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@@ -1275,3 +1275,5 @@ videochat2/lib/python3.10/site-packages/pandas/_libs/tslibs/conversion.cpython-3
 videochat2/lib/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 videochat2/lib/python3.10/site-packages/pandas/_libs/tslibs/timezones.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 videochat2/lib/python3.10/site-packages/pandas/core/groupby/__pycache__/groupby.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+videochat2/lib/python3.10/site-packages/pandas/core/indexes/__pycache__/base.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+videochat2/lib/python3.10/site-packages/pandas/core/__pycache__/series.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text

videochat2/lib/python3.10/site-packages/pandas/core/__pycache__/series.cpython-310.pyc

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videochat2/lib/python3.10/site-packages/pandas/core/indexes/__pycache__/base.cpython-310.pyc

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

@@ -0,0 +1,947 @@
+from __future__ import annotations
+
+from datetime import datetime
+import gc
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import Timestamp
+
+from pandas.core.dtypes.common import (
+    is_datetime64tz_dtype,
+    is_integer_dtype,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    TimedeltaIndex,
+    isna,
+)
+import pandas._testing as tm
+from pandas.core.arrays import BaseMaskedArray
+
+
+class Base:
+    """
+    Base class for index sub-class tests.
+    """
+
+    _index_cls: type[Index]
+
+    @pytest.fixture
+    def simple_index(self):
+        raise NotImplementedError("Method not implemented")
+
+    def create_index(self) -> Index:
+        raise NotImplementedError("Method not implemented")
+
+    def test_pickle_compat_construction(self):
+        # need an object to create with
+        msg = "|".join(
+            [
+                r"Index\(\.\.\.\) must be called with a collection of some "
+                r"kind, None was passed",
+                r"DatetimeIndex\(\) must be called with a collection of some "
+                r"kind, None was passed",
+                r"TimedeltaIndex\(\) must be called with a collection of some "
+                r"kind, None was passed",
+                r"__new__\(\) missing 1 required positional argument: 'data'",
+                r"__new__\(\) takes at least 2 arguments \(1 given\)",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            self._index_cls()
+
+    def test_shift(self, simple_index):
+        # GH8083 test the base class for shift
+        idx = simple_index
+        msg = (
+            f"This method is only implemented for DatetimeIndex, PeriodIndex and "
+            f"TimedeltaIndex; Got type {type(idx).__name__}"
+        )
+        with pytest.raises(NotImplementedError, match=msg):
+            idx.shift(1)
+        with pytest.raises(NotImplementedError, match=msg):
+            idx.shift(1, 2)
+
+    def test_constructor_name_unhashable(self, simple_index):
+        # GH#29069 check that name is hashable
+        # See also same-named test in tests.series.test_constructors
+        idx = simple_index
+        with pytest.raises(TypeError, match="Index.name must be a hashable type"):
+            type(idx)(idx, name=[])
+
+    def test_create_index_existing_name(self, simple_index):
+        # GH11193, when an existing index is passed, and a new name is not
+        # specified, the new index should inherit the previous object name
+        expected = simple_index
+        if not isinstance(expected, MultiIndex):
+            expected.name = "foo"
+            result = Index(expected)
+            tm.assert_index_equal(result, expected)
+
+            result = Index(expected, name="bar")
+            expected.name = "bar"
+            tm.assert_index_equal(result, expected)
+        else:
+            expected.names = ["foo", "bar"]
+            result = Index(expected)
+            tm.assert_index_equal(
+                result,
+                Index(
+                    Index(
+                        [
+                            ("foo", "one"),
+                            ("foo", "two"),
+                            ("bar", "one"),
+                            ("baz", "two"),
+                            ("qux", "one"),
+                            ("qux", "two"),
+                        ],
+                        dtype="object",
+                    ),
+                    names=["foo", "bar"],
+                ),
+            )
+
+            result = Index(expected, names=["A", "B"])
+            tm.assert_index_equal(
+                result,
+                Index(
+                    Index(
+                        [
+                            ("foo", "one"),
+                            ("foo", "two"),
+                            ("bar", "one"),
+                            ("baz", "two"),
+                            ("qux", "one"),
+                            ("qux", "two"),
+                        ],
+                        dtype="object",
+                    ),
+                    names=["A", "B"],
+                ),
+            )
+
+    def test_numeric_compat(self, simple_index):
+        idx = simple_index
+        # Check that this doesn't cover MultiIndex case, if/when it does,
+        #  we can remove multi.test_compat.test_numeric_compat
+        assert not isinstance(idx, MultiIndex)
+        if type(idx) is Index:
+            return
+
+        typ = type(idx._data).__name__
+        cls = type(idx).__name__
+        lmsg = "|".join(
+            [
+                rf"unsupported operand type\(s\) for \*: '{typ}' and 'int'",
+                "cannot perform (__mul__|__truediv__|__floordiv__) with "
+                f"this index type: ({cls}|{typ})",
+            ]
+        )
+        with pytest.raises(TypeError, match=lmsg):
+            idx * 1
+        rmsg = "|".join(
+            [
+                rf"unsupported operand type\(s\) for \*: 'int' and '{typ}'",
+                "cannot perform (__rmul__|__rtruediv__|__rfloordiv__) with "
+                f"this index type: ({cls}|{typ})",
+            ]
+        )
+        with pytest.raises(TypeError, match=rmsg):
+            1 * idx
+
+        div_err = lmsg.replace("*", "/")
+        with pytest.raises(TypeError, match=div_err):
+            idx / 1
+        div_err = rmsg.replace("*", "/")
+        with pytest.raises(TypeError, match=div_err):
+            1 / idx
+
+        floordiv_err = lmsg.replace("*", "//")
+        with pytest.raises(TypeError, match=floordiv_err):
+            idx // 1
+        floordiv_err = rmsg.replace("*", "//")
+        with pytest.raises(TypeError, match=floordiv_err):
+            1 // idx
+
+    def test_logical_compat(self, simple_index):
+        idx = simple_index
+        with pytest.raises(TypeError, match="cannot perform all"):
+            idx.all()
+        with pytest.raises(TypeError, match="cannot perform any"):
+            idx.any()
+
+    def test_repr_roundtrip(self, simple_index):
+        idx = simple_index
+        tm.assert_index_equal(eval(repr(idx)), idx)
+
+    def test_repr_max_seq_item_setting(self, simple_index):
+        # GH10182
+        idx = simple_index
+        idx = idx.repeat(50)
+        with pd.option_context("display.max_seq_items", None):
+            repr(idx)
+            assert "..." not in str(idx)
+
+    def test_ensure_copied_data(self, index):
+        # Check the "copy" argument of each Index.__new__ is honoured
+        # GH12309
+        init_kwargs = {}
+        if isinstance(index, PeriodIndex):
+            # Needs "freq" specification:
+            init_kwargs["freq"] = index.freq
+        elif isinstance(index, (RangeIndex, MultiIndex, CategoricalIndex)):
+            # RangeIndex cannot be initialized from data
+            # MultiIndex and CategoricalIndex are tested separately
+            return
+        elif index.dtype == object and index.inferred_type == "boolean":
+            init_kwargs["dtype"] = index.dtype
+
+        index_type = type(index)
+        result = index_type(index.values, copy=True, **init_kwargs)
+        if is_datetime64tz_dtype(index.dtype):
+            result = result.tz_localize("UTC").tz_convert(index.tz)
+        if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
+            index = index._with_freq(None)
+
+        tm.assert_index_equal(index, result)
+
+        if isinstance(index, PeriodIndex):
+            # .values an object array of Period, thus copied
+            result = index_type(ordinal=index.asi8, copy=False, **init_kwargs)
+            tm.assert_numpy_array_equal(index.asi8, result.asi8, check_same="same")
+        elif isinstance(index, IntervalIndex):
+            # checked in test_interval.py
+            pass
+        elif type(index) is Index and not isinstance(index.dtype, np.dtype):
+            result = index_type(index.values, copy=False, **init_kwargs)
+            tm.assert_index_equal(result, index)
+
+            if isinstance(index._values, BaseMaskedArray):
+                assert np.shares_memory(index._values._data, result._values._data)
+                tm.assert_numpy_array_equal(
+                    index._values._data, result._values._data, check_same="same"
+                )
+                assert np.shares_memory(index._values._mask, result._values._mask)
+                tm.assert_numpy_array_equal(
+                    index._values._mask, result._values._mask, check_same="same"
+                )
+            elif index.dtype == "string[python]":
+                assert np.shares_memory(index._values._ndarray, result._values._ndarray)
+                tm.assert_numpy_array_equal(
+                    index._values._ndarray, result._values._ndarray, check_same="same"
+                )
+            elif index.dtype == "string[pyarrow]":
+                assert tm.shares_memory(result._values, index._values)
+            else:
+                raise NotImplementedError(index.dtype)
+        else:
+            result = index_type(index.values, copy=False, **init_kwargs)
+            tm.assert_numpy_array_equal(index.values, result.values, check_same="same")
+
+    def test_memory_usage(self, index):
+        index._engine.clear_mapping()
+        result = index.memory_usage()
+        if index.empty:
+            # we report 0 for no-length
+            assert result == 0
+            return
+
+        # non-zero length
+        index.get_loc(index[0])
+        result2 = index.memory_usage()
+        result3 = index.memory_usage(deep=True)
+
+        # RangeIndex, IntervalIndex
+        # don't have engines
+        # Index[EA] has engine but it does not have a Hashtable .mapping
+        if not isinstance(index, (RangeIndex, IntervalIndex)) and not (
+            type(index) is Index and not isinstance(index.dtype, np.dtype)
+        ):
+            assert result2 > result
+
+        if index.inferred_type == "object":
+            assert result3 > result2
+
+    def test_argsort(self, index):
+        # separately tested
+        if isinstance(index, CategoricalIndex):
+            return
+
+        result = index.argsort()
+        expected = np.array(index).argsort()
+        tm.assert_numpy_array_equal(result, expected, check_dtype=False)
+
+    def test_numpy_argsort(self, index):
+        result = np.argsort(index)
+        expected = index.argsort()
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.argsort(index, kind="mergesort")
+        expected = index.argsort(kind="mergesort")
+        tm.assert_numpy_array_equal(result, expected)
+
+        # these are the only two types that perform
+        # pandas compatibility input validation - the
+        # rest already perform separate (or no) such
+        # validation via their 'values' attribute as
+        # defined in pandas.core.indexes/base.py - they
+        # cannot be changed at the moment due to
+        # backwards compatibility concerns
+        if isinstance(index, (CategoricalIndex, RangeIndex)):
+            msg = "the 'axis' parameter is not supported"
+            with pytest.raises(ValueError, match=msg):
+                np.argsort(index, axis=1)
+
+            msg = "the 'order' parameter is not supported"
+            with pytest.raises(ValueError, match=msg):
+                np.argsort(index, order=("a", "b"))
+
+    def test_repeat(self, simple_index):
+        rep = 2
+        idx = simple_index.copy()
+        new_index_cls = idx._constructor
+        expected = new_index_cls(idx.values.repeat(rep), name=idx.name)
+        tm.assert_index_equal(idx.repeat(rep), expected)
+
+        idx = simple_index
+        rep = np.arange(len(idx))
+        expected = new_index_cls(idx.values.repeat(rep), name=idx.name)
+        tm.assert_index_equal(idx.repeat(rep), expected)
+
+    def test_numpy_repeat(self, simple_index):
+        rep = 2
+        idx = simple_index
+        expected = idx.repeat(rep)
+        tm.assert_index_equal(np.repeat(idx, rep), expected)
+
+        msg = "the 'axis' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            np.repeat(idx, rep, axis=0)
+
+    def test_where(self, listlike_box, simple_index):
+        klass = listlike_box
+
+        idx = simple_index
+        if isinstance(idx, (DatetimeIndex, TimedeltaIndex)):
+            # where does not preserve freq
+            idx = idx._with_freq(None)
+
+        cond = [True] * len(idx)
+        result = idx.where(klass(cond))
+        expected = idx
+        tm.assert_index_equal(result, expected)
+
+        cond = [False] + [True] * len(idx[1:])
+        expected = Index([idx._na_value] + idx[1:].tolist(), dtype=idx.dtype)
+        result = idx.where(klass(cond))
+        tm.assert_index_equal(result, expected)
+
+    def test_insert_base(self, index):
+        result = index[1:4]
+
+        if not len(index):
+            return
+
+        # test 0th element
+        assert index[0:4].equals(result.insert(0, index[0]))
+
+    def test_insert_out_of_bounds(self, index):
+        # TypeError/IndexError matches what np.insert raises in these cases
+
+        if len(index) > 0:
+            err = TypeError
+        else:
+            err = IndexError
+        if len(index) == 0:
+            # 0 vs 0.5 in error message varies with numpy version
+            msg = "index (0|0.5) is out of bounds for axis 0 with size 0"
+        else:
+            msg = "slice indices must be integers or None or have an __index__ method"
+        with pytest.raises(err, match=msg):
+            index.insert(0.5, "foo")
+
+        msg = "|".join(
+            [
+                r"index -?\d+ is out of bounds for axis 0 with size \d+",
+                "loc must be an integer between",
+            ]
+        )
+        with pytest.raises(IndexError, match=msg):
+            index.insert(len(index) + 1, 1)
+
+        with pytest.raises(IndexError, match=msg):
+            index.insert(-len(index) - 1, 1)
+
+    def test_delete_base(self, index):
+        if not len(index):
+            return
+
+        if isinstance(index, RangeIndex):
+            # tested in class
+            return
+
+        expected = index[1:]
+        result = index.delete(0)
+        assert result.equals(expected)
+        assert result.name == expected.name
+
+        expected = index[:-1]
+        result = index.delete(-1)
+        assert result.equals(expected)
+        assert result.name == expected.name
+
+        length = len(index)
+        msg = f"index {length} is out of bounds for axis 0 with size {length}"
+        with pytest.raises(IndexError, match=msg):
+            index.delete(length)
+
+    def test_equals(self, index):
+        if isinstance(index, IntervalIndex):
+            # IntervalIndex tested separately, the index.equals(index.astype(object))
+            #  fails for IntervalIndex
+            return
+
+        is_ea_idx = type(index) is Index and not isinstance(index.dtype, np.dtype)
+
+        assert index.equals(index)
+        assert index.equals(index.copy())
+        if not is_ea_idx:
+            # doesn't hold for e.g. IntegerDtype
+            assert index.equals(index.astype(object))
+
+        assert not index.equals(list(index))
+        assert not index.equals(np.array(index))
+
+        # Cannot pass in non-int64 dtype to RangeIndex
+        if not isinstance(index, RangeIndex) and not is_ea_idx:
+            same_values = Index(index, dtype=object)
+            assert index.equals(same_values)
+            assert same_values.equals(index)
+
+        if index.nlevels == 1:
+            # do not test MultiIndex
+            assert not index.equals(Series(index))
+
+    def test_equals_op(self, simple_index):
+        # GH9947, GH10637
+        index_a = simple_index
+
+        n = len(index_a)
+        index_b = index_a[0:-1]
+        index_c = index_a[0:-1].append(index_a[-2:-1])
+        index_d = index_a[0:1]
+
+        msg = "Lengths must match|could not be broadcast"
+        with pytest.raises(ValueError, match=msg):
+            index_a == index_b
+        expected1 = np.array([True] * n)
+        expected2 = np.array([True] * (n - 1) + [False])
+        tm.assert_numpy_array_equal(index_a == index_a, expected1)
+        tm.assert_numpy_array_equal(index_a == index_c, expected2)
+
+        # test comparisons with numpy arrays
+        array_a = np.array(index_a)
+        array_b = np.array(index_a[0:-1])
+        array_c = np.array(index_a[0:-1].append(index_a[-2:-1]))
+        array_d = np.array(index_a[0:1])
+        with pytest.raises(ValueError, match=msg):
+            index_a == array_b
+        tm.assert_numpy_array_equal(index_a == array_a, expected1)
+        tm.assert_numpy_array_equal(index_a == array_c, expected2)
+
+        # test comparisons with Series
+        series_a = Series(array_a)
+        series_b = Series(array_b)
+        series_c = Series(array_c)
+        series_d = Series(array_d)
+        with pytest.raises(ValueError, match=msg):
+            index_a == series_b
+
+        tm.assert_numpy_array_equal(index_a == series_a, expected1)
+        tm.assert_numpy_array_equal(index_a == series_c, expected2)
+
+        # cases where length is 1 for one of them
+        with pytest.raises(ValueError, match="Lengths must match"):
+            index_a == index_d
+        with pytest.raises(ValueError, match="Lengths must match"):
+            index_a == series_d
+        with pytest.raises(ValueError, match="Lengths must match"):
+            index_a == array_d
+        msg = "Can only compare identically-labeled Series objects"
+        with pytest.raises(ValueError, match=msg):
+            series_a == series_d
+        with pytest.raises(ValueError, match="Lengths must match"):
+            series_a == array_d
+
+        # comparing with a scalar should broadcast; note that we are excluding
+        # MultiIndex because in this case each item in the index is a tuple of
+        # length 2, and therefore is considered an array of length 2 in the
+        # comparison instead of a scalar
+        if not isinstance(index_a, MultiIndex):
+            expected3 = np.array([False] * (len(index_a) - 2) + [True, False])
+            # assuming the 2nd to last item is unique in the data
+            item = index_a[-2]
+            tm.assert_numpy_array_equal(index_a == item, expected3)
+            tm.assert_series_equal(series_a == item, Series(expected3))
+
+    def test_format(self, simple_index):
+        # GH35439
+        idx = simple_index
+        expected = [str(x) for x in idx]
+        assert idx.format() == expected
+
+    def test_format_empty(self):
+        # GH35712
+        empty_idx = self._index_cls([])
+        assert empty_idx.format() == []
+        assert empty_idx.format(name=True) == [""]
+
+    def test_fillna(self, index):
+        # GH 11343
+        if len(index) == 0:
+            return
+        elif index.dtype == bool:
+            # can't hold NAs
+            return
+        elif isinstance(index, Index) and is_integer_dtype(index.dtype):
+            return
+        elif isinstance(index, MultiIndex):
+            idx = index.copy(deep=True)
+            msg = "isna is not defined for MultiIndex"
+            with pytest.raises(NotImplementedError, match=msg):
+                idx.fillna(idx[0])
+        else:
+            idx = index.copy(deep=True)
+            result = idx.fillna(idx[0])
+            tm.assert_index_equal(result, idx)
+            assert result is not idx
+
+            msg = "'value' must be a scalar, passed: "
+            with pytest.raises(TypeError, match=msg):
+                idx.fillna([idx[0]])
+
+            idx = index.copy(deep=True)
+            values = idx._values
+
+            values[1] = np.nan
+
+            idx = type(index)(values)
+
+            msg = "does not support 'downcast'"
+            with pytest.raises(NotImplementedError, match=msg):
+                # For now at least, we only raise if there are NAs present
+                idx.fillna(idx[0], downcast="infer")
+
+            expected = np.array([False] * len(idx), dtype=bool)
+            expected[1] = True
+            tm.assert_numpy_array_equal(idx._isnan, expected)
+            assert idx.hasnans is True
+
+    def test_nulls(self, index):
+        # this is really a smoke test for the methods
+        # as these are adequately tested for function elsewhere
+        if len(index) == 0:
+            tm.assert_numpy_array_equal(index.isna(), np.array([], dtype=bool))
+        elif isinstance(index, MultiIndex):
+            idx = index.copy()
+            msg = "isna is not defined for MultiIndex"
+            with pytest.raises(NotImplementedError, match=msg):
+                idx.isna()
+        elif not index.hasnans:
+            tm.assert_numpy_array_equal(index.isna(), np.zeros(len(index), dtype=bool))
+            tm.assert_numpy_array_equal(index.notna(), np.ones(len(index), dtype=bool))
+        else:
+            result = isna(index)
+            tm.assert_numpy_array_equal(index.isna(), result)
+            tm.assert_numpy_array_equal(index.notna(), ~result)
+
+    def test_empty(self, simple_index):
+        # GH 15270
+        idx = simple_index
+        assert not idx.empty
+        assert idx[:0].empty
+
+    def test_join_self_unique(self, join_type, simple_index):
+        idx = simple_index
+        if idx.is_unique:
+            joined = idx.join(idx, how=join_type)
+            assert (idx == joined).all()
+
+    def test_map(self, simple_index):
+        # callable
+        idx = simple_index
+
+        result = idx.map(lambda x: x)
+        # RangeIndex are equivalent to the similar Index with int64 dtype
+        tm.assert_index_equal(result, idx, exact="equiv")
+
+    @pytest.mark.parametrize(
+        "mapper",
+        [
+            lambda values, index: {i: e for e, i in zip(values, index)},
+            lambda values, index: Series(values, index),
+        ],
+    )
+    def test_map_dictlike(self, mapper, simple_index):
+        idx = simple_index
+        if isinstance(idx, CategoricalIndex):
+            # FIXME: this fails with CategoricalIndex bc it goes through
+            # Categorical.map which ends up calling get_indexer with
+            #  non-unique values, which raises.  This _should_ work fine for
+            #  CategoricalIndex.
+            pytest.skip(f"skipping tests for {type(idx)}")
+
+        identity = mapper(idx.values, idx)
+
+        result = idx.map(identity)
+        # RangeIndex are equivalent to the similar Index with int64 dtype
+        tm.assert_index_equal(result, idx, exact="equiv")
+
+        # empty mappable
+        dtype = None
+        if idx.dtype.kind == "f":
+            dtype = idx.dtype
+
+        expected = Index([np.nan] * len(idx), dtype=dtype)
+        result = idx.map(mapper(expected, idx))
+        tm.assert_index_equal(result, expected)
+
+    def test_map_str(self, simple_index):
+        # GH 31202
+        idx = simple_index
+        result = idx.map(str)
+        expected = Index([str(x) for x in idx], dtype=object)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("copy", [True, False])
+    @pytest.mark.parametrize("name", [None, "foo"])
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_astype_category(self, copy, name, ordered, simple_index):
+        # GH 18630
+        idx = simple_index
+        if name:
+            idx = idx.rename(name)
+
+        # standard categories
+        dtype = CategoricalDtype(ordered=ordered)
+        result = idx.astype(dtype, copy=copy)
+        expected = CategoricalIndex(idx, name=name, ordered=ordered)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        # non-standard categories
+        dtype = CategoricalDtype(idx.unique().tolist()[:-1], ordered)
+        result = idx.astype(dtype, copy=copy)
+        expected = CategoricalIndex(idx, name=name, dtype=dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+        if ordered is False:
+            # dtype='category' defaults to ordered=False, so only test once
+            result = idx.astype("category", copy=copy)
+            expected = CategoricalIndex(idx, name=name)
+            tm.assert_index_equal(result, expected, exact=True)
+
+    def test_is_unique(self, simple_index):
+        # initialize a unique index
+        index = simple_index.drop_duplicates()
+        assert index.is_unique is True
+
+        # empty index should be unique
+        index_empty = index[:0]
+        assert index_empty.is_unique is True
+
+        # test basic dupes
+        index_dup = index.insert(0, index[0])
+        assert index_dup.is_unique is False
+
+        # single NA should be unique
+        index_na = index.insert(0, np.nan)
+        assert index_na.is_unique is True
+
+        # multiple NA should not be unique
+        index_na_dup = index_na.insert(0, np.nan)
+        assert index_na_dup.is_unique is False
+
+    @pytest.mark.arm_slow
+    def test_engine_reference_cycle(self, simple_index):
+        # GH27585
+        index = simple_index
+        nrefs_pre = len(gc.get_referrers(index))
+        index._engine
+        assert len(gc.get_referrers(index)) == nrefs_pre
+
+    def test_getitem_2d_deprecated(self, simple_index):
+        # GH#30588, GH#31479
+        idx = simple_index
+        msg = "Multi-dimensional indexing"
+        with pytest.raises(ValueError, match=msg):
+            idx[:, None]
+
+        if not isinstance(idx, RangeIndex):
+            # GH#44051 RangeIndex already raised pre-2.0 with a different message
+            with pytest.raises(ValueError, match=msg):
+                idx[True]
+            with pytest.raises(ValueError, match=msg):
+                idx[False]
+        else:
+            msg = "only integers, slices"
+            with pytest.raises(IndexError, match=msg):
+                idx[True]
+            with pytest.raises(IndexError, match=msg):
+                idx[False]
+
+    def test_copy_shares_cache(self, simple_index):
+        # GH32898, GH36840
+        idx = simple_index
+        idx.get_loc(idx[0])  # populates the _cache.
+        copy = idx.copy()
+
+        assert copy._cache is idx._cache
+
+    def test_shallow_copy_shares_cache(self, simple_index):
+        # GH32669, GH36840
+        idx = simple_index
+        idx.get_loc(idx[0])  # populates the _cache.
+        shallow_copy = idx._view()
+
+        assert shallow_copy._cache is idx._cache
+
+        shallow_copy = idx._shallow_copy(idx._data)
+        assert shallow_copy._cache is not idx._cache
+        assert shallow_copy._cache == {}
+
+    def test_index_groupby(self, simple_index):
+        idx = simple_index[:5]
+        to_groupby = np.array([1, 2, np.nan, 2, 1])
+        tm.assert_dict_equal(
+            idx.groupby(to_groupby), {1.0: idx[[0, 4]], 2.0: idx[[1, 3]]}
+        )
+
+        to_groupby = DatetimeIndex(
+            [
+                datetime(2011, 11, 1),
+                datetime(2011, 12, 1),
+                pd.NaT,
+                datetime(2011, 12, 1),
+                datetime(2011, 11, 1),
+            ],
+            tz="UTC",
+        ).values
+
+        ex_keys = [Timestamp("2011-11-01"), Timestamp("2011-12-01")]
+        expected = {ex_keys[0]: idx[[0, 4]], ex_keys[1]: idx[[1, 3]]}
+        tm.assert_dict_equal(idx.groupby(to_groupby), expected)
+
+    def test_append_preserves_dtype(self, simple_index):
+        # In particular Index with dtype float32
+        index = simple_index
+        N = len(index)
+
+        result = index.append(index)
+        assert result.dtype == index.dtype
+        tm.assert_index_equal(result[:N], index, check_exact=True)
+        tm.assert_index_equal(result[N:], index, check_exact=True)
+
+        alt = index.take(list(range(N)) * 2)
+        tm.assert_index_equal(result, alt, check_exact=True)
+
+    def test_inv(self, simple_index):
+        idx = simple_index
+
+        if idx.dtype.kind in ["i", "u"]:
+            res = ~idx
+            expected = Index(~idx.values, name=idx.name)
+            tm.assert_index_equal(res, expected)
+
+            # check that we are matching Series behavior
+            res2 = ~Series(idx)
+            tm.assert_series_equal(res2, Series(expected))
+        else:
+            if idx.dtype.kind == "f":
+                msg = "ufunc 'invert' not supported for the input types"
+            else:
+                msg = "bad operand"
+            with pytest.raises(TypeError, match=msg):
+                ~idx
+
+            # check that we get the same behavior with Series
+            with pytest.raises(TypeError, match=msg):
+                ~Series(idx)
+
+    def test_is_boolean_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(FutureWarning):
+            idx.is_boolean()
+
+    def test_is_floating_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(FutureWarning):
+            idx.is_floating()
+
+    def test_is_integer_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(FutureWarning):
+            idx.is_integer()
+
+    def test_holds_integer_deprecated(self, simple_index):
+        # GH50243
+        idx = simple_index
+        msg = f"{type(idx).__name__}.holds_integer is deprecated. "
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            idx.holds_integer()
+
+    def test_is_numeric_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(
+            FutureWarning,
+            match=f"{type(idx).__name__}.is_numeric is deprecated. ",
+        ):
+            idx.is_numeric()
+
+    def test_is_categorical_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(
+            FutureWarning,
+            match=r"Use pandas\.api\.types\.is_categorical_dtype instead",
+        ):
+            idx.is_categorical()
+
+    def test_is_interval_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(FutureWarning):
+            idx.is_interval()
+
+    def test_is_object_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(FutureWarning):
+            idx.is_object()
+
+
+class NumericBase(Base):
+    """
+    Base class for numeric index (incl. RangeIndex) sub-class tests.
+    """
+
+    def test_constructor_unwraps_index(self, dtype):
+        index_cls = self._index_cls
+
+        idx = Index([1, 2], dtype=dtype)
+        result = index_cls(idx)
+        expected = np.array([1, 2], dtype=idx.dtype)
+        tm.assert_numpy_array_equal(result._data, expected)
+
+    def test_where(self):
+        # Tested in numeric.test_indexing
+        pass
+
+    def test_can_hold_identifiers(self, simple_index):
+        idx = simple_index
+        key = idx[0]
+        assert idx._can_hold_identifiers_and_holds_name(key) is False
+
+    def test_view(self, dtype):
+        index_cls = self._index_cls
+
+        idx = index_cls([], dtype=dtype, name="Foo")
+        idx_view = idx.view()
+        assert idx_view.name == "Foo"
+
+        idx_view = idx.view(dtype)
+        tm.assert_index_equal(idx, index_cls(idx_view, name="Foo"), exact=True)
+
+        idx_view = idx.view(index_cls)
+        tm.assert_index_equal(idx, index_cls(idx_view, name="Foo"), exact=True)
+
+    def test_format(self, simple_index):
+        # GH35439
+        idx = simple_index
+        max_width = max(len(str(x)) for x in idx)
+        expected = [str(x).ljust(max_width) for x in idx]
+        assert idx.format() == expected
+
+    def test_numeric_compat(self):
+        pass  # override Base method
+
+    def test_insert_non_na(self, simple_index):
+        # GH#43921 inserting an element that we know we can hold should
+        #  not change dtype or type (except for RangeIndex)
+        index = simple_index
+
+        result = index.insert(0, index[0])
+
+        expected = Index([index[0]] + list(index), dtype=index.dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_insert_na(self, nulls_fixture, simple_index):
+        # GH 18295 (test missing)
+        index = simple_index
+        na_val = nulls_fixture
+
+        if na_val is pd.NaT:
+            expected = Index([index[0], pd.NaT] + list(index[1:]), dtype=object)
+        else:
+            expected = Index([index[0], np.nan] + list(index[1:]))
+            # GH#43921 we preserve float dtype
+            if index.dtype.kind == "f":
+                expected = Index(expected, dtype=index.dtype)
+
+        result = index.insert(1, na_val)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_arithmetic_explicit_conversions(self):
+        # GH 8608
+        # add/sub are overridden explicitly for Float/Int Index
+        index_cls = self._index_cls
+        if index_cls is RangeIndex:
+            idx = RangeIndex(5)
+        else:
+            idx = index_cls(np.arange(5, dtype="int64"))
+
+        # float conversions
+        arr = np.arange(5, dtype="int64") * 3.2
+        expected = Index(arr, dtype=np.float64)
+        fidx = idx * 3.2
+        tm.assert_index_equal(fidx, expected)
+        fidx = 3.2 * idx
+        tm.assert_index_equal(fidx, expected)
+
+        # interops with numpy arrays
+        expected = Index(arr, dtype=np.float64)
+        a = np.zeros(5, dtype="float64")
+        result = fidx - a
+        tm.assert_index_equal(result, expected)
+
+        expected = Index(-arr, dtype=np.float64)
+        a = np.zeros(5, dtype="float64")
+        result = a - fidx
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("complex_dtype", [np.complex64, np.complex128])
+    def test_astype_to_complex(self, complex_dtype, simple_index):
+        result = simple_index.astype(complex_dtype)
+
+        assert type(result) is Index and result.dtype == complex_dtype
+
+    def test_cast_string(self, dtype):
+        result = self._index_cls(["0", "1", "2"], dtype=dtype)
+        expected = self._index_cls([0, 1, 2], dtype=dtype)
+        tm.assert_index_equal(result, expected)

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

@@ -0,0 +1,61 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Series,
+    array,
+)
+import pandas._testing as tm
+
+
+@pytest.fixture(params=[None, False])
+def sort(request):
+    """
+    Valid values for the 'sort' parameter used in the Index
+    setops methods (intersection, union, etc.)
+
+    Caution:
+        Don't confuse this one with the "sort" fixture used
+        for DataFrame.append or concat. That one has
+        parameters [True, False].
+
+        We can't combine them as sort=True is not permitted
+        in the Index setops methods.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["D", "3D", "-3D", "H", "2H", "-2H", "T", "2T", "S", "-3S"])
+def freq_sample(request):
+    """
+    Valid values for 'freq' parameter used to create date_range and
+    timedelta_range..
+    """
+    return request.param
+
+
+@pytest.fixture(params=[list, tuple, np.array, array, Series])
+def listlike_box(request):
+    """
+    Types that may be passed as the indexer to searchsorted.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=tm.ALL_REAL_NUMPY_DTYPES
+    + [
+        "object",
+        "category",
+        "datetime64[ns]",
+        "timedelta64[ns]",
+    ]
+)
+def any_dtype_for_small_pos_integer_indexes(request):
+    """
+    Dtypes that can be given to an Index with small positive integers.
+
+    This means that for any dtype `x` in the params list, `Index([1, 2, 3], dtype=x)` is
+    valid and gives the correct Index (sub-)class.
+    """
+    return request.param

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

@@ -0,0 +1,137 @@
+""" generic datetimelike tests """
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.tests.indexes.common import Base
+
+
+class DatetimeLike(Base):
+    def test_isin(self, simple_index):
+        index = simple_index[:4]
+        result = index.isin(index)
+        assert result.all()
+
+        result = index.isin(list(index))
+        assert result.all()
+
+        result = index.isin([index[2], 5])
+        expected = np.array([False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_argsort_matches_array(self, simple_index):
+        idx = simple_index
+        idx = idx.insert(1, pd.NaT)
+
+        result = idx.argsort()
+        expected = idx._data.argsort()
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_can_hold_identifiers(self, simple_index):
+        idx = simple_index
+        key = idx[0]
+        assert idx._can_hold_identifiers_and_holds_name(key) is False
+
+    def test_shift_identity(self, simple_index):
+        idx = simple_index
+        tm.assert_index_equal(idx, idx.shift(0))
+
+    def test_shift_empty(self, simple_index):
+        # GH#14811
+        idx = simple_index[:0]
+        tm.assert_index_equal(idx, idx.shift(1))
+
+    def test_str(self, simple_index):
+        # test the string repr
+        idx = simple_index
+        idx.name = "foo"
+        assert f"length={len(idx)}" not in str(idx)
+        assert "'foo'" in str(idx)
+        assert type(idx).__name__ in str(idx)
+
+        if hasattr(idx, "tz"):
+            if idx.tz is not None:
+                assert idx.tz in str(idx)
+        if isinstance(idx, pd.PeriodIndex):
+            assert f"dtype='period[{idx.freqstr}]'" in str(idx)
+        else:
+            assert f"freq='{idx.freqstr}'" in str(idx)
+
+    def test_view(self, simple_index):
+        idx = simple_index
+
+        idx_view = idx.view("i8")
+        result = self._index_cls(idx)
+        tm.assert_index_equal(result, idx)
+
+        idx_view = idx.view(self._index_cls)
+        result = self._index_cls(idx)
+        tm.assert_index_equal(result, idx_view)
+
+    def test_map_callable(self, simple_index):
+        index = simple_index
+        expected = index + index.freq
+        result = index.map(lambda x: x + x.freq)
+        tm.assert_index_equal(result, expected)
+
+        # map to NaT
+        result = index.map(lambda x: pd.NaT if x == index[0] else x)
+        expected = pd.Index([pd.NaT] + index[1:].tolist())
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "mapper",
+        [
+            lambda values, index: {i: e for e, i in zip(values, index)},
+            lambda values, index: pd.Series(values, index, dtype=object),
+        ],
+    )
+    def test_map_dictlike(self, mapper, simple_index):
+        index = simple_index
+        expected = index + index.freq
+
+        # don't compare the freqs
+        if isinstance(expected, (pd.DatetimeIndex, pd.TimedeltaIndex)):
+            expected = expected._with_freq(None)
+
+        result = index.map(mapper(expected, index))
+        tm.assert_index_equal(result, expected)
+
+        expected = pd.Index([pd.NaT] + index[1:].tolist())
+        result = index.map(mapper(expected, index))
+        tm.assert_index_equal(result, expected)
+
+        # empty map; these map to np.nan because we cannot know
+        # to re-infer things
+        expected = pd.Index([np.nan] * len(index))
+        result = index.map(mapper([], []))
+        tm.assert_index_equal(result, expected)
+
+    def test_getitem_preserves_freq(self, simple_index):
+        index = simple_index
+        assert index.freq is not None
+
+        result = index[:]
+        assert result.freq == index.freq
+
+    def test_where_cast_str(self, simple_index):
+        index = simple_index
+
+        mask = np.ones(len(index), dtype=bool)
+        mask[-1] = False
+
+        result = index.where(mask, str(index[0]))
+        expected = index.where(mask, index[0])
+        tm.assert_index_equal(result, expected)
+
+        result = index.where(mask, [str(index[0])])
+        tm.assert_index_equal(result, expected)
+
+        expected = index.astype(object).where(mask, "foo")
+        result = index.where(mask, "foo")
+        tm.assert_index_equal(result, expected)
+
+        result = index.where(mask, ["foo"])
+        tm.assert_index_equal(result, expected)

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

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

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

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

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_join.py

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

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_numeric.py

@@ -0,0 +1,551 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    Series,
+)
+import pandas._testing as tm
+from pandas.tests.indexes.common import NumericBase
+
+
+class TestFloatNumericIndex(NumericBase):
+    _index_cls = Index
+
+    @pytest.fixture(params=[np.float64, np.float32])
+    def dtype(self, request):
+        return request.param
+
+    @pytest.fixture
+    def simple_index(self, dtype):
+        values = np.arange(5, dtype=dtype)
+        return self._index_cls(values)
+
+    @pytest.fixture(
+        params=[
+            [1.5, 2, 3, 4, 5],
+            [0.0, 2.5, 5.0, 7.5, 10.0],
+            [5, 4, 3, 2, 1.5],
+            [10.0, 7.5, 5.0, 2.5, 0.0],
+        ],
+        ids=["mixed", "float", "mixed_dec", "float_dec"],
+    )
+    def index(self, request, dtype):
+        return self._index_cls(request.param, dtype=dtype)
+
+    @pytest.fixture
+    def mixed_index(self, dtype):
+        return self._index_cls([1.5, 2, 3, 4, 5], dtype=dtype)
+
+    @pytest.fixture
+    def float_index(self, dtype):
+        return self._index_cls([0.0, 2.5, 5.0, 7.5, 10.0], dtype=dtype)
+
+    def test_repr_roundtrip(self, index):
+        tm.assert_index_equal(eval(repr(index)), index, exact=True)
+
+    def check_coerce(self, a, b, is_float_index=True):
+        assert a.equals(b)
+        tm.assert_index_equal(a, b, exact=False)
+        if is_float_index:
+            assert isinstance(b, self._index_cls)
+        else:
+            assert type(b) is Index
+
+    def test_constructor_from_list_no_dtype(self):
+        index = self._index_cls([1.5, 2.5, 3.5])
+        assert index.dtype == np.float64
+
+    def test_constructor(self, dtype):
+        index_cls = self._index_cls
+
+        # explicit construction
+        index = index_cls([1, 2, 3, 4, 5], dtype=dtype)
+
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        expected = np.array([1, 2, 3, 4, 5], dtype=dtype)
+        tm.assert_numpy_array_equal(index.values, expected)
+
+        index = index_cls(np.array([1, 2, 3, 4, 5]), dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        index = index_cls([1.0, 2, 3, 4, 5], dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        index = index_cls(np.array([1.0, 2, 3, 4, 5]), dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        index = index_cls([1.0, 2, 3, 4, 5], dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        index = index_cls(np.array([1.0, 2, 3, 4, 5]), dtype=dtype)
+        assert isinstance(index, index_cls)
+        assert index.dtype == dtype
+
+        # nan handling
+        result = index_cls([np.nan, np.nan], dtype=dtype)
+        assert pd.isna(result.values).all()
+
+        result = index_cls(np.array([np.nan]), dtype=dtype)
+        assert pd.isna(result.values).all()
+
+    def test_constructor_invalid(self):
+        index_cls = self._index_cls
+        cls_name = index_cls.__name__
+        # invalid
+        msg = (
+            rf"{cls_name}\(\.\.\.\) must be called with a collection of "
+            r"some kind, 0\.0 was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            index_cls(0.0)
+
+    def test_constructor_coerce(self, mixed_index, float_index):
+        self.check_coerce(mixed_index, Index([1.5, 2, 3, 4, 5]))
+        self.check_coerce(float_index, Index(np.arange(5) * 2.5))
+
+        result = Index(np.array(np.arange(5) * 2.5, dtype=object))
+        assert result.dtype == object  # as of 2.0 to match Series
+        self.check_coerce(float_index, result.astype("float64"))
+
+    def test_constructor_explicit(self, mixed_index, float_index):
+        # these don't auto convert
+        self.check_coerce(
+            float_index, Index((np.arange(5) * 2.5), dtype=object), is_float_index=False
+        )
+        self.check_coerce(
+            mixed_index, Index([1.5, 2, 3, 4, 5], dtype=object), is_float_index=False
+        )
+
+    def test_type_coercion_fail(self, any_int_numpy_dtype):
+        # see gh-15832
+        msg = "Trying to coerce float values to integers"
+        with pytest.raises(ValueError, match=msg):
+            Index([1, 2, 3.5], dtype=any_int_numpy_dtype)
+
+    def test_equals_numeric(self):
+        index_cls = self._index_cls
+
+        idx = index_cls([1.0, 2.0])
+        assert idx.equals(idx)
+        assert idx.identical(idx)
+
+        idx2 = index_cls([1.0, 2.0])
+        assert idx.equals(idx2)
+
+        idx = index_cls([1.0, np.nan])
+        assert idx.equals(idx)
+        assert idx.identical(idx)
+
+        idx2 = index_cls([1.0, np.nan])
+        assert idx.equals(idx2)
+
+    @pytest.mark.parametrize(
+        "other",
+        (
+            Index([1, 2], dtype=np.int64),
+            Index([1.0, 2.0], dtype=object),
+            Index([1, 2], dtype=object),
+        ),
+    )
+    def test_equals_numeric_other_index_type(self, other):
+        idx = self._index_cls([1.0, 2.0])
+        assert idx.equals(other)
+        assert other.equals(idx)
+
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            pd.date_range("2016-01-01", periods=3),
+            pd.timedelta_range("1 Day", periods=3),
+        ],
+    )
+    def test_lookups_datetimelike_values(self, vals, dtype):
+        # If we have datetime64 or timedelta64 values, make sure they are
+        #  wrapped correctly  GH#31163
+        ser = Series(vals, index=range(3, 6))
+        ser.index = ser.index.astype(dtype)
+
+        expected = vals[1]
+
+        result = ser[4.0]
+        assert isinstance(result, type(expected)) and result == expected
+        result = ser[4]
+        assert isinstance(result, type(expected)) and result == expected
+
+        result = ser.loc[4.0]
+        assert isinstance(result, type(expected)) and result == expected
+        result = ser.loc[4]
+        assert isinstance(result, type(expected)) and result == expected
+
+        result = ser.at[4.0]
+        assert isinstance(result, type(expected)) and result == expected
+        # GH#31329 .at[4] should cast to 4.0, matching .loc behavior
+        result = ser.at[4]
+        assert isinstance(result, type(expected)) and result == expected
+
+        result = ser.iloc[1]
+        assert isinstance(result, type(expected)) and result == expected
+
+        result = ser.iat[1]
+        assert isinstance(result, type(expected)) and result == expected
+
+    def test_doesnt_contain_all_the_things(self):
+        idx = self._index_cls([np.nan])
+        assert not idx.isin([0]).item()
+        assert not idx.isin([1]).item()
+        assert idx.isin([np.nan]).item()
+
+    def test_nan_multiple_containment(self):
+        index_cls = self._index_cls
+
+        idx = index_cls([1.0, np.nan])
+        tm.assert_numpy_array_equal(idx.isin([1.0]), np.array([True, False]))
+        tm.assert_numpy_array_equal(idx.isin([2.0, np.pi]), np.array([False, False]))
+        tm.assert_numpy_array_equal(idx.isin([np.nan]), np.array([False, True]))
+        tm.assert_numpy_array_equal(idx.isin([1.0, np.nan]), np.array([True, True]))
+        idx = index_cls([1.0, 2.0])
+        tm.assert_numpy_array_equal(idx.isin([np.nan]), np.array([False, False]))
+
+    def test_fillna_float64(self):
+        index_cls = self._index_cls
+        # GH 11343
+        idx = Index([1.0, np.nan, 3.0], dtype=float, name="x")
+        # can't downcast
+        exp = Index([1.0, 0.1, 3.0], name="x")
+        tm.assert_index_equal(idx.fillna(0.1), exp, exact=True)
+
+        # downcast
+        exp = index_cls([1.0, 2.0, 3.0], name="x")
+        tm.assert_index_equal(idx.fillna(2), exp)
+
+        # object
+        exp = Index([1.0, "obj", 3.0], name="x")
+        tm.assert_index_equal(idx.fillna("obj"), exp, exact=True)
+
+
+class NumericInt(NumericBase):
+    _index_cls = Index
+
+    def test_is_monotonic(self):
+        index_cls = self._index_cls
+
+        index = index_cls([1, 2, 3, 4])
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_increasing is True
+        assert index._is_strictly_monotonic_increasing is True
+        assert index.is_monotonic_decreasing is False
+        assert index._is_strictly_monotonic_decreasing is False
+
+        index = index_cls([4, 3, 2, 1])
+        assert index.is_monotonic_increasing is False
+        assert index._is_strictly_monotonic_increasing is False
+        assert index._is_strictly_monotonic_decreasing is True
+
+        index = index_cls([1])
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_increasing is True
+        assert index.is_monotonic_decreasing is True
+        assert index._is_strictly_monotonic_increasing is True
+        assert index._is_strictly_monotonic_decreasing is True
+
+    def test_is_strictly_monotonic(self):
+        index_cls = self._index_cls
+
+        index = index_cls([1, 1, 2, 3])
+        assert index.is_monotonic_increasing is True
+        assert index._is_strictly_monotonic_increasing is False
+
+        index = index_cls([3, 2, 1, 1])
+        assert index.is_monotonic_decreasing is True
+        assert index._is_strictly_monotonic_decreasing is False
+
+        index = index_cls([1, 1])
+        assert index.is_monotonic_increasing
+        assert index.is_monotonic_decreasing
+        assert not index._is_strictly_monotonic_increasing
+        assert not index._is_strictly_monotonic_decreasing
+
+    def test_logical_compat(self, simple_index):
+        idx = simple_index
+        assert idx.all() == idx.values.all()
+        assert idx.any() == idx.values.any()
+
+    def test_identical(self, simple_index, dtype):
+        index = simple_index
+
+        idx = Index(index.copy())
+        assert idx.identical(index)
+
+        same_values_different_type = Index(idx, dtype=object)
+        assert not idx.identical(same_values_different_type)
+
+        idx = index.astype(dtype=object)
+        idx = idx.rename("foo")
+        same_values = Index(idx, dtype=object)
+        assert same_values.identical(idx)
+
+        assert not idx.identical(index)
+        assert Index(same_values, name="foo", dtype=object).identical(idx)
+
+        assert not index.astype(dtype=object).identical(index.astype(dtype=dtype))
+
+    def test_cant_or_shouldnt_cast(self, dtype):
+        msg = r"invalid literal for int\(\) with base 10: 'foo'"
+
+        # can't
+        data = ["foo", "bar", "baz"]
+        with pytest.raises(ValueError, match=msg):
+            self._index_cls(data, dtype=dtype)
+
+    def test_view_index(self, simple_index):
+        index = simple_index
+        index.view(Index)
+
+    def test_prevent_casting(self, simple_index):
+        index = simple_index
+        result = index.astype("O")
+        assert result.dtype == np.object_
+
+
+class TestIntNumericIndex(NumericInt):
+    @pytest.fixture(params=[np.int64, np.int32, np.int16, np.int8])
+    def dtype(self, request):
+        return request.param
+
+    @pytest.fixture
+    def simple_index(self, dtype):
+        return self._index_cls(range(0, 20, 2), dtype=dtype)
+
+    @pytest.fixture(
+        params=[range(0, 20, 2), range(19, -1, -1)], ids=["index_inc", "index_dec"]
+    )
+    def index(self, request, dtype):
+        return self._index_cls(request.param, dtype=dtype)
+
+    def test_constructor_from_list_no_dtype(self):
+        index = self._index_cls([1, 2, 3])
+        assert index.dtype == np.int64
+
+    def test_constructor(self, dtype):
+        index_cls = self._index_cls
+
+        # scalar raise Exception
+        msg = (
+            rf"{index_cls.__name__}\(\.\.\.\) must be called with a collection of some "
+            "kind, 5 was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            index_cls(5)
+
+        # copy
+        # pass list, coerce fine
+        index = index_cls([-5, 0, 1, 2], dtype=dtype)
+        arr = index.values
+        new_index = index_cls(arr, copy=True)
+        tm.assert_index_equal(new_index, index, exact=True)
+        val = arr[0] + 3000
+
+        # this should not change index
+        arr[0] = val
+        assert new_index[0] != val
+
+        if dtype == np.int64:
+            # pass list, coerce fine
+            index = index_cls([-5, 0, 1, 2], dtype=dtype)
+            expected = Index([-5, 0, 1, 2], dtype=dtype)
+            tm.assert_index_equal(index, expected)
+
+            # from iterable
+            index = index_cls(iter([-5, 0, 1, 2]), dtype=dtype)
+            expected = index_cls([-5, 0, 1, 2], dtype=dtype)
+            tm.assert_index_equal(index, expected, exact=True)
+
+            # interpret list-like
+            expected = index_cls([5, 0], dtype=dtype)
+            for cls in [Index, index_cls]:
+                for idx in [
+                    cls([5, 0], dtype=dtype),
+                    cls(np.array([5, 0]), dtype=dtype),
+                    cls(Series([5, 0]), dtype=dtype),
+                ]:
+                    tm.assert_index_equal(idx, expected)
+
+    def test_constructor_corner(self, dtype):
+        index_cls = self._index_cls
+
+        arr = np.array([1, 2, 3, 4], dtype=object)
+
+        index = index_cls(arr, dtype=dtype)
+        assert index.values.dtype == index.dtype
+        if dtype == np.int64:
+            without_dtype = Index(arr)
+            # as of 2.0 we do not infer a dtype when we get an object-dtype
+            #  ndarray of numbers, matching Series behavior
+            assert without_dtype.dtype == object
+
+            tm.assert_index_equal(index, without_dtype.astype(np.int64))
+
+        # preventing casting
+        arr = np.array([1, "2", 3, "4"], dtype=object)
+        msg = "Trying to coerce float values to integers"
+        with pytest.raises(ValueError, match=msg):
+            index_cls(arr, dtype=dtype)
+
+    def test_constructor_coercion_signed_to_unsigned(
+        self,
+        any_unsigned_int_numpy_dtype,
+    ):
+        # see gh-15832
+        msg = "Trying to coerce negative values to unsigned integers"
+
+        with pytest.raises(OverflowError, match=msg):
+            Index([-1], dtype=any_unsigned_int_numpy_dtype)
+
+    def test_constructor_np_signed(self, any_signed_int_numpy_dtype):
+        # GH#47475
+        scalar = np.dtype(any_signed_int_numpy_dtype).type(1)
+        result = Index([scalar])
+        expected = Index([1], dtype=any_signed_int_numpy_dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_constructor_np_unsigned(self, any_unsigned_int_numpy_dtype):
+        # GH#47475
+        scalar = np.dtype(any_unsigned_int_numpy_dtype).type(1)
+        result = Index([scalar])
+        expected = Index([1], dtype=any_unsigned_int_numpy_dtype)
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_coerce_list(self):
+        # coerce things
+        arr = Index([1, 2, 3, 4])
+        assert isinstance(arr, self._index_cls)
+
+        # but not if explicit dtype passed
+        arr = Index([1, 2, 3, 4], dtype=object)
+        assert type(arr) is Index
+
+
+class TestFloat16Index:
+    # float 16 indexes not supported
+    # GH 49535
+    _index_cls = Index
+
+    def test_constructor(self):
+        index_cls = self._index_cls
+        dtype = np.float16
+
+        msg = "float16 indexes are not supported"
+
+        # explicit construction
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls([1, 2, 3, 4, 5], dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls(np.array([1, 2, 3, 4, 5]), dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls([1.0, 2, 3, 4, 5], dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls(np.array([1.0, 2, 3, 4, 5]), dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls([1.0, 2, 3, 4, 5], dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls(np.array([1.0, 2, 3, 4, 5]), dtype=dtype)
+
+        # nan handling
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls([np.nan, np.nan], dtype=dtype)
+
+        with pytest.raises(NotImplementedError, match=msg):
+            index_cls(np.array([np.nan]), dtype=dtype)
+
+
+class TestUIntNumericIndex(NumericInt):
+    @pytest.fixture(params=[np.uint64])
+    def dtype(self, request):
+        return request.param
+
+    @pytest.fixture
+    def simple_index(self, dtype):
+        # compat with shared Int64/Float64 tests
+        return self._index_cls(np.arange(5, dtype=dtype))
+
+    @pytest.fixture(
+        params=[
+            [2**63, 2**63 + 10, 2**63 + 15, 2**63 + 20, 2**63 + 25],
+            [2**63 + 25, 2**63 + 20, 2**63 + 15, 2**63 + 10, 2**63],
+        ],
+        ids=["index_inc", "index_dec"],
+    )
+    def index(self, request):
+        return self._index_cls(request.param, dtype=np.uint64)
+
+
+@pytest.mark.parametrize(
+    "box",
+    [list, lambda x: np.array(x, dtype=object), lambda x: Index(x, dtype=object)],
+)
+def test_uint_index_does_not_convert_to_float64(box):
+    # https://github.com/pandas-dev/pandas/issues/28279
+    # https://github.com/pandas-dev/pandas/issues/28023
+    series = Series(
+        [0, 1, 2, 3, 4, 5],
+        index=[
+            7606741985629028552,
+            17876870360202815256,
+            17876870360202815256,
+            13106359306506049338,
+            8991270399732411471,
+            8991270399732411472,
+        ],
+    )
+
+    result = series.loc[box([7606741985629028552, 17876870360202815256])]
+
+    expected = Index(
+        [7606741985629028552, 17876870360202815256, 17876870360202815256],
+        dtype="uint64",
+    )
+    tm.assert_index_equal(result.index, expected)
+
+    tm.assert_equal(result, series.iloc[:3])
+
+
+def test_float64_index_equals():
+    # https://github.com/pandas-dev/pandas/issues/35217
+    float_index = Index([1.0, 2, 3])
+    string_index = Index(["1", "2", "3"])
+
+    result = float_index.equals(string_index)
+    assert result is False
+
+    result = string_index.equals(float_index)
+    assert result is False
+
+
+def test_map_dtype_inference_unsigned_to_signed():
+    # GH#44609 cases where we don't retain dtype
+    idx = Index([1, 2, 3], dtype=np.uint64)
+    result = idx.map(lambda x: -x)
+    expected = Index([-1, -2, -3], dtype=np.int64)
+    tm.assert_index_equal(result, expected)
+
+
+def test_map_dtype_inference_overflows():
+    # GH#44609 case where we have to upcast
+    idx = Index(np.array([1, 2, 3], dtype=np.int8))
+    result = idx.map(lambda x: x * 1000)
+    # TODO: we could plausibly try to infer down to int16 here
+    expected = Index([1000, 2000, 3000], dtype=np.int64)
+    tm.assert_index_equal(result, expected)

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

@@ -0,0 +1,154 @@
+from datetime import (
+    datetime,
+    timedelta,
+)
+
+import numpy as np
+import pytest
+
+import pandas._testing as tm
+from pandas.core.indexes.api import (
+    Index,
+    RangeIndex,
+)
+
+
+@pytest.fixture
+def index_large():
+    # large values used in TestUInt64Index where no compat needed with int64/float64
+    large = [2**63, 2**63 + 10, 2**63 + 15, 2**63 + 20, 2**63 + 25]
+    return Index(large, dtype=np.uint64)
+
+
+class TestSetOps:
+    @pytest.mark.parametrize("dtype", ["f8", "u8", "i8"])
+    def test_union_non_numeric(self, dtype):
+        # corner case, non-numeric
+        index = Index(np.arange(5, dtype=dtype), dtype=dtype)
+        assert index.dtype == dtype
+
+        other = Index([datetime.now() + timedelta(i) for i in range(4)], dtype=object)
+        result = index.union(other)
+        expected = Index(np.concatenate((index, other)))
+        tm.assert_index_equal(result, expected)
+
+        result = other.union(index)
+        expected = Index(np.concatenate((other, index)))
+        tm.assert_index_equal(result, expected)
+
+    def test_intersection(self):
+        index = Index(range(5), dtype=np.int64)
+
+        other = Index([1, 2, 3, 4, 5])
+        result = index.intersection(other)
+        expected = Index(np.sort(np.intersect1d(index.values, other.values)))
+        tm.assert_index_equal(result, expected)
+
+        result = other.intersection(index)
+        expected = Index(
+            np.sort(np.asarray(np.intersect1d(index.values, other.values)))
+        )
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["int64", "uint64"])
+    def test_int_float_union_dtype(self, dtype):
+        # https://github.com/pandas-dev/pandas/issues/26778
+        # [u]int | float -> float
+        index = Index([0, 2, 3], dtype=dtype)
+        other = Index([0.5, 1.5], dtype=np.float64)
+        expected = Index([0.0, 0.5, 1.5, 2.0, 3.0], dtype=np.float64)
+        result = index.union(other)
+        tm.assert_index_equal(result, expected)
+
+        result = other.union(index)
+        tm.assert_index_equal(result, expected)
+
+    def test_range_float_union_dtype(self):
+        # https://github.com/pandas-dev/pandas/issues/26778
+        index = RangeIndex(start=0, stop=3)
+        other = Index([0.5, 1.5], dtype=np.float64)
+        result = index.union(other)
+        expected = Index([0.0, 0.5, 1, 1.5, 2.0], dtype=np.float64)
+        tm.assert_index_equal(result, expected)
+
+        result = other.union(index)
+        tm.assert_index_equal(result, expected)
+
+    def test_float64_index_difference(self):
+        # https://github.com/pandas-dev/pandas/issues/35217
+        float_index = Index([1.0, 2, 3])
+        string_index = Index(["1", "2", "3"])
+
+        result = float_index.difference(string_index)
+        tm.assert_index_equal(result, float_index)
+
+        result = string_index.difference(float_index)
+        tm.assert_index_equal(result, string_index)
+
+    def test_intersection_uint64_outside_int64_range(self, index_large):
+        other = Index([2**63, 2**63 + 5, 2**63 + 10, 2**63 + 15, 2**63 + 20])
+        result = index_large.intersection(other)
+        expected = Index(np.sort(np.intersect1d(index_large.values, other.values)))
+        tm.assert_index_equal(result, expected)
+
+        result = other.intersection(index_large)
+        expected = Index(
+            np.sort(np.asarray(np.intersect1d(index_large.values, other.values)))
+        )
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index2,keeps_name",
+        [
+            (Index([4, 7, 6, 5, 3], name="index"), True),
+            (Index([4, 7, 6, 5, 3], name="other"), False),
+        ],
+    )
+    def test_intersection_monotonic(self, index2, keeps_name, sort):
+        index1 = Index([5, 3, 2, 4, 1], name="index")
+        expected = Index([5, 3, 4])
+
+        if keeps_name:
+            expected.name = "index"
+
+        result = index1.intersection(index2, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+
+    def test_symmetric_difference(self, sort):
+        # smoke
+        index1 = Index([5, 2, 3, 4], name="index1")
+        index2 = Index([2, 3, 4, 1])
+        result = index1.symmetric_difference(index2, sort=sort)
+        expected = Index([5, 1])
+        assert tm.equalContents(result, expected)
+        assert result.name is None
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+
+
+class TestSetOpsSort:
+    @pytest.mark.parametrize("slice_", [slice(None), slice(0)])
+    def test_union_sort_other_special(self, slice_):
+        # https://github.com/pandas-dev/pandas/issues/24959
+
+        idx = Index([1, 0, 2])
+        # default, sort=None
+        other = idx[slice_]
+        tm.assert_index_equal(idx.union(other), idx)
+        tm.assert_index_equal(other.union(idx), idx)
+
+        # sort=False
+        tm.assert_index_equal(idx.union(other, sort=False), idx)
+
+    @pytest.mark.parametrize("slice_", [slice(None), slice(0)])
+    def test_union_sort_special_true(self, slice_):
+        idx = Index([1, 0, 2])
+        # default, sort=None
+        other = idx[slice_]
+
+        result = idx.union(other, sort=True)
+        expected = Index([0, 1, 2])
+        tm.assert_index_equal(result, expected)

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

@@ -0,0 +1,171 @@
+"""
+Tests that can be parametrized over _any_ Index object.
+"""
+import re
+
+import numpy as np
+import pytest
+
+from pandas.errors import InvalidIndexError
+
+import pandas._testing as tm
+
+
+def test_boolean_context_compat(index):
+    # GH#7897
+    with pytest.raises(ValueError, match="The truth value of a"):
+        if index:
+            pass
+
+    with pytest.raises(ValueError, match="The truth value of a"):
+        bool(index)
+
+
+def test_sort(index):
+    msg = "cannot sort an Index object in-place, use sort_values instead"
+    with pytest.raises(TypeError, match=msg):
+        index.sort()
+
+
+def test_hash_error(index):
+    with pytest.raises(TypeError, match=f"unhashable type: '{type(index).__name__}'"):
+        hash(index)
+
+
+def test_mutability(index):
+    if not len(index):
+        return
+    msg = "Index does not support mutable operations"
+    with pytest.raises(TypeError, match=msg):
+        index[0] = index[0]
+
+
+def test_map_identity_mapping(index, request):
+    # GH#12766
+
+    result = index.map(lambda x: x)
+    if index.dtype == object and result.dtype == bool:
+        assert (index == result).all()
+        # TODO: could work that into the 'exact="equiv"'?
+        return  # FIXME: doesn't belong in this file anymore!
+    tm.assert_index_equal(result, index, exact="equiv")
+
+
+def test_wrong_number_names(index):
+    names = index.nlevels * ["apple", "banana", "carrot"]
+    with pytest.raises(ValueError, match="^Length"):
+        index.names = names
+
+
+def test_view_preserves_name(index):
+    assert index.view().name == index.name
+
+
+def test_ravel(index):
+    # GH#19956 ravel returning ndarray is deprecated, in 2.0 returns a view on self
+    res = index.ravel()
+    tm.assert_index_equal(res, index)
+
+
+class TestConversion:
+    def test_to_series(self, index):
+        # assert that we are creating a copy of the index
+
+        ser = index.to_series()
+        assert ser.values is not index.values
+        assert ser.index is not index
+        assert ser.name == index.name
+
+    def test_to_series_with_arguments(self, index):
+        # GH#18699
+
+        # index kwarg
+        ser = index.to_series(index=index)
+
+        assert ser.values is not index.values
+        assert ser.index is index
+        assert ser.name == index.name
+
+        # name kwarg
+        ser = index.to_series(name="__test")
+
+        assert ser.values is not index.values
+        assert ser.index is not index
+        assert ser.name != index.name
+
+    def test_tolist_matches_list(self, index):
+        assert index.tolist() == list(index)
+
+
+class TestRoundTrips:
+    def test_pickle_roundtrip(self, index):
+        result = tm.round_trip_pickle(index)
+        tm.assert_index_equal(result, index, exact=True)
+        if result.nlevels > 1:
+            # GH#8367 round-trip with timezone
+            assert index.equal_levels(result)
+
+    def test_pickle_preserves_name(self, index):
+        original_name, index.name = index.name, "foo"
+        unpickled = tm.round_trip_pickle(index)
+        assert index.equals(unpickled)
+        index.name = original_name
+
+
+class TestIndexing:
+    def test_get_loc_listlike_raises_invalid_index_error(self, index):
+        # and never TypeError
+        key = np.array([0, 1], dtype=np.intp)
+
+        with pytest.raises(InvalidIndexError, match=r"\[0 1\]"):
+            index.get_loc(key)
+
+        with pytest.raises(InvalidIndexError, match=r"\[False  True\]"):
+            index.get_loc(key.astype(bool))
+
+    def test_getitem_ellipsis(self, index):
+        # GH#21282
+        result = index[...]
+        assert result.equals(index)
+        assert result is not index
+
+    def test_slice_keeps_name(self, index):
+        assert index.name == index[1:].name
+
+    @pytest.mark.parametrize("item", [101, "no_int", 2.5])
+    def test_getitem_error(self, index, item):
+        msg = "|".join(
+            [
+                r"index 101 is out of bounds for axis 0 with size [\d]+",
+                re.escape(
+                    "only integers, slices (`:`), ellipsis (`...`), "
+                    "numpy.newaxis (`None`) and integer or boolean arrays "
+                    "are valid indices"
+                ),
+                "index out of bounds",  # string[pyarrow]
+            ]
+        )
+        with pytest.raises(IndexError, match=msg):
+            index[item]
+
+
+class TestRendering:
+    def test_str(self, index):
+        # test the string repr
+        index.name = "foo"
+        assert "'foo'" in str(index)
+        assert type(index).__name__ in str(index)
+
+
+class TestReductions:
+    def test_argmax_axis_invalid(self, index):
+        # GH#23081
+        msg = r"`axis` must be fewer than the number of dimensions \(1\)"
+        with pytest.raises(ValueError, match=msg):
+            index.argmax(axis=1)
+        with pytest.raises(ValueError, match=msg):
+            index.argmin(axis=2)
+        with pytest.raises(ValueError, match=msg):
+            index.min(axis=-2)
+        with pytest.raises(ValueError, match=msg):
+            index.max(axis=-3)

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

@@ -0,0 +1,488 @@
+"""
+Collection of tests asserting things that should be true for
+any index subclass except for MultiIndex. Makes use of the `index_flat`
+fixture defined in pandas/conftest.py.
+"""
+from copy import (
+    copy,
+    deepcopy,
+)
+import re
+
+import numpy as np
+import pytest
+
+from pandas.compat import IS64
+
+from pandas.core.dtypes.common import (
+    is_integer_dtype,
+    is_numeric_dtype,
+)
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+)
+import pandas._testing as tm
+
+
+class TestCommon:
+    @pytest.mark.parametrize("name", [None, "new_name"])
+    def test_to_frame(self, name, index_flat):
+        # see GH#15230, GH#22580
+        idx = index_flat
+
+        if name:
+            idx_name = name
+        else:
+            idx_name = idx.name or 0
+
+        df = idx.to_frame(name=idx_name)
+
+        assert df.index is idx
+        assert len(df.columns) == 1
+        assert df.columns[0] == idx_name
+        assert df[idx_name].values is not idx.values
+
+        df = idx.to_frame(index=False, name=idx_name)
+        assert df.index is not idx
+
+    def test_droplevel(self, index_flat):
+        # GH 21115
+        # MultiIndex is tested separately in test_multi.py
+        index = index_flat
+
+        assert index.droplevel([]).equals(index)
+
+        for level in [index.name, [index.name]]:
+            if isinstance(index.name, tuple) and level is index.name:
+                # GH 21121 : droplevel with tuple name
+                continue
+            msg = (
+                "Cannot remove 1 levels from an index with 1 levels: at least one "
+                "level must be left."
+            )
+            with pytest.raises(ValueError, match=msg):
+                index.droplevel(level)
+
+        for level in "wrong", ["wrong"]:
+            with pytest.raises(
+                KeyError,
+                match=r"'Requested level \(wrong\) does not match index name \(None\)'",
+            ):
+                index.droplevel(level)
+
+    def test_constructor_non_hashable_name(self, index_flat):
+        # GH 20527
+        index = index_flat
+
+        message = "Index.name must be a hashable type"
+        renamed = [["1"]]
+
+        # With .rename()
+        with pytest.raises(TypeError, match=message):
+            index.rename(name=renamed)
+
+        # With .set_names()
+        with pytest.raises(TypeError, match=message):
+            index.set_names(names=renamed)
+
+    def test_constructor_unwraps_index(self, index_flat):
+        a = index_flat
+        # Passing dtype is necessary for Index([True, False], dtype=object)
+        #  case.
+        b = type(a)(a, dtype=a.dtype)
+        tm.assert_equal(a._data, b._data)
+
+    def test_to_flat_index(self, index_flat):
+        # 22866
+        index = index_flat
+
+        result = index.to_flat_index()
+        tm.assert_index_equal(result, index)
+
+    def test_set_name_methods(self, index_flat):
+        # MultiIndex tested separately
+        index = index_flat
+        new_name = "This is the new name for this index"
+
+        original_name = index.name
+        new_ind = index.set_names([new_name])
+        assert new_ind.name == new_name
+        assert index.name == original_name
+        res = index.rename(new_name, inplace=True)
+
+        # should return None
+        assert res is None
+        assert index.name == new_name
+        assert index.names == [new_name]
+        # FIXME: dont leave commented-out
+        # with pytest.raises(TypeError, match="list-like"):
+        #    # should still fail even if it would be the right length
+        #    ind.set_names("a")
+        with pytest.raises(ValueError, match="Level must be None"):
+            index.set_names("a", level=0)
+
+        # rename in place just leaves tuples and other containers alone
+        name = ("A", "B")
+        index.rename(name, inplace=True)
+        assert index.name == name
+        assert index.names == [name]
+
+    def test_copy_and_deepcopy(self, index_flat):
+        index = index_flat
+
+        for func in (copy, deepcopy):
+            idx_copy = func(index)
+            assert idx_copy is not index
+            assert idx_copy.equals(index)
+
+        new_copy = index.copy(deep=True, name="banana")
+        assert new_copy.name == "banana"
+
+    def test_copy_name(self, index_flat):
+        # GH#12309: Check that the "name" argument
+        # passed at initialization is honored.
+        index = index_flat
+
+        first = type(index)(index, copy=True, name="mario")
+        second = type(first)(first, copy=False)
+
+        # Even though "copy=False", we want a new object.
+        assert first is not second
+        tm.assert_index_equal(first, second)
+
+        # Not using tm.assert_index_equal() since names differ.
+        assert index.equals(first)
+
+        assert first.name == "mario"
+        assert second.name == "mario"
+
+        # TODO: belongs in series arithmetic tests?
+        s1 = pd.Series(2, index=first)
+        s2 = pd.Series(3, index=second[:-1])
+        # See GH#13365
+        s3 = s1 * s2
+        assert s3.index.name == "mario"
+
+    def test_copy_name2(self, index_flat):
+        # GH#35592
+        index = index_flat
+
+        assert index.copy(name="mario").name == "mario"
+
+        with pytest.raises(ValueError, match="Length of new names must be 1, got 2"):
+            index.copy(name=["mario", "luigi"])
+
+        msg = f"{type(index).__name__}.name must be a hashable type"
+        with pytest.raises(TypeError, match=msg):
+            index.copy(name=[["mario"]])
+
+    def test_unique_level(self, index_flat):
+        # don't test a MultiIndex here (as its tested separated)
+        index = index_flat
+
+        # GH 17896
+        expected = index.drop_duplicates()
+        for level in [0, index.name, None]:
+            result = index.unique(level=level)
+            tm.assert_index_equal(result, expected)
+
+        msg = "Too many levels: Index has only 1 level, not 4"
+        with pytest.raises(IndexError, match=msg):
+            index.unique(level=3)
+
+        msg = (
+            rf"Requested level \(wrong\) does not match index name "
+            rf"\({re.escape(index.name.__repr__())}\)"
+        )
+        with pytest.raises(KeyError, match=msg):
+            index.unique(level="wrong")
+
+    def test_unique(self, index_flat):
+        # MultiIndex tested separately
+        index = index_flat
+        if not len(index):
+            pytest.skip("Skip check for empty Index and MultiIndex")
+
+        idx = index[[0] * 5]
+        idx_unique = index[[0]]
+
+        # We test against `idx_unique`, so first we make sure it's unique
+        # and doesn't contain nans.
+        assert idx_unique.is_unique is True
+        try:
+            assert idx_unique.hasnans is False
+        except NotImplementedError:
+            pass
+
+        result = idx.unique()
+        tm.assert_index_equal(result, idx_unique)
+
+        # nans:
+        if not index._can_hold_na:
+            pytest.skip("Skip na-check if index cannot hold na")
+
+        vals = index._values[[0] * 5]
+        vals[0] = np.nan
+
+        vals_unique = vals[:2]
+        idx_nan = index._shallow_copy(vals)
+        idx_unique_nan = index._shallow_copy(vals_unique)
+        assert idx_unique_nan.is_unique is True
+
+        assert idx_nan.dtype == index.dtype
+        assert idx_unique_nan.dtype == index.dtype
+
+        expected = idx_unique_nan
+        for pos, i in enumerate([idx_nan, idx_unique_nan]):
+            result = i.unique()
+            tm.assert_index_equal(result, expected)
+
+    def test_searchsorted_monotonic(self, index_flat, request):
+        # GH17271
+        index = index_flat
+        # not implemented for tuple searches in MultiIndex
+        # or Intervals searches in IntervalIndex
+        if isinstance(index, pd.IntervalIndex):
+            mark = pytest.mark.xfail(
+                reason="IntervalIndex.searchsorted does not support Interval arg",
+                raises=NotImplementedError,
+            )
+            request.node.add_marker(mark)
+
+        # nothing to test if the index is empty
+        if index.empty:
+            pytest.skip("Skip check for empty Index")
+        value = index[0]
+
+        # determine the expected results (handle dupes for 'right')
+        expected_left, expected_right = 0, (index == value).argmin()
+        if expected_right == 0:
+            # all values are the same, expected_right should be length
+            expected_right = len(index)
+
+        # test _searchsorted_monotonic in all cases
+        # test searchsorted only for increasing
+        if index.is_monotonic_increasing:
+            ssm_left = index._searchsorted_monotonic(value, side="left")
+            assert expected_left == ssm_left
+
+            ssm_right = index._searchsorted_monotonic(value, side="right")
+            assert expected_right == ssm_right
+
+            ss_left = index.searchsorted(value, side="left")
+            assert expected_left == ss_left
+
+            ss_right = index.searchsorted(value, side="right")
+            assert expected_right == ss_right
+
+        elif index.is_monotonic_decreasing:
+            ssm_left = index._searchsorted_monotonic(value, side="left")
+            assert expected_left == ssm_left
+
+            ssm_right = index._searchsorted_monotonic(value, side="right")
+            assert expected_right == ssm_right
+        else:
+            # non-monotonic should raise.
+            msg = "index must be monotonic increasing or decreasing"
+            with pytest.raises(ValueError, match=msg):
+                index._searchsorted_monotonic(value, side="left")
+
+    def test_drop_duplicates(self, index_flat, keep):
+        # MultiIndex is tested separately
+        index = index_flat
+        if isinstance(index, RangeIndex):
+            pytest.skip(
+                "RangeIndex is tested in test_drop_duplicates_no_duplicates "
+                "as it cannot hold duplicates"
+            )
+        if len(index) == 0:
+            pytest.skip(
+                "empty index is tested in test_drop_duplicates_no_duplicates "
+                "as it cannot hold duplicates"
+            )
+
+        # make unique index
+        holder = type(index)
+        unique_values = list(set(index))
+        dtype = index.dtype if is_numeric_dtype(index) else None
+        unique_idx = holder(unique_values, dtype=dtype)
+
+        # make duplicated index
+        n = len(unique_idx)
+        duplicated_selection = np.random.choice(n, int(n * 1.5))
+        idx = holder(unique_idx.values[duplicated_selection])
+
+        # Series.duplicated is tested separately
+        expected_duplicated = (
+            pd.Series(duplicated_selection).duplicated(keep=keep).values
+        )
+        tm.assert_numpy_array_equal(idx.duplicated(keep=keep), expected_duplicated)
+
+        # Series.drop_duplicates is tested separately
+        expected_dropped = holder(pd.Series(idx).drop_duplicates(keep=keep))
+        tm.assert_index_equal(idx.drop_duplicates(keep=keep), expected_dropped)
+
+    def test_drop_duplicates_no_duplicates(self, index_flat):
+        # MultiIndex is tested separately
+        index = index_flat
+
+        # make unique index
+        if isinstance(index, RangeIndex):
+            # RangeIndex cannot have duplicates
+            unique_idx = index
+        else:
+            holder = type(index)
+            unique_values = list(set(index))
+            dtype = index.dtype if is_numeric_dtype(index) else None
+            unique_idx = holder(unique_values, dtype=dtype)
+
+        # check on unique index
+        expected_duplicated = np.array([False] * len(unique_idx), dtype="bool")
+        tm.assert_numpy_array_equal(unique_idx.duplicated(), expected_duplicated)
+        result_dropped = unique_idx.drop_duplicates()
+        tm.assert_index_equal(result_dropped, unique_idx)
+        # validate shallow copy
+        assert result_dropped is not unique_idx
+
+    def test_drop_duplicates_inplace(self, index):
+        msg = r"drop_duplicates\(\) got an unexpected keyword argument"
+        with pytest.raises(TypeError, match=msg):
+            index.drop_duplicates(inplace=True)
+
+    def test_has_duplicates(self, index_flat):
+        # MultiIndex tested separately in:
+        #   tests/indexes/multi/test_unique_and_duplicates.
+        index = index_flat
+        holder = type(index)
+        if not len(index) or isinstance(index, RangeIndex):
+            # MultiIndex tested separately in:
+            #   tests/indexes/multi/test_unique_and_duplicates.
+            # RangeIndex is unique by definition.
+            pytest.skip("Skip check for empty Index, MultiIndex, and RangeIndex")
+
+        idx = holder([index[0]] * 5)
+        assert idx.is_unique is False
+        assert idx.has_duplicates is True
+
+    @pytest.mark.parametrize(
+        "dtype",
+        ["int64", "uint64", "float64", "category", "datetime64[ns]", "timedelta64[ns]"],
+    )
+    def test_astype_preserves_name(self, index, dtype):
+        # https://github.com/pandas-dev/pandas/issues/32013
+        if isinstance(index, MultiIndex):
+            index.names = ["idx" + str(i) for i in range(index.nlevels)]
+        else:
+            index.name = "idx"
+
+        warn = None
+        if index.dtype.kind == "c" and dtype in ["float64", "int64", "uint64"]:
+            # imaginary components discarded
+            warn = np.ComplexWarning
+
+        is_pyarrow_str = str(index.dtype) == "string[pyarrow]" and dtype == "category"
+        try:
+            # Some of these conversions cannot succeed so we use a try / except
+            with tm.assert_produces_warning(
+                warn,
+                raise_on_extra_warnings=is_pyarrow_str,
+                check_stacklevel=False,
+            ):
+                result = index.astype(dtype)
+        except (ValueError, TypeError, NotImplementedError, SystemError):
+            return
+
+        if isinstance(index, MultiIndex):
+            assert result.names == index.names
+        else:
+            assert result.name == index.name
+
+    def test_hasnans_isnans(self, index_flat):
+        # GH#11343, added tests for hasnans / isnans
+        index = index_flat
+
+        # cases in indices doesn't include NaN
+        idx = index.copy(deep=True)
+        expected = np.array([False] * len(idx), dtype=bool)
+        tm.assert_numpy_array_equal(idx._isnan, expected)
+        assert idx.hasnans is False
+
+        idx = index.copy(deep=True)
+        values = idx._values
+
+        if len(index) == 0:
+            return
+        elif is_integer_dtype(index.dtype):
+            return
+        elif index.dtype == bool:
+            # values[1] = np.nan below casts to True!
+            return
+
+        values[1] = np.nan
+
+        idx = type(index)(values)
+
+        expected = np.array([False] * len(idx), dtype=bool)
+        expected[1] = True
+        tm.assert_numpy_array_equal(idx._isnan, expected)
+        assert idx.hasnans is True
+
+
+@pytest.mark.parametrize("na_position", [None, "middle"])
+def test_sort_values_invalid_na_position(index_with_missing, na_position):
+    with pytest.raises(ValueError, match=f"invalid na_position: {na_position}"):
+        index_with_missing.sort_values(na_position=na_position)
+
+
+@pytest.mark.parametrize("na_position", ["first", "last"])
+def test_sort_values_with_missing(index_with_missing, na_position, request):
+    # GH 35584. Test that sort_values works with missing values,
+    # sort non-missing and place missing according to na_position
+
+    if isinstance(index_with_missing, CategoricalIndex):
+        request.node.add_marker(
+            pytest.mark.xfail(
+                reason="missing value sorting order not well-defined", strict=False
+            )
+        )
+
+    missing_count = np.sum(index_with_missing.isna())
+    not_na_vals = index_with_missing[index_with_missing.notna()].values
+    sorted_values = np.sort(not_na_vals)
+    if na_position == "first":
+        sorted_values = np.concatenate([[None] * missing_count, sorted_values])
+    else:
+        sorted_values = np.concatenate([sorted_values, [None] * missing_count])
+
+    # Explicitly pass dtype needed for Index backed by EA e.g. IntegerArray
+    expected = type(index_with_missing)(sorted_values, dtype=index_with_missing.dtype)
+
+    result = index_with_missing.sort_values(na_position=na_position)
+    tm.assert_index_equal(result, expected)
+
+
+def test_ndarray_compat_properties(index):
+    if isinstance(index, PeriodIndex) and not IS64:
+        pytest.skip("Overflow")
+    idx = index
+    assert idx.T.equals(idx)
+    assert idx.transpose().equals(idx)
+
+    values = idx.values
+
+    assert idx.shape == values.shape
+    assert idx.ndim == values.ndim
+    assert idx.size == values.size
+
+    if not isinstance(index, (RangeIndex, MultiIndex)):
+        # These two are not backed by an ndarray
+        assert idx.nbytes == values.nbytes
+
+    # test for validity
+    idx.nbytes
+    idx.values.nbytes

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

@@ -0,0 +1,192 @@
+import re
+
+import numpy as np
+import pytest
+
+from pandas._libs import index as libindex
+
+import pandas as pd
+
+
+@pytest.fixture(
+    params=[
+        (libindex.Int64Engine, np.int64),
+        (libindex.Int32Engine, np.int32),
+        (libindex.Int16Engine, np.int16),
+        (libindex.Int8Engine, np.int8),
+        (libindex.UInt64Engine, np.uint64),
+        (libindex.UInt32Engine, np.uint32),
+        (libindex.UInt16Engine, np.uint16),
+        (libindex.UInt8Engine, np.uint8),
+        (libindex.Float64Engine, np.float64),
+        (libindex.Float32Engine, np.float32),
+    ],
+    ids=lambda x: x[0].__name__,
+)
+def numeric_indexing_engine_type_and_dtype(request):
+    return request.param
+
+
+class TestDatetimeEngine:
+    @pytest.mark.parametrize(
+        "scalar",
+        [
+            pd.Timedelta(pd.Timestamp("2016-01-01").asm8.view("m8[ns]")),
+            pd.Timestamp("2016-01-01")._value,
+            pd.Timestamp("2016-01-01").to_pydatetime(),
+            pd.Timestamp("2016-01-01").to_datetime64(),
+        ],
+    )
+    def test_not_contains_requires_timestamp(self, scalar):
+        dti1 = pd.date_range("2016-01-01", periods=3)
+        dti2 = dti1.insert(1, pd.NaT)  # non-monotonic
+        dti3 = dti1.insert(3, dti1[0])  # non-unique
+        dti4 = pd.date_range("2016-01-01", freq="ns", periods=2_000_000)
+        dti5 = dti4.insert(0, dti4[0])  # over size threshold, not unique
+
+        msg = "|".join([re.escape(str(scalar)), re.escape(repr(scalar))])
+        for dti in [dti1, dti2, dti3, dti4, dti5]:
+            with pytest.raises(TypeError, match=msg):
+                scalar in dti._engine
+
+            with pytest.raises(KeyError, match=msg):
+                dti._engine.get_loc(scalar)
+
+
+class TestTimedeltaEngine:
+    @pytest.mark.parametrize(
+        "scalar",
+        [
+            pd.Timestamp(pd.Timedelta(days=42).asm8.view("datetime64[ns]")),
+            pd.Timedelta(days=42)._value,
+            pd.Timedelta(days=42).to_pytimedelta(),
+            pd.Timedelta(days=42).to_timedelta64(),
+        ],
+    )
+    def test_not_contains_requires_timedelta(self, scalar):
+        tdi1 = pd.timedelta_range("42 days", freq="9h", periods=1234)
+        tdi2 = tdi1.insert(1, pd.NaT)  # non-monotonic
+        tdi3 = tdi1.insert(3, tdi1[0])  # non-unique
+        tdi4 = pd.timedelta_range("42 days", freq="ns", periods=2_000_000)
+        tdi5 = tdi4.insert(0, tdi4[0])  # over size threshold, not unique
+
+        msg = "|".join([re.escape(str(scalar)), re.escape(repr(scalar))])
+        for tdi in [tdi1, tdi2, tdi3, tdi4, tdi5]:
+            with pytest.raises(TypeError, match=msg):
+                scalar in tdi._engine
+
+            with pytest.raises(KeyError, match=msg):
+                tdi._engine.get_loc(scalar)
+
+
+class TestNumericEngine:
+    def test_is_monotonic(self, numeric_indexing_engine_type_and_dtype):
+        engine_type, dtype = numeric_indexing_engine_type_and_dtype
+        num = 1000
+        arr = np.array([1] * num + [2] * num + [3] * num, dtype=dtype)
+
+        # monotonic increasing
+        engine = engine_type(arr)
+        assert engine.is_monotonic_increasing is True
+        assert engine.is_monotonic_decreasing is False
+
+        # monotonic decreasing
+        engine = engine_type(arr[::-1])
+        assert engine.is_monotonic_increasing is False
+        assert engine.is_monotonic_decreasing is True
+
+        # neither monotonic increasing or decreasing
+        arr = np.array([1] * num + [2] * num + [1] * num, dtype=dtype)
+        engine = engine_type(arr[::-1])
+        assert engine.is_monotonic_increasing is False
+        assert engine.is_monotonic_decreasing is False
+
+    def test_is_unique(self, numeric_indexing_engine_type_and_dtype):
+        engine_type, dtype = numeric_indexing_engine_type_and_dtype
+
+        # unique
+        arr = np.array([1, 3, 2], dtype=dtype)
+        engine = engine_type(arr)
+        assert engine.is_unique is True
+
+        # not unique
+        arr = np.array([1, 2, 1], dtype=dtype)
+        engine = engine_type(arr)
+        assert engine.is_unique is False
+
+    def test_get_loc(self, numeric_indexing_engine_type_and_dtype):
+        engine_type, dtype = numeric_indexing_engine_type_and_dtype
+
+        # unique
+        arr = np.array([1, 2, 3], dtype=dtype)
+        engine = engine_type(arr)
+        assert engine.get_loc(2) == 1
+
+        # monotonic
+        num = 1000
+        arr = np.array([1] * num + [2] * num + [3] * num, dtype=dtype)
+        engine = engine_type(arr)
+        assert engine.get_loc(2) == slice(1000, 2000)
+
+        # not monotonic
+        arr = np.array([1, 2, 3] * num, dtype=dtype)
+        engine = engine_type(arr)
+        expected = np.array([False, True, False] * num, dtype=bool)
+        result = engine.get_loc(2)
+        assert (result == expected).all()
+
+
+class TestObjectEngine:
+    engine_type = libindex.ObjectEngine
+    dtype = np.object_
+    values = list("abc")
+
+    def test_is_monotonic(self):
+        num = 1000
+        arr = np.array(["a"] * num + ["a"] * num + ["c"] * num, dtype=self.dtype)
+
+        # monotonic increasing
+        engine = self.engine_type(arr)
+        assert engine.is_monotonic_increasing is True
+        assert engine.is_monotonic_decreasing is False
+
+        # monotonic decreasing
+        engine = self.engine_type(arr[::-1])
+        assert engine.is_monotonic_increasing is False
+        assert engine.is_monotonic_decreasing is True
+
+        # neither monotonic increasing or decreasing
+        arr = np.array(["a"] * num + ["b"] * num + ["a"] * num, dtype=self.dtype)
+        engine = self.engine_type(arr[::-1])
+        assert engine.is_monotonic_increasing is False
+        assert engine.is_monotonic_decreasing is False
+
+    def test_is_unique(self):
+        # unique
+        arr = np.array(self.values, dtype=self.dtype)
+        engine = self.engine_type(arr)
+        assert engine.is_unique is True
+
+        # not unique
+        arr = np.array(["a", "b", "a"], dtype=self.dtype)
+        engine = self.engine_type(arr)
+        assert engine.is_unique is False
+
+    def test_get_loc(self):
+        # unique
+        arr = np.array(self.values, dtype=self.dtype)
+        engine = self.engine_type(arr)
+        assert engine.get_loc("b") == 1
+
+        # monotonic
+        num = 1000
+        arr = np.array(["a"] * num + ["b"] * num + ["c"] * num, dtype=self.dtype)
+        engine = self.engine_type(arr)
+        assert engine.get_loc("b") == slice(1000, 2000)
+
+        # not monotonic
+        arr = np.array(self.values * num, dtype=self.dtype)
+        engine = self.engine_type(arr)
+        expected = np.array([False, True, False] * num, dtype=bool)
+        result = engine.get_loc("b")
+        assert (result == expected).all()

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

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

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

@@ -0,0 +1,360 @@
+"""
+test_indexing tests the following Index methods:
+    __getitem__
+    get_loc
+    get_value
+    __contains__
+    take
+    where
+    get_indexer
+    get_indexer_for
+    slice_locs
+    asof_locs
+
+The corresponding tests.indexes.[index_type].test_indexing files
+contain tests for the corresponding methods specific to those Index subclasses.
+"""
+import numpy as np
+import pytest
+
+from pandas.errors import InvalidIndexError
+
+from pandas.core.dtypes.common import (
+    is_float_dtype,
+    is_scalar,
+)
+
+from pandas import (
+    NA,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    PeriodIndex,
+    TimedeltaIndex,
+)
+import pandas._testing as tm
+
+
+class TestTake:
+    def test_take_invalid_kwargs(self, index):
+        indices = [1, 2]
+
+        msg = r"take\(\) got an unexpected keyword argument 'foo'"
+        with pytest.raises(TypeError, match=msg):
+            index.take(indices, foo=2)
+
+        msg = "the 'out' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            index.take(indices, out=indices)
+
+        msg = "the 'mode' parameter is not supported"
+        with pytest.raises(ValueError, match=msg):
+            index.take(indices, mode="clip")
+
+    def test_take(self, index):
+        indexer = [4, 3, 0, 2]
+        if len(index) < 5:
+            # not enough elements; ignore
+            return
+
+        result = index.take(indexer)
+        expected = index[indexer]
+        assert result.equals(expected)
+
+        if not isinstance(index, (DatetimeIndex, PeriodIndex, TimedeltaIndex)):
+            # GH 10791
+            msg = r"'(.*Index)' object has no attribute 'freq'"
+            with pytest.raises(AttributeError, match=msg):
+                index.freq
+
+    def test_take_indexer_type(self):
+        # GH#42875
+        integer_index = Index([0, 1, 2, 3])
+        scalar_index = 1
+        msg = "Expected indices to be array-like"
+        with pytest.raises(TypeError, match=msg):
+            integer_index.take(scalar_index)
+
+    def test_take_minus1_without_fill(self, index):
+        # -1 does not get treated as NA unless allow_fill=True is passed
+        if len(index) == 0:
+            # Test is not applicable
+            return
+
+        result = index.take([0, 0, -1])
+
+        expected = index.take([0, 0, len(index) - 1])
+        tm.assert_index_equal(result, expected)
+
+
+class TestContains:
+    @pytest.mark.parametrize(
+        "index,val",
+        [
+            (Index([0, 1, 2]), 2),
+            (Index([0, 1, "2"]), "2"),
+            (Index([0, 1, 2, np.inf, 4]), 4),
+            (Index([0, 1, 2, np.nan, 4]), 4),
+            (Index([0, 1, 2, np.inf]), np.inf),
+            (Index([0, 1, 2, np.nan]), np.nan),
+        ],
+    )
+    def test_index_contains(self, index, val):
+        assert val in index
+
+    @pytest.mark.parametrize(
+        "index,val",
+        [
+            (Index([0, 1, 2]), "2"),
+            (Index([0, 1, "2"]), 2),
+            (Index([0, 1, 2, np.inf]), 4),
+            (Index([0, 1, 2, np.nan]), 4),
+            (Index([0, 1, 2, np.inf]), np.nan),
+            (Index([0, 1, 2, np.nan]), np.inf),
+            # Checking if np.inf in int64 Index should not cause an OverflowError
+            # Related to GH 16957
+            (Index([0, 1, 2], dtype=np.int64), np.inf),
+            (Index([0, 1, 2], dtype=np.int64), np.nan),
+            (Index([0, 1, 2], dtype=np.uint64), np.inf),
+            (Index([0, 1, 2], dtype=np.uint64), np.nan),
+        ],
+    )
+    def test_index_not_contains(self, index, val):
+        assert val not in index
+
+    @pytest.mark.parametrize(
+        "index,val", [(Index([0, 1, "2"]), 0), (Index([0, 1, "2"]), "2")]
+    )
+    def test_mixed_index_contains(self, index, val):
+        # GH#19860
+        assert val in index
+
+    @pytest.mark.parametrize(
+        "index,val", [(Index([0, 1, "2"]), "1"), (Index([0, 1, "2"]), 2)]
+    )
+    def test_mixed_index_not_contains(self, index, val):
+        # GH#19860
+        assert val not in index
+
+    def test_contains_with_float_index(self, any_real_numpy_dtype):
+        # GH#22085
+        dtype = any_real_numpy_dtype
+        data = [0, 1, 2, 3] if not is_float_dtype(dtype) else [0.1, 1.1, 2.2, 3.3]
+        index = Index(data, dtype=dtype)
+
+        if not is_float_dtype(index.dtype):
+            assert 1.1 not in index
+            assert 1.0 in index
+            assert 1 in index
+        else:
+            assert 1.1 in index
+            assert 1.0 not in index
+            assert 1 not in index
+
+    def test_contains_requires_hashable_raises(self, index):
+        if isinstance(index, MultiIndex):
+            return  # TODO: do we want this to raise?
+
+        msg = "unhashable type: 'list'"
+        with pytest.raises(TypeError, match=msg):
+            [] in index
+
+        msg = "|".join(
+            [
+                r"unhashable type: 'dict'",
+                r"must be real number, not dict",
+                r"an integer is required",
+                r"\{\}",
+                r"pandas\._libs\.interval\.IntervalTree' is not iterable",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            {} in index._engine
+
+
+class TestGetLoc:
+    def test_get_loc_non_hashable(self, index):
+        # MultiIndex and Index raise TypeError, others InvalidIndexError
+
+        with pytest.raises((TypeError, InvalidIndexError), match="slice"):
+            index.get_loc(slice(0, 1))
+
+    def test_get_loc_non_scalar_hashable(self, index):
+        # GH52877
+        from enum import Enum
+
+        class E(Enum):
+            X1 = "x1"
+
+        assert not is_scalar(E.X1)
+
+        exc = KeyError
+        msg = "<E.X1: 'x1'>"
+        if isinstance(
+            index,
+            (
+                DatetimeIndex,
+                TimedeltaIndex,
+                PeriodIndex,
+                IntervalIndex,
+            ),
+        ):
+            # TODO: make these more consistent?
+            exc = InvalidIndexError
+            msg = "E.X1"
+        with pytest.raises(exc, match=msg):
+            index.get_loc(E.X1)
+
+    def test_get_loc_generator(self, index):
+        exc = KeyError
+        if isinstance(
+            index,
+            (
+                DatetimeIndex,
+                TimedeltaIndex,
+                PeriodIndex,
+                IntervalIndex,
+                MultiIndex,
+            ),
+        ):
+            # TODO: make these more consistent?
+            exc = InvalidIndexError
+        with pytest.raises(exc, match="generator object"):
+            # MultiIndex specifically checks for generator; others for scalar
+            index.get_loc(x for x in range(5))
+
+    def test_get_loc_masked_duplicated_na(self):
+        # GH#48411
+        idx = Index([1, 2, NA, NA], dtype="Int64")
+        result = idx.get_loc(NA)
+        expected = np.array([False, False, True, True])
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestGetIndexer:
+    def test_get_indexer_base(self, index):
+        if index._index_as_unique:
+            expected = np.arange(index.size, dtype=np.intp)
+            actual = index.get_indexer(index)
+            tm.assert_numpy_array_equal(expected, actual)
+        else:
+            msg = "Reindexing only valid with uniquely valued Index objects"
+            with pytest.raises(InvalidIndexError, match=msg):
+                index.get_indexer(index)
+
+        with pytest.raises(ValueError, match="Invalid fill method"):
+            index.get_indexer(index, method="invalid")
+
+    def test_get_indexer_consistency(self, index):
+        # See GH#16819
+
+        if index._index_as_unique:
+            indexer = index.get_indexer(index[0:2])
+            assert isinstance(indexer, np.ndarray)
+            assert indexer.dtype == np.intp
+        else:
+            msg = "Reindexing only valid with uniquely valued Index objects"
+            with pytest.raises(InvalidIndexError, match=msg):
+                index.get_indexer(index[0:2])
+
+        indexer, _ = index.get_indexer_non_unique(index[0:2])
+        assert isinstance(indexer, np.ndarray)
+        assert indexer.dtype == np.intp
+
+    def test_get_indexer_masked_duplicated_na(self):
+        # GH#48411
+        idx = Index([1, 2, NA, NA], dtype="Int64")
+        result = idx.get_indexer_for(Index([1, NA], dtype="Int64"))
+        expected = np.array([0, 2, 3], dtype=result.dtype)
+        tm.assert_numpy_array_equal(result, expected)
+
+
+class TestConvertSliceIndexer:
+    def test_convert_almost_null_slice(self, index):
+        # slice with None at both ends, but not step
+
+        key = slice(None, None, "foo")
+
+        if isinstance(index, IntervalIndex):
+            msg = "label-based slicing with step!=1 is not supported for IntervalIndex"
+            with pytest.raises(ValueError, match=msg):
+                index._convert_slice_indexer(key, "loc")
+        else:
+            msg = "'>=' not supported between instances of 'str' and 'int'"
+            with pytest.raises(TypeError, match=msg):
+                index._convert_slice_indexer(key, "loc")
+
+
+class TestPutmask:
+    def test_putmask_with_wrong_mask(self, index):
+        # GH#18368
+        if not len(index):
+            return
+
+        fill = index[0]
+
+        msg = "putmask: mask and data must be the same size"
+        with pytest.raises(ValueError, match=msg):
+            index.putmask(np.ones(len(index) + 1, np.bool_), fill)
+
+        with pytest.raises(ValueError, match=msg):
+            index.putmask(np.ones(len(index) - 1, np.bool_), fill)
+
+        with pytest.raises(ValueError, match=msg):
+            index.putmask("foo", fill)
+
+
+@pytest.mark.parametrize(
+    "idx", [Index([1, 2, 3]), Index([0.1, 0.2, 0.3]), Index(["a", "b", "c"])]
+)
+def test_getitem_deprecated_float(idx):
+    # https://github.com/pandas-dev/pandas/issues/34191
+
+    msg = "Indexing with a float is no longer supported"
+    with pytest.raises(IndexError, match=msg):
+        idx[1.0]
+
+
+@pytest.mark.parametrize(
+    "idx,target,expected",
+    [
+        ([np.nan, "var1", np.nan], [np.nan], np.array([0, 2], dtype=np.intp)),
+        (
+            [np.nan, "var1", np.nan],
+            [np.nan, "var1"],
+            np.array([0, 2, 1], dtype=np.intp),
+        ),
+        (
+            np.array([np.nan, "var1", np.nan], dtype=object),
+            [np.nan],
+            np.array([0, 2], dtype=np.intp),
+        ),
+        (
+            DatetimeIndex(["2020-08-05", NaT, NaT]),
+            [NaT],
+            np.array([1, 2], dtype=np.intp),
+        ),
+        (["a", "b", "a", np.nan], [np.nan], np.array([3], dtype=np.intp)),
+        (
+            np.array(["b", np.nan, float("NaN"), "b"], dtype=object),
+            Index([np.nan], dtype=object),
+            np.array([1, 2], dtype=np.intp),
+        ),
+    ],
+)
+def test_get_indexer_non_unique_multiple_nans(idx, target, expected):
+    # GH 35392
+    axis = Index(idx)
+    actual = axis.get_indexer_for(target)
+    tm.assert_numpy_array_equal(actual, expected)
+
+
+def test_get_indexer_non_unique_nans_in_object_dtype_target(nulls_fixture):
+    idx = Index([1.0, 2.0])
+    target = Index([1, nulls_fixture], dtype="object")
+
+    result_idx, result_missing = idx.get_indexer_non_unique(target)
+    tm.assert_numpy_array_equal(result_idx, np.array([0, -1], dtype=np.intp))
+    tm.assert_numpy_array_equal(result_missing, np.array([1], dtype=np.intp))

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

@@ -0,0 +1,191 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    CategoricalIndex,
+    DatetimeIndex,
+    Index,
+    PeriodIndex,
+    TimedeltaIndex,
+    isna,
+)
+import pandas._testing as tm
+from pandas.api.types import (
+    is_complex_dtype,
+    is_numeric_dtype,
+)
+from pandas.core.arrays import BooleanArray
+from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
+
+
+def test_numpy_ufuncs_out(index):
+    result = index == index
+
+    out = np.empty(index.shape, dtype=bool)
+    np.equal(index, index, out=out)
+    tm.assert_numpy_array_equal(out, result)
+
+    if not index._is_multi:
+        # same thing on the ExtensionArray
+        out = np.empty(index.shape, dtype=bool)
+        np.equal(index.array, index.array, out=out)
+        tm.assert_numpy_array_equal(out, result)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        np.exp,
+        np.exp2,
+        np.expm1,
+        np.log,
+        np.log2,
+        np.log10,
+        np.log1p,
+        np.sqrt,
+        np.sin,
+        np.cos,
+        np.tan,
+        np.arcsin,
+        np.arccos,
+        np.arctan,
+        np.sinh,
+        np.cosh,
+        np.tanh,
+        np.arcsinh,
+        np.arccosh,
+        np.arctanh,
+        np.deg2rad,
+        np.rad2deg,
+    ],
+    ids=lambda x: x.__name__,
+)
+def test_numpy_ufuncs_basic(index, func):
+    # test ufuncs of numpy, see:
+    # https://numpy.org/doc/stable/reference/ufuncs.html
+
+    if isinstance(index, DatetimeIndexOpsMixin):
+        with tm.external_error_raised((TypeError, AttributeError)):
+            with np.errstate(all="ignore"):
+                func(index)
+    elif is_numeric_dtype(index) and not (
+        is_complex_dtype(index) and func in [np.deg2rad, np.rad2deg]
+    ):
+        # coerces to float (e.g. np.sin)
+        with np.errstate(all="ignore"):
+            result = func(index)
+            arr_result = func(index.values)
+            if arr_result.dtype == np.float16:
+                arr_result = arr_result.astype(np.float32)
+            exp = Index(arr_result, name=index.name)
+
+        tm.assert_index_equal(result, exp)
+        if isinstance(index.dtype, np.dtype) and is_numeric_dtype(index):
+            if is_complex_dtype(index):
+                assert result.dtype == index.dtype
+            elif index.dtype in ["bool", "int8", "uint8"]:
+                assert result.dtype in ["float16", "float32"]
+            elif index.dtype in ["int16", "uint16", "float32"]:
+                assert result.dtype == "float32"
+            else:
+                assert result.dtype == "float64"
+        else:
+            # e.g. np.exp with Int64 -> Float64
+            assert type(result) is Index
+    else:
+        # raise AttributeError or TypeError
+        if len(index) == 0:
+            pass
+        else:
+            with tm.external_error_raised((TypeError, AttributeError)):
+                with np.errstate(all="ignore"):
+                    func(index)
+
+
+@pytest.mark.parametrize(
+    "func", [np.isfinite, np.isinf, np.isnan, np.signbit], ids=lambda x: x.__name__
+)
+def test_numpy_ufuncs_other(index, func):
+    # test ufuncs of numpy, see:
+    # https://numpy.org/doc/stable/reference/ufuncs.html
+    if isinstance(index, (DatetimeIndex, TimedeltaIndex)):
+        if func in (np.isfinite, np.isinf, np.isnan):
+            # numpy 1.18 changed isinf and isnan to not raise on dt64/td64
+            result = func(index)
+            assert isinstance(result, np.ndarray)
+
+            out = np.empty(index.shape, dtype=bool)
+            func(index, out=out)
+            tm.assert_numpy_array_equal(out, result)
+        else:
+            with tm.external_error_raised(TypeError):
+                func(index)
+
+    elif isinstance(index, PeriodIndex):
+        with tm.external_error_raised(TypeError):
+            func(index)
+
+    elif is_numeric_dtype(index) and not (
+        is_complex_dtype(index) and func is np.signbit
+    ):
+        # Results in bool array
+        result = func(index)
+        if not isinstance(index.dtype, np.dtype):
+            # e.g. Int64 we expect to get BooleanArray back
+            assert isinstance(result, BooleanArray)
+        else:
+            assert isinstance(result, np.ndarray)
+
+        out = np.empty(index.shape, dtype=bool)
+        func(index, out=out)
+
+        if not isinstance(index.dtype, np.dtype):
+            tm.assert_numpy_array_equal(out, result._data)
+        else:
+            tm.assert_numpy_array_equal(out, result)
+
+    else:
+        if len(index) == 0:
+            pass
+        else:
+            with tm.external_error_raised(TypeError):
+                func(index)
+
+
+@pytest.mark.parametrize("func", [np.maximum, np.minimum])
+def test_numpy_ufuncs_reductions(index, func, request):
+    # TODO: overlap with tests.series.test_ufunc.test_reductions
+    if len(index) == 0:
+        return
+
+    if isinstance(index, CategoricalIndex) and index.dtype.ordered is False:
+        with pytest.raises(TypeError, match="is not ordered for"):
+            func.reduce(index)
+        return
+    else:
+        result = func.reduce(index)
+
+    if func is np.maximum:
+        expected = index.max(skipna=False)
+    else:
+        expected = index.min(skipna=False)
+        # TODO: do we have cases both with and without NAs?
+
+    assert type(result) is type(expected)
+    if isna(result):
+        assert isna(expected)
+    else:
+        assert result == expected
+
+
+@pytest.mark.parametrize("func", [np.bitwise_and, np.bitwise_or, np.bitwise_xor])
+def test_numpy_ufuncs_bitwise(func):
+    # https://github.com/pandas-dev/pandas/issues/46769
+    idx1 = Index([1, 2, 3, 4], dtype="int64")
+    idx2 = Index([3, 4, 5, 6], dtype="int64")
+
+    with tm.assert_produces_warning(None):
+        result = func(idx1, idx2)
+
+    expected = Index(func(idx1.values, idx2.values))
+    tm.assert_index_equal(result, expected)

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

@@ -0,0 +1,888 @@
+"""
+The tests in this package are to ensure the proper resultant dtypes of
+set operations.
+"""
+from datetime import datetime
+import operator
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.cast import find_common_type
+
+from pandas import (
+    CategoricalIndex,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+    Timestamp,
+)
+import pandas._testing as tm
+from pandas.api.types import (
+    is_bool_dtype,
+    is_datetime64tz_dtype,
+    is_signed_integer_dtype,
+    pandas_dtype,
+)
+
+
+def test_union_same_types(index):
+    # Union with a non-unique, non-monotonic index raises error
+    # Only needed for bool index factory
+    idx1 = index.sort_values()
+    idx2 = index.sort_values()
+    assert idx1.union(idx2).dtype == idx1.dtype
+
+
+def test_union_different_types(index_flat, index_flat2, request):
+    # This test only considers combinations of indices
+    # GH 23525
+    idx1 = index_flat
+    idx2 = index_flat2
+
+    if (
+        not idx1.is_unique
+        and not idx2.is_unique
+        and idx1.dtype.kind == "i"
+        and idx2.dtype.kind == "b"
+    ) or (
+        not idx2.is_unique
+        and not idx1.is_unique
+        and idx2.dtype.kind == "i"
+        and idx1.dtype.kind == "b"
+    ):
+        # Each condition had idx[1|2].is_monotonic_decreasing
+        # but failed when e.g.
+        # idx1 = Index(
+        # [True, True, True, True, True, True, True, True, False, False], dtype='bool'
+        # )
+        # idx2 = Index([0, 0, 1, 1, 2, 2], dtype='int64')
+        mark = pytest.mark.xfail(
+            reason="GH#44000 True==1", raises=ValueError, strict=False
+        )
+        request.node.add_marker(mark)
+
+    common_dtype = find_common_type([idx1.dtype, idx2.dtype])
+
+    warn = None
+    if not len(idx1) or not len(idx2):
+        pass
+    elif (
+        idx1.dtype.kind == "c"
+        and (
+            idx2.dtype.kind not in ["i", "u", "f", "c"]
+            or not isinstance(idx2.dtype, np.dtype)
+        )
+    ) or (
+        idx2.dtype.kind == "c"
+        and (
+            idx1.dtype.kind not in ["i", "u", "f", "c"]
+            or not isinstance(idx1.dtype, np.dtype)
+        )
+    ):
+        # complex objects non-sortable
+        warn = RuntimeWarning
+
+    any_uint64 = np.uint64 in (idx1.dtype, idx2.dtype)
+    idx1_signed = is_signed_integer_dtype(idx1.dtype)
+    idx2_signed = is_signed_integer_dtype(idx2.dtype)
+
+    # Union with a non-unique, non-monotonic index raises error
+    # This applies to the boolean index
+    idx1 = idx1.sort_values()
+    idx2 = idx2.sort_values()
+
+    with tm.assert_produces_warning(warn, match="'<' not supported between"):
+        res1 = idx1.union(idx2)
+        res2 = idx2.union(idx1)
+
+    if any_uint64 and (idx1_signed or idx2_signed):
+        assert res1.dtype == np.dtype("O")
+        assert res2.dtype == np.dtype("O")
+    else:
+        assert res1.dtype == common_dtype
+        assert res2.dtype == common_dtype
+
+
+@pytest.mark.parametrize(
+    "idx_fact1,idx_fact2",
+    [
+        (tm.makeIntIndex, tm.makeRangeIndex),
+        (tm.makeFloatIndex, tm.makeIntIndex),
+        (tm.makeFloatIndex, tm.makeRangeIndex),
+        (tm.makeFloatIndex, tm.makeUIntIndex),
+    ],
+)
+def test_compatible_inconsistent_pairs(idx_fact1, idx_fact2):
+    # GH 23525
+    idx1 = idx_fact1(10)
+    idx2 = idx_fact2(20)
+
+    res1 = idx1.union(idx2)
+    res2 = idx2.union(idx1)
+
+    assert res1.dtype in (idx1.dtype, idx2.dtype)
+    assert res2.dtype in (idx1.dtype, idx2.dtype)
+
+
+@pytest.mark.parametrize(
+    "left, right, expected",
+    [
+        ("int64", "int64", "int64"),
+        ("int64", "uint64", "object"),
+        ("int64", "float64", "float64"),
+        ("uint64", "float64", "float64"),
+        ("uint64", "uint64", "uint64"),
+        ("float64", "float64", "float64"),
+        ("datetime64[ns]", "int64", "object"),
+        ("datetime64[ns]", "uint64", "object"),
+        ("datetime64[ns]", "float64", "object"),
+        ("datetime64[ns, CET]", "int64", "object"),
+        ("datetime64[ns, CET]", "uint64", "object"),
+        ("datetime64[ns, CET]", "float64", "object"),
+        ("Period[D]", "int64", "object"),
+        ("Period[D]", "uint64", "object"),
+        ("Period[D]", "float64", "object"),
+    ],
+)
+@pytest.mark.parametrize("names", [("foo", "foo", "foo"), ("foo", "bar", None)])
+def test_union_dtypes(left, right, expected, names):
+    left = pandas_dtype(left)
+    right = pandas_dtype(right)
+    a = Index([], dtype=left, name=names[0])
+    b = Index([], dtype=right, name=names[1])
+    result = a.union(b)
+    assert result.dtype == expected
+    assert result.name == names[2]
+
+    # Testing name retention
+    # TODO: pin down desired dtype; do we want it to be commutative?
+    result = a.intersection(b)
+    assert result.name == names[2]
+
+
+@pytest.mark.parametrize("values", [[1, 2, 2, 3], [3, 3]])
+def test_intersection_duplicates(values):
+    # GH#31326
+    a = Index(values)
+    b = Index([3, 3])
+    result = a.intersection(b)
+    expected = Index([3])
+    tm.assert_index_equal(result, expected)
+
+
+class TestSetOps:
+    # Set operation tests shared by all indexes in the `index` fixture
+    @pytest.mark.parametrize("case", [0.5, "xxx"])
+    @pytest.mark.parametrize(
+        "method", ["intersection", "union", "difference", "symmetric_difference"]
+    )
+    def test_set_ops_error_cases(self, case, method, index):
+        # non-iterable input
+        msg = "Input must be Index or array-like"
+        with pytest.raises(TypeError, match=msg):
+            getattr(index, method)(case)
+
+    def test_intersection_base(self, index):
+        if isinstance(index, CategoricalIndex):
+            return
+
+        first = index[:5]
+        second = index[:3]
+        intersect = first.intersection(second)
+        assert tm.equalContents(intersect, second)
+
+        if is_datetime64tz_dtype(index.dtype):
+            # The second.values below will drop tz, so the rest of this test
+            #  is not applicable.
+            return
+
+        # GH#10149
+        cases = [second.to_numpy(), second.to_series(), second.to_list()]
+        for case in cases:
+            result = first.intersection(case)
+            assert tm.equalContents(result, second)
+
+        if isinstance(index, MultiIndex):
+            msg = "other must be a MultiIndex or a list of tuples"
+            with pytest.raises(TypeError, match=msg):
+                first.intersection([1, 2, 3])
+
+    @pytest.mark.filterwarnings(
+        "ignore:Falling back on a non-pyarrow:pandas.errors.PerformanceWarning"
+    )
+    def test_union_base(self, index):
+        first = index[3:]
+        second = index[:5]
+        everything = index
+
+        union = first.union(second)
+        assert tm.equalContents(union, everything)
+
+        if is_datetime64tz_dtype(index.dtype):
+            # The second.values below will drop tz, so the rest of this test
+            #  is not applicable.
+            return
+
+        # GH#10149
+        cases = [second.to_numpy(), second.to_series(), second.to_list()]
+        for case in cases:
+            result = first.union(case)
+            assert tm.equalContents(result, everything)
+
+        if isinstance(index, MultiIndex):
+            msg = "other must be a MultiIndex or a list of tuples"
+            with pytest.raises(TypeError, match=msg):
+                first.union([1, 2, 3])
+
+    @pytest.mark.filterwarnings(
+        "ignore:Falling back on a non-pyarrow:pandas.errors.PerformanceWarning"
+    )
+    def test_difference_base(self, sort, index):
+        first = index[2:]
+        second = index[:4]
+        if is_bool_dtype(index):
+            # i think (TODO: be sure) there assumptions baked in about
+            #  the index fixture that don't hold here?
+            answer = set(first).difference(set(second))
+        elif isinstance(index, CategoricalIndex):
+            answer = []
+        else:
+            answer = index[4:]
+        result = first.difference(second, sort)
+        assert tm.equalContents(result, answer)
+
+        # GH#10149
+        cases = [second.to_numpy(), second.to_series(), second.to_list()]
+        for case in cases:
+            result = first.difference(case, sort)
+            assert tm.equalContents(result, answer)
+
+        if isinstance(index, MultiIndex):
+            msg = "other must be a MultiIndex or a list of tuples"
+            with pytest.raises(TypeError, match=msg):
+                first.difference([1, 2, 3], sort)
+
+    @pytest.mark.filterwarnings(
+        "ignore:Falling back on a non-pyarrow:pandas.errors.PerformanceWarning"
+    )
+    def test_symmetric_difference(self, index):
+        if isinstance(index, CategoricalIndex):
+            return
+        if len(index) < 2:
+            return
+        if index[0] in index[1:] or index[-1] in index[:-1]:
+            # index fixture has e.g. an index of bools that does not satisfy this,
+            #  another with [0, 0, 1, 1, 2, 2]
+            return
+
+        first = index[1:]
+        second = index[:-1]
+        answer = index[[0, -1]]
+        result = first.symmetric_difference(second)
+        assert tm.equalContents(result, answer)
+
+        # GH#10149
+        cases = [second.to_numpy(), second.to_series(), second.to_list()]
+        for case in cases:
+            result = first.symmetric_difference(case)
+            assert tm.equalContents(result, answer)
+
+        if isinstance(index, MultiIndex):
+            msg = "other must be a MultiIndex or a list of tuples"
+            with pytest.raises(TypeError, match=msg):
+                first.symmetric_difference([1, 2, 3])
+
+    @pytest.mark.parametrize(
+        "fname, sname, expected_name",
+        [
+            ("A", "A", "A"),
+            ("A", "B", None),
+            ("A", None, None),
+            (None, "B", None),
+            (None, None, None),
+        ],
+    )
+    def test_corner_union(self, index_flat, fname, sname, expected_name):
+        # GH#9943, GH#9862
+        # Test unions with various name combinations
+        # Do not test MultiIndex or repeats
+        if not index_flat.is_unique:
+            pytest.skip("Randomly generated index_flat was not unique.")
+        index = index_flat
+
+        # Test copy.union(copy)
+        first = index.copy().set_names(fname)
+        second = index.copy().set_names(sname)
+        union = first.union(second)
+        expected = index.copy().set_names(expected_name)
+        tm.assert_index_equal(union, expected)
+
+        # Test copy.union(empty)
+        first = index.copy().set_names(fname)
+        second = index.drop(index).set_names(sname)
+        union = first.union(second)
+        expected = index.copy().set_names(expected_name)
+        tm.assert_index_equal(union, expected)
+
+        # Test empty.union(copy)
+        first = index.drop(index).set_names(fname)
+        second = index.copy().set_names(sname)
+        union = first.union(second)
+        expected = index.copy().set_names(expected_name)
+        tm.assert_index_equal(union, expected)
+
+        # Test empty.union(empty)
+        first = index.drop(index).set_names(fname)
+        second = index.drop(index).set_names(sname)
+        union = first.union(second)
+        expected = index.drop(index).set_names(expected_name)
+        tm.assert_index_equal(union, expected)
+
+    @pytest.mark.parametrize(
+        "fname, sname, expected_name",
+        [
+            ("A", "A", "A"),
+            ("A", "B", None),
+            ("A", None, None),
+            (None, "B", None),
+            (None, None, None),
+        ],
+    )
+    def test_union_unequal(self, index_flat, fname, sname, expected_name):
+        if not index_flat.is_unique:
+            pytest.skip("Randomly generated index_flat was not unique.")
+        index = index_flat
+
+        # test copy.union(subset) - need sort for unicode and string
+        first = index.copy().set_names(fname)
+        second = index[1:].set_names(sname)
+        union = first.union(second).sort_values()
+        expected = index.set_names(expected_name).sort_values()
+        tm.assert_index_equal(union, expected)
+
+    @pytest.mark.parametrize(
+        "fname, sname, expected_name",
+        [
+            ("A", "A", "A"),
+            ("A", "B", None),
+            ("A", None, None),
+            (None, "B", None),
+            (None, None, None),
+        ],
+    )
+    def test_corner_intersect(self, index_flat, fname, sname, expected_name):
+        # GH#35847
+        # Test intersections with various name combinations
+        if not index_flat.is_unique:
+            pytest.skip("Randomly generated index_flat was not unique.")
+        index = index_flat
+
+        # Test copy.intersection(copy)
+        first = index.copy().set_names(fname)
+        second = index.copy().set_names(sname)
+        intersect = first.intersection(second)
+        expected = index.copy().set_names(expected_name)
+        tm.assert_index_equal(intersect, expected)
+
+        # Test copy.intersection(empty)
+        first = index.copy().set_names(fname)
+        second = index.drop(index).set_names(sname)
+        intersect = first.intersection(second)
+        expected = index.drop(index).set_names(expected_name)
+        tm.assert_index_equal(intersect, expected)
+
+        # Test empty.intersection(copy)
+        first = index.drop(index).set_names(fname)
+        second = index.copy().set_names(sname)
+        intersect = first.intersection(second)
+        expected = index.drop(index).set_names(expected_name)
+        tm.assert_index_equal(intersect, expected)
+
+        # Test empty.intersection(empty)
+        first = index.drop(index).set_names(fname)
+        second = index.drop(index).set_names(sname)
+        intersect = first.intersection(second)
+        expected = index.drop(index).set_names(expected_name)
+        tm.assert_index_equal(intersect, expected)
+
+    @pytest.mark.parametrize(
+        "fname, sname, expected_name",
+        [
+            ("A", "A", "A"),
+            ("A", "B", None),
+            ("A", None, None),
+            (None, "B", None),
+            (None, None, None),
+        ],
+    )
+    def test_intersect_unequal(self, index_flat, fname, sname, expected_name):
+        if not index_flat.is_unique:
+            pytest.skip("Randomly generated index_flat was not unique.")
+        index = index_flat
+
+        # test copy.intersection(subset) - need sort for unicode and string
+        first = index.copy().set_names(fname)
+        second = index[1:].set_names(sname)
+        intersect = first.intersection(second).sort_values()
+        expected = index[1:].set_names(expected_name).sort_values()
+        tm.assert_index_equal(intersect, expected)
+
+    def test_intersection_name_retention_with_nameless(self, index):
+        if isinstance(index, MultiIndex):
+            index = index.rename(list(range(index.nlevels)))
+        else:
+            index = index.rename("foo")
+
+        other = np.asarray(index)
+
+        result = index.intersection(other)
+        assert result.name == index.name
+
+        # empty other, same dtype
+        result = index.intersection(other[:0])
+        assert result.name == index.name
+
+        # empty `self`
+        result = index[:0].intersection(other)
+        assert result.name == index.name
+
+    def test_difference_preserves_type_empty(self, index, sort):
+        # GH#20040
+        # If taking difference of a set and itself, it
+        # needs to preserve the type of the index
+        if not index.is_unique:
+            return
+        result = index.difference(index, sort=sort)
+        expected = index[:0]
+        tm.assert_index_equal(result, expected, exact=True)
+
+    def test_difference_name_retention_equals(self, index, names):
+        if isinstance(index, MultiIndex):
+            names = [[x] * index.nlevels for x in names]
+        index = index.rename(names[0])
+        other = index.rename(names[1])
+
+        assert index.equals(other)
+
+        result = index.difference(other)
+        expected = index[:0].rename(names[2])
+        tm.assert_index_equal(result, expected)
+
+    def test_intersection_difference_match_empty(self, index, sort):
+        # GH#20040
+        # Test that the intersection of an index with an
+        # empty index produces the same index as the difference
+        # of an index with itself.  Test for all types
+        if not index.is_unique:
+            return
+        inter = index.intersection(index[:0])
+        diff = index.difference(index, sort=sort)
+        tm.assert_index_equal(inter, diff, exact=True)
+
+
+@pytest.mark.filterwarnings(
+    "ignore:Falling back on a non-pyarrow:pandas.errors.PerformanceWarning"
+)
+@pytest.mark.parametrize(
+    "method", ["intersection", "union", "difference", "symmetric_difference"]
+)
+def test_setop_with_categorical(index_flat, sort, method):
+    # MultiIndex tested separately in tests.indexes.multi.test_setops
+    index = index_flat
+
+    other = index.astype("category")
+    exact = "equiv" if isinstance(index, RangeIndex) else True
+
+    result = getattr(index, method)(other, sort=sort)
+    expected = getattr(index, method)(index, sort=sort)
+    tm.assert_index_equal(result, expected, exact=exact)
+
+    result = getattr(index, method)(other[:5], sort=sort)
+    expected = getattr(index, method)(index[:5], sort=sort)
+    tm.assert_index_equal(result, expected, exact=exact)
+
+
+def test_intersection_duplicates_all_indexes(index):
+    # GH#38743
+    if index.empty:
+        # No duplicates in empty indexes
+        return
+
+    idx = index
+    idx_non_unique = idx[[0, 0, 1, 2]]
+
+    assert idx.intersection(idx_non_unique).equals(idx_non_unique.intersection(idx))
+    assert idx.intersection(idx_non_unique).is_unique
+
+
+def test_union_duplicate_index_subsets_of_each_other(
+    any_dtype_for_small_pos_integer_indexes,
+):
+    # GH#31326
+    dtype = any_dtype_for_small_pos_integer_indexes
+    a = Index([1, 2, 2, 3], dtype=dtype)
+    b = Index([3, 3, 4], dtype=dtype)
+
+    expected = Index([1, 2, 2, 3, 3, 4], dtype=dtype)
+    if isinstance(a, CategoricalIndex):
+        expected = Index([1, 2, 2, 3, 3, 4])
+    result = a.union(b)
+    tm.assert_index_equal(result, expected)
+    result = a.union(b, sort=False)
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_with_duplicate_index_and_non_monotonic(
+    any_dtype_for_small_pos_integer_indexes,
+):
+    # GH#36289
+    dtype = any_dtype_for_small_pos_integer_indexes
+    a = Index([1, 0, 0], dtype=dtype)
+    b = Index([0, 1], dtype=dtype)
+    expected = Index([0, 0, 1], dtype=dtype)
+
+    result = a.union(b)
+    tm.assert_index_equal(result, expected)
+
+    result = b.union(a)
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_duplicate_index_different_dtypes():
+    # GH#36289
+    a = Index([1, 2, 2, 3])
+    b = Index(["1", "0", "0"])
+    expected = Index([1, 2, 2, 3, "1", "0", "0"])
+    result = a.union(b, sort=False)
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_same_value_duplicated_in_both():
+    # GH#36289
+    a = Index([0, 0, 1])
+    b = Index([0, 0, 1, 2])
+    result = a.union(b)
+    expected = Index([0, 0, 1, 2])
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("dup", [1, np.nan])
+def test_union_nan_in_both(dup):
+    # GH#36289
+    a = Index([np.nan, 1, 2, 2])
+    b = Index([np.nan, dup, 1, 2])
+    result = a.union(b, sort=False)
+    expected = Index([np.nan, dup, 1.0, 2.0, 2.0])
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_rangeindex_sort_true():
+    # GH 53490
+    idx1 = RangeIndex(1, 100, 6)
+    idx2 = RangeIndex(1, 50, 3)
+    result = idx1.union(idx2, sort=True)
+    expected = Index(
+        [
+            1,
+            4,
+            7,
+            10,
+            13,
+            16,
+            19,
+            22,
+            25,
+            28,
+            31,
+            34,
+            37,
+            40,
+            43,
+            46,
+            49,
+            55,
+            61,
+            67,
+            73,
+            79,
+            85,
+            91,
+            97,
+        ]
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_with_duplicate_index_not_subset_and_non_monotonic(
+    any_dtype_for_small_pos_integer_indexes,
+):
+    # GH#36289
+    dtype = any_dtype_for_small_pos_integer_indexes
+    a = Index([1, 0, 2], dtype=dtype)
+    b = Index([0, 0, 1], dtype=dtype)
+    expected = Index([0, 0, 1, 2], dtype=dtype)
+    if isinstance(a, CategoricalIndex):
+        expected = Index([0, 0, 1, 2])
+
+    result = a.union(b)
+    tm.assert_index_equal(result, expected)
+
+    result = b.union(a)
+    tm.assert_index_equal(result, expected)
+
+
+def test_union_int_categorical_with_nan():
+    ci = CategoricalIndex([1, 2, np.nan])
+    assert ci.categories.dtype.kind == "i"
+
+    idx = Index([1, 2])
+
+    result = idx.union(ci)
+    expected = Index([1, 2, np.nan], dtype=np.float64)
+    tm.assert_index_equal(result, expected)
+
+    result = ci.union(idx)
+    tm.assert_index_equal(result, expected)
+
+
+class TestSetOpsUnsorted:
+    # These may eventually belong in a dtype-specific test_setops, or
+    #  parametrized over a more general fixture
+    def test_intersect_str_dates(self):
+        dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]
+
+        index1 = Index(dt_dates, dtype=object)
+        index2 = Index(["aa"], dtype=object)
+        result = index2.intersection(index1)
+
+        expected = Index([], dtype=object)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_intersection(self, index, sort):
+        first = index[:20]
+        second = index[:10]
+        intersect = first.intersection(second, sort=sort)
+        if sort is None:
+            tm.assert_index_equal(intersect, second.sort_values())
+        assert tm.equalContents(intersect, second)
+
+        # Corner cases
+        inter = first.intersection(first, sort=sort)
+        assert inter is first
+
+    @pytest.mark.parametrize(
+        "index2,keeps_name",
+        [
+            (Index([3, 4, 5, 6, 7], name="index"), True),  # preserve same name
+            (Index([3, 4, 5, 6, 7], name="other"), False),  # drop diff names
+            (Index([3, 4, 5, 6, 7]), False),
+        ],
+    )
+    def test_intersection_name_preservation(self, index2, keeps_name, sort):
+        index1 = Index([1, 2, 3, 4, 5], name="index")
+        expected = Index([3, 4, 5])
+        result = index1.intersection(index2, sort)
+
+        if keeps_name:
+            expected.name = "index"
+
+        assert result.name == expected.name
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    @pytest.mark.parametrize(
+        "first_name,second_name,expected_name",
+        [("A", "A", "A"), ("A", "B", None), (None, "B", None)],
+    )
+    def test_intersection_name_preservation2(
+        self, index, first_name, second_name, expected_name, sort
+    ):
+        first = index[5:20]
+        second = index[:10]
+        first.name = first_name
+        second.name = second_name
+        intersect = first.intersection(second, sort=sort)
+        assert intersect.name == expected_name
+
+    def test_chained_union(self, sort):
+        # Chained unions handles names correctly
+        i1 = Index([1, 2], name="i1")
+        i2 = Index([5, 6], name="i2")
+        i3 = Index([3, 4], name="i3")
+        union = i1.union(i2.union(i3, sort=sort), sort=sort)
+        expected = i1.union(i2, sort=sort).union(i3, sort=sort)
+        tm.assert_index_equal(union, expected)
+
+        j1 = Index([1, 2], name="j1")
+        j2 = Index([], name="j2")
+        j3 = Index([], name="j3")
+        union = j1.union(j2.union(j3, sort=sort), sort=sort)
+        expected = j1.union(j2, sort=sort).union(j3, sort=sort)
+        tm.assert_index_equal(union, expected)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_union(self, index, sort):
+        first = index[5:20]
+        second = index[:10]
+        everything = index[:20]
+
+        union = first.union(second, sort=sort)
+        if sort is None:
+            tm.assert_index_equal(union, everything.sort_values())
+        assert tm.equalContents(union, everything)
+
+    @pytest.mark.parametrize("klass", [np.array, Series, list])
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_union_from_iterables(self, index, klass, sort):
+        # GH#10149
+        first = index[5:20]
+        second = index[:10]
+        everything = index[:20]
+
+        case = klass(second.values)
+        result = first.union(case, sort=sort)
+        if sort is None:
+            tm.assert_index_equal(result, everything.sort_values())
+        assert tm.equalContents(result, everything)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_union_identity(self, index, sort):
+        first = index[5:20]
+
+        union = first.union(first, sort=sort)
+        # i.e. identity is not preserved when sort is True
+        assert (union is first) is (not sort)
+
+        # This should no longer be the same object, since [] is not consistent,
+        # both objects will be recast to dtype('O')
+        union = first.union([], sort=sort)
+        assert (union is first) is (not sort)
+
+        union = Index([]).union(first, sort=sort)
+        assert (union is first) is (not sort)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    @pytest.mark.parametrize("second_name,expected", [(None, None), ("name", "name")])
+    def test_difference_name_preservation(self, index, second_name, expected, sort):
+        first = index[5:20]
+        second = index[:10]
+        answer = index[10:20]
+
+        first.name = "name"
+        second.name = second_name
+        result = first.difference(second, sort=sort)
+
+        assert tm.equalContents(result, answer)
+
+        if expected is None:
+            assert result.name is None
+        else:
+            assert result.name == expected
+
+    def test_difference_empty_arg(self, index, sort):
+        first = index[5:20]
+        first.name = "name"
+        result = first.difference([], sort)
+
+        tm.assert_index_equal(result, first)
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_difference_identity(self, index, sort):
+        first = index[5:20]
+        first.name = "name"
+        result = first.difference(first, sort)
+
+        assert len(result) == 0
+        assert result.name == first.name
+
+    @pytest.mark.parametrize("index", ["string"], indirect=True)
+    def test_difference_sort(self, index, sort):
+        first = index[5:20]
+        second = index[:10]
+
+        result = first.difference(second, sort)
+        expected = index[10:20]
+
+        if sort is None:
+            expected = expected.sort_values()
+
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("opname", ["difference", "symmetric_difference"])
+    def test_difference_incomparable(self, opname):
+        a = Index([3, Timestamp("2000"), 1])
+        b = Index([2, Timestamp("1999"), 1])
+        op = operator.methodcaller(opname, b)
+
+        with tm.assert_produces_warning(RuntimeWarning):
+            # sort=None, the default
+            result = op(a)
+        expected = Index([3, Timestamp("2000"), 2, Timestamp("1999")])
+        if opname == "difference":
+            expected = expected[:2]
+        tm.assert_index_equal(result, expected)
+
+        # sort=False
+        op = operator.methodcaller(opname, b, sort=False)
+        result = op(a)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize("opname", ["difference", "symmetric_difference"])
+    def test_difference_incomparable_true(self, opname):
+        a = Index([3, Timestamp("2000"), 1])
+        b = Index([2, Timestamp("1999"), 1])
+        op = operator.methodcaller(opname, b, sort=True)
+
+        msg = "'<' not supported between instances of 'Timestamp' and 'int'"
+        with pytest.raises(TypeError, match=msg):
+            op(a)
+
+    def test_symmetric_difference_mi(self, sort):
+        index1 = MultiIndex.from_tuples(zip(["foo", "bar", "baz"], [1, 2, 3]))
+        index2 = MultiIndex.from_tuples([("foo", 1), ("bar", 3)])
+        result = index1.symmetric_difference(index2, sort=sort)
+        expected = MultiIndex.from_tuples([("bar", 2), ("baz", 3), ("bar", 3)])
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+        assert tm.equalContents(result, expected)
+
+    @pytest.mark.parametrize(
+        "index2,expected",
+        [
+            (Index([0, 1, np.nan]), Index([2.0, 3.0, 0.0])),
+            (Index([0, 1]), Index([np.nan, 2.0, 3.0, 0.0])),
+        ],
+    )
+    def test_symmetric_difference_missing(self, index2, expected, sort):
+        # GH#13514 change: {nan} - {nan} == {}
+        # (GH#6444, sorting of nans, is no longer an issue)
+        index1 = Index([1, np.nan, 2, 3])
+
+        result = index1.symmetric_difference(index2, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+
+    def test_symmetric_difference_non_index(self, sort):
+        index1 = Index([1, 2, 3, 4], name="index1")
+        index2 = np.array([2, 3, 4, 5])
+        expected = Index([1, 5])
+        result = index1.symmetric_difference(index2, sort=sort)
+        assert tm.equalContents(result, expected)
+        assert result.name == "index1"
+
+        result = index1.symmetric_difference(index2, result_name="new_name", sort=sort)
+        assert tm.equalContents(result, expected)
+        assert result.name == "new_name"
+
+    def test_union_ea_dtypes(self, any_numeric_ea_and_arrow_dtype):
+        # GH#51365
+        idx = Index([1, 2, 3], dtype=any_numeric_ea_and_arrow_dtype)
+        idx2 = Index([3, 4, 5], dtype=any_numeric_ea_and_arrow_dtype)
+        result = idx.union(idx2)
+        expected = Index([1, 2, 3, 4, 5], dtype=any_numeric_ea_and_arrow_dtype)
+        tm.assert_index_equal(result, expected)

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

@@ -0,0 +1,38 @@
+"""
+Tests involving custom Index subclasses
+"""
+import numpy as np
+
+from pandas import (
+    DataFrame,
+    Index,
+)
+import pandas._testing as tm
+
+
+class CustomIndex(Index):
+    def __new__(cls, data, name=None):
+        # assert that this index class cannot hold strings
+        if any(isinstance(val, str) for val in data):
+            raise TypeError("CustomIndex cannot hold strings")
+
+        if name is None and hasattr(data, "name"):
+            name = data.name
+        data = np.array(data, dtype="O")
+
+        return cls._simple_new(data, name)
+
+
+def test_insert_fallback_to_base_index():
+    # https://github.com/pandas-dev/pandas/issues/47071
+
+    idx = CustomIndex([1, 2, 3])
+    result = idx.insert(0, "string")
+    expected = Index(["string", 1, 2, 3], dtype=object)
+    tm.assert_index_equal(result, expected)
+
+    df = DataFrame(
+        np.random.randn(2, 3), columns=idx, index=Index([1, 2], name="string")
+    )
+    result = df.reset_index()
+    tm.assert_index_equal(result.columns, expected)

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

@@ -0,0 +1,275 @@
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Timedelta,
+    TimedeltaIndex,
+    timedelta_range,
+    to_timedelta,
+)
+import pandas._testing as tm
+from pandas.core.arrays.timedeltas import (
+    TimedeltaArray,
+    sequence_to_td64ns,
+)
+
+
+class TestTimedeltaIndex:
+    def test_array_of_dt64_nat_raises(self):
+        # GH#39462
+        nat = np.datetime64("NaT", "ns")
+        arr = np.array([nat], dtype=object)
+
+        msg = "Invalid type for timedelta scalar"
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaIndex(arr)
+
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaArray._from_sequence(arr)
+
+        with pytest.raises(TypeError, match=msg):
+            sequence_to_td64ns(arr)
+
+        with pytest.raises(TypeError, match=msg):
+            to_timedelta(arr)
+
+    @pytest.mark.parametrize("unit", ["Y", "y", "M"])
+    def test_unit_m_y_raises(self, unit):
+        msg = "Units 'M', 'Y', and 'y' are no longer supported"
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaIndex([1, 3, 7], unit)
+
+    def test_int64_nocopy(self):
+        # GH#23539 check that a copy isn't made when we pass int64 data
+        #  and copy=False
+        arr = np.arange(10, dtype=np.int64)
+        tdi = TimedeltaIndex(arr, copy=False)
+        assert tdi._data._ndarray.base is arr
+
+    def test_infer_from_tdi(self):
+        # GH#23539
+        # fast-path for inferring a frequency if the passed data already
+        #  has one
+        tdi = timedelta_range("1 second", periods=10**7, freq="1s")
+
+        result = TimedeltaIndex(tdi, freq="infer")
+        assert result.freq == tdi.freq
+
+        # check that inferred_freq was not called by checking that the
+        #  value has not been cached
+        assert "inferred_freq" not in getattr(result, "_cache", {})
+
+    def test_infer_from_tdi_mismatch(self):
+        # GH#23539
+        # fast-path for invalidating a frequency if the passed data already
+        #  has one and it does not match the `freq` input
+        tdi = timedelta_range("1 second", periods=100, freq="1s")
+
+        msg = (
+            "Inferred frequency .* from passed values does "
+            "not conform to passed frequency"
+        )
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaIndex(tdi, freq="D")
+
+        with pytest.raises(ValueError, match=msg):
+            # GH#23789
+            TimedeltaArray(tdi, freq="D")
+
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaIndex(tdi._data, freq="D")
+
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaArray(tdi._data, freq="D")
+
+    def test_dt64_data_invalid(self):
+        # GH#23539
+        # passing tz-aware DatetimeIndex raises, naive or ndarray[datetime64]
+        #  raise as of GH#29794
+        dti = pd.date_range("2016-01-01", periods=3)
+
+        msg = "cannot be converted to timedelta64"
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaIndex(dti.tz_localize("Europe/Brussels"))
+
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaIndex(dti)
+
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaIndex(np.asarray(dti))
+
+    def test_float64_ns_rounded(self):
+        # GH#23539 without specifying a unit, floats are regarded as nanos,
+        #  and fractional portions are truncated
+        tdi = TimedeltaIndex([2.3, 9.7])
+        expected = TimedeltaIndex([2, 9])
+        tm.assert_index_equal(tdi, expected)
+
+        # integral floats are non-lossy
+        tdi = TimedeltaIndex([2.0, 9.0])
+        expected = TimedeltaIndex([2, 9])
+        tm.assert_index_equal(tdi, expected)
+
+        # NaNs get converted to NaT
+        tdi = TimedeltaIndex([2.0, np.nan])
+        expected = TimedeltaIndex([Timedelta(nanoseconds=2), pd.NaT])
+        tm.assert_index_equal(tdi, expected)
+
+    def test_float64_unit_conversion(self):
+        # GH#23539
+        tdi = TimedeltaIndex([1.5, 2.25], unit="D")
+        expected = TimedeltaIndex([Timedelta(days=1.5), Timedelta(days=2.25)])
+        tm.assert_index_equal(tdi, expected)
+
+    def test_construction_base_constructor(self):
+        arr = [Timedelta("1 days"), pd.NaT, Timedelta("3 days")]
+        tm.assert_index_equal(pd.Index(arr), TimedeltaIndex(arr))
+        tm.assert_index_equal(pd.Index(np.array(arr)), TimedeltaIndex(np.array(arr)))
+
+        arr = [np.nan, pd.NaT, Timedelta("1 days")]
+        tm.assert_index_equal(pd.Index(arr), TimedeltaIndex(arr))
+        tm.assert_index_equal(pd.Index(np.array(arr)), TimedeltaIndex(np.array(arr)))
+
+    def test_constructor(self):
+        expected = TimedeltaIndex(
+            [
+                "1 days",
+                "1 days 00:00:05",
+                "2 days",
+                "2 days 00:00:02",
+                "0 days 00:00:03",
+            ]
+        )
+        result = TimedeltaIndex(
+            [
+                "1 days",
+                "1 days, 00:00:05",
+                np.timedelta64(2, "D"),
+                timedelta(days=2, seconds=2),
+                pd.offsets.Second(3),
+            ]
+        )
+        tm.assert_index_equal(result, expected)
+
+        expected = TimedeltaIndex(
+            ["0 days 00:00:00", "0 days 00:00:01", "0 days 00:00:02"]
+        )
+        tm.assert_index_equal(TimedeltaIndex(range(3), unit="s"), expected)
+        expected = TimedeltaIndex(
+            ["0 days 00:00:00", "0 days 00:00:05", "0 days 00:00:09"]
+        )
+        tm.assert_index_equal(TimedeltaIndex([0, 5, 9], unit="s"), expected)
+        expected = TimedeltaIndex(
+            ["0 days 00:00:00.400", "0 days 00:00:00.450", "0 days 00:00:01.200"]
+        )
+        tm.assert_index_equal(TimedeltaIndex([400, 450, 1200], unit="ms"), expected)
+
+    def test_constructor_iso(self):
+        # GH #21877
+        expected = timedelta_range("1s", periods=9, freq="s")
+        durations = [f"P0DT0H0M{i}S" for i in range(1, 10)]
+        result = to_timedelta(durations)
+        tm.assert_index_equal(result, expected)
+
+    def test_constructor_coverage(self):
+        rng = timedelta_range("1 days", periods=10.5)
+        exp = timedelta_range("1 days", periods=10)
+        tm.assert_index_equal(rng, exp)
+
+        msg = "periods must be a number, got foo"
+        with pytest.raises(TypeError, match=msg):
+            timedelta_range(start="1 days", periods="foo", freq="D")
+
+        msg = (
+            r"TimedeltaIndex\(\.\.\.\) must be called with a collection of some kind, "
+            "'1 days' was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            TimedeltaIndex("1 days")
+
+        # generator expression
+        gen = (timedelta(i) for i in range(10))
+        result = TimedeltaIndex(gen)
+        expected = TimedeltaIndex([timedelta(i) for i in range(10)])
+        tm.assert_index_equal(result, expected)
+
+        # NumPy string array
+        strings = np.array(["1 days", "2 days", "3 days"])
+        result = TimedeltaIndex(strings)
+        expected = to_timedelta([1, 2, 3], unit="d")
+        tm.assert_index_equal(result, expected)
+
+        from_ints = TimedeltaIndex(expected.asi8)
+        tm.assert_index_equal(from_ints, expected)
+
+        # non-conforming freq
+        msg = (
+            "Inferred frequency None from passed values does not conform to "
+            "passed frequency D"
+        )
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaIndex(["1 days", "2 days", "4 days"], freq="D")
+
+        msg = (
+            "Of the four parameters: start, end, periods, and freq, exactly "
+            "three must be specified"
+        )
+        with pytest.raises(ValueError, match=msg):
+            timedelta_range(periods=10, freq="D")
+
+    def test_constructor_name(self):
+        idx = timedelta_range(start="1 days", periods=1, freq="D", name="TEST")
+        assert idx.name == "TEST"
+
+        # GH10025
+        idx2 = TimedeltaIndex(idx, name="something else")
+        assert idx2.name == "something else"
+
+    def test_constructor_no_precision_raises(self):
+        # GH-24753, GH-24739
+
+        msg = "with no precision is not allowed"
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaIndex(["2000"], dtype="timedelta64")
+
+        msg = "The 'timedelta64' dtype has no unit. Please pass in"
+        with pytest.raises(ValueError, match=msg):
+            pd.Index(["2000"], dtype="timedelta64")
+
+    def test_constructor_wrong_precision_raises(self):
+        msg = r"dtype timedelta64\[D\] cannot be converted to timedelta64\[ns\]"
+        with pytest.raises(ValueError, match=msg):
+            TimedeltaIndex(["2000"], dtype="timedelta64[D]")
+
+        # "timedelta64[us]" was unsupported pre-2.0, but now this works.
+        tdi = TimedeltaIndex(["2000"], dtype="timedelta64[us]")
+        assert tdi.dtype == "m8[us]"
+
+    def test_explicit_none_freq(self):
+        # Explicitly passing freq=None is respected
+        tdi = timedelta_range(1, periods=5)
+        assert tdi.freq is not None
+
+        result = TimedeltaIndex(tdi, freq=None)
+        assert result.freq is None
+
+        result = TimedeltaIndex(tdi._data, freq=None)
+        assert result.freq is None
+
+        tda = TimedeltaArray(tdi, freq=None)
+        assert tda.freq is None
+
+    def test_from_categorical(self):
+        tdi = timedelta_range(1, periods=5)
+
+        cat = pd.Categorical(tdi)
+
+        result = TimedeltaIndex(cat)
+        tm.assert_index_equal(result, tdi)
+
+        ci = pd.CategoricalIndex(tdi)
+        result = TimedeltaIndex(ci)
+        tm.assert_index_equal(result, tdi)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_freq_attr.py

@@ -0,0 +1,61 @@
+import pytest
+
+from pandas import TimedeltaIndex
+
+from pandas.tseries.offsets import (
+    DateOffset,
+    Day,
+    Hour,
+)
+
+
+class TestFreq:
+    @pytest.mark.parametrize("values", [["0 days", "2 days", "4 days"], []])
+    @pytest.mark.parametrize("freq", ["2D", Day(2), "48H", Hour(48)])
+    def test_freq_setter(self, values, freq):
+        # GH#20678
+        idx = TimedeltaIndex(values)
+
+        # can set to an offset, converting from string if necessary
+        idx._data.freq = freq
+        assert idx.freq == freq
+        assert isinstance(idx.freq, DateOffset)
+
+        # can reset to None
+        idx._data.freq = None
+        assert idx.freq is None
+
+    def test_freq_setter_errors(self):
+        # GH#20678
+        idx = TimedeltaIndex(["0 days", "2 days", "4 days"])
+
+        # setting with an incompatible freq
+        msg = (
+            "Inferred frequency 2D from passed values does not conform to "
+            "passed frequency 5D"
+        )
+        with pytest.raises(ValueError, match=msg):
+            idx._data.freq = "5D"
+
+        # setting with a non-fixed frequency
+        msg = r"<2 \* BusinessDays> is a non-fixed frequency"
+        with pytest.raises(ValueError, match=msg):
+            idx._data.freq = "2B"
+
+        # setting with non-freq string
+        with pytest.raises(ValueError, match="Invalid frequency"):
+            idx._data.freq = "foo"
+
+    def test_freq_view_safe(self):
+        # Setting the freq for one TimedeltaIndex shouldn't alter the freq
+        #  for another that views the same data
+
+        tdi = TimedeltaIndex(["0 days", "2 days", "4 days"], freq="2D")
+        tda = tdi._data
+
+        tdi2 = TimedeltaIndex(tda)._with_freq(None)
+        assert tdi2.freq is None
+
+        # Original was not altered
+        assert tdi.freq == "2D"
+        assert tda.freq == "2D"

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

@@ -0,0 +1,347 @@
+from datetime import datetime
+import re
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Index,
+    NaT,
+    Timedelta,
+    TimedeltaIndex,
+    Timestamp,
+    notna,
+    offsets,
+    timedelta_range,
+    to_timedelta,
+)
+import pandas._testing as tm
+
+
+class TestGetItem:
+    def test_getitem_slice_keeps_name(self):
+        # GH#4226
+        tdi = timedelta_range("1d", "5d", freq="H", name="timebucket")
+        assert tdi[1:].name == tdi.name
+
+    def test_getitem(self):
+        idx1 = timedelta_range("1 day", "31 day", freq="D", name="idx")
+
+        for idx in [idx1]:
+            result = idx[0]
+            assert result == Timedelta("1 day")
+
+            result = idx[0:5]
+            expected = timedelta_range("1 day", "5 day", freq="D", name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+            result = idx[0:10:2]
+            expected = timedelta_range("1 day", "9 day", freq="2D", name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+            result = idx[-20:-5:3]
+            expected = timedelta_range("12 day", "24 day", freq="3D", name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+            result = idx[4::-1]
+            expected = TimedeltaIndex(
+                ["5 day", "4 day", "3 day", "2 day", "1 day"], freq="-1D", name="idx"
+            )
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+    @pytest.mark.parametrize(
+        "key",
+        [
+            Timestamp("1970-01-01"),
+            Timestamp("1970-01-02"),
+            datetime(1970, 1, 1),
+            Timestamp("1970-01-03").to_datetime64(),
+            # non-matching NA values
+            np.datetime64("NaT"),
+        ],
+    )
+    def test_timestamp_invalid_key(self, key):
+        # GH#20464
+        tdi = timedelta_range(0, periods=10)
+        with pytest.raises(KeyError, match=re.escape(repr(key))):
+            tdi.get_loc(key)
+
+
+class TestGetLoc:
+    def test_get_loc_key_unit_mismatch(self):
+        idx = to_timedelta(["0 days", "1 days", "2 days"])
+        key = idx[1].as_unit("ms")
+        loc = idx.get_loc(key)
+        assert loc == 1
+
+    def test_get_loc_key_unit_mismatch_not_castable(self):
+        tdi = to_timedelta(["0 days", "1 days", "2 days"]).astype("m8[s]")
+        assert tdi.dtype == "m8[s]"
+        key = tdi[0].as_unit("ns") + Timedelta(1)
+
+        with pytest.raises(KeyError, match=r"Timedelta\('0 days 00:00:00.000000001'\)"):
+            tdi.get_loc(key)
+
+        assert key not in tdi
+
+    def test_get_loc(self):
+        idx = to_timedelta(["0 days", "1 days", "2 days"])
+
+        # GH 16909
+        assert idx.get_loc(idx[1].to_timedelta64()) == 1
+
+        # GH 16896
+        assert idx.get_loc("0 days") == 0
+
+    def test_get_loc_nat(self):
+        tidx = TimedeltaIndex(["1 days 01:00:00", "NaT", "2 days 01:00:00"])
+
+        assert tidx.get_loc(NaT) == 1
+        assert tidx.get_loc(None) == 1
+        assert tidx.get_loc(float("nan")) == 1
+        assert tidx.get_loc(np.nan) == 1
+
+
+class TestGetIndexer:
+    def test_get_indexer(self):
+        idx = to_timedelta(["0 days", "1 days", "2 days"])
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(idx), np.array([0, 1, 2], dtype=np.intp)
+        )
+
+        target = to_timedelta(["-1 hour", "12 hours", "1 day 1 hour"])
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "pad"), np.array([-1, 0, 1], dtype=np.intp)
+        )
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "backfill"), np.array([0, 1, 2], dtype=np.intp)
+        )
+        tm.assert_numpy_array_equal(
+            idx.get_indexer(target, "nearest"), np.array([0, 1, 1], dtype=np.intp)
+        )
+
+        res = idx.get_indexer(target, "nearest", tolerance=Timedelta("1 hour"))
+        tm.assert_numpy_array_equal(res, np.array([0, -1, 1], dtype=np.intp))
+
+
+class TestWhere:
+    def test_where_doesnt_retain_freq(self):
+        tdi = timedelta_range("1 day", periods=3, freq="D", name="idx")
+        cond = [True, True, False]
+        expected = TimedeltaIndex([tdi[0], tdi[1], tdi[0]], freq=None, name="idx")
+
+        result = tdi.where(cond, tdi[::-1])
+        tm.assert_index_equal(result, expected)
+
+    def test_where_invalid_dtypes(self, fixed_now_ts):
+        tdi = timedelta_range("1 day", periods=3, freq="D", name="idx")
+
+        tail = tdi[2:].tolist()
+        i2 = Index([NaT, NaT] + tail)
+        mask = notna(i2)
+
+        expected = Index([NaT._value, NaT._value] + tail, dtype=object, name="idx")
+        assert isinstance(expected[0], int)
+        result = tdi.where(mask, i2.asi8)
+        tm.assert_index_equal(result, expected)
+
+        ts = i2 + fixed_now_ts
+        expected = Index([ts[0], ts[1]] + tail, dtype=object, name="idx")
+        result = tdi.where(mask, ts)
+        tm.assert_index_equal(result, expected)
+
+        per = (i2 + fixed_now_ts).to_period("D")
+        expected = Index([per[0], per[1]] + tail, dtype=object, name="idx")
+        result = tdi.where(mask, per)
+        tm.assert_index_equal(result, expected)
+
+        ts = fixed_now_ts
+        expected = Index([ts, ts] + tail, dtype=object, name="idx")
+        result = tdi.where(mask, ts)
+        tm.assert_index_equal(result, expected)
+
+    def test_where_mismatched_nat(self):
+        tdi = timedelta_range("1 day", periods=3, freq="D", name="idx")
+        cond = np.array([True, False, False])
+
+        dtnat = np.datetime64("NaT", "ns")
+        expected = Index([tdi[0], dtnat, dtnat], dtype=object, name="idx")
+        assert expected[2] is dtnat
+        result = tdi.where(cond, dtnat)
+        tm.assert_index_equal(result, expected)
+
+
+class TestTake:
+    def test_take(self):
+        # GH 10295
+        idx1 = timedelta_range("1 day", "31 day", freq="D", name="idx")
+
+        for idx in [idx1]:
+            result = idx.take([0])
+            assert result == Timedelta("1 day")
+
+            result = idx.take([-1])
+            assert result == Timedelta("31 day")
+
+            result = idx.take([0, 1, 2])
+            expected = timedelta_range("1 day", "3 day", freq="D", name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+            result = idx.take([0, 2, 4])
+            expected = timedelta_range("1 day", "5 day", freq="2D", name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+            result = idx.take([7, 4, 1])
+            expected = timedelta_range("8 day", "2 day", freq="-3D", name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq == expected.freq
+
+            result = idx.take([3, 2, 5])
+            expected = TimedeltaIndex(["4 day", "3 day", "6 day"], name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq is None
+
+            result = idx.take([-3, 2, 5])
+            expected = TimedeltaIndex(["29 day", "3 day", "6 day"], name="idx")
+            tm.assert_index_equal(result, expected)
+            assert result.freq is None
+
+    def test_take_invalid_kwargs(self):
+        idx = timedelta_range("1 day", "31 day", freq="D", name="idx")
+        indices = [1, 6, 5, 9, 10, 13, 15, 3]
+
+        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")
+
+    def test_take_equiv_getitem(self):
+        tds = ["1day 02:00:00", "1 day 04:00:00", "1 day 10:00:00"]
+        idx = timedelta_range(start="1d", end="2d", freq="H", name="idx")
+        expected = TimedeltaIndex(tds, freq=None, name="idx")
+
+        taken1 = idx.take([2, 4, 10])
+        taken2 = idx[[2, 4, 10]]
+
+        for taken in [taken1, taken2]:
+            tm.assert_index_equal(taken, expected)
+            assert isinstance(taken, TimedeltaIndex)
+            assert taken.freq is None
+            assert taken.name == expected.name
+
+    def test_take_fill_value(self):
+        # GH 12631
+        idx = TimedeltaIndex(["1 days", "2 days", "3 days"], name="xxx")
+        result = idx.take(np.array([1, 0, -1]))
+        expected = TimedeltaIndex(["2 days", "1 days", "3 days"], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # fill_value
+        result = idx.take(np.array([1, 0, -1]), fill_value=True)
+        expected = TimedeltaIndex(["2 days", "1 days", "NaT"], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        # allow_fill=False
+        result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
+        expected = TimedeltaIndex(["2 days", "1 days", "3 days"], name="xxx")
+        tm.assert_index_equal(result, expected)
+
+        msg = (
+            "When allow_fill=True and fill_value is not None, "
+            "all indices must be >= -1"
+        )
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -2]), fill_value=True)
+        with pytest.raises(ValueError, match=msg):
+            idx.take(np.array([1, 0, -5]), fill_value=True)
+
+        msg = "index -5 is out of bounds for (axis 0 with )?size 3"
+        with pytest.raises(IndexError, match=msg):
+            idx.take(np.array([1, -5]))
+
+
+class TestMaybeCastSliceBound:
+    @pytest.fixture(params=["increasing", "decreasing", None])
+    def monotonic(self, request):
+        return request.param
+
+    @pytest.fixture
+    def tdi(self, monotonic):
+        tdi = timedelta_range("1 Day", periods=10)
+        if monotonic == "decreasing":
+            tdi = tdi[::-1]
+        elif monotonic is None:
+            taker = np.arange(10, dtype=np.intp)
+            np.random.shuffle(taker)
+            tdi = tdi.take(taker)
+        return tdi
+
+    def test_maybe_cast_slice_bound_invalid_str(self, tdi):
+        # test the low-level _maybe_cast_slice_bound and that we get the
+        #  expected exception+message all the way up the stack
+        msg = (
+            "cannot do slice indexing on TimedeltaIndex with these "
+            r"indexers \[foo\] of type str"
+        )
+        with pytest.raises(TypeError, match=msg):
+            tdi._maybe_cast_slice_bound("foo", side="left")
+        with pytest.raises(TypeError, match=msg):
+            tdi.get_slice_bound("foo", side="left")
+        with pytest.raises(TypeError, match=msg):
+            tdi.slice_locs("foo", None, None)
+
+    def test_slice_invalid_str_with_timedeltaindex(
+        self, tdi, frame_or_series, indexer_sl
+    ):
+        obj = frame_or_series(range(10), index=tdi)
+
+        msg = (
+            "cannot do slice indexing on TimedeltaIndex with these "
+            r"indexers \[foo\] of type str"
+        )
+        with pytest.raises(TypeError, match=msg):
+            indexer_sl(obj)["foo":]
+        with pytest.raises(TypeError, match=msg):
+            indexer_sl(obj)["foo":-1]
+        with pytest.raises(TypeError, match=msg):
+            indexer_sl(obj)[:"foo"]
+        with pytest.raises(TypeError, match=msg):
+            indexer_sl(obj)[tdi[0] : "foo"]
+
+
+class TestContains:
+    def test_contains_nonunique(self):
+        # GH#9512
+        for vals in (
+            [0, 1, 0],
+            [0, 0, -1],
+            [0, -1, -1],
+            ["00:01:00", "00:01:00", "00:02:00"],
+            ["00:01:00", "00:01:00", "00:00:01"],
+        ):
+            idx = TimedeltaIndex(vals)
+            assert idx[0] in idx
+
+    def test_contains(self):
+        # Checking for any NaT-like objects
+        # GH#13603
+        td = to_timedelta(range(5), unit="d") + offsets.Hour(1)
+        for v in [NaT, None, float("nan"), np.nan]:
+            assert v not in td
+
+        td = to_timedelta([NaT])
+        for v in [NaT, None, float("nan"), np.nan]:
+            assert v in td

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_join.py

@@ -0,0 +1,53 @@
+import numpy as np
+
+from pandas import (
+    Index,
+    Timedelta,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+
+class TestJoin:
+    def test_append_join_nondatetimeindex(self):
+        rng = timedelta_range("1 days", periods=10)
+        idx = Index(["a", "b", "c", "d"])
+
+        result = rng.append(idx)
+        assert isinstance(result[0], Timedelta)
+
+        # it works
+        rng.join(idx, how="outer")
+
+    def test_join_self(self, join_type):
+        index = timedelta_range("1 day", periods=10)
+        joined = index.join(index, how=join_type)
+        tm.assert_index_equal(index, joined)
+
+    def test_does_not_convert_mixed_integer(self):
+        df = tm.makeCustomDataframe(
+            10,
+            10,
+            data_gen_f=lambda *args, **kwargs: np.random.randn(),
+            r_idx_type="i",
+            c_idx_type="td",
+        )
+        str(df)
+
+        cols = df.columns.join(df.index, how="outer")
+        joined = cols.join(df.columns)
+        assert cols.dtype == np.dtype("O")
+        assert cols.dtype == joined.dtype
+        tm.assert_index_equal(cols, joined)
+
+    def test_join_preserves_freq(self):
+        # GH#32157
+        tdi = timedelta_range("1 day", periods=10)
+        result = tdi[:5].join(tdi[5:], how="outer")
+        assert result.freq == tdi.freq
+        tm.assert_index_equal(result, tdi)
+
+        result = tdi[:5].join(tdi[6:], how="outer")
+        assert result.freq is None
+        expected = tdi.delete(5)
+        tm.assert_index_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_ops.py

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

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

@@ -0,0 +1,252 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+
+from pandas.tseries.offsets import Hour
+
+
+class TestTimedeltaIndex:
+    def test_union(self):
+        i1 = timedelta_range("1day", periods=5)
+        i2 = timedelta_range("3day", periods=5)
+        result = i1.union(i2)
+        expected = timedelta_range("1day", periods=7)
+        tm.assert_index_equal(result, expected)
+
+        i1 = Index(np.arange(0, 20, 2, dtype=np.int64))
+        i2 = timedelta_range(start="1 day", periods=10, freq="D")
+        i1.union(i2)  # Works
+        i2.union(i1)  # Fails with "AttributeError: can't set attribute"
+
+    def test_union_sort_false(self):
+        tdi = timedelta_range("1day", periods=5)
+
+        left = tdi[3:]
+        right = tdi[:3]
+
+        # Check that we are testing the desired code path
+        assert left._can_fast_union(right)
+
+        result = left.union(right)
+        tm.assert_index_equal(result, tdi)
+
+        result = left.union(right, sort=False)
+        expected = TimedeltaIndex(["4 Days", "5 Days", "1 Days", "2 Day", "3 Days"])
+        tm.assert_index_equal(result, expected)
+
+    def test_union_coverage(self):
+        idx = TimedeltaIndex(["3d", "1d", "2d"])
+        ordered = TimedeltaIndex(idx.sort_values(), freq="infer")
+        result = ordered.union(idx)
+        tm.assert_index_equal(result, ordered)
+
+        result = ordered[:0].union(ordered)
+        tm.assert_index_equal(result, ordered)
+        assert result.freq == ordered.freq
+
+    def test_union_bug_1730(self):
+        rng_a = timedelta_range("1 day", periods=4, freq="3H")
+        rng_b = timedelta_range("1 day", periods=4, freq="4H")
+
+        result = rng_a.union(rng_b)
+        exp = TimedeltaIndex(sorted(set(rng_a) | set(rng_b)))
+        tm.assert_index_equal(result, exp)
+
+    def test_union_bug_1745(self):
+        left = TimedeltaIndex(["1 day 15:19:49.695000"])
+        right = TimedeltaIndex(
+            ["2 day 13:04:21.322000", "1 day 15:27:24.873000", "1 day 15:31:05.350000"]
+        )
+
+        result = left.union(right)
+        exp = TimedeltaIndex(sorted(set(left) | set(right)))
+        tm.assert_index_equal(result, exp)
+
+    def test_union_bug_4564(self):
+        left = timedelta_range("1 day", "30d")
+        right = left + pd.offsets.Minute(15)
+
+        result = left.union(right)
+        exp = TimedeltaIndex(sorted(set(left) | set(right)))
+        tm.assert_index_equal(result, exp)
+
+    def test_union_freq_infer(self):
+        # When taking the union of two TimedeltaIndexes, we infer
+        #  a freq even if the arguments don't have freq.  This matches
+        #  DatetimeIndex behavior.
+        tdi = timedelta_range("1 Day", periods=5)
+        left = tdi[[0, 1, 3, 4]]
+        right = tdi[[2, 3, 1]]
+
+        assert left.freq is None
+        assert right.freq is None
+
+        result = left.union(right)
+        tm.assert_index_equal(result, tdi)
+        assert result.freq == "D"
+
+    def test_intersection_bug_1708(self):
+        index_1 = timedelta_range("1 day", periods=4, freq="h")
+        index_2 = index_1 + pd.offsets.Hour(5)
+
+        result = index_1.intersection(index_2)
+        assert len(result) == 0
+
+        index_1 = timedelta_range("1 day", periods=4, freq="h")
+        index_2 = index_1 + pd.offsets.Hour(1)
+
+        result = index_1.intersection(index_2)
+        expected = timedelta_range("1 day 01:00:00", periods=3, freq="h")
+        tm.assert_index_equal(result, expected)
+        assert result.freq == expected.freq
+
+    def test_intersection_equal(self, sort):
+        # GH 24471 Test intersection outcome given the sort keyword
+        # for equal indices intersection should return the original index
+        first = timedelta_range("1 day", periods=4, freq="h")
+        second = timedelta_range("1 day", periods=4, freq="h")
+        intersect = first.intersection(second, sort=sort)
+        if sort is None:
+            tm.assert_index_equal(intersect, second.sort_values())
+        assert tm.equalContents(intersect, second)
+
+        # Corner cases
+        inter = first.intersection(first, sort=sort)
+        assert inter is first
+
+    @pytest.mark.parametrize("period_1, period_2", [(0, 4), (4, 0)])
+    def test_intersection_zero_length(self, period_1, period_2, sort):
+        # GH 24471 test for non overlap the intersection should be zero length
+        index_1 = timedelta_range("1 day", periods=period_1, freq="h")
+        index_2 = timedelta_range("1 day", periods=period_2, freq="h")
+        expected = timedelta_range("1 day", periods=0, freq="h")
+        result = index_1.intersection(index_2, sort=sort)
+        tm.assert_index_equal(result, expected)
+
+    def test_zero_length_input_index(self, sort):
+        # GH 24966 test for 0-len intersections are copied
+        index_1 = timedelta_range("1 day", periods=0, freq="h")
+        index_2 = timedelta_range("1 day", periods=3, freq="h")
+        result = index_1.intersection(index_2, sort=sort)
+        assert index_1 is not result
+        assert index_2 is not result
+        tm.assert_copy(result, index_1)
+
+    @pytest.mark.parametrize(
+        "rng, expected",
+        # if target has the same name, it is preserved
+        [
+            (
+                timedelta_range("1 day", periods=5, freq="h", name="idx"),
+                timedelta_range("1 day", periods=4, freq="h", name="idx"),
+            ),
+            # if target name is different, it will be reset
+            (
+                timedelta_range("1 day", periods=5, freq="h", name="other"),
+                timedelta_range("1 day", periods=4, freq="h", name=None),
+            ),
+            # if no overlap exists return empty index
+            (
+                timedelta_range("1 day", periods=10, freq="h", name="idx")[5:],
+                TimedeltaIndex([], freq="h", name="idx"),
+            ),
+        ],
+    )
+    def test_intersection(self, rng, expected, sort):
+        # GH 4690 (with tz)
+        base = timedelta_range("1 day", periods=4, freq="h", name="idx")
+        result = base.intersection(rng, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+        assert result.freq == expected.freq
+
+    @pytest.mark.parametrize(
+        "rng, expected",
+        # part intersection works
+        [
+            (
+                TimedeltaIndex(["5 hour", "2 hour", "4 hour", "9 hour"], name="idx"),
+                TimedeltaIndex(["2 hour", "4 hour"], name="idx"),
+            ),
+            # reordered part intersection
+            (
+                TimedeltaIndex(["2 hour", "5 hour", "5 hour", "1 hour"], name="other"),
+                TimedeltaIndex(["1 hour", "2 hour"], name=None),
+            ),
+            # reversed index
+            (
+                TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx")[
+                    ::-1
+                ],
+                TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx"),
+            ),
+        ],
+    )
+    def test_intersection_non_monotonic(self, rng, expected, sort):
+        # 24471 non-monotonic
+        base = TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx")
+        result = base.intersection(rng, sort=sort)
+        if sort is None:
+            expected = expected.sort_values()
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+
+        # if reversed order, frequency is still the same
+        if all(base == rng[::-1]) and sort is None:
+            assert isinstance(result.freq, Hour)
+        else:
+            assert result.freq is None
+
+
+class TestTimedeltaIndexDifference:
+    def test_difference_freq(self, sort):
+        # GH14323: Difference of TimedeltaIndex should not preserve frequency
+
+        index = timedelta_range("0 days", "5 days", freq="D")
+
+        other = timedelta_range("1 days", "4 days", freq="D")
+        expected = TimedeltaIndex(["0 days", "5 days"], freq=None)
+        idx_diff = index.difference(other, sort)
+        tm.assert_index_equal(idx_diff, expected)
+        tm.assert_attr_equal("freq", idx_diff, expected)
+
+        other = timedelta_range("2 days", "5 days", freq="D")
+        idx_diff = index.difference(other, sort)
+        expected = TimedeltaIndex(["0 days", "1 days"], freq=None)
+        tm.assert_index_equal(idx_diff, expected)
+        tm.assert_attr_equal("freq", idx_diff, expected)
+
+    def test_difference_sort(self, sort):
+        index = TimedeltaIndex(
+            ["5 days", "3 days", "2 days", "4 days", "1 days", "0 days"]
+        )
+
+        other = timedelta_range("1 days", "4 days", freq="D")
+        idx_diff = index.difference(other, sort)
+
+        expected = TimedeltaIndex(["5 days", "0 days"], freq=None)
+
+        if sort is None:
+            expected = expected.sort_values()
+
+        tm.assert_index_equal(idx_diff, expected)
+        tm.assert_attr_equal("freq", idx_diff, expected)
+
+        other = timedelta_range("2 days", "5 days", freq="D")
+        idx_diff = index.difference(other, sort)
+        expected = TimedeltaIndex(["1 days", "0 days"], freq=None)
+
+        if sort is None:
+            expected = expected.sort_values()
+
+        tm.assert_index_equal(idx_diff, expected)
+        tm.assert_attr_equal("freq", idx_diff, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta.py

@@ -0,0 +1,159 @@
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Index,
+    NaT,
+    Series,
+    Timedelta,
+    TimedeltaIndex,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import TimedeltaArray
+from pandas.tests.indexes.datetimelike import DatetimeLike
+
+randn = np.random.randn
+
+
+class TestTimedeltaIndex(DatetimeLike):
+    _index_cls = TimedeltaIndex
+
+    @pytest.fixture
+    def simple_index(self) -> TimedeltaIndex:
+        index = pd.to_timedelta(range(5), unit="d")._with_freq("infer")
+        assert index.freq == "D"
+        ret = index + pd.offsets.Hour(1)
+        assert ret.freq == "D"
+        return ret
+
+    @pytest.fixture
+    def index(self):
+        return tm.makeTimedeltaIndex(10)
+
+    def test_numeric_compat(self):
+        # Dummy method to override super's version; this test is now done
+        # in test_arithmetic.py
+        pass
+
+    def test_shift(self):
+        pass  # this is handled in test_arithmetic.py
+
+    def test_misc_coverage(self):
+        rng = timedelta_range("1 day", periods=5)
+        result = rng.groupby(rng.days)
+        assert isinstance(list(result.values())[0][0], Timedelta)
+
+    def test_map(self):
+        # test_map_dictlike generally tests
+
+        rng = timedelta_range("1 day", periods=10)
+
+        f = lambda x: x.days
+        result = rng.map(f)
+        exp = Index([f(x) for x in rng], dtype=np.int64)
+        tm.assert_index_equal(result, exp)
+
+    def test_pass_TimedeltaIndex_to_index(self):
+        rng = timedelta_range("1 days", "10 days")
+        idx = Index(rng, dtype=object)
+
+        expected = Index(rng.to_pytimedelta(), dtype=object)
+
+        tm.assert_numpy_array_equal(idx.values, expected.values)
+
+    def test_fields(self):
+        rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s")
+        tm.assert_index_equal(rng.days, Index([1, 1], dtype=np.int64))
+        tm.assert_index_equal(
+            rng.seconds,
+            Index([10 * 3600 + 11 * 60 + 12, 10 * 3600 + 11 * 60 + 13], dtype=np.int32),
+        )
+        tm.assert_index_equal(
+            rng.microseconds,
+            Index([100 * 1000 + 123, 100 * 1000 + 123], dtype=np.int32),
+        )
+        tm.assert_index_equal(rng.nanoseconds, Index([456, 456], dtype=np.int32))
+
+        msg = "'TimedeltaIndex' object has no attribute '{}'"
+        with pytest.raises(AttributeError, match=msg.format("hours")):
+            rng.hours
+        with pytest.raises(AttributeError, match=msg.format("minutes")):
+            rng.minutes
+        with pytest.raises(AttributeError, match=msg.format("milliseconds")):
+            rng.milliseconds
+
+        # with nat
+        s = Series(rng)
+        s[1] = np.nan
+
+        tm.assert_series_equal(s.dt.days, Series([1, np.nan], index=[0, 1]))
+        tm.assert_series_equal(
+            s.dt.seconds, Series([10 * 3600 + 11 * 60 + 12, np.nan], index=[0, 1])
+        )
+
+        # preserve name (GH15589)
+        rng.name = "name"
+        assert rng.days.name == "name"
+
+    def test_freq_conversion_always_floating(self):
+        # pre-2.0 td64 astype converted to float64. now for supported units
+        #  (s, ms, us, ns) this converts to the requested dtype.
+        # This matches TDA and Series
+        tdi = timedelta_range("1 Day", periods=30)
+
+        res = tdi.astype("m8[s]")
+        exp_values = np.asarray(tdi).astype("m8[s]")
+        exp_tda = TimedeltaArray._simple_new(
+            exp_values, dtype=exp_values.dtype, freq=tdi.freq
+        )
+        expected = Index(exp_tda)
+        assert expected.dtype == "m8[s]"
+        tm.assert_index_equal(res, expected)
+
+        # check this matches Series and TimedeltaArray
+        res = tdi._data.astype("m8[s]")
+        tm.assert_equal(res, expected._values)
+
+        res = tdi.to_series().astype("m8[s]")
+        tm.assert_equal(res._values, expected._values._with_freq(None))
+
+    def test_freq_conversion(self, index_or_series):
+        # doc example
+
+        scalar = Timedelta(days=31)
+        td = index_or_series(
+            [scalar, scalar, scalar + timedelta(minutes=5, seconds=3), NaT],
+            dtype="m8[ns]",
+        )
+
+        result = td / np.timedelta64(1, "D")
+        expected = index_or_series(
+            [31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan]
+        )
+        tm.assert_equal(result, expected)
+
+        # We don't support "D" reso, so we use the pre-2.0 behavior
+        #  casting to float64
+        msg = (
+            r"Cannot convert from timedelta64\[ns\] to timedelta64\[D\]. "
+            "Supported resolutions are 's', 'ms', 'us', 'ns'"
+        )
+        with pytest.raises(ValueError, match=msg):
+            td.astype("timedelta64[D]")
+
+        result = td / np.timedelta64(1, "s")
+        expected = index_or_series(
+            [31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan]
+        )
+        tm.assert_equal(result, expected)
+
+        exp_values = np.asarray(td).astype("m8[s]")
+        exp_tda = TimedeltaArray._simple_new(exp_values, dtype=exp_values.dtype)
+        expected = index_or_series(exp_tda)
+        assert expected.dtype == "m8[s]"
+        result = td.astype("timedelta64[s]")
+        tm.assert_equal(result, expected)