.uint64)
+    result = idx.map(lambda x: -x)
+    expected = Index([-1, -2, -3], dtype=np.int64)
+    tm.assert_index_equal(result, expected)
+
+
+def test_map_dtype_inference_overflows():
+    # GH#44609 case where we have to upcast
+    idx = Index(np.array([1, 2, 3], dtype=np.int8))
+    result = idx.map(lambda x: x * 1000)
+    # TODO: we could plausibly try to infer down to int16 here
+    expected = Index([1000, 2000, 3000], dtype=np.int64)
+    tm.assert_index_equal(result, expected)
+
+
+def test_view_to_datetimelike():
+    # GH#55710
+    idx = Index([1, 2, 3])
+    res = idx.view("m8[s]")
+    expected = pd.TimedeltaIndex(idx.values.view("m8[s]"))
+    tm.assert_index_equal(res, expected)
+
+    res2 = idx.view("m8[D]")
+    expected2 = idx.values.view("m8[D]")
+    tm.assert_numpy_array_equal(res2, expected2)
+
+    res3 = idx.view("M8[h]")
+    expected3 = idx.values.view("M8[h]")
+    tm.assert_numpy_array_equal(res3, expected3)

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_setops.py

@@ -0,0 +1,168 @@
+from datetime import (
+    datetime,
+    timedelta,
+)
+
+import numpy as np
+import pytest
+
+import pandas._testing as tm
+from pandas.core.indexes.api import (
+    Index,
+    RangeIndex,
+)
+
+
+@pytest.fixture
+def index_large():
+    # large values used in TestUInt64Index where no compat needed with int64/float64
+    large = [2**63, 2**63 + 10, 2**63 + 15, 2**63 + 20, 2**63 + 25]
+    return Index(large, dtype=np.uint64)
+
+
+class TestSetOps:
+    @pytest.mark.parametrize("dtype", ["f8", "u8", "i8"])
+    def test_union_non_numeric(self, dtype):
+        # corner case, non-numeric
+        index = Index(np.arange(5, dtype=dtype), dtype=dtype)
+        assert index.dtype == dtype
+
+        other = Index([datetime.now() + timedelta(i) for i in range(4)], dtype=object)
+        result = index.union(other)
+        expected = Index(np.concatenate((index, other)))
+        tm.assert_index_equal(result, expected)
+
+        result = other.union(index)
+        expected = Index(np.concatenate((other, index)))
+        tm.assert_index_equal(result, expected)
+
+    def test_intersection(self):
+        index = Index(range(5), dtype=np.int64)
+
+        other = Index([1, 2, 3, 4, 5])
+        result = index.intersection(other)
+        expected = Index(np.sort(np.intersect1d(index.values, other.values)))
+        tm.assert_index_equal(result, expected)
+
+        result = other.intersection(index)
+        expected = Index(
+            np.sort(np.asarray(np.intersect1d(index.values, other.values)))
+        )
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["int64", "uint64"])
+    def test_int_float_union_dtype(self, dtype):
+        # https://github.com/pandas-dev/pandas/issues/26778
+        # [u]int | float -> float
+        index = Index([0, 2, 3], dtype=dtype)
+        other = Index([0.5, 1.5], dtype=np.float64)
+        expected = Index([0.0, 0.5, 1.5, 2.0, 3.0], dtype=np.float64)
+        result = index.union(other)
+        tm.assert_index_equal(result, expected)
+
+        result = other.union(index)
+        tm.assert_index_equal(result, expected)
+
+    def test_range_float_union_dtype(self):
+        # https://github.com/pandas-dev/pandas/issues/26778
+        index = RangeIndex(start=0, stop=3)
+        other = Index([0.5, 1.5], dtype=np.float64)
+        result = index.union(other)
+        expected = Index([0.0, 0.5, 1, 1.5, 2.0], dtype=np.float64)
+        tm.assert_index_equal(result, expected)
+
+        result = other.union(index)
+        tm.assert_index_equal(result, expected)
+
+    def test_range_uint64_union_dtype(self):
+        # https://github.com/pandas-dev/pandas/issues/26778
+        index = RangeIndex(start=0, stop=3)
+        other = Index([0, 10], dtype=np.uint64)
+        result = index.union(other)
+        expected = Index([0, 1, 2, 10], dtype=object)
+        tm.assert_index_equal(result, expected)
+
+        result = other.union(index)
+        tm.assert_index_equal(result, expected)
+
+    def test_float64_index_difference(self):
+        # https://github.com/pandas-dev/pandas/issues/35217
+        float_index = Index([1.0, 2, 3])
+        string_index = Index(["1", "2", "3"])
+
+        result = float_index.difference(string_index)
+        tm.assert_index_equal(result, float_index)
+
+        result = string_index.difference(float_index)
+        tm.assert_index_equal(result, string_index)
+
+    def test_intersection_uint64_outside_int64_range(self, index_large):
+        other = Index([2**63, 2**63 + 5, 2**63 + 10, 2**63 + 15, 2**63 + 20])
+        result = index_large.intersection(other)
+        expected = Index(np.sort(np.intersect1d(index_large.values, other.values)))
+        tm.assert_index_equal(result, expected)
+
+        result = other.intersection(index_large)
+        expected = Index(
+            np.sort(np.asarray(np.intersect1d(index_large.values, other.values)))
+        )
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index2,keeps_name",
+        [
+            (Index([4, 7, 6, 5, 3], name="index"), True),
+            (Index([4, 7, 6, 5, 3], name="other"), False),
+        ],
+    )
+    def test_intersection_monotonic(self, index2, keeps_name, sort):
+        index1 = Index([5, 3, 2, 4, 1], name="index")
+        expected = Index([5, 3, 4])
+
+        if keeps_name:
+            expected.name = "index"
+
+        result = index1.intersection(index2, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+
+    def test_symmetric_difference(self, sort):
+        # smoke
+        index1 = Index([5, 2, 3, 4], name="index1")
+        index2 = Index([2, 3, 4, 1])
+        result = index1.symmetric_difference(index2, sort=sort)
+        expected = Index([5, 1])
+        if sort is not None:
+            tm.assert_index_equal(result, expected)
+        else:
+            tm.assert_index_equal(result, expected.sort_values())
+        assert result.name is None
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+
+
+class TestSetOpsSort:
+    @pytest.mark.parametrize("slice_", [slice(None), slice(0)])
+    def test_union_sort_other_special(self, slice_):
+        # https://github.com/pandas-dev/pandas/issues/24959
+
+        idx = Index([1, 0, 2])
+        # default, sort=None
+        other = idx[slice_]
+        tm.assert_index_equal(idx.union(other), idx)
+        tm.assert_index_equal(other.union(idx), idx)
+
+        # sort=False
+        tm.assert_index_equal(idx.union(other, sort=False), idx)
+
+    @pytest.mark.parametrize("slice_", [slice(None), slice(0)])
+    def test_union_sort_special_true(self, slice_):
+        idx = Index([1, 0, 2])
+        # default, sort=None
+        other = idx[slice_]
+
+        result = idx.union(other, sort=True)
+        expected = Index([0, 1, 2])
+        tm.assert_index_equal(result, expected)

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/test_any_index.py

@@ -0,0 +1,172 @@
+"""
+Tests that can be parametrized over _any_ Index object.
+"""
+import re
+
+import numpy as np
+import pytest
+
+from pandas.errors import InvalidIndexError
+
+import pandas._testing as tm
+
+
+def test_boolean_context_compat(index):
+    # GH#7897
+    with pytest.raises(ValueError, match="The truth value of a"):
+        if index:
+            pass
+
+    with pytest.raises(ValueError, match="The truth value of a"):
+        bool(index)
+
+
+def test_sort(index):
+    msg = "cannot sort an Index object in-place, use sort_values instead"
+    with pytest.raises(TypeError, match=msg):
+        index.sort()
+
+
+def test_hash_error(index):
+    with pytest.raises(TypeError, match=f"unhashable type: '{type(index).__name__}'"):
+        hash(index)
+
+
+def test_mutability(index):
+    if not len(index):
+        pytest.skip("Test doesn't make sense for empty index")
+    msg = "Index does not support mutable operations"
+    with pytest.raises(TypeError, match=msg):
+        index[0] = index[0]
+
+
+@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+def test_map_identity_mapping(index, request):
+    # GH#12766
+
+    result = index.map(lambda x: x)
+    if index.dtype == object and result.dtype == bool:
+        assert (index == result).all()
+        # TODO: could work that into the 'exact="equiv"'?
+        return  # FIXME: doesn't belong in this file anymore!
+    tm.assert_index_equal(result, index, exact="equiv")
+
+
+def test_wrong_number_names(index):
+    names = index.nlevels * ["apple", "banana", "carrot"]
+    with pytest.raises(ValueError, match="^Length"):
+        index.names = names
+
+
+def test_view_preserves_name(index):
+    assert index.view().name == index.name
+
+
+def test_ravel(index):
+    # GH#19956 ravel returning ndarray is deprecated, in 2.0 returns a view on self
+    res = index.ravel()
+    tm.assert_index_equal(res, index)
+
+
+class TestConversion:
+    def test_to_series(self, index):
+        # assert that we are creating a copy of the index
+
+        ser = index.to_series()
+        assert ser.values is not index.values
+        assert ser.index is not index
+        assert ser.name == index.name
+
+    def test_to_series_with_arguments(self, index):
+        # GH#18699
+
+        # index kwarg
+        ser = index.to_series(index=index)
+
+        assert ser.values is not index.values
+        assert ser.index is index
+        assert ser.name == index.name
+
+        # name kwarg
+        ser = index.to_series(name="__test")
+
+        assert ser.values is not index.values
+        assert ser.index is not index
+        assert ser.name != index.name
+
+    def test_tolist_matches_list(self, index):
+        assert index.tolist() == list(index)
+
+
+class TestRoundTrips:
+    def test_pickle_roundtrip(self, index):
+        result = tm.round_trip_pickle(index)
+        tm.assert_index_equal(result, index, exact=True)
+        if result.nlevels > 1:
+            # GH#8367 round-trip with timezone
+            assert index.equal_levels(result)
+
+    def test_pickle_preserves_name(self, index):
+        original_name, index.name = index.name, "foo"
+        unpickled = tm.round_trip_pickle(index)
+        assert index.equals(unpickled)
+        index.name = original_name
+
+
+class TestIndexing:
+    def test_get_loc_listlike_raises_invalid_index_error(self, index):
+        # and never TypeError
+        key = np.array([0, 1], dtype=np.intp)
+
+        with pytest.raises(InvalidIndexError, match=r"\[0 1\]"):
+            index.get_loc(key)
+
+        with pytest.raises(InvalidIndexError, match=r"\[False  True\]"):
+            index.get_loc(key.astype(bool))
+
+    def test_getitem_ellipsis(self, index):
+        # GH#21282
+        result = index[...]
+        assert result.equals(index)
+        assert result is not index
+
+    def test_slice_keeps_name(self, index):
+        assert index.name == index[1:].name
+
+    @pytest.mark.parametrize("item", [101, "no_int", 2.5])
+    def test_getitem_error(self, index, item):
+        msg = "|".join(
+            [
+                r"index 101 is out of bounds for axis 0 with size [\d]+",
+                re.escape(
+                    "only integers, slices (`:`), ellipsis (`...`), "
+                    "numpy.newaxis (`None`) and integer or boolean arrays "
+                    "are valid indices"
+                ),
+                "index out of bounds",  # string[pyarrow]
+            ]
+        )
+        with pytest.raises(IndexError, match=msg):
+            index[item]
+
+
+class TestRendering:
+    def test_str(self, index):
+        # test the string repr
+        index.name = "foo"
+        assert "'foo'" in str(index)
+        assert type(index).__name__ in str(index)
+
+
+class TestReductions:
+    def test_argmax_axis_invalid(self, index):
+        # GH#23081
+        msg = r"`axis` must be fewer than the number of dimensions \(1\)"
+        with pytest.raises(ValueError, match=msg):
+            index.argmax(axis=1)
+        with pytest.raises(ValueError, match=msg):
+            index.argmin(axis=2)
+        with pytest.raises(ValueError, match=msg):
+            index.min(axis=-2)
+        with pytest.raises(ValueError, match=msg):
+            index.max(axis=-3)

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/test_base.py

@@ -0,0 +1,1737 @@
+from collections import defaultdict
+from datetime import datetime
+from functools import partial
+import math
+import operator
+import re
+
+import numpy as np
+import pytest
+
+from pandas.compat import IS64
+from pandas.errors import InvalidIndexError
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.common import (
+    is_any_real_numeric_dtype,
+    is_numeric_dtype,
+    is_object_dtype,
+)
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    DataFrame,
+    DatetimeIndex,
+    IntervalIndex,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    TimedeltaIndex,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    _get_combined_index,
+    ensure_index,
+    ensure_index_from_sequences,
+)
+
+
+class TestIndex:
+    @pytest.fixture
+    def simple_index(self) -> Index:
+        return Index(list("abcde"))
+
+    def test_can_hold_identifiers(self, simple_index):
+        index = simple_index
+        key = index[0]
+        assert index._can_hold_identifiers_and_holds_name(key) is True
+
+    @pytest.mark.parametrize("index", ["datetime"], indirect=True)
+    def test_new_axis(self, index):
+        # TODO: a bunch of scattered tests check this deprecation is enforced.
+        #  de-duplicate/centralize them.
+        with pytest.raises(ValueError, match="Multi-dimensional indexing"):
+            # GH#30588 multi-dimensional indexing deprecated
+            index[None, :]
+
+    def test_constructor_regular(self, index):
+        tm.assert_contains_all(index, index)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_constructor_casting(self, index):
+        # casting
+        arr = np.array(index)
+        new_index = Index(arr)
+        tm.assert_contains_all(arr, new_index)
+        tm.assert_index_equal(index, new_index)
+
+    def test_constructor_copy(self, using_infer_string):
+        index = Index(list("abc"), name="name")
+        arr = np.array(index)
+        new_index = Index(arr, copy=True, name="name")
+        assert isinstance(new_index, Index)
+        assert new_index.name == "name"
+        if using_infer_string:
+            tm.assert_extension_array_equal(
+                new_index.values, pd.array(arr, dtype="string[pyarrow_numpy]")
+            )
+        else:
+            tm.assert_numpy_array_equal(arr, new_index.values)
+        arr[0] = "SOMEBIGLONGSTRING"
+        assert new_index[0] != "SOMEBIGLONGSTRING"
+
+    @pytest.mark.parametrize("cast_as_obj", [True, False])
+    @pytest.mark.parametrize(
+        "index",
+        [
+            date_range(
+                "2015-01-01 10:00",
+                freq="D",
+                periods=3,
+                tz="US/Eastern",
+                name="Green Eggs & Ham",
+            ),  # DTI with tz
+            date_range("2015-01-01 10:00", freq="D", periods=3),  # DTI no tz
+            timedelta_range("1 days", freq="D", periods=3),  # td
+            period_range("2015-01-01", freq="D", periods=3),  # period
+        ],
+    )
+    def test_constructor_from_index_dtlike(self, cast_as_obj, index):
+        if cast_as_obj:
+            with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+                result = Index(index.astype(object))
+        else:
+            result = Index(index)
+
+        tm.assert_index_equal(result, index)
+
+        if isinstance(index, DatetimeIndex):
+            assert result.tz == index.tz
+            if cast_as_obj:
+                # GH#23524 check that Index(dti, dtype=object) does not
+                #  incorrectly raise ValueError, and that nanoseconds are not
+                #  dropped
+                index += pd.Timedelta(nanoseconds=50)
+                result = Index(index, dtype=object)
+                assert result.dtype == np.object_
+                assert list(result) == list(index)
+
+    @pytest.mark.parametrize(
+        "index,has_tz",
+        [
+            (
+                date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern"),
+                True,
+            ),  # datetimetz
+            (timedelta_range("1 days", freq="D", periods=3), False),  # td
+            (period_range("2015-01-01", freq="D", periods=3), False),  # period
+        ],
+    )
+    def test_constructor_from_series_dtlike(self, index, has_tz):
+        result = Index(Series(index))
+        tm.assert_index_equal(result, index)
+
+        if has_tz:
+            assert result.tz == index.tz
+
+    def test_constructor_from_series_freq(self):
+        # GH 6273
+        # create from a series, passing a freq
+        dts = ["1-1-1990", "2-1-1990", "3-1-1990", "4-1-1990", "5-1-1990"]
+        expected = DatetimeIndex(dts, freq="MS")
+
+        s = Series(pd.to_datetime(dts))
+        result = DatetimeIndex(s, freq="MS")
+
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_from_frame_series_freq(self, using_infer_string):
+        # GH 6273
+        # create from a series, passing a freq
+        dts = ["1-1-1990", "2-1-1990", "3-1-1990", "4-1-1990", "5-1-1990"]
+        expected = DatetimeIndex(dts, freq="MS")
+
+        df = DataFrame(np.random.default_rng(2).random((5, 3)))
+        df["date"] = dts
+        result = DatetimeIndex(df["date"], freq="MS")
+        dtype = object if not using_infer_string else "string"
+        assert df["date"].dtype == dtype
+        expected.name = "date"
+        tm.assert_index_equal(result, expected)
+
+        expected = Series(dts, name="date")
+        tm.assert_series_equal(df["date"], expected)
+
+        # GH 6274
+        # infer freq of same
+        if not using_infer_string:
+            # Doesn't work with arrow strings
+            freq = pd.infer_freq(df["date"])
+            assert freq == "MS"
+
+    def test_constructor_int_dtype_nan(self):
+        # see gh-15187
+        data = [np.nan]
+        expected = Index(data, dtype=np.float64)
+        result = Index(data, dtype="float")
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "klass,dtype,na_val",
+        [
+            (Index, np.float64, np.nan),
+            (DatetimeIndex, "datetime64[ns]", pd.NaT),
+        ],
+    )
+    def test_index_ctor_infer_nan_nat(self, klass, dtype, na_val):
+        # GH 13467
+        na_list = [na_val, na_val]
+        expected = klass(na_list)
+        assert expected.dtype == dtype
+
+        result = Index(na_list)
+        tm.assert_index_equal(result, expected)
+
+        result = Index(np.array(na_list))
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "vals,dtype",
+        [
+            ([1, 2, 3, 4, 5], "int"),
+            ([1.1, np.nan, 2.2, 3.0], "float"),
+            (["A", "B", "C", np.nan], "obj"),
+        ],
+    )
+    def test_constructor_simple_new(self, vals, dtype):
+        index = Index(vals, name=dtype)
+        result = index._simple_new(index.values, dtype)
+        tm.assert_index_equal(result, index)
+
+    @pytest.mark.parametrize("attr", ["values", "asi8"])
+    @pytest.mark.parametrize("klass", [Index, DatetimeIndex])
+    def test_constructor_dtypes_datetime(self, tz_naive_fixture, attr, klass):
+        # Test constructing with a datetimetz dtype
+        # .values produces numpy datetimes, so these are considered naive
+        # .asi8 produces integers, so these are considered epoch timestamps
+        # ^the above will be true in a later version. Right now we `.view`
+        # the i8 values as NS_DTYPE, effectively treating them as wall times.
+        index = date_range("2011-01-01", periods=5)
+        arg = getattr(index, attr)
+        index = index.tz_localize(tz_naive_fixture)
+        dtype = index.dtype
+
+        # As of 2.0 astype raises on dt64.astype(dt64tz)
+        err = tz_naive_fixture is not None
+        msg = "Cannot use .astype to convert from timezone-naive dtype to"
+
+        if attr == "asi8":
+            result = DatetimeIndex(arg).tz_localize(tz_naive_fixture)
+            tm.assert_index_equal(result, index)
+        elif klass is Index:
+            with pytest.raises(TypeError, match="unexpected keyword"):
+                klass(arg, tz=tz_naive_fixture)
+        else:
+            result = klass(arg, tz=tz_naive_fixture)
+            tm.assert_index_equal(result, index)
+
+        if attr == "asi8":
+            if err:
+                with pytest.raises(TypeError, match=msg):
+                    DatetimeIndex(arg).astype(dtype)
+            else:
+                result = DatetimeIndex(arg).astype(dtype)
+                tm.assert_index_equal(result, index)
+        else:
+            result = klass(arg, dtype=dtype)
+            tm.assert_index_equal(result, index)
+
+        if attr == "asi8":
+            result = DatetimeIndex(list(arg)).tz_localize(tz_naive_fixture)
+            tm.assert_index_equal(result, index)
+        elif klass is Index:
+            with pytest.raises(TypeError, match="unexpected keyword"):
+                klass(arg, tz=tz_naive_fixture)
+        else:
+            result = klass(list(arg), tz=tz_naive_fixture)
+            tm.assert_index_equal(result, index)
+
+        if attr == "asi8":
+            if err:
+                with pytest.raises(TypeError, match=msg):
+                    DatetimeIndex(list(arg)).astype(dtype)
+            else:
+                result = DatetimeIndex(list(arg)).astype(dtype)
+                tm.assert_index_equal(result, index)
+        else:
+            result = klass(list(arg), dtype=dtype)
+            tm.assert_index_equal(result, index)
+
+    @pytest.mark.parametrize("attr", ["values", "asi8"])
+    @pytest.mark.parametrize("klass", [Index, TimedeltaIndex])
+    def test_constructor_dtypes_timedelta(self, attr, klass):
+        index = timedelta_range("1 days", periods=5)
+        index = index._with_freq(None)  # won't be preserved by constructors
+        dtype = index.dtype
+
+        values = getattr(index, attr)
+
+        result = klass(values, dtype=dtype)
+        tm.assert_index_equal(result, index)
+
+        result = klass(list(values), dtype=dtype)
+        tm.assert_index_equal(result, index)
+
+    @pytest.mark.parametrize("value", [[], iter([]), (_ for _ in [])])
+    @pytest.mark.parametrize(
+        "klass",
+        [
+            Index,
+            CategoricalIndex,
+            DatetimeIndex,
+            TimedeltaIndex,
+        ],
+    )
+    def test_constructor_empty(self, value, klass):
+        empty = klass(value)
+        assert isinstance(empty, klass)
+        assert not len(empty)
+
+    @pytest.mark.parametrize(
+        "empty,klass",
+        [
+            (PeriodIndex([], freq="D"), PeriodIndex),
+            (PeriodIndex(iter([]), freq="D"), PeriodIndex),
+            (PeriodIndex((_ for _ in []), freq="D"), PeriodIndex),
+            (RangeIndex(step=1), RangeIndex),
+            (MultiIndex(levels=[[1, 2], ["blue", "red"]], codes=[[], []]), MultiIndex),
+        ],
+    )
+    def test_constructor_empty_special(self, empty, klass):
+        assert isinstance(empty, klass)
+        assert not len(empty)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            "datetime",
+            "float64",
+            "float32",
+            "int64",
+            "int32",
+            "period",
+            "range",
+            "repeats",
+            "timedelta",
+            "tuples",
+            "uint64",
+            "uint32",
+        ],
+        indirect=True,
+    )
+    def test_view_with_args(self, index):
+        index.view("i8")
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            "string",
+            pytest.param("categorical", marks=pytest.mark.xfail(reason="gh-25464")),
+            "bool-object",
+            "bool-dtype",
+            "empty",
+        ],
+        indirect=True,
+    )
+    def test_view_with_args_object_array_raises(self, index):
+        if index.dtype == bool:
+            msg = "When changing to a larger dtype"
+            with pytest.raises(ValueError, match=msg):
+                index.view("i8")
+        elif index.dtype == "string":
+            with pytest.raises(NotImplementedError, match="i8"):
+                index.view("i8")
+        else:
+            msg = (
+                "Cannot change data-type for array of references|"
+                "Cannot change data-type for object array|"
+            )
+            with pytest.raises(TypeError, match=msg):
+                index.view("i8")
+
+    @pytest.mark.parametrize(
+        "index",
+        ["int64", "int32", "range"],
+        indirect=True,
+    )
+    def test_astype(self, index):
+        casted = index.astype("i8")
+
+        # it works!
+        casted.get_loc(5)
+
+        # pass on name
+        index.name = "foobar"
+        casted = index.astype("i8")
+        assert casted.name == "foobar"
+
+    def test_equals_object(self):
+        # same
+        assert Index(["a", "b", "c"]).equals(Index(["a", "b", "c"]))
+
+    @pytest.mark.parametrize(
+        "comp", [Index(["a", "b"]), Index(["a", "b", "d"]), ["a", "b", "c"]]
+    )
+    def test_not_equals_object(self, comp):
+        assert not Index(["a", "b", "c"]).equals(comp)
+
+    def test_identical(self):
+        # index
+        i1 = Index(["a", "b", "c"])
+        i2 = Index(["a", "b", "c"])
+
+        assert i1.identical(i2)
+
+        i1 = i1.rename("foo")
+        assert i1.equals(i2)
+        assert not i1.identical(i2)
+
+        i2 = i2.rename("foo")
+        assert i1.identical(i2)
+
+        i3 = Index([("a", "a"), ("a", "b"), ("b", "a")])
+        i4 = Index([("a", "a"), ("a", "b"), ("b", "a")], tupleize_cols=False)
+        assert not i3.identical(i4)
+
+    def test_is_(self):
+        ind = Index(range(10))
+        assert ind.is_(ind)
+        assert ind.is_(ind.view().view().view().view())
+        assert not ind.is_(Index(range(10)))
+        assert not ind.is_(ind.copy())
+        assert not ind.is_(ind.copy(deep=False))
+        assert not ind.is_(ind[:])
+        assert not ind.is_(np.array(range(10)))
+
+        # quasi-implementation dependent
+        assert ind.is_(ind.view())
+        ind2 = ind.view()
+        ind2.name = "bob"
+        assert ind.is_(ind2)
+        assert ind2.is_(ind)
+        # doesn't matter if Indices are *actually* views of underlying data,
+        assert not ind.is_(Index(ind.values))
+        arr = np.array(range(1, 11))
+        ind1 = Index(arr, copy=False)
+        ind2 = Index(arr, copy=False)
+        assert not ind1.is_(ind2)
+
+    def test_asof_numeric_vs_bool_raises(self):
+        left = Index([1, 2, 3])
+        right = Index([True, False], dtype=object)
+
+        msg = "Cannot compare dtypes int64 and bool"
+        with pytest.raises(TypeError, match=msg):
+            left.asof(right[0])
+        # TODO: should right.asof(left[0]) also raise?
+
+        with pytest.raises(InvalidIndexError, match=re.escape(str(right))):
+            left.asof(right)
+
+        with pytest.raises(InvalidIndexError, match=re.escape(str(left))):
+            right.asof(left)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_booleanindex(self, index):
+        bool_index = np.ones(len(index), dtype=bool)
+        bool_index[5:30:2] = False
+
+        sub_index = index[bool_index]
+
+        for i, val in enumerate(sub_index):
+            assert sub_index.get_loc(val) == i
+
+        sub_index = index[list(bool_index)]
+        for i, val in enumerate(sub_index):
+            assert sub_index.get_loc(val) == i
+
+    def test_fancy(self, simple_index):
+        index = simple_index
+        sl = index[[1, 2, 3]]
+        for i in sl:
+            assert i == sl[sl.get_loc(i)]
+
+    @pytest.mark.parametrize(
+        "index",
+        ["string", "int64", "int32", "uint64", "uint32", "float64", "float32"],
+        indirect=True,
+    )
+    @pytest.mark.parametrize("dtype", [int, np.bool_])
+    def test_empty_fancy(self, index, dtype, request, using_infer_string):
+        if dtype is np.bool_ and using_infer_string and index.dtype == "string":
+            request.applymarker(pytest.mark.xfail(reason="numpy behavior is buggy"))
+        empty_arr = np.array([], dtype=dtype)
+        empty_index = type(index)([], dtype=index.dtype)
+
+        assert index[[]].identical(empty_index)
+        if dtype == np.bool_:
+            with tm.assert_produces_warning(FutureWarning, match="is deprecated"):
+                assert index[empty_arr].identical(empty_index)
+        else:
+            assert index[empty_arr].identical(empty_index)
+
+    @pytest.mark.parametrize(
+        "index",
+        ["string", "int64", "int32", "uint64", "uint32", "float64", "float32"],
+        indirect=True,
+    )
+    def test_empty_fancy_raises(self, index):
+        # DatetimeIndex is excluded, because it overrides getitem and should
+        # be tested separately.
+        empty_farr = np.array([], dtype=np.float64)
+        empty_index = type(index)([], dtype=index.dtype)
+
+        assert index[[]].identical(empty_index)
+        # np.ndarray only accepts ndarray of int & bool dtypes, so should Index
+        msg = r"arrays used as indices must be of integer"
+        with pytest.raises(IndexError, match=msg):
+            index[empty_farr]
+
+    def test_union_dt_as_obj(self, simple_index):
+        # TODO: Replace with fixturesult
+        index = simple_index
+        date_index = date_range("2019-01-01", periods=10)
+        first_cat = index.union(date_index)
+        second_cat = index.union(index)
+
+        appended = Index(np.append(index, date_index.astype("O")))
+
+        tm.assert_index_equal(first_cat, appended)
+        tm.assert_index_equal(second_cat, index)
+        tm.assert_contains_all(index, first_cat)
+        tm.assert_contains_all(index, second_cat)
+        tm.assert_contains_all(date_index, first_cat)
+
+    def test_map_with_tuples(self):
+        # GH 12766
+
+        # Test that returning a single tuple from an Index
+        #   returns an Index.
+        index = Index(np.arange(3), dtype=np.int64)
+        result = index.map(lambda x: (x,))
+        expected = Index([(i,) for i in index])
+        tm.assert_index_equal(result, expected)
+
+        # Test that returning a tuple from a map of a single index
+        #   returns a MultiIndex object.
+        result = index.map(lambda x: (x, x == 1))
+        expected = MultiIndex.from_tuples([(i, i == 1) for i in index])
+        tm.assert_index_equal(result, expected)
+
+    def test_map_with_tuples_mi(self):
+        # Test that returning a single object from a MultiIndex
+        #   returns an Index.
+        first_level = ["foo", "bar", "baz"]
+        multi_index = MultiIndex.from_tuples(zip(first_level, [1, 2, 3]))
+        reduced_index = multi_index.map(lambda x: x[0])
+        tm.assert_index_equal(reduced_index, Index(first_level))
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            date_range("2020-01-01", freq="D", periods=10),
+            period_range("2020-01-01", freq="D", periods=10),
+            timedelta_range("1 day", periods=10),
+        ],
+    )
+    def test_map_tseries_indices_return_index(self, index):
+        expected = Index([1] * 10)
+        result = index.map(lambda x: 1)
+        tm.assert_index_equal(expected, result)
+
+    def test_map_tseries_indices_accsr_return_index(self):
+        date_index = DatetimeIndex(
+            date_range("2020-01-01", periods=24, freq="h"), name="hourly"
+        )
+        result = date_index.map(lambda x: x.hour)
+        expected = Index(np.arange(24, dtype="int64"), name="hourly")
+        tm.assert_index_equal(result, expected, exact=True)
+
+    @pytest.mark.parametrize(
+        "mapper",
+        [
+            lambda values, index: {i: e for e, i in zip(values, index)},
+            lambda values, index: Series(values, index),
+        ],
+    )
+    def test_map_dictlike_simple(self, mapper):
+        # GH 12756
+        expected = Index(["foo", "bar", "baz"])
+        index = Index(np.arange(3), dtype=np.int64)
+        result = index.map(mapper(expected.values, index))
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "mapper",
+        [
+            lambda values, index: {i: e for e, i in zip(values, index)},
+            lambda values, index: Series(values, index),
+        ],
+    )
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_map_dictlike(self, index, mapper, request):
+        # GH 12756
+        if isinstance(index, CategoricalIndex):
+            pytest.skip("Tested in test_categorical")
+        elif not index.is_unique:
+            pytest.skip("Cannot map duplicated index")
+
+        rng = np.arange(len(index), 0, -1, dtype=np.int64)
+
+        if index.empty:
+            # to match proper result coercion for uints
+            expected = Index([])
+        elif is_numeric_dtype(index.dtype):
+            expected = index._constructor(rng, dtype=index.dtype)
+        elif type(index) is Index and index.dtype != object:
+            # i.e. EA-backed, for now just Nullable
+            expected = Index(rng, dtype=index.dtype)
+        else:
+            expected = Index(rng)
+
+        result = index.map(mapper(expected, index))
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "mapper",
+        [Series(["foo", 2.0, "baz"], index=[0, 2, -1]), {0: "foo", 2: 2.0, -1: "baz"}],
+    )
+    def test_map_with_non_function_missing_values(self, mapper):
+        # GH 12756
+        expected = Index([2.0, np.nan, "foo"])
+        result = Index([2, 1, 0]).map(mapper)
+
+        tm.assert_index_equal(expected, result)
+
+    def test_map_na_exclusion(self):
+        index = Index([1.5, np.nan, 3, np.nan, 5])
+
+        result = index.map(lambda x: x * 2, na_action="ignore")
+        expected = index * 2
+        tm.assert_index_equal(result, expected)
+
+    def test_map_defaultdict(self):
+        index = Index([1, 2, 3])
+        default_dict = defaultdict(lambda: "blank")
+        default_dict[1] = "stuff"
+        result = index.map(default_dict)
+        expected = Index(["stuff", "blank", "blank"])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("name,expected", [("foo", "foo"), ("bar", None)])
+    def test_append_empty_preserve_name(self, name, expected):
+        left = Index([], name="foo")
+        right = Index([1, 2, 3], name=name)
+
+        msg = "The behavior of array concatenation with empty entries is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = left.append(right)
+        assert result.name == expected
+
+    @pytest.mark.parametrize(
+        "index, expected",
+        [
+            ("string", False),
+            ("bool-object", False),
+            ("bool-dtype", False),
+            ("categorical", False),
+            ("int64", True),
+            ("int32", True),
+            ("uint64", True),
+            ("uint32", True),
+            ("datetime", False),
+            ("float64", True),
+            ("float32", True),
+        ],
+        indirect=["index"],
+    )
+    def test_is_numeric(self, index, expected):
+        assert is_any_real_numeric_dtype(index) is expected
+
+    @pytest.mark.parametrize(
+        "index, expected",
+        [
+            ("string", True),
+            ("bool-object", True),
+            ("bool-dtype", False),
+            ("categorical", False),
+            ("int64", False),
+            ("int32", False),
+            ("uint64", False),
+            ("uint32", False),
+            ("datetime", False),
+            ("float64", False),
+            ("float32", False),
+        ],
+        indirect=["index"],
+    )
+    def test_is_object(self, index, expected, using_infer_string):
+        if using_infer_string and index.dtype == "string" and expected:
+            expected = False
+        assert is_object_dtype(index) is expected
+
+    def test_summary(self, index):
+        index._summary()
+
+    def test_format_bug(self):
+        # GH 14626
+        # windows has different precision on datetime.datetime.now (it doesn't
+        # include us since the default for Timestamp shows these but Index
+        # formatting does not we are skipping)
+        now = datetime.now()
+        msg = r"Index\.format is deprecated"
+
+        if not str(now).endswith("000"):
+            index = Index([now])
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                formatted = index.format()
+            expected = [str(index[0])]
+            assert formatted == expected
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            Index([]).format()
+
+    @pytest.mark.parametrize("vals", [[1, 2.0 + 3.0j, 4.0], ["a", "b", "c"]])
+    def test_format_missing(self, vals, nulls_fixture):
+        # 2845
+        vals = list(vals)  # Copy for each iteration
+        vals.append(nulls_fixture)
+        index = Index(vals, dtype=object)
+        # TODO: case with complex dtype?
+
+        msg = r"Index\.format is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            formatted = index.format()
+        null_repr = "NaN" if isinstance(nulls_fixture, float) else str(nulls_fixture)
+        expected = [str(index[0]), str(index[1]), str(index[2]), null_repr]
+
+        assert formatted == expected
+        assert index[3] is nulls_fixture
+
+    @pytest.mark.parametrize("op", ["any", "all"])
+    def test_logical_compat(self, op, simple_index):
+        index = simple_index
+        left = getattr(index, op)()
+        assert left == getattr(index.values, op)()
+        right = getattr(index.to_series(), op)()
+        # left might not match right exactly in e.g. string cases where the
+        # because we use np.any/all instead of .any/all
+        assert bool(left) == bool(right)
+
+    @pytest.mark.parametrize(
+        "index", ["string", "int64", "int32", "float64", "float32"], indirect=True
+    )
+    def test_drop_by_str_label(self, index):
+        n = len(index)
+        drop = index[list(range(5, 10))]
+        dropped = index.drop(drop)
+
+        expected = index[list(range(5)) + list(range(10, n))]
+        tm.assert_index_equal(dropped, expected)
+
+        dropped = index.drop(index[0])
+        expected = index[1:]
+        tm.assert_index_equal(dropped, expected)
+
+    @pytest.mark.parametrize(
+        "index", ["string", "int64", "int32", "float64", "float32"], indirect=True
+    )
+    @pytest.mark.parametrize("keys", [["foo", "bar"], ["1", "bar"]])
+    def test_drop_by_str_label_raises_missing_keys(self, index, keys):
+        with pytest.raises(KeyError, match=""):
+            index.drop(keys)
+
+    @pytest.mark.parametrize(
+        "index", ["string", "int64", "int32", "float64", "float32"], indirect=True
+    )
+    def test_drop_by_str_label_errors_ignore(self, index):
+        n = len(index)
+        drop = index[list(range(5, 10))]
+        mixed = drop.tolist() + ["foo"]
+        dropped = index.drop(mixed, errors="ignore")
+
+        expected = index[list(range(5)) + list(range(10, n))]
+        tm.assert_index_equal(dropped, expected)
+
+        dropped = index.drop(["foo", "bar"], errors="ignore")
+        expected = index[list(range(n))]
+        tm.assert_index_equal(dropped, expected)
+
+    def test_drop_by_numeric_label_loc(self):
+        # TODO: Parametrize numeric and str tests after self.strIndex fixture
+        index = Index([1, 2, 3])
+        dropped = index.drop(1)
+        expected = Index([2, 3])
+
+        tm.assert_index_equal(dropped, expected)
+
+    def test_drop_by_numeric_label_raises_missing_keys(self):
+        index = Index([1, 2, 3])
+        with pytest.raises(KeyError, match=""):
+            index.drop([3, 4])
+
+    @pytest.mark.parametrize(
+        "key,expected", [(4, Index([1, 2, 3])), ([3, 4, 5], Index([1, 2]))]
+    )
+    def test_drop_by_numeric_label_errors_ignore(self, key, expected):
+        index = Index([1, 2, 3])
+        dropped = index.drop(key, errors="ignore")
+
+        tm.assert_index_equal(dropped, expected)
+
+    @pytest.mark.parametrize(
+        "values",
+        [["a", "b", ("c", "d")], ["a", ("c", "d"), "b"], [("c", "d"), "a", "b"]],
+    )
+    @pytest.mark.parametrize("to_drop", [[("c", "d"), "a"], ["a", ("c", "d")]])
+    def test_drop_tuple(self, values, to_drop):
+        # GH 18304
+        index = Index(values)
+        expected = Index(["b"], dtype=object)
+
+        result = index.drop(to_drop)
+        tm.assert_index_equal(result, expected)
+
+        removed = index.drop(to_drop[0])
+        for drop_me in to_drop[1], [to_drop[1]]:
+            result = removed.drop(drop_me)
+            tm.assert_index_equal(result, expected)
+
+        removed = index.drop(to_drop[1])
+        msg = rf"\"\[{re.escape(to_drop[1].__repr__())}\] not found in axis\""
+        for drop_me in to_drop[1], [to_drop[1]]:
+            with pytest.raises(KeyError, match=msg):
+                removed.drop(drop_me)
+
+    @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
+    def test_drop_with_duplicates_in_index(self, index):
+        # GH38051
+        if len(index) == 0 or isinstance(index, MultiIndex):
+            pytest.skip("Test doesn't make sense for empty MultiIndex")
+        if isinstance(index, IntervalIndex) and not IS64:
+            pytest.skip("Cannot test IntervalIndex with int64 dtype on 32 bit platform")
+        index = index.unique().repeat(2)
+        expected = index[2:]
+        result = index.drop(index[0])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "attr",
+        [
+            "is_monotonic_increasing",
+            "is_monotonic_decreasing",
+            "_is_strictly_monotonic_increasing",
+            "_is_strictly_monotonic_decreasing",
+        ],
+    )
+    def test_is_monotonic_incomparable(self, attr):
+        index = Index([5, datetime.now(), 7])
+        assert not getattr(index, attr)
+
+    @pytest.mark.parametrize("values", [["foo", "bar", "quux"], {"foo", "bar", "quux"}])
+    @pytest.mark.parametrize(
+        "index,expected",
+        [
+            (Index(["qux", "baz", "foo", "bar"]), np.array([False, False, True, True])),
+            (Index([]), np.array([], dtype=bool)),  # empty
+        ],
+    )
+    def test_isin(self, values, index, expected):
+        result = index.isin(values)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_isin_nan_common_object(
+        self, nulls_fixture, nulls_fixture2, using_infer_string
+    ):
+        # Test cartesian product of null fixtures and ensure that we don't
+        # mangle the various types (save a corner case with PyPy)
+        idx = Index(["a", nulls_fixture])
+
+        # all nans are the same
+        if (
+            isinstance(nulls_fixture, float)
+            and isinstance(nulls_fixture2, float)
+            and math.isnan(nulls_fixture)
+            and math.isnan(nulls_fixture2)
+        ):
+            tm.assert_numpy_array_equal(
+                idx.isin([nulls_fixture2]),
+                np.array([False, True]),
+            )
+
+        elif nulls_fixture is nulls_fixture2:  # should preserve NA type
+            tm.assert_numpy_array_equal(
+                idx.isin([nulls_fixture2]),
+                np.array([False, True]),
+            )
+
+        elif using_infer_string and idx.dtype == "string":
+            tm.assert_numpy_array_equal(
+                idx.isin([nulls_fixture2]),
+                np.array([False, True]),
+            )
+
+        else:
+            tm.assert_numpy_array_equal(
+                idx.isin([nulls_fixture2]),
+                np.array([False, False]),
+            )
+
+    def test_isin_nan_common_float64(self, nulls_fixture, float_numpy_dtype):
+        dtype = float_numpy_dtype
+
+        if nulls_fixture is pd.NaT or nulls_fixture is pd.NA:
+            # Check 1) that we cannot construct a float64 Index with this value
+            #  and 2) that with an NaN we do not have .isin(nulls_fixture)
+            msg = (
+                r"float\(\) argument must be a string or a (real )?number, "
+                f"not {repr(type(nulls_fixture).__name__)}"
+            )
+            with pytest.raises(TypeError, match=msg):
+                Index([1.0, nulls_fixture], dtype=dtype)
+
+            idx = Index([1.0, np.nan], dtype=dtype)
+            assert not idx.isin([nulls_fixture]).any()
+            return
+
+        idx = Index([1.0, nulls_fixture], dtype=dtype)
+        res = idx.isin([np.nan])
+        tm.assert_numpy_array_equal(res, np.array([False, True]))
+
+        # we cannot compare NaT with NaN
+        res = idx.isin([pd.NaT])
+        tm.assert_numpy_array_equal(res, np.array([False, False]))
+
+    @pytest.mark.parametrize("level", [0, -1])
+    @pytest.mark.parametrize(
+        "index",
+        [
+            Index(["qux", "baz", "foo", "bar"]),
+            Index([1.0, 2.0, 3.0, 4.0], dtype=np.float64),
+        ],
+    )
+    def test_isin_level_kwarg(self, level, index):
+        values = index.tolist()[-2:] + ["nonexisting"]
+
+        expected = np.array([False, False, True, True])
+        tm.assert_numpy_array_equal(expected, index.isin(values, level=level))
+
+        index.name = "foobar"
+        tm.assert_numpy_array_equal(expected, index.isin(values, level="foobar"))
+
+    def test_isin_level_kwarg_bad_level_raises(self, index):
+        for level in [10, index.nlevels, -(index.nlevels + 1)]:
+            with pytest.raises(IndexError, match="Too many levels"):
+                index.isin([], level=level)
+
+    @pytest.mark.parametrize("label", [1.0, "foobar", "xyzzy", np.nan])
+    def test_isin_level_kwarg_bad_label_raises(self, label, index):
+        if isinstance(index, MultiIndex):
+            index = index.rename(["foo", "bar"] + index.names[2:])
+            msg = f"'Level {label} not found'"
+        else:
+            index = index.rename("foo")
+            msg = rf"Requested level \({label}\) does not match index name \(foo\)"
+        with pytest.raises(KeyError, match=msg):
+            index.isin([], level=label)
+
+    @pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])])
+    def test_isin_empty(self, empty):
+        # see gh-16991
+        index = Index(["a", "b"])
+        expected = np.array([False, False])
+
+        result = index.isin(empty)
+        tm.assert_numpy_array_equal(expected, result)
+
+    @td.skip_if_no("pyarrow")
+    def test_isin_arrow_string_null(self):
+        # GH#55821
+        index = Index(["a", "b"], dtype="string[pyarrow_numpy]")
+        result = index.isin([None])
+        expected = np.array([False, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            [1, 2, 3, 4],
+            [1.0, 2.0, 3.0, 4.0],
+            [True, True, True, True],
+            ["foo", "bar", "baz", "qux"],
+            date_range("2018-01-01", freq="D", periods=4),
+        ],
+    )
+    def test_boolean_cmp(self, values):
+        index = Index(values)
+        result = index == values
+        expected = np.array([True, True, True, True], dtype=bool)
+
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    @pytest.mark.parametrize("name,level", [(None, 0), ("a", "a")])
+    def test_get_level_values(self, index, name, level):
+        expected = index.copy()
+        if name:
+            expected.name = name
+
+        result = expected.get_level_values(level)
+        tm.assert_index_equal(result, expected)
+
+    def test_slice_keep_name(self):
+        index = Index(["a", "b"], name="asdf")
+        assert index.name == index[1:].name
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            "string",
+            "datetime",
+            "int64",
+            "int32",
+            "uint64",
+            "uint32",
+            "float64",
+            "float32",
+        ],
+        indirect=True,
+    )
+    def test_join_self(self, index, join_type):
+        result = index.join(index, how=join_type)
+        expected = index
+        if join_type == "outer":
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("method", ["strip", "rstrip", "lstrip"])
+    def test_str_attribute(self, method):
+        # GH9068
+        index = Index([" jack", "jill ", " jesse ", "frank"])
+        expected = Index([getattr(str, method)(x) for x in index.values])
+
+        result = getattr(index.str, method)()
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            Index(range(5)),
+            date_range("2020-01-01", periods=10),
+            MultiIndex.from_tuples([("foo", "1"), ("bar", "3")]),
+            period_range(start="2000", end="2010", freq="Y"),
+        ],
+    )
+    def test_str_attribute_raises(self, index):
+        with pytest.raises(AttributeError, match="only use .str accessor"):
+            index.str.repeat(2)
+
+    @pytest.mark.parametrize(
+        "expand,expected",
+        [
+            (None, Index([["a", "b", "c"], ["d", "e"], ["f"]])),
+            (False, Index([["a", "b", "c"], ["d", "e"], ["f"]])),
+            (
+                True,
+                MultiIndex.from_tuples(
+                    [("a", "b", "c"), ("d", "e", np.nan), ("f", np.nan, np.nan)]
+                ),
+            ),
+        ],
+    )
+    def test_str_split(self, expand, expected):
+        index = Index(["a b c", "d e", "f"])
+        if expand is not None:
+            result = index.str.split(expand=expand)
+        else:
+            result = index.str.split()
+
+        tm.assert_index_equal(result, expected)
+
+    def test_str_bool_return(self):
+        # test boolean case, should return np.array instead of boolean Index
+        index = Index(["a1", "a2", "b1", "b2"])
+        result = index.str.startswith("a")
+        expected = np.array([True, True, False, False])
+
+        tm.assert_numpy_array_equal(result, expected)
+        assert isinstance(result, np.ndarray)
+
+    def test_str_bool_series_indexing(self):
+        index = Index(["a1", "a2", "b1", "b2"])
+        s = Series(range(4), index=index)
+
+        result = s[s.index.str.startswith("a")]
+        expected = Series(range(2), index=["a1", "a2"])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index,expected", [(Index(list("abcd")), True), (Index(range(4)), False)]
+    )
+    def test_tab_completion(self, index, expected):
+        # GH 9910
+        result = "str" in dir(index)
+        assert result == expected
+
+    def test_indexing_doesnt_change_class(self):
+        index = Index([1, 2, 3, "a", "b", "c"])
+
+        assert index[1:3].identical(Index([2, 3], dtype=np.object_))
+        assert index[[0, 1]].identical(Index([1, 2], dtype=np.object_))
+
+    def test_outer_join_sort(self):
+        left_index = Index(np.random.default_rng(2).permutation(15))
+        right_index = date_range("2020-01-01", periods=10)
+
+        with tm.assert_produces_warning(RuntimeWarning):
+            result = left_index.join(right_index, how="outer")
+
+        with tm.assert_produces_warning(RuntimeWarning):
+            expected = left_index.astype(object).union(right_index.astype(object))
+
+        tm.assert_index_equal(result, expected)
+
+    def test_take_fill_value(self):
+        # GH 12631
+        index = Index(list("ABC"), name="xxx")
+        result = index.take(np.array([1, 0, -1]))
+        expected = Index(list("BAC"), name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # fill_value
+        result = index.take(np.array([1, 0, -1]), fill_value=True)
+        expected = Index(["B", "A", np.nan], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # allow_fill=False
+        result = index.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = Index(["B", "A", "C"], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+    def test_take_fill_value_none_raises(self):
+        index = Index(list("ABC"), name="xxx")
+        msg = (
+            "When allow_fill=True and fill_value is not None, "
+            "all indices must be >= -1"
+        )
+
+        with pytest.raises(ValueError, match=msg):
+            index.take(np.array([1, 0, -2]), fill_value=True)
+        with pytest.raises(ValueError, match=msg):
+            index.take(np.array([1, 0, -5]), fill_value=True)
+
+    def test_take_bad_bounds_raises(self):
+        index = Index(list("ABC"), name="xxx")
+        with pytest.raises(IndexError, match="out of bounds"):
+            index.take(np.array([1, -5]))
+
+    @pytest.mark.parametrize("name", [None, "foobar"])
+    @pytest.mark.parametrize(
+        "labels",
+        [
+            [],
+            np.array([]),
+            ["A", "B", "C"],
+            ["C", "B", "A"],
+            np.array(["A", "B", "C"]),
+            np.array(["C", "B", "A"]),
+            # Must preserve name even if dtype changes
+            date_range("20130101", periods=3).values,
+            date_range("20130101", periods=3).tolist(),
+        ],
+    )
+    def test_reindex_preserves_name_if_target_is_list_or_ndarray(self, name, labels):
+        # GH6552
+        index = Index([0, 1, 2])
+        index.name = name
+        assert index.reindex(labels)[0].name == name
+
+    @pytest.mark.parametrize("labels", [[], np.array([]), np.array([], dtype=np.int64)])
+    def test_reindex_preserves_type_if_target_is_empty_list_or_array(self, labels):
+        # GH7774
+        index = Index(list("abc"))
+        assert index.reindex(labels)[0].dtype.type == index.dtype.type
+
+    @pytest.mark.parametrize(
+        "labels,dtype",
+        [
+            (DatetimeIndex([]), np.datetime64),
+        ],
+    )
+    def test_reindex_doesnt_preserve_type_if_target_is_empty_index(self, labels, dtype):
+        # GH7774
+        index = Index(list("abc"))
+        assert index.reindex(labels)[0].dtype.type == dtype
+
+    def test_reindex_doesnt_preserve_type_if_target_is_empty_index_numeric(
+        self, any_real_numpy_dtype
+    ):
+        # GH7774
+        dtype = any_real_numpy_dtype
+        index = Index(list("abc"))
+        labels = Index([], dtype=dtype)
+        assert index.reindex(labels)[0].dtype == dtype
+
+    def test_reindex_no_type_preserve_target_empty_mi(self):
+        index = Index(list("abc"))
+        result = index.reindex(
+            MultiIndex([Index([], np.int64), Index([], np.float64)], [[], []])
+        )[0]
+        assert result.levels[0].dtype.type == np.int64
+        assert result.levels[1].dtype.type == np.float64
+
+    def test_reindex_ignoring_level(self):
+        # GH#35132
+        idx = Index([1, 2, 3], name="x")
+        idx2 = Index([1, 2, 3, 4], name="x")
+        expected = Index([1, 2, 3, 4], name="x")
+        result, _ = idx.reindex(idx2, level="x")
+        tm.assert_index_equal(result, expected)
+
+    def test_groupby(self):
+        index = Index(range(5))
+        result = index.groupby(np.array([1, 1, 2, 2, 2]))
+        expected = {1: Index([0, 1]), 2: Index([2, 3, 4])}
+
+        tm.assert_dict_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "mi,expected",
+        [
+            (MultiIndex.from_tuples([(1, 2), (4, 5)]), np.array([True, True])),
+            (MultiIndex.from_tuples([(1, 2), (4, 6)]), np.array([True, False])),
+        ],
+    )
+    def test_equals_op_multiindex(self, mi, expected):
+        # GH9785
+        # test comparisons of multiindex
+        df = DataFrame(
+            [3, 6],
+            columns=["c"],
+            index=MultiIndex.from_arrays([[1, 4], [2, 5]], names=["a", "b"]),
+        )
+
+        result = df.index == mi
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_equals_op_multiindex_identify(self):
+        df = DataFrame(
+            [3, 6],
+            columns=["c"],
+            index=MultiIndex.from_arrays([[1, 4], [2, 5]], names=["a", "b"]),
+        )
+
+        result = df.index == df.index
+        expected = np.array([True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            MultiIndex.from_tuples([(1, 2), (4, 5), (8, 9)]),
+            Index(["foo", "bar", "baz"]),
+        ],
+    )
+    def test_equals_op_mismatched_multiindex_raises(self, index):
+        df = DataFrame(
+            [3, 6],
+            columns=["c"],
+            index=MultiIndex.from_arrays([[1, 4], [2, 5]], names=["a", "b"]),
+        )
+
+        with pytest.raises(ValueError, match="Lengths must match"):
+            df.index == index
+
+    def test_equals_op_index_vs_mi_same_length(self, using_infer_string):
+        mi = MultiIndex.from_tuples([(1, 2), (4, 5), (8, 9)])
+        index = Index(["foo", "bar", "baz"])
+
+        result = mi == index
+        expected = np.array([False, False, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dt_conv, arg",
+        [
+            (pd.to_datetime, ["2000-01-01", "2000-01-02"]),
+            (pd.to_timedelta, ["01:02:03", "01:02:04"]),
+        ],
+    )
+    def test_dt_conversion_preserves_name(self, dt_conv, arg):
+        # GH 10875
+        index = Index(arg, name="label")
+        assert index.name == dt_conv(index).name
+
+    def test_cached_properties_not_settable(self):
+        index = Index([1, 2, 3])
+        with pytest.raises(AttributeError, match="Can't set attribute"):
+            index.is_unique = False
+
+    def test_tab_complete_warning(self, ip):
+        # https://github.com/pandas-dev/pandas/issues/16409
+        pytest.importorskip("IPython", minversion="6.0.0")
+        from IPython.core.completer import provisionalcompleter
+
+        code = "import pandas as pd; idx = pd.Index([1, 2])"
+        ip.run_cell(code)
+
+        # GH 31324 newer jedi version raises Deprecation warning;
+        #  appears resolved 2021-02-02
+        with tm.assert_produces_warning(None, raise_on_extra_warnings=False):
+            with provisionalcompleter("ignore"):
+                list(ip.Completer.completions("idx.", 4))
+
+    def test_contains_method_removed(self, index):
+        # GH#30103 method removed for all types except IntervalIndex
+        if isinstance(index, IntervalIndex):
+            index.contains(1)
+        else:
+            msg = f"'{type(index).__name__}' object has no attribute 'contains'"
+            with pytest.raises(AttributeError, match=msg):
+                index.contains(1)
+
+    def test_sortlevel(self):
+        index = Index([5, 4, 3, 2, 1])
+        with pytest.raises(Exception, match="ascending must be a single bool value or"):
+            index.sortlevel(ascending="True")
+
+        with pytest.raises(
+            Exception, match="ascending must be a list of bool values of length 1"
+        ):
+            index.sortlevel(ascending=[True, True])
+
+        with pytest.raises(Exception, match="ascending must be a bool value"):
+            index.sortlevel(ascending=["True"])
+
+        expected = Index([1, 2, 3, 4, 5])
+        result = index.sortlevel(ascending=[True])
+        tm.assert_index_equal(result[0], expected)
+
+        expected = Index([1, 2, 3, 4, 5])
+        result = index.sortlevel(ascending=True)
+        tm.assert_index_equal(result[0], expected)
+
+        expected = Index([5, 4, 3, 2, 1])
+        result = index.sortlevel(ascending=False)
+        tm.assert_index_equal(result[0], expected)
+
+    def test_sortlevel_na_position(self):
+        # GH#51612
+        idx = Index([1, np.nan])
+        result = idx.sortlevel(na_position="first")[0]
+        expected = Index([np.nan, 1])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "periods, expected_results",
+        [
+            (1, [np.nan, 10, 10, 10, 10]),
+            (2, [np.nan, np.nan, 20, 20, 20]),
+            (3, [np.nan, np.nan, np.nan, 30, 30]),
+        ],
+    )
+    def test_index_diff(self, periods, expected_results):
+        # GH#19708
+        idx = Index([10, 20, 30, 40, 50])
+        result = idx.diff(periods)
+        expected = Index(expected_results)
+
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "decimals, expected_results",
+        [
+            (0, [1.0, 2.0, 3.0]),
+            (1, [1.2, 2.3, 3.5]),
+            (2, [1.23, 2.35, 3.46]),
+        ],
+    )
+    def test_index_round(self, decimals, expected_results):
+        # GH#19708
+        idx = Index([1.234, 2.345, 3.456])
+        result = idx.round(decimals)
+        expected = Index(expected_results)
+
+        tm.assert_index_equal(result, expected)
+
+
+class TestMixedIntIndex:
+    # Mostly the tests from common.py for which the results differ
+    # in py2 and py3 because ints and strings are uncomparable in py3
+    # (GH 13514)
+    @pytest.fixture
+    def simple_index(self) -> Index:
+        return Index([0, "a", 1, "b", 2, "c"])
+
+    def test_argsort(self, simple_index):
+        index = simple_index
+        with pytest.raises(TypeError, match="'>|<' not supported"):
+            index.argsort()
+
+    def test_numpy_argsort(self, simple_index):
+        index = simple_index
+        with pytest.raises(TypeError, match="'>|<' not supported"):
+            np.argsort(index)
+
+    def test_copy_name(self, simple_index):
+        # Check that "name" argument passed at initialization is honoured
+        # GH12309
+        index = simple_index
+
+        first = type(index)(index, copy=True, name="mario")
+        second = type(first)(first, copy=False)
+
+        # Even though "copy=False", we want a new object.
+        assert first is not second
+        tm.assert_index_equal(first, second)
+
+        assert first.name == "mario"
+        assert second.name == "mario"
+
+        s1 = Series(2, index=first)
+        s2 = Series(3, index=second[:-1])
+
+        s3 = s1 * s2
+
+        assert s3.index.name == "mario"
+
+    def test_copy_name2(self):
+        # Check that adding a "name" parameter to the copy is honored
+        # GH14302
+        index = Index([1, 2], name="MyName")
+        index1 = index.copy()
+
+        tm.assert_index_equal(index, index1)
+
+        index2 = index.copy(name="NewName")
+        tm.assert_index_equal(index, index2, check_names=False)
+        assert index.name == "MyName"
+        assert index2.name == "NewName"
+
+    def test_unique_na(self):
+        idx = Index([2, np.nan, 2, 1], name="my_index")
+        expected = Index([2, np.nan, 1], name="my_index")
+        result = idx.unique()
+        tm.assert_index_equal(result, expected)
+
+    def test_logical_compat(self, simple_index):
+        index = simple_index
+        assert index.all() == index.values.all()
+        assert index.any() == index.values.any()
+
+    @pytest.mark.parametrize("how", ["any", "all"])
+    @pytest.mark.parametrize("dtype", [None, object, "category"])
+    @pytest.mark.parametrize(
+        "vals,expected",
+        [
+            ([1, 2, 3], [1, 2, 3]),
+            ([1.0, 2.0, 3.0], [1.0, 2.0, 3.0]),
+            ([1.0, 2.0, np.nan, 3.0], [1.0, 2.0, 3.0]),
+            (["A", "B", "C"], ["A", "B", "C"]),
+            (["A", np.nan, "B", "C"], ["A", "B", "C"]),
+        ],
+    )
+    def test_dropna(self, how, dtype, vals, expected):
+        # GH 6194
+        index = Index(vals, dtype=dtype)
+        result = index.dropna(how=how)
+        expected = Index(expected, dtype=dtype)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("how", ["any", "all"])
+    @pytest.mark.parametrize(
+        "index,expected",
+        [
+            (
+                DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),
+                DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),
+            ),
+            (
+                DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03", pd.NaT]),
+                DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"]),
+            ),
+            (
+                TimedeltaIndex(["1 days", "2 days", "3 days"]),
+                TimedeltaIndex(["1 days", "2 days", "3 days"]),
+            ),
+            (
+                TimedeltaIndex([pd.NaT, "1 days", "2 days", "3 days", pd.NaT]),
+                TimedeltaIndex(["1 days", "2 days", "3 days"]),
+            ),
+            (
+                PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),
+                PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),
+            ),
+            (
+                PeriodIndex(["2012-02", "2012-04", "NaT", "2012-05"], freq="M"),
+                PeriodIndex(["2012-02", "2012-04", "2012-05"], freq="M"),
+            ),
+        ],
+    )
+    def test_dropna_dt_like(self, how, index, expected):
+        result = index.dropna(how=how)
+        tm.assert_index_equal(result, expected)
+
+    def test_dropna_invalid_how_raises(self):
+        msg = "invalid how option: xxx"
+        with pytest.raises(ValueError, match=msg):
+            Index([1, 2, 3]).dropna(how="xxx")
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            Index([np.nan]),
+            Index([np.nan, 1]),
+            Index([1, 2, np.nan]),
+            Index(["a", "b", np.nan]),
+            pd.to_datetime(["NaT"]),
+            pd.to_datetime(["NaT", "2000-01-01"]),
+            pd.to_datetime(["2000-01-01", "NaT", "2000-01-02"]),
+            pd.to_timedelta(["1 day", "NaT"]),
+        ],
+    )
+    def test_is_monotonic_na(self, index):
+        assert index.is_monotonic_increasing is False
+        assert index.is_monotonic_decreasing is False
+        assert index._is_strictly_monotonic_increasing is False
+        assert index._is_strictly_monotonic_decreasing is False
+
+    @pytest.mark.parametrize("dtype", ["f8", "m8[ns]", "M8[us]"])
+    @pytest.mark.parametrize("unique_first", [True, False])
+    def test_is_monotonic_unique_na(self, dtype, unique_first):
+        # GH 55755
+        index = Index([None, 1, 1], dtype=dtype)
+        if unique_first:
+            assert index.is_unique is False
+            assert index.is_monotonic_increasing is False
+            assert index.is_monotonic_decreasing is False
+        else:
+            assert index.is_monotonic_increasing is False
+            assert index.is_monotonic_decreasing is False
+            assert index.is_unique is False
+
+    def test_int_name_format(self, frame_or_series):
+        index = Index(["a", "b", "c"], name=0)
+        result = frame_or_series(list(range(3)), index=index)
+        assert "0" in repr(result)
+
+    def test_str_to_bytes_raises(self):
+        # GH 26447
+        index = Index([str(x) for x in range(10)])
+        msg = "^'str' object cannot be interpreted as an integer$"
+        with pytest.raises(TypeError, match=msg):
+            bytes(index)
+
+    @pytest.mark.filterwarnings("ignore:elementwise comparison failed:FutureWarning")
+    def test_index_with_tuple_bool(self):
+        # GH34123
+        # TODO: also this op right now produces FutureWarning from numpy
+        #  https://github.com/numpy/numpy/issues/11521
+        idx = Index([("a", "b"), ("b", "c"), ("c", "a")])
+        result = idx == ("c", "a")
+        expected = np.array([False, False, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestIndexUtils:
+    @pytest.mark.parametrize(
+        "data, names, expected",
+        [
+            ([[1, 2, 3]], None, Index([1, 2, 3])),
+            ([[1, 2, 3]], ["name"], Index([1, 2, 3], name="name")),
+            (
+                [["a", "a"], ["c", "d"]],
+                None,
+                MultiIndex([["a"], ["c", "d"]], [[0, 0], [0, 1]]),
+            ),
+            (
+                [["a", "a"], ["c", "d"]],
+                ["L1", "L2"],
+                MultiIndex([["a"], ["c", "d"]], [[0, 0], [0, 1]], names=["L1", "L2"]),
+            ),
+        ],
+    )
+    def test_ensure_index_from_sequences(self, data, names, expected):
+        result = ensure_index_from_sequences(data, names)
+        tm.assert_index_equal(result, expected)
+
+    def test_ensure_index_mixed_closed_intervals(self):
+        # GH27172
+        intervals = [
+            pd.Interval(0, 1, closed="left"),
+            pd.Interval(1, 2, closed="right"),
+            pd.Interval(2, 3, closed="neither"),
+            pd.Interval(3, 4, closed="both"),
+        ]
+        result = ensure_index(intervals)
+        expected = Index(intervals, dtype=object)
+        tm.assert_index_equal(result, expected)
+
+    def test_ensure_index_uint64(self):
+        # with both 0 and a large-uint64, np.array will infer to float64
+        #  https://github.com/numpy/numpy/issues/19146
+        #  but a more accurate choice would be uint64
+        values = [0, np.iinfo(np.uint64).max]
+
+        result = ensure_index(values)
+        assert list(result) == values
+
+        expected = Index(values, dtype="uint64")
+        tm.assert_index_equal(result, expected)
+
+    def test_get_combined_index(self):
+        result = _get_combined_index([])
+        expected = Index([])
+        tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "opname",
+    [
+        "eq",
+        "ne",
+        "le",
+        "lt",
+        "ge",
+        "gt",
+        "add",
+        "radd",
+        "sub",
+        "rsub",
+        "mul",
+        "rmul",
+        "truediv",
+        "rtruediv",
+        "floordiv",
+        "rfloordiv",
+        "pow",
+        "rpow",
+        "mod",
+        "divmod",
+    ],
+)
+def test_generated_op_names(opname, index):
+    opname = f"__{opname}__"
+    method = getattr(index, opname)
+    assert method.__name__ == opname
+
+
+@pytest.mark.parametrize(
+    "klass",
+    [
+        partial(CategoricalIndex, data=[1]),
+        partial(DatetimeIndex, data=["2020-01-01"]),
+        partial(PeriodIndex, data=["2020-01-01"]),
+        partial(TimedeltaIndex, data=["1 day"]),
+        partial(RangeIndex, data=range(1)),
+        partial(IntervalIndex, data=[pd.Interval(0, 1)]),
+        partial(Index, data=["a"], dtype=object),
+        partial(MultiIndex, levels=[1], codes=[0]),
+    ],
+)
+def test_index_subclass_constructor_wrong_kwargs(klass):
+    # GH #19348
+    with pytest.raises(TypeError, match="unexpected keyword argument"):
+        klass(foo="bar")
+
+
+def test_deprecated_fastpath():
+    msg = "[Uu]nexpected keyword argument"
+    with pytest.raises(TypeError, match=msg):
+        Index(np.array(["a", "b"], dtype=object), name="test", fastpath=True)
+
+    with pytest.raises(TypeError, match=msg):
+        Index(np.array([1, 2, 3], dtype="int64"), name="test", fastpath=True)
+
+    with pytest.raises(TypeError, match=msg):
+        RangeIndex(0, 5, 2, name="test", fastpath=True)
+
+    with pytest.raises(TypeError, match=msg):
+        CategoricalIndex(["a", "b", "c"], name="test", fastpath=True)
+
+
+def test_shape_of_invalid_index():
+    # Pre-2.0, it was possible to create "invalid" index objects backed by
+    # a multi-dimensional array (see https://github.com/pandas-dev/pandas/issues/27125
+    # about this). However, as long as this is not solved in general,this test ensures
+    # that the returned shape is consistent with this underlying array for
+    # compat with matplotlib (see https://github.com/pandas-dev/pandas/issues/27775)
+    idx = Index([0, 1, 2, 3])
+    with pytest.raises(ValueError, match="Multi-dimensional indexing"):
+        # GH#30588 multi-dimensional indexing deprecated
+        idx[:, None]
+
+
+@pytest.mark.parametrize("dtype", [None, np.int64, np.uint64, np.float64])
+def test_validate_1d_input(dtype):
+    # GH#27125 check that we do not have >1-dimensional input
+    msg = "Index data must be 1-dimensional"
+
+    arr = np.arange(8).reshape(2, 2, 2)
+    with pytest.raises(ValueError, match=msg):
+        Index(arr, dtype=dtype)
+
+    df = DataFrame(arr.reshape(4, 2))
+    with pytest.raises(ValueError, match=msg):
+        Index(df, dtype=dtype)
+
+    # GH#13601 trying to assign a multi-dimensional array to an index is not allowed
+    ser = Series(0, range(4))
+    with pytest.raises(ValueError, match=msg):
+        ser.index = np.array([[2, 3]] * 4, dtype=dtype)
+
+
+@pytest.mark.parametrize(
+    "klass, extra_kwargs",
+    [
+        [Index, {}],
+        *[[lambda x: Index(x, dtype=dtyp), {}] for dtyp in tm.ALL_REAL_NUMPY_DTYPES],
+        [DatetimeIndex, {}],
+        [TimedeltaIndex, {}],
+        [PeriodIndex, {"freq": "Y"}],
+    ],
+)
+def test_construct_from_memoryview(klass, extra_kwargs):
+    # GH 13120
+    result = klass(memoryview(np.arange(2000, 2005)), **extra_kwargs)
+    expected = klass(list(range(2000, 2005)), **extra_kwargs)
+    tm.assert_index_equal(result, expected, exact=True)
+
+
+@pytest.mark.parametrize("op", [operator.lt, operator.gt])
+def test_nan_comparison_same_object(op):
+    # GH#47105
+    idx = Index([np.nan])
+    expected = np.array([False])
+
+    result = op(idx, idx)
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = op(idx, idx.copy())
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@td.skip_if_no("pyarrow")
+def test_is_monotonic_pyarrow_list_type():
+    # GH 57333
+    import pyarrow as pa
+
+    idx = Index([[1], [2, 3]], dtype=pd.ArrowDtype(pa.list_(pa.int64())))
+    assert not idx.is_monotonic_increasing
+    assert not idx.is_monotonic_decreasing

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/test_frozen.py

@@ -0,0 +1,113 @@
+import re
+
+import pytest
+
+from pandas.core.indexes.frozen import FrozenList
+
+
+@pytest.fixture
+def lst():
+    return [1, 2, 3, 4, 5]
+
+
+@pytest.fixture
+def container(lst):
+    return FrozenList(lst)
+
+
+@pytest.fixture
+def unicode_container():
+    return FrozenList(["\u05d0", "\u05d1", "c"])
+
+
+class TestFrozenList:
+    def check_mutable_error(self, *args, **kwargs):
+        # Pass whatever function you normally would to pytest.raises
+        # (after the Exception kind).
+        mutable_regex = re.compile("does not support mutable operations")
+        msg = "'(_s)?re.(SRE_)?Pattern' object is not callable"
+        with pytest.raises(TypeError, match=msg):
+            mutable_regex(*args, **kwargs)
+
+    def test_no_mutable_funcs(self, container):
+        def setitem():
+            container[0] = 5
+
+        self.check_mutable_error(setitem)
+
+        def setslice():
+            container[1:2] = 3
+
+        self.check_mutable_error(setslice)
+
+        def delitem():
+            del container[0]
+
+        self.check_mutable_error(delitem)
+
+        def delslice():
+            del container[0:3]
+
+        self.check_mutable_error(delslice)
+
+        mutable_methods = ("extend", "pop", "remove", "insert")
+
+        for meth in mutable_methods:
+            self.check_mutable_error(getattr(container, meth))
+
+    def test_slicing_maintains_type(self, container, lst):
+        result = container[1:2]
+        expected = lst[1:2]
+        self.check_result(result, expected)
+
+    def check_result(self, result, expected):
+        assert isinstance(result, FrozenList)
+        assert result == expected
+
+    def test_string_methods_dont_fail(self, container):
+        repr(container)
+        str(container)
+        bytes(container)
+
+    def test_tricky_container(self, unicode_container):
+        repr(unicode_container)
+        str(unicode_container)
+
+    def test_add(self, container, lst):
+        result = container + (1, 2, 3)
+        expected = FrozenList(lst + [1, 2, 3])
+        self.check_result(result, expected)
+
+        result = (1, 2, 3) + container
+        expected = FrozenList([1, 2, 3] + lst)
+        self.check_result(result, expected)
+
+    def test_iadd(self, container, lst):
+        q = r = container
+
+        q += [5]
+        self.check_result(q, lst + [5])
+
+        # Other shouldn't be mutated.
+        self.check_result(r, lst)
+
+    def test_union(self, container, lst):
+        result = container.union((1, 2, 3))
+        expected = FrozenList(lst + [1, 2, 3])
+        self.check_result(result, expected)
+
+    def test_difference(self, container):
+        result = container.difference([2])
+        expected = FrozenList([1, 3, 4, 5])
+        self.check_result(result, expected)
+
+    def test_difference_dupe(self):
+        result = FrozenList([1, 2, 3, 2]).difference([2])
+        expected = FrozenList([1, 3])
+        self.check_result(result, expected)
+
+    def test_tricky_container_to_bytes_raises(self, unicode_container):
+        # GH 26447
+        msg = "^'str' object cannot be interpreted as an integer$"
+        with pytest.raises(TypeError, match=msg):
+            bytes(unicode_container)

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/test_index_new.py

@@ -0,0 +1,432 @@
+"""
+Tests for the Index constructor conducting inference.
+"""
+from datetime import (
+    datetime,
+    timedelta,
+    timezone,
+)
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs.timezones import maybe_get_tz
+
+from pandas import (
+    NA,
+    Categorical,
+    CategoricalIndex,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    PeriodIndex,
+    Series,
+    TimedeltaIndex,
+    Timestamp,
+    array,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestIndexConstructorInference:
+    def test_object_all_bools(self):
+        # GH#49594 match Series behavior on ndarray[object] of all bools
+        arr = np.array([True, False], dtype=object)
+        res = Index(arr)
+        assert res.dtype == object
+
+        # since the point is matching Series behavior, let's double check
+        assert Series(arr).dtype == object
+
+    def test_object_all_complex(self):
+        # GH#49594 match Series behavior on ndarray[object] of all complex
+        arr = np.array([complex(1), complex(2)], dtype=object)
+        res = Index(arr)
+        assert res.dtype == object
+
+        # since the point is matching Series behavior, let's double check
+        assert Series(arr).dtype == object
+
+    @pytest.mark.parametrize("val", [NaT, None, np.nan, float("nan")])
+    def test_infer_nat(self, val):
+        # GH#49340 all NaT/None/nan and at least 1 NaT -> datetime64[ns],
+        #  matching Series behavior
+        values = [NaT, val]
+
+        idx = Index(values)
+        assert idx.dtype == "datetime64[ns]" and idx.isna().all()
+
+        idx = Index(values[::-1])
+        assert idx.dtype == "datetime64[ns]" and idx.isna().all()
+
+        idx = Index(np.array(values, dtype=object))
+        assert idx.dtype == "datetime64[ns]" and idx.isna().all()
+
+        idx = Index(np.array(values, dtype=object)[::-1])
+        assert idx.dtype == "datetime64[ns]" and idx.isna().all()
+
+    @pytest.mark.parametrize("na_value", [None, np.nan])
+    @pytest.mark.parametrize("vtype", [list, tuple, iter])
+    def test_construction_list_tuples_nan(self, na_value, vtype):
+        # GH#18505 : valid tuples containing NaN
+        values = [(1, "two"), (3.0, na_value)]
+        result = Index(vtype(values))
+        expected = MultiIndex.from_tuples(values)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype",
+        [int, "int64", "int32", "int16", "int8", "uint64", "uint32", "uint16", "uint8"],
+    )
+    def test_constructor_int_dtype_float(self, dtype):
+        # GH#18400
+        expected = Index([0, 1, 2, 3], dtype=dtype)
+        result = Index([0.0, 1.0, 2.0, 3.0], dtype=dtype)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("cast_index", [True, False])
+    @pytest.mark.parametrize(
+        "vals", [[True, False, True], np.array([True, False, True], dtype=bool)]
+    )
+    def test_constructor_dtypes_to_object(self, cast_index, vals):
+        if cast_index:
+            index = Index(vals, dtype=bool)
+        else:
+            index = Index(vals)
+
+        assert type(index) is Index
+        assert index.dtype == bool
+
+    def test_constructor_categorical_to_object(self):
+        # GH#32167 Categorical data and dtype=object should return object-dtype
+        ci = CategoricalIndex(range(5))
+        result = Index(ci, dtype=object)
+        assert not isinstance(result, CategoricalIndex)
+
+    def test_constructor_infer_periodindex(self):
+        xp = period_range("2012-1-1", freq="M", periods=3)
+        rs = Index(xp)
+        tm.assert_index_equal(rs, xp)
+        assert isinstance(rs, PeriodIndex)
+
+    def test_from_list_of_periods(self):
+        rng = period_range("1/1/2000", periods=20, freq="D")
+        periods = list(rng)
+
+        result = Index(periods)
+        assert isinstance(result, PeriodIndex)
+
+    @pytest.mark.parametrize("pos", [0, 1])
+    @pytest.mark.parametrize(
+        "klass,dtype,ctor",
+        [
+            (DatetimeIndex, "datetime64[ns]", np.datetime64("nat")),
+            (TimedeltaIndex, "timedelta64[ns]", np.timedelta64("nat")),
+        ],
+    )
+    def test_constructor_infer_nat_dt_like(
+        self, pos, klass, dtype, ctor, nulls_fixture, request
+    ):
+        if isinstance(nulls_fixture, Decimal):
+            # We dont cast these to datetime64/timedelta64
+            pytest.skip(
+                f"We don't cast {type(nulls_fixture).__name__} to "
+                "datetime64/timedelta64"
+            )
+
+        expected = klass([NaT, NaT])
+        assert expected.dtype == dtype
+        data = [ctor]
+        data.insert(pos, nulls_fixture)
+
+        warn = None
+        if nulls_fixture is NA:
+            expected = Index([NA, NaT])
+            mark = pytest.mark.xfail(reason="Broken with np.NaT ctor; see GH 31884")
+            request.applymarker(mark)
+            # GH#35942 numpy will emit a DeprecationWarning within the
+            #  assert_index_equal calls.  Since we can't do anything
+            #  about it until GH#31884 is fixed, we suppress that warning.
+            warn = DeprecationWarning
+
+        result = Index(data)
+
+        with tm.assert_produces_warning(warn):
+            tm.assert_index_equal(result, expected)
+
+        result = Index(np.array(data, dtype=object))
+
+        with tm.assert_produces_warning(warn):
+            tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("swap_objs", [True, False])
+    def test_constructor_mixed_nat_objs_infers_object(self, swap_objs):
+        # mixed np.datetime64/timedelta64 nat results in object
+        data = [np.datetime64("nat"), np.timedelta64("nat")]
+        if swap_objs:
+            data = data[::-1]
+
+        expected = Index(data, dtype=object)
+        tm.assert_index_equal(Index(data), expected)
+        tm.assert_index_equal(Index(np.array(data, dtype=object)), expected)
+
+    @pytest.mark.parametrize("swap_objs", [True, False])
+    def test_constructor_datetime_and_datetime64(self, swap_objs):
+        data = [Timestamp(2021, 6, 8, 9, 42), np.datetime64("now")]
+        if swap_objs:
+            data = data[::-1]
+        expected = DatetimeIndex(data)
+
+        tm.assert_index_equal(Index(data), expected)
+        tm.assert_index_equal(Index(np.array(data, dtype=object)), expected)
+
+    def test_constructor_datetimes_mixed_tzs(self):
+        # https://github.com/pandas-dev/pandas/pull/55793/files#r1383719998
+        tz = maybe_get_tz("US/Central")
+        dt1 = datetime(2020, 1, 1, tzinfo=tz)
+        dt2 = datetime(2020, 1, 1, tzinfo=timezone.utc)
+        result = Index([dt1, dt2])
+        expected = Index([dt1, dt2], dtype=object)
+        tm.assert_index_equal(result, expected)
+
+
+class TestDtypeEnforced:
+    # check we don't silently ignore the dtype keyword
+
+    def test_constructor_object_dtype_with_ea_data(self, any_numeric_ea_dtype):
+        # GH#45206
+        arr = array([0], dtype=any_numeric_ea_dtype)
+
+        idx = Index(arr, dtype=object)
+        assert idx.dtype == object
+
+    @pytest.mark.parametrize("dtype", [object, "float64", "uint64", "category"])
+    def test_constructor_range_values_mismatched_dtype(self, dtype):
+        rng = Index(range(5))
+
+        result = Index(rng, dtype=dtype)
+        assert result.dtype == dtype
+
+        result = Index(range(5), dtype=dtype)
+        assert result.dtype == dtype
+
+    @pytest.mark.parametrize("dtype", [object, "float64", "uint64", "category"])
+    def test_constructor_categorical_values_mismatched_non_ea_dtype(self, dtype):
+        cat = Categorical([1, 2, 3])
+
+        result = Index(cat, dtype=dtype)
+        assert result.dtype == dtype
+
+    def test_constructor_categorical_values_mismatched_dtype(self):
+        dti = date_range("2016-01-01", periods=3)
+        cat = Categorical(dti)
+        result = Index(cat, dti.dtype)
+        tm.assert_index_equal(result, dti)
+
+        dti2 = dti.tz_localize("Asia/Tokyo")
+        cat2 = Categorical(dti2)
+        result = Index(cat2, dti2.dtype)
+        tm.assert_index_equal(result, dti2)
+
+        ii = IntervalIndex.from_breaks(range(5))
+        cat3 = Categorical(ii)
+        result = Index(cat3, dtype=ii.dtype)
+        tm.assert_index_equal(result, ii)
+
+    def test_constructor_ea_values_mismatched_categorical_dtype(self):
+        dti = date_range("2016-01-01", periods=3)
+        result = Index(dti, dtype="category")
+        expected = CategoricalIndex(dti)
+        tm.assert_index_equal(result, expected)
+
+        dti2 = date_range("2016-01-01", periods=3, tz="US/Pacific")
+        result = Index(dti2, dtype="category")
+        expected = CategoricalIndex(dti2)
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_period_values_mismatched_dtype(self):
+        pi = period_range("2016-01-01", periods=3, freq="D")
+        result = Index(pi, dtype="category")
+        expected = CategoricalIndex(pi)
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_timedelta64_values_mismatched_dtype(self):
+        # check we don't silently ignore the dtype keyword
+        tdi = timedelta_range("4 Days", periods=5)
+        result = Index(tdi, dtype="category")
+        expected = CategoricalIndex(tdi)
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_interval_values_mismatched_dtype(self):
+        dti = date_range("2016-01-01", periods=3)
+        ii = IntervalIndex.from_breaks(dti)
+        result = Index(ii, dtype="category")
+        expected = CategoricalIndex(ii)
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_datetime64_values_mismatched_period_dtype(self):
+        dti = date_range("2016-01-01", periods=3)
+        result = Index(dti, dtype="Period[D]")
+        expected = dti.to_period("D")
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["int64", "uint64"])
+    def test_constructor_int_dtype_nan_raises(self, dtype):
+        # see GH#15187
+        data = [np.nan]
+        msg = "cannot convert"
+        with pytest.raises(ValueError, match=msg):
+            Index(data, dtype=dtype)
+
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            [1, 2, 3],
+            np.array([1, 2, 3]),
+            np.array([1, 2, 3], dtype=int),
+            # below should coerce
+            [1.0, 2.0, 3.0],
+            np.array([1.0, 2.0, 3.0], dtype=float),
+        ],
+    )
+    def test_constructor_dtypes_to_int(self, vals, any_int_numpy_dtype):
+        dtype = any_int_numpy_dtype
+        index = Index(vals, dtype=dtype)
+        assert index.dtype == dtype
+
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            [1, 2, 3],
+            [1.0, 2.0, 3.0],
+            np.array([1.0, 2.0, 3.0]),
+            np.array([1, 2, 3], dtype=int),
+            np.array([1.0, 2.0, 3.0], dtype=float),
+        ],
+    )
+    def test_constructor_dtypes_to_float(self, vals, float_numpy_dtype):
+        dtype = float_numpy_dtype
+        index = Index(vals, dtype=dtype)
+        assert index.dtype == dtype
+
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            [1, 2, 3],
+            np.array([1, 2, 3], dtype=int),
+            np.array(["2011-01-01", "2011-01-02"], dtype="datetime64[ns]"),
+            [datetime(2011, 1, 1), datetime(2011, 1, 2)],
+        ],
+    )
+    def test_constructor_dtypes_to_categorical(self, vals):
+        index = Index(vals, dtype="category")
+        assert isinstance(index, CategoricalIndex)
+
+    @pytest.mark.parametrize("cast_index", [True, False])
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            Index(np.array([np.datetime64("2011-01-01"), np.datetime64("2011-01-02")])),
+            Index([datetime(2011, 1, 1), datetime(2011, 1, 2)]),
+        ],
+    )
+    def test_constructor_dtypes_to_datetime(self, cast_index, vals):
+        if cast_index:
+            index = Index(vals, dtype=object)
+            assert isinstance(index, Index)
+            assert index.dtype == object
+        else:
+            index = Index(vals)
+            assert isinstance(index, DatetimeIndex)
+
+    @pytest.mark.parametrize("cast_index", [True, False])
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            np.array([np.timedelta64(1, "D"), np.timedelta64(1, "D")]),
+            [timedelta(1), timedelta(1)],
+        ],
+    )
+    def test_constructor_dtypes_to_timedelta(self, cast_index, vals):
+        if cast_index:
+            index = Index(vals, dtype=object)
+            assert isinstance(index, Index)
+            assert index.dtype == object
+        else:
+            index = Index(vals)
+            assert isinstance(index, TimedeltaIndex)
+
+    def test_pass_timedeltaindex_to_index(self):
+        rng = timedelta_range("1 days", "10 days")
+        idx = Index(rng, dtype=object)
+
+        expected = Index(rng.to_pytimedelta(), dtype=object)
+
+        tm.assert_numpy_array_equal(idx.values, expected.values)
+
+    def test_pass_datetimeindex_to_index(self):
+        # GH#1396
+        rng = date_range("1/1/2000", "3/1/2000")
+        idx = Index(rng, dtype=object)
+
+        expected = Index(rng.to_pydatetime(), dtype=object)
+
+        tm.assert_numpy_array_equal(idx.values, expected.values)
+
+
+class TestIndexConstructorUnwrapping:
+    # Test passing different arraylike values to pd.Index
+
+    @pytest.mark.parametrize("klass", [Index, DatetimeIndex])
+    def test_constructor_from_series_dt64(self, klass):
+        stamps = [Timestamp("20110101"), Timestamp("20120101"), Timestamp("20130101")]
+        expected = DatetimeIndex(stamps)
+        ser = Series(stamps)
+        result = klass(ser)
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_no_pandas_array(self):
+        ser = Series([1, 2, 3])
+        result = Index(ser.array)
+        expected = Index([1, 2, 3])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "array",
+        [
+            np.arange(5),
+            np.array(["a", "b", "c"]),
+            date_range("2000-01-01", periods=3).values,
+        ],
+    )
+    def test_constructor_ndarray_like(self, array):
+        # GH#5460#issuecomment-44474502
+        # it should be possible to convert any object that satisfies the numpy
+        # ndarray interface directly into an Index
+        class ArrayLike:
+            def __init__(self, array) -> None:
+                self.array = array
+
+            def __array__(self, dtype=None, copy=None) -> np.ndarray:
+                return self.array
+
+        expected = Index(array)
+        result = Index(ArrayLike(array))
+        tm.assert_index_equal(result, expected)
+
+
+class TestIndexConstructionErrors:
+    def test_constructor_overflow_int64(self):
+        # see GH#15832
+        msg = (
+            "The elements provided in the data cannot "
+            "all be casted to the dtype int64"
+        )
+        with pytest.raises(OverflowError, match=msg):
+            Index([np.iinfo(np.uint64).max - 1], dtype="int64")

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/test_numpy_compat.py

@@ -0,0 +1,189 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    CategoricalIndex,
+    DatetimeIndex,
+    Index,
+    PeriodIndex,
+    TimedeltaIndex,
+    isna,
+)
+import pandas._testing as tm
+from pandas.api.types import (
+    is_complex_dtype,
+    is_numeric_dtype,
+)
+from pandas.core.arrays import BooleanArray
+from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
+
+
+def test_numpy_ufuncs_out(index):
+    result = index == index
+
+    out = np.empty(index.shape, dtype=bool)
+    np.equal(index, index, out=out)
+    tm.assert_numpy_array_equal(out, result)
+
+    if not index._is_multi:
+        # same thing on the ExtensionArray
+        out = np.empty(index.shape, dtype=bool)
+        np.equal(index.array, index.array, out=out)
+        tm.assert_numpy_array_equal(out, result)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        np.exp,
+        np.exp2,
+        np.expm1,
+        np.log,
+        np.log2,
+        np.log10,
+        np.log1p,
+        np.sqrt,
+        np.sin,
+        np.cos,
+        np.tan,
+        np.arcsin,
+        np.arccos,
+        np.arctan,
+        np.sinh,
+        np.cosh,
+        np.tanh,
+        np.arcsinh,
+        np.arccosh,
+        np.arctanh,
+        np.deg2rad,
+        np.rad2deg,
+    ],
+    ids=lambda x: x.__name__,
+)
+def test_numpy_ufuncs_basic(index, func):
+    # test ufuncs of numpy, see:
+    # https://numpy.org/doc/stable/reference/ufuncs.html
+
+    if isinstance(index, DatetimeIndexOpsMixin):
+        with tm.external_error_raised((TypeError, AttributeError)):
+            with np.errstate(all="ignore"):
+                func(index)
+    elif is_numeric_dtype(index) and not (
+        is_complex_dtype(index) and func in [np.deg2rad, np.rad2deg]
+    ):
+        # coerces to float (e.g. np.sin)
+        with np.errstate(all="ignore"):
+            result = func(index)
+            arr_result = func(index.values)
+            if arr_result.dtype == np.float16:
+                arr_result = arr_result.astype(np.float32)
+            exp = Index(arr_result, name=index.name)
+
+        tm.assert_index_equal(result, exp)
+        if isinstance(index.dtype, np.dtype) and is_numeric_dtype(index):
+            if is_complex_dtype(index):
+                assert result.dtype == index.dtype
+            elif index.dtype in ["bool", "int8", "uint8"]:
+                assert result.dtype in ["float16", "float32"]
+            elif index.dtype in ["int16", "uint16", "float32"]:
+                assert result.dtype == "float32"
+            else:
+                assert result.dtype == "float64"
+        else:
+            # e.g. np.exp with Int64 -> Float64
+            assert type(result) is Index
+    # raise AttributeError or TypeError
+    elif len(index) == 0:
+        pass
+    else:
+        with tm.external_error_raised((TypeError, AttributeError)):
+            with np.errstate(all="ignore"):
+                func(index)
+
+
+@pytest.mark.parametrize(
+    "func", [np.isfinite, np.isinf, np.isnan, np.signbit], ids=lambda x: x.__name__
+)
+def test_numpy_ufuncs_other(index, func):
+    # test ufuncs of numpy, see:
+    # https://numpy.org/doc/stable/reference/ufuncs.html
+    if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
+        if func in (np.isfinite, np.isinf, np.isnan):
+            # numpy 1.18 changed isinf and isnan to not raise on dt64/td64
+            result = func(index)
+            assert isinstance(result, np.ndarray)
+
+            out = np.empty(index.shape, dtype=bool)
+            func(index, out=out)
+            tm.assert_numpy_array_equal(out, result)
+        else:
+            with tm.external_error_raised(TypeError):
+                func(index)
+
+    elif isinstance(index, PeriodIndex):
+        with tm.external_error_raised(TypeError):
+            func(index)
+
+    elif is_numeric_dtype(index) and not (
+        is_complex_dtype(index) and func is np.signbit
+    ):
+        # Results in bool array
+        result = func(index)
+        if not isinstance(index.dtype, np.dtype):
+            # e.g. Int64 we expect to get BooleanArray back
+            assert isinstance(result, BooleanArray)
+        else:
+            assert isinstance(result, np.ndarray)
+
+        out = np.empty(index.shape, dtype=bool)
+        func(index, out=out)
+
+        if not isinstance(index.dtype, np.dtype):
+            tm.assert_numpy_array_equal(out, result._data)
+        else:
+            tm.assert_numpy_array_equal(out, result)
+
+    elif len(index) == 0:
+        pass
+    else:
+        with tm.external_error_raised(TypeError):
+            func(index)
+
+
+@pytest.mark.parametrize("func", [np.maximum, np.minimum])
+def test_numpy_ufuncs_reductions(index, func, request):
+    # TODO: overlap with tests.series.test_ufunc.test_reductions
+    if len(index) == 0:
+        pytest.skip("Test doesn't make sense for empty index.")
+
+    if isinstance(index, CategoricalIndex) and index.dtype.ordered is False:
+        with pytest.raises(TypeError, match="is not ordered for"):
+            func.reduce(index)
+        return
+    else:
+        result = func.reduce(index)
+
+    if func is np.maximum:
+        expected = index.max(skipna=False)
+    else:
+        expected = index.min(skipna=False)
+        # TODO: do we have cases both with and without NAs?
+
+    assert type(result) is type(expected)
+    if isna(result):
+        assert isna(expected)
+    else:
+        assert result == expected
+
+
+@pytest.mark.parametrize("func", [np.bitwise_and, np.bitwise_or, np.bitwise_xor])
+def test_numpy_ufuncs_bitwise(func):
+    # https://github.com/pandas-dev/pandas/issues/46769
+    idx1 = Index([1, 2, 3, 4], dtype="int64")
+    idx2 = Index([3, 4, 5, 6], dtype="int64")
+
+    with tm.assert_produces_warning(None):
+        result = func(idx1, idx2)
+
+    expected = Index(func(idx1.values, idx2.values))
+    tm.assert_index_equal(result, expected)

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/test_subclass.py

@@ -0,0 +1,40 @@
+"""
+Tests involving custom Index subclasses
+"""
+import numpy as np
+
+from pandas import (
+    DataFrame,
+    Index,
+)
+import pandas._testing as tm
+
+
+class CustomIndex(Index):
+    def __new__(cls, data, name=None):
+        # assert that this index class cannot hold strings
+        if any(isinstance(val, str) for val in data):
+            raise TypeError("CustomIndex cannot hold strings")
+
+        if name is None and hasattr(data, "name"):
+            name = data.name
+        data = np.array(data, dtype="O")
+
+        return cls._simple_new(data, name)
+
+
+def test_insert_fallback_to_base_index():
+    # https://github.com/pandas-dev/pandas/issues/47071
+
+    idx = CustomIndex([1, 2, 3])
+    result = idx.insert(0, "string")
+    expected = Index(["string", 1, 2, 3], dtype=object)
+    tm.assert_index_equal(result, expected)
+
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((2, 3)),
+        columns=idx,
+        index=Index([1, 2], name="string"),
+    )
+    result = df.reset_index()
+    tm.assert_index_equal(result.columns, expected)

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_join.py

@@ -0,0 +1,47 @@
+import numpy as np
+
+from pandas import (
+    DataFrame,
+    Index,
+    Timedelta,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestJoin:
+    def test_append_join_nondatetimeindex(self):
+        rng = timedelta_range("1 days", periods=10)
+        idx = Index(["a", "b", "c", "d"])
+
+        result = rng.append(idx)
+        assert isinstance(result[0], Timedelta)
+
+        # it works
+        rng.join(idx, how="outer")
+
+    def test_join_self(self, join_type):
+        index = timedelta_range("1 day", periods=10)
+        joined = index.join(index, how=join_type)
+        tm.assert_index_equal(index, joined)
+
+    def test_does_not_convert_mixed_integer(self):
+        df = DataFrame(np.ones((5, 5)), columns=timedelta_range("1 day", periods=5))
+
+        cols = df.columns.join(df.index, how="outer")
+        joined = cols.join(df.columns)
+        assert cols.dtype == np.dtype("O")
+        assert cols.dtype == joined.dtype
+        tm.assert_index_equal(cols, joined)
+
+    def test_join_preserves_freq(self):
+        # GH#32157
+        tdi = timedelta_range("1 day", periods=10)
+        result = tdi[:5].join(tdi[5:], how="outer")
+        assert result.freq == tdi.freq
+        tm.assert_index_equal(result, tdi)
+
+        result = tdi[:5].join(tdi[6:], how="outer")
+        assert result.freq is None
+        expected = tdi.delete(5)
+        tm.assert_index_equal(result, expected)

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_ops.py

@@ -0,0 +1,14 @@
+from pandas import (
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestTimedeltaIndexOps:
+    def test_infer_freq(self, freq_sample):
+        # GH#11018
+        idx = timedelta_range("1", freq=freq_sample, periods=10)
+        result = TimedeltaIndex(idx.asi8, freq="infer")
+        tm.assert_index_equal(idx, result)
+        assert result.freq == freq_sample

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_setops.py

@@ -0,0 +1,254 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+from pandas.tseries.offsets import Hour
+
+
+class TestTimedeltaIndex:
+    def test_union(self):
+        i1 = timedelta_range("1day", periods=5)
+        i2 = timedelta_range("3day", periods=5)
+        result = i1.union(i2)
+        expected = timedelta_range("1day", periods=7)
+        tm.assert_index_equal(result, expected)
+
+        i1 = Index(np.arange(0, 20, 2, dtype=np.int64))
+        i2 = timedelta_range(start="1 day", periods=10, freq="D")
+        i1.union(i2)  # Works
+        i2.union(i1)  # Fails with "AttributeError: can't set attribute"
+
+    def test_union_sort_false(self):
+        tdi = timedelta_range("1day", periods=5)
+
+        left = tdi[3:]
+        right = tdi[:3]
+
+        # Check that we are testing the desired code path
+        assert left._can_fast_union(right)
+
+        result = left.union(right)
+        tm.assert_index_equal(result, tdi)
+
+        result = left.union(right, sort=False)
+        expected = TimedeltaIndex(["4 Days", "5 Days", "1 Days", "2 Day", "3 Days"])
+        tm.assert_index_equal(result, expected)
+
+    def test_union_coverage(self):
+        idx = TimedeltaIndex(["3d", "1d", "2d"])
+        ordered = TimedeltaIndex(idx.sort_values(), freq="infer")
+        result = ordered.union(idx)
+        tm.assert_index_equal(result, ordered)
+
+        result = ordered[:0].union(ordered)
+        tm.assert_index_equal(result, ordered)
+        assert result.freq == ordered.freq
+
+    def test_union_bug_1730(self):
+        rng_a = timedelta_range("1 day", periods=4, freq="3h")
+        rng_b = timedelta_range("1 day", periods=4, freq="4h")
+
+        result = rng_a.union(rng_b)
+        exp = TimedeltaIndex(sorted(set(rng_a) | set(rng_b)))
+        tm.assert_index_equal(result, exp)
+
+    def test_union_bug_1745(self):
+        left = TimedeltaIndex(["1 day 15:19:49.695000"])
+        right = TimedeltaIndex(
+            ["2 day 13:04:21.322000", "1 day 15:27:24.873000", "1 day 15:31:05.350000"]
+        )
+
+        result = left.union(right)
+        exp = TimedeltaIndex(sorted(set(left) | set(right)))
+        tm.assert_index_equal(result, exp)
+
+    def test_union_bug_4564(self):
+        left = timedelta_range("1 day", "30d")
+        right = left + pd.offsets.Minute(15)
+
+        result = left.union(right)
+        exp = TimedeltaIndex(sorted(set(left) | set(right)))
+        tm.assert_index_equal(result, exp)
+
+    def test_union_freq_infer(self):
+        # When taking the union of two TimedeltaIndexes, we infer
+        #  a freq even if the arguments don't have freq.  This matches
+        #  DatetimeIndex behavior.
+        tdi = timedelta_range("1 Day", periods=5)
+        left = tdi[[0, 1, 3, 4]]
+        right = tdi[[2, 3, 1]]
+
+        assert left.freq is None
+        assert right.freq is None
+
+        result = left.union(right)
+        tm.assert_index_equal(result, tdi)
+        assert result.freq == "D"
+
+    def test_intersection_bug_1708(self):
+        index_1 = timedelta_range("1 day", periods=4, freq="h")
+        index_2 = index_1 + pd.offsets.Hour(5)
+
+        result = index_1.intersection(index_2)
+        assert len(result) == 0
+
+        index_1 = timedelta_range("1 day", periods=4, freq="h")
+        index_2 = index_1 + pd.offsets.Hour(1)
+
+        result = index_1.intersection(index_2)
+        expected = timedelta_range("1 day 01:00:00", periods=3, freq="h")
+        tm.assert_index_equal(result, expected)
+        assert result.freq == expected.freq
+
+    def test_intersection_equal(self, sort):
+        # GH 24471 Test intersection outcome given the sort keyword
+        # for equal indices intersection should return the original index
+        first = timedelta_range("1 day", periods=4, freq="h")
+        second = timedelta_range("1 day", periods=4, freq="h")
+        intersect = first.intersection(second, sort=sort)
+        if sort is None:
+            tm.assert_index_equal(intersect, second.sort_values())
+        tm.assert_index_equal(intersect, second)
+
+        # Corner cases
+        inter = first.intersection(first, sort=sort)
+        assert inter is first
+
+    @pytest.mark.parametrize("period_1, period_2", [(0, 4), (4, 0)])
+    def test_intersection_zero_length(self, period_1, period_2, sort):
+        # GH 24471 test for non overlap the intersection should be zero length
+        index_1 = timedelta_range("1 day", periods=period_1, freq="h")
+        index_2 = timedelta_range("1 day", periods=period_2, freq="h")
+        expected = timedelta_range("1 day", periods=0, freq="h")
+        result = index_1.intersection(index_2, sort=sort)
+        tm.assert_index_equal(result, expected)
+
+    def test_zero_length_input_index(self, sort):
+        # GH 24966 test for 0-len intersections are copied
+        index_1 = timedelta_range("1 day", periods=0, freq="h")
+        index_2 = timedelta_range("1 day", periods=3, freq="h")
+        result = index_1.intersection(index_2, sort=sort)
+        assert index_1 is not result
+        assert index_2 is not result
+        tm.assert_copy(result, index_1)
+
+    @pytest.mark.parametrize(
+        "rng, expected",
+        # if target has the same name, it is preserved
+        [
+            (
+                timedelta_range("1 day", periods=5, freq="h", name="idx"),
+                timedelta_range("1 day", periods=4, freq="h", name="idx"),
+            ),
+            # if target name is different, it will be reset
+            (
+                timedelta_range("1 day", periods=5, freq="h", name="other"),
+                timedelta_range("1 day", periods=4, freq="h", name=None),
+            ),
+            # if no overlap exists return empty index
+            (
+                timedelta_range("1 day", periods=10, freq="h", name="idx")[5:],
+                TimedeltaIndex([], freq="h", name="idx"),
+            ),
+        ],
+    )
+    def test_intersection(self, rng, expected, sort):
+        # GH 4690 (with tz)
+        base = timedelta_range("1 day", periods=4, freq="h", name="idx")
+        result = base.intersection(rng, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+        assert result.freq == expected.freq
+
+    @pytest.mark.parametrize(
+        "rng, expected",
+        # part intersection works
+        [
+            (
+                TimedeltaIndex(["5 hour", "2 hour", "4 hour", "9 hour"], name="idx"),
+                TimedeltaIndex(["2 hour", "4 hour"], name="idx"),
+            ),
+            # reordered part intersection
+            (
+                TimedeltaIndex(["2 hour", "5 hour", "5 hour", "1 hour"], name="other"),
+                TimedeltaIndex(["1 hour", "2 hour"], name=None),
+            ),
+            # reversed index
+            (
+                TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx")[
+                    ::-1
+                ],
+                TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx"),
+            ),
+        ],
+    )
+    def test_intersection_non_monotonic(self, rng, expected, sort):
+        # 24471 non-monotonic
+        base = TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx")
+        result = base.intersection(rng, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+
+        # if reversed order, frequency is still the same
+        if all(base == rng[::-1]) and sort is None:
+            assert isinstance(result.freq, Hour)
+        else:
+            assert result.freq is None
+
+
+class TestTimedeltaIndexDifference:
+    def test_difference_freq(self, sort):
+        # GH14323: Difference of TimedeltaIndex should not preserve frequency
+
+        index = timedelta_range("0 days", "5 days", freq="D")
+
+        other = timedelta_range("1 days", "4 days", freq="D")
+        expected = TimedeltaIndex(["0 days", "5 days"], freq=None)
+        idx_diff = index.difference(other, sort)
+        tm.assert_index_equal(idx_diff, expected)
+        tm.assert_attr_equal("freq", idx_diff, expected)
+
+        # preserve frequency when the difference is a contiguous
+        # subset of the original range
+        other = timedelta_range("2 days", "5 days", freq="D")
+        idx_diff = index.difference(other, sort)
+        expected = TimedeltaIndex(["0 days", "1 days"], freq="D")
+        tm.assert_index_equal(idx_diff, expected)
+        tm.assert_attr_equal("freq", idx_diff, expected)
+
+    def test_difference_sort(self, sort):
+        index = TimedeltaIndex(
+            ["5 days", "3 days", "2 days", "4 days", "1 days", "0 days"]
+        )
+
+        other = timedelta_range("1 days", "4 days", freq="D")
+        idx_diff = index.difference(other, sort)
+
+        expected = TimedeltaIndex(["5 days", "0 days"], freq=None)
+
+        if sort is None:
+            expected = expected.sort_values()
+
+        tm.assert_index_equal(idx_diff, expected)
+        tm.assert_attr_equal("freq", idx_diff, expected)
+
+        other = timedelta_range("2 days", "5 days", freq="D")
+        idx_diff = index.difference(other, sort)
+        expected = TimedeltaIndex(["1 days", "0 days"], freq=None)
+
+        if sort is None:
+            expected = expected.sort_values()
+
+        tm.assert_index_equal(idx_diff, expected)
+        tm.assert_attr_equal("freq", idx_diff, expected)

mplug_owl2/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta.py

@@ -0,0 +1,61 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    Series,
+    Timedelta,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestTimedeltaIndex:
+    def test_misc_coverage(self):
+        rng = timedelta_range("1 day", periods=5)
+        result = rng.groupby(rng.days)
+        assert isinstance(next(iter(result.values()))[0], Timedelta)
+
+    def test_map(self):
+        # test_map_dictlike generally tests
+
+        rng = timedelta_range("1 day", periods=10)
+
+        f = lambda x: x.days
+        result = rng.map(f)
+        exp = Index([f(x) for x in rng], dtype=np.int64)
+        tm.assert_index_equal(result, exp)
+
+    def test_fields(self):
+        rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s")
+        tm.assert_index_equal(rng.days, Index([1, 1], dtype=np.int64))
+        tm.assert_index_equal(
+            rng.seconds,
+            Index([10 * 3600 + 11 * 60 + 12, 10 * 3600 + 11 * 60 + 13], dtype=np.int32),
+        )
+        tm.assert_index_equal(
+            rng.microseconds,
+            Index([100 * 1000 + 123, 100 * 1000 + 123], dtype=np.int32),
+        )
+        tm.assert_index_equal(rng.nanoseconds, Index([456, 456], dtype=np.int32))
+
+        msg = "'TimedeltaIndex' object has no attribute '{}'"
+        with pytest.raises(AttributeError, match=msg.format("hours")):
+            rng.hours
+        with pytest.raises(AttributeError, match=msg.format("minutes")):
+            rng.minutes
+        with pytest.raises(AttributeError, match=msg.format("milliseconds")):
+            rng.milliseconds
+
+        # with nat
+        s = Series(rng)
+        s[1] = np.nan
+
+        tm.assert_series_equal(s.dt.days, Series([1, np.nan], index=[0, 1]))
+        tm.assert_series_equal(
+            s.dt.seconds, Series([10 * 3600 + 11 * 60 + 12, np.nan], index=[0, 1])
+        )
+
+        # preserve name (GH15589)
+        rng.name = "name"
+        assert rng.days.name == "name"

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/BUILD_SHARED_LIBS.rst

@@ -0,0 +1,43 @@
+BUILD_SHARED_LIBS
+-----------------
+
+Tell :command:`add_library` to default to ``SHARED`` libraries,
+instead of ``STATIC`` libraries, when called with no explicit library type.
+
+Calls to :command:`add_library` without any explicit library type check
+the current ``BUILD_SHARED_LIBS`` variable value.  If it is true, then the
+default library type is ``SHARED``.  Otherwise, the default is ``STATIC``.
+
+For example, the code:
+
+.. code-block:: cmake
+
+  add_library(example ${sources})
+
+behaves as if written
+
+.. code-block:: cmake
+
+  if(BUILD_SHARED_LIBS)
+    add_library(example SHARED ${sources})
+  else()
+    add_library(example STATIC ${sources})
+  endif()
+
+CMake does not define ``BUILD_SHARED_LIBS`` by default, but projects
+often create a cache entry for it using the :command:`option` command:
+
+.. code-block:: cmake
+
+  option(BUILD_SHARED_LIBS "Build using shared libraries" ON)
+
+This provides a switch that users can control, e.g., with :option:`cmake -D`.
+If adding such an option to the project, do so in the top level
+``CMakeLists.txt`` file, before any :command:`add_library` calls.
+Note that if bringing external dependencies directly into the build, such as
+with :module:`FetchContent` or a direct call to :command:`add_subdirectory`,
+and one of those dependencies has such a call to
+:command:`option(BUILD_SHARED_LIBS ...) <option>`, the top level project must
+also call :command:`option(BUILD_SHARED_LIBS ...) <option>` before bringing in
+its dependencies.  Failure to do so can lead to different behavior between the
+first and subsequent CMake runs.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_ANDROID_JAVA_SOURCE_DIR.rst

@@ -0,0 +1,7 @@
+CMAKE_ANDROID_JAVA_SOURCE_DIR
+-----------------------------
+
+.. versionadded:: 3.4
+
+Default value for the :prop_tgt:`ANDROID_JAVA_SOURCE_DIR` target property.
+See that target property for additional information.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_ANDROID_NDK_DEPRECATED_HEADERS.rst

@@ -0,0 +1,11 @@
+CMAKE_ANDROID_NDK_DEPRECATED_HEADERS
+------------------------------------
+
+.. versionadded:: 3.9
+
+When :ref:`Cross Compiling for Android with the NDK`, this variable
+may be set to specify whether to use the deprecated per-api-level
+headers instead of the unified headers.
+
+If not specified, the default will be *false* if using a NDK version
+that provides the unified headers and *true* otherwise.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_AUTOMOC_COMPILER_PREDEFINES.rst

@@ -0,0 +1,10 @@
+CMAKE_AUTOMOC_COMPILER_PREDEFINES
+---------------------------------
+
+.. versionadded:: 3.10
+
+This variable is used to initialize the :prop_tgt:`AUTOMOC_COMPILER_PREDEFINES`
+property on all the targets. See that target property for additional
+information.
+
+By default it is ON.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_AUTOMOC_PATH_PREFIX.rst

@@ -0,0 +1,13 @@
+CMAKE_AUTOMOC_PATH_PREFIX
+-------------------------
+
+.. versionadded:: 3.16
+
+Whether to generate the ``-p`` path prefix option for ``moc`` on
+:prop_tgt:`AUTOMOC` enabled Qt targets.
+
+This variable is used to initialize the :prop_tgt:`AUTOMOC_PATH_PREFIX`
+property on all the targets.  See that target property for additional
+information.
+
+The default value is ``OFF``.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_COMPILER_2005.rst

@@ -0,0 +1,6 @@
+CMAKE_COMPILER_2005
+-------------------
+
+Using the Visual Studio 2005 compiler from Microsoft
+
+Set to true when using the Visual Studio 2005 compiler from Microsoft.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_COMPILE_WARNING_AS_ERROR.rst

@@ -0,0 +1,9 @@
+CMAKE_COMPILE_WARNING_AS_ERROR
+------------------------------
+
+.. versionadded:: 3.24
+
+Specify whether to treat warnings on compile as errors.
+
+This variable is used to initialize the
+:prop_tgt:`COMPILE_WARNING_AS_ERROR` property on all the targets.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_CUDA_STANDARD_REQUIRED.rst

@@ -0,0 +1,10 @@
+CMAKE_CUDA_STANDARD_REQUIRED
+----------------------------
+
+.. versionadded:: 3.8
+
+Default value for :prop_tgt:`CUDA_STANDARD_REQUIRED` target property if set
+when a target is created.
+
+See the :manual:`cmake-compile-features(7)` manual for information on
+compile features and a list of supported compilers.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_CXX_EXTENSIONS.rst

@@ -0,0 +1,10 @@
+CMAKE_CXX_EXTENSIONS
+--------------------
+
+.. versionadded:: 3.1
+
+Default value for :prop_tgt:`CXX_EXTENSIONS` target property if set when a
+target is created.
+
+See the :manual:`cmake-compile-features(7)` manual for information on
+compile features and a list of supported compilers.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_CXX_STANDARD_REQUIRED.rst

@@ -0,0 +1,10 @@
+CMAKE_CXX_STANDARD_REQUIRED
+---------------------------
+
+.. versionadded:: 3.1
+
+Default value for :prop_tgt:`CXX_STANDARD_REQUIRED` target property if set when
+a target is created.
+
+See the :manual:`cmake-compile-features(7)` manual for information on
+compile features and a list of supported compilers.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_DISABLE_FIND_PACKAGE_PackageName.rst

@@ -0,0 +1,28 @@
+CMAKE_DISABLE_FIND_PACKAGE_<PackageName>
+----------------------------------------
+
+Variable for disabling :command:`find_package` calls.
+
+Every non-``REQUIRED`` :command:`find_package` call in a project can be
+disabled by setting the variable
+``CMAKE_DISABLE_FIND_PACKAGE_<PackageName>`` to ``TRUE``.
+This can be used to build a project without an optional package,
+although that package is installed.
+
+This switch should be used during the initial CMake run.  Otherwise if
+the package has already been found in a previous CMake run, the
+variables which have been stored in the cache will still be there.  In
+that case it is recommended to remove the cache variables for this
+package from the cache using the cache editor or :option:`cmake -U`.
+
+Note that this variable can lead to inconsistent results within the project.
+Consider the case where a dependency is requested via :command:`find_package`
+from two different places within the project.  If the first call does not
+have the ``REQUIRED`` keyword, it will not find the dependency when
+``CMAKE_DISABLE_FIND_PACKAGE_<PackageName>`` is set to true for that
+dependency.  The project will proceed under the assumption that the dependency
+isn't available.  If the second call elsewhere in the project *does* have the
+``REQUIRED`` keyword, it can succeed.  Two different parts of the same project
+have then seen opposite results for the same dependency.
+
+See also the :variable:`CMAKE_REQUIRE_FIND_PACKAGE_<PackageName>` variable.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_EDIT_COMMAND.rst

@@ -0,0 +1,9 @@
+CMAKE_EDIT_COMMAND
+------------------
+
+Full path to :manual:`cmake-gui(1)` or :manual:`ccmake(1)`.  Defined only for
+:ref:`Makefile Generators` and :ref:`Ninja Generators` when not using any
+:ref:`Extra Generators`.
+
+This is the full path to the CMake executable that can graphically
+edit the cache.  For example, :manual:`cmake-gui(1)` or :manual:`ccmake(1)`.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_EXPORT_BUILD_DATABASE.rst

@@ -0,0 +1,86 @@
+CMAKE_EXPORT_BUILD_DATABASE
+---------------------------
+
+.. versionadded:: 3.31
+
+.. note ::
+
+   This variable is meaningful only when experimental support for build
+   databases has been enabled by the
+   ``CMAKE_EXPERIMENTAL_EXPORT_BUILD_DATABASE`` gate.
+
+Enable/Disable output of module compile commands during the build.
+
+If enabled, generates a ``build_database.json`` file containing the
+information necessary to compile a target's C++ module sources with any
+tooling. The format of the JSON file looks like:
+
+.. code-block:: javascript
+
+  {
+    "version": 1,
+    "revision": 0,
+    "sets": [
+      {
+        "family-name" : "export_build_database",
+        "name" : "export_build_database@Debug",
+        "translation-units" : [
+          {
+            "arguments": [
+              "/path/to/compiler",
+              "...",
+            ],
+            "baseline-arguments" :
+            [
+              "...",
+            ],
+            "local-arguments" :
+            [
+              "...",
+            ],
+            "object": "CMakeFiles/target.dir/source.cxx.o",
+            "private": true,
+            "provides": {
+              "importable": "path/to/bmi"
+            },
+            "requires" : [],
+            "source": "path/to/source.cxx",
+            "work-directory": "/path/to/working/directory"
+          }
+        ],
+        "visible-sets" : []
+      }
+    ]
+  }
+
+This is initialized by the :envvar:`CMAKE_EXPORT_BUILD_DATABASE` environment
+variable, and initializes the :prop_tgt:`EXPORT_BUILD_DATABASE` target
+property for all targets.
+
+.. note::
+  This option is implemented only by the :ref:`Ninja Generators`.  It is
+  ignored on other generators.
+
+When supported and enabled, numerous targets are created in order to make it
+possible to build a file containing just the commands that are needed for the
+tool in question.
+
+``cmake_build_database-<CONFIG>``
+  Writes ``build_database_<CONFIG>.json``. Writes a build database for the
+  entire build for the given configuration and all languages. Not available if
+  the configuration name is the empty string.
+
+``cmake_build_database-<LANG>-<CONFIG>``
+  Writes ``build_database_<LANG>_<CONFIG>.json``. Writes build database for
+  the entire build for the given configuration and language. Not available if
+  the configuration name is the empty string.
+
+``cmake_build_database-<LANG>``
+  Writes ``build_database_<LANG>.json``. Writes build database for the entire
+  build for the given language and all configurations. In a multi-config
+  generator, other build configuration database may be assumed to exist.
+
+``cmake_build_database``
+  Writes to ``build_database.json``. Writes build database for all languages
+  and configurations. In a multi-config generator, other build configuration
+  database may be assumed to exist.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_EXPORT_FIND_PACKAGE_NAME.rst

@@ -0,0 +1,8 @@
+CMAKE_EXPORT_FIND_PACKAGE_NAME
+------------------------------
+
+.. note::
+
+  Experimental. Gated by ``CMAKE_EXPERIMENTAL_EXPORT_PACKAGE_DEPENDENCIES``.
+
+Initializes the value of :prop_tgt:`EXPORT_FIND_PACKAGE_NAME`.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_FIND_APPBUNDLE.rst

@@ -0,0 +1,24 @@
+CMAKE_FIND_APPBUNDLE
+--------------------
+
+.. versionadded:: 3.4
+
+This variable affects how ``find_*`` commands choose between
+macOS Application Bundles and unix-style package components.
+
+On Darwin or systems supporting macOS Application Bundles, the
+``CMAKE_FIND_APPBUNDLE`` variable can be set to empty or
+one of the following:
+
+``FIRST``
+  Try to find application bundles before standard programs.
+  This is the default on Darwin.
+
+``LAST``
+  Try to find application bundles after standard programs.
+
+``ONLY``
+  Only try to find application bundles.
+
+``NEVER``
+  Never try to find application bundles.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_FIND_LIBRARY_SUFFIXES.rst

@@ -0,0 +1,12 @@
+CMAKE_FIND_LIBRARY_SUFFIXES
+---------------------------
+
+Suffixes to append when looking for libraries.
+
+This specifies what suffixes to add to library names when the
+:command:`find_library` command looks for libraries.  On Windows systems this
+is typically ``.lib`` and, depending on the compiler, ``.dll.lib``, ``.dll.a``,
+``.a`` (e.g. rustc, GCC, or Clang), so when it tries to find the ``foo``
+library, it will look for ``[<prefix>]foo[.dll].lib`` and/or
+``[<prefix>]foo[.dll].a``, depending on the compiler used and the ``<prefix>``
+specified in the :variable:`CMAKE_FIND_LIBRARY_PREFIXES`.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_FIND_PACKAGE_REDIRECTS_DIR.rst

@@ -0,0 +1,28 @@
+CMAKE_FIND_PACKAGE_REDIRECTS_DIR
+--------------------------------
+
+.. versionadded:: 3.24
+
+This read-only variable specifies a directory that the :command:`find_package`
+command will check first before searching anywhere else for a module or config
+package file.  A config package file in this directory will always be found in
+preference to any other Find module file or config package file.
+
+The primary purpose of this variable is to facilitate integration between
+:command:`find_package` and :command:`FetchContent_MakeAvailable`.  The latter
+command may create files in the ``CMAKE_FIND_PACKAGE_REDIRECTS_DIR`` directory
+when it populates a dependency.  This allows subsequent calls to
+:command:`find_package` for the same dependency to reuse the populated
+contents instead of trying to satisfy the dependency from somewhere external
+to the build.  Projects may also want to write files into this directory in
+some situations (see :ref:`FetchContent-find_package-integration-examples`
+for examples).
+
+The directory that ``CMAKE_FIND_PACKAGE_REDIRECTS_DIR`` points to will always
+be erased and recreated empty at the start of every CMake run.  Any files
+written into this directory during the CMake run will be lost the next time
+CMake configures the project.
+
+``CMAKE_FIND_PACKAGE_REDIRECTS_DIR`` is only set in CMake project mode.
+It is not set when CMake is run in script mode
+(i.e. :option:`cmake -P`).

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_HIP_PLATFORM.rst

@@ -0,0 +1,22 @@
+CMAKE_HIP_PLATFORM
+------------------
+
+.. versionadded:: 3.28
+
+GPU platform for which HIP language sources are to be compiled.
+
+The value must be one of:
+
+``amd``
+  AMD GPUs
+
+``nvidia``
+  NVIDIA GPUs
+
+If not specified, a default is computed via ``hipconfig --platform``.
+
+:variable:`CMAKE_HIP_ARCHITECTURES` entries are interpreted with
+as architectures of the GPU platform.
+
+:variable:`CMAKE_HIP_COMPILER <CMAKE_<LANG>_COMPILER>` must target
+the same GPU platform.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_HOST_LINUX.rst

@@ -0,0 +1,6 @@
+CMAKE_HOST_LINUX
+----------------
+
+.. versionadded:: 3.25
+
+Set to true when the host system is Linux.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_HOST_SYSTEM.rst

@@ -0,0 +1,10 @@
+CMAKE_HOST_SYSTEM
+-----------------
+
+Composite Name of OS CMake is being run on.
+
+This variable is the composite of :variable:`CMAKE_HOST_SYSTEM_NAME` and
+:variable:`CMAKE_HOST_SYSTEM_VERSION`, e.g.
+``${CMAKE_HOST_SYSTEM_NAME}-${CMAKE_HOST_SYSTEM_VERSION}``.  If
+:variable:`CMAKE_HOST_SYSTEM_VERSION` is not set, then this variable is
+the same as :variable:`CMAKE_HOST_SYSTEM_NAME`.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_HOST_SYSTEM_PROCESSOR.rst

@@ -0,0 +1,42 @@
+CMAKE_HOST_SYSTEM_PROCESSOR
+---------------------------
+
+The name of the CPU CMake is running on.
+
+Windows Platforms
+^^^^^^^^^^^^^^^^^
+
+On Windows, this variable is set to the value of the environment variable
+``PROCESSOR_ARCHITECTURE``.
+
+Unix Platforms
+^^^^^^^^^^^^^^
+
+On systems that support ``uname``, this variable is set to the output of:
+
+- ``uname -m`` on GNU, Linux, Cygwin, Android, or
+- ``arch`` on OpenBSD, or
+- on other systems,
+
+  * ``uname -p`` if its exit code is nonzero, or
+  * ``uname -m`` otherwise.
+
+macOS Platforms
+^^^^^^^^^^^^^^^
+
+The value of ``uname -m`` is used by default.
+
+On Apple Silicon hosts, the architecture printed by ``uname -m`` may vary
+based on CMake's own architecture and that of the invoking process tree.
+
+.. versionadded:: 3.19.2
+
+  On Apple Silicon hosts:
+
+  * The :variable:`CMAKE_APPLE_SILICON_PROCESSOR` variable or
+    the :envvar:`CMAKE_APPLE_SILICON_PROCESSOR` environment variable
+    may be set to specify the host architecture explicitly.
+
+  * If :variable:`CMAKE_OSX_ARCHITECTURES` is not set, CMake adds explicit
+    flags to tell the compiler to build for the host architecture so the
+    toolchain does not have to guess based on the process tree's architecture.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_HOST_WIN32.rst

@@ -0,0 +1,6 @@
+CMAKE_HOST_WIN32
+----------------
+
+``True`` if the host system is running Windows, including Windows 64-bit and MSYS.
+
+Set to ``false`` on Cygwin.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_INCLUDE_DIRECTORIES_BEFORE.rst

@@ -0,0 +1,9 @@
+CMAKE_INCLUDE_DIRECTORIES_BEFORE
+--------------------------------
+
+Whether to append or prepend directories by default in
+:command:`include_directories`.
+
+This variable affects the default behavior of the :command:`include_directories`
+command.  Setting this variable to ``ON`` is equivalent to using the ``BEFORE``
+option in all uses of that command.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_INSTALL_MESSAGE.rst

@@ -0,0 +1,32 @@
+CMAKE_INSTALL_MESSAGE
+---------------------
+
+.. versionadded:: 3.1
+
+Specify verbosity of installation script code generated by the
+:command:`install` command (using the :command:`file(INSTALL)` command).
+For paths that are newly installed or updated, installation
+may print lines like::
+
+  -- Installing: /some/destination/path
+
+For paths that are already up to date, installation may print
+lines like::
+
+  -- Up-to-date: /some/destination/path
+
+The ``CMAKE_INSTALL_MESSAGE`` variable may be set to control
+which messages are printed:
+
+``ALWAYS``
+  Print both ``Installing`` and ``Up-to-date`` messages.
+
+``LAZY``
+  Print ``Installing`` but not ``Up-to-date`` messages.
+
+``NEVER``
+  Print neither ``Installing`` nor ``Up-to-date`` messages.
+
+Other values have undefined behavior and may not be diagnosed.
+
+If this variable is not set, the default behavior is ``ALWAYS``.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_INSTALL_RPATH_USE_LINK_PATH.rst

@@ -0,0 +1,13 @@
+CMAKE_INSTALL_RPATH_USE_LINK_PATH
+---------------------------------
+
+Add paths to linker search and installed rpath.
+
+``CMAKE_INSTALL_RPATH_USE_LINK_PATH`` is a boolean that if set to ``True``
+will append to the runtime search path (rpath) of installed binaries
+any directories outside the project that are in the linker search path or
+contain linked library files.  The directories are appended after the
+value of the :prop_tgt:`INSTALL_RPATH` target property.
+
+This variable is used to initialize the target property
+:prop_tgt:`INSTALL_RPATH_USE_LINK_PATH` for all targets.

openflamingo/lib/python3.10/site-packages/cmake/data/share/cmake-3.31/Help/variable/CMAKE_LANG_COMPILER_EXTERNAL_TOOLCHAIN.rst

@@ -0,0 +1,13 @@
+CMAKE_<LANG>_COMPILER_EXTERNAL_TOOLCHAIN
+----------------------------------------
+
+The external toolchain for cross-compiling, if supported.
+
+Some compiler toolchains do not ship their own auxiliary utilities such as
+archivers and linkers.  The compiler driver may support a command-line argument
+to specify the location of such tools.
+``CMAKE_<LANG>_COMPILER_EXTERNAL_TOOLCHAIN`` may be set to a path to
+the external toolchain and will be passed to the compiler driver if supported.
+
+This variable may only be set in a toolchain file specified by
+the :variable:`CMAKE_TOOLCHAIN_FILE` variable.