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@@ -1277,3 +1277,7 @@ videochat2/lib/python3.10/site-packages/pandas/_libs/tslibs/timezones.cpython-31
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videochat2/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_reshape.py

@@ -0,0 +1,85 @@
+"""
+Tests for ndarray-like method on the base Index class
+"""
+import numpy as np
+import pytest
+
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestReshape:
+    def test_repeat(self):
+        repeats = 2
+        index = Index([1, 2, 3])
+        expected = Index([1, 1, 2, 2, 3, 3])
+
+        result = index.repeat(repeats)
+        tm.assert_index_equal(result, expected)
+
+    def test_insert(self):
+        # GH 7256
+        # validate neg/pos inserts
+        result = Index(["b", "c", "d"])
+
+        # test 0th element
+        tm.assert_index_equal(Index(["a", "b", "c", "d"]), result.insert(0, "a"))
+
+        # test Nth element that follows Python list behavior
+        tm.assert_index_equal(Index(["b", "c", "e", "d"]), result.insert(-1, "e"))
+
+        # test loc +/- neq (0, -1)
+        tm.assert_index_equal(result.insert(1, "z"), result.insert(-2, "z"))
+
+        # test empty
+        null_index = Index([])
+        tm.assert_index_equal(Index(["a"]), null_index.insert(0, "a"))
+
+    def test_insert_missing(self, nulls_fixture):
+        # GH#22295
+        # test there is no mangling of NA values
+        expected = Index(["a", nulls_fixture, "b", "c"])
+        result = Index(list("abc")).insert(1, nulls_fixture)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "val", [(1, 2), np.datetime64("2019-12-31"), np.timedelta64(1, "D")]
+    )
+    @pytest.mark.parametrize("loc", [-1, 2])
+    def test_insert_datetime_into_object(self, loc, val):
+        # GH#44509
+        idx = Index(["1", "2", "3"])
+        result = idx.insert(loc, val)
+        expected = Index(["1", "2", val, "3"])
+        tm.assert_index_equal(result, expected)
+        assert type(expected[2]) is type(val)
+
+    @pytest.mark.parametrize(
+        "pos,expected",
+        [
+            (0, Index(["b", "c", "d"], name="index")),
+            (-1, Index(["a", "b", "c"], name="index")),
+        ],
+    )
+    def test_delete(self, pos, expected):
+        index = Index(["a", "b", "c", "d"], name="index")
+        result = index.delete(pos)
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+
+    def test_delete_raises(self):
+        index = Index(["a", "b", "c", "d"], name="index")
+        msg = "index 5 is out of bounds for axis 0 with size 4"
+        with pytest.raises(IndexError, match=msg):
+            index.delete(5)
+
+    def test_append_multiple(self):
+        index = Index(["a", "b", "c", "d", "e", "f"])
+
+        foos = [index[:2], index[2:4], index[4:]]
+        result = foos[0].append(foos[1:])
+        tm.assert_index_equal(result, index)
+
+        # empty
+        result = index.append([])
+        tm.assert_index_equal(result, index)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_setops.py

@@ -0,0 +1,259 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    Series,
+)
+import pandas._testing as tm
+from pandas.core.algorithms import safe_sort
+
+
+class TestIndexSetOps:
+    @pytest.mark.parametrize(
+        "method", ["union", "intersection", "difference", "symmetric_difference"]
+    )
+    def test_setops_sort_validation(self, method):
+        idx1 = Index(["a", "b"])
+        idx2 = Index(["b", "c"])
+
+        with pytest.raises(ValueError, match="The 'sort' keyword only takes"):
+            getattr(idx1, method)(idx2, sort=2)
+
+        # sort=True is supported as of GH#??
+        getattr(idx1, method)(idx2, sort=True)
+
+    def test_setops_preserve_object_dtype(self):
+        idx = Index([1, 2, 3], dtype=object)
+        result = idx.intersection(idx[1:])
+        expected = idx[1:]
+        tm.assert_index_equal(result, expected)
+
+        # if other is not monotonic increasing, intersection goes through
+        #  a different route
+        result = idx.intersection(idx[1:][::-1])
+        tm.assert_index_equal(result, expected)
+
+        result = idx._union(idx[1:], sort=None)
+        expected = idx
+        tm.assert_numpy_array_equal(result, expected.values)
+
+        result = idx.union(idx[1:], sort=None)
+        tm.assert_index_equal(result, expected)
+
+        # if other is not monotonic increasing, _union goes through
+        #  a different route
+        result = idx._union(idx[1:][::-1], sort=None)
+        tm.assert_numpy_array_equal(result, expected.values)
+
+        result = idx.union(idx[1:][::-1], sort=None)
+        tm.assert_index_equal(result, expected)
+
+    def test_union_base(self):
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[3:]
+        second = index[:5]
+
+        result = first.union(second)
+
+        expected = Index([0, 1, 2, "a", "b", "c"])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("klass", [np.array, Series, list])
+    def test_union_different_type_base(self, klass):
+        # GH 10149
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[3:]
+        second = index[:5]
+
+        result = first.union(klass(second.values))
+
+        assert tm.equalContents(result, index)
+
+    def test_union_sort_other_incomparable(self):
+        # https://github.com/pandas-dev/pandas/issues/24959
+        idx = Index([1, pd.Timestamp("2000")])
+        # default (sort=None)
+        with tm.assert_produces_warning(RuntimeWarning):
+            result = idx.union(idx[:1])
+
+        tm.assert_index_equal(result, idx)
+
+        # sort=None
+        with tm.assert_produces_warning(RuntimeWarning):
+            result = idx.union(idx[:1], sort=None)
+        tm.assert_index_equal(result, idx)
+
+        # sort=False
+        result = idx.union(idx[:1], sort=False)
+        tm.assert_index_equal(result, idx)
+
+    def test_union_sort_other_incomparable_true(self):
+        idx = Index([1, pd.Timestamp("2000")])
+        with pytest.raises(TypeError, match=".*"):
+            idx.union(idx[:1], sort=True)
+
+    def test_intersection_equal_sort_true(self):
+        idx = Index(["c", "a", "b"])
+        sorted_ = Index(["a", "b", "c"])
+        tm.assert_index_equal(idx.intersection(idx, sort=True), sorted_)
+
+    def test_intersection_base(self, sort):
+        # (same results for py2 and py3 but sortedness not tested elsewhere)
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[:5]
+        second = index[:3]
+
+        expected = Index([0, 1, "a"]) if sort is None else Index([0, "a", 1])
+        result = first.intersection(second, sort=sort)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("klass", [np.array, Series, list])
+    def test_intersection_different_type_base(self, klass, sort):
+        # GH 10149
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[:5]
+        second = index[:3]
+
+        result = first.intersection(klass(second.values), sort=sort)
+        assert tm.equalContents(result, second)
+
+    def test_intersection_nosort(self):
+        result = Index(["c", "b", "a"]).intersection(["b", "a"])
+        expected = Index(["b", "a"])
+        tm.assert_index_equal(result, expected)
+
+    def test_intersection_equal_sort(self):
+        idx = Index(["c", "a", "b"])
+        tm.assert_index_equal(idx.intersection(idx, sort=False), idx)
+        tm.assert_index_equal(idx.intersection(idx, sort=None), idx)
+
+    def test_intersection_str_dates(self, sort):
+        dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]
+
+        i1 = Index(dt_dates, dtype=object)
+        i2 = Index(["aa"], dtype=object)
+        result = i2.intersection(i1, sort=sort)
+
+        assert len(result) == 0
+
+    @pytest.mark.parametrize(
+        "index2,expected_arr",
+        [(Index(["B", "D"]), ["B"]), (Index(["B", "D", "A"]), ["A", "B"])],
+    )
+    def test_intersection_non_monotonic_non_unique(self, index2, expected_arr, sort):
+        # non-monotonic non-unique
+        index1 = Index(["A", "B", "A", "C"])
+        expected = Index(expected_arr, dtype="object")
+        result = index1.intersection(index2, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+
+    def test_difference_base(self, sort):
+        # (same results for py2 and py3 but sortedness not tested elsewhere)
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[:4]
+        second = index[3:]
+
+        result = first.difference(second, sort)
+        expected = Index([0, "a", 1])
+        if sort is None:
+            expected = Index(safe_sort(expected))
+        tm.assert_index_equal(result, expected)
+
+    def test_symmetric_difference(self):
+        # (same results for py2 and py3 but sortedness not tested elsewhere)
+        index = Index([0, "a", 1, "b", 2, "c"])
+        first = index[:4]
+        second = index[3:]
+
+        result = first.symmetric_difference(second)
+        expected = Index([0, 1, 2, "a", "c"])
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "method,expected,sort",
+        [
+            (
+                "intersection",
+                np.array(
+                    [(1, "A"), (2, "A"), (1, "B"), (2, "B")],
+                    dtype=[("num", int), ("let", "a1")],
+                ),
+                False,
+            ),
+            (
+                "intersection",
+                np.array(
+                    [(1, "A"), (1, "B"), (2, "A"), (2, "B")],
+                    dtype=[("num", int), ("let", "a1")],
+                ),
+                None,
+            ),
+            (
+                "union",
+                np.array(
+                    [(1, "A"), (1, "B"), (1, "C"), (2, "A"), (2, "B"), (2, "C")],
+                    dtype=[("num", int), ("let", "a1")],
+                ),
+                None,
+            ),
+        ],
+    )
+    def test_tuple_union_bug(self, method, expected, sort):
+        index1 = Index(
+            np.array(
+                [(1, "A"), (2, "A"), (1, "B"), (2, "B")],
+                dtype=[("num", int), ("let", "a1")],
+            )
+        )
+        index2 = Index(
+            np.array(
+                [(1, "A"), (2, "A"), (1, "B"), (2, "B"), (1, "C"), (2, "C")],
+                dtype=[("num", int), ("let", "a1")],
+            )
+        )
+
+        result = getattr(index1, method)(index2, sort=sort)
+        assert result.ndim == 1
+
+        expected = Index(expected)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("first_list", [["b", "a"], []])
+    @pytest.mark.parametrize("second_list", [["a", "b"], []])
+    @pytest.mark.parametrize(
+        "first_name, second_name, expected_name",
+        [("A", "B", None), (None, "B", None), ("A", None, None)],
+    )
+    def test_union_name_preservation(
+        self, first_list, second_list, first_name, second_name, expected_name, sort
+    ):
+        first = Index(first_list, name=first_name)
+        second = Index(second_list, name=second_name)
+        union = first.union(second, sort=sort)
+
+        vals = set(first_list).union(second_list)
+
+        if sort is None and len(first_list) > 0 and len(second_list) > 0:
+            expected = Index(sorted(vals), name=expected_name)
+            tm.assert_index_equal(union, expected)
+        else:
+            expected = Index(vals, name=expected_name)
+            tm.equalContents(union, expected)
+
+    @pytest.mark.parametrize(
+        "diff_type, expected",
+        [["difference", [1, "B"]], ["symmetric_difference", [1, 2, "B", "C"]]],
+    )
+    def test_difference_object_type(self, diff_type, expected):
+        # GH 13432
+        idx1 = Index([0, 1, "A", "B"])
+        idx2 = Index([0, 2, "A", "C"])
+        result = getattr(idx1, diff_type)(idx2)
+        expected = Index(expected)
+        tm.assert_index_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_append.py

@@ -0,0 +1,62 @@
+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)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_astype.py

@@ -0,0 +1,90 @@
+from datetime import date
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    CategoricalIndex,
+    Index,
+    IntervalIndex,
+)
+import pandas._testing as tm
+
+
+class TestAstype:
+    def test_astype(self):
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False)
+
+        result = ci.astype(object)
+        tm.assert_index_equal(result, Index(np.array(ci)))
+
+        # this IS equal, but not the same class
+        assert result.equals(ci)
+        assert isinstance(result, Index)
+        assert not isinstance(result, CategoricalIndex)
+
+        # interval
+        ii = IntervalIndex.from_arrays(left=[-0.001, 2.0], right=[2, 4], closed="right")
+
+        ci = CategoricalIndex(
+            Categorical.from_codes([0, 1, -1], categories=ii, ordered=True)
+        )
+
+        result = ci.astype("interval")
+        expected = ii.take([0, 1, -1], allow_fill=True, fill_value=np.nan)
+        tm.assert_index_equal(result, expected)
+
+        result = IntervalIndex(result.values)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("name", [None, "foo"])
+    @pytest.mark.parametrize("dtype_ordered", [True, False])
+    @pytest.mark.parametrize("index_ordered", [True, False])
+    def test_astype_category(self, name, dtype_ordered, index_ordered):
+        # GH#18630
+        index = CategoricalIndex(
+            list("aabbca"), categories=list("cab"), ordered=index_ordered
+        )
+        if name:
+            index = index.rename(name)
+
+        # standard categories
+        dtype = CategoricalDtype(ordered=dtype_ordered)
+        result = index.astype(dtype)
+        expected = CategoricalIndex(
+            index.tolist(),
+            name=name,
+            categories=index.categories,
+            ordered=dtype_ordered,
+        )
+        tm.assert_index_equal(result, expected)
+
+        # non-standard categories
+        dtype = CategoricalDtype(index.unique().tolist()[:-1], dtype_ordered)
+        result = index.astype(dtype)
+        expected = CategoricalIndex(index.tolist(), name=name, dtype=dtype)
+        tm.assert_index_equal(result, expected)
+
+        if dtype_ordered is False:
+            # dtype='category' can't specify ordered, so only test once
+            result = index.astype("category")
+            expected = index
+            tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("box", [True, False])
+    def test_categorical_date_roundtrip(self, box):
+        # astype to categorical and back should preserve date objects
+        v = date.today()
+
+        obj = Index([v, v])
+        assert obj.dtype == object
+        if box:
+            obj = obj.array
+
+        cat = obj.astype("category")
+
+        rtrip = cat.astype(object)
+        assert rtrip.dtype == object
+        assert type(rtrip[0]) is date

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_category.py

@@ -0,0 +1,396 @@
+import numpy as np
+import pytest
+
+from pandas._libs import index as libindex
+from pandas._libs.arrays import NDArrayBacked
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+)
+import pandas._testing as tm
+from pandas.core.indexes.api import (
+    CategoricalIndex,
+    Index,
+)
+from pandas.tests.indexes.common import Base
+
+
+class TestCategoricalIndex(Base):
+    _index_cls = CategoricalIndex
+
+    @pytest.fixture
+    def simple_index(self) -> CategoricalIndex:
+        return self._index_cls(list("aabbca"), categories=list("cab"), ordered=False)
+
+    @pytest.fixture
+    def index(self):
+        return tm.makeCategoricalIndex(100)
+
+    def create_index(self, *, categories=None, ordered=False):
+        if categories is None:
+            categories = list("cab")
+        return CategoricalIndex(list("aabbca"), categories=categories, ordered=ordered)
+
+    def test_can_hold_identifiers(self):
+        idx = self.create_index(categories=list("abcd"))
+        key = idx[0]
+        assert idx._can_hold_identifiers_and_holds_name(key) is True
+
+    def test_insert(self, simple_index):
+        ci = simple_index
+        categories = ci.categories
+
+        # test 0th element
+        result = ci.insert(0, "a")
+        expected = CategoricalIndex(list("aaabbca"), categories=categories)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # test Nth element that follows Python list behavior
+        result = ci.insert(-1, "a")
+        expected = CategoricalIndex(list("aabbcaa"), categories=categories)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # test empty
+        result = CategoricalIndex([], categories=categories).insert(0, "a")
+        expected = CategoricalIndex(["a"], categories=categories)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # invalid -> cast to object
+        expected = ci.astype(object).insert(0, "d")
+        result = ci.insert(0, "d")
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # GH 18295 (test missing)
+        expected = CategoricalIndex(["a", np.nan, "a", "b", "c", "b"])
+        for na in (np.nan, pd.NaT, None):
+            result = CategoricalIndex(list("aabcb")).insert(1, na)
+            tm.assert_index_equal(result, expected)
+
+    def test_insert_na_mismatched_dtype(self):
+        ci = CategoricalIndex([0, 1, 1])
+        result = ci.insert(0, pd.NaT)
+        expected = Index([pd.NaT, 0, 1, 1], dtype=object)
+        tm.assert_index_equal(result, expected)
+
+    def test_delete(self, simple_index):
+        ci = simple_index
+        categories = ci.categories
+
+        result = ci.delete(0)
+        expected = CategoricalIndex(list("abbca"), categories=categories)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        result = ci.delete(-1)
+        expected = CategoricalIndex(list("aabbc"), categories=categories)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        with tm.external_error_raised((IndexError, ValueError)):
+            # Either depending on NumPy version
+            ci.delete(10)
+
+    @pytest.mark.parametrize(
+        "data, non_lexsorted_data",
+        [[[1, 2, 3], [9, 0, 1, 2, 3]], [list("abc"), list("fabcd")]],
+    )
+    def test_is_monotonic(self, data, non_lexsorted_data):
+        c = CategoricalIndex(data)
+        assert c.is_monotonic_increasing is True
+        assert c.is_monotonic_decreasing is False
+
+        c = CategoricalIndex(data, ordered=True)
+        assert c.is_monotonic_increasing is True
+        assert c.is_monotonic_decreasing is False
+
+        c = CategoricalIndex(data, categories=reversed(data))
+        assert c.is_monotonic_increasing is False
+        assert c.is_monotonic_decreasing is True
+
+        c = CategoricalIndex(data, categories=reversed(data), ordered=True)
+        assert c.is_monotonic_increasing is False
+        assert c.is_monotonic_decreasing is True
+
+        # test when data is neither monotonic increasing nor decreasing
+        reordered_data = [data[0], data[2], data[1]]
+        c = CategoricalIndex(reordered_data, categories=reversed(data))
+        assert c.is_monotonic_increasing is False
+        assert c.is_monotonic_decreasing is False
+
+        # non lexsorted categories
+        categories = non_lexsorted_data
+
+        c = CategoricalIndex(categories[:2], categories=categories)
+        assert c.is_monotonic_increasing is True
+        assert c.is_monotonic_decreasing is False
+
+        c = CategoricalIndex(categories[1:3], categories=categories)
+        assert c.is_monotonic_increasing is True
+        assert c.is_monotonic_decreasing is False
+
+    def test_has_duplicates(self):
+        idx = CategoricalIndex([0, 0, 0], name="foo")
+        assert idx.is_unique is False
+        assert idx.has_duplicates is True
+
+        idx = CategoricalIndex([0, 1], categories=[2, 3], name="foo")
+        assert idx.is_unique is False
+        assert idx.has_duplicates is True
+
+        idx = CategoricalIndex([0, 1, 2, 3], categories=[1, 2, 3], name="foo")
+        assert idx.is_unique is True
+        assert idx.has_duplicates is False
+
+    @pytest.mark.parametrize(
+        "data, categories, expected",
+        [
+            (
+                [1, 1, 1],
+                [1, 2, 3],
+                {
+                    "first": np.array([False, True, True]),
+                    "last": np.array([True, True, False]),
+                    False: np.array([True, True, True]),
+                },
+            ),
+            (
+                [1, 1, 1],
+                list("abc"),
+                {
+                    "first": np.array([False, True, True]),
+                    "last": np.array([True, True, False]),
+                    False: np.array([True, True, True]),
+                },
+            ),
+            (
+                [2, "a", "b"],
+                list("abc"),
+                {
+                    "first": np.zeros(shape=(3), dtype=np.bool_),
+                    "last": np.zeros(shape=(3), dtype=np.bool_),
+                    False: np.zeros(shape=(3), dtype=np.bool_),
+                },
+            ),
+            (
+                list("abb"),
+                list("abc"),
+                {
+                    "first": np.array([False, False, True]),
+                    "last": np.array([False, True, False]),
+                    False: np.array([False, True, True]),
+                },
+            ),
+        ],
+    )
+    def test_drop_duplicates(self, data, categories, expected):
+        idx = CategoricalIndex(data, categories=categories, name="foo")
+        for keep, e in expected.items():
+            tm.assert_numpy_array_equal(idx.duplicated(keep=keep), e)
+            e = idx[~e]
+            result = idx.drop_duplicates(keep=keep)
+            tm.assert_index_equal(result, e)
+
+    @pytest.mark.parametrize(
+        "data, categories, expected_data",
+        [
+            ([1, 1, 1], [1, 2, 3], [1]),
+            ([1, 1, 1], list("abc"), [np.nan]),
+            ([1, 2, "a"], [1, 2, 3], [1, 2, np.nan]),
+            ([2, "a", "b"], list("abc"), [np.nan, "a", "b"]),
+        ],
+    )
+    def test_unique(self, data, categories, expected_data, ordered):
+        dtype = CategoricalDtype(categories, ordered=ordered)
+
+        idx = CategoricalIndex(data, dtype=dtype)
+        expected = CategoricalIndex(expected_data, dtype=dtype)
+        tm.assert_index_equal(idx.unique(), expected)
+
+    def test_repr_roundtrip(self):
+        ci = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True)
+        str(ci)
+        tm.assert_index_equal(eval(repr(ci)), ci, exact=True)
+
+        # formatting
+        str(ci)
+
+        # long format
+        # this is not reprable
+        ci = CategoricalIndex(np.random.randint(0, 5, size=100))
+        str(ci)
+
+    def test_isin(self):
+        ci = CategoricalIndex(list("aabca") + [np.nan], categories=["c", "a", "b"])
+        tm.assert_numpy_array_equal(
+            ci.isin(["c"]), np.array([False, False, False, True, False, False])
+        )
+        tm.assert_numpy_array_equal(
+            ci.isin(["c", "a", "b"]), np.array([True] * 5 + [False])
+        )
+        tm.assert_numpy_array_equal(
+            ci.isin(["c", "a", "b", np.nan]), np.array([True] * 6)
+        )
+
+        # mismatched categorical -> coerced to ndarray so doesn't matter
+        result = ci.isin(ci.set_categories(list("abcdefghi")))
+        expected = np.array([True] * 6)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = ci.isin(ci.set_categories(list("defghi")))
+        expected = np.array([False] * 5 + [True])
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_identical(self):
+        ci1 = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True)
+        ci2 = CategoricalIndex(["a", "b"], categories=["a", "b", "c"], ordered=True)
+        assert ci1.identical(ci1)
+        assert ci1.identical(ci1.copy())
+        assert not ci1.identical(ci2)
+
+    def test_ensure_copied_data(self, index):
+        # gh-12309: Check the "copy" argument of each
+        # Index.__new__ is honored.
+        #
+        # Must be tested separately from other indexes because
+        # self.values is not an ndarray.
+
+        result = CategoricalIndex(index.values, copy=True)
+        tm.assert_index_equal(index, result)
+        assert not np.shares_memory(result._data._codes, index._data._codes)
+
+        result = CategoricalIndex(index.values, copy=False)
+        assert result._data._codes is index._data._codes
+
+    def test_frame_repr(self):
+        df = pd.DataFrame({"A": [1, 2, 3]}, index=CategoricalIndex(["a", "b", "c"]))
+        result = repr(df)
+        expected = "   A\na  1\nb  2\nc  3"
+        assert result == expected
+
+    def test_reindex_base(self):
+        # See test_reindex.py
+        pass
+
+    def test_map_str(self):
+        # See test_map.py
+        pass
+
+
+class TestCategoricalIndex2:
+    # Tests that are not overriding a test in Base
+
+    def test_view_i8(self):
+        # GH#25464
+        ci = tm.makeCategoricalIndex(100)
+        msg = "When changing to a larger dtype, its size must be a divisor"
+        with pytest.raises(ValueError, match=msg):
+            ci.view("i8")
+        with pytest.raises(ValueError, match=msg):
+            ci._data.view("i8")
+
+        ci = ci[:-4]  # length divisible by 8
+
+        res = ci.view("i8")
+        expected = ci._data.codes.view("i8")
+        tm.assert_numpy_array_equal(res, expected)
+
+        cat = ci._data
+        tm.assert_numpy_array_equal(cat.view("i8"), expected)
+
+    @pytest.mark.parametrize(
+        "dtype, engine_type",
+        [
+            (np.int8, libindex.Int8Engine),
+            (np.int16, libindex.Int16Engine),
+            (np.int32, libindex.Int32Engine),
+            (np.int64, libindex.Int64Engine),
+        ],
+    )
+    def test_engine_type(self, dtype, engine_type):
+        if dtype != np.int64:
+            # num. of uniques required to push CategoricalIndex.codes to a
+            # dtype (128 categories required for .codes dtype to be int16 etc.)
+            num_uniques = {np.int8: 1, np.int16: 128, np.int32: 32768}[dtype]
+            ci = CategoricalIndex(range(num_uniques))
+        else:
+            # having 2**32 - 2**31 categories would be very memory-intensive,
+            # so we cheat a bit with the dtype
+            ci = CategoricalIndex(range(32768))  # == 2**16 - 2**(16 - 1)
+            arr = ci.values._ndarray.astype("int64")
+            NDArrayBacked.__init__(ci._data, arr, ci.dtype)
+        assert np.issubdtype(ci.codes.dtype, dtype)
+        assert isinstance(ci._engine, engine_type)
+
+    @pytest.mark.parametrize(
+        "func,op_name",
+        [
+            (lambda idx: idx - idx, "__sub__"),
+            (lambda idx: idx + idx, "__add__"),
+            (lambda idx: idx - ["a", "b"], "__sub__"),
+            (lambda idx: idx + ["a", "b"], "__add__"),
+            (lambda idx: ["a", "b"] - idx, "__rsub__"),
+            (lambda idx: ["a", "b"] + idx, "__radd__"),
+        ],
+    )
+    def test_disallow_addsub_ops(self, func, op_name):
+        # GH 10039
+        # set ops (+/-) raise TypeError
+        idx = Index(Categorical(["a", "b"]))
+        cat_or_list = "'(Categorical|list)' and '(Categorical|list)'"
+        msg = "|".join(
+            [
+                f"cannot perform {op_name} with this index type: CategoricalIndex",
+                "can only concatenate list",
+                rf"unsupported operand type\(s\) for [\+-]: {cat_or_list}",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            func(idx)
+
+    def test_method_delegation(self):
+        ci = CategoricalIndex(list("aabbca"), categories=list("cabdef"))
+        result = ci.set_categories(list("cab"))
+        tm.assert_index_equal(
+            result, CategoricalIndex(list("aabbca"), categories=list("cab"))
+        )
+
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
+        result = ci.rename_categories(list("efg"))
+        tm.assert_index_equal(
+            result, CategoricalIndex(list("ffggef"), categories=list("efg"))
+        )
+
+        # GH18862 (let rename_categories take callables)
+        result = ci.rename_categories(lambda x: x.upper())
+        tm.assert_index_equal(
+            result, CategoricalIndex(list("AABBCA"), categories=list("CAB"))
+        )
+
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
+        result = ci.add_categories(["d"])
+        tm.assert_index_equal(
+            result, CategoricalIndex(list("aabbca"), categories=list("cabd"))
+        )
+
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"))
+        result = ci.remove_categories(["c"])
+        tm.assert_index_equal(
+            result,
+            CategoricalIndex(list("aabb") + [np.nan] + ["a"], categories=list("ab")),
+        )
+
+        ci = CategoricalIndex(list("aabbca"), categories=list("cabdef"))
+        result = ci.as_unordered()
+        tm.assert_index_equal(result, ci)
+
+        ci = CategoricalIndex(list("aabbca"), categories=list("cabdef"))
+        result = ci.as_ordered()
+        tm.assert_index_equal(
+            result,
+            CategoricalIndex(list("aabbca"), categories=list("cabdef"), ordered=True),
+        )
+
+        # invalid
+        msg = "cannot use inplace with CategoricalIndex"
+        with pytest.raises(ValueError, match=msg):
+            ci.set_categories(list("cab"), inplace=True)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_constructors.py

@@ -0,0 +1,142 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalDtype,
+    CategoricalIndex,
+    Index,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalIndexConstructors:
+    def test_construction_disallows_scalar(self):
+        msg = "must be called with a collection of some kind"
+        with pytest.raises(TypeError, match=msg):
+            CategoricalIndex(data=1, categories=list("abcd"), ordered=False)
+        with pytest.raises(TypeError, match=msg):
+            CategoricalIndex(categories=list("abcd"), ordered=False)
+
+    def test_construction(self):
+        ci = CategoricalIndex(list("aabbca"), categories=list("abcd"), ordered=False)
+        categories = ci.categories
+
+        result = Index(ci)
+        tm.assert_index_equal(result, ci, exact=True)
+        assert not result.ordered
+
+        result = Index(ci.values)
+        tm.assert_index_equal(result, ci, exact=True)
+        assert not result.ordered
+
+        # empty
+        result = CategoricalIndex([], categories=categories)
+        tm.assert_index_equal(result.categories, Index(categories))
+        tm.assert_numpy_array_equal(result.codes, np.array([], dtype="int8"))
+        assert not result.ordered
+
+        # passing categories
+        result = CategoricalIndex(list("aabbca"), categories=categories)
+        tm.assert_index_equal(result.categories, Index(categories))
+        tm.assert_numpy_array_equal(
+            result.codes, np.array([0, 0, 1, 1, 2, 0], dtype="int8")
+        )
+
+        c = Categorical(list("aabbca"))
+        result = CategoricalIndex(c)
+        tm.assert_index_equal(result.categories, Index(list("abc")))
+        tm.assert_numpy_array_equal(
+            result.codes, np.array([0, 0, 1, 1, 2, 0], dtype="int8")
+        )
+        assert not result.ordered
+
+        result = CategoricalIndex(c, categories=categories)
+        tm.assert_index_equal(result.categories, Index(categories))
+        tm.assert_numpy_array_equal(
+            result.codes, np.array([0, 0, 1, 1, 2, 0], dtype="int8")
+        )
+        assert not result.ordered
+
+        ci = CategoricalIndex(c, categories=list("abcd"))
+        result = CategoricalIndex(ci)
+        tm.assert_index_equal(result.categories, Index(categories))
+        tm.assert_numpy_array_equal(
+            result.codes, np.array([0, 0, 1, 1, 2, 0], dtype="int8")
+        )
+        assert not result.ordered
+
+        result = CategoricalIndex(ci, categories=list("ab"))
+        tm.assert_index_equal(result.categories, Index(list("ab")))
+        tm.assert_numpy_array_equal(
+            result.codes, np.array([0, 0, 1, 1, -1, 0], dtype="int8")
+        )
+        assert not result.ordered
+
+        result = CategoricalIndex(ci, categories=list("ab"), ordered=True)
+        tm.assert_index_equal(result.categories, Index(list("ab")))
+        tm.assert_numpy_array_equal(
+            result.codes, np.array([0, 0, 1, 1, -1, 0], dtype="int8")
+        )
+        assert result.ordered
+
+        result = CategoricalIndex(ci, categories=list("ab"), ordered=True)
+        expected = CategoricalIndex(
+            ci, categories=list("ab"), ordered=True, dtype="category"
+        )
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # turn me to an Index
+        result = Index(np.array(ci))
+        assert isinstance(result, Index)
+        assert not isinstance(result, CategoricalIndex)
+
+    def test_construction_with_dtype(self):
+        # specify dtype
+        ci = CategoricalIndex(list("aabbca"), categories=list("abc"), ordered=False)
+
+        result = Index(np.array(ci), dtype="category")
+        tm.assert_index_equal(result, ci, exact=True)
+
+        result = Index(np.array(ci).tolist(), dtype="category")
+        tm.assert_index_equal(result, ci, exact=True)
+
+        # these are generally only equal when the categories are reordered
+        ci = CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False)
+
+        result = Index(np.array(ci), dtype="category").reorder_categories(ci.categories)
+        tm.assert_index_equal(result, ci, exact=True)
+
+        # make sure indexes are handled
+        idx = Index(range(3))
+        expected = CategoricalIndex([0, 1, 2], categories=idx, ordered=True)
+        result = CategoricalIndex(idx, categories=idx, ordered=True)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_construction_empty_with_bool_categories(self):
+        # see GH#22702
+        cat = CategoricalIndex([], categories=[True, False])
+        categories = sorted(cat.categories.tolist())
+        assert categories == [False, True]
+
+    def test_construction_with_categorical_dtype(self):
+        # construction with CategoricalDtype
+        # GH#18109
+        data, cats, ordered = "a a b b".split(), "c b a".split(), True
+        dtype = CategoricalDtype(categories=cats, ordered=ordered)
+
+        result = CategoricalIndex(data, dtype=dtype)
+        expected = CategoricalIndex(data, categories=cats, ordered=ordered)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # GH#19032
+        result = Index(data, dtype=dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # error when combining categories/ordered and dtype kwargs
+        msg = "Cannot specify `categories` or `ordered` together with `dtype`."
+        with pytest.raises(ValueError, match=msg):
+            CategoricalIndex(data, categories=cats, dtype=dtype)
+
+        with pytest.raises(ValueError, match=msg):
+            CategoricalIndex(data, ordered=ordered, dtype=dtype)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_equals.py

@@ -0,0 +1,90 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    Index,
+    MultiIndex,
+)
+
+
+class TestEquals:
+    def test_equals_categorical(self):
+        ci1 = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True)
+        ci2 = CategoricalIndex(["a", "b"], categories=["a", "b", "c"], ordered=True)
+
+        assert ci1.equals(ci1)
+        assert not ci1.equals(ci2)
+        assert ci1.equals(ci1.astype(object))
+        assert ci1.astype(object).equals(ci1)
+
+        assert (ci1 == ci1).all()
+        assert not (ci1 != ci1).all()
+        assert not (ci1 > ci1).all()
+        assert not (ci1 < ci1).all()
+        assert (ci1 <= ci1).all()
+        assert (ci1 >= ci1).all()
+
+        assert not (ci1 == 1).all()
+        assert (ci1 == Index(["a", "b"])).all()
+        assert (ci1 == ci1.values).all()
+
+        # invalid comparisons
+        with pytest.raises(ValueError, match="Lengths must match"):
+            ci1 == Index(["a", "b", "c"])
+
+        msg = "Categoricals can only be compared if 'categories' are the same"
+        with pytest.raises(TypeError, match=msg):
+            ci1 == ci2
+        with pytest.raises(TypeError, match=msg):
+            ci1 == Categorical(ci1.values, ordered=False)
+        with pytest.raises(TypeError, match=msg):
+            ci1 == Categorical(ci1.values, categories=list("abc"))
+
+        # tests
+        # make sure that we are testing for category inclusion properly
+        ci = CategoricalIndex(list("aabca"), categories=["c", "a", "b"])
+        assert not ci.equals(list("aabca"))
+        # Same categories, but different order
+        # Unordered
+        assert ci.equals(CategoricalIndex(list("aabca")))
+        # Ordered
+        assert not ci.equals(CategoricalIndex(list("aabca"), ordered=True))
+        assert ci.equals(ci.copy())
+
+        ci = CategoricalIndex(list("aabca") + [np.nan], categories=["c", "a", "b"])
+        assert not ci.equals(list("aabca"))
+        assert not ci.equals(CategoricalIndex(list("aabca")))
+        assert ci.equals(ci.copy())
+
+        ci = CategoricalIndex(list("aabca") + [np.nan], categories=["c", "a", "b"])
+        assert not ci.equals(list("aabca") + [np.nan])
+        assert ci.equals(CategoricalIndex(list("aabca") + [np.nan]))
+        assert not ci.equals(CategoricalIndex(list("aabca") + [np.nan], ordered=True))
+        assert ci.equals(ci.copy())
+
+    def test_equals_categorical_unordered(self):
+        # https://github.com/pandas-dev/pandas/issues/16603
+        a = CategoricalIndex(["A"], categories=["A", "B"])
+        b = CategoricalIndex(["A"], categories=["B", "A"])
+        c = CategoricalIndex(["C"], categories=["B", "A"])
+        assert a.equals(b)
+        assert not a.equals(c)
+        assert not b.equals(c)
+
+    def test_equals_non_category(self):
+        # GH#37667 Case where other contains a value not among ci's
+        #  categories ("D") and also contains np.nan
+        ci = CategoricalIndex(["A", "B", np.nan, np.nan])
+        other = Index(["A", "B", "D", np.nan])
+
+        assert not ci.equals(other)
+
+    def test_equals_multiindex(self):
+        # dont raise NotImplementedError when calling is_dtype_compat
+
+        mi = MultiIndex.from_arrays([["A", "B", "C", "D"], range(4)])
+        ci = mi.to_flat_index().astype("category")
+
+        assert not ci.equals(mi)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_fillna.py

@@ -0,0 +1,54 @@
+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)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_formats.py

@@ -0,0 +1,113 @@
+"""
+Tests for CategoricalIndex.__repr__ and related methods.
+"""
+import pandas._config.config as cf
+
+from pandas import CategoricalIndex
+
+
+class TestCategoricalIndexRepr:
+    def test_format_different_scalar_lengths(self):
+        # GH#35439
+        idx = CategoricalIndex(["aaaaaaaaa", "b"])
+        expected = ["aaaaaaaaa", "b"]
+        assert idx.format() == expected
+
+    def test_string_categorical_index_repr(self):
+        # short
+        idx = CategoricalIndex(["a", "bb", "ccc"])
+        expected = """CategoricalIndex(['a', 'bb', 'ccc'], categories=['a', 'bb', 'ccc'], ordered=False, dtype='category')"""  # noqa:E501
+        assert repr(idx) == expected
+
+        # multiple lines
+        idx = CategoricalIndex(["a", "bb", "ccc"] * 10)
+        expected = """CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',
+                  'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb',
+                  'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],
+                 categories=['a', 'bb', 'ccc'], ordered=False, dtype='category')"""  # noqa:E501
+
+        assert repr(idx) == expected
+
+        # truncated
+        idx = CategoricalIndex(["a", "bb", "ccc"] * 100)
+        expected = """CategoricalIndex(['a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a',
+                  ...
+                  'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc', 'a', 'bb', 'ccc'],
+                 categories=['a', 'bb', 'ccc'], ordered=False, dtype='category', length=300)"""  # noqa:E501
+
+        assert repr(idx) == expected
+
+        # larger categories
+        idx = CategoricalIndex(list("abcdefghijklmmo"))
+        expected = """CategoricalIndex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l',
+                  'm', 'm', 'o'],
+                 categories=['a', 'b', 'c', 'd', ..., 'k', 'l', 'm', 'o'], ordered=False, dtype='category')"""  # noqa:E501
+
+        assert repr(idx) == expected
+
+        # short
+        idx = CategoricalIndex(["あ", "いい", "ううう"])
+        expected = """CategoricalIndex(['あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa:E501
+        assert repr(idx) == expected
+
+        # multiple lines
+        idx = CategoricalIndex(["あ", "いい", "ううう"] * 10)
+        expected = """CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ',
+                  'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
+                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'],
+                 categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa:E501
+
+        assert repr(idx) == expected
+
+        # truncated
+        idx = CategoricalIndex(["あ", "いい", "ううう"] * 100)
+        expected = """CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ',
+                  ...
+                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'],
+                 categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)"""  # noqa:E501
+
+        assert repr(idx) == expected
+
+        # larger categories
+        idx = CategoricalIndex(list("あいうえおかきくけこさしすせそ"))
+        expected = """CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ', 'さ', 'し',
+                  'す', 'せ', 'そ'],
+                 categories=['あ', 'い', 'う', 'え', ..., 'し', 'す', 'せ', 'そ'], ordered=False, dtype='category')"""  # noqa:E501
+
+        assert repr(idx) == expected
+
+        # Enable Unicode option -----------------------------------------
+        with cf.option_context("display.unicode.east_asian_width", True):
+            # short
+            idx = CategoricalIndex(["あ", "いい", "ううう"])
+            expected = """CategoricalIndex(['あ', 'いい', 'ううう'], categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa:E501
+            assert repr(idx) == expected
+
+            # multiple lines
+            idx = CategoricalIndex(["あ", "いい", "ううう"] * 10)
+            expected = """CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
+                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',
+                  'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
+                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう'],
+                 categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category')"""  # noqa:E501
+
+            assert repr(idx) == expected
+
+            # truncated
+            idx = CategoricalIndex(["あ", "いい", "ううう"] * 100)
+            expected = """CategoricalIndex(['あ', 'いい', 'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい',
+                  'ううう', 'あ',
+                  ...
+                  'ううう', 'あ', 'いい', 'ううう', 'あ', 'いい', 'ううう',
+                  'あ', 'いい', 'ううう'],
+                 categories=['あ', 'いい', 'ううう'], ordered=False, dtype='category', length=300)"""  # noqa:E501
+
+            assert repr(idx) == expected
+
+            # larger categories
+            idx = CategoricalIndex(list("あいうえおかきくけこさしすせそ"))
+            expected = """CategoricalIndex(['あ', 'い', 'う', 'え', 'お', 'か', 'き', 'く', 'け', 'こ',
+                  'さ', 'し', 'す', 'せ', 'そ'],
+                 categories=['あ', 'い', 'う', 'え', ..., 'し', 'す', 'せ', 'そ'], ordered=False, dtype='category')"""  # noqa:E501
+
+            assert repr(idx) == expected

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_indexing.py

@@ -0,0 +1,422 @@
+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):
+        np.random.seed(123456789)
+
+        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.randint(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

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_map.py

@@ -0,0 +1,115 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestMap:
+    @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(self, 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(self):
+        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(self):
+        # 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"),
+        (
+            ([1, 1, np.nan], pd.isna),
+            ([1, 2, np.nan], pd.isna),
+            ([1, 1, np.nan], {1: False}),
+            ([1, 2, np.nan], {1: False, 2: False}),
+            ([1, 1, np.nan], Series([False, False])),
+            ([1, 2, np.nan], Series([False, False, False])),
+        ),
+    )
+    def test_map_with_nan(self, data, f):  # GH 24241
+        values = pd.Categorical(data)
+        result = values.map(f)
+        if data[1] == 1:
+            expected = pd.Categorical([False, False, np.nan])
+            tm.assert_categorical_equal(result, expected)
+        else:
+            expected = Index([False, False, np.nan])
+            tm.assert_index_equal(result, expected)
+
+    def test_map_with_dict_or_series(self):
+        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)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/categorical/test_reindex.py

@@ -0,0 +1,78 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    Index,
+    Interval,
+)
+import pandas._testing as tm
+
+
+class TestReindex:
+    def test_reindex_list_non_unique(self):
+        # GH#11586
+        msg = "cannot reindex on an axis with duplicate labels"
+        ci = CategoricalIndex(["a", "b", "c", "a"])
+        with pytest.raises(ValueError, match=msg):
+            ci.reindex(["a", "c"])
+
+    def test_reindex_categorical_non_unique(self):
+        msg = "cannot reindex on an axis with duplicate labels"
+        ci = CategoricalIndex(["a", "b", "c", "a"])
+        with pytest.raises(ValueError, match=msg):
+            ci.reindex(Categorical(["a", "c"]))
+
+    def test_reindex_list_non_unique_unused_category(self):
+        msg = "cannot reindex on an axis with duplicate labels"
+        ci = CategoricalIndex(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
+        with pytest.raises(ValueError, match=msg):
+            ci.reindex(["a", "c"])
+
+    def test_reindex_categorical_non_unique_unused_category(self):
+        msg = "cannot reindex on an axis with duplicate labels"
+        ci = CategoricalIndex(["a", "b", "c", "a"], categories=["a", "b", "c", "d"])
+        with pytest.raises(ValueError, match=msg):
+            ci.reindex(Categorical(["a", "c"]))
+
+    def test_reindex_duplicate_target(self):
+        # See GH25459
+        cat = CategoricalIndex(["a", "b", "c"], categories=["a", "b", "c", "d"])
+        res, indexer = cat.reindex(["a", "c", "c"])
+        exp = Index(["a", "c", "c"], dtype="object")
+        tm.assert_index_equal(res, exp, exact=True)
+        tm.assert_numpy_array_equal(indexer, np.array([0, 2, 2], dtype=np.intp))
+
+        res, indexer = cat.reindex(
+            CategoricalIndex(["a", "c", "c"], categories=["a", "b", "c", "d"])
+        )
+        exp = CategoricalIndex(["a", "c", "c"], categories=["a", "b", "c", "d"])
+        tm.assert_index_equal(res, exp, exact=True)
+        tm.assert_numpy_array_equal(indexer, np.array([0, 2, 2], dtype=np.intp))
+
+    def test_reindex_empty_index(self):
+        # See GH16770
+        c = CategoricalIndex([])
+        res, indexer = c.reindex(["a", "b"])
+        tm.assert_index_equal(res, Index(["a", "b"]), exact=True)
+        tm.assert_numpy_array_equal(indexer, np.array([-1, -1], dtype=np.intp))
+
+    def test_reindex_categorical_added_category(self):
+        # GH 42424
+        ci = CategoricalIndex(
+            [Interval(0, 1, closed="right"), Interval(1, 2, closed="right")],
+            ordered=True,
+        )
+        ci_add = CategoricalIndex(
+            [
+                Interval(0, 1, closed="right"),
+                Interval(1, 2, closed="right"),
+                Interval(2, 3, closed="right"),
+                Interval(3, 4, closed="right"),
+            ],
+            ordered=True,
+        )
+        result, _ = ci.reindex(ci_add)
+        expected = ci_add
+        tm.assert_index_equal(expected, result)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/datetimelike_/test_drop_duplicates.py

@@ -0,0 +1,89 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    PeriodIndex,
+    Series,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class DropDuplicates:
+    def test_drop_duplicates_metadata(self, idx):
+        # GH#10115
+        result = idx.drop_duplicates()
+        tm.assert_index_equal(idx, result)
+        assert idx.freq == result.freq
+
+        idx_dup = idx.append(idx)
+        result = idx_dup.drop_duplicates()
+
+        expected = idx
+        if not isinstance(idx, PeriodIndex):
+            # freq is reset except for PeriodIndex
+            assert idx_dup.freq is None
+            assert result.freq is None
+            expected = idx._with_freq(None)
+        else:
+            assert result.freq == expected.freq
+
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "keep, expected, index",
+        [
+            (
+                "first",
+                np.concatenate(([False] * 10, [True] * 5)),
+                np.arange(0, 10, dtype=np.int64),
+            ),
+            (
+                "last",
+                np.concatenate(([True] * 5, [False] * 10)),
+                np.arange(5, 15, dtype=np.int64),
+            ),
+            (
+                False,
+                np.concatenate(([True] * 5, [False] * 5, [True] * 5)),
+                np.arange(5, 10, dtype=np.int64),
+            ),
+        ],
+    )
+    def test_drop_duplicates(self, keep, expected, index, idx):
+        # to check Index/Series compat
+        idx = idx.append(idx[:5])
+
+        tm.assert_numpy_array_equal(idx.duplicated(keep=keep), expected)
+        expected = idx[~expected]
+
+        result = idx.drop_duplicates(keep=keep)
+        tm.assert_index_equal(result, expected)
+
+        result = Series(idx).drop_duplicates(keep=keep)
+        expected = Series(expected, index=index)
+        tm.assert_series_equal(result, expected)
+
+
+class TestDropDuplicatesPeriodIndex(DropDuplicates):
+    @pytest.fixture(params=["D", "3D", "H", "2H", "T", "2T", "S", "3S"])
+    def freq(self, request):
+        return request.param
+
+    @pytest.fixture
+    def idx(self, freq):
+        return period_range("2011-01-01", periods=10, freq=freq, name="idx")
+
+
+class TestDropDuplicatesDatetimeIndex(DropDuplicates):
+    @pytest.fixture
+    def idx(self, freq_sample):
+        return date_range("2011-01-01", freq=freq_sample, periods=10, name="idx")
+
+
+class TestDropDuplicatesTimedeltaIndex(DropDuplicates):
+    @pytest.fixture
+    def idx(self, freq_sample):
+        return timedelta_range("1 day", periods=10, freq=freq_sample, name="idx")

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/datetimelike_/test_equals.py

@@ -0,0 +1,180 @@
+"""
+Tests shared for DatetimeIndex/TimedeltaIndex/PeriodIndex
+"""
+from datetime import (
+    datetime,
+    timedelta,
+)
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    DatetimeIndex,
+    Index,
+    PeriodIndex,
+    TimedeltaIndex,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class EqualsTests:
+    def test_not_equals_numeric(self, index):
+        assert not index.equals(Index(index.asi8))
+        assert not index.equals(Index(index.asi8.astype("u8")))
+        assert not index.equals(Index(index.asi8).astype("f8"))
+
+    def test_equals(self, index):
+        assert index.equals(index)
+        assert index.equals(index.astype(object))
+        assert index.equals(CategoricalIndex(index))
+        assert index.equals(CategoricalIndex(index.astype(object)))
+
+    def test_not_equals_non_arraylike(self, index):
+        assert not index.equals(list(index))
+
+    def test_not_equals_strings(self, index):
+        other = Index([str(x) for x in index], dtype=object)
+        assert not index.equals(other)
+        assert not index.equals(CategoricalIndex(other))
+
+    def test_not_equals_misc_strs(self, index):
+        other = Index(list("abc"))
+        assert not index.equals(other)
+
+
+class TestPeriodIndexEquals(EqualsTests):
+    @pytest.fixture
+    def index(self):
+        return period_range("2013-01-01", periods=5, freq="D")
+
+    # TODO: de-duplicate with other test_equals2 methods
+    @pytest.mark.parametrize("freq", ["D", "M"])
+    def test_equals2(self, freq):
+        # GH#13107
+        idx = PeriodIndex(["2011-01-01", "2011-01-02", "NaT"], freq=freq)
+        assert idx.equals(idx)
+        assert idx.equals(idx.copy())
+        assert idx.equals(idx.astype(object))
+        assert idx.astype(object).equals(idx)
+        assert idx.astype(object).equals(idx.astype(object))
+        assert not idx.equals(list(idx))
+        assert not idx.equals(pd.Series(idx))
+
+        idx2 = PeriodIndex(["2011-01-01", "2011-01-02", "NaT"], freq="H")
+        assert not idx.equals(idx2)
+        assert not idx.equals(idx2.copy())
+        assert not idx.equals(idx2.astype(object))
+        assert not idx.astype(object).equals(idx2)
+        assert not idx.equals(list(idx2))
+        assert not idx.equals(pd.Series(idx2))
+
+        # same internal, different tz
+        idx3 = PeriodIndex._simple_new(
+            idx._values._simple_new(idx._values.asi8, freq="H")
+        )
+        tm.assert_numpy_array_equal(idx.asi8, idx3.asi8)
+        assert not idx.equals(idx3)
+        assert not idx.equals(idx3.copy())
+        assert not idx.equals(idx3.astype(object))
+        assert not idx.astype(object).equals(idx3)
+        assert not idx.equals(list(idx3))
+        assert not idx.equals(pd.Series(idx3))
+
+
+class TestDatetimeIndexEquals(EqualsTests):
+    @pytest.fixture
+    def index(self):
+        return date_range("2013-01-01", periods=5)
+
+    def test_equals2(self):
+        # GH#13107
+        idx = DatetimeIndex(["2011-01-01", "2011-01-02", "NaT"])
+        assert idx.equals(idx)
+        assert idx.equals(idx.copy())
+        assert idx.equals(idx.astype(object))
+        assert idx.astype(object).equals(idx)
+        assert idx.astype(object).equals(idx.astype(object))
+        assert not idx.equals(list(idx))
+        assert not idx.equals(pd.Series(idx))
+
+        idx2 = DatetimeIndex(["2011-01-01", "2011-01-02", "NaT"], tz="US/Pacific")
+        assert not idx.equals(idx2)
+        assert not idx.equals(idx2.copy())
+        assert not idx.equals(idx2.astype(object))
+        assert not idx.astype(object).equals(idx2)
+        assert not idx.equals(list(idx2))
+        assert not idx.equals(pd.Series(idx2))
+
+        # same internal, different tz
+        idx3 = DatetimeIndex(idx.asi8, tz="US/Pacific")
+        tm.assert_numpy_array_equal(idx.asi8, idx3.asi8)
+        assert not idx.equals(idx3)
+        assert not idx.equals(idx3.copy())
+        assert not idx.equals(idx3.astype(object))
+        assert not idx.astype(object).equals(idx3)
+        assert not idx.equals(list(idx3))
+        assert not idx.equals(pd.Series(idx3))
+
+        # check that we do not raise when comparing with OutOfBounds objects
+        oob = Index([datetime(2500, 1, 1)] * 3, dtype=object)
+        assert not idx.equals(oob)
+        assert not idx2.equals(oob)
+        assert not idx3.equals(oob)
+
+        # check that we do not raise when comparing with OutOfBounds dt64
+        oob2 = oob.map(np.datetime64)
+        assert not idx.equals(oob2)
+        assert not idx2.equals(oob2)
+        assert not idx3.equals(oob2)
+
+    @pytest.mark.parametrize("freq", ["B", "C"])
+    def test_not_equals_bday(self, freq):
+        rng = date_range("2009-01-01", "2010-01-01", freq=freq)
+        assert not rng.equals(list(rng))
+
+
+class TestTimedeltaIndexEquals(EqualsTests):
+    @pytest.fixture
+    def index(self):
+        return tm.makeTimedeltaIndex(10)
+
+    def test_equals2(self):
+        # GH#13107
+        idx = TimedeltaIndex(["1 days", "2 days", "NaT"])
+        assert idx.equals(idx)
+        assert idx.equals(idx.copy())
+        assert idx.equals(idx.astype(object))
+        assert idx.astype(object).equals(idx)
+        assert idx.astype(object).equals(idx.astype(object))
+        assert not idx.equals(list(idx))
+        assert not idx.equals(pd.Series(idx))
+
+        idx2 = TimedeltaIndex(["2 days", "1 days", "NaT"])
+        assert not idx.equals(idx2)
+        assert not idx.equals(idx2.copy())
+        assert not idx.equals(idx2.astype(object))
+        assert not idx.astype(object).equals(idx2)
+        assert not idx.astype(object).equals(idx2.astype(object))
+        assert not idx.equals(list(idx2))
+        assert not idx.equals(pd.Series(idx2))
+
+        # Check that we dont raise OverflowError on comparisons outside the
+        #  implementation range GH#28532
+        oob = Index([timedelta(days=10**6)] * 3, dtype=object)
+        assert not idx.equals(oob)
+        assert not idx2.equals(oob)
+
+        oob2 = Index([np.timedelta64(x) for x in oob], dtype=object)
+        assert (oob == oob2).all()
+        assert not idx.equals(oob2)
+        assert not idx2.equals(oob2)
+
+        oob3 = oob.map(np.timedelta64)
+        assert (oob3 == oob).all()
+        assert not idx.equals(oob3)
+        assert not idx2.equals(oob3)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/datetimelike_/test_indexing.py

@@ -0,0 +1,45 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DatetimeIndex,
+    Index,
+)
+import pandas._testing as tm
+
+dtlike_dtypes = [
+    np.dtype("timedelta64[ns]"),
+    np.dtype("datetime64[ns]"),
+    pd.DatetimeTZDtype("ns", "Asia/Tokyo"),
+    pd.PeriodDtype("ns"),
+]
+
+
+@pytest.mark.parametrize("ldtype", dtlike_dtypes)
+@pytest.mark.parametrize("rdtype", dtlike_dtypes)
+def test_get_indexer_non_unique_wrong_dtype(ldtype, rdtype):
+    vals = np.tile(3600 * 10**9 * np.arange(3), 2)
+
+    def construct(dtype):
+        if dtype is dtlike_dtypes[-1]:
+            # PeriodArray will try to cast ints to strings
+            return DatetimeIndex(vals).astype(dtype)
+        return Index(vals, dtype=dtype)
+
+    left = construct(ldtype)
+    right = construct(rdtype)
+
+    result = left.get_indexer_non_unique(right)
+
+    if ldtype is rdtype:
+        ex1 = np.array([0, 3, 1, 4, 2, 5] * 2, dtype=np.intp)
+        ex2 = np.array([], dtype=np.intp)
+        tm.assert_numpy_array_equal(result[0], ex1)
+        tm.assert_numpy_array_equal(result[1], ex2)
+
+    else:
+        no_matches = np.array([-1] * 6, dtype=np.intp)
+        missing = np.arange(6, dtype=np.intp)
+        tm.assert_numpy_array_equal(result[0], no_matches)
+        tm.assert_numpy_array_equal(result[1], missing)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/datetimelike_/test_is_monotonic.py

@@ -0,0 +1,46 @@
+from pandas import (
+    Index,
+    NaT,
+    date_range,
+)
+
+
+def test_is_monotonic_with_nat():
+    # GH#31437
+    # PeriodIndex.is_monotonic_increasing should behave analogously to DatetimeIndex,
+    #  in particular never be monotonic when we have NaT
+    dti = date_range("2016-01-01", periods=3)
+    pi = dti.to_period("D")
+    tdi = Index(dti.view("timedelta64[ns]"))
+
+    for obj in [pi, pi._engine, dti, dti._engine, tdi, tdi._engine]:
+        if isinstance(obj, Index):
+            # i.e. not Engines
+            assert obj.is_monotonic_increasing
+        assert obj.is_monotonic_increasing
+        assert not obj.is_monotonic_decreasing
+        assert obj.is_unique
+
+    dti1 = dti.insert(0, NaT)
+    pi1 = dti1.to_period("D")
+    tdi1 = Index(dti1.view("timedelta64[ns]"))
+
+    for obj in [pi1, pi1._engine, dti1, dti1._engine, tdi1, tdi1._engine]:
+        if isinstance(obj, Index):
+            # i.e. not Engines
+            assert not obj.is_monotonic_increasing
+        assert not obj.is_monotonic_increasing
+        assert not obj.is_monotonic_decreasing
+        assert obj.is_unique
+
+    dti2 = dti.insert(3, NaT)
+    pi2 = dti2.to_period("H")
+    tdi2 = Index(dti2.view("timedelta64[ns]"))
+
+    for obj in [pi2, pi2._engine, dti2, dti2._engine, tdi2, tdi2._engine]:
+        if isinstance(obj, Index):
+            # i.e. not Engines
+            assert not obj.is_monotonic_increasing
+        assert not obj.is_monotonic_increasing
+        assert not obj.is_monotonic_decreasing
+        assert obj.is_unique

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/datetimelike_/test_nat.py

@@ -0,0 +1,53 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DatetimeIndex,
+    NaT,
+    PeriodIndex,
+    TimedeltaIndex,
+)
+import pandas._testing as tm
+
+
+class NATests:
+    def test_nat(self, index_without_na):
+        empty_index = index_without_na[:0]
+
+        index_with_na = index_without_na.copy(deep=True)
+        index_with_na._data[1] = NaT
+
+        assert empty_index._na_value is NaT
+        assert index_with_na._na_value is NaT
+        assert index_without_na._na_value is NaT
+
+        idx = index_without_na
+        assert idx._can_hold_na
+
+        tm.assert_numpy_array_equal(idx._isnan, np.array([False, False]))
+        assert idx.hasnans is False
+
+        idx = index_with_na
+        assert idx._can_hold_na
+
+        tm.assert_numpy_array_equal(idx._isnan, np.array([False, True]))
+        assert idx.hasnans is True
+
+
+class TestDatetimeIndexNA(NATests):
+    @pytest.fixture
+    def index_without_na(self, tz_naive_fixture):
+        tz = tz_naive_fixture
+        return DatetimeIndex(["2011-01-01", "2011-01-02"], tz=tz)
+
+
+class TestTimedeltaIndexNA(NATests):
+    @pytest.fixture
+    def index_without_na(self):
+        return TimedeltaIndex(["1 days", "2 days"])
+
+
+class TestPeriodIndexNA(NATests):
+    @pytest.fixture
+    def index_without_na(self):
+        return PeriodIndex(["2011-01-01", "2011-01-02"], freq="D")

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/datetimelike_/test_sort_values.py

@@ -0,0 +1,315 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DatetimeIndex,
+    Index,
+    NaT,
+    PeriodIndex,
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+def check_freq_ascending(ordered, orig, ascending):
+    """
+    Check the expected freq on a PeriodIndex/DatetimeIndex/TimedeltaIndex
+    when the original index is generated (or generate-able) with
+    period_range/date_range/timedelta_range.
+    """
+    if isinstance(ordered, PeriodIndex):
+        assert ordered.freq == orig.freq
+    elif isinstance(ordered, (DatetimeIndex, TimedeltaIndex)):
+        if ascending:
+            assert ordered.freq.n == orig.freq.n
+        else:
+            assert ordered.freq.n == -1 * orig.freq.n
+
+
+def check_freq_nonmonotonic(ordered, orig):
+    """
+    Check the expected freq on a PeriodIndex/DatetimeIndex/TimedeltaIndex
+    when the original index is _not_ generated (or generate-able) with
+    period_range/date_range//timedelta_range.
+    """
+    if isinstance(ordered, PeriodIndex):
+        assert ordered.freq == orig.freq
+    elif isinstance(ordered, (DatetimeIndex, TimedeltaIndex)):
+        assert ordered.freq is None
+
+
+class TestSortValues:
+    @pytest.fixture(params=[DatetimeIndex, TimedeltaIndex, PeriodIndex])
+    def non_monotonic_idx(self, request):
+        if request.param is DatetimeIndex:
+            return DatetimeIndex(["2000-01-04", "2000-01-01", "2000-01-02"])
+        elif request.param is PeriodIndex:
+            dti = DatetimeIndex(["2000-01-04", "2000-01-01", "2000-01-02"])
+            return dti.to_period("D")
+        else:
+            return TimedeltaIndex(
+                ["1 day 00:00:05", "1 day 00:00:01", "1 day 00:00:02"]
+            )
+
+    def test_argmin_argmax(self, non_monotonic_idx):
+        assert non_monotonic_idx.argmin() == 1
+        assert non_monotonic_idx.argmax() == 0
+
+    def test_sort_values(self, non_monotonic_idx):
+        idx = non_monotonic_idx
+        ordered = idx.sort_values()
+        assert ordered.is_monotonic_increasing
+        ordered = idx.sort_values(ascending=False)
+        assert ordered[::-1].is_monotonic_increasing
+
+        ordered, dexer = idx.sort_values(return_indexer=True)
+        assert ordered.is_monotonic_increasing
+        tm.assert_numpy_array_equal(dexer, np.array([1, 2, 0], dtype=np.intp))
+
+        ordered, dexer = idx.sort_values(return_indexer=True, ascending=False)
+        assert ordered[::-1].is_monotonic_increasing
+        tm.assert_numpy_array_equal(dexer, np.array([0, 2, 1], dtype=np.intp))
+
+    def check_sort_values_with_freq(self, idx):
+        ordered = idx.sort_values()
+        tm.assert_index_equal(ordered, idx)
+        check_freq_ascending(ordered, idx, True)
+
+        ordered = idx.sort_values(ascending=False)
+        expected = idx[::-1]
+        tm.assert_index_equal(ordered, expected)
+        check_freq_ascending(ordered, idx, False)
+
+        ordered, indexer = idx.sort_values(return_indexer=True)
+        tm.assert_index_equal(ordered, idx)
+        tm.assert_numpy_array_equal(indexer, np.array([0, 1, 2], dtype=np.intp))
+        check_freq_ascending(ordered, idx, True)
+
+        ordered, indexer = idx.sort_values(return_indexer=True, ascending=False)
+        expected = idx[::-1]
+        tm.assert_index_equal(ordered, expected)
+        tm.assert_numpy_array_equal(indexer, np.array([2, 1, 0], dtype=np.intp))
+        check_freq_ascending(ordered, idx, False)
+
+    @pytest.mark.parametrize("freq", ["D", "H"])
+    def test_sort_values_with_freq_timedeltaindex(self, freq):
+        # GH#10295
+        idx = timedelta_range(start=f"1{freq}", periods=3, freq=freq).rename("idx")
+
+        self.check_sort_values_with_freq(idx)
+
+    @pytest.mark.parametrize(
+        "idx",
+        [
+            DatetimeIndex(
+                ["2011-01-01", "2011-01-02", "2011-01-03"], freq="D", name="idx"
+            ),
+            DatetimeIndex(
+                ["2011-01-01 09:00", "2011-01-01 10:00", "2011-01-01 11:00"],
+                freq="H",
+                name="tzidx",
+                tz="Asia/Tokyo",
+            ),
+        ],
+    )
+    def test_sort_values_with_freq_datetimeindex(self, idx):
+        self.check_sort_values_with_freq(idx)
+
+    @pytest.mark.parametrize("freq", ["D", "2D", "4D"])
+    def test_sort_values_with_freq_periodindex(self, freq):
+        # here with_freq refers to being period_range-like
+        idx = PeriodIndex(
+            ["2011-01-01", "2011-01-02", "2011-01-03"], freq=freq, name="idx"
+        )
+        self.check_sort_values_with_freq(idx)
+
+    @pytest.mark.parametrize(
+        "idx",
+        [
+            PeriodIndex(["2011", "2012", "2013"], name="pidx", freq="A"),
+            Index([2011, 2012, 2013], name="idx"),  # for compatibility check
+        ],
+    )
+    def test_sort_values_with_freq_periodindex2(self, idx):
+        # here with_freq indicates this is period_range-like
+        self.check_sort_values_with_freq(idx)
+
+    def check_sort_values_without_freq(self, idx, expected):
+        ordered = idx.sort_values(na_position="first")
+        tm.assert_index_equal(ordered, expected)
+        check_freq_nonmonotonic(ordered, idx)
+
+        if not idx.isna().any():
+            ordered = idx.sort_values()
+            tm.assert_index_equal(ordered, expected)
+            check_freq_nonmonotonic(ordered, idx)
+
+        ordered = idx.sort_values(ascending=False)
+        tm.assert_index_equal(ordered, expected[::-1])
+        check_freq_nonmonotonic(ordered, idx)
+
+        ordered, indexer = idx.sort_values(return_indexer=True, na_position="first")
+        tm.assert_index_equal(ordered, expected)
+
+        exp = np.array([0, 4, 3, 1, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, exp)
+        check_freq_nonmonotonic(ordered, idx)
+
+        if not idx.isna().any():
+            ordered, indexer = idx.sort_values(return_indexer=True)
+            tm.assert_index_equal(ordered, expected)
+
+            exp = np.array([0, 4, 3, 1, 2], dtype=np.intp)
+            tm.assert_numpy_array_equal(indexer, exp)
+            check_freq_nonmonotonic(ordered, idx)
+
+        ordered, indexer = idx.sort_values(return_indexer=True, ascending=False)
+        tm.assert_index_equal(ordered, expected[::-1])
+
+        exp = np.array([2, 1, 3, 0, 4], dtype=np.intp)
+        tm.assert_numpy_array_equal(indexer, exp)
+        check_freq_nonmonotonic(ordered, idx)
+
+    def test_sort_values_without_freq_timedeltaindex(self):
+        # GH#10295
+
+        idx = TimedeltaIndex(
+            ["1 hour", "3 hour", "5 hour", "2 hour ", "1 hour"], name="idx1"
+        )
+        expected = TimedeltaIndex(
+            ["1 hour", "1 hour", "2 hour", "3 hour", "5 hour"], name="idx1"
+        )
+        self.check_sort_values_without_freq(idx, expected)
+
+    @pytest.mark.parametrize(
+        "index_dates,expected_dates",
+        [
+            (
+                ["2011-01-01", "2011-01-03", "2011-01-05", "2011-01-02", "2011-01-01"],
+                ["2011-01-01", "2011-01-01", "2011-01-02", "2011-01-03", "2011-01-05"],
+            ),
+            (
+                ["2011-01-01", "2011-01-03", "2011-01-05", "2011-01-02", "2011-01-01"],
+                ["2011-01-01", "2011-01-01", "2011-01-02", "2011-01-03", "2011-01-05"],
+            ),
+            (
+                [NaT, "2011-01-03", "2011-01-05", "2011-01-02", NaT],
+                [NaT, NaT, "2011-01-02", "2011-01-03", "2011-01-05"],
+            ),
+        ],
+    )
+    def test_sort_values_without_freq_datetimeindex(
+        self, index_dates, expected_dates, tz_naive_fixture
+    ):
+        tz = tz_naive_fixture
+
+        # without freq
+        idx = DatetimeIndex(index_dates, tz=tz, name="idx")
+        expected = DatetimeIndex(expected_dates, tz=tz, name="idx")
+
+        self.check_sort_values_without_freq(idx, expected)
+
+    @pytest.mark.parametrize(
+        "idx,expected",
+        [
+            (
+                PeriodIndex(
+                    [
+                        "2011-01-01",
+                        "2011-01-03",
+                        "2011-01-05",
+                        "2011-01-02",
+                        "2011-01-01",
+                    ],
+                    freq="D",
+                    name="idx1",
+                ),
+                PeriodIndex(
+                    [
+                        "2011-01-01",
+                        "2011-01-01",
+                        "2011-01-02",
+                        "2011-01-03",
+                        "2011-01-05",
+                    ],
+                    freq="D",
+                    name="idx1",
+                ),
+            ),
+            (
+                PeriodIndex(
+                    [
+                        "2011-01-01",
+                        "2011-01-03",
+                        "2011-01-05",
+                        "2011-01-02",
+                        "2011-01-01",
+                    ],
+                    freq="D",
+                    name="idx2",
+                ),
+                PeriodIndex(
+                    [
+                        "2011-01-01",
+                        "2011-01-01",
+                        "2011-01-02",
+                        "2011-01-03",
+                        "2011-01-05",
+                    ],
+                    freq="D",
+                    name="idx2",
+                ),
+            ),
+            (
+                PeriodIndex(
+                    [NaT, "2011-01-03", "2011-01-05", "2011-01-02", NaT],
+                    freq="D",
+                    name="idx3",
+                ),
+                PeriodIndex(
+                    [NaT, NaT, "2011-01-02", "2011-01-03", "2011-01-05"],
+                    freq="D",
+                    name="idx3",
+                ),
+            ),
+            (
+                PeriodIndex(
+                    ["2011", "2013", "2015", "2012", "2011"], name="pidx", freq="A"
+                ),
+                PeriodIndex(
+                    ["2011", "2011", "2012", "2013", "2015"], name="pidx", freq="A"
+                ),
+            ),
+            (
+                # For compatibility check
+                Index([2011, 2013, 2015, 2012, 2011], name="idx"),
+                Index([2011, 2011, 2012, 2013, 2015], name="idx"),
+            ),
+        ],
+    )
+    def test_sort_values_without_freq_periodindex(self, idx, expected):
+        # here without_freq means not generateable by period_range
+        self.check_sort_values_without_freq(idx, expected)
+
+    def test_sort_values_without_freq_periodindex_nat(self):
+        # doesn't quite fit into check_sort_values_without_freq
+        idx = PeriodIndex(["2011", "2013", "NaT", "2011"], name="pidx", freq="D")
+        expected = PeriodIndex(["NaT", "2011", "2011", "2013"], name="pidx", freq="D")
+
+        ordered = idx.sort_values(na_position="first")
+        tm.assert_index_equal(ordered, expected)
+        check_freq_nonmonotonic(ordered, idx)
+
+        ordered = idx.sort_values(ascending=False)
+        tm.assert_index_equal(ordered, expected[::-1])
+        check_freq_nonmonotonic(ordered, idx)
+
+
+def test_order_stability_compat():
+    # GH#35922. sort_values is stable both for normal and datetime-like Index
+    pidx = PeriodIndex(["2011", "2013", "2015", "2012", "2011"], name="pidx", freq="A")
+    iidx = Index([2011, 2013, 2015, 2012, 2011], name="idx")
+    ordered1, indexer1 = pidx.sort_values(return_indexer=True, ascending=False)
+    ordered2, indexer2 = iidx.sort_values(return_indexer=True, ascending=False)
+    tm.assert_numpy_array_equal(indexer1, indexer2)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/datetimelike_/test_value_counts.py

@@ -0,0 +1,103 @@
+import numpy as np
+
+from pandas import (
+    DatetimeIndex,
+    NaT,
+    PeriodIndex,
+    Series,
+    TimedeltaIndex,
+    date_range,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestValueCounts:
+    # GH#7735
+
+    def test_value_counts_unique_datetimeindex(self, tz_naive_fixture):
+        tz = tz_naive_fixture
+        orig = date_range("2011-01-01 09:00", freq="H", periods=10, tz=tz)
+        self._check_value_counts_with_repeats(orig)
+
+    def test_value_counts_unique_timedeltaindex(self):
+        orig = timedelta_range("1 days 09:00:00", freq="H", periods=10)
+        self._check_value_counts_with_repeats(orig)
+
+    def test_value_counts_unique_periodindex(self):
+        orig = period_range("2011-01-01 09:00", freq="H", periods=10)
+        self._check_value_counts_with_repeats(orig)
+
+    def _check_value_counts_with_repeats(self, orig):
+        # create repeated values, 'n'th element is repeated by n+1 times
+        idx = type(orig)(
+            np.repeat(orig._values, range(1, len(orig) + 1)), dtype=orig.dtype
+        )
+
+        exp_idx = orig[::-1]
+        if not isinstance(exp_idx, PeriodIndex):
+            exp_idx = exp_idx._with_freq(None)
+        expected = Series(range(10, 0, -1), index=exp_idx, dtype="int64", name="count")
+
+        for obj in [idx, Series(idx)]:
+            tm.assert_series_equal(obj.value_counts(), expected)
+
+        tm.assert_index_equal(idx.unique(), orig)
+
+    def test_value_counts_unique_datetimeindex2(self, tz_naive_fixture):
+        tz = tz_naive_fixture
+        idx = DatetimeIndex(
+            [
+                "2013-01-01 09:00",
+                "2013-01-01 09:00",
+                "2013-01-01 09:00",
+                "2013-01-01 08:00",
+                "2013-01-01 08:00",
+                NaT,
+            ],
+            tz=tz,
+        )
+        self._check_value_counts_dropna(idx)
+
+    def test_value_counts_unique_timedeltaindex2(self):
+        idx = TimedeltaIndex(
+            [
+                "1 days 09:00:00",
+                "1 days 09:00:00",
+                "1 days 09:00:00",
+                "1 days 08:00:00",
+                "1 days 08:00:00",
+                NaT,
+            ]
+        )
+        self._check_value_counts_dropna(idx)
+
+    def test_value_counts_unique_periodindex2(self):
+        idx = PeriodIndex(
+            [
+                "2013-01-01 09:00",
+                "2013-01-01 09:00",
+                "2013-01-01 09:00",
+                "2013-01-01 08:00",
+                "2013-01-01 08:00",
+                NaT,
+            ],
+            freq="H",
+        )
+        self._check_value_counts_dropna(idx)
+
+    def _check_value_counts_dropna(self, idx):
+        exp_idx = idx[[2, 3]]
+        expected = Series([3, 2], index=exp_idx, name="count")
+
+        for obj in [idx, Series(idx)]:
+            tm.assert_series_equal(obj.value_counts(), expected)
+
+        exp_idx = idx[[2, 3, -1]]
+        expected = Series([3, 2, 1], index=exp_idx, name="count")
+
+        for obj in [idx, Series(idx)]:
+            tm.assert_series_equal(obj.value_counts(dropna=False), expected)
+
+        tm.assert_index_equal(idx.unique(), exp_idx)