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llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_append.py

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+import pytest
+
+from pandas import (
+    CategoricalIndex,
+    Index,
+)
+import pandas._testing as tm
+
+
+class TestAppend:
+    @pytest.fixture
+    def ci(self):
+        categories = list("cab")
+        return CategoricalIndex(list("aabbca"), categories=categories, ordered=False)
+
+    def test_append(self, ci):
+        # append cats with the same categories
+        result = ci[:3].append(ci[3:])
+        tm.assert_index_equal(result, ci, exact=True)
+
+        foos = [ci[:1], ci[1:3], ci[3:]]
+        result = foos[0].append(foos[1:])
+        tm.assert_index_equal(result, ci, exact=True)
+
+    def test_append_empty(self, ci):
+        # empty
+        result = ci.append([])
+        tm.assert_index_equal(result, ci, exact=True)
+
+    def test_append_mismatched_categories(self, ci):
+        # appending with different categories or reordered is not ok
+        msg = "all inputs must be Index"
+        with pytest.raises(TypeError, match=msg):
+            ci.append(ci.values.set_categories(list("abcd")))
+        with pytest.raises(TypeError, match=msg):
+            ci.append(ci.values.reorder_categories(list("abc")))
+
+    def test_append_category_objects(self, ci):
+        # with objects
+        result = ci.append(Index(["c", "a"]))
+        expected = CategoricalIndex(list("aabbcaca"), categories=ci.categories)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_append_non_categories(self, ci):
+        # invalid objects -> cast to object via concat_compat
+        result = ci.append(Index(["a", "d"]))
+        expected = Index(["a", "a", "b", "b", "c", "a", "a", "d"])
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_append_object(self, ci):
+        # GH#14298 - if base object is not categorical -> coerce to object
+        result = Index(["c", "a"]).append(ci)
+        expected = Index(list("caaabbca"))
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_append_to_another(self):
+        # hits Index._concat
+        fst = Index(["a", "b"])
+        snd = CategoricalIndex(["d", "e"])
+        result = fst.append(snd)
+        expected = Index(["a", "b", "d", "e"])
+        tm.assert_index_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_fillna.py

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+import numpy as np
+import pytest
+
+from pandas import CategoricalIndex
+import pandas._testing as tm
+
+
+class TestFillNA:
+    def test_fillna_categorical(self):
+        # GH#11343
+        idx = CategoricalIndex([1.0, np.nan, 3.0, 1.0], name="x")
+        # fill by value in categories
+        exp = CategoricalIndex([1.0, 1.0, 3.0, 1.0], name="x")
+        tm.assert_index_equal(idx.fillna(1.0), exp)
+
+        cat = idx._data
+
+        # fill by value not in categories raises TypeError on EA, casts on CI
+        msg = "Cannot setitem on a Categorical with a new category"
+        with pytest.raises(TypeError, match=msg):
+            cat.fillna(2.0)
+
+        result = idx.fillna(2.0)
+        expected = idx.astype(object).fillna(2.0)
+        tm.assert_index_equal(result, expected)
+
+    def test_fillna_copies_with_no_nas(self):
+        # Nothing to fill, should still get a copy for the Categorical method,
+        #  but OK to get a view on CategoricalIndex method
+        ci = CategoricalIndex([0, 1, 1])
+        result = ci.fillna(0)
+        assert result is not ci
+        assert tm.shares_memory(result, ci)
+
+        # But at the EA level we always get a copy.
+        cat = ci._data
+        result = cat.fillna(0)
+        assert result._ndarray is not cat._ndarray
+        assert result._ndarray.base is None
+        assert not tm.shares_memory(result, cat)
+
+    def test_fillna_validates_with_no_nas(self):
+        # We validate the fill value even if fillna is a no-op
+        ci = CategoricalIndex([2, 3, 3])
+        cat = ci._data
+
+        msg = "Cannot setitem on a Categorical with a new category"
+        res = ci.fillna(False)
+        # nothing to fill, so we dont cast
+        tm.assert_index_equal(res, ci)
+
+        # Same check directly on the Categorical
+        with pytest.raises(TypeError, match=msg):
+            cat.fillna(False)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_indexing.py

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+import numpy as np
+import pytest
+
+from pandas.errors import InvalidIndexError
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    Index,
+    IntervalIndex,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestTake:
+    def test_take_fill_value(self):
+        # GH 12631
+
+        # numeric category
+        idx = CategoricalIndex([1, 2, 3], name="xxx")
+        result = idx.take(np.array([1, 0, -1]))
+        expected = CategoricalIndex([2, 1, 3], name="xxx")
+        tm.assert_index_equal(result, expected)
+        tm.assert_categorical_equal(result.values, expected.values)
+
+        # fill_value
+        result = idx.take(np.array([1, 0, -1]), fill_value=True)
+        expected = CategoricalIndex([2, 1, np.nan], categories=[1, 2, 3], name="xxx")
+        tm.assert_index_equal(result, expected)
+        tm.assert_categorical_equal(result.values, expected.values)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = CategoricalIndex([2, 1, 3], name="xxx")
+        tm.assert_index_equal(result, expected)
+        tm.assert_categorical_equal(result.values, expected.values)
+
+        # object category
+        idx = CategoricalIndex(
+            list("CBA"), categories=list("ABC"), ordered=True, name="xxx"
+        )
+        result = idx.take(np.array([1, 0, -1]))
+        expected = CategoricalIndex(
+            list("BCA"), categories=list("ABC"), ordered=True, name="xxx"
+        )
+        tm.assert_index_equal(result, expected)
+        tm.assert_categorical_equal(result.values, expected.values)
+
+        # fill_value
+        result = idx.take(np.array([1, 0, -1]), fill_value=True)
+        expected = CategoricalIndex(
+            ["B", "C", np.nan], categories=list("ABC"), ordered=True, name="xxx"
+        )
+        tm.assert_index_equal(result, expected)
+        tm.assert_categorical_equal(result.values, expected.values)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = CategoricalIndex(
+            list("BCA"), categories=list("ABC"), ordered=True, name="xxx"
+        )
+        tm.assert_index_equal(result, expected)
+        tm.assert_categorical_equal(result.values, expected.values)
+
+        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]))
+
+    def test_take_fill_value_datetime(self):
+        # datetime category
+        idx = pd.DatetimeIndex(["2011-01-01", "2011-02-01", "2011-03-01"], name="xxx")
+        idx = CategoricalIndex(idx)
+        result = idx.take(np.array([1, 0, -1]))
+        expected = pd.DatetimeIndex(
+            ["2011-02-01", "2011-01-01", "2011-03-01"], name="xxx"
+        )
+        expected = CategoricalIndex(expected)
+        tm.assert_index_equal(result, expected)
+
+        # fill_value
+        result = idx.take(np.array([1, 0, -1]), fill_value=True)
+        expected = pd.DatetimeIndex(["2011-02-01", "2011-01-01", "NaT"], name="xxx")
+        exp_cats = pd.DatetimeIndex(["2011-01-01", "2011-02-01", "2011-03-01"])
+        expected = CategoricalIndex(expected, categories=exp_cats)
+        tm.assert_index_equal(result, expected)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = pd.DatetimeIndex(
+            ["2011-02-01", "2011-01-01", "2011-03-01"], name="xxx"
+        )
+        expected = CategoricalIndex(expected)
+        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]))
+
+    def test_take_invalid_kwargs(self):
+        idx = CategoricalIndex([1, 2, 3], name="foo")
+        indices = [1, 0, -1]
+
+        msg = r"take\(\) got an unexpected keyword argument 'foo'"
+        with pytest.raises(TypeError, match=msg):
+            idx.take(indices, foo=2)
+
+        msg = "the 'out' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            idx.take(indices, out=indices)
+
+        msg = "the 'mode' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            idx.take(indices, mode="clip")
+
+
+class TestGetLoc:
+    def test_get_loc(self):
+        # GH 12531
+        cidx1 = CategoricalIndex(list("abcde"), categories=list("edabc"))
+        idx1 = Index(list("abcde"))
+        assert cidx1.get_loc("a") == idx1.get_loc("a")
+        assert cidx1.get_loc("e") == idx1.get_loc("e")
+
+        for i in [cidx1, idx1]:
+            with pytest.raises(KeyError, match="'NOT-EXIST'"):
+                i.get_loc("NOT-EXIST")
+
+        # non-unique
+        cidx2 = CategoricalIndex(list("aacded"), categories=list("edabc"))
+        idx2 = Index(list("aacded"))
+
+        # results in bool array
+        res = cidx2.get_loc("d")
+        tm.assert_numpy_array_equal(res, idx2.get_loc("d"))
+        tm.assert_numpy_array_equal(
+            res, np.array([False, False, False, True, False, True])
+        )
+        # unique element results in scalar
+        res = cidx2.get_loc("e")
+        assert res == idx2.get_loc("e")
+        assert res == 4
+
+        for i in [cidx2, idx2]:
+            with pytest.raises(KeyError, match="'NOT-EXIST'"):
+                i.get_loc("NOT-EXIST")
+
+        # non-unique, sliceable
+        cidx3 = CategoricalIndex(list("aabbb"), categories=list("abc"))
+        idx3 = Index(list("aabbb"))
+
+        # results in slice
+        res = cidx3.get_loc("a")
+        assert res == idx3.get_loc("a")
+        assert res == slice(0, 2, None)
+
+        res = cidx3.get_loc("b")
+        assert res == idx3.get_loc("b")
+        assert res == slice(2, 5, None)
+
+        for i in [cidx3, idx3]:
+            with pytest.raises(KeyError, match="'c'"):
+                i.get_loc("c")
+
+    def test_get_loc_unique(self):
+        cidx = CategoricalIndex(list("abc"))
+        result = cidx.get_loc("b")
+        assert result == 1
+
+    def test_get_loc_monotonic_nonunique(self):
+        cidx = CategoricalIndex(list("abbc"))
+        result = cidx.get_loc("b")
+        expected = slice(1, 3, None)
+        assert result == expected
+
+    def test_get_loc_nonmonotonic_nonunique(self):
+        cidx = CategoricalIndex(list("abcb"))
+        result = cidx.get_loc("b")
+        expected = np.array([False, True, False, True], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_loc_nan(self):
+        # GH#41933
+        ci = CategoricalIndex(["A", "B", np.nan])
+        res = ci.get_loc(np.nan)
+
+        assert res == 2
+
+
+class TestGetIndexer:
+    def test_get_indexer_base(self):
+        # Determined by cat ordering.
+        idx = CategoricalIndex(list("cab"), categories=list("cab"))
+        expected = np.arange(len(idx), dtype=np.intp)
+
+        actual = idx.get_indexer(idx)
+        tm.assert_numpy_array_equal(expected, actual)
+
+        with pytest.raises(ValueError, match="Invalid fill method"):
+            idx.get_indexer(idx, method="invalid")
+
+    def test_get_indexer_requires_unique(self):
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False)
+        oidx = Index(np.array(ci))
+
+        msg = "Reindexing only valid with uniquely valued Index objects"
+
+        for n in [1, 2, 5, len(ci)]:
+            finder = oidx[np.random.default_rng(2).integers(0, len(ci), size=n)]
+
+            with pytest.raises(InvalidIndexError, match=msg):
+                ci.get_indexer(finder)
+
+        # see gh-17323
+        #
+        # Even when indexer is equal to the
+        # members in the index, we should
+        # respect duplicates instead of taking
+        # the fast-track path.
+        for finder in [list("aabbca"), list("aababca")]:
+            with pytest.raises(InvalidIndexError, match=msg):
+                ci.get_indexer(finder)
+
+    def test_get_indexer_non_unique(self):
+        idx1 = CategoricalIndex(list("aabcde"), categories=list("edabc"))
+        idx2 = CategoricalIndex(list("abf"))
+
+        for indexer in [idx2, list("abf"), Index(list("abf"))]:
+            msg = "Reindexing only valid with uniquely valued Index objects"
+            with pytest.raises(InvalidIndexError, match=msg):
+                idx1.get_indexer(indexer)
+
+            r1, _ = idx1.get_indexer_non_unique(indexer)
+            expected = np.array([0, 1, 2, -1], dtype=np.intp)
+            tm.assert_almost_equal(r1, expected)
+
+    def test_get_indexer_method(self):
+        idx1 = CategoricalIndex(list("aabcde"), categories=list("edabc"))
+        idx2 = CategoricalIndex(list("abf"))
+
+        msg = "method pad not yet implemented for CategoricalIndex"
+        with pytest.raises(NotImplementedError, match=msg):
+            idx2.get_indexer(idx1, method="pad")
+        msg = "method backfill not yet implemented for CategoricalIndex"
+        with pytest.raises(NotImplementedError, match=msg):
+            idx2.get_indexer(idx1, method="backfill")
+
+        msg = "method nearest not yet implemented for CategoricalIndex"
+        with pytest.raises(NotImplementedError, match=msg):
+            idx2.get_indexer(idx1, method="nearest")
+
+    def test_get_indexer_array(self):
+        arr = np.array(
+            [Timestamp("1999-12-31 00:00:00"), Timestamp("2000-12-31 00:00:00")],
+            dtype=object,
+        )
+        cats = [Timestamp("1999-12-31 00:00:00"), Timestamp("2000-12-31 00:00:00")]
+        ci = CategoricalIndex(cats, categories=cats, ordered=False, dtype="category")
+        result = ci.get_indexer(arr)
+        expected = np.array([0, 1], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_same_categories_same_order(self):
+        ci = CategoricalIndex(["a", "b"], categories=["a", "b"])
+
+        result = ci.get_indexer(CategoricalIndex(["b", "b"], categories=["a", "b"]))
+        expected = np.array([1, 1], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_same_categories_different_order(self):
+        # https://github.com/pandas-dev/pandas/issues/19551
+        ci = CategoricalIndex(["a", "b"], categories=["a", "b"])
+
+        result = ci.get_indexer(CategoricalIndex(["b", "b"], categories=["b", "a"]))
+        expected = np.array([1, 1], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_nans_in_index_and_target(self):
+        # GH 45361
+        ci = CategoricalIndex([1, 2, np.nan, 3])
+        other1 = [2, 3, 4, np.nan]
+        res1 = ci.get_indexer(other1)
+        expected1 = np.array([1, 3, -1, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(res1, expected1)
+        other2 = [1, 4, 2, 3]
+        res2 = ci.get_indexer(other2)
+        expected2 = np.array([0, -1, 1, 3], dtype=np.intp)
+        tm.assert_numpy_array_equal(res2, expected2)
+
+
+class TestWhere:
+    def test_where(self, listlike_box):
+        klass = listlike_box
+
+        i = CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False)
+        cond = [True] * len(i)
+        expected = i
+        result = i.where(klass(cond))
+        tm.assert_index_equal(result, expected)
+
+        cond = [False] + [True] * (len(i) - 1)
+        expected = CategoricalIndex([np.nan] + i[1:].tolist(), categories=i.categories)
+        result = i.where(klass(cond))
+        tm.assert_index_equal(result, expected)
+
+    def test_where_non_categories(self):
+        ci = CategoricalIndex(["a", "b", "c", "d"])
+        mask = np.array([True, False, True, False])
+
+        result = ci.where(mask, 2)
+        expected = Index(["a", 2, "c", 2], dtype=object)
+        tm.assert_index_equal(result, expected)
+
+        msg = "Cannot setitem on a Categorical with a new category"
+        with pytest.raises(TypeError, match=msg):
+            # Test the Categorical method directly
+            ci._data._where(mask, 2)
+
+
+class TestContains:
+    def test_contains(self):
+        ci = CategoricalIndex(list("aabbca"), categories=list("cabdef"), ordered=False)
+
+        assert "a" in ci
+        assert "z" not in ci
+        assert "e" not in ci
+        assert np.nan not in ci
+
+        # assert codes NOT in index
+        assert 0 not in ci
+        assert 1 not in ci
+
+    def test_contains_nan(self):
+        ci = CategoricalIndex(list("aabbca") + [np.nan], categories=list("cabdef"))
+        assert np.nan in ci
+
+    @pytest.mark.parametrize("unwrap", [True, False])
+    def test_contains_na_dtype(self, unwrap):
+        dti = pd.date_range("2016-01-01", periods=100).insert(0, pd.NaT)
+        pi = dti.to_period("D")
+        tdi = dti - dti[-1]
+        ci = CategoricalIndex(dti)
+
+        obj = ci
+        if unwrap:
+            obj = ci._data
+
+        assert np.nan in obj
+        assert None in obj
+        assert pd.NaT in obj
+        assert np.datetime64("NaT") in obj
+        assert np.timedelta64("NaT") not in obj
+
+        obj2 = CategoricalIndex(tdi)
+        if unwrap:
+            obj2 = obj2._data
+
+        assert np.nan in obj2
+        assert None in obj2
+        assert pd.NaT in obj2
+        assert np.datetime64("NaT") not in obj2
+        assert np.timedelta64("NaT") in obj2
+
+        obj3 = CategoricalIndex(pi)
+        if unwrap:
+            obj3 = obj3._data
+
+        assert np.nan in obj3
+        assert None in obj3
+        assert pd.NaT in obj3
+        assert np.datetime64("NaT") not in obj3
+        assert np.timedelta64("NaT") not in obj3
+
+    @pytest.mark.parametrize(
+        "item, expected",
+        [
+            (pd.Interval(0, 1), True),
+            (1.5, True),
+            (pd.Interval(0.5, 1.5), False),
+            ("a", False),
+            (Timestamp(1), False),
+            (pd.Timedelta(1), False),
+        ],
+        ids=str,
+    )
+    def test_contains_interval(self, item, expected):
+        # GH 23705
+        ci = CategoricalIndex(IntervalIndex.from_breaks(range(3)))
+        result = item in ci
+        assert result is expected
+
+    def test_contains_list(self):
+        # GH#21729
+        idx = CategoricalIndex([1, 2, 3])
+
+        assert "a" not in idx
+
+        with pytest.raises(TypeError, match="unhashable type"):
+            ["a"] in idx
+
+        with pytest.raises(TypeError, match="unhashable type"):
+            ["a", "b"] in idx

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_map.py

@@ -0,0 +1,144 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "data, categories",
+    [
+        (list("abcbca"), list("cab")),
+        (pd.interval_range(0, 3).repeat(3), pd.interval_range(0, 3)),
+    ],
+    ids=["string", "interval"],
+)
+def test_map_str(data, categories, ordered):
+    # GH 31202 - override base class since we want to maintain categorical/ordered
+    index = CategoricalIndex(data, categories=categories, ordered=ordered)
+    result = index.map(str)
+    expected = CategoricalIndex(
+        map(str, data), categories=map(str, categories), ordered=ordered
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_map():
+    ci = CategoricalIndex(list("ABABC"), categories=list("CBA"), ordered=True)
+    result = ci.map(lambda x: x.lower())
+    exp = CategoricalIndex(list("ababc"), categories=list("cba"), ordered=True)
+    tm.assert_index_equal(result, exp)
+
+    ci = CategoricalIndex(
+        list("ABABC"), categories=list("BAC"), ordered=False, name="XXX"
+    )
+    result = ci.map(lambda x: x.lower())
+    exp = CategoricalIndex(
+        list("ababc"), categories=list("bac"), ordered=False, name="XXX"
+    )
+    tm.assert_index_equal(result, exp)
+
+    # GH 12766: Return an index not an array
+    tm.assert_index_equal(
+        ci.map(lambda x: 1), Index(np.array([1] * 5, dtype=np.int64), name="XXX")
+    )
+
+    # change categories dtype
+    ci = CategoricalIndex(list("ABABC"), categories=list("BAC"), ordered=False)
+
+    def f(x):
+        return {"A": 10, "B": 20, "C": 30}.get(x)
+
+    result = ci.map(f)
+    exp = CategoricalIndex([10, 20, 10, 20, 30], categories=[20, 10, 30], ordered=False)
+    tm.assert_index_equal(result, exp)
+
+    result = ci.map(Series([10, 20, 30], index=["A", "B", "C"]))
+    tm.assert_index_equal(result, exp)
+
+    result = ci.map({"A": 10, "B": 20, "C": 30})
+    tm.assert_index_equal(result, exp)
+
+
+def test_map_with_categorical_series():
+    # GH 12756
+    a = Index([1, 2, 3, 4])
+    b = Series(["even", "odd", "even", "odd"], dtype="category")
+    c = Series(["even", "odd", "even", "odd"])
+
+    exp = CategoricalIndex(["odd", "even", "odd", np.nan])
+    tm.assert_index_equal(a.map(b), exp)
+    exp = Index(["odd", "even", "odd", np.nan])
+    tm.assert_index_equal(a.map(c), exp)
+
+
+@pytest.mark.parametrize(
+    ("data", "f", "expected"),
+    (
+        ([1, 1, np.nan], pd.isna, CategoricalIndex([False, False, np.nan])),
+        ([1, 2, np.nan], pd.isna, Index([False, False, np.nan])),
+        ([1, 1, np.nan], {1: False}, CategoricalIndex([False, False, np.nan])),
+        ([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])),
+        (
+            [1, 1, np.nan],
+            Series([False, False]),
+            CategoricalIndex([False, False, np.nan]),
+        ),
+        (
+            [1, 2, np.nan],
+            Series([False, False, False]),
+            Index([False, False, np.nan]),
+        ),
+    ),
+)
+def test_map_with_nan_ignore(data, f, expected):  # GH 24241
+    values = CategoricalIndex(data)
+    result = values.map(f, na_action="ignore")
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("data", "f", "expected"),
+    (
+        ([1, 1, np.nan], pd.isna, Index([False, False, True])),
+        ([1, 2, np.nan], pd.isna, Index([False, False, True])),
+        ([1, 1, np.nan], {1: False}, CategoricalIndex([False, False, np.nan])),
+        ([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])),
+        (
+            [1, 1, np.nan],
+            Series([False, False]),
+            CategoricalIndex([False, False, np.nan]),
+        ),
+        (
+            [1, 2, np.nan],
+            Series([False, False, False]),
+            Index([False, False, np.nan]),
+        ),
+    ),
+)
+def test_map_with_nan_none(data, f, expected):  # GH 24241
+    values = CategoricalIndex(data)
+    result = values.map(f, na_action=None)
+    tm.assert_index_equal(result, expected)
+
+
+def test_map_with_dict_or_series():
+    orig_values = ["a", "B", 1, "a"]
+    new_values = ["one", 2, 3.0, "one"]
+    cur_index = CategoricalIndex(orig_values, name="XXX")
+    expected = CategoricalIndex(new_values, name="XXX", categories=[3.0, 2, "one"])
+
+    mapper = Series(new_values[:-1], index=orig_values[:-1])
+    result = cur_index.map(mapper)
+    # Order of categories in result can be different
+    tm.assert_index_equal(result, expected)
+
+    mapper = dict(zip(orig_values[:-1], new_values[:-1]))
+    result = cur_index.map(mapper)
+    # Order of categories in result can be different
+    tm.assert_index_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_setops.py

@@ -0,0 +1,18 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    CategoricalIndex,
+    Index,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("na_value", [None, np.nan])
+def test_difference_with_na(na_value):
+    # GH 57318
+    ci = CategoricalIndex(["a", "b", "c", None])
+    other = Index(["c", na_value])
+    result = ci.difference(other)
+    expected = CategoricalIndex(["a", "b"], categories=["a", "b", "c"])
+    tm.assert_index_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_constructors.py

@@ -0,0 +1,535 @@
+from functools import partial
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.common import is_unsigned_integer_dtype
+from pandas.core.dtypes.dtypes import IntervalDtype
+
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    CategoricalIndex,
+    Index,
+    Interval,
+    IntervalIndex,
+    date_range,
+    notna,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import IntervalArray
+import pandas.core.common as com
+
+
+@pytest.fixture(params=[None, "foo"])
+def name(request):
+    return request.param
+
+
+class ConstructorTests:
+    """
+    Common tests for all variations of IntervalIndex construction. Input data
+    to be supplied in breaks format, then converted by the subclass method
+    get_kwargs_from_breaks to the expected format.
+    """
+
+    @pytest.fixture(
+        params=[
+            ([3, 14, 15, 92, 653], np.int64),
+            (np.arange(10, dtype="int64"), np.int64),
+            (Index(np.arange(-10, 11, dtype=np.int64)), np.int64),
+            (Index(np.arange(10, 31, dtype=np.uint64)), np.uint64),
+            (Index(np.arange(20, 30, 0.5), dtype=np.float64), np.float64),
+            (date_range("20180101", periods=10), "<M8[ns]"),
+            (
+                date_range("20180101", periods=10, tz="US/Eastern"),
+                "datetime64[ns, US/Eastern]",
+            ),
+            (timedelta_range("1 day", periods=10), "<m8[ns]"),
+        ]
+    )
+    def breaks_and_expected_subtype(self, request):
+        return request.param
+
+    def test_constructor(self, constructor, breaks_and_expected_subtype, closed, name):
+        breaks, expected_subtype = breaks_and_expected_subtype
+
+        result_kwargs = self.get_kwargs_from_breaks(breaks, closed)
+
+        result = constructor(closed=closed, name=name, **result_kwargs)
+
+        assert result.closed == closed
+        assert result.name == name
+        assert result.dtype.subtype == expected_subtype
+        tm.assert_index_equal(result.left, Index(breaks[:-1], dtype=expected_subtype))
+        tm.assert_index_equal(result.right, Index(breaks[1:], dtype=expected_subtype))
+
+    @pytest.mark.parametrize(
+        "breaks, subtype",
+        [
+            (Index([0, 1, 2, 3, 4], dtype=np.int64), "float64"),
+            (Index([0, 1, 2, 3, 4], dtype=np.int64), "datetime64[ns]"),
+            (Index([0, 1, 2, 3, 4], dtype=np.int64), "timedelta64[ns]"),
+            (Index([0, 1, 2, 3, 4], dtype=np.float64), "int64"),
+            (date_range("2017-01-01", periods=5), "int64"),
+            (timedelta_range("1 day", periods=5), "int64"),
+        ],
+    )
+    def test_constructor_dtype(self, constructor, breaks, subtype):
+        # GH 19262: conversion via dtype parameter
+        expected_kwargs = self.get_kwargs_from_breaks(breaks.astype(subtype))
+        expected = constructor(**expected_kwargs)
+
+        result_kwargs = self.get_kwargs_from_breaks(breaks)
+        iv_dtype = IntervalDtype(subtype, "right")
+        for dtype in (iv_dtype, str(iv_dtype)):
+            result = constructor(dtype=dtype, **result_kwargs)
+            tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "breaks",
+        [
+            Index([0, 1, 2, 3, 4], dtype=np.int64),
+            Index([0, 1, 2, 3, 4], dtype=np.uint64),
+            Index([0, 1, 2, 3, 4], dtype=np.float64),
+            date_range("2017-01-01", periods=5),
+            timedelta_range("1 day", periods=5),
+        ],
+    )
+    def test_constructor_pass_closed(self, constructor, breaks):
+        # not passing closed to IntervalDtype, but to IntervalArray constructor
+        iv_dtype = IntervalDtype(breaks.dtype)
+
+        result_kwargs = self.get_kwargs_from_breaks(breaks)
+
+        for dtype in (iv_dtype, str(iv_dtype)):
+            with tm.assert_produces_warning(None):
+                result = constructor(dtype=dtype, closed="left", **result_kwargs)
+            assert result.dtype.closed == "left"
+
+    @pytest.mark.parametrize("breaks", [[np.nan] * 2, [np.nan] * 4, [np.nan] * 50])
+    def test_constructor_nan(self, constructor, breaks, closed):
+        # GH 18421
+        result_kwargs = self.get_kwargs_from_breaks(breaks)
+        result = constructor(closed=closed, **result_kwargs)
+
+        expected_subtype = np.float64
+        expected_values = np.array(breaks[:-1], dtype=object)
+
+        assert result.closed == closed
+        assert result.dtype.subtype == expected_subtype
+        tm.assert_numpy_array_equal(np.array(result), expected_values)
+
+    @pytest.mark.parametrize(
+        "breaks",
+        [
+            [],
+            np.array([], dtype="int64"),
+            np.array([], dtype="uint64"),
+            np.array([], dtype="float64"),
+            np.array([], dtype="datetime64[ns]"),
+            np.array([], dtype="timedelta64[ns]"),
+        ],
+    )
+    def test_constructor_empty(self, constructor, breaks, closed):
+        # GH 18421
+        result_kwargs = self.get_kwargs_from_breaks(breaks)
+        result = constructor(closed=closed, **result_kwargs)
+
+        expected_values = np.array([], dtype=object)
+        expected_subtype = getattr(breaks, "dtype", np.int64)
+
+        assert result.empty
+        assert result.closed == closed
+        assert result.dtype.subtype == expected_subtype
+        tm.assert_numpy_array_equal(np.array(result), expected_values)
+
+    @pytest.mark.parametrize(
+        "breaks",
+        [
+            tuple("0123456789"),
+            list("abcdefghij"),
+            np.array(list("abcdefghij"), dtype=object),
+            np.array(list("abcdefghij"), dtype="<U1"),
+        ],
+    )
+    def test_constructor_string(self, constructor, breaks):
+        # GH 19016
+        msg = (
+            "category, object, and string subtypes are not supported "
+            "for IntervalIndex"
+        )
+        with pytest.raises(TypeError, match=msg):
+            constructor(**self.get_kwargs_from_breaks(breaks))
+
+    @pytest.mark.parametrize("cat_constructor", [Categorical, CategoricalIndex])
+    def test_constructor_categorical_valid(self, constructor, cat_constructor):
+        # GH 21243/21253
+
+        breaks = np.arange(10, dtype="int64")
+        expected = IntervalIndex.from_breaks(breaks)
+
+        cat_breaks = cat_constructor(breaks)
+        result_kwargs = self.get_kwargs_from_breaks(cat_breaks)
+        result = constructor(**result_kwargs)
+        tm.assert_index_equal(result, expected)
+
+    def test_generic_errors(self, constructor):
+        # filler input data to be used when supplying invalid kwargs
+        filler = self.get_kwargs_from_breaks(range(10))
+
+        # invalid closed
+        msg = "closed must be one of 'right', 'left', 'both', 'neither'"
+        with pytest.raises(ValueError, match=msg):
+            constructor(closed="invalid", **filler)
+
+        # unsupported dtype
+        msg = "dtype must be an IntervalDtype, got int64"
+        with pytest.raises(TypeError, match=msg):
+            constructor(dtype="int64", **filler)
+
+        # invalid dtype
+        msg = "data type [\"']invalid[\"'] not understood"
+        with pytest.raises(TypeError, match=msg):
+            constructor(dtype="invalid", **filler)
+
+        # no point in nesting periods in an IntervalIndex
+        periods = period_range("2000-01-01", periods=10)
+        periods_kwargs = self.get_kwargs_from_breaks(periods)
+        msg = "Period dtypes are not supported, use a PeriodIndex instead"
+        with pytest.raises(ValueError, match=msg):
+            constructor(**periods_kwargs)
+
+        # decreasing values
+        decreasing_kwargs = self.get_kwargs_from_breaks(range(10, -1, -1))
+        msg = "left side of interval must be <= right side"
+        with pytest.raises(ValueError, match=msg):
+            constructor(**decreasing_kwargs)
+
+
+class TestFromArrays(ConstructorTests):
+    """Tests specific to IntervalIndex.from_arrays"""
+
+    @pytest.fixture
+    def constructor(self):
+        return IntervalIndex.from_arrays
+
+    def get_kwargs_from_breaks(self, breaks, closed="right"):
+        """
+        converts intervals in breaks format to a dictionary of kwargs to
+        specific to the format expected by IntervalIndex.from_arrays
+        """
+        return {"left": breaks[:-1], "right": breaks[1:]}
+
+    def test_constructor_errors(self):
+        # GH 19016: categorical data
+        data = Categorical(list("01234abcde"), ordered=True)
+        msg = (
+            "category, object, and string subtypes are not supported "
+            "for IntervalIndex"
+        )
+        with pytest.raises(TypeError, match=msg):
+            IntervalIndex.from_arrays(data[:-1], data[1:])
+
+        # unequal length
+        left = [0, 1, 2]
+        right = [2, 3]
+        msg = "left and right must have the same length"
+        with pytest.raises(ValueError, match=msg):
+            IntervalIndex.from_arrays(left, right)
+
+    @pytest.mark.parametrize(
+        "left_subtype, right_subtype", [(np.int64, np.float64), (np.float64, np.int64)]
+    )
+    def test_mixed_float_int(self, left_subtype, right_subtype):
+        """mixed int/float left/right results in float for both sides"""
+        left = np.arange(9, dtype=left_subtype)
+        right = np.arange(1, 10, dtype=right_subtype)
+        result = IntervalIndex.from_arrays(left, right)
+
+        expected_left = Index(left, dtype=np.float64)
+        expected_right = Index(right, dtype=np.float64)
+        expected_subtype = np.float64
+
+        tm.assert_index_equal(result.left, expected_left)
+        tm.assert_index_equal(result.right, expected_right)
+        assert result.dtype.subtype == expected_subtype
+
+    @pytest.mark.parametrize("interval_cls", [IntervalArray, IntervalIndex])
+    def test_from_arrays_mismatched_datetimelike_resos(self, interval_cls):
+        # GH#55714
+        left = date_range("2016-01-01", periods=3, unit="s")
+        right = date_range("2017-01-01", periods=3, unit="ms")
+        result = interval_cls.from_arrays(left, right)
+        expected = interval_cls.from_arrays(left.as_unit("ms"), right)
+        tm.assert_equal(result, expected)
+
+        # td64
+        left2 = left - left[0]
+        right2 = right - left[0]
+        result2 = interval_cls.from_arrays(left2, right2)
+        expected2 = interval_cls.from_arrays(left2.as_unit("ms"), right2)
+        tm.assert_equal(result2, expected2)
+
+        # dt64tz
+        left3 = left.tz_localize("UTC")
+        right3 = right.tz_localize("UTC")
+        result3 = interval_cls.from_arrays(left3, right3)
+        expected3 = interval_cls.from_arrays(left3.as_unit("ms"), right3)
+        tm.assert_equal(result3, expected3)
+
+
+class TestFromBreaks(ConstructorTests):
+    """Tests specific to IntervalIndex.from_breaks"""
+
+    @pytest.fixture
+    def constructor(self):
+        return IntervalIndex.from_breaks
+
+    def get_kwargs_from_breaks(self, breaks, closed="right"):
+        """
+        converts intervals in breaks format to a dictionary of kwargs to
+        specific to the format expected by IntervalIndex.from_breaks
+        """
+        return {"breaks": breaks}
+
+    def test_constructor_errors(self):
+        # GH 19016: categorical data
+        data = Categorical(list("01234abcde"), ordered=True)
+        msg = (
+            "category, object, and string subtypes are not supported "
+            "for IntervalIndex"
+        )
+        with pytest.raises(TypeError, match=msg):
+            IntervalIndex.from_breaks(data)
+
+    def test_length_one(self):
+        """breaks of length one produce an empty IntervalIndex"""
+        breaks = [0]
+        result = IntervalIndex.from_breaks(breaks)
+        expected = IntervalIndex.from_breaks([])
+        tm.assert_index_equal(result, expected)
+
+    def test_left_right_dont_share_data(self):
+        # GH#36310
+        breaks = np.arange(5)
+        result = IntervalIndex.from_breaks(breaks)._data
+        assert result._left.base is None or result._left.base is not result._right.base
+
+
+class TestFromTuples(ConstructorTests):
+    """Tests specific to IntervalIndex.from_tuples"""
+
+    @pytest.fixture
+    def constructor(self):
+        return IntervalIndex.from_tuples
+
+    def get_kwargs_from_breaks(self, breaks, closed="right"):
+        """
+        converts intervals in breaks format to a dictionary of kwargs to
+        specific to the format expected by IntervalIndex.from_tuples
+        """
+        if is_unsigned_integer_dtype(breaks):
+            pytest.skip(f"{breaks.dtype} not relevant IntervalIndex.from_tuples tests")
+
+        if len(breaks) == 0:
+            return {"data": breaks}
+
+        tuples = list(zip(breaks[:-1], breaks[1:]))
+        if isinstance(breaks, (list, tuple)):
+            return {"data": tuples}
+        elif isinstance(getattr(breaks, "dtype", None), CategoricalDtype):
+            return {"data": breaks._constructor(tuples)}
+        return {"data": com.asarray_tuplesafe(tuples)}
+
+    def test_constructor_errors(self):
+        # non-tuple
+        tuples = [(0, 1), 2, (3, 4)]
+        msg = "IntervalIndex.from_tuples received an invalid item, 2"
+        with pytest.raises(TypeError, match=msg.format(t=tuples)):
+            IntervalIndex.from_tuples(tuples)
+
+        # too few/many items
+        tuples = [(0, 1), (2,), (3, 4)]
+        msg = "IntervalIndex.from_tuples requires tuples of length 2, got {t}"
+        with pytest.raises(ValueError, match=msg.format(t=tuples)):
+            IntervalIndex.from_tuples(tuples)
+
+        tuples = [(0, 1), (2, 3, 4), (5, 6)]
+        with pytest.raises(ValueError, match=msg.format(t=tuples)):
+            IntervalIndex.from_tuples(tuples)
+
+    def test_na_tuples(self):
+        # tuple (NA, NA) evaluates the same as NA as an element
+        na_tuple = [(0, 1), (np.nan, np.nan), (2, 3)]
+        idx_na_tuple = IntervalIndex.from_tuples(na_tuple)
+        idx_na_element = IntervalIndex.from_tuples([(0, 1), np.nan, (2, 3)])
+        tm.assert_index_equal(idx_na_tuple, idx_na_element)
+
+
+class TestClassConstructors(ConstructorTests):
+    """Tests specific to the IntervalIndex/Index constructors"""
+
+    @pytest.fixture(
+        params=[IntervalIndex, partial(Index, dtype="interval")],
+        ids=["IntervalIndex", "Index"],
+    )
+    def klass(self, request):
+        # We use a separate fixture here to include Index.__new__ with dtype kwarg
+        return request.param
+
+    @pytest.fixture
+    def constructor(self):
+        return IntervalIndex
+
+    def get_kwargs_from_breaks(self, breaks, closed="right"):
+        """
+        converts intervals in breaks format to a dictionary of kwargs to
+        specific to the format expected by the IntervalIndex/Index constructors
+        """
+        if is_unsigned_integer_dtype(breaks):
+            pytest.skip(f"{breaks.dtype} not relevant for class constructor tests")
+
+        if len(breaks) == 0:
+            return {"data": breaks}
+
+        ivs = [
+            Interval(left, right, closed) if notna(left) else left
+            for left, right in zip(breaks[:-1], breaks[1:])
+        ]
+
+        if isinstance(breaks, list):
+            return {"data": ivs}
+        elif isinstance(getattr(breaks, "dtype", None), CategoricalDtype):
+            return {"data": breaks._constructor(ivs)}
+        return {"data": np.array(ivs, dtype=object)}
+
+    def test_generic_errors(self, constructor):
+        """
+        override the base class implementation since errors are handled
+        differently; checks unnecessary since caught at the Interval level
+        """
+
+    def test_constructor_string(self):
+        # GH23013
+        # When forming the interval from breaks,
+        # the interval of strings is already forbidden.
+        pass
+
+    def test_constructor_errors(self, klass):
+        # mismatched closed within intervals with no constructor override
+        ivs = [Interval(0, 1, closed="right"), Interval(2, 3, closed="left")]
+        msg = "intervals must all be closed on the same side"
+        with pytest.raises(ValueError, match=msg):
+            klass(ivs)
+
+        # scalar
+        msg = (
+            r"(IntervalIndex|Index)\(...\) must be called with a collection of "
+            "some kind, 5 was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            klass(5)
+
+        # not an interval; dtype depends on 32bit/windows builds
+        msg = "type <class 'numpy.int(32|64)'> with value 0 is not an interval"
+        with pytest.raises(TypeError, match=msg):
+            klass([0, 1])
+
+    @pytest.mark.parametrize(
+        "data, closed",
+        [
+            ([], "both"),
+            ([np.nan, np.nan], "neither"),
+            (
+                [Interval(0, 3, closed="neither"), Interval(2, 5, closed="neither")],
+                "left",
+            ),
+            (
+                [Interval(0, 3, closed="left"), Interval(2, 5, closed="right")],
+                "neither",
+            ),
+            (IntervalIndex.from_breaks(range(5), closed="both"), "right"),
+        ],
+    )
+    def test_override_inferred_closed(self, constructor, data, closed):
+        # GH 19370
+        if isinstance(data, IntervalIndex):
+            tuples = data.to_tuples()
+        else:
+            tuples = [(iv.left, iv.right) if notna(iv) else iv for iv in data]
+        expected = IntervalIndex.from_tuples(tuples, closed=closed)
+        result = constructor(data, closed=closed)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "values_constructor", [list, np.array, IntervalIndex, IntervalArray]
+    )
+    def test_index_object_dtype(self, values_constructor):
+        # Index(intervals, dtype=object) is an Index (not an IntervalIndex)
+        intervals = [Interval(0, 1), Interval(1, 2), Interval(2, 3)]
+        values = values_constructor(intervals)
+        result = Index(values, dtype=object)
+
+        assert type(result) is Index
+        tm.assert_numpy_array_equal(result.values, np.array(values))
+
+    def test_index_mixed_closed(self):
+        # GH27172
+        intervals = [
+            Interval(0, 1, closed="left"),
+            Interval(1, 2, closed="right"),
+            Interval(2, 3, closed="neither"),
+            Interval(3, 4, closed="both"),
+        ]
+        result = Index(intervals)
+        expected = Index(intervals, dtype=object)
+        tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("timezone", ["UTC", "US/Pacific", "GMT"])
+def test_interval_index_subtype(timezone, inclusive_endpoints_fixture):
+    # GH#46999
+    dates = date_range("2022", periods=3, tz=timezone)
+    dtype = f"interval[datetime64[ns, {timezone}], {inclusive_endpoints_fixture}]"
+    result = IntervalIndex.from_arrays(
+        ["2022-01-01", "2022-01-02"],
+        ["2022-01-02", "2022-01-03"],
+        closed=inclusive_endpoints_fixture,
+        dtype=dtype,
+    )
+    expected = IntervalIndex.from_arrays(
+        dates[:-1], dates[1:], closed=inclusive_endpoints_fixture
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_dtype_closed_mismatch():
+    # GH#38394 closed specified in both dtype and IntervalIndex constructor
+
+    dtype = IntervalDtype(np.int64, "left")
+
+    msg = "closed keyword does not match dtype.closed"
+    with pytest.raises(ValueError, match=msg):
+        IntervalIndex([], dtype=dtype, closed="neither")
+
+    with pytest.raises(ValueError, match=msg):
+        IntervalArray([], dtype=dtype, closed="neither")
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    ["Float64", pytest.param("float64[pyarrow]", marks=td.skip_if_no("pyarrow"))],
+)
+def test_ea_dtype(dtype):
+    # GH#56765
+    bins = [(0.0, 0.4), (0.4, 0.6)]
+    interval_dtype = IntervalDtype(subtype=dtype, closed="left")
+    result = IntervalIndex.from_tuples(bins, closed="left", dtype=interval_dtype)
+    assert result.dtype == interval_dtype
+    expected = IntervalIndex.from_tuples(bins, closed="left").astype(interval_dtype)
+    tm.assert_index_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_equals.py

@@ -0,0 +1,36 @@
+import numpy as np
+
+from pandas import (
+    IntervalIndex,
+    date_range,
+)
+
+
+class TestEquals:
+    def test_equals(self, closed):
+        expected = IntervalIndex.from_breaks(np.arange(5), closed=closed)
+        assert expected.equals(expected)
+        assert expected.equals(expected.copy())
+
+        assert not expected.equals(expected.astype(object))
+        assert not expected.equals(np.array(expected))
+        assert not expected.equals(list(expected))
+
+        assert not expected.equals([1, 2])
+        assert not expected.equals(np.array([1, 2]))
+        assert not expected.equals(date_range("20130101", periods=2))
+
+        expected_name1 = IntervalIndex.from_breaks(
+            np.arange(5), closed=closed, name="foo"
+        )
+        expected_name2 = IntervalIndex.from_breaks(
+            np.arange(5), closed=closed, name="bar"
+        )
+        assert expected.equals(expected_name1)
+        assert expected_name1.equals(expected_name2)
+
+        for other_closed in {"left", "right", "both", "neither"} - {closed}:
+            expected_other_closed = IntervalIndex.from_breaks(
+                np.arange(5), closed=other_closed
+            )
+            assert not expected.equals(expected_other_closed)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_formats.py

@@ -0,0 +1,119 @@
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    Interval,
+    IntervalIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestIntervalIndexRendering:
+    # TODO: this is a test for DataFrame/Series, not IntervalIndex
+    @pytest.mark.parametrize(
+        "constructor,expected",
+        [
+            (
+                Series,
+                (
+                    "(0.0, 1.0]    a\n"
+                    "NaN           b\n"
+                    "(2.0, 3.0]    c\n"
+                    "dtype: object"
+                ),
+            ),
+            (DataFrame, ("            0\n(0.0, 1.0]  a\nNaN         b\n(2.0, 3.0]  c")),
+        ],
+    )
+    def test_repr_missing(self, constructor, expected, using_infer_string, request):
+        # GH 25984
+        if using_infer_string and constructor is Series:
+            request.applymarker(pytest.mark.xfail(reason="repr different"))
+        index = IntervalIndex.from_tuples([(0, 1), np.nan, (2, 3)])
+        obj = constructor(list("abc"), index=index)
+        result = repr(obj)
+        assert result == expected
+
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="repr different")
+    def test_repr_floats(self):
+        # GH 32553
+
+        markers = Series(
+            ["foo", "bar"],
+            index=IntervalIndex(
+                [
+                    Interval(left, right)
+                    for left, right in zip(
+                        Index([329.973, 345.137], dtype="float64"),
+                        Index([345.137, 360.191], dtype="float64"),
+                    )
+                ]
+            ),
+        )
+        result = str(markers)
+        expected = "(329.973, 345.137]    foo\n(345.137, 360.191]    bar\ndtype: object"
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "tuples, closed, expected_data",
+        [
+            ([(0, 1), (1, 2), (2, 3)], "left", ["[0, 1)", "[1, 2)", "[2, 3)"]),
+            (
+                [(0.5, 1.0), np.nan, (2.0, 3.0)],
+                "right",
+                ["(0.5, 1.0]", "NaN", "(2.0, 3.0]"],
+            ),
+            (
+                [
+                    (Timestamp("20180101"), Timestamp("20180102")),
+                    np.nan,
+                    ((Timestamp("20180102"), Timestamp("20180103"))),
+                ],
+                "both",
+                [
+                    "[2018-01-01 00:00:00, 2018-01-02 00:00:00]",
+                    "NaN",
+                    "[2018-01-02 00:00:00, 2018-01-03 00:00:00]",
+                ],
+            ),
+            (
+                [
+                    (Timedelta("0 days"), Timedelta("1 days")),
+                    (Timedelta("1 days"), Timedelta("2 days")),
+                    np.nan,
+                ],
+                "neither",
+                [
+                    "(0 days 00:00:00, 1 days 00:00:00)",
+                    "(1 days 00:00:00, 2 days 00:00:00)",
+                    "NaN",
+                ],
+            ),
+        ],
+    )
+    def test_get_values_for_csv(self, tuples, closed, expected_data):
+        # GH 28210
+        index = IntervalIndex.from_tuples(tuples, closed=closed)
+        result = index._get_values_for_csv(na_rep="NaN")
+        expected = np.array(expected_data)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_timestamp_with_timezone(self, unit):
+        # GH 55035
+        left = DatetimeIndex(["2020-01-01"], dtype=f"M8[{unit}, UTC]")
+        right = DatetimeIndex(["2020-01-02"], dtype=f"M8[{unit}, UTC]")
+        index = IntervalIndex.from_arrays(left, right)
+        result = repr(index)
+        expected = (
+            "IntervalIndex([(2020-01-01 00:00:00+00:00, 2020-01-02 00:00:00+00:00]], "
+            f"dtype='interval[datetime64[{unit}, UTC], right]')"
+        )
+        assert result == expected

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_indexing.py

@@ -0,0 +1,671 @@
+import re
+
+import numpy as np
+import pytest
+
+from pandas.errors import InvalidIndexError
+
+from pandas import (
+    NA,
+    CategoricalIndex,
+    DatetimeIndex,
+    Index,
+    Interval,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    Timedelta,
+    Timestamp,
+    array,
+    date_range,
+    interval_range,
+    isna,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestGetItem:
+    def test_getitem(self, closed):
+        idx = IntervalIndex.from_arrays((0, 1, np.nan), (1, 2, np.nan), closed=closed)
+        assert idx[0] == Interval(0.0, 1.0, closed=closed)
+        assert idx[1] == Interval(1.0, 2.0, closed=closed)
+        assert isna(idx[2])
+
+        result = idx[0:1]
+        expected = IntervalIndex.from_arrays((0.0,), (1.0,), closed=closed)
+        tm.assert_index_equal(result, expected)
+
+        result = idx[0:2]
+        expected = IntervalIndex.from_arrays((0.0, 1), (1.0, 2.0), closed=closed)
+        tm.assert_index_equal(result, expected)
+
+        result = idx[1:3]
+        expected = IntervalIndex.from_arrays(
+            (1.0, np.nan), (2.0, np.nan), closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+    def test_getitem_2d_deprecated(self):
+        # GH#30588 multi-dim indexing is deprecated, but raising is also acceptable
+        idx = IntervalIndex.from_breaks(range(11), closed="right")
+        with pytest.raises(ValueError, match="multi-dimensional indexing not allowed"):
+            idx[:, None]
+        with pytest.raises(ValueError, match="multi-dimensional indexing not allowed"):
+            # GH#44051
+            idx[True]
+        with pytest.raises(ValueError, match="multi-dimensional indexing not allowed"):
+            # GH#44051
+            idx[False]
+
+
+class TestWhere:
+    def test_where(self, listlike_box):
+        klass = listlike_box
+
+        idx = IntervalIndex.from_breaks(range(11), closed="right")
+        cond = [True] * len(idx)
+        expected = idx
+        result = expected.where(klass(cond))
+        tm.assert_index_equal(result, expected)
+
+        cond = [False] + [True] * len(idx[1:])
+        expected = IntervalIndex([np.nan] + idx[1:].tolist())
+        result = idx.where(klass(cond))
+        tm.assert_index_equal(result, expected)
+
+
+class TestTake:
+    def test_take(self, closed):
+        index = IntervalIndex.from_breaks(range(11), closed=closed)
+
+        result = index.take(range(10))
+        tm.assert_index_equal(result, index)
+
+        result = index.take([0, 0, 1])
+        expected = IntervalIndex.from_arrays([0, 0, 1], [1, 1, 2], closed=closed)
+        tm.assert_index_equal(result, expected)
+
+
+class TestGetLoc:
+    @pytest.mark.parametrize("side", ["right", "left", "both", "neither"])
+    def test_get_loc_interval(self, closed, side):
+        idx = IntervalIndex.from_tuples([(0, 1), (2, 3)], closed=closed)
+
+        for bound in [[0, 1], [1, 2], [2, 3], [3, 4], [0, 2], [2.5, 3], [-1, 4]]:
+            # if get_loc is supplied an interval, it should only search
+            # for exact matches, not overlaps or covers, else KeyError.
+            msg = re.escape(f"Interval({bound[0]}, {bound[1]}, closed='{side}')")
+            if closed == side:
+                if bound == [0, 1]:
+                    assert idx.get_loc(Interval(0, 1, closed=side)) == 0
+                elif bound == [2, 3]:
+                    assert idx.get_loc(Interval(2, 3, closed=side)) == 1
+                else:
+                    with pytest.raises(KeyError, match=msg):
+                        idx.get_loc(Interval(*bound, closed=side))
+            else:
+                with pytest.raises(KeyError, match=msg):
+                    idx.get_loc(Interval(*bound, closed=side))
+
+    @pytest.mark.parametrize("scalar", [-0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5])
+    def test_get_loc_scalar(self, closed, scalar):
+        # correct = {side: {query: answer}}.
+        # If query is not in the dict, that query should raise a KeyError
+        correct = {
+            "right": {0.5: 0, 1: 0, 2.5: 1, 3: 1},
+            "left": {0: 0, 0.5: 0, 2: 1, 2.5: 1},
+            "both": {0: 0, 0.5: 0, 1: 0, 2: 1, 2.5: 1, 3: 1},
+            "neither": {0.5: 0, 2.5: 1},
+        }
+
+        idx = IntervalIndex.from_tuples([(0, 1), (2, 3)], closed=closed)
+
+        # if get_loc is supplied a scalar, it should return the index of
+        # the interval which contains the scalar, or KeyError.
+        if scalar in correct[closed].keys():
+            assert idx.get_loc(scalar) == correct[closed][scalar]
+        else:
+            with pytest.raises(KeyError, match=str(scalar)):
+                idx.get_loc(scalar)
+
+    @pytest.mark.parametrize("scalar", [-1, 0, 0.5, 3, 4.5, 5, 6])
+    def test_get_loc_length_one_scalar(self, scalar, closed):
+        # GH 20921
+        index = IntervalIndex.from_tuples([(0, 5)], closed=closed)
+        if scalar in index[0]:
+            result = index.get_loc(scalar)
+            assert result == 0
+        else:
+            with pytest.raises(KeyError, match=str(scalar)):
+                index.get_loc(scalar)
+
+    @pytest.mark.parametrize("other_closed", ["left", "right", "both", "neither"])
+    @pytest.mark.parametrize("left, right", [(0, 5), (-1, 4), (-1, 6), (6, 7)])
+    def test_get_loc_length_one_interval(self, left, right, closed, other_closed):
+        # GH 20921
+        index = IntervalIndex.from_tuples([(0, 5)], closed=closed)
+        interval = Interval(left, right, closed=other_closed)
+        if interval == index[0]:
+            result = index.get_loc(interval)
+            assert result == 0
+        else:
+            with pytest.raises(
+                KeyError,
+                match=re.escape(f"Interval({left}, {right}, closed='{other_closed}')"),
+            ):
+                index.get_loc(interval)
+
+    # Make consistent with test_interval_new.py (see #16316, #16386)
+    @pytest.mark.parametrize(
+        "breaks",
+        [
+            date_range("20180101", periods=4),
+            date_range("20180101", periods=4, tz="US/Eastern"),
+            timedelta_range("0 days", periods=4),
+        ],
+        ids=lambda x: str(x.dtype),
+    )
+    def test_get_loc_datetimelike_nonoverlapping(self, breaks):
+        # GH 20636
+        # nonoverlapping = IntervalIndex method and no i8 conversion
+        index = IntervalIndex.from_breaks(breaks)
+
+        value = index[0].mid
+        result = index.get_loc(value)
+        expected = 0
+        assert result == expected
+
+        interval = Interval(index[0].left, index[0].right)
+        result = index.get_loc(interval)
+        expected = 0
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "arrays",
+        [
+            (date_range("20180101", periods=4), date_range("20180103", periods=4)),
+            (
+                date_range("20180101", periods=4, tz="US/Eastern"),
+                date_range("20180103", periods=4, tz="US/Eastern"),
+            ),
+            (
+                timedelta_range("0 days", periods=4),
+                timedelta_range("2 days", periods=4),
+            ),
+        ],
+        ids=lambda x: str(x[0].dtype),
+    )
+    def test_get_loc_datetimelike_overlapping(self, arrays):
+        # GH 20636
+        index = IntervalIndex.from_arrays(*arrays)
+
+        value = index[0].mid + Timedelta("12 hours")
+        result = index.get_loc(value)
+        expected = slice(0, 2, None)
+        assert result == expected
+
+        interval = Interval(index[0].left, index[0].right)
+        result = index.get_loc(interval)
+        expected = 0
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            date_range("2018-01-04", periods=4, freq="-1D"),
+            date_range("2018-01-04", periods=4, freq="-1D", tz="US/Eastern"),
+            timedelta_range("3 days", periods=4, freq="-1D"),
+            np.arange(3.0, -1.0, -1.0),
+            np.arange(3, -1, -1),
+        ],
+        ids=lambda x: str(x.dtype),
+    )
+    def test_get_loc_decreasing(self, values):
+        # GH 25860
+        index = IntervalIndex.from_arrays(values[1:], values[:-1])
+        result = index.get_loc(index[0])
+        expected = 0
+        assert result == expected
+
+    @pytest.mark.parametrize("key", [[5], (2, 3)])
+    def test_get_loc_non_scalar_errors(self, key):
+        # GH 31117
+        idx = IntervalIndex.from_tuples([(1, 3), (2, 4), (3, 5), (7, 10), (3, 10)])
+
+        msg = str(key)
+        with pytest.raises(InvalidIndexError, match=msg):
+            idx.get_loc(key)
+
+    def test_get_indexer_with_nans(self):
+        # GH#41831
+        index = IntervalIndex([np.nan, Interval(1, 2), np.nan])
+
+        expected = np.array([True, False, True])
+        for key in [None, np.nan, NA]:
+            assert key in index
+            result = index.get_loc(key)
+            tm.assert_numpy_array_equal(result, expected)
+
+        for key in [NaT, np.timedelta64("NaT", "ns"), np.datetime64("NaT", "ns")]:
+            with pytest.raises(KeyError, match=str(key)):
+                index.get_loc(key)
+
+
+class TestGetIndexer:
+    @pytest.mark.parametrize(
+        "query, expected",
+        [
+            ([Interval(2, 4, closed="right")], [1]),
+            ([Interval(2, 4, closed="left")], [-1]),
+            ([Interval(2, 4, closed="both")], [-1]),
+            ([Interval(2, 4, closed="neither")], [-1]),
+            ([Interval(1, 4, closed="right")], [-1]),
+            ([Interval(0, 4, closed="right")], [-1]),
+            ([Interval(0.5, 1.5, closed="right")], [-1]),
+            ([Interval(2, 4, closed="right"), Interval(0, 1, closed="right")], [1, -1]),
+            ([Interval(2, 4, closed="right"), Interval(2, 4, closed="right")], [1, 1]),
+            ([Interval(5, 7, closed="right"), Interval(2, 4, closed="right")], [2, 1]),
+            ([Interval(2, 4, closed="right"), Interval(2, 4, closed="left")], [1, -1]),
+        ],
+    )
+    def test_get_indexer_with_interval(self, query, expected):
+        tuples = [(0, 2), (2, 4), (5, 7)]
+        index = IntervalIndex.from_tuples(tuples, closed="right")
+
+        result = index.get_indexer(query)
+        expected = np.array(expected, dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "query, expected",
+        [
+            ([-0.5], [-1]),
+            ([0], [-1]),
+            ([0.5], [0]),
+            ([1], [0]),
+            ([1.5], [1]),
+            ([2], [1]),
+            ([2.5], [-1]),
+            ([3], [-1]),
+            ([3.5], [2]),
+            ([4], [2]),
+            ([4.5], [-1]),
+            ([1, 2], [0, 1]),
+            ([1, 2, 3], [0, 1, -1]),
+            ([1, 2, 3, 4], [0, 1, -1, 2]),
+            ([1, 2, 3, 4, 2], [0, 1, -1, 2, 1]),
+        ],
+    )
+    def test_get_indexer_with_int_and_float(self, query, expected):
+        tuples = [(0, 1), (1, 2), (3, 4)]
+        index = IntervalIndex.from_tuples(tuples, closed="right")
+
+        result = index.get_indexer(query)
+        expected = np.array(expected, dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("item", [[3], np.arange(0.5, 5, 0.5)])
+    def test_get_indexer_length_one(self, item, closed):
+        # GH 17284
+        index = IntervalIndex.from_tuples([(0, 5)], closed=closed)
+        result = index.get_indexer(item)
+        expected = np.array([0] * len(item), dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("size", [1, 5])
+    def test_get_indexer_length_one_interval(self, size, closed):
+        # GH 17284
+        index = IntervalIndex.from_tuples([(0, 5)], closed=closed)
+        result = index.get_indexer([Interval(0, 5, closed)] * size)
+        expected = np.array([0] * size, dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "target",
+        [
+            IntervalIndex.from_tuples([(7, 8), (1, 2), (3, 4), (0, 1)]),
+            IntervalIndex.from_tuples([(0, 1), (1, 2), (3, 4), np.nan]),
+            IntervalIndex.from_tuples([(0, 1), (1, 2), (3, 4)], closed="both"),
+            [-1, 0, 0.5, 1, 2, 2.5, np.nan],
+            ["foo", "foo", "bar", "baz"],
+        ],
+    )
+    def test_get_indexer_categorical(self, target, ordered):
+        # GH 30063: categorical and non-categorical results should be consistent
+        index = IntervalIndex.from_tuples([(0, 1), (1, 2), (3, 4)])
+        categorical_target = CategoricalIndex(target, ordered=ordered)
+
+        result = index.get_indexer(categorical_target)
+        expected = index.get_indexer(target)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_categorical_with_nans(self):
+        # GH#41934 nans in both index and in target
+        ii = IntervalIndex.from_breaks(range(5))
+        ii2 = ii.append(IntervalIndex([np.nan]))
+        ci2 = CategoricalIndex(ii2)
+
+        result = ii2.get_indexer(ci2)
+        expected = np.arange(5, dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # not-all-matches
+        result = ii2[1:].get_indexer(ci2[::-1])
+        expected = np.array([3, 2, 1, 0, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # non-unique target, non-unique nans
+        result = ii2.get_indexer(ci2.append(ci2))
+        expected = np.array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_datetime(self):
+        ii = IntervalIndex.from_breaks(date_range("2018-01-01", periods=4))
+        # TODO: with mismatched resolution get_indexer currently raises;
+        #  this should probably coerce?
+        target = DatetimeIndex(["2018-01-02"], dtype="M8[ns]")
+        result = ii.get_indexer(target)
+        expected = np.array([0], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = ii.get_indexer(target.astype(str))
+        tm.assert_numpy_array_equal(result, expected)
+
+        # https://github.com/pandas-dev/pandas/issues/47772
+        result = ii.get_indexer(target.asi8)
+        expected = np.array([-1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "tuples, closed",
+        [
+            ([(0, 2), (1, 3), (3, 4)], "neither"),
+            ([(0, 5), (1, 4), (6, 7)], "left"),
+            ([(0, 1), (0, 1), (1, 2)], "right"),
+            ([(0, 1), (2, 3), (3, 4)], "both"),
+        ],
+    )
+    def test_get_indexer_errors(self, tuples, closed):
+        # IntervalIndex needs non-overlapping for uniqueness when querying
+        index = IntervalIndex.from_tuples(tuples, closed=closed)
+
+        msg = (
+            "cannot handle overlapping indices; use "
+            "IntervalIndex.get_indexer_non_unique"
+        )
+        with pytest.raises(InvalidIndexError, match=msg):
+            index.get_indexer([0, 2])
+
+    @pytest.mark.parametrize(
+        "query, expected",
+        [
+            ([-0.5], ([-1], [0])),
+            ([0], ([0], [])),
+            ([0.5], ([0], [])),
+            ([1], ([0, 1], [])),
+            ([1.5], ([0, 1], [])),
+            ([2], ([0, 1, 2], [])),
+            ([2.5], ([1, 2], [])),
+            ([3], ([2], [])),
+            ([3.5], ([2], [])),
+            ([4], ([-1], [0])),
+            ([4.5], ([-1], [0])),
+            ([1, 2], ([0, 1, 0, 1, 2], [])),
+            ([1, 2, 3], ([0, 1, 0, 1, 2, 2], [])),
+            ([1, 2, 3, 4], ([0, 1, 0, 1, 2, 2, -1], [3])),
+            ([1, 2, 3, 4, 2], ([0, 1, 0, 1, 2, 2, -1, 0, 1, 2], [3])),
+        ],
+    )
+    def test_get_indexer_non_unique_with_int_and_float(self, query, expected):
+        tuples = [(0, 2.5), (1, 3), (2, 4)]
+        index = IntervalIndex.from_tuples(tuples, closed="left")
+
+        result_indexer, result_missing = index.get_indexer_non_unique(query)
+        expected_indexer = np.array(expected[0], dtype="intp")
+        expected_missing = np.array(expected[1], dtype="intp")
+
+        tm.assert_numpy_array_equal(result_indexer, expected_indexer)
+        tm.assert_numpy_array_equal(result_missing, expected_missing)
+
+        # TODO we may also want to test get_indexer for the case when
+        # the intervals are duplicated, decreasing, non-monotonic, etc..
+
+    def test_get_indexer_non_monotonic(self):
+        # GH 16410
+        idx1 = IntervalIndex.from_tuples([(2, 3), (4, 5), (0, 1)])
+        idx2 = IntervalIndex.from_tuples([(0, 1), (2, 3), (6, 7), (8, 9)])
+        result = idx1.get_indexer(idx2)
+        expected = np.array([2, 0, -1, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = idx1.get_indexer(idx1[1:])
+        expected = np.array([1, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_with_nans(self):
+        # GH#41831
+        index = IntervalIndex([np.nan, np.nan])
+        other = IntervalIndex([np.nan])
+
+        assert not index._index_as_unique
+
+        result = index.get_indexer_for(other)
+        expected = np.array([0, 1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_index_non_unique_non_monotonic(self):
+        # GH#44084 (root cause)
+        index = IntervalIndex.from_tuples(
+            [(0.0, 1.0), (1.0, 2.0), (0.0, 1.0), (1.0, 2.0)]
+        )
+
+        result, _ = index.get_indexer_non_unique([Interval(1.0, 2.0)])
+        expected = np.array([1, 3], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_multiindex_with_intervals(self):
+        # GH#44084 (MultiIndex case as reported)
+        interval_index = IntervalIndex.from_tuples(
+            [(2.0, 3.0), (0.0, 1.0), (1.0, 2.0)], name="interval"
+        )
+        foo_index = Index([1, 2, 3], name="foo")
+
+        multi_index = MultiIndex.from_product([foo_index, interval_index])
+
+        result = multi_index.get_level_values("interval").get_indexer_for(
+            [Interval(0.0, 1.0)]
+        )
+        expected = np.array([1, 4, 7], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("box", [IntervalIndex, array, list])
+    def test_get_indexer_interval_index(self, box):
+        # GH#30178
+        rng = period_range("2022-07-01", freq="D", periods=3)
+        idx = box(interval_range(Timestamp("2022-07-01"), freq="3D", periods=3))
+
+        actual = rng.get_indexer(idx)
+        expected = np.array([-1, -1, -1], dtype=np.intp)
+        tm.assert_numpy_array_equal(actual, expected)
+
+    def test_get_indexer_read_only(self):
+        idx = interval_range(start=0, end=5)
+        arr = np.array([1, 2])
+        arr.flags.writeable = False
+        result = idx.get_indexer(arr)
+        expected = np.array([0, 1])
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
+        result = idx.get_indexer_non_unique(arr)[0]
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
+
+class TestSliceLocs:
+    def test_slice_locs_with_interval(self):
+        # increasing monotonically
+        index = IntervalIndex.from_tuples([(0, 2), (1, 3), (2, 4)])
+
+        assert index.slice_locs(start=Interval(0, 2), end=Interval(2, 4)) == (0, 3)
+        assert index.slice_locs(start=Interval(0, 2)) == (0, 3)
+        assert index.slice_locs(end=Interval(2, 4)) == (0, 3)
+        assert index.slice_locs(end=Interval(0, 2)) == (0, 1)
+        assert index.slice_locs(start=Interval(2, 4), end=Interval(0, 2)) == (2, 1)
+
+        # decreasing monotonically
+        index = IntervalIndex.from_tuples([(2, 4), (1, 3), (0, 2)])
+
+        assert index.slice_locs(start=Interval(0, 2), end=Interval(2, 4)) == (2, 1)
+        assert index.slice_locs(start=Interval(0, 2)) == (2, 3)
+        assert index.slice_locs(end=Interval(2, 4)) == (0, 1)
+        assert index.slice_locs(end=Interval(0, 2)) == (0, 3)
+        assert index.slice_locs(start=Interval(2, 4), end=Interval(0, 2)) == (0, 3)
+
+        # sorted duplicates
+        index = IntervalIndex.from_tuples([(0, 2), (0, 2), (2, 4)])
+
+        assert index.slice_locs(start=Interval(0, 2), end=Interval(2, 4)) == (0, 3)
+        assert index.slice_locs(start=Interval(0, 2)) == (0, 3)
+        assert index.slice_locs(end=Interval(2, 4)) == (0, 3)
+        assert index.slice_locs(end=Interval(0, 2)) == (0, 2)
+        assert index.slice_locs(start=Interval(2, 4), end=Interval(0, 2)) == (2, 2)
+
+        # unsorted duplicates
+        index = IntervalIndex.from_tuples([(0, 2), (2, 4), (0, 2)])
+
+        with pytest.raises(
+            KeyError,
+            match=re.escape(
+                '"Cannot get left slice bound for non-unique label: '
+                "Interval(0, 2, closed='right')\""
+            ),
+        ):
+            index.slice_locs(start=Interval(0, 2), end=Interval(2, 4))
+
+        with pytest.raises(
+            KeyError,
+            match=re.escape(
+                '"Cannot get left slice bound for non-unique label: '
+                "Interval(0, 2, closed='right')\""
+            ),
+        ):
+            index.slice_locs(start=Interval(0, 2))
+
+        assert index.slice_locs(end=Interval(2, 4)) == (0, 2)
+
+        with pytest.raises(
+            KeyError,
+            match=re.escape(
+                '"Cannot get right slice bound for non-unique label: '
+                "Interval(0, 2, closed='right')\""
+            ),
+        ):
+            index.slice_locs(end=Interval(0, 2))
+
+        with pytest.raises(
+            KeyError,
+            match=re.escape(
+                '"Cannot get right slice bound for non-unique label: '
+                "Interval(0, 2, closed='right')\""
+            ),
+        ):
+            index.slice_locs(start=Interval(2, 4), end=Interval(0, 2))
+
+        # another unsorted duplicates
+        index = IntervalIndex.from_tuples([(0, 2), (0, 2), (2, 4), (1, 3)])
+
+        assert index.slice_locs(start=Interval(0, 2), end=Interval(2, 4)) == (0, 3)
+        assert index.slice_locs(start=Interval(0, 2)) == (0, 4)
+        assert index.slice_locs(end=Interval(2, 4)) == (0, 3)
+        assert index.slice_locs(end=Interval(0, 2)) == (0, 2)
+        assert index.slice_locs(start=Interval(2, 4), end=Interval(0, 2)) == (2, 2)
+
+    def test_slice_locs_with_ints_and_floats_succeeds(self):
+        # increasing non-overlapping
+        index = IntervalIndex.from_tuples([(0, 1), (1, 2), (3, 4)])
+
+        assert index.slice_locs(0, 1) == (0, 1)
+        assert index.slice_locs(0, 2) == (0, 2)
+        assert index.slice_locs(0, 3) == (0, 2)
+        assert index.slice_locs(3, 1) == (2, 1)
+        assert index.slice_locs(3, 4) == (2, 3)
+        assert index.slice_locs(0, 4) == (0, 3)
+
+        # decreasing non-overlapping
+        index = IntervalIndex.from_tuples([(3, 4), (1, 2), (0, 1)])
+        assert index.slice_locs(0, 1) == (3, 3)
+        assert index.slice_locs(0, 2) == (3, 2)
+        assert index.slice_locs(0, 3) == (3, 1)
+        assert index.slice_locs(3, 1) == (1, 3)
+        assert index.slice_locs(3, 4) == (1, 1)
+        assert index.slice_locs(0, 4) == (3, 1)
+
+    @pytest.mark.parametrize("query", [[0, 1], [0, 2], [0, 3], [0, 4]])
+    @pytest.mark.parametrize(
+        "tuples",
+        [
+            [(0, 2), (1, 3), (2, 4)],
+            [(2, 4), (1, 3), (0, 2)],
+            [(0, 2), (0, 2), (2, 4)],
+            [(0, 2), (2, 4), (0, 2)],
+            [(0, 2), (0, 2), (2, 4), (1, 3)],
+        ],
+    )
+    def test_slice_locs_with_ints_and_floats_errors(self, tuples, query):
+        start, stop = query
+        index = IntervalIndex.from_tuples(tuples)
+        with pytest.raises(
+            KeyError,
+            match=(
+                "'can only get slices from an IntervalIndex if bounds are "
+                "non-overlapping and all monotonic increasing or decreasing'"
+            ),
+        ):
+            index.slice_locs(start, stop)
+
+
+class TestPutmask:
+    @pytest.mark.parametrize("tz", ["US/Pacific", None])
+    def test_putmask_dt64(self, tz):
+        # GH#37968
+        dti = date_range("2016-01-01", periods=9, tz=tz)
+        idx = IntervalIndex.from_breaks(dti)
+        mask = np.zeros(idx.shape, dtype=bool)
+        mask[0:3] = True
+
+        result = idx.putmask(mask, idx[-1])
+        expected = IntervalIndex([idx[-1]] * 3 + list(idx[3:]))
+        tm.assert_index_equal(result, expected)
+
+    def test_putmask_td64(self):
+        # GH#37968
+        dti = date_range("2016-01-01", periods=9)
+        tdi = dti - dti[0]
+        idx = IntervalIndex.from_breaks(tdi)
+        mask = np.zeros(idx.shape, dtype=bool)
+        mask[0:3] = True
+
+        result = idx.putmask(mask, idx[-1])
+        expected = IntervalIndex([idx[-1]] * 3 + list(idx[3:]))
+        tm.assert_index_equal(result, expected)
+
+
+class TestContains:
+    # .__contains__, not .contains
+
+    def test_contains_dunder(self):
+        index = IntervalIndex.from_arrays([0, 1], [1, 2], closed="right")
+
+        # __contains__ requires perfect matches to intervals.
+        assert 0 not in index
+        assert 1 not in index
+        assert 2 not in index
+
+        assert Interval(0, 1, closed="right") in index
+        assert Interval(0, 2, closed="right") not in index
+        assert Interval(0, 0.5, closed="right") not in index
+        assert Interval(3, 5, closed="right") not in index
+        assert Interval(-1, 0, closed="left") not in index
+        assert Interval(0, 1, closed="left") not in index
+        assert Interval(0, 1, closed="both") not in index

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_interval.py

@@ -0,0 +1,918 @@
+from itertools import permutations
+import re
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    Interval,
+    IntervalIndex,
+    Timedelta,
+    Timestamp,
+    date_range,
+    interval_range,
+    isna,
+    notna,
+    timedelta_range,
+)
+import pandas._testing as tm
+import pandas.core.common as com
+
+
+@pytest.fixture(params=[None, "foo"])
+def name(request):
+    return request.param
+
+
+class TestIntervalIndex:
+    index = IntervalIndex.from_arrays([0, 1], [1, 2])
+
+    def create_index(self, closed="right"):
+        return IntervalIndex.from_breaks(range(11), closed=closed)
+
+    def create_index_with_nan(self, closed="right"):
+        mask = [True, False] + [True] * 8
+        return IntervalIndex.from_arrays(
+            np.where(mask, np.arange(10), np.nan),
+            np.where(mask, np.arange(1, 11), np.nan),
+            closed=closed,
+        )
+
+    def test_properties(self, closed):
+        index = self.create_index(closed=closed)
+        assert len(index) == 10
+        assert index.size == 10
+        assert index.shape == (10,)
+
+        tm.assert_index_equal(index.left, Index(np.arange(10, dtype=np.int64)))
+        tm.assert_index_equal(index.right, Index(np.arange(1, 11, dtype=np.int64)))
+        tm.assert_index_equal(index.mid, Index(np.arange(0.5, 10.5, dtype=np.float64)))
+
+        assert index.closed == closed
+
+        ivs = [
+            Interval(left, right, closed)
+            for left, right in zip(range(10), range(1, 11))
+        ]
+        expected = np.array(ivs, dtype=object)
+        tm.assert_numpy_array_equal(np.asarray(index), expected)
+
+        # with nans
+        index = self.create_index_with_nan(closed=closed)
+        assert len(index) == 10
+        assert index.size == 10
+        assert index.shape == (10,)
+
+        expected_left = Index([0, np.nan, 2, 3, 4, 5, 6, 7, 8, 9])
+        expected_right = expected_left + 1
+        expected_mid = expected_left + 0.5
+        tm.assert_index_equal(index.left, expected_left)
+        tm.assert_index_equal(index.right, expected_right)
+        tm.assert_index_equal(index.mid, expected_mid)
+
+        assert index.closed == closed
+
+        ivs = [
+            Interval(left, right, closed) if notna(left) else np.nan
+            for left, right in zip(expected_left, expected_right)
+        ]
+        expected = np.array(ivs, dtype=object)
+        tm.assert_numpy_array_equal(np.asarray(index), expected)
+
+    @pytest.mark.parametrize(
+        "breaks",
+        [
+            [1, 1, 2, 5, 15, 53, 217, 1014, 5335, 31240, 201608],
+            [-np.inf, -100, -10, 0.5, 1, 1.5, 3.8, 101, 202, np.inf],
+            date_range("2017-01-01", "2017-01-04"),
+            pytest.param(
+                date_range("2017-01-01", "2017-01-04", unit="s"),
+                marks=pytest.mark.xfail(reason="mismatched result unit"),
+            ),
+            pd.to_timedelta(["1ns", "2ms", "3s", "4min", "5h", "6D"]),
+        ],
+    )
+    def test_length(self, closed, breaks):
+        # GH 18789
+        index = IntervalIndex.from_breaks(breaks, closed=closed)
+        result = index.length
+        expected = Index(iv.length for iv in index)
+        tm.assert_index_equal(result, expected)
+
+        # with NA
+        index = index.insert(1, np.nan)
+        result = index.length
+        expected = Index(iv.length if notna(iv) else iv for iv in index)
+        tm.assert_index_equal(result, expected)
+
+    def test_with_nans(self, closed):
+        index = self.create_index(closed=closed)
+        assert index.hasnans is False
+
+        result = index.isna()
+        expected = np.zeros(len(index), dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = index.notna()
+        expected = np.ones(len(index), dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+        index = self.create_index_with_nan(closed=closed)
+        assert index.hasnans is True
+
+        result = index.isna()
+        expected = np.array([False, True] + [False] * (len(index) - 2))
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = index.notna()
+        expected = np.array([True, False] + [True] * (len(index) - 2))
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_copy(self, closed):
+        expected = self.create_index(closed=closed)
+
+        result = expected.copy()
+        assert result.equals(expected)
+
+        result = expected.copy(deep=True)
+        assert result.equals(expected)
+        assert result.left is not expected.left
+
+    def test_ensure_copied_data(self, closed):
+        # exercise the copy flag in the constructor
+
+        # not copying
+        index = self.create_index(closed=closed)
+        result = IntervalIndex(index, copy=False)
+        tm.assert_numpy_array_equal(
+            index.left.values, result.left.values, check_same="same"
+        )
+        tm.assert_numpy_array_equal(
+            index.right.values, result.right.values, check_same="same"
+        )
+
+        # by-definition make a copy
+        result = IntervalIndex(np.array(index), copy=False)
+        tm.assert_numpy_array_equal(
+            index.left.values, result.left.values, check_same="copy"
+        )
+        tm.assert_numpy_array_equal(
+            index.right.values, result.right.values, check_same="copy"
+        )
+
+    def test_delete(self, closed):
+        breaks = np.arange(1, 11, dtype=np.int64)
+        expected = IntervalIndex.from_breaks(breaks, closed=closed)
+        result = self.create_index(closed=closed).delete(0)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data",
+        [
+            interval_range(0, periods=10, closed="neither"),
+            interval_range(1.7, periods=8, freq=2.5, closed="both"),
+            interval_range(Timestamp("20170101"), periods=12, closed="left"),
+            interval_range(Timedelta("1 day"), periods=6, closed="right"),
+        ],
+    )
+    def test_insert(self, data):
+        item = data[0]
+        idx_item = IntervalIndex([item])
+
+        # start
+        expected = idx_item.append(data)
+        result = data.insert(0, item)
+        tm.assert_index_equal(result, expected)
+
+        # end
+        expected = data.append(idx_item)
+        result = data.insert(len(data), item)
+        tm.assert_index_equal(result, expected)
+
+        # mid
+        expected = data[:3].append(idx_item).append(data[3:])
+        result = data.insert(3, item)
+        tm.assert_index_equal(result, expected)
+
+        # invalid type
+        res = data.insert(1, "foo")
+        expected = data.astype(object).insert(1, "foo")
+        tm.assert_index_equal(res, expected)
+
+        msg = "can only insert Interval objects and NA into an IntervalArray"
+        with pytest.raises(TypeError, match=msg):
+            data._data.insert(1, "foo")
+
+        # invalid closed
+        msg = "'value.closed' is 'left', expected 'right'."
+        for closed in {"left", "right", "both", "neither"} - {item.closed}:
+            msg = f"'value.closed' is '{closed}', expected '{item.closed}'."
+            bad_item = Interval(item.left, item.right, closed=closed)
+            res = data.insert(1, bad_item)
+            expected = data.astype(object).insert(1, bad_item)
+            tm.assert_index_equal(res, expected)
+            with pytest.raises(ValueError, match=msg):
+                data._data.insert(1, bad_item)
+
+        # GH 18295 (test missing)
+        na_idx = IntervalIndex([np.nan], closed=data.closed)
+        for na in [np.nan, None, pd.NA]:
+            expected = data[:1].append(na_idx).append(data[1:])
+            result = data.insert(1, na)
+            tm.assert_index_equal(result, expected)
+
+        if data.left.dtype.kind not in ["m", "M"]:
+            # trying to insert pd.NaT into a numeric-dtyped Index should cast
+            expected = data.astype(object).insert(1, pd.NaT)
+
+            msg = "can only insert Interval objects and NA into an IntervalArray"
+            with pytest.raises(TypeError, match=msg):
+                data._data.insert(1, pd.NaT)
+
+        result = data.insert(1, pd.NaT)
+        tm.assert_index_equal(result, expected)
+
+    def test_is_unique_interval(self, closed):
+        """
+        Interval specific tests for is_unique in addition to base class tests
+        """
+        # unique overlapping - distinct endpoints
+        idx = IntervalIndex.from_tuples([(0, 1), (0.5, 1.5)], closed=closed)
+        assert idx.is_unique is True
+
+        # unique overlapping - shared endpoints
+        idx = IntervalIndex.from_tuples([(1, 2), (1, 3), (2, 3)], closed=closed)
+        assert idx.is_unique is True
+
+        # unique nested
+        idx = IntervalIndex.from_tuples([(-1, 1), (-2, 2)], closed=closed)
+        assert idx.is_unique is True
+
+        # unique NaN
+        idx = IntervalIndex.from_tuples([(np.nan, np.nan)], closed=closed)
+        assert idx.is_unique is True
+
+        # non-unique NaN
+        idx = IntervalIndex.from_tuples(
+            [(np.nan, np.nan), (np.nan, np.nan)], closed=closed
+        )
+        assert idx.is_unique is False
+
+    def test_monotonic(self, closed):
+        # increasing non-overlapping
+        idx = IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)], closed=closed)
+        assert idx.is_monotonic_increasing is True
+        assert idx._is_strictly_monotonic_increasing is True
+        assert idx.is_monotonic_decreasing is False
+        assert idx._is_strictly_monotonic_decreasing is False
+
+        # decreasing non-overlapping
+        idx = IntervalIndex.from_tuples([(4, 5), (2, 3), (1, 2)], closed=closed)
+        assert idx.is_monotonic_increasing is False
+        assert idx._is_strictly_monotonic_increasing is False
+        assert idx.is_monotonic_decreasing is True
+        assert idx._is_strictly_monotonic_decreasing is True
+
+        # unordered non-overlapping
+        idx = IntervalIndex.from_tuples([(0, 1), (4, 5), (2, 3)], closed=closed)
+        assert idx.is_monotonic_increasing is False
+        assert idx._is_strictly_monotonic_increasing is False
+        assert idx.is_monotonic_decreasing is False
+        assert idx._is_strictly_monotonic_decreasing is False
+
+        # increasing overlapping
+        idx = IntervalIndex.from_tuples([(0, 2), (0.5, 2.5), (1, 3)], closed=closed)
+        assert idx.is_monotonic_increasing is True
+        assert idx._is_strictly_monotonic_increasing is True
+        assert idx.is_monotonic_decreasing is False
+        assert idx._is_strictly_monotonic_decreasing is False
+
+        # decreasing overlapping
+        idx = IntervalIndex.from_tuples([(1, 3), (0.5, 2.5), (0, 2)], closed=closed)
+        assert idx.is_monotonic_increasing is False
+        assert idx._is_strictly_monotonic_increasing is False
+        assert idx.is_monotonic_decreasing is True
+        assert idx._is_strictly_monotonic_decreasing is True
+
+        # unordered overlapping
+        idx = IntervalIndex.from_tuples([(0.5, 2.5), (0, 2), (1, 3)], closed=closed)
+        assert idx.is_monotonic_increasing is False
+        assert idx._is_strictly_monotonic_increasing is False
+        assert idx.is_monotonic_decreasing is False
+        assert idx._is_strictly_monotonic_decreasing is False
+
+        # increasing overlapping shared endpoints
+        idx = IntervalIndex.from_tuples([(1, 2), (1, 3), (2, 3)], closed=closed)
+        assert idx.is_monotonic_increasing is True
+        assert idx._is_strictly_monotonic_increasing is True
+        assert idx.is_monotonic_decreasing is False
+        assert idx._is_strictly_monotonic_decreasing is False
+
+        # decreasing overlapping shared endpoints
+        idx = IntervalIndex.from_tuples([(2, 3), (1, 3), (1, 2)], closed=closed)
+        assert idx.is_monotonic_increasing is False
+        assert idx._is_strictly_monotonic_increasing is False
+        assert idx.is_monotonic_decreasing is True
+        assert idx._is_strictly_monotonic_decreasing is True
+
+        # stationary
+        idx = IntervalIndex.from_tuples([(0, 1), (0, 1)], closed=closed)
+        assert idx.is_monotonic_increasing is True
+        assert idx._is_strictly_monotonic_increasing is False
+        assert idx.is_monotonic_decreasing is True
+        assert idx._is_strictly_monotonic_decreasing is False
+
+        # empty
+        idx = IntervalIndex([], closed=closed)
+        assert idx.is_monotonic_increasing is True
+        assert idx._is_strictly_monotonic_increasing is True
+        assert idx.is_monotonic_decreasing is True
+        assert idx._is_strictly_monotonic_decreasing is True
+
+    def test_is_monotonic_with_nans(self):
+        # GH#41831
+        index = IntervalIndex([np.nan, np.nan])
+
+        assert not index.is_monotonic_increasing
+        assert not index._is_strictly_monotonic_increasing
+        assert not index.is_monotonic_increasing
+        assert not index._is_strictly_monotonic_decreasing
+        assert not index.is_monotonic_decreasing
+
+    @pytest.mark.parametrize(
+        "breaks",
+        [
+            date_range("20180101", periods=4),
+            date_range("20180101", periods=4, tz="US/Eastern"),
+            timedelta_range("0 days", periods=4),
+        ],
+        ids=lambda x: str(x.dtype),
+    )
+    def test_maybe_convert_i8(self, breaks):
+        # GH 20636
+        index = IntervalIndex.from_breaks(breaks)
+
+        # intervalindex
+        result = index._maybe_convert_i8(index)
+        expected = IntervalIndex.from_breaks(breaks.asi8)
+        tm.assert_index_equal(result, expected)
+
+        # interval
+        interval = Interval(breaks[0], breaks[1])
+        result = index._maybe_convert_i8(interval)
+        expected = Interval(breaks[0]._value, breaks[1]._value)
+        assert result == expected
+
+        # datetimelike index
+        result = index._maybe_convert_i8(breaks)
+        expected = Index(breaks.asi8)
+        tm.assert_index_equal(result, expected)
+
+        # datetimelike scalar
+        result = index._maybe_convert_i8(breaks[0])
+        expected = breaks[0]._value
+        assert result == expected
+
+        # list-like of datetimelike scalars
+        result = index._maybe_convert_i8(list(breaks))
+        expected = Index(breaks.asi8)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "breaks",
+        [date_range("2018-01-01", periods=5), timedelta_range("0 days", periods=5)],
+    )
+    def test_maybe_convert_i8_nat(self, breaks):
+        # GH 20636
+        index = IntervalIndex.from_breaks(breaks)
+
+        to_convert = breaks._constructor([pd.NaT] * 3).as_unit("ns")
+        expected = Index([np.nan] * 3, dtype=np.float64)
+        result = index._maybe_convert_i8(to_convert)
+        tm.assert_index_equal(result, expected)
+
+        to_convert = to_convert.insert(0, breaks[0])
+        expected = expected.insert(0, float(breaks[0]._value))
+        result = index._maybe_convert_i8(to_convert)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "make_key",
+        [lambda breaks: breaks, list],
+        ids=["lambda", "list"],
+    )
+    def test_maybe_convert_i8_numeric(self, make_key, any_real_numpy_dtype):
+        # GH 20636
+        breaks = np.arange(5, dtype=any_real_numpy_dtype)
+        index = IntervalIndex.from_breaks(breaks)
+        key = make_key(breaks)
+
+        result = index._maybe_convert_i8(key)
+        kind = breaks.dtype.kind
+        expected_dtype = {"i": np.int64, "u": np.uint64, "f": np.float64}[kind]
+        expected = Index(key, dtype=expected_dtype)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "make_key",
+        [
+            IntervalIndex.from_breaks,
+            lambda breaks: Interval(breaks[0], breaks[1]),
+            lambda breaks: breaks[0],
+        ],
+        ids=["IntervalIndex", "Interval", "scalar"],
+    )
+    def test_maybe_convert_i8_numeric_identical(self, make_key, any_real_numpy_dtype):
+        # GH 20636
+        breaks = np.arange(5, dtype=any_real_numpy_dtype)
+        index = IntervalIndex.from_breaks(breaks)
+        key = make_key(breaks)
+
+        # test if _maybe_convert_i8 won't change key if an Interval or IntervalIndex
+        result = index._maybe_convert_i8(key)
+        assert result is key
+
+    @pytest.mark.parametrize(
+        "breaks1, breaks2",
+        permutations(
+            [
+                date_range("20180101", periods=4),
+                date_range("20180101", periods=4, tz="US/Eastern"),
+                timedelta_range("0 days", periods=4),
+            ],
+            2,
+        ),
+        ids=lambda x: str(x.dtype),
+    )
+    @pytest.mark.parametrize(
+        "make_key",
+        [
+            IntervalIndex.from_breaks,
+            lambda breaks: Interval(breaks[0], breaks[1]),
+            lambda breaks: breaks,
+            lambda breaks: breaks[0],
+            list,
+        ],
+        ids=["IntervalIndex", "Interval", "Index", "scalar", "list"],
+    )
+    def test_maybe_convert_i8_errors(self, breaks1, breaks2, make_key):
+        # GH 20636
+        index = IntervalIndex.from_breaks(breaks1)
+        key = make_key(breaks2)
+
+        msg = (
+            f"Cannot index an IntervalIndex of subtype {breaks1.dtype} with "
+            f"values of dtype {breaks2.dtype}"
+        )
+        msg = re.escape(msg)
+        with pytest.raises(ValueError, match=msg):
+            index._maybe_convert_i8(key)
+
+    def test_contains_method(self):
+        # can select values that are IN the range of a value
+        i = IntervalIndex.from_arrays([0, 1], [1, 2])
+
+        expected = np.array([False, False], dtype="bool")
+        actual = i.contains(0)
+        tm.assert_numpy_array_equal(actual, expected)
+        actual = i.contains(3)
+        tm.assert_numpy_array_equal(actual, expected)
+
+        expected = np.array([True, False], dtype="bool")
+        actual = i.contains(0.5)
+        tm.assert_numpy_array_equal(actual, expected)
+        actual = i.contains(1)
+        tm.assert_numpy_array_equal(actual, expected)
+
+        # __contains__ not implemented for "interval in interval", follow
+        # that for the contains method for now
+        with pytest.raises(
+            NotImplementedError, match="contains not implemented for two"
+        ):
+            i.contains(Interval(0, 1))
+
+    def test_dropna(self, closed):
+        expected = IntervalIndex.from_tuples([(0.0, 1.0), (1.0, 2.0)], closed=closed)
+
+        ii = IntervalIndex.from_tuples([(0, 1), (1, 2), np.nan], closed=closed)
+        result = ii.dropna()
+        tm.assert_index_equal(result, expected)
+
+        ii = IntervalIndex.from_arrays([0, 1, np.nan], [1, 2, np.nan], closed=closed)
+        result = ii.dropna()
+        tm.assert_index_equal(result, expected)
+
+    def test_non_contiguous(self, closed):
+        index = IntervalIndex.from_tuples([(0, 1), (2, 3)], closed=closed)
+        target = [0.5, 1.5, 2.5]
+        actual = index.get_indexer(target)
+        expected = np.array([0, -1, 1], dtype="intp")
+        tm.assert_numpy_array_equal(actual, expected)
+
+        assert 1.5 not in index
+
+    def test_isin(self, closed):
+        index = self.create_index(closed=closed)
+
+        expected = np.array([True] + [False] * (len(index) - 1))
+        result = index.isin(index[:1])
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = index.isin([index[0]])
+        tm.assert_numpy_array_equal(result, expected)
+
+        other = IntervalIndex.from_breaks(np.arange(-2, 10), closed=closed)
+        expected = np.array([True] * (len(index) - 1) + [False])
+        result = index.isin(other)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = index.isin(other.tolist())
+        tm.assert_numpy_array_equal(result, expected)
+
+        for other_closed in ["right", "left", "both", "neither"]:
+            other = self.create_index(closed=other_closed)
+            expected = np.repeat(closed == other_closed, len(index))
+            result = index.isin(other)
+            tm.assert_numpy_array_equal(result, expected)
+
+            result = index.isin(other.tolist())
+            tm.assert_numpy_array_equal(result, expected)
+
+    def test_comparison(self):
+        actual = Interval(0, 1) < self.index
+        expected = np.array([False, True])
+        tm.assert_numpy_array_equal(actual, expected)
+
+        actual = Interval(0.5, 1.5) < self.index
+        expected = np.array([False, True])
+        tm.assert_numpy_array_equal(actual, expected)
+        actual = self.index > Interval(0.5, 1.5)
+        tm.assert_numpy_array_equal(actual, expected)
+
+        actual = self.index == self.index
+        expected = np.array([True, True])
+        tm.assert_numpy_array_equal(actual, expected)
+        actual = self.index <= self.index
+        tm.assert_numpy_array_equal(actual, expected)
+        actual = self.index >= self.index
+        tm.assert_numpy_array_equal(actual, expected)
+
+        actual = self.index < self.index
+        expected = np.array([False, False])
+        tm.assert_numpy_array_equal(actual, expected)
+        actual = self.index > self.index
+        tm.assert_numpy_array_equal(actual, expected)
+
+        actual = self.index == IntervalIndex.from_breaks([0, 1, 2], "left")
+        tm.assert_numpy_array_equal(actual, expected)
+
+        actual = self.index == self.index.values
+        tm.assert_numpy_array_equal(actual, np.array([True, True]))
+        actual = self.index.values == self.index
+        tm.assert_numpy_array_equal(actual, np.array([True, True]))
+        actual = self.index <= self.index.values
+        tm.assert_numpy_array_equal(actual, np.array([True, True]))
+        actual = self.index != self.index.values
+        tm.assert_numpy_array_equal(actual, np.array([False, False]))
+        actual = self.index > self.index.values
+        tm.assert_numpy_array_equal(actual, np.array([False, False]))
+        actual = self.index.values > self.index
+        tm.assert_numpy_array_equal(actual, np.array([False, False]))
+
+        # invalid comparisons
+        actual = self.index == 0
+        tm.assert_numpy_array_equal(actual, np.array([False, False]))
+        actual = self.index == self.index.left
+        tm.assert_numpy_array_equal(actual, np.array([False, False]))
+
+        msg = "|".join(
+            [
+                "not supported between instances of 'int' and '.*.Interval'",
+                r"Invalid comparison between dtype=interval\[int64, right\] and ",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            self.index > 0
+        with pytest.raises(TypeError, match=msg):
+            self.index <= 0
+        with pytest.raises(TypeError, match=msg):
+            self.index > np.arange(2)
+
+        msg = "Lengths must match to compare"
+        with pytest.raises(ValueError, match=msg):
+            self.index > np.arange(3)
+
+    def test_missing_values(self, closed):
+        idx = Index(
+            [np.nan, Interval(0, 1, closed=closed), Interval(1, 2, closed=closed)]
+        )
+        idx2 = IntervalIndex.from_arrays([np.nan, 0, 1], [np.nan, 1, 2], closed=closed)
+        assert idx.equals(idx2)
+
+        msg = (
+            "missing values must be missing in the same location both left "
+            "and right sides"
+        )
+        with pytest.raises(ValueError, match=msg):
+            IntervalIndex.from_arrays(
+                [np.nan, 0, 1], np.array([0, 1, 2]), closed=closed
+            )
+
+        tm.assert_numpy_array_equal(isna(idx), np.array([True, False, False]))
+
+    def test_sort_values(self, closed):
+        index = self.create_index(closed=closed)
+
+        result = index.sort_values()
+        tm.assert_index_equal(result, index)
+
+        result = index.sort_values(ascending=False)
+        tm.assert_index_equal(result, index[::-1])
+
+        # with nan
+        index = IntervalIndex([Interval(1, 2), np.nan, Interval(0, 1)])
+
+        result = index.sort_values()
+        expected = IntervalIndex([Interval(0, 1), Interval(1, 2), np.nan])
+        tm.assert_index_equal(result, expected)
+
+        result = index.sort_values(ascending=False, na_position="first")
+        expected = IntervalIndex([np.nan, Interval(1, 2), Interval(0, 1)])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("tz", [None, "US/Eastern"])
+    def test_datetime(self, tz):
+        start = Timestamp("2000-01-01", tz=tz)
+        dates = date_range(start=start, periods=10)
+        index = IntervalIndex.from_breaks(dates)
+
+        # test mid
+        start = Timestamp("2000-01-01T12:00", tz=tz)
+        expected = date_range(start=start, periods=9)
+        tm.assert_index_equal(index.mid, expected)
+
+        # __contains__ doesn't check individual points
+        assert Timestamp("2000-01-01", tz=tz) not in index
+        assert Timestamp("2000-01-01T12", tz=tz) not in index
+        assert Timestamp("2000-01-02", tz=tz) not in index
+        iv_true = Interval(
+            Timestamp("2000-01-02", tz=tz), Timestamp("2000-01-03", tz=tz)
+        )
+        iv_false = Interval(
+            Timestamp("1999-12-31", tz=tz), Timestamp("2000-01-01", tz=tz)
+        )
+        assert iv_true in index
+        assert iv_false not in index
+
+        # .contains does check individual points
+        assert not index.contains(Timestamp("2000-01-01", tz=tz)).any()
+        assert index.contains(Timestamp("2000-01-01T12", tz=tz)).any()
+        assert index.contains(Timestamp("2000-01-02", tz=tz)).any()
+
+        # test get_indexer
+        start = Timestamp("1999-12-31T12:00", tz=tz)
+        target = date_range(start=start, periods=7, freq="12h")
+        actual = index.get_indexer(target)
+        expected = np.array([-1, -1, 0, 0, 1, 1, 2], dtype="intp")
+        tm.assert_numpy_array_equal(actual, expected)
+
+        start = Timestamp("2000-01-08T18:00", tz=tz)
+        target = date_range(start=start, periods=7, freq="6h")
+        actual = index.get_indexer(target)
+        expected = np.array([7, 7, 8, 8, 8, 8, -1], dtype="intp")
+        tm.assert_numpy_array_equal(actual, expected)
+
+    def test_append(self, closed):
+        index1 = IntervalIndex.from_arrays([0, 1], [1, 2], closed=closed)
+        index2 = IntervalIndex.from_arrays([1, 2], [2, 3], closed=closed)
+
+        result = index1.append(index2)
+        expected = IntervalIndex.from_arrays([0, 1, 1, 2], [1, 2, 2, 3], closed=closed)
+        tm.assert_index_equal(result, expected)
+
+        result = index1.append([index1, index2])
+        expected = IntervalIndex.from_arrays(
+            [0, 1, 0, 1, 1, 2], [1, 2, 1, 2, 2, 3], closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+        for other_closed in {"left", "right", "both", "neither"} - {closed}:
+            index_other_closed = IntervalIndex.from_arrays(
+                [0, 1], [1, 2], closed=other_closed
+            )
+            result = index1.append(index_other_closed)
+            expected = index1.astype(object).append(index_other_closed.astype(object))
+            tm.assert_index_equal(result, expected)
+
+    def test_is_non_overlapping_monotonic(self, closed):
+        # Should be True in all cases
+        tpls = [(0, 1), (2, 3), (4, 5), (6, 7)]
+        idx = IntervalIndex.from_tuples(tpls, closed=closed)
+        assert idx.is_non_overlapping_monotonic is True
+
+        idx = IntervalIndex.from_tuples(tpls[::-1], closed=closed)
+        assert idx.is_non_overlapping_monotonic is True
+
+        # Should be False in all cases (overlapping)
+        tpls = [(0, 2), (1, 3), (4, 5), (6, 7)]
+        idx = IntervalIndex.from_tuples(tpls, closed=closed)
+        assert idx.is_non_overlapping_monotonic is False
+
+        idx = IntervalIndex.from_tuples(tpls[::-1], closed=closed)
+        assert idx.is_non_overlapping_monotonic is False
+
+        # Should be False in all cases (non-monotonic)
+        tpls = [(0, 1), (2, 3), (6, 7), (4, 5)]
+        idx = IntervalIndex.from_tuples(tpls, closed=closed)
+        assert idx.is_non_overlapping_monotonic is False
+
+        idx = IntervalIndex.from_tuples(tpls[::-1], closed=closed)
+        assert idx.is_non_overlapping_monotonic is False
+
+        # Should be False for closed='both', otherwise True (GH16560)
+        if closed == "both":
+            idx = IntervalIndex.from_breaks(range(4), closed=closed)
+            assert idx.is_non_overlapping_monotonic is False
+        else:
+            idx = IntervalIndex.from_breaks(range(4), closed=closed)
+            assert idx.is_non_overlapping_monotonic is True
+
+    @pytest.mark.parametrize(
+        "start, shift, na_value",
+        [
+            (0, 1, np.nan),
+            (Timestamp("2018-01-01"), Timedelta("1 day"), pd.NaT),
+            (Timedelta("0 days"), Timedelta("1 day"), pd.NaT),
+        ],
+    )
+    def test_is_overlapping(self, start, shift, na_value, closed):
+        # GH 23309
+        # see test_interval_tree.py for extensive tests; interface tests here
+
+        # non-overlapping
+        tuples = [(start + n * shift, start + (n + 1) * shift) for n in (0, 2, 4)]
+        index = IntervalIndex.from_tuples(tuples, closed=closed)
+        assert index.is_overlapping is False
+
+        # non-overlapping with NA
+        tuples = [(na_value, na_value)] + tuples + [(na_value, na_value)]
+        index = IntervalIndex.from_tuples(tuples, closed=closed)
+        assert index.is_overlapping is False
+
+        # overlapping
+        tuples = [(start + n * shift, start + (n + 2) * shift) for n in range(3)]
+        index = IntervalIndex.from_tuples(tuples, closed=closed)
+        assert index.is_overlapping is True
+
+        # overlapping with NA
+        tuples = [(na_value, na_value)] + tuples + [(na_value, na_value)]
+        index = IntervalIndex.from_tuples(tuples, closed=closed)
+        assert index.is_overlapping is True
+
+        # common endpoints
+        tuples = [(start + n * shift, start + (n + 1) * shift) for n in range(3)]
+        index = IntervalIndex.from_tuples(tuples, closed=closed)
+        result = index.is_overlapping
+        expected = closed == "both"
+        assert result is expected
+
+        # common endpoints with NA
+        tuples = [(na_value, na_value)] + tuples + [(na_value, na_value)]
+        index = IntervalIndex.from_tuples(tuples, closed=closed)
+        result = index.is_overlapping
+        assert result is expected
+
+        # intervals with duplicate left values
+        a = [10, 15, 20, 25, 30, 35, 40, 45, 45, 50, 55, 60, 65, 70, 75, 80, 85]
+        b = [15, 20, 25, 30, 35, 40, 45, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90]
+        index = IntervalIndex.from_arrays(a, b, closed="right")
+        result = index.is_overlapping
+        assert result is False
+
+    @pytest.mark.parametrize(
+        "tuples",
+        [
+            list(zip(range(10), range(1, 11))),
+            list(
+                zip(
+                    date_range("20170101", periods=10),
+                    date_range("20170101", periods=10),
+                )
+            ),
+            list(
+                zip(
+                    timedelta_range("0 days", periods=10),
+                    timedelta_range("1 day", periods=10),
+                )
+            ),
+        ],
+    )
+    def test_to_tuples(self, tuples):
+        # GH 18756
+        idx = IntervalIndex.from_tuples(tuples)
+        result = idx.to_tuples()
+        expected = Index(com.asarray_tuplesafe(tuples))
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "tuples",
+        [
+            list(zip(range(10), range(1, 11))) + [np.nan],
+            list(
+                zip(
+                    date_range("20170101", periods=10),
+                    date_range("20170101", periods=10),
+                )
+            )
+            + [np.nan],
+            list(
+                zip(
+                    timedelta_range("0 days", periods=10),
+                    timedelta_range("1 day", periods=10),
+                )
+            )
+            + [np.nan],
+        ],
+    )
+    @pytest.mark.parametrize("na_tuple", [True, False])
+    def test_to_tuples_na(self, tuples, na_tuple):
+        # GH 18756
+        idx = IntervalIndex.from_tuples(tuples)
+        result = idx.to_tuples(na_tuple=na_tuple)
+
+        # check the non-NA portion
+        expected_notna = Index(com.asarray_tuplesafe(tuples[:-1]))
+        result_notna = result[:-1]
+        tm.assert_index_equal(result_notna, expected_notna)
+
+        # check the NA portion
+        result_na = result[-1]
+        if na_tuple:
+            assert isinstance(result_na, tuple)
+            assert len(result_na) == 2
+            assert all(isna(x) for x in result_na)
+        else:
+            assert isna(result_na)
+
+    def test_nbytes(self):
+        # GH 19209
+        left = np.arange(0, 4, dtype="i8")
+        right = np.arange(1, 5, dtype="i8")
+
+        result = IntervalIndex.from_arrays(left, right).nbytes
+        expected = 64  # 4 * 8 * 2
+        assert result == expected
+
+    @pytest.mark.parametrize("new_closed", ["left", "right", "both", "neither"])
+    def test_set_closed(self, name, closed, new_closed):
+        # GH 21670
+        index = interval_range(0, 5, closed=closed, name=name)
+        result = index.set_closed(new_closed)
+        expected = interval_range(0, 5, closed=new_closed, name=name)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("bad_closed", ["foo", 10, "LEFT", True, False])
+    def test_set_closed_errors(self, bad_closed):
+        # GH 21670
+        index = interval_range(0, 5)
+        msg = f"invalid option for 'closed': {bad_closed}"
+        with pytest.raises(ValueError, match=msg):
+            index.set_closed(bad_closed)
+
+    def test_is_all_dates(self):
+        # GH 23576
+        year_2017 = Interval(
+            Timestamp("2017-01-01 00:00:00"), Timestamp("2018-01-01 00:00:00")
+        )
+        year_2017_index = IntervalIndex([year_2017])
+        assert not year_2017_index._is_all_dates
+
+
+def test_dir():
+    # GH#27571 dir(interval_index) should not raise
+    index = IntervalIndex.from_arrays([0, 1], [1, 2])
+    result = dir(index)
+    assert "str" not in result
+
+
+def test_searchsorted_different_argument_classes(listlike_box):
+    # https://github.com/pandas-dev/pandas/issues/32762
+    values = IntervalIndex([Interval(0, 1), Interval(1, 2)])
+    result = values.searchsorted(listlike_box(values))
+    expected = np.array([0, 1], dtype=result.dtype)
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = values._data.searchsorted(listlike_box(values))
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "arg", [[1, 2], ["a", "b"], [Timestamp("2020-01-01", tz="Europe/London")] * 2]
+)
+def test_searchsorted_invalid_argument(arg):
+    values = IntervalIndex([Interval(0, 1), Interval(1, 2)])
+    msg = "'<' not supported between instances of 'pandas._libs.interval.Interval' and "
+    with pytest.raises(TypeError, match=msg):
+        values.searchsorted(arg)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_interval_range.py

@@ -0,0 +1,369 @@
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import is_integer
+
+from pandas import (
+    DateOffset,
+    Interval,
+    IntervalIndex,
+    Timedelta,
+    Timestamp,
+    date_range,
+    interval_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+from pandas.tseries.offsets import Day
+
+
+@pytest.fixture(params=[None, "foo"])
+def name(request):
+    return request.param
+
+
+class TestIntervalRange:
+    @pytest.mark.parametrize("freq, periods", [(1, 100), (2.5, 40), (5, 20), (25, 4)])
+    def test_constructor_numeric(self, closed, name, freq, periods):
+        start, end = 0, 100
+        breaks = np.arange(101, step=freq)
+        expected = IntervalIndex.from_breaks(breaks, name=name, closed=closed)
+
+        # defined from start/end/freq
+        result = interval_range(
+            start=start, end=end, freq=freq, name=name, closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+        # defined from start/periods/freq
+        result = interval_range(
+            start=start, periods=periods, freq=freq, name=name, closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+        # defined from end/periods/freq
+        result = interval_range(
+            end=end, periods=periods, freq=freq, name=name, closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+        # GH 20976: linspace behavior defined from start/end/periods
+        result = interval_range(
+            start=start, end=end, periods=periods, name=name, closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("tz", [None, "US/Eastern"])
+    @pytest.mark.parametrize(
+        "freq, periods", [("D", 364), ("2D", 182), ("22D18h", 16), ("ME", 11)]
+    )
+    def test_constructor_timestamp(self, closed, name, freq, periods, tz):
+        start, end = Timestamp("20180101", tz=tz), Timestamp("20181231", tz=tz)
+        breaks = date_range(start=start, end=end, freq=freq)
+        expected = IntervalIndex.from_breaks(breaks, name=name, closed=closed)
+
+        # defined from start/end/freq
+        result = interval_range(
+            start=start, end=end, freq=freq, name=name, closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+        # defined from start/periods/freq
+        result = interval_range(
+            start=start, periods=periods, freq=freq, name=name, closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+        # defined from end/periods/freq
+        result = interval_range(
+            end=end, periods=periods, freq=freq, name=name, closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+        # GH 20976: linspace behavior defined from start/end/periods
+        if not breaks.freq.n == 1 and tz is None:
+            result = interval_range(
+                start=start, end=end, periods=periods, name=name, closed=closed
+            )
+            tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "freq, periods", [("D", 100), ("2D12h", 40), ("5D", 20), ("25D", 4)]
+    )
+    def test_constructor_timedelta(self, closed, name, freq, periods):
+        start, end = Timedelta("0 days"), Timedelta("100 days")
+        breaks = timedelta_range(start=start, end=end, freq=freq)
+        expected = IntervalIndex.from_breaks(breaks, name=name, closed=closed)
+
+        # defined from start/end/freq
+        result = interval_range(
+            start=start, end=end, freq=freq, name=name, closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+        # defined from start/periods/freq
+        result = interval_range(
+            start=start, periods=periods, freq=freq, name=name, closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+        # defined from end/periods/freq
+        result = interval_range(
+            end=end, periods=periods, freq=freq, name=name, closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+        # GH 20976: linspace behavior defined from start/end/periods
+        result = interval_range(
+            start=start, end=end, periods=periods, name=name, closed=closed
+        )
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "start, end, freq, expected_endpoint",
+        [
+            (0, 10, 3, 9),
+            (0, 10, 1.5, 9),
+            (0.5, 10, 3, 9.5),
+            (Timedelta("0D"), Timedelta("10D"), "2D4h", Timedelta("8D16h")),
+            (
+                Timestamp("2018-01-01"),
+                Timestamp("2018-02-09"),
+                "MS",
+                Timestamp("2018-02-01"),
+            ),
+            (
+                Timestamp("2018-01-01", tz="US/Eastern"),
+                Timestamp("2018-01-20", tz="US/Eastern"),
+                "5D12h",
+                Timestamp("2018-01-17 12:00:00", tz="US/Eastern"),
+            ),
+        ],
+    )
+    def test_early_truncation(self, start, end, freq, expected_endpoint):
+        # index truncates early if freq causes end to be skipped
+        result = interval_range(start=start, end=end, freq=freq)
+        result_endpoint = result.right[-1]
+        assert result_endpoint == expected_endpoint
+
+    @pytest.mark.parametrize(
+        "start, end, freq",
+        [(0.5, None, None), (None, 4.5, None), (0.5, None, 1.5), (None, 6.5, 1.5)],
+    )
+    def test_no_invalid_float_truncation(self, start, end, freq):
+        # GH 21161
+        if freq is None:
+            breaks = [0.5, 1.5, 2.5, 3.5, 4.5]
+        else:
+            breaks = [0.5, 2.0, 3.5, 5.0, 6.5]
+        expected = IntervalIndex.from_breaks(breaks)
+
+        result = interval_range(start=start, end=end, periods=4, freq=freq)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "start, mid, end",
+        [
+            (
+                Timestamp("2018-03-10", tz="US/Eastern"),
+                Timestamp("2018-03-10 23:30:00", tz="US/Eastern"),
+                Timestamp("2018-03-12", tz="US/Eastern"),
+            ),
+            (
+                Timestamp("2018-11-03", tz="US/Eastern"),
+                Timestamp("2018-11-04 00:30:00", tz="US/Eastern"),
+                Timestamp("2018-11-05", tz="US/Eastern"),
+            ),
+        ],
+    )
+    def test_linspace_dst_transition(self, start, mid, end):
+        # GH 20976: linspace behavior defined from start/end/periods
+        # accounts for the hour gained/lost during DST transition
+        start = start.as_unit("ns")
+        mid = mid.as_unit("ns")
+        end = end.as_unit("ns")
+        result = interval_range(start=start, end=end, periods=2)
+        expected = IntervalIndex.from_breaks([start, mid, end])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("freq", [2, 2.0])
+    @pytest.mark.parametrize("end", [10, 10.0])
+    @pytest.mark.parametrize("start", [0, 0.0])
+    def test_float_subtype(self, start, end, freq):
+        # Has float subtype if any of start/end/freq are float, even if all
+        # resulting endpoints can safely be upcast to integers
+
+        # defined from start/end/freq
+        index = interval_range(start=start, end=end, freq=freq)
+        result = index.dtype.subtype
+        expected = "int64" if is_integer(start + end + freq) else "float64"
+        assert result == expected
+
+        # defined from start/periods/freq
+        index = interval_range(start=start, periods=5, freq=freq)
+        result = index.dtype.subtype
+        expected = "int64" if is_integer(start + freq) else "float64"
+        assert result == expected
+
+        # defined from end/periods/freq
+        index = interval_range(end=end, periods=5, freq=freq)
+        result = index.dtype.subtype
+        expected = "int64" if is_integer(end + freq) else "float64"
+        assert result == expected
+
+        # GH 20976: linspace behavior defined from start/end/periods
+        index = interval_range(start=start, end=end, periods=5)
+        result = index.dtype.subtype
+        expected = "int64" if is_integer(start + end) else "float64"
+        assert result == expected
+
+    def test_interval_range_fractional_period(self):
+        # float value for periods
+        expected = interval_range(start=0, periods=10)
+        msg = "Non-integer 'periods' in pd.date_range, .* pd.interval_range"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = interval_range(start=0, periods=10.5)
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_coverage(self):
+        # equivalent timestamp-like start/end
+        start, end = Timestamp("2017-01-01"), Timestamp("2017-01-15")
+        expected = interval_range(start=start, end=end)
+
+        result = interval_range(start=start.to_pydatetime(), end=end.to_pydatetime())
+        tm.assert_index_equal(result, expected)
+
+        result = interval_range(start=start.asm8, end=end.asm8)
+        tm.assert_index_equal(result, expected)
+
+        # equivalent freq with timestamp
+        equiv_freq = [
+            "D",
+            Day(),
+            Timedelta(days=1),
+            timedelta(days=1),
+            DateOffset(days=1),
+        ]
+        for freq in equiv_freq:
+            result = interval_range(start=start, end=end, freq=freq)
+            tm.assert_index_equal(result, expected)
+
+        # equivalent timedelta-like start/end
+        start, end = Timedelta(days=1), Timedelta(days=10)
+        expected = interval_range(start=start, end=end)
+
+        result = interval_range(start=start.to_pytimedelta(), end=end.to_pytimedelta())
+        tm.assert_index_equal(result, expected)
+
+        result = interval_range(start=start.asm8, end=end.asm8)
+        tm.assert_index_equal(result, expected)
+
+        # equivalent freq with timedelta
+        equiv_freq = ["D", Day(), Timedelta(days=1), timedelta(days=1)]
+        for freq in equiv_freq:
+            result = interval_range(start=start, end=end, freq=freq)
+            tm.assert_index_equal(result, expected)
+
+    def test_errors(self):
+        # not enough params
+        msg = (
+            "Of the four parameters: start, end, periods, and freq, "
+            "exactly three must be specified"
+        )
+
+        with pytest.raises(ValueError, match=msg):
+            interval_range(start=0)
+
+        with pytest.raises(ValueError, match=msg):
+            interval_range(end=5)
+
+        with pytest.raises(ValueError, match=msg):
+            interval_range(periods=2)
+
+        with pytest.raises(ValueError, match=msg):
+            interval_range()
+
+        # too many params
+        with pytest.raises(ValueError, match=msg):
+            interval_range(start=0, end=5, periods=6, freq=1.5)
+
+        # mixed units
+        msg = "start, end, freq need to be type compatible"
+        with pytest.raises(TypeError, match=msg):
+            interval_range(start=0, end=Timestamp("20130101"), freq=2)
+
+        with pytest.raises(TypeError, match=msg):
+            interval_range(start=0, end=Timedelta("1 day"), freq=2)
+
+        with pytest.raises(TypeError, match=msg):
+            interval_range(start=0, end=10, freq="D")
+
+        with pytest.raises(TypeError, match=msg):
+            interval_range(start=Timestamp("20130101"), end=10, freq="D")
+
+        with pytest.raises(TypeError, match=msg):
+            interval_range(
+                start=Timestamp("20130101"), end=Timedelta("1 day"), freq="D"
+            )
+
+        with pytest.raises(TypeError, match=msg):
+            interval_range(
+                start=Timestamp("20130101"), end=Timestamp("20130110"), freq=2
+            )
+
+        with pytest.raises(TypeError, match=msg):
+            interval_range(start=Timedelta("1 day"), end=10, freq="D")
+
+        with pytest.raises(TypeError, match=msg):
+            interval_range(
+                start=Timedelta("1 day"), end=Timestamp("20130110"), freq="D"
+            )
+
+        with pytest.raises(TypeError, match=msg):
+            interval_range(start=Timedelta("1 day"), end=Timedelta("10 days"), freq=2)
+
+        # invalid periods
+        msg = "periods must be a number, got foo"
+        with pytest.raises(TypeError, match=msg):
+            interval_range(start=0, periods="foo")
+
+        # invalid start
+        msg = "start must be numeric or datetime-like, got foo"
+        with pytest.raises(ValueError, match=msg):
+            interval_range(start="foo", periods=10)
+
+        # invalid end
+        msg = r"end must be numeric or datetime-like, got \(0, 1\]"
+        with pytest.raises(ValueError, match=msg):
+            interval_range(end=Interval(0, 1), periods=10)
+
+        # invalid freq for datetime-like
+        msg = "freq must be numeric or convertible to DateOffset, got foo"
+        with pytest.raises(ValueError, match=msg):
+            interval_range(start=0, end=10, freq="foo")
+
+        with pytest.raises(ValueError, match=msg):
+            interval_range(start=Timestamp("20130101"), periods=10, freq="foo")
+
+        with pytest.raises(ValueError, match=msg):
+            interval_range(end=Timedelta("1 day"), periods=10, freq="foo")
+
+        # mixed tz
+        start = Timestamp("2017-01-01", tz="US/Eastern")
+        end = Timestamp("2017-01-07", tz="US/Pacific")
+        msg = "Start and end cannot both be tz-aware with different timezones"
+        with pytest.raises(TypeError, match=msg):
+            interval_range(start=start, end=end)
+
+    def test_float_freq(self):
+        # GH 54477
+        result = interval_range(0, 1, freq=0.1)
+        expected = IntervalIndex.from_breaks([0 + 0.1 * n for n in range(11)])
+        tm.assert_index_equal(result, expected)
+
+        result = interval_range(0, 1, freq=0.6)
+        expected = IntervalIndex.from_breaks([0, 0.6])
+        tm.assert_index_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_interval_tree.py

@@ -0,0 +1,209 @@
+from itertools import permutations
+
+import numpy as np
+import pytest
+
+from pandas._libs.interval import IntervalTree
+from pandas.compat import IS64
+
+import pandas._testing as tm
+
+
+def skipif_32bit(param):
+    """
+    Skip parameters in a parametrize on 32bit systems. Specifically used
+    here to skip leaf_size parameters related to GH 23440.
+    """
+    marks = pytest.mark.skipif(not IS64, reason="GH 23440: int type mismatch on 32bit")
+    return pytest.param(param, marks=marks)
+
+
+@pytest.fixture(params=["int64", "float64", "uint64"])
+def dtype(request):
+    return request.param
+
+
+@pytest.fixture(params=[skipif_32bit(1), skipif_32bit(2), 10])
+def leaf_size(request):
+    """
+    Fixture to specify IntervalTree leaf_size parameter; to be used with the
+    tree fixture.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        np.arange(5, dtype="int64"),
+        np.arange(5, dtype="uint64"),
+        np.arange(5, dtype="float64"),
+        np.array([0, 1, 2, 3, 4, np.nan], dtype="float64"),
+    ]
+)
+def tree(request, leaf_size):
+    left = request.param
+    return IntervalTree(left, left + 2, leaf_size=leaf_size)
+
+
+class TestIntervalTree:
+    def test_get_indexer(self, tree):
+        result = tree.get_indexer(np.array([1.0, 5.5, 6.5]))
+        expected = np.array([0, 4, -1], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+        with pytest.raises(
+            KeyError, match="'indexer does not intersect a unique set of intervals'"
+        ):
+            tree.get_indexer(np.array([3.0]))
+
+    @pytest.mark.parametrize(
+        "dtype, target_value, target_dtype",
+        [("int64", 2**63 + 1, "uint64"), ("uint64", -1, "int64")],
+    )
+    def test_get_indexer_overflow(self, dtype, target_value, target_dtype):
+        left, right = np.array([0, 1], dtype=dtype), np.array([1, 2], dtype=dtype)
+        tree = IntervalTree(left, right)
+
+        result = tree.get_indexer(np.array([target_value], dtype=target_dtype))
+        expected = np.array([-1], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_get_indexer_non_unique(self, tree):
+        indexer, missing = tree.get_indexer_non_unique(np.array([1.0, 2.0, 6.5]))
+
+        result = indexer[:1]
+        expected = np.array([0], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.sort(indexer[1:3])
+        expected = np.array([0, 1], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.sort(indexer[3:])
+        expected = np.array([-1], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = missing
+        expected = np.array([2], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype, target_value, target_dtype",
+        [("int64", 2**63 + 1, "uint64"), ("uint64", -1, "int64")],
+    )
+    def test_get_indexer_non_unique_overflow(self, dtype, target_value, target_dtype):
+        left, right = np.array([0, 2], dtype=dtype), np.array([1, 3], dtype=dtype)
+        tree = IntervalTree(left, right)
+        target = np.array([target_value], dtype=target_dtype)
+
+        result_indexer, result_missing = tree.get_indexer_non_unique(target)
+        expected_indexer = np.array([-1], dtype="intp")
+        tm.assert_numpy_array_equal(result_indexer, expected_indexer)
+
+        expected_missing = np.array([0], dtype="intp")
+        tm.assert_numpy_array_equal(result_missing, expected_missing)
+
+    def test_duplicates(self, dtype):
+        left = np.array([0, 0, 0], dtype=dtype)
+        tree = IntervalTree(left, left + 1)
+
+        with pytest.raises(
+            KeyError, match="'indexer does not intersect a unique set of intervals'"
+        ):
+            tree.get_indexer(np.array([0.5]))
+
+        indexer, missing = tree.get_indexer_non_unique(np.array([0.5]))
+        result = np.sort(indexer)
+        expected = np.array([0, 1, 2], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = missing
+        expected = np.array([], dtype="intp")
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "leaf_size", [skipif_32bit(1), skipif_32bit(10), skipif_32bit(100), 10000]
+    )
+    def test_get_indexer_closed(self, closed, leaf_size):
+        x = np.arange(1000, dtype="float64")
+        found = x.astype("intp")
+        not_found = (-1 * np.ones(1000)).astype("intp")
+
+        tree = IntervalTree(x, x + 0.5, closed=closed, leaf_size=leaf_size)
+        tm.assert_numpy_array_equal(found, tree.get_indexer(x + 0.25))
+
+        expected = found if tree.closed_left else not_found
+        tm.assert_numpy_array_equal(expected, tree.get_indexer(x + 0.0))
+
+        expected = found if tree.closed_right else not_found
+        tm.assert_numpy_array_equal(expected, tree.get_indexer(x + 0.5))
+
+    @pytest.mark.parametrize(
+        "left, right, expected",
+        [
+            (np.array([0, 1, 4], dtype="int64"), np.array([2, 3, 5]), True),
+            (np.array([0, 1, 2], dtype="int64"), np.array([5, 4, 3]), True),
+            (np.array([0, 1, np.nan]), np.array([5, 4, np.nan]), True),
+            (np.array([0, 2, 4], dtype="int64"), np.array([1, 3, 5]), False),
+            (np.array([0, 2, np.nan]), np.array([1, 3, np.nan]), False),
+        ],
+    )
+    @pytest.mark.parametrize("order", (list(x) for x in permutations(range(3))))
+    def test_is_overlapping(self, closed, order, left, right, expected):
+        # GH 23309
+        tree = IntervalTree(left[order], right[order], closed=closed)
+        result = tree.is_overlapping
+        assert result is expected
+
+    @pytest.mark.parametrize("order", (list(x) for x in permutations(range(3))))
+    def test_is_overlapping_endpoints(self, closed, order):
+        """shared endpoints are marked as overlapping"""
+        # GH 23309
+        left, right = np.arange(3, dtype="int64"), np.arange(1, 4)
+        tree = IntervalTree(left[order], right[order], closed=closed)
+        result = tree.is_overlapping
+        expected = closed == "both"
+        assert result is expected
+
+    @pytest.mark.parametrize(
+        "left, right",
+        [
+            (np.array([], dtype="int64"), np.array([], dtype="int64")),
+            (np.array([0], dtype="int64"), np.array([1], dtype="int64")),
+            (np.array([np.nan]), np.array([np.nan])),
+            (np.array([np.nan] * 3), np.array([np.nan] * 3)),
+        ],
+    )
+    def test_is_overlapping_trivial(self, closed, left, right):
+        # GH 23309
+        tree = IntervalTree(left, right, closed=closed)
+        assert tree.is_overlapping is False
+
+    @pytest.mark.skipif(not IS64, reason="GH 23440")
+    def test_construction_overflow(self):
+        # GH 25485
+        left, right = np.arange(101, dtype="int64"), [np.iinfo(np.int64).max] * 101
+        tree = IntervalTree(left, right)
+
+        # pivot should be average of left/right medians
+        result = tree.root.pivot
+        expected = (50 + np.iinfo(np.int64).max) / 2
+        assert result == expected
+
+    @pytest.mark.xfail(not IS64, reason="GH 23440")
+    @pytest.mark.parametrize(
+        "left, right, expected",
+        [
+            ([-np.inf, 1.0], [1.0, 2.0], 0.0),
+            ([-np.inf, -2.0], [-2.0, -1.0], -2.0),
+            ([-2.0, -1.0], [-1.0, np.inf], 0.0),
+            ([1.0, 2.0], [2.0, np.inf], 2.0),
+        ],
+    )
+    def test_inf_bound_infinite_recursion(self, left, right, expected):
+        # GH 46658
+
+        tree = IntervalTree(left * 101, right * 101)
+
+        result = tree.root.pivot
+        assert result == expected

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_join.py

@@ -0,0 +1,44 @@
+import pytest
+
+from pandas import (
+    IntervalIndex,
+    MultiIndex,
+    RangeIndex,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture
+def range_index():
+    return RangeIndex(3, name="range_index")
+
+
+@pytest.fixture
+def interval_index():
+    return IntervalIndex.from_tuples(
+        [(0.0, 1.0), (1.0, 2.0), (1.5, 2.5)], name="interval_index"
+    )
+
+
+def test_join_overlapping_in_mi_to_same_intervalindex(range_index, interval_index):
+    #  GH-45661
+    multi_index = MultiIndex.from_product([interval_index, range_index])
+    result = multi_index.join(interval_index)
+
+    tm.assert_index_equal(result, multi_index)
+
+
+def test_join_overlapping_to_multiindex_with_same_interval(range_index, interval_index):
+    #  GH-45661
+    multi_index = MultiIndex.from_product([interval_index, range_index])
+    result = interval_index.join(multi_index)
+
+    tm.assert_index_equal(result, multi_index)
+
+
+def test_join_overlapping_interval_to_another_intervalindex(interval_index):
+    #  GH-45661
+    flipped_interval_index = interval_index[::-1]
+    result = interval_index.join(flipped_interval_index)
+
+    tm.assert_index_equal(result, interval_index)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_analytics.py

@@ -0,0 +1,263 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+def test_infer_objects(idx):
+    with pytest.raises(NotImplementedError, match="to_frame"):
+        idx.infer_objects()
+
+
+def test_shift(idx):
+    # GH8083 test the base class for shift
+    msg = (
+        "This method is only implemented for DatetimeIndex, PeriodIndex and "
+        "TimedeltaIndex; Got type MultiIndex"
+    )
+    with pytest.raises(NotImplementedError, match=msg):
+        idx.shift(1)
+    with pytest.raises(NotImplementedError, match=msg):
+        idx.shift(1, 2)
+
+
+def test_groupby(idx):
+    groups = idx.groupby(np.array([1, 1, 1, 2, 2, 2]))
+    labels = idx.tolist()
+    exp = {1: labels[:3], 2: labels[3:]}
+    tm.assert_dict_equal(groups, exp)
+
+    # GH5620
+    groups = idx.groupby(idx)
+    exp = {key: [key] for key in idx}
+    tm.assert_dict_equal(groups, exp)
+
+
+def test_truncate_multiindex():
+    # GH 34564 for MultiIndex level names check
+    major_axis = Index(list(range(4)))
+    minor_axis = Index(list(range(2)))
+
+    major_codes = np.array([0, 0, 1, 2, 3, 3])
+    minor_codes = np.array([0, 1, 0, 1, 0, 1])
+
+    index = MultiIndex(
+        levels=[major_axis, minor_axis],
+        codes=[major_codes, minor_codes],
+        names=["L1", "L2"],
+    )
+
+    result = index.truncate(before=1)
+    assert "foo" not in result.levels[0]
+    assert 1 in result.levels[0]
+    assert index.names == result.names
+
+    result = index.truncate(after=1)
+    assert 2 not in result.levels[0]
+    assert 1 in result.levels[0]
+    assert index.names == result.names
+
+    result = index.truncate(before=1, after=2)
+    assert len(result.levels[0]) == 2
+    assert index.names == result.names
+
+    msg = "after < before"
+    with pytest.raises(ValueError, match=msg):
+        index.truncate(3, 1)
+
+
+# TODO: reshape
+
+
+def test_reorder_levels(idx):
+    # this blows up
+    with pytest.raises(IndexError, match="^Too many levels"):
+        idx.reorder_levels([2, 1, 0])
+
+
+def test_numpy_repeat():
+    reps = 2
+    numbers = [1, 2, 3]
+    names = np.array(["foo", "bar"])
+
+    m = MultiIndex.from_product([numbers, names], names=names)
+    expected = MultiIndex.from_product([numbers, names.repeat(reps)], names=names)
+    tm.assert_index_equal(np.repeat(m, reps), expected)
+
+    msg = "the 'axis' parameter is not supported"
+    with pytest.raises(ValueError, match=msg):
+        np.repeat(m, reps, axis=1)
+
+
+def test_append_mixed_dtypes():
+    # GH 13660
+    dti = date_range("2011-01-01", freq="ME", periods=3)
+    dti_tz = date_range("2011-01-01", freq="ME", periods=3, tz="US/Eastern")
+    pi = period_range("2011-01", freq="M", periods=3)
+
+    mi = MultiIndex.from_arrays(
+        [[1, 2, 3], [1.1, np.nan, 3.3], ["a", "b", "c"], dti, dti_tz, pi]
+    )
+    assert mi.nlevels == 6
+
+    res = mi.append(mi)
+    exp = MultiIndex.from_arrays(
+        [
+            [1, 2, 3, 1, 2, 3],
+            [1.1, np.nan, 3.3, 1.1, np.nan, 3.3],
+            ["a", "b", "c", "a", "b", "c"],
+            dti.append(dti),
+            dti_tz.append(dti_tz),
+            pi.append(pi),
+        ]
+    )
+    tm.assert_index_equal(res, exp)
+
+    other = MultiIndex.from_arrays(
+        [
+            ["x", "y", "z"],
+            ["x", "y", "z"],
+            ["x", "y", "z"],
+            ["x", "y", "z"],
+            ["x", "y", "z"],
+            ["x", "y", "z"],
+        ]
+    )
+
+    res = mi.append(other)
+    exp = MultiIndex.from_arrays(
+        [
+            [1, 2, 3, "x", "y", "z"],
+            [1.1, np.nan, 3.3, "x", "y", "z"],
+            ["a", "b", "c", "x", "y", "z"],
+            dti.append(Index(["x", "y", "z"])),
+            dti_tz.append(Index(["x", "y", "z"])),
+            pi.append(Index(["x", "y", "z"])),
+        ]
+    )
+    tm.assert_index_equal(res, exp)
+
+
+def test_iter(idx):
+    result = list(idx)
+    expected = [
+        ("foo", "one"),
+        ("foo", "two"),
+        ("bar", "one"),
+        ("baz", "two"),
+        ("qux", "one"),
+        ("qux", "two"),
+    ]
+    assert result == expected
+
+
+def test_sub(idx):
+    first = idx
+
+    # - now raises (previously was set op difference)
+    msg = "cannot perform __sub__ with this index type: MultiIndex"
+    with pytest.raises(TypeError, match=msg):
+        first - idx[-3:]
+    with pytest.raises(TypeError, match=msg):
+        idx[-3:] - first
+    with pytest.raises(TypeError, match=msg):
+        idx[-3:] - first.tolist()
+    msg = "cannot perform __rsub__ with this index type: MultiIndex"
+    with pytest.raises(TypeError, match=msg):
+        first.tolist() - idx[-3:]
+
+
+def test_map(idx):
+    # callable
+    index = idx
+
+    result = index.map(lambda x: x)
+    tm.assert_index_equal(result, index)
+
+
+@pytest.mark.parametrize(
+    "mapper",
+    [
+        lambda values, idx: {i: e for e, i in zip(values, idx)},
+        lambda values, idx: pd.Series(values, idx),
+    ],
+)
+def test_map_dictlike(idx, mapper):
+    identity = mapper(idx.values, idx)
+
+    # we don't infer to uint64 dtype for a dict
+    if idx.dtype == np.uint64 and isinstance(identity, dict):
+        expected = idx.astype("int64")
+    else:
+        expected = idx
+
+    result = idx.map(identity)
+    tm.assert_index_equal(result, expected)
+
+    # empty mappable
+    expected = Index([np.nan] * len(idx))
+    result = idx.map(mapper(expected, idx))
+    tm.assert_index_equal(result, expected)
+
+
+@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 func: func.__name__,
+)
+def test_numpy_ufuncs(idx, func):
+    # test ufuncs of numpy. see:
+    # https://numpy.org/doc/stable/reference/ufuncs.html
+
+    expected_exception = TypeError
+    msg = (
+        "loop of ufunc does not support argument 0 of type tuple which "
+        f"has no callable {func.__name__} method"
+    )
+    with pytest.raises(expected_exception, match=msg):
+        func(idx)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [np.isfinite, np.isinf, np.isnan, np.signbit],
+    ids=lambda func: func.__name__,
+)
+def test_numpy_type_funcs(idx, func):
+    msg = (
+        f"ufunc '{func.__name__}' not supported for the input types, and the inputs "
+        "could not be safely coerced to any supported types according to "
+        "the casting rule ''safe''"
+    )
+    with pytest.raises(TypeError, match=msg):
+        func(idx)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_compat.py

@@ -0,0 +1,122 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import MultiIndex
+import pandas._testing as tm
+
+
+def test_numeric_compat(idx):
+    with pytest.raises(TypeError, match="cannot perform __mul__"):
+        idx * 1
+
+    with pytest.raises(TypeError, match="cannot perform __rmul__"):
+        1 * idx
+
+    div_err = "cannot perform __truediv__"
+    with pytest.raises(TypeError, match=div_err):
+        idx / 1
+
+    div_err = div_err.replace(" __", " __r")
+    with pytest.raises(TypeError, match=div_err):
+        1 / idx
+
+    with pytest.raises(TypeError, match="cannot perform __floordiv__"):
+        idx // 1
+
+    with pytest.raises(TypeError, match="cannot perform __rfloordiv__"):
+        1 // idx
+
+
+@pytest.mark.parametrize("method", ["all", "any", "__invert__"])
+def test_logical_compat(idx, method):
+    msg = f"cannot perform {method}"
+
+    with pytest.raises(TypeError, match=msg):
+        getattr(idx, method)()
+
+
+def test_inplace_mutation_resets_values():
+    levels = [["a", "b", "c"], [4]]
+    levels2 = [[1, 2, 3], ["a"]]
+    codes = [[0, 1, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0]]
+
+    mi1 = MultiIndex(levels=levels, codes=codes)
+    mi2 = MultiIndex(levels=levels2, codes=codes)
+
+    # instantiating MultiIndex should not access/cache _.values
+    assert "_values" not in mi1._cache
+    assert "_values" not in mi2._cache
+
+    vals = mi1.values.copy()
+    vals2 = mi2.values.copy()
+
+    # accessing .values should cache ._values
+    assert mi1._values is mi1._cache["_values"]
+    assert mi1.values is mi1._cache["_values"]
+    assert isinstance(mi1._cache["_values"], np.ndarray)
+
+    # Make sure level setting works
+    new_vals = mi1.set_levels(levels2).values
+    tm.assert_almost_equal(vals2, new_vals)
+
+    #  Doesn't drop _values from _cache [implementation detail]
+    tm.assert_almost_equal(mi1._cache["_values"], vals)
+
+    # ...and values is still same too
+    tm.assert_almost_equal(mi1.values, vals)
+
+    # Make sure label setting works too
+    codes2 = [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
+    exp_values = np.empty((6,), dtype=object)
+    exp_values[:] = [(1, "a")] * 6
+
+    # Must be 1d array of tuples
+    assert exp_values.shape == (6,)
+
+    new_mi = mi2.set_codes(codes2)
+    assert "_values" not in new_mi._cache
+    new_values = new_mi.values
+    assert "_values" in new_mi._cache
+
+    # Shouldn't change cache
+    tm.assert_almost_equal(mi2._cache["_values"], vals2)
+
+    # Should have correct values
+    tm.assert_almost_equal(exp_values, new_values)
+
+
+def test_boxable_categorical_values():
+    cat = pd.Categorical(pd.date_range("2012-01-01", periods=3, freq="h"))
+    result = MultiIndex.from_product([["a", "b", "c"], cat]).values
+    expected = pd.Series(
+        [
+            ("a", pd.Timestamp("2012-01-01 00:00:00")),
+            ("a", pd.Timestamp("2012-01-01 01:00:00")),
+            ("a", pd.Timestamp("2012-01-01 02:00:00")),
+            ("b", pd.Timestamp("2012-01-01 00:00:00")),
+            ("b", pd.Timestamp("2012-01-01 01:00:00")),
+            ("b", pd.Timestamp("2012-01-01 02:00:00")),
+            ("c", pd.Timestamp("2012-01-01 00:00:00")),
+            ("c", pd.Timestamp("2012-01-01 01:00:00")),
+            ("c", pd.Timestamp("2012-01-01 02:00:00")),
+        ]
+    ).values
+    tm.assert_numpy_array_equal(result, expected)
+    result = pd.DataFrame({"a": ["a", "b", "c"], "b": cat, "c": np.array(cat)}).values
+    expected = pd.DataFrame(
+        {
+            "a": ["a", "b", "c"],
+            "b": [
+                pd.Timestamp("2012-01-01 00:00:00"),
+                pd.Timestamp("2012-01-01 01:00:00"),
+                pd.Timestamp("2012-01-01 02:00:00"),
+            ],
+            "c": [
+                pd.Timestamp("2012-01-01 00:00:00"),
+                pd.Timestamp("2012-01-01 01:00:00"),
+                pd.Timestamp("2012-01-01 02:00:00"),
+            ],
+        }
+    ).values
+    tm.assert_numpy_array_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_drop.py

@@ -0,0 +1,190 @@
+import numpy as np
+import pytest
+
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+def test_drop(idx):
+    dropped = idx.drop([("foo", "two"), ("qux", "one")])
+
+    index = MultiIndex.from_tuples([("foo", "two"), ("qux", "one")])
+    dropped2 = idx.drop(index)
+
+    expected = idx[[0, 2, 3, 5]]
+    tm.assert_index_equal(dropped, expected)
+    tm.assert_index_equal(dropped2, expected)
+
+    dropped = idx.drop(["bar"])
+    expected = idx[[0, 1, 3, 4, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    dropped = idx.drop("foo")
+    expected = idx[[2, 3, 4, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    index = MultiIndex.from_tuples([("bar", "two")])
+    with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"):
+        idx.drop([("bar", "two")])
+    with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"):
+        idx.drop(index)
+    with pytest.raises(KeyError, match=r"^'two'$"):
+        idx.drop(["foo", "two"])
+
+    # partially correct argument
+    mixed_index = MultiIndex.from_tuples([("qux", "one"), ("bar", "two")])
+    with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"):
+        idx.drop(mixed_index)
+
+    # error='ignore'
+    dropped = idx.drop(index, errors="ignore")
+    expected = idx[[0, 1, 2, 3, 4, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    dropped = idx.drop(mixed_index, errors="ignore")
+    expected = idx[[0, 1, 2, 3, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    dropped = idx.drop(["foo", "two"], errors="ignore")
+    expected = idx[[2, 3, 4, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    # mixed partial / full drop
+    dropped = idx.drop(["foo", ("qux", "one")])
+    expected = idx[[2, 3, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+    # mixed partial / full drop / error='ignore'
+    mixed_index = ["foo", ("qux", "one"), "two"]
+    with pytest.raises(KeyError, match=r"^'two'$"):
+        idx.drop(mixed_index)
+    dropped = idx.drop(mixed_index, errors="ignore")
+    expected = idx[[2, 3, 5]]
+    tm.assert_index_equal(dropped, expected)
+
+
+def test_droplevel_with_names(idx):
+    index = idx[idx.get_loc("foo")]
+    dropped = index.droplevel(0)
+    assert dropped.name == "second"
+
+    index = MultiIndex(
+        levels=[Index(range(4)), Index(range(4)), Index(range(4))],
+        codes=[
+            np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+            np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+            np.array([1, 0, 1, 1, 0, 0, 1, 0]),
+        ],
+        names=["one", "two", "three"],
+    )
+    dropped = index.droplevel(0)
+    assert dropped.names == ("two", "three")
+
+    dropped = index.droplevel("two")
+    expected = index.droplevel(1)
+    assert dropped.equals(expected)
+
+
+def test_droplevel_list():
+    index = MultiIndex(
+        levels=[Index(range(4)), Index(range(4)), Index(range(4))],
+        codes=[
+            np.array([0, 0, 1, 2, 2, 2, 3, 3]),
+            np.array([0, 1, 0, 0, 0, 1, 0, 1]),
+            np.array([1, 0, 1, 1, 0, 0, 1, 0]),
+        ],
+        names=["one", "two", "three"],
+    )
+
+    dropped = index[:2].droplevel(["three", "one"])
+    expected = index[:2].droplevel(2).droplevel(0)
+    assert dropped.equals(expected)
+
+    dropped = index[:2].droplevel([])
+    expected = index[:2]
+    assert dropped.equals(expected)
+
+    msg = (
+        "Cannot remove 3 levels from an index with 3 levels: "
+        "at least one level must be left"
+    )
+    with pytest.raises(ValueError, match=msg):
+        index[:2].droplevel(["one", "two", "three"])
+
+    with pytest.raises(KeyError, match="'Level four not found'"):
+        index[:2].droplevel(["one", "four"])
+
+
+def test_drop_not_lexsorted():
+    # GH 12078
+
+    # define the lexsorted version of the multi-index
+    tuples = [("a", ""), ("b1", "c1"), ("b2", "c2")]
+    lexsorted_mi = MultiIndex.from_tuples(tuples, names=["b", "c"])
+    assert lexsorted_mi._is_lexsorted()
+
+    # and the not-lexsorted version
+    df = pd.DataFrame(
+        columns=["a", "b", "c", "d"], data=[[1, "b1", "c1", 3], [1, "b2", "c2", 4]]
+    )
+    df = df.pivot_table(index="a", columns=["b", "c"], values="d")
+    df = df.reset_index()
+    not_lexsorted_mi = df.columns
+    assert not not_lexsorted_mi._is_lexsorted()
+
+    # compare the results
+    tm.assert_index_equal(lexsorted_mi, not_lexsorted_mi)
+    with tm.assert_produces_warning(PerformanceWarning):
+        tm.assert_index_equal(lexsorted_mi.drop("a"), not_lexsorted_mi.drop("a"))
+
+
+def test_drop_with_nan_in_index(nulls_fixture):
+    # GH#18853
+    mi = MultiIndex.from_tuples([("blah", nulls_fixture)], names=["name", "date"])
+    msg = r"labels \[Timestamp\('2001-01-01 00:00:00'\)\] not found in level"
+    with pytest.raises(KeyError, match=msg):
+        mi.drop(pd.Timestamp("2001"), level="date")
+
+
+@pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning")
+def test_drop_with_non_monotonic_duplicates():
+    # GH#33494
+    mi = MultiIndex.from_tuples([(1, 2), (2, 3), (1, 2)])
+    result = mi.drop((1, 2))
+    expected = MultiIndex.from_tuples([(2, 3)])
+    tm.assert_index_equal(result, expected)
+
+
+def test_single_level_drop_partially_missing_elements():
+    # GH 37820
+
+    mi = MultiIndex.from_tuples([(1, 2), (2, 2), (3, 2)])
+    msg = r"labels \[4\] not found in level"
+    with pytest.raises(KeyError, match=msg):
+        mi.drop(4, level=0)
+    with pytest.raises(KeyError, match=msg):
+        mi.drop([1, 4], level=0)
+    msg = r"labels \[nan\] not found in level"
+    with pytest.raises(KeyError, match=msg):
+        mi.drop([np.nan], level=0)
+    with pytest.raises(KeyError, match=msg):
+        mi.drop([np.nan, 1, 2, 3], level=0)
+
+    mi = MultiIndex.from_tuples([(np.nan, 1), (1, 2)])
+    msg = r"labels \['a'\] not found in level"
+    with pytest.raises(KeyError, match=msg):
+        mi.drop([np.nan, 1, "a"], level=0)
+
+
+def test_droplevel_multiindex_one_level():
+    # GH#37208
+    index = MultiIndex.from_tuples([(2,)], names=("b",))
+    result = index.droplevel([])
+    expected = Index([2], name="b")
+    tm.assert_index_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_duplicates.py

@@ -0,0 +1,363 @@
+from itertools import product
+
+import numpy as np
+import pytest
+
+from pandas._libs import (
+    hashtable,
+    index as libindex,
+)
+
+from pandas import (
+    NA,
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture
+def idx_dup():
+    # compare tests/indexes/multi/conftest.py
+    major_axis = Index(["foo", "bar", "baz", "qux"])
+    minor_axis = Index(["one", "two"])
+
+    major_codes = np.array([0, 0, 1, 0, 1, 1])
+    minor_codes = np.array([0, 1, 0, 1, 0, 1])
+    index_names = ["first", "second"]
+    mi = MultiIndex(
+        levels=[major_axis, minor_axis],
+        codes=[major_codes, minor_codes],
+        names=index_names,
+        verify_integrity=False,
+    )
+    return mi
+
+
+@pytest.mark.parametrize("names", [None, ["first", "second"]])
+def test_unique(names):
+    mi = MultiIndex.from_arrays([[1, 2, 1, 2], [1, 1, 1, 2]], names=names)
+
+    res = mi.unique()
+    exp = MultiIndex.from_arrays([[1, 2, 2], [1, 1, 2]], names=mi.names)
+    tm.assert_index_equal(res, exp)
+
+    mi = MultiIndex.from_arrays([list("aaaa"), list("abab")], names=names)
+    res = mi.unique()
+    exp = MultiIndex.from_arrays([list("aa"), list("ab")], names=mi.names)
+    tm.assert_index_equal(res, exp)
+
+    mi = MultiIndex.from_arrays([list("aaaa"), list("aaaa")], names=names)
+    res = mi.unique()
+    exp = MultiIndex.from_arrays([["a"], ["a"]], names=mi.names)
+    tm.assert_index_equal(res, exp)
+
+    # GH #20568 - empty MI
+    mi = MultiIndex.from_arrays([[], []], names=names)
+    res = mi.unique()
+    tm.assert_index_equal(mi, res)
+
+
+def test_unique_datetimelike():
+    idx1 = DatetimeIndex(
+        ["2015-01-01", "2015-01-01", "2015-01-01", "2015-01-01", "NaT", "NaT"]
+    )
+    idx2 = DatetimeIndex(
+        ["2015-01-01", "2015-01-01", "2015-01-02", "2015-01-02", "NaT", "2015-01-01"],
+        tz="Asia/Tokyo",
+    )
+    result = MultiIndex.from_arrays([idx1, idx2]).unique()
+
+    eidx1 = DatetimeIndex(["2015-01-01", "2015-01-01", "NaT", "NaT"])
+    eidx2 = DatetimeIndex(
+        ["2015-01-01", "2015-01-02", "NaT", "2015-01-01"], tz="Asia/Tokyo"
+    )
+    exp = MultiIndex.from_arrays([eidx1, eidx2])
+    tm.assert_index_equal(result, exp)
+
+
+@pytest.mark.parametrize("level", [0, "first", 1, "second"])
+def test_unique_level(idx, level):
+    # GH #17896 - with level= argument
+    result = idx.unique(level=level)
+    expected = idx.get_level_values(level).unique()
+    tm.assert_index_equal(result, expected)
+
+    # With already unique level
+    mi = MultiIndex.from_arrays([[1, 3, 2, 4], [1, 3, 2, 5]], names=["first", "second"])
+    result = mi.unique(level=level)
+    expected = mi.get_level_values(level)
+    tm.assert_index_equal(result, expected)
+
+    # With empty MI
+    mi = MultiIndex.from_arrays([[], []], names=["first", "second"])
+    result = mi.unique(level=level)
+    expected = mi.get_level_values(level)
+    tm.assert_index_equal(result, expected)
+
+
+def test_duplicate_multiindex_codes():
+    # GH 17464
+    # Make sure that a MultiIndex with duplicate levels throws a ValueError
+    msg = r"Level values must be unique: \[[A', ]+\] on level 0"
+    with pytest.raises(ValueError, match=msg):
+        mi = MultiIndex([["A"] * 10, range(10)], [[0] * 10, range(10)])
+
+    # And that using set_levels with duplicate levels fails
+    mi = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]])
+    msg = r"Level values must be unique: \[[AB', ]+\] on level 0"
+    with pytest.raises(ValueError, match=msg):
+        mi.set_levels([["A", "B", "A", "A", "B"], [2, 1, 3, -2, 5]])
+
+
+@pytest.mark.parametrize("names", [["a", "b", "a"], [1, 1, 2], [1, "a", 1]])
+def test_duplicate_level_names(names):
+    # GH18872, GH19029
+    mi = MultiIndex.from_product([[0, 1]] * 3, names=names)
+    assert mi.names == names
+
+    # With .rename()
+    mi = MultiIndex.from_product([[0, 1]] * 3)
+    mi = mi.rename(names)
+    assert mi.names == names
+
+    # With .rename(., level=)
+    mi.rename(names[1], level=1, inplace=True)
+    mi = mi.rename([names[0], names[2]], level=[0, 2])
+    assert mi.names == names
+
+
+def test_duplicate_meta_data():
+    # GH 10115
+    mi = MultiIndex(
+        levels=[[0, 1], [0, 1, 2]], codes=[[0, 0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 0, 1, 2]]
+    )
+
+    for idx in [
+        mi,
+        mi.set_names([None, None]),
+        mi.set_names([None, "Num"]),
+        mi.set_names(["Upper", "Num"]),
+    ]:
+        assert idx.has_duplicates
+        assert idx.drop_duplicates().names == idx.names
+
+
+def test_has_duplicates(idx, idx_dup):
+    # see fixtures
+    assert idx.is_unique is True
+    assert idx.has_duplicates is False
+    assert idx_dup.is_unique is False
+    assert idx_dup.has_duplicates is True
+
+    mi = MultiIndex(
+        levels=[[0, 1], [0, 1, 2]], codes=[[0, 0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 0, 1, 2]]
+    )
+    assert mi.is_unique is False
+    assert mi.has_duplicates is True
+
+    # single instance of NaN
+    mi_nan = MultiIndex(
+        levels=[["a", "b"], [0, 1]], codes=[[-1, 0, 0, 1, 1], [-1, 0, 1, 0, 1]]
+    )
+    assert mi_nan.is_unique is True
+    assert mi_nan.has_duplicates is False
+
+    # multiple instances of NaN
+    mi_nan_dup = MultiIndex(
+        levels=[["a", "b"], [0, 1]], codes=[[-1, -1, 0, 0, 1, 1], [-1, -1, 0, 1, 0, 1]]
+    )
+    assert mi_nan_dup.is_unique is False
+    assert mi_nan_dup.has_duplicates is True
+
+
+def test_has_duplicates_from_tuples():
+    # GH 9075
+    t = [
+        ("x", "out", "z", 5, "y", "in", "z", 169),
+        ("x", "out", "z", 7, "y", "in", "z", 119),
+        ("x", "out", "z", 9, "y", "in", "z", 135),
+        ("x", "out", "z", 13, "y", "in", "z", 145),
+        ("x", "out", "z", 14, "y", "in", "z", 158),
+        ("x", "out", "z", 16, "y", "in", "z", 122),
+        ("x", "out", "z", 17, "y", "in", "z", 160),
+        ("x", "out", "z", 18, "y", "in", "z", 180),
+        ("x", "out", "z", 20, "y", "in", "z", 143),
+        ("x", "out", "z", 21, "y", "in", "z", 128),
+        ("x", "out", "z", 22, "y", "in", "z", 129),
+        ("x", "out", "z", 25, "y", "in", "z", 111),
+        ("x", "out", "z", 28, "y", "in", "z", 114),
+        ("x", "out", "z", 29, "y", "in", "z", 121),
+        ("x", "out", "z", 31, "y", "in", "z", 126),
+        ("x", "out", "z", 32, "y", "in", "z", 155),
+        ("x", "out", "z", 33, "y", "in", "z", 123),
+        ("x", "out", "z", 12, "y", "in", "z", 144),
+    ]
+
+    mi = MultiIndex.from_tuples(t)
+    assert not mi.has_duplicates
+
+
+@pytest.mark.parametrize("nlevels", [4, 8])
+@pytest.mark.parametrize("with_nulls", [True, False])
+def test_has_duplicates_overflow(nlevels, with_nulls):
+    # handle int64 overflow if possible
+    # no overflow with 4
+    # overflow possible with 8
+    codes = np.tile(np.arange(500), 2)
+    level = np.arange(500)
+
+    if with_nulls:  # inject some null values
+        codes[500] = -1  # common nan value
+        codes = [codes.copy() for i in range(nlevels)]
+        for i in range(nlevels):
+            codes[i][500 + i - nlevels // 2] = -1
+
+        codes += [np.array([-1, 1]).repeat(500)]
+    else:
+        codes = [codes] * nlevels + [np.arange(2).repeat(500)]
+
+    levels = [level] * nlevels + [[0, 1]]
+
+    # no dups
+    mi = MultiIndex(levels=levels, codes=codes)
+    assert not mi.has_duplicates
+
+    # with a dup
+    if with_nulls:
+
+        def f(a):
+            return np.insert(a, 1000, a[0])
+
+        codes = list(map(f, codes))
+        mi = MultiIndex(levels=levels, codes=codes)
+    else:
+        values = mi.values.tolist()
+        mi = MultiIndex.from_tuples(values + [values[0]])
+
+    assert mi.has_duplicates
+
+
+@pytest.mark.parametrize(
+    "keep, expected",
+    [
+        ("first", np.array([False, False, False, True, True, False])),
+        ("last", np.array([False, True, True, False, False, False])),
+        (False, np.array([False, True, True, True, True, False])),
+    ],
+)
+def test_duplicated(idx_dup, keep, expected):
+    result = idx_dup.duplicated(keep=keep)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.arm_slow
+def test_duplicated_hashtable_impl(keep, monkeypatch):
+    # GH 9125
+    n, k = 6, 10
+    levels = [np.arange(n), [str(i) for i in range(n)], 1000 + np.arange(n)]
+    codes = [np.random.default_rng(2).choice(n, k * n) for _ in levels]
+    with monkeypatch.context() as m:
+        m.setattr(libindex, "_SIZE_CUTOFF", 50)
+        mi = MultiIndex(levels=levels, codes=codes)
+
+        result = mi.duplicated(keep=keep)
+        expected = hashtable.duplicated(mi.values, keep=keep)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize("val", [101, 102])
+def test_duplicated_with_nan(val):
+    # GH5873
+    mi = MultiIndex.from_arrays([[101, val], [3.5, np.nan]])
+    assert not mi.has_duplicates
+
+    tm.assert_numpy_array_equal(mi.duplicated(), np.zeros(2, dtype="bool"))
+
+
+@pytest.mark.parametrize("n", range(1, 6))
+@pytest.mark.parametrize("m", range(1, 5))
+def test_duplicated_with_nan_multi_shape(n, m):
+    # GH5873
+    # all possible unique combinations, including nan
+    codes = product(range(-1, n), range(-1, m))
+    mi = MultiIndex(
+        levels=[list("abcde")[:n], list("WXYZ")[:m]],
+        codes=np.random.default_rng(2).permutation(list(codes)).T,
+    )
+    assert len(mi) == (n + 1) * (m + 1)
+    assert not mi.has_duplicates
+
+    tm.assert_numpy_array_equal(mi.duplicated(), np.zeros(len(mi), dtype="bool"))
+
+
+def test_duplicated_drop_duplicates():
+    # GH#4060
+    idx = MultiIndex.from_arrays(([1, 2, 3, 1, 2, 3], [1, 1, 1, 1, 2, 2]))
+
+    expected = np.array([False, False, False, True, False, False], dtype=bool)
+    duplicated = idx.duplicated()
+    tm.assert_numpy_array_equal(duplicated, expected)
+    assert duplicated.dtype == bool
+    expected = MultiIndex.from_arrays(([1, 2, 3, 2, 3], [1, 1, 1, 2, 2]))
+    tm.assert_index_equal(idx.drop_duplicates(), expected)
+
+    expected = np.array([True, False, False, False, False, False])
+    duplicated = idx.duplicated(keep="last")
+    tm.assert_numpy_array_equal(duplicated, expected)
+    assert duplicated.dtype == bool
+    expected = MultiIndex.from_arrays(([2, 3, 1, 2, 3], [1, 1, 1, 2, 2]))
+    tm.assert_index_equal(idx.drop_duplicates(keep="last"), expected)
+
+    expected = np.array([True, False, False, True, False, False])
+    duplicated = idx.duplicated(keep=False)
+    tm.assert_numpy_array_equal(duplicated, expected)
+    assert duplicated.dtype == bool
+    expected = MultiIndex.from_arrays(([2, 3, 2, 3], [1, 1, 2, 2]))
+    tm.assert_index_equal(idx.drop_duplicates(keep=False), expected)
+
+
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        np.complex64,
+        np.complex128,
+    ],
+)
+def test_duplicated_series_complex_numbers(dtype):
+    # GH 17927
+    expected = Series(
+        [False, False, False, True, False, False, False, True, False, True],
+        dtype=bool,
+    )
+    result = Series(
+        [
+            np.nan + np.nan * 1j,
+            0,
+            1j,
+            1j,
+            1,
+            1 + 1j,
+            1 + 2j,
+            1 + 1j,
+            np.nan,
+            np.nan + np.nan * 1j,
+        ],
+        dtype=dtype,
+    ).duplicated()
+    tm.assert_series_equal(result, expected)
+
+
+def test_midx_unique_ea_dtype():
+    # GH#48335
+    vals_a = Series([1, 2, NA, NA], dtype="Int64")
+    vals_b = np.array([1, 2, 3, 3])
+    midx = MultiIndex.from_arrays([vals_a, vals_b], names=["a", "b"])
+    result = midx.unique()
+
+    exp_vals_a = Series([1, 2, NA], dtype="Int64")
+    exp_vals_b = np.array([1, 2, 3])
+    expected = MultiIndex.from_arrays([exp_vals_a, exp_vals_b], names=["a", "b"])
+    tm.assert_index_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_formats.py

@@ -0,0 +1,249 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+def test_format(idx):
+    msg = "MultiIndex.format is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        idx.format()
+        idx[:0].format()
+
+
+def test_format_integer_names():
+    index = MultiIndex(
+        levels=[[0, 1], [0, 1]], codes=[[0, 0, 1, 1], [0, 1, 0, 1]], names=[0, 1]
+    )
+    msg = "MultiIndex.format is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        index.format(names=True)
+
+
+def test_format_sparse_config(idx):
+    # GH1538
+    msg = "MultiIndex.format is deprecated"
+    with pd.option_context("display.multi_sparse", False):
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = idx.format()
+    assert result[1] == "foo  two"
+
+
+def test_format_sparse_display():
+    index = MultiIndex(
+        levels=[[0, 1], [0, 1], [0, 1], [0]],
+        codes=[
+            [0, 0, 0, 1, 1, 1],
+            [0, 0, 1, 0, 0, 1],
+            [0, 1, 0, 0, 1, 0],
+            [0, 0, 0, 0, 0, 0],
+        ],
+    )
+    msg = "MultiIndex.format is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = index.format()
+    assert result[3] == "1  0  0  0"
+
+
+def test_repr_with_unicode_data():
+    with pd.option_context("display.encoding", "UTF-8"):
+        d = {"a": ["\u05d0", 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}
+        index = pd.DataFrame(d).set_index(["a", "b"]).index
+        assert "\\" not in repr(index)  # we don't want unicode-escaped
+
+
+def test_repr_roundtrip_raises():
+    mi = MultiIndex.from_product([list("ab"), range(3)], names=["first", "second"])
+    msg = "Must pass both levels and codes"
+    with pytest.raises(TypeError, match=msg):
+        eval(repr(mi))
+
+
+def test_unicode_string_with_unicode():
+    d = {"a": ["\u05d0", 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}
+    idx = pd.DataFrame(d).set_index(["a", "b"]).index
+    str(idx)
+
+
+def test_repr_max_seq_item_setting(idx):
+    # GH10182
+    idx = idx.repeat(50)
+    with pd.option_context("display.max_seq_items", None):
+        repr(idx)
+        assert "..." not in str(idx)
+
+
+class TestRepr:
+    def test_unicode_repr_issues(self):
+        levels = [Index(["a/\u03c3", "b/\u03c3", "c/\u03c3"]), Index([0, 1])]
+        codes = [np.arange(3).repeat(2), np.tile(np.arange(2), 3)]
+        index = MultiIndex(levels=levels, codes=codes)
+
+        repr(index.levels)
+        repr(index.get_level_values(1))
+
+    def test_repr_max_seq_items_equal_to_n(self, idx):
+        # display.max_seq_items == n
+        with pd.option_context("display.max_seq_items", 6):
+            result = idx.__repr__()
+            expected = """\
+MultiIndex([('foo', 'one'),
+            ('foo', 'two'),
+            ('bar', 'one'),
+            ('baz', 'two'),
+            ('qux', 'one'),
+            ('qux', 'two')],
+           names=['first', 'second'])"""
+            assert result == expected
+
+    def test_repr(self, idx):
+        result = idx[:1].__repr__()
+        expected = """\
+MultiIndex([('foo', 'one')],
+           names=['first', 'second'])"""
+        assert result == expected
+
+        result = idx.__repr__()
+        expected = """\
+MultiIndex([('foo', 'one'),
+            ('foo', 'two'),
+            ('bar', 'one'),
+            ('baz', 'two'),
+            ('qux', 'one'),
+            ('qux', 'two')],
+           names=['first', 'second'])"""
+        assert result == expected
+
+        with pd.option_context("display.max_seq_items", 5):
+            result = idx.__repr__()
+            expected = """\
+MultiIndex([('foo', 'one'),
+            ('foo', 'two'),
+            ...
+            ('qux', 'one'),
+            ('qux', 'two')],
+           names=['first', 'second'], length=6)"""
+            assert result == expected
+
+        # display.max_seq_items == 1
+        with pd.option_context("display.max_seq_items", 1):
+            result = idx.__repr__()
+            expected = """\
+MultiIndex([...
+            ('qux', 'two')],
+           names=['first', ...], length=6)"""
+            assert result == expected
+
+    def test_rjust(self):
+        n = 1000
+        ci = pd.CategoricalIndex(list("a" * n) + (["abc"] * n))
+        dti = pd.date_range("2000-01-01", freq="s", periods=n * 2)
+        mi = MultiIndex.from_arrays([ci, ci.codes + 9, dti], names=["a", "b", "dti"])
+        result = mi[:1].__repr__()
+        expected = """\
+MultiIndex([('a', 9, '2000-01-01 00:00:00')],
+           names=['a', 'b', 'dti'])"""
+        assert result == expected
+
+        result = mi[::500].__repr__()
+        expected = """\
+MultiIndex([(  'a',  9, '2000-01-01 00:00:00'),
+            (  'a',  9, '2000-01-01 00:08:20'),
+            ('abc', 10, '2000-01-01 00:16:40'),
+            ('abc', 10, '2000-01-01 00:25:00')],
+           names=['a', 'b', 'dti'])"""
+        assert result == expected
+
+        result = mi.__repr__()
+        expected = """\
+MultiIndex([(  'a',  9, '2000-01-01 00:00:00'),
+            (  'a',  9, '2000-01-01 00:00:01'),
+            (  'a',  9, '2000-01-01 00:00:02'),
+            (  'a',  9, '2000-01-01 00:00:03'),
+            (  'a',  9, '2000-01-01 00:00:04'),
+            (  'a',  9, '2000-01-01 00:00:05'),
+            (  'a',  9, '2000-01-01 00:00:06'),
+            (  'a',  9, '2000-01-01 00:00:07'),
+            (  'a',  9, '2000-01-01 00:00:08'),
+            (  'a',  9, '2000-01-01 00:00:09'),
+            ...
+            ('abc', 10, '2000-01-01 00:33:10'),
+            ('abc', 10, '2000-01-01 00:33:11'),
+            ('abc', 10, '2000-01-01 00:33:12'),
+            ('abc', 10, '2000-01-01 00:33:13'),
+            ('abc', 10, '2000-01-01 00:33:14'),
+            ('abc', 10, '2000-01-01 00:33:15'),
+            ('abc', 10, '2000-01-01 00:33:16'),
+            ('abc', 10, '2000-01-01 00:33:17'),
+            ('abc', 10, '2000-01-01 00:33:18'),
+            ('abc', 10, '2000-01-01 00:33:19')],
+           names=['a', 'b', 'dti'], length=2000)"""
+        assert result == expected
+
+    def test_tuple_width(self):
+        n = 1000
+        ci = pd.CategoricalIndex(list("a" * n) + (["abc"] * n))
+        dti = pd.date_range("2000-01-01", freq="s", periods=n * 2)
+        levels = [ci, ci.codes + 9, dti, dti, dti]
+        names = ["a", "b", "dti_1", "dti_2", "dti_3"]
+        mi = MultiIndex.from_arrays(levels, names=names)
+        result = mi[:1].__repr__()
+        expected = """MultiIndex([('a', 9, '2000-01-01 00:00:00', '2000-01-01 00:00:00', ...)],
+           names=['a', 'b', 'dti_1', 'dti_2', 'dti_3'])"""  # noqa: E501
+        assert result == expected
+
+        result = mi[:10].__repr__()
+        expected = """\
+MultiIndex([('a', 9, '2000-01-01 00:00:00', '2000-01-01 00:00:00', ...),
+            ('a', 9, '2000-01-01 00:00:01', '2000-01-01 00:00:01', ...),
+            ('a', 9, '2000-01-01 00:00:02', '2000-01-01 00:00:02', ...),
+            ('a', 9, '2000-01-01 00:00:03', '2000-01-01 00:00:03', ...),
+            ('a', 9, '2000-01-01 00:00:04', '2000-01-01 00:00:04', ...),
+            ('a', 9, '2000-01-01 00:00:05', '2000-01-01 00:00:05', ...),
+            ('a', 9, '2000-01-01 00:00:06', '2000-01-01 00:00:06', ...),
+            ('a', 9, '2000-01-01 00:00:07', '2000-01-01 00:00:07', ...),
+            ('a', 9, '2000-01-01 00:00:08', '2000-01-01 00:00:08', ...),
+            ('a', 9, '2000-01-01 00:00:09', '2000-01-01 00:00:09', ...)],
+           names=['a', 'b', 'dti_1', 'dti_2', 'dti_3'])"""
+        assert result == expected
+
+        result = mi.__repr__()
+        expected = """\
+MultiIndex([(  'a',  9, '2000-01-01 00:00:00', '2000-01-01 00:00:00', ...),
+            (  'a',  9, '2000-01-01 00:00:01', '2000-01-01 00:00:01', ...),
+            (  'a',  9, '2000-01-01 00:00:02', '2000-01-01 00:00:02', ...),
+            (  'a',  9, '2000-01-01 00:00:03', '2000-01-01 00:00:03', ...),
+            (  'a',  9, '2000-01-01 00:00:04', '2000-01-01 00:00:04', ...),
+            (  'a',  9, '2000-01-01 00:00:05', '2000-01-01 00:00:05', ...),
+            (  'a',  9, '2000-01-01 00:00:06', '2000-01-01 00:00:06', ...),
+            (  'a',  9, '2000-01-01 00:00:07', '2000-01-01 00:00:07', ...),
+            (  'a',  9, '2000-01-01 00:00:08', '2000-01-01 00:00:08', ...),
+            (  'a',  9, '2000-01-01 00:00:09', '2000-01-01 00:00:09', ...),
+            ...
+            ('abc', 10, '2000-01-01 00:33:10', '2000-01-01 00:33:10', ...),
+            ('abc', 10, '2000-01-01 00:33:11', '2000-01-01 00:33:11', ...),
+            ('abc', 10, '2000-01-01 00:33:12', '2000-01-01 00:33:12', ...),
+            ('abc', 10, '2000-01-01 00:33:13', '2000-01-01 00:33:13', ...),
+            ('abc', 10, '2000-01-01 00:33:14', '2000-01-01 00:33:14', ...),
+            ('abc', 10, '2000-01-01 00:33:15', '2000-01-01 00:33:15', ...),
+            ('abc', 10, '2000-01-01 00:33:16', '2000-01-01 00:33:16', ...),
+            ('abc', 10, '2000-01-01 00:33:17', '2000-01-01 00:33:17', ...),
+            ('abc', 10, '2000-01-01 00:33:18', '2000-01-01 00:33:18', ...),
+            ('abc', 10, '2000-01-01 00:33:19', '2000-01-01 00:33:19', ...)],
+           names=['a', 'b', 'dti_1', 'dti_2', 'dti_3'], length=2000)"""
+        assert result == expected
+
+    def test_multiindex_long_element(self):
+        # Non-regression test towards GH#52960
+        data = MultiIndex.from_tuples([("c" * 62,)])
+
+        expected = (
+            "MultiIndex([('cccccccccccccccccccccccccccccccccccccccc"
+            "cccccccccccccccccccccc',)],\n           )"
+        )
+        assert str(data) == expected

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_level_values.py

@@ -0,0 +1,124 @@
+import numpy as np
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    Index,
+    MultiIndex,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestGetLevelValues:
+    def test_get_level_values_box_datetime64(self):
+        dates = date_range("1/1/2000", periods=4)
+        levels = [dates, [0, 1]]
+        codes = [[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]]
+
+        index = MultiIndex(levels=levels, codes=codes)
+
+        assert isinstance(index.get_level_values(0)[0], Timestamp)
+
+
+def test_get_level_values(idx):
+    result = idx.get_level_values(0)
+    expected = Index(["foo", "foo", "bar", "baz", "qux", "qux"], name="first")
+    tm.assert_index_equal(result, expected)
+    assert result.name == "first"
+
+    result = idx.get_level_values("first")
+    expected = idx.get_level_values(0)
+    tm.assert_index_equal(result, expected)
+
+    # GH 10460
+    index = MultiIndex(
+        levels=[CategoricalIndex(["A", "B"]), CategoricalIndex([1, 2, 3])],
+        codes=[np.array([0, 0, 0, 1, 1, 1]), np.array([0, 1, 2, 0, 1, 2])],
+    )
+
+    exp = CategoricalIndex(["A", "A", "A", "B", "B", "B"])
+    tm.assert_index_equal(index.get_level_values(0), exp)
+    exp = CategoricalIndex([1, 2, 3, 1, 2, 3])
+    tm.assert_index_equal(index.get_level_values(1), exp)
+
+
+def test_get_level_values_all_na():
+    # GH#17924 when level entirely consists of nan
+    arrays = [[np.nan, np.nan, np.nan], ["a", np.nan, 1]]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.get_level_values(0)
+    expected = Index([np.nan, np.nan, np.nan], dtype=np.float64)
+    tm.assert_index_equal(result, expected)
+
+    result = index.get_level_values(1)
+    expected = Index(["a", np.nan, 1], dtype=object)
+    tm.assert_index_equal(result, expected)
+
+
+def test_get_level_values_int_with_na():
+    # GH#17924
+    arrays = [["a", "b", "b"], [1, np.nan, 2]]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.get_level_values(1)
+    expected = Index([1, np.nan, 2])
+    tm.assert_index_equal(result, expected)
+
+    arrays = [["a", "b", "b"], [np.nan, np.nan, 2]]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.get_level_values(1)
+    expected = Index([np.nan, np.nan, 2])
+    tm.assert_index_equal(result, expected)
+
+
+def test_get_level_values_na():
+    arrays = [[np.nan, np.nan, np.nan], ["a", np.nan, 1]]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.get_level_values(0)
+    expected = Index([np.nan, np.nan, np.nan])
+    tm.assert_index_equal(result, expected)
+
+    result = index.get_level_values(1)
+    expected = Index(["a", np.nan, 1])
+    tm.assert_index_equal(result, expected)
+
+    arrays = [["a", "b", "b"], pd.DatetimeIndex([0, 1, pd.NaT])]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.get_level_values(1)
+    expected = pd.DatetimeIndex([0, 1, pd.NaT])
+    tm.assert_index_equal(result, expected)
+
+    arrays = [[], []]
+    index = MultiIndex.from_arrays(arrays)
+    result = index.get_level_values(0)
+    expected = Index([], dtype=object)
+    tm.assert_index_equal(result, expected)
+
+
+def test_get_level_values_when_periods():
+    # GH33131. See also discussion in GH32669.
+    # This test can probably be removed when PeriodIndex._engine is removed.
+    from pandas import (
+        Period,
+        PeriodIndex,
+    )
+
+    idx = MultiIndex.from_arrays(
+        [PeriodIndex([Period("2019Q1"), Period("2019Q2")], name="b")]
+    )
+    idx2 = MultiIndex.from_arrays(
+        [idx._get_level_values(level) for level in range(idx.nlevels)]
+    )
+    assert all(x.is_monotonic_increasing for x in idx2.levels)
+
+
+def test_values_loses_freq_of_underlying_index():
+    # GH#49054
+    idx = pd.DatetimeIndex(date_range("20200101", periods=3, freq="BME"))
+    expected = idx.copy(deep=True)
+    idx2 = Index([1, 2, 3])
+    midx = MultiIndex(levels=[idx, idx2], codes=[[0, 1, 2], [0, 1, 2]])
+    midx.values
+    assert idx.freq is not None
+    tm.assert_index_equal(idx, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_isin.py

@@ -0,0 +1,103 @@
+import numpy as np
+import pytest
+
+from pandas import MultiIndex
+import pandas._testing as tm
+
+
+def test_isin_nan():
+    idx = MultiIndex.from_arrays([["foo", "bar"], [1.0, np.nan]])
+    tm.assert_numpy_array_equal(idx.isin([("bar", np.nan)]), np.array([False, True]))
+    tm.assert_numpy_array_equal(
+        idx.isin([("bar", float("nan"))]), np.array([False, True])
+    )
+
+
+def test_isin_missing(nulls_fixture):
+    # GH48905
+    mi1 = MultiIndex.from_tuples([(1, nulls_fixture)])
+    mi2 = MultiIndex.from_tuples([(1, 1), (1, 2)])
+    result = mi2.isin(mi1)
+    expected = np.array([False, False])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_isin():
+    values = [("foo", 2), ("bar", 3), ("quux", 4)]
+
+    idx = MultiIndex.from_arrays([["qux", "baz", "foo", "bar"], np.arange(4)])
+    result = idx.isin(values)
+    expected = np.array([False, False, True, True])
+    tm.assert_numpy_array_equal(result, expected)
+
+    # empty, return dtype bool
+    idx = MultiIndex.from_arrays([[], []])
+    result = idx.isin(values)
+    assert len(result) == 0
+    assert result.dtype == np.bool_
+
+
+def test_isin_level_kwarg():
+    idx = MultiIndex.from_arrays([["qux", "baz", "foo", "bar"], np.arange(4)])
+
+    vals_0 = ["foo", "bar", "quux"]
+    vals_1 = [2, 3, 10]
+
+    expected = np.array([False, False, True, True])
+    tm.assert_numpy_array_equal(expected, idx.isin(vals_0, level=0))
+    tm.assert_numpy_array_equal(expected, idx.isin(vals_0, level=-2))
+
+    tm.assert_numpy_array_equal(expected, idx.isin(vals_1, level=1))
+    tm.assert_numpy_array_equal(expected, idx.isin(vals_1, level=-1))
+
+    msg = "Too many levels: Index has only 2 levels, not 6"
+    with pytest.raises(IndexError, match=msg):
+        idx.isin(vals_0, level=5)
+    msg = "Too many levels: Index has only 2 levels, -5 is not a valid level number"
+    with pytest.raises(IndexError, match=msg):
+        idx.isin(vals_0, level=-5)
+
+    with pytest.raises(KeyError, match=r"'Level 1\.0 not found'"):
+        idx.isin(vals_0, level=1.0)
+    with pytest.raises(KeyError, match=r"'Level -1\.0 not found'"):
+        idx.isin(vals_1, level=-1.0)
+    with pytest.raises(KeyError, match="'Level A not found'"):
+        idx.isin(vals_1, level="A")
+
+    idx.names = ["A", "B"]
+    tm.assert_numpy_array_equal(expected, idx.isin(vals_0, level="A"))
+    tm.assert_numpy_array_equal(expected, idx.isin(vals_1, level="B"))
+
+    with pytest.raises(KeyError, match="'Level C not found'"):
+        idx.isin(vals_1, level="C")
+
+
+@pytest.mark.parametrize(
+    "labels,expected,level",
+    [
+        ([("b", np.nan)], np.array([False, False, True]), None),
+        ([np.nan, "a"], np.array([True, True, False]), 0),
+        (["d", np.nan], np.array([False, True, True]), 1),
+    ],
+)
+def test_isin_multi_index_with_missing_value(labels, expected, level):
+    # GH 19132
+    midx = MultiIndex.from_arrays([[np.nan, "a", "b"], ["c", "d", np.nan]])
+    result = midx.isin(labels, level=level)
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_isin_empty():
+    # GH#51599
+    midx = MultiIndex.from_arrays([[1, 2], [3, 4]])
+    result = midx.isin([])
+    expected = np.array([False, False])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_isin_generator():
+    # GH#52568
+    midx = MultiIndex.from_tuples([(1, 2)])
+    result = midx.isin(x for x in [(1, 2)])
+    expected = np.array([True])
+    tm.assert_numpy_array_equal(result, expected)

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_monotonic.py

@@ -0,0 +1,188 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    MultiIndex,
+)
+
+
+def test_is_monotonic_increasing_lexsorted(lexsorted_two_level_string_multiindex):
+    # string ordering
+    mi = lexsorted_two_level_string_multiindex
+    assert mi.is_monotonic_increasing is False
+    assert Index(mi.values).is_monotonic_increasing is False
+    assert mi._is_strictly_monotonic_increasing is False
+    assert Index(mi.values)._is_strictly_monotonic_increasing is False
+
+
+def test_is_monotonic_increasing():
+    i = MultiIndex.from_product([np.arange(10), np.arange(10)], names=["one", "two"])
+    assert i.is_monotonic_increasing is True
+    assert i._is_strictly_monotonic_increasing is True
+    assert Index(i.values).is_monotonic_increasing is True
+    assert i._is_strictly_monotonic_increasing is True
+
+    i = MultiIndex.from_product(
+        [np.arange(10, 0, -1), np.arange(10)], names=["one", "two"]
+    )
+    assert i.is_monotonic_increasing is False
+    assert i._is_strictly_monotonic_increasing is False
+    assert Index(i.values).is_monotonic_increasing is False
+    assert Index(i.values)._is_strictly_monotonic_increasing is False
+
+    i = MultiIndex.from_product(
+        [np.arange(10), np.arange(10, 0, -1)], names=["one", "two"]
+    )
+    assert i.is_monotonic_increasing is False
+    assert i._is_strictly_monotonic_increasing is False
+    assert Index(i.values).is_monotonic_increasing is False
+    assert Index(i.values)._is_strictly_monotonic_increasing is False
+
+    i = MultiIndex.from_product([[1.0, np.nan, 2.0], ["a", "b", "c"]])
+    assert i.is_monotonic_increasing is False
+    assert i._is_strictly_monotonic_increasing is False
+    assert Index(i.values).is_monotonic_increasing is False
+    assert Index(i.values)._is_strictly_monotonic_increasing is False
+
+    i = MultiIndex(
+        levels=[["bar", "baz", "foo", "qux"], ["mom", "next", "zenith"]],
+        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+        names=["first", "second"],
+    )
+    assert i.is_monotonic_increasing is True
+    assert Index(i.values).is_monotonic_increasing is True
+    assert i._is_strictly_monotonic_increasing is True
+    assert Index(i.values)._is_strictly_monotonic_increasing is True
+
+    # mixed levels, hits the TypeError
+    i = MultiIndex(
+        levels=[
+            [1, 2, 3, 4],
+            [
+                "gb00b03mlx29",
+                "lu0197800237",
+                "nl0000289783",
+                "nl0000289965",
+                "nl0000301109",
+            ],
+        ],
+        codes=[[0, 1, 1, 2, 2, 2, 3], [4, 2, 0, 0, 1, 3, -1]],
+        names=["household_id", "asset_id"],
+    )
+
+    assert i.is_monotonic_increasing is False
+    assert i._is_strictly_monotonic_increasing is False
+
+    # empty
+    i = MultiIndex.from_arrays([[], []])
+    assert i.is_monotonic_increasing is True
+    assert Index(i.values).is_monotonic_increasing is True
+    assert i._is_strictly_monotonic_increasing is True
+    assert Index(i.values)._is_strictly_monotonic_increasing is True
+
+
+def test_is_monotonic_decreasing():
+    i = MultiIndex.from_product(
+        [np.arange(9, -1, -1), np.arange(9, -1, -1)], names=["one", "two"]
+    )
+    assert i.is_monotonic_decreasing is True
+    assert i._is_strictly_monotonic_decreasing is True
+    assert Index(i.values).is_monotonic_decreasing is True
+    assert i._is_strictly_monotonic_decreasing is True
+
+    i = MultiIndex.from_product(
+        [np.arange(10), np.arange(10, 0, -1)], names=["one", "two"]
+    )
+    assert i.is_monotonic_decreasing is False
+    assert i._is_strictly_monotonic_decreasing is False
+    assert Index(i.values).is_monotonic_decreasing is False
+    assert Index(i.values)._is_strictly_monotonic_decreasing is False
+
+    i = MultiIndex.from_product(
+        [np.arange(10, 0, -1), np.arange(10)], names=["one", "two"]
+    )
+    assert i.is_monotonic_decreasing is False
+    assert i._is_strictly_monotonic_decreasing is False
+    assert Index(i.values).is_monotonic_decreasing is False
+    assert Index(i.values)._is_strictly_monotonic_decreasing is False
+
+    i = MultiIndex.from_product([[2.0, np.nan, 1.0], ["c", "b", "a"]])
+    assert i.is_monotonic_decreasing is False
+    assert i._is_strictly_monotonic_decreasing is False
+    assert Index(i.values).is_monotonic_decreasing is False
+    assert Index(i.values)._is_strictly_monotonic_decreasing is False
+
+    # string ordering
+    i = MultiIndex(
+        levels=[["qux", "foo", "baz", "bar"], ["three", "two", "one"]],
+        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+        names=["first", "second"],
+    )
+    assert i.is_monotonic_decreasing is False
+    assert Index(i.values).is_monotonic_decreasing is False
+    assert i._is_strictly_monotonic_decreasing is False
+    assert Index(i.values)._is_strictly_monotonic_decreasing is False
+
+    i = MultiIndex(
+        levels=[["qux", "foo", "baz", "bar"], ["zenith", "next", "mom"]],
+        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
+        names=["first", "second"],
+    )
+    assert i.is_monotonic_decreasing is True
+    assert Index(i.values).is_monotonic_decreasing is True
+    assert i._is_strictly_monotonic_decreasing is True
+    assert Index(i.values)._is_strictly_monotonic_decreasing is True
+
+    # mixed levels, hits the TypeError
+    i = MultiIndex(
+        levels=[
+            [4, 3, 2, 1],
+            [
+                "nl0000301109",
+                "nl0000289965",
+                "nl0000289783",
+                "lu0197800237",
+                "gb00b03mlx29",
+            ],
+        ],
+        codes=[[0, 1, 1, 2, 2, 2, 3], [4, 2, 0, 0, 1, 3, -1]],
+        names=["household_id", "asset_id"],
+    )
+
+    assert i.is_monotonic_decreasing is False
+    assert i._is_strictly_monotonic_decreasing is False
+
+    # empty
+    i = MultiIndex.from_arrays([[], []])
+    assert i.is_monotonic_decreasing is True
+    assert Index(i.values).is_monotonic_decreasing is True
+    assert i._is_strictly_monotonic_decreasing is True
+    assert Index(i.values)._is_strictly_monotonic_decreasing is True
+
+
+def test_is_strictly_monotonic_increasing():
+    idx = MultiIndex(
+        levels=[["bar", "baz"], ["mom", "next"]], codes=[[0, 0, 1, 1], [0, 0, 0, 1]]
+    )
+    assert idx.is_monotonic_increasing is True
+    assert idx._is_strictly_monotonic_increasing is False
+
+
+def test_is_strictly_monotonic_decreasing():
+    idx = MultiIndex(
+        levels=[["baz", "bar"], ["next", "mom"]], codes=[[0, 0, 1, 1], [0, 0, 0, 1]]
+    )
+    assert idx.is_monotonic_decreasing is True
+    assert idx._is_strictly_monotonic_decreasing is False
+
+
+@pytest.mark.parametrize("attr", ["is_monotonic_increasing", "is_monotonic_decreasing"])
+@pytest.mark.parametrize(
+    "values",
+    [[(np.nan,), (1,), (2,)], [(1,), (np.nan,), (2,)], [(1,), (2,), (np.nan,)]],
+)
+def test_is_monotonic_with_nans(values, attr):
+    # GH: 37220
+    idx = MultiIndex.from_tuples(values, names=["test"])
+    assert getattr(idx, attr) is False

llmeval-env/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reshape.py

@@ -0,0 +1,224 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+import pytz
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+def test_insert(idx):
+    # key contained in all levels
+    new_index = idx.insert(0, ("bar", "two"))
+    assert new_index.equal_levels(idx)
+    assert new_index[0] == ("bar", "two")
+
+    # key not contained in all levels
+    new_index = idx.insert(0, ("abc", "three"))
+
+    exp0 = Index(list(idx.levels[0]) + ["abc"], name="first")
+    tm.assert_index_equal(new_index.levels[0], exp0)
+    assert new_index.names == ["first", "second"]
+
+    exp1 = Index(list(idx.levels[1]) + ["three"], name="second")
+    tm.assert_index_equal(new_index.levels[1], exp1)
+    assert new_index[0] == ("abc", "three")
+
+    # key wrong length
+    msg = "Item must have length equal to number of levels"
+    with pytest.raises(ValueError, match=msg):
+        idx.insert(0, ("foo2",))
+
+    left = pd.DataFrame([["a", "b", 0], ["b", "d", 1]], columns=["1st", "2nd", "3rd"])
+    left.set_index(["1st", "2nd"], inplace=True)
+    ts = left["3rd"].copy(deep=True)
+
+    left.loc[("b", "x"), "3rd"] = 2
+    left.loc[("b", "a"), "3rd"] = -1
+    left.loc[("b", "b"), "3rd"] = 3
+    left.loc[("a", "x"), "3rd"] = 4
+    left.loc[("a", "w"), "3rd"] = 5
+    left.loc[("a", "a"), "3rd"] = 6
+
+    ts.loc[("b", "x")] = 2
+    ts.loc["b", "a"] = -1
+    ts.loc[("b", "b")] = 3
+    ts.loc["a", "x"] = 4
+    ts.loc[("a", "w")] = 5
+    ts.loc["a", "a"] = 6
+
+    right = pd.DataFrame(
+        [
+            ["a", "b", 0],
+            ["b", "d", 1],
+            ["b", "x", 2],
+            ["b", "a", -1],
+            ["b", "b", 3],
+            ["a", "x", 4],
+            ["a", "w", 5],
+            ["a", "a", 6],
+        ],
+        columns=["1st", "2nd", "3rd"],
+    )
+    right.set_index(["1st", "2nd"], inplace=True)
+    # FIXME data types changes to float because
+    # of intermediate nan insertion;
+    tm.assert_frame_equal(left, right, check_dtype=False)
+    tm.assert_series_equal(ts, right["3rd"])
+
+
+def test_insert2():
+    # GH9250
+    idx = (
+        [("test1", i) for i in range(5)]
+        + [("test2", i) for i in range(6)]
+        + [("test", 17), ("test", 18)]
+    )
+
+    left = pd.Series(np.linspace(0, 10, 11), MultiIndex.from_tuples(idx[:-2]))
+
+    left.loc[("test", 17)] = 11
+    left.loc[("test", 18)] = 12
+
+    right = pd.Series(np.linspace(0, 12, 13), MultiIndex.from_tuples(idx))
+
+    tm.assert_series_equal(left, right)
+
+
+def test_append(idx):
+    result = idx[:3].append(idx[3:])
+    assert result.equals(idx)
+
+    foos = [idx[:1], idx[1:3], idx[3:]]
+    result = foos[0].append(foos[1:])
+    assert result.equals(idx)
+
+    # empty
+    result = idx.append([])
+    assert result.equals(idx)
+
+
+def test_append_index():
+    idx1 = Index([1.1, 1.2, 1.3])
+    idx2 = pd.date_range("2011-01-01", freq="D", periods=3, tz="Asia/Tokyo")
+    idx3 = Index(["A", "B", "C"])
+
+    midx_lv2 = MultiIndex.from_arrays([idx1, idx2])
+    midx_lv3 = MultiIndex.from_arrays([idx1, idx2, idx3])
+
+    result = idx1.append(midx_lv2)
+
+    # see gh-7112
+    tz = pytz.timezone("Asia/Tokyo")
+    expected_tuples = [
+        (1.1, tz.localize(datetime(2011, 1, 1))),
+        (1.2, tz.localize(datetime(2011, 1, 2))),
+        (1.3, tz.localize(datetime(2011, 1, 3))),
+    ]
+    expected = Index([1.1, 1.2, 1.3] + expected_tuples)
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv2.append(idx1)
+    expected = Index(expected_tuples + [1.1, 1.2, 1.3])
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv2.append(midx_lv2)
+    expected = MultiIndex.from_arrays([idx1.append(idx1), idx2.append(idx2)])
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv2.append(midx_lv3)
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv3.append(midx_lv2)
+    expected = Index._simple_new(
+        np.array(
+            [
+                (1.1, tz.localize(datetime(2011, 1, 1)), "A"),
+                (1.2, tz.localize(datetime(2011, 1, 2)), "B"),
+                (1.3, tz.localize(datetime(2011, 1, 3)), "C"),
+            ]
+            + expected_tuples,
+            dtype=object,
+        ),
+        None,
+    )
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("name, exp", [("b", "b"), ("c", None)])
+def test_append_names_match(name, exp):
+    # GH#48288
+    midx = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"])
+    midx2 = MultiIndex.from_arrays([[3], [5]], names=["a", name])
+    result = midx.append(midx2)
+    expected = MultiIndex.from_arrays([[1, 2, 3], [3, 4, 5]], names=["a", exp])
+    tm.assert_index_equal(result, expected)
+
+
+def test_append_names_dont_match():
+    # GH#48288
+    midx = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"])
+    midx2 = MultiIndex.from_arrays([[3], [5]], names=["x", "y"])
+    result = midx.append(midx2)
+    expected = MultiIndex.from_arrays([[1, 2, 3], [3, 4, 5]], names=None)
+    tm.assert_index_equal(result, expected)
+
+
+def test_append_overlapping_interval_levels():
+    # GH 54934
+    ivl1 = pd.IntervalIndex.from_breaks([0.0, 1.0, 2.0])
+    ivl2 = pd.IntervalIndex.from_breaks([0.5, 1.5, 2.5])
+    mi1 = MultiIndex.from_product([ivl1, ivl1])
+    mi2 = MultiIndex.from_product([ivl2, ivl2])
+    result = mi1.append(mi2)
+    expected = MultiIndex.from_tuples(
+        [
+            (pd.Interval(0.0, 1.0), pd.Interval(0.0, 1.0)),
+            (pd.Interval(0.0, 1.0), pd.Interval(1.0, 2.0)),
+            (pd.Interval(1.0, 2.0), pd.Interval(0.0, 1.0)),
+            (pd.Interval(1.0, 2.0), pd.Interval(1.0, 2.0)),
+            (pd.Interval(0.5, 1.5), pd.Interval(0.5, 1.5)),
+            (pd.Interval(0.5, 1.5), pd.Interval(1.5, 2.5)),
+            (pd.Interval(1.5, 2.5), pd.Interval(0.5, 1.5)),
+            (pd.Interval(1.5, 2.5), pd.Interval(1.5, 2.5)),
+        ]
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_repeat():
+    reps = 2
+    numbers = [1, 2, 3]
+    names = np.array(["foo", "bar"])
+
+    m = MultiIndex.from_product([numbers, names], names=names)
+    expected = MultiIndex.from_product([numbers, names.repeat(reps)], names=names)
+    tm.assert_index_equal(m.repeat(reps), expected)
+
+
+def test_insert_base(idx):
+    result = idx[1:4]
+
+    # test 0th element
+    assert idx[0:4].equals(result.insert(0, idx[0]))
+
+
+def test_delete_base(idx):
+    expected = idx[1:]
+    result = idx.delete(0)
+    assert result.equals(expected)
+    assert result.name == expected.name
+
+    expected = idx[:-1]
+    result = idx.delete(-1)
+    assert result.equals(expected)
+    assert result.name == expected.name
+
+    msg = "index 6 is out of bounds for axis 0 with size 6"
+    with pytest.raises(IndexError, match=msg):
+        idx.delete(len(idx))