Add files using upload-large-folder tool

py311/lib/python3.11/site-packages/pandas/tests/frame/constructors/test_from_dict.py

@@ -0,0 +1,223 @@
+from collections import OrderedDict
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestFromDict:
+    # Note: these tests are specific to the from_dict method, not for
+    #  passing dictionaries to DataFrame.__init__
+
+    def test_constructor_list_of_odicts(self):
+        data = [
+            OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]]),
+            OrderedDict([["a", 1.5], ["b", 3], ["d", 6]]),
+            OrderedDict([["a", 1.5], ["d", 6]]),
+            OrderedDict(),
+            OrderedDict([["a", 1.5], ["b", 3], ["c", 4]]),
+            OrderedDict([["b", 3], ["c", 4], ["d", 6]]),
+        ]
+
+        result = DataFrame(data)
+        expected = DataFrame.from_dict(
+            dict(zip(range(len(data)), data)), orient="index"
+        )
+        tm.assert_frame_equal(result, expected.reindex(result.index))
+
+    def test_constructor_single_row(self):
+        data = [OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]])]
+
+        result = DataFrame(data)
+        expected = DataFrame.from_dict(dict(zip([0], data)), orient="index").reindex(
+            result.index
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_list_of_series(self):
+        data = [
+            OrderedDict([["a", 1.5], ["b", 3.0], ["c", 4.0]]),
+            OrderedDict([["a", 1.5], ["b", 3.0], ["c", 6.0]]),
+        ]
+        sdict = OrderedDict(zip(["x", "y"], data))
+        idx = Index(["a", "b", "c"])
+
+        # all named
+        data2 = [
+            Series([1.5, 3, 4], idx, dtype="O", name="x"),
+            Series([1.5, 3, 6], idx, name="y"),
+        ]
+        result = DataFrame(data2)
+        expected = DataFrame.from_dict(sdict, orient="index")
+        tm.assert_frame_equal(result, expected)
+
+        # some unnamed
+        data2 = [
+            Series([1.5, 3, 4], idx, dtype="O", name="x"),
+            Series([1.5, 3, 6], idx),
+        ]
+        result = DataFrame(data2)
+
+        sdict = OrderedDict(zip(["x", "Unnamed 0"], data))
+        expected = DataFrame.from_dict(sdict, orient="index")
+        tm.assert_frame_equal(result, expected)
+
+        # none named
+        data = [
+            OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]]),
+            OrderedDict([["a", 1.5], ["b", 3], ["d", 6]]),
+            OrderedDict([["a", 1.5], ["d", 6]]),
+            OrderedDict(),
+            OrderedDict([["a", 1.5], ["b", 3], ["c", 4]]),
+            OrderedDict([["b", 3], ["c", 4], ["d", 6]]),
+        ]
+        data = [Series(d) for d in data]
+
+        result = DataFrame(data)
+        sdict = OrderedDict(zip(range(len(data)), data))
+        expected = DataFrame.from_dict(sdict, orient="index")
+        tm.assert_frame_equal(result, expected.reindex(result.index))
+
+        result2 = DataFrame(data, index=np.arange(6, dtype=np.int64))
+        tm.assert_frame_equal(result, result2)
+
+        result = DataFrame([Series(dtype=object)])
+        expected = DataFrame(index=[0])
+        tm.assert_frame_equal(result, expected)
+
+        data = [
+            OrderedDict([["a", 1.5], ["b", 3.0], ["c", 4.0]]),
+            OrderedDict([["a", 1.5], ["b", 3.0], ["c", 6.0]]),
+        ]
+        sdict = OrderedDict(zip(range(len(data)), data))
+
+        idx = Index(["a", "b", "c"])
+        data2 = [Series([1.5, 3, 4], idx, dtype="O"), Series([1.5, 3, 6], idx)]
+        result = DataFrame(data2)
+        expected = DataFrame.from_dict(sdict, orient="index")
+        tm.assert_frame_equal(result, expected)
+
+    def test_constructor_orient(self, float_string_frame):
+        data_dict = float_string_frame.T._series
+        recons = DataFrame.from_dict(data_dict, orient="index")
+        expected = float_string_frame.reindex(index=recons.index)
+        tm.assert_frame_equal(recons, expected)
+
+        # dict of sequence
+        a = {"hi": [32, 3, 3], "there": [3, 5, 3]}
+        rs = DataFrame.from_dict(a, orient="index")
+        xp = DataFrame.from_dict(a).T.reindex(list(a.keys()))
+        tm.assert_frame_equal(rs, xp)
+
+    def test_constructor_from_ordered_dict(self):
+        # GH#8425
+        a = OrderedDict(
+            [
+                ("one", OrderedDict([("col_a", "foo1"), ("col_b", "bar1")])),
+                ("two", OrderedDict([("col_a", "foo2"), ("col_b", "bar2")])),
+                ("three", OrderedDict([("col_a", "foo3"), ("col_b", "bar3")])),
+            ]
+        )
+        expected = DataFrame.from_dict(a, orient="columns").T
+        result = DataFrame.from_dict(a, orient="index")
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_dict_columns_parameter(self):
+        # GH#18529
+        # Test new columns parameter for from_dict that was added to make
+        # from_items(..., orient='index', columns=[...]) easier to replicate
+        result = DataFrame.from_dict(
+            OrderedDict([("A", [1, 2]), ("B", [4, 5])]),
+            orient="index",
+            columns=["one", "two"],
+        )
+        expected = DataFrame([[1, 2], [4, 5]], index=["A", "B"], columns=["one", "two"])
+        tm.assert_frame_equal(result, expected)
+
+        msg = "cannot use columns parameter with orient='columns'"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame.from_dict(
+                {"A": [1, 2], "B": [4, 5]},
+                orient="columns",
+                columns=["one", "two"],
+            )
+        with pytest.raises(ValueError, match=msg):
+            DataFrame.from_dict({"A": [1, 2], "B": [4, 5]}, columns=["one", "two"])
+
+    @pytest.mark.parametrize(
+        "data_dict, orient, expected",
+        [
+            ({}, "index", RangeIndex(0)),
+            (
+                [{("a",): 1}, {("a",): 2}],
+                "columns",
+                Index([("a",)], tupleize_cols=False),
+            ),
+            (
+                [OrderedDict([(("a",), 1), (("b",), 2)])],
+                "columns",
+                Index([("a",), ("b",)], tupleize_cols=False),
+            ),
+            ([{("a", "b"): 1}], "columns", Index([("a", "b")], tupleize_cols=False)),
+        ],
+    )
+    def test_constructor_from_dict_tuples(self, data_dict, orient, expected):
+        # GH#16769
+        df = DataFrame.from_dict(data_dict, orient)
+        result = df.columns
+        tm.assert_index_equal(result, expected)
+
+    def test_frame_dict_constructor_empty_series(self):
+        s1 = Series(
+            [1, 2, 3, 4], index=MultiIndex.from_tuples([(1, 2), (1, 3), (2, 2), (2, 4)])
+        )
+        s2 = Series(
+            [1, 2, 3, 4], index=MultiIndex.from_tuples([(1, 2), (1, 3), (3, 2), (3, 4)])
+        )
+        s3 = Series(dtype=object)
+
+        # it works!
+        DataFrame({"foo": s1, "bar": s2, "baz": s3})
+        DataFrame.from_dict({"foo": s1, "baz": s3, "bar": s2})
+
+    def test_from_dict_scalars_requires_index(self):
+        msg = "If using all scalar values, you must pass an index"
+        with pytest.raises(ValueError, match=msg):
+            DataFrame.from_dict(OrderedDict([("b", 8), ("a", 5), ("a", 6)]))
+
+    def test_from_dict_orient_invalid(self):
+        msg = (
+            "Expected 'index', 'columns' or 'tight' for orient parameter. "
+            "Got 'abc' instead"
+        )
+        with pytest.raises(ValueError, match=msg):
+            DataFrame.from_dict({"foo": 1, "baz": 3, "bar": 2}, orient="abc")
+
+    def test_from_dict_order_with_single_column(self):
+        data = {
+            "alpha": {
+                "value2": 123,
+                "value1": 532,
+                "animal": 222,
+                "plant": False,
+                "name": "test",
+            }
+        }
+        result = DataFrame.from_dict(
+            data,
+            orient="columns",
+        )
+        expected = DataFrame(
+            [[123], [532], [222], [False], ["test"]],
+            index=["value2", "value1", "animal", "plant", "name"],
+            columns=["alpha"],
+        )
+        tm.assert_frame_equal(result, expected)

py311/lib/python3.11/site-packages/pandas/tests/frame/constructors/test_from_records.py

@@ -0,0 +1,503 @@
+from collections.abc import Iterator
+from datetime import datetime
+from decimal import Decimal
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas._config import using_string_dtype
+
+from pandas.compat import is_platform_little_endian
+
+from pandas import (
+    CategoricalIndex,
+    DataFrame,
+    Index,
+    Interval,
+    RangeIndex,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestFromRecords:
+    def test_from_records_dt64tz_frame(self):
+        # GH#51162 don't lose tz when calling from_records with DataFrame input
+        dti = date_range("2016-01-01", periods=10, tz="US/Pacific")
+        df = DataFrame({i: dti for i in range(4)})
+        with tm.assert_produces_warning(FutureWarning):
+            res = DataFrame.from_records(df)
+        tm.assert_frame_equal(res, df)
+
+    def test_from_records_with_datetimes(self):
+        # this may fail on certain platforms because of a numpy issue
+        # related GH#6140
+        if not is_platform_little_endian():
+            pytest.skip("known failure of test on non-little endian")
+
+        # construction with a null in a recarray
+        # GH#6140
+        expected = DataFrame({"EXPIRY": [datetime(2005, 3, 1, 0, 0), None]})
+
+        arrdata = [np.array([datetime(2005, 3, 1, 0, 0), None])]
+        dtypes = [("EXPIRY", "<M8[ns]")]
+
+        recarray = np.rec.fromarrays(arrdata, dtype=dtypes)
+
+        result = DataFrame.from_records(recarray)
+        tm.assert_frame_equal(result, expected)
+
+        # coercion should work too
+        arrdata = [np.array([datetime(2005, 3, 1, 0, 0), None])]
+        dtypes = [("EXPIRY", "<M8[m]")]
+        recarray = np.rec.fromarrays(arrdata, dtype=dtypes)
+        result = DataFrame.from_records(recarray)
+        # we get the closest supported unit, "s"
+        expected["EXPIRY"] = expected["EXPIRY"].astype("M8[s]")
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.xfail(using_string_dtype(), reason="dtype checking logic doesn't work")
+    def test_from_records_sequencelike(self):
+        df = DataFrame(
+            {
+                "A": np.array(
+                    np.random.default_rng(2).standard_normal(6), dtype=np.float64
+                ),
+                "A1": np.array(
+                    np.random.default_rng(2).standard_normal(6), dtype=np.float64
+                ),
+                "B": np.array(np.arange(6), dtype=np.int64),
+                "C": ["foo"] * 6,
+                "D": np.array([True, False] * 3, dtype=bool),
+                "E": np.array(
+                    np.random.default_rng(2).standard_normal(6), dtype=np.float32
+                ),
+                "E1": np.array(
+                    np.random.default_rng(2).standard_normal(6), dtype=np.float32
+                ),
+                "F": np.array(np.arange(6), dtype=np.int32),
+            }
+        )
+
+        # this is actually tricky to create the recordlike arrays and
+        # have the dtypes be intact
+        blocks = df._to_dict_of_blocks()
+        tuples = []
+        columns = []
+        dtypes = []
+        for dtype, b in blocks.items():
+            columns.extend(b.columns)
+            dtypes.extend([(c, np.dtype(dtype).descr[0][1]) for c in b.columns])
+        for i in range(len(df.index)):
+            tup = []
+            for _, b in blocks.items():
+                tup.extend(b.iloc[i].values)
+            tuples.append(tuple(tup))
+
+        recarray = np.array(tuples, dtype=dtypes).view(np.rec.recarray)
+        recarray2 = df.to_records()
+        lists = [list(x) for x in tuples]
+
+        # tuples (lose the dtype info)
+        result = DataFrame.from_records(tuples, columns=columns).reindex(
+            columns=df.columns
+        )
+
+        # created recarray and with to_records recarray (have dtype info)
+        result2 = DataFrame.from_records(recarray, columns=columns).reindex(
+            columns=df.columns
+        )
+        result3 = DataFrame.from_records(recarray2, columns=columns).reindex(
+            columns=df.columns
+        )
+
+        # list of tuples (no dtype info)
+        result4 = DataFrame.from_records(lists, columns=columns).reindex(
+            columns=df.columns
+        )
+
+        tm.assert_frame_equal(result, df, check_dtype=False)
+        tm.assert_frame_equal(result2, df)
+        tm.assert_frame_equal(result3, df)
+        tm.assert_frame_equal(result4, df, check_dtype=False)
+
+        # tuples is in the order of the columns
+        result = DataFrame.from_records(tuples)
+        tm.assert_index_equal(result.columns, RangeIndex(8))
+
+        # test exclude parameter & we are casting the results here (as we don't
+        # have dtype info to recover)
+        columns_to_test = [columns.index("C"), columns.index("E1")]
+
+        exclude = list(set(range(8)) - set(columns_to_test))
+        result = DataFrame.from_records(tuples, exclude=exclude)
+        result.columns = [columns[i] for i in sorted(columns_to_test)]
+        tm.assert_series_equal(result["C"], df["C"])
+        tm.assert_series_equal(result["E1"], df["E1"])
+
+    def test_from_records_sequencelike_empty(self):
+        # empty case
+        result = DataFrame.from_records([], columns=["foo", "bar", "baz"])
+        assert len(result) == 0
+        tm.assert_index_equal(result.columns, Index(["foo", "bar", "baz"]))
+
+        result = DataFrame.from_records([])
+        assert len(result) == 0
+        assert len(result.columns) == 0
+
+    def test_from_records_dictlike(self):
+        # test the dict methods
+        df = DataFrame(
+            {
+                "A": np.array(
+                    np.random.default_rng(2).standard_normal(6), dtype=np.float64
+                ),
+                "A1": np.array(
+                    np.random.default_rng(2).standard_normal(6), dtype=np.float64
+                ),
+                "B": np.array(np.arange(6), dtype=np.int64),
+                "C": ["foo"] * 6,
+                "D": np.array([True, False] * 3, dtype=bool),
+                "E": np.array(
+                    np.random.default_rng(2).standard_normal(6), dtype=np.float32
+                ),
+                "E1": np.array(
+                    np.random.default_rng(2).standard_normal(6), dtype=np.float32
+                ),
+                "F": np.array(np.arange(6), dtype=np.int32),
+            }
+        )
+
+        # columns is in a different order here than the actual items iterated
+        # from the dict
+        blocks = df._to_dict_of_blocks()
+        columns = []
+        for b in blocks.values():
+            columns.extend(b.columns)
+
+        asdict = dict(df.items())
+        asdict2 = {x: y.values for x, y in df.items()}
+
+        # dict of series & dict of ndarrays (have dtype info)
+        results = []
+        results.append(DataFrame.from_records(asdict).reindex(columns=df.columns))
+        results.append(
+            DataFrame.from_records(asdict, columns=columns).reindex(columns=df.columns)
+        )
+        results.append(
+            DataFrame.from_records(asdict2, columns=columns).reindex(columns=df.columns)
+        )
+
+        for r in results:
+            tm.assert_frame_equal(r, df)
+
+    def test_from_records_with_index_data(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 3)), columns=["A", "B", "C"]
+        )
+
+        data = np.random.default_rng(2).standard_normal(10)
+        with tm.assert_produces_warning(FutureWarning):
+            df1 = DataFrame.from_records(df, index=data)
+        tm.assert_index_equal(df1.index, Index(data))
+
+    def test_from_records_bad_index_column(self):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((10, 3)), columns=["A", "B", "C"]
+        )
+
+        # should pass
+        with tm.assert_produces_warning(FutureWarning):
+            df1 = DataFrame.from_records(df, index=["C"])
+        tm.assert_index_equal(df1.index, Index(df.C))
+
+        with tm.assert_produces_warning(FutureWarning):
+            df1 = DataFrame.from_records(df, index="C")
+        tm.assert_index_equal(df1.index, Index(df.C))
+
+        # should fail
+        msg = "|".join(
+            [
+                r"'None of \[2\] are in the columns'",
+            ]
+        )
+        with pytest.raises(KeyError, match=msg):
+            with tm.assert_produces_warning(FutureWarning):
+                DataFrame.from_records(df, index=[2])
+        with pytest.raises(KeyError, match=msg):
+            with tm.assert_produces_warning(FutureWarning):
+                DataFrame.from_records(df, index=2)
+
+    def test_from_records_non_tuple(self):
+        class Record:
+            def __init__(self, *args) -> None:
+                self.args = args
+
+            def __getitem__(self, i):
+                return self.args[i]
+
+            def __iter__(self) -> Iterator:
+                return iter(self.args)
+
+        recs = [Record(1, 2, 3), Record(4, 5, 6), Record(7, 8, 9)]
+        tups = [tuple(rec) for rec in recs]
+
+        result = DataFrame.from_records(recs)
+        expected = DataFrame.from_records(tups)
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_len0_with_columns(self):
+        # GH#2633
+        result = DataFrame.from_records([], index="foo", columns=["foo", "bar"])
+        expected = Index(["bar"])
+
+        assert len(result) == 0
+        assert result.index.name == "foo"
+        tm.assert_index_equal(result.columns, expected)
+
+    def test_from_records_series_list_dict(self):
+        # GH#27358
+        expected = DataFrame([[{"a": 1, "b": 2}, {"a": 3, "b": 4}]]).T
+        data = Series([[{"a": 1, "b": 2}], [{"a": 3, "b": 4}]])
+        result = DataFrame.from_records(data)
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_series_categorical_index(self):
+        # GH#32805
+        index = CategoricalIndex(
+            [Interval(-20, -10), Interval(-10, 0), Interval(0, 10)]
+        )
+        series_of_dicts = Series([{"a": 1}, {"a": 2}, {"b": 3}], index=index)
+        frame = DataFrame.from_records(series_of_dicts, index=index)
+        expected = DataFrame(
+            {"a": [1, 2, np.nan], "b": [np.nan, np.nan, 3]}, index=index
+        )
+        tm.assert_frame_equal(frame, expected)
+
+    def test_frame_from_records_utc(self):
+        rec = {"datum": 1.5, "begin_time": datetime(2006, 4, 27, tzinfo=pytz.utc)}
+
+        # it works
+        DataFrame.from_records([rec], index="begin_time")
+
+    def test_from_records_to_records(self):
+        # from numpy documentation
+        arr = np.zeros((2,), dtype=("i4,f4,S10"))
+        arr[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")]
+
+        DataFrame.from_records(arr)
+
+        index = Index(np.arange(len(arr))[::-1])
+        indexed_frame = DataFrame.from_records(arr, index=index)
+        tm.assert_index_equal(indexed_frame.index, index)
+
+        # without names, it should go to last ditch
+        arr2 = np.zeros((2, 3))
+        tm.assert_frame_equal(DataFrame.from_records(arr2), DataFrame(arr2))
+
+        # wrong length
+        msg = "|".join(
+            [
+                r"Length of values \(2\) does not match length of index \(1\)",
+            ]
+        )
+        with pytest.raises(ValueError, match=msg):
+            DataFrame.from_records(arr, index=index[:-1])
+
+        indexed_frame = DataFrame.from_records(arr, index="f1")
+
+        # what to do?
+        records = indexed_frame.to_records()
+        assert len(records.dtype.names) == 3
+
+        records = indexed_frame.to_records(index=False)
+        assert len(records.dtype.names) == 2
+        assert "index" not in records.dtype.names
+
+    def test_from_records_nones(self):
+        tuples = [(1, 2, None, 3), (1, 2, None, 3), (None, 2, 5, 3)]
+
+        df = DataFrame.from_records(tuples, columns=["a", "b", "c", "d"])
+        assert np.isnan(df["c"][0])
+
+    def test_from_records_iterator(self):
+        arr = np.array(
+            [(1.0, 1.0, 2, 2), (3.0, 3.0, 4, 4), (5.0, 5.0, 6, 6), (7.0, 7.0, 8, 8)],
+            dtype=[
+                ("x", np.float64),
+                ("u", np.float32),
+                ("y", np.int64),
+                ("z", np.int32),
+            ],
+        )
+        df = DataFrame.from_records(iter(arr), nrows=2)
+        xp = DataFrame(
+            {
+                "x": np.array([1.0, 3.0], dtype=np.float64),
+                "u": np.array([1.0, 3.0], dtype=np.float32),
+                "y": np.array([2, 4], dtype=np.int64),
+                "z": np.array([2, 4], dtype=np.int32),
+            }
+        )
+        tm.assert_frame_equal(df.reindex_like(xp), xp)
+
+        # no dtypes specified here, so just compare with the default
+        arr = [(1.0, 2), (3.0, 4), (5.0, 6), (7.0, 8)]
+        df = DataFrame.from_records(iter(arr), columns=["x", "y"], nrows=2)
+        tm.assert_frame_equal(df, xp.reindex(columns=["x", "y"]), check_dtype=False)
+
+    def test_from_records_tuples_generator(self):
+        def tuple_generator(length):
+            for i in range(length):
+                letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+                yield (i, letters[i % len(letters)], i / length)
+
+        columns_names = ["Integer", "String", "Float"]
+        columns = [
+            [i[j] for i in tuple_generator(10)] for j in range(len(columns_names))
+        ]
+        data = {"Integer": columns[0], "String": columns[1], "Float": columns[2]}
+        expected = DataFrame(data, columns=columns_names)
+
+        generator = tuple_generator(10)
+        result = DataFrame.from_records(generator, columns=columns_names)
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_lists_generator(self):
+        def list_generator(length):
+            for i in range(length):
+                letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
+                yield [i, letters[i % len(letters)], i / length]
+
+        columns_names = ["Integer", "String", "Float"]
+        columns = [
+            [i[j] for i in list_generator(10)] for j in range(len(columns_names))
+        ]
+        data = {"Integer": columns[0], "String": columns[1], "Float": columns[2]}
+        expected = DataFrame(data, columns=columns_names)
+
+        generator = list_generator(10)
+        result = DataFrame.from_records(generator, columns=columns_names)
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_columns_not_modified(self):
+        tuples = [(1, 2, 3), (1, 2, 3), (2, 5, 3)]
+
+        columns = ["a", "b", "c"]
+        original_columns = list(columns)
+
+        DataFrame.from_records(tuples, columns=columns, index="a")
+
+        assert columns == original_columns
+
+    def test_from_records_decimal(self):
+        tuples = [(Decimal("1.5"),), (Decimal("2.5"),), (None,)]
+
+        df = DataFrame.from_records(tuples, columns=["a"])
+        assert df["a"].dtype == object
+
+        df = DataFrame.from_records(tuples, columns=["a"], coerce_float=True)
+        assert df["a"].dtype == np.float64
+        assert np.isnan(df["a"].values[-1])
+
+    def test_from_records_duplicates(self):
+        result = DataFrame.from_records([(1, 2, 3), (4, 5, 6)], columns=["a", "b", "a"])
+
+        expected = DataFrame([(1, 2, 3), (4, 5, 6)], columns=["a", "b", "a"])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_set_index_name(self):
+        def create_dict(order_id):
+            return {
+                "order_id": order_id,
+                "quantity": np.random.default_rng(2).integers(1, 10),
+                "price": np.random.default_rng(2).integers(1, 10),
+            }
+
+        documents = [create_dict(i) for i in range(10)]
+        # demo missing data
+        documents.append({"order_id": 10, "quantity": 5})
+
+        result = DataFrame.from_records(documents, index="order_id")
+        assert result.index.name == "order_id"
+
+        # MultiIndex
+        result = DataFrame.from_records(documents, index=["order_id", "quantity"])
+        assert result.index.names == ("order_id", "quantity")
+
+    def test_from_records_misc_brokenness(self):
+        # GH#2179
+
+        data = {1: ["foo"], 2: ["bar"]}
+
+        result = DataFrame.from_records(data, columns=["a", "b"])
+        exp = DataFrame(data, columns=["a", "b"])
+        tm.assert_frame_equal(result, exp)
+
+        # overlap in index/index_names
+
+        data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+
+        result = DataFrame.from_records(data, index=["a", "b", "c"])
+        exp = DataFrame(data, index=["a", "b", "c"])
+        tm.assert_frame_equal(result, exp)
+
+    def test_from_records_misc_brokenness2(self):
+        # GH#2623
+        rows = []
+        rows.append([datetime(2010, 1, 1), 1])
+        rows.append([datetime(2010, 1, 2), "hi"])  # test col upconverts to obj
+        result = DataFrame.from_records(rows, columns=["date", "test"])
+        expected = DataFrame(
+            {"date": [row[0] for row in rows], "test": [row[1] for row in rows]}
+        )
+        tm.assert_frame_equal(result, expected)
+        assert result.dtypes["test"] == np.dtype(object)
+
+    def test_from_records_misc_brokenness3(self):
+        rows = []
+        rows.append([datetime(2010, 1, 1), 1])
+        rows.append([datetime(2010, 1, 2), 1])
+        result = DataFrame.from_records(rows, columns=["date", "test"])
+        expected = DataFrame(
+            {"date": [row[0] for row in rows], "test": [row[1] for row in rows]}
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_empty(self):
+        # GH#3562
+        result = DataFrame.from_records([], columns=["a", "b", "c"])
+        expected = DataFrame(columns=["a", "b", "c"])
+        tm.assert_frame_equal(result, expected)
+
+        result = DataFrame.from_records([], columns=["a", "b", "b"])
+        expected = DataFrame(columns=["a", "b", "b"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_records_empty_with_nonempty_fields_gh3682(self):
+        a = np.array([(1, 2)], dtype=[("id", np.int64), ("value", np.int64)])
+        df = DataFrame.from_records(a, index="id")
+
+        ex_index = Index([1], name="id")
+        expected = DataFrame({"value": [2]}, index=ex_index, columns=["value"])
+        tm.assert_frame_equal(df, expected)
+
+        b = a[:0]
+        df2 = DataFrame.from_records(b, index="id")
+        tm.assert_frame_equal(df2, df.iloc[:0])
+
+    def test_from_records_empty2(self):
+        # GH#42456
+        dtype = [("prop", int)]
+        shape = (0, len(dtype))
+        arr = np.empty(shape, dtype=dtype)
+
+        result = DataFrame.from_records(arr)
+        expected = DataFrame({"prop": np.array([], dtype=int)})
+        tm.assert_frame_equal(result, expected)
+
+        alt = DataFrame(arr)
+        tm.assert_frame_equal(alt, expected)

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_add_prefix_suffix.py

@@ -0,0 +1,49 @@
+import pytest
+
+from pandas import Index
+import pandas._testing as tm
+
+
+def test_add_prefix_suffix(float_frame):
+    with_prefix = float_frame.add_prefix("foo#")
+    expected = Index([f"foo#{c}" for c in float_frame.columns])
+    tm.assert_index_equal(with_prefix.columns, expected)
+
+    with_suffix = float_frame.add_suffix("#foo")
+    expected = Index([f"{c}#foo" for c in float_frame.columns])
+    tm.assert_index_equal(with_suffix.columns, expected)
+
+    with_pct_prefix = float_frame.add_prefix("%")
+    expected = Index([f"%{c}" for c in float_frame.columns])
+    tm.assert_index_equal(with_pct_prefix.columns, expected)
+
+    with_pct_suffix = float_frame.add_suffix("%")
+    expected = Index([f"{c}%" for c in float_frame.columns])
+    tm.assert_index_equal(with_pct_suffix.columns, expected)
+
+
+def test_add_prefix_suffix_axis(float_frame):
+    # GH 47819
+    with_prefix = float_frame.add_prefix("foo#", axis=0)
+    expected = Index([f"foo#{c}" for c in float_frame.index])
+    tm.assert_index_equal(with_prefix.index, expected)
+
+    with_prefix = float_frame.add_prefix("foo#", axis=1)
+    expected = Index([f"foo#{c}" for c in float_frame.columns])
+    tm.assert_index_equal(with_prefix.columns, expected)
+
+    with_pct_suffix = float_frame.add_suffix("#foo", axis=0)
+    expected = Index([f"{c}#foo" for c in float_frame.index])
+    tm.assert_index_equal(with_pct_suffix.index, expected)
+
+    with_pct_suffix = float_frame.add_suffix("#foo", axis=1)
+    expected = Index([f"{c}#foo" for c in float_frame.columns])
+    tm.assert_index_equal(with_pct_suffix.columns, expected)
+
+
+def test_add_prefix_suffix_invalid_axis(float_frame):
+    with pytest.raises(ValueError, match="No axis named 2 for object type DataFrame"):
+        float_frame.add_prefix("foo#", axis=2)
+
+    with pytest.raises(ValueError, match="No axis named 2 for object type DataFrame"):
+        float_frame.add_suffix("foo#", axis=2)

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_align.py

@@ -0,0 +1,484 @@
+from datetime import timezone
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameAlign:
+    def test_align_asfreq_method_raises(self):
+        df = DataFrame({"A": [1, np.nan, 2]})
+        msg = "Invalid fill method"
+        msg2 = "The 'method', 'limit', and 'fill_axis' keywords"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=msg2):
+                df.align(df.iloc[::-1], method="asfreq")
+
+    def test_frame_align_aware(self):
+        idx1 = date_range("2001", periods=5, freq="h", tz="US/Eastern")
+        idx2 = date_range("2001", periods=5, freq="2h", tz="US/Eastern")
+        df1 = DataFrame(np.random.default_rng(2).standard_normal((len(idx1), 3)), idx1)
+        df2 = DataFrame(np.random.default_rng(2).standard_normal((len(idx2), 3)), idx2)
+        new1, new2 = df1.align(df2)
+        assert df1.index.tz == new1.index.tz
+        assert df2.index.tz == new2.index.tz
+
+        # different timezones convert to UTC
+
+        # frame with frame
+        df1_central = df1.tz_convert("US/Central")
+        new1, new2 = df1.align(df1_central)
+        assert new1.index.tz is timezone.utc
+        assert new2.index.tz is timezone.utc
+
+        # frame with Series
+        new1, new2 = df1.align(df1_central[0], axis=0)
+        assert new1.index.tz is timezone.utc
+        assert new2.index.tz is timezone.utc
+
+        df1[0].align(df1_central, axis=0)
+        assert new1.index.tz is timezone.utc
+        assert new2.index.tz is timezone.utc
+
+    def test_align_float(self, float_frame, using_copy_on_write):
+        af, bf = float_frame.align(float_frame)
+        assert af._mgr is not float_frame._mgr
+
+        af, bf = float_frame.align(float_frame, copy=False)
+        if not using_copy_on_write:
+            assert af._mgr is float_frame._mgr
+        else:
+            assert af._mgr is not float_frame._mgr
+
+        # axis = 0
+        other = float_frame.iloc[:-5, :3]
+        af, bf = float_frame.align(other, axis=0, fill_value=-1)
+
+        tm.assert_index_equal(bf.columns, other.columns)
+
+        # test fill value
+        join_idx = float_frame.index.join(other.index)
+        diff_a = float_frame.index.difference(join_idx)
+        diff_a_vals = af.reindex(diff_a).values
+        assert (diff_a_vals == -1).all()
+
+        af, bf = float_frame.align(other, join="right", axis=0)
+        tm.assert_index_equal(bf.columns, other.columns)
+        tm.assert_index_equal(bf.index, other.index)
+        tm.assert_index_equal(af.index, other.index)
+
+        # axis = 1
+        other = float_frame.iloc[:-5, :3].copy()
+        af, bf = float_frame.align(other, axis=1)
+        tm.assert_index_equal(bf.columns, float_frame.columns)
+        tm.assert_index_equal(bf.index, other.index)
+
+        # test fill value
+        join_idx = float_frame.index.join(other.index)
+        diff_a = float_frame.index.difference(join_idx)
+        diff_a_vals = af.reindex(diff_a).values
+
+        assert (diff_a_vals == -1).all()
+
+        af, bf = float_frame.align(other, join="inner", axis=1)
+        tm.assert_index_equal(bf.columns, other.columns)
+
+        msg = (
+            "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align "
+            "are deprecated"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            af, bf = float_frame.align(other, join="inner", axis=1, method="pad")
+        tm.assert_index_equal(bf.columns, other.columns)
+
+        msg = (
+            "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align "
+            "are deprecated"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            af, bf = float_frame.align(
+                other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=None
+            )
+        tm.assert_index_equal(bf.index, Index([]).astype(bf.index.dtype))
+
+        msg = (
+            "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align "
+            "are deprecated"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            af, bf = float_frame.align(
+                other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=0
+            )
+        tm.assert_index_equal(bf.index, Index([]).astype(bf.index.dtype))
+
+        # Try to align DataFrame to Series along bad axis
+        msg = "No axis named 2 for object type DataFrame"
+        with pytest.raises(ValueError, match=msg):
+            float_frame.align(af.iloc[0, :3], join="inner", axis=2)
+
+    def test_align_frame_with_series(self, float_frame):
+        # align dataframe to series with broadcast or not
+        idx = float_frame.index
+        s = Series(range(len(idx)), index=idx)
+
+        left, right = float_frame.align(s, axis=0)
+        tm.assert_index_equal(left.index, float_frame.index)
+        tm.assert_index_equal(right.index, float_frame.index)
+        assert isinstance(right, Series)
+
+        msg = "The 'broadcast_axis' keyword in DataFrame.align is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            left, right = float_frame.align(s, broadcast_axis=1)
+        tm.assert_index_equal(left.index, float_frame.index)
+        expected = {c: s for c in float_frame.columns}
+        expected = DataFrame(
+            expected, index=float_frame.index, columns=float_frame.columns
+        )
+        tm.assert_frame_equal(right, expected)
+
+    def test_align_series_condition(self):
+        # see gh-9558
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+        result = df[df["a"] == 2]
+        expected = DataFrame([[2, 5]], index=[1], columns=["a", "b"])
+        tm.assert_frame_equal(result, expected)
+
+        result = df.where(df["a"] == 2, 0)
+        expected = DataFrame({"a": [0, 2, 0], "b": [0, 5, 0]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_align_int(self, int_frame):
+        # test other non-float types
+        other = DataFrame(index=range(5), columns=["A", "B", "C"])
+
+        msg = (
+            "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align "
+            "are deprecated"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            af, bf = int_frame.align(other, join="inner", axis=1, method="pad")
+        tm.assert_index_equal(bf.columns, other.columns)
+
+    def test_align_mixed_type(self, float_string_frame):
+        msg = (
+            "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align "
+            "are deprecated"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            af, bf = float_string_frame.align(
+                float_string_frame, join="inner", axis=1, method="pad"
+            )
+        tm.assert_index_equal(bf.columns, float_string_frame.columns)
+
+    def test_align_mixed_float(self, mixed_float_frame):
+        # mixed floats/ints
+        other = DataFrame(index=range(5), columns=["A", "B", "C"])
+
+        msg = (
+            "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align "
+            "are deprecated"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            af, bf = mixed_float_frame.align(
+                other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=0
+            )
+        tm.assert_index_equal(bf.index, Index([]))
+
+    def test_align_mixed_int(self, mixed_int_frame):
+        other = DataFrame(index=range(5), columns=["A", "B", "C"])
+
+        msg = (
+            "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align "
+            "are deprecated"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            af, bf = mixed_int_frame.align(
+                other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=0
+            )
+        tm.assert_index_equal(bf.index, Index([]))
+
+    @pytest.mark.parametrize(
+        "l_ordered,r_ordered,expected",
+        [
+            [True, True, pd.CategoricalIndex],
+            [True, False, Index],
+            [False, True, Index],
+            [False, False, pd.CategoricalIndex],
+        ],
+    )
+    def test_align_categorical(self, l_ordered, r_ordered, expected):
+        # GH-28397
+        df_1 = DataFrame(
+            {
+                "A": np.arange(6, dtype="int64"),
+                "B": Series(list("aabbca")).astype(
+                    pd.CategoricalDtype(list("cab"), ordered=l_ordered)
+                ),
+            }
+        ).set_index("B")
+        df_2 = DataFrame(
+            {
+                "A": np.arange(5, dtype="int64"),
+                "B": Series(list("babca")).astype(
+                    pd.CategoricalDtype(list("cab"), ordered=r_ordered)
+                ),
+            }
+        ).set_index("B")
+
+        aligned_1, aligned_2 = df_1.align(df_2)
+        assert isinstance(aligned_1.index, expected)
+        assert isinstance(aligned_2.index, expected)
+        tm.assert_index_equal(aligned_1.index, aligned_2.index)
+
+    def test_align_multiindex(self):
+        # GH#10665
+        # same test cases as test_align_multiindex in test_series.py
+
+        midx = pd.MultiIndex.from_product(
+            [range(2), range(3), range(2)], names=("a", "b", "c")
+        )
+        idx = Index(range(2), name="b")
+        df1 = DataFrame(np.arange(12, dtype="int64"), index=midx)
+        df2 = DataFrame(np.arange(2, dtype="int64"), index=idx)
+
+        # these must be the same results (but flipped)
+        res1l, res1r = df1.align(df2, join="left")
+        res2l, res2r = df2.align(df1, join="right")
+
+        expl = df1
+        tm.assert_frame_equal(expl, res1l)
+        tm.assert_frame_equal(expl, res2r)
+        expr = DataFrame([0, 0, 1, 1, np.nan, np.nan] * 2, index=midx)
+        tm.assert_frame_equal(expr, res1r)
+        tm.assert_frame_equal(expr, res2l)
+
+        res1l, res1r = df1.align(df2, join="right")
+        res2l, res2r = df2.align(df1, join="left")
+
+        exp_idx = pd.MultiIndex.from_product(
+            [range(2), range(2), range(2)], names=("a", "b", "c")
+        )
+        expl = DataFrame([0, 1, 2, 3, 6, 7, 8, 9], index=exp_idx)
+        tm.assert_frame_equal(expl, res1l)
+        tm.assert_frame_equal(expl, res2r)
+        expr = DataFrame([0, 0, 1, 1] * 2, index=exp_idx)
+        tm.assert_frame_equal(expr, res1r)
+        tm.assert_frame_equal(expr, res2l)
+
+    def test_align_series_combinations(self):
+        df = DataFrame({"a": [1, 3, 5], "b": [1, 3, 5]}, index=list("ACE"))
+        s = Series([1, 2, 4], index=list("ABD"), name="x")
+
+        # frame + series
+        res1, res2 = df.align(s, axis=0)
+        exp1 = DataFrame(
+            {"a": [1, np.nan, 3, np.nan, 5], "b": [1, np.nan, 3, np.nan, 5]},
+            index=list("ABCDE"),
+        )
+        exp2 = Series([1, 2, np.nan, 4, np.nan], index=list("ABCDE"), name="x")
+
+        tm.assert_frame_equal(res1, exp1)
+        tm.assert_series_equal(res2, exp2)
+
+        # series + frame
+        res1, res2 = s.align(df)
+        tm.assert_series_equal(res1, exp2)
+        tm.assert_frame_equal(res2, exp1)
+
+    def test_multiindex_align_to_series_with_common_index_level(self):
+        #  GH-46001
+        foo_index = Index([1, 2, 3], name="foo")
+        bar_index = Index([1, 2], name="bar")
+
+        series = Series([1, 2], index=bar_index, name="foo_series")
+        df = DataFrame(
+            {"col": np.arange(6)},
+            index=pd.MultiIndex.from_product([foo_index, bar_index]),
+        )
+
+        expected_r = Series([1, 2] * 3, index=df.index, name="foo_series")
+        result_l, result_r = df.align(series, axis=0)
+
+        tm.assert_frame_equal(result_l, df)
+        tm.assert_series_equal(result_r, expected_r)
+
+    def test_multiindex_align_to_series_with_common_index_level_missing_in_left(self):
+        #  GH-46001
+        foo_index = Index([1, 2, 3], name="foo")
+        bar_index = Index([1, 2], name="bar")
+
+        series = Series(
+            [1, 2, 3, 4], index=Index([1, 2, 3, 4], name="bar"), name="foo_series"
+        )
+        df = DataFrame(
+            {"col": np.arange(6)},
+            index=pd.MultiIndex.from_product([foo_index, bar_index]),
+        )
+
+        expected_r = Series([1, 2] * 3, index=df.index, name="foo_series")
+        result_l, result_r = df.align(series, axis=0)
+
+        tm.assert_frame_equal(result_l, df)
+        tm.assert_series_equal(result_r, expected_r)
+
+    def test_multiindex_align_to_series_with_common_index_level_missing_in_right(self):
+        #  GH-46001
+        foo_index = Index([1, 2, 3], name="foo")
+        bar_index = Index([1, 2, 3, 4], name="bar")
+
+        series = Series([1, 2], index=Index([1, 2], name="bar"), name="foo_series")
+        df = DataFrame(
+            {"col": np.arange(12)},
+            index=pd.MultiIndex.from_product([foo_index, bar_index]),
+        )
+
+        expected_r = Series(
+            [1, 2, np.nan, np.nan] * 3, index=df.index, name="foo_series"
+        )
+        result_l, result_r = df.align(series, axis=0)
+
+        tm.assert_frame_equal(result_l, df)
+        tm.assert_series_equal(result_r, expected_r)
+
+    def test_multiindex_align_to_series_with_common_index_level_missing_in_both(self):
+        #  GH-46001
+        foo_index = Index([1, 2, 3], name="foo")
+        bar_index = Index([1, 3, 4], name="bar")
+
+        series = Series(
+            [1, 2, 3], index=Index([1, 2, 4], name="bar"), name="foo_series"
+        )
+        df = DataFrame(
+            {"col": np.arange(9)},
+            index=pd.MultiIndex.from_product([foo_index, bar_index]),
+        )
+
+        expected_r = Series([1, np.nan, 3] * 3, index=df.index, name="foo_series")
+        result_l, result_r = df.align(series, axis=0)
+
+        tm.assert_frame_equal(result_l, df)
+        tm.assert_series_equal(result_r, expected_r)
+
+    def test_multiindex_align_to_series_with_common_index_level_non_unique_cols(self):
+        #  GH-46001
+        foo_index = Index([1, 2, 3], name="foo")
+        bar_index = Index([1, 2], name="bar")
+
+        series = Series([1, 2], index=bar_index, name="foo_series")
+        df = DataFrame(
+            np.arange(18).reshape(6, 3),
+            index=pd.MultiIndex.from_product([foo_index, bar_index]),
+        )
+        df.columns = ["cfoo", "cbar", "cfoo"]
+
+        expected = Series([1, 2] * 3, index=df.index, name="foo_series")
+        result_left, result_right = df.align(series, axis=0)
+
+        tm.assert_series_equal(result_right, expected)
+        tm.assert_index_equal(result_left.columns, df.columns)
+
+    def test_missing_axis_specification_exception(self):
+        df = DataFrame(np.arange(50).reshape((10, 5)))
+        series = Series(np.arange(5))
+
+        with pytest.raises(ValueError, match=r"axis=0 or 1"):
+            df.align(series)
+
+    @pytest.mark.parametrize("method", ["pad", "bfill"])
+    @pytest.mark.parametrize("axis", [0, 1, None])
+    @pytest.mark.parametrize("fill_axis", [0, 1])
+    @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"])
+    @pytest.mark.parametrize(
+        "left_slice",
+        [
+            [slice(4), slice(10)],
+            [slice(0), slice(0)],
+        ],
+    )
+    @pytest.mark.parametrize(
+        "right_slice",
+        [
+            [slice(2, None), slice(6, None)],
+            [slice(0), slice(0)],
+        ],
+    )
+    @pytest.mark.parametrize("limit", [1, None])
+    def test_align_fill_method(
+        self, how, method, axis, fill_axis, float_frame, left_slice, right_slice, limit
+    ):
+        frame = float_frame
+        left = frame.iloc[left_slice[0], left_slice[1]]
+        right = frame.iloc[right_slice[0], right_slice[1]]
+
+        msg = (
+            "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align "
+            "are deprecated"
+        )
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            aa, ab = left.align(
+                right,
+                axis=axis,
+                join=how,
+                method=method,
+                limit=limit,
+                fill_axis=fill_axis,
+            )
+
+        join_index, join_columns = None, None
+
+        ea, eb = left, right
+        if axis is None or axis == 0:
+            join_index = left.index.join(right.index, how=how)
+            ea = ea.reindex(index=join_index)
+            eb = eb.reindex(index=join_index)
+
+        if axis is None or axis == 1:
+            join_columns = left.columns.join(right.columns, how=how)
+            ea = ea.reindex(columns=join_columns)
+            eb = eb.reindex(columns=join_columns)
+
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            ea = ea.fillna(axis=fill_axis, method=method, limit=limit)
+            eb = eb.fillna(axis=fill_axis, method=method, limit=limit)
+
+        tm.assert_frame_equal(aa, ea)
+        tm.assert_frame_equal(ab, eb)
+
+    def test_align_series_check_copy(self):
+        # GH#
+        df = DataFrame({0: [1, 2]})
+        ser = Series([1], name=0)
+        expected = ser.copy()
+        result, other = df.align(ser, axis=1)
+        ser.iloc[0] = 100
+        tm.assert_series_equal(other, expected)
+
+    def test_align_identical_different_object(self):
+        # GH#51032
+        df = DataFrame({"a": [1, 2]})
+        ser = Series([3, 4])
+        result, result2 = df.align(ser, axis=0)
+        tm.assert_frame_equal(result, df)
+        tm.assert_series_equal(result2, ser)
+        assert df is not result
+        assert ser is not result2
+
+    def test_align_identical_different_object_columns(self):
+        # GH#51032
+        df = DataFrame({"a": [1, 2]})
+        ser = Series([1], index=["a"])
+        result, result2 = df.align(ser, axis=1)
+        tm.assert_frame_equal(result, df)
+        tm.assert_series_equal(result2, ser)
+        assert df is not result
+        assert ser is not result2

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_at_time.py

@@ -0,0 +1,132 @@
+from datetime import time
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs.tslibs import timezones
+
+from pandas import (
+    DataFrame,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestAtTime:
+    @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"])
+    def test_localized_at_time(self, tzstr, frame_or_series):
+        tz = timezones.maybe_get_tz(tzstr)
+
+        rng = date_range("4/16/2012", "5/1/2012", freq="h")
+        ts = frame_or_series(
+            np.random.default_rng(2).standard_normal(len(rng)), index=rng
+        )
+
+        ts_local = ts.tz_localize(tzstr)
+
+        result = ts_local.at_time(time(10, 0))
+        expected = ts.at_time(time(10, 0)).tz_localize(tzstr)
+        tm.assert_equal(result, expected)
+        assert timezones.tz_compare(result.index.tz, tz)
+
+    def test_at_time(self, frame_or_series):
+        rng = date_range("1/1/2000", "1/5/2000", freq="5min")
+        ts = DataFrame(
+            np.random.default_rng(2).standard_normal((len(rng), 2)), index=rng
+        )
+        ts = tm.get_obj(ts, frame_or_series)
+        rs = ts.at_time(rng[1])
+        assert (rs.index.hour == rng[1].hour).all()
+        assert (rs.index.minute == rng[1].minute).all()
+        assert (rs.index.second == rng[1].second).all()
+
+        result = ts.at_time("9:30")
+        expected = ts.at_time(time(9, 30))
+        tm.assert_equal(result, expected)
+
+    def test_at_time_midnight(self, frame_or_series):
+        # midnight, everything
+        rng = date_range("1/1/2000", "1/31/2000")
+        ts = DataFrame(
+            np.random.default_rng(2).standard_normal((len(rng), 3)), index=rng
+        )
+        ts = tm.get_obj(ts, frame_or_series)
+
+        result = ts.at_time(time(0, 0))
+        tm.assert_equal(result, ts)
+
+    def test_at_time_nonexistent(self, frame_or_series):
+        # time doesn't exist
+        rng = date_range("1/1/2012", freq="23Min", periods=384)
+        ts = DataFrame(np.random.default_rng(2).standard_normal(len(rng)), rng)
+        ts = tm.get_obj(ts, frame_or_series)
+        rs = ts.at_time("16:00")
+        assert len(rs) == 0
+
+    @pytest.mark.parametrize(
+        "hour", ["1:00", "1:00AM", time(1), time(1, tzinfo=pytz.UTC)]
+    )
+    def test_at_time_errors(self, hour):
+        # GH#24043
+        dti = date_range("2018", periods=3, freq="h")
+        df = DataFrame(list(range(len(dti))), index=dti)
+        if getattr(hour, "tzinfo", None) is None:
+            result = df.at_time(hour)
+            expected = df.iloc[1:2]
+            tm.assert_frame_equal(result, expected)
+        else:
+            with pytest.raises(ValueError, match="Index must be timezone"):
+                df.at_time(hour)
+
+    def test_at_time_tz(self):
+        # GH#24043
+        dti = date_range("2018", periods=3, freq="h", tz="US/Pacific")
+        df = DataFrame(list(range(len(dti))), index=dti)
+        result = df.at_time(time(4, tzinfo=pytz.timezone("US/Eastern")))
+        expected = df.iloc[1:2]
+        tm.assert_frame_equal(result, expected)
+
+    def test_at_time_raises(self, frame_or_series):
+        # GH#20725
+        obj = DataFrame([[1, 2, 3], [4, 5, 6]])
+        obj = tm.get_obj(obj, frame_or_series)
+        msg = "Index must be DatetimeIndex"
+        with pytest.raises(TypeError, match=msg):  # index is not a DatetimeIndex
+            obj.at_time("00:00")
+
+    @pytest.mark.parametrize("axis", ["index", "columns", 0, 1])
+    def test_at_time_axis(self, axis):
+        # issue 8839
+        rng = date_range("1/1/2000", "1/5/2000", freq="5min")
+        ts = DataFrame(np.random.default_rng(2).standard_normal((len(rng), len(rng))))
+        ts.index, ts.columns = rng, rng
+
+        indices = rng[(rng.hour == 9) & (rng.minute == 30) & (rng.second == 0)]
+
+        if axis in ["index", 0]:
+            expected = ts.loc[indices, :]
+        elif axis in ["columns", 1]:
+            expected = ts.loc[:, indices]
+
+        result = ts.at_time("9:30", axis=axis)
+
+        # Without clearing freq, result has freq 1440T and expected 5T
+        result.index = result.index._with_freq(None)
+        expected.index = expected.index._with_freq(None)
+        tm.assert_frame_equal(result, expected)
+
+    def test_at_time_datetimeindex(self):
+        index = date_range("2012-01-01", "2012-01-05", freq="30min")
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((len(index), 5)), index=index
+        )
+        akey = time(12, 0, 0)
+        ainds = [24, 72, 120, 168]
+
+        result = df.at_time(akey)
+        expected = df.loc[akey]
+        expected2 = df.iloc[ainds]
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(result, expected2)
+        assert len(result) == 4

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_between_time.py

@@ -0,0 +1,227 @@
+from datetime import (
+    datetime,
+    time,
+)
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import timezones
+import pandas.util._test_decorators as td
+
+from pandas import (
+    DataFrame,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestBetweenTime:
+    @td.skip_if_not_us_locale
+    def test_between_time_formats(self, frame_or_series):
+        # GH#11818
+        rng = date_range("1/1/2000", "1/5/2000", freq="5min")
+        ts = DataFrame(
+            np.random.default_rng(2).standard_normal((len(rng), 2)), index=rng
+        )
+        ts = tm.get_obj(ts, frame_or_series)
+
+        strings = [
+            ("2:00", "2:30"),
+            ("0200", "0230"),
+            ("2:00am", "2:30am"),
+            ("0200am", "0230am"),
+            ("2:00:00", "2:30:00"),
+            ("020000", "023000"),
+            ("2:00:00am", "2:30:00am"),
+            ("020000am", "023000am"),
+        ]
+        expected_length = 28
+
+        for time_string in strings:
+            assert len(ts.between_time(*time_string)) == expected_length
+
+    @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"])
+    def test_localized_between_time(self, tzstr, frame_or_series):
+        tz = timezones.maybe_get_tz(tzstr)
+
+        rng = date_range("4/16/2012", "5/1/2012", freq="h")
+        ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+        if frame_or_series is DataFrame:
+            ts = ts.to_frame()
+
+        ts_local = ts.tz_localize(tzstr)
+
+        t1, t2 = time(10, 0), time(11, 0)
+        result = ts_local.between_time(t1, t2)
+        expected = ts.between_time(t1, t2).tz_localize(tzstr)
+        tm.assert_equal(result, expected)
+        assert timezones.tz_compare(result.index.tz, tz)
+
+    def test_between_time_types(self, frame_or_series):
+        # GH11818
+        rng = date_range("1/1/2000", "1/5/2000", freq="5min")
+        obj = DataFrame({"A": 0}, index=rng)
+        obj = tm.get_obj(obj, frame_or_series)
+
+        msg = r"Cannot convert arg \[datetime\.datetime\(2010, 1, 2, 1, 0\)\] to a time"
+        with pytest.raises(ValueError, match=msg):
+            obj.between_time(datetime(2010, 1, 2, 1), datetime(2010, 1, 2, 5))
+
+    def test_between_time(self, inclusive_endpoints_fixture, frame_or_series):
+        rng = date_range("1/1/2000", "1/5/2000", freq="5min")
+        ts = DataFrame(
+            np.random.default_rng(2).standard_normal((len(rng), 2)), index=rng
+        )
+        ts = tm.get_obj(ts, frame_or_series)
+
+        stime = time(0, 0)
+        etime = time(1, 0)
+        inclusive = inclusive_endpoints_fixture
+
+        filtered = ts.between_time(stime, etime, inclusive=inclusive)
+        exp_len = 13 * 4 + 1
+
+        if inclusive in ["right", "neither"]:
+            exp_len -= 5
+        if inclusive in ["left", "neither"]:
+            exp_len -= 4
+
+        assert len(filtered) == exp_len
+        for rs in filtered.index:
+            t = rs.time()
+            if inclusive in ["left", "both"]:
+                assert t >= stime
+            else:
+                assert t > stime
+
+            if inclusive in ["right", "both"]:
+                assert t <= etime
+            else:
+                assert t < etime
+
+        result = ts.between_time("00:00", "01:00")
+        expected = ts.between_time(stime, etime)
+        tm.assert_equal(result, expected)
+
+        # across midnight
+        rng = date_range("1/1/2000", "1/5/2000", freq="5min")
+        ts = DataFrame(
+            np.random.default_rng(2).standard_normal((len(rng), 2)), index=rng
+        )
+        ts = tm.get_obj(ts, frame_or_series)
+        stime = time(22, 0)
+        etime = time(9, 0)
+
+        filtered = ts.between_time(stime, etime, inclusive=inclusive)
+        exp_len = (12 * 11 + 1) * 4 + 1
+        if inclusive in ["right", "neither"]:
+            exp_len -= 4
+        if inclusive in ["left", "neither"]:
+            exp_len -= 4
+
+        assert len(filtered) == exp_len
+        for rs in filtered.index:
+            t = rs.time()
+            if inclusive in ["left", "both"]:
+                assert (t >= stime) or (t <= etime)
+            else:
+                assert (t > stime) or (t <= etime)
+
+            if inclusive in ["right", "both"]:
+                assert (t <= etime) or (t >= stime)
+            else:
+                assert (t < etime) or (t >= stime)
+
+    def test_between_time_raises(self, frame_or_series):
+        # GH#20725
+        obj = DataFrame([[1, 2, 3], [4, 5, 6]])
+        obj = tm.get_obj(obj, frame_or_series)
+
+        msg = "Index must be DatetimeIndex"
+        with pytest.raises(TypeError, match=msg):  # index is not a DatetimeIndex
+            obj.between_time(start_time="00:00", end_time="12:00")
+
+    def test_between_time_axis(self, frame_or_series):
+        # GH#8839
+        rng = date_range("1/1/2000", periods=100, freq="10min")
+        ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
+        if frame_or_series is DataFrame:
+            ts = ts.to_frame()
+
+        stime, etime = ("08:00:00", "09:00:00")
+        expected_length = 7
+
+        assert len(ts.between_time(stime, etime)) == expected_length
+        assert len(ts.between_time(stime, etime, axis=0)) == expected_length
+        msg = f"No axis named {ts.ndim} for object type {type(ts).__name__}"
+        with pytest.raises(ValueError, match=msg):
+            ts.between_time(stime, etime, axis=ts.ndim)
+
+    def test_between_time_axis_aliases(self, axis):
+        # GH#8839
+        rng = date_range("1/1/2000", periods=100, freq="10min")
+        ts = DataFrame(np.random.default_rng(2).standard_normal((len(rng), len(rng))))
+        stime, etime = ("08:00:00", "09:00:00")
+        exp_len = 7
+
+        if axis in ["index", 0]:
+            ts.index = rng
+            assert len(ts.between_time(stime, etime)) == exp_len
+            assert len(ts.between_time(stime, etime, axis=0)) == exp_len
+
+        if axis in ["columns", 1]:
+            ts.columns = rng
+            selected = ts.between_time(stime, etime, axis=1).columns
+            assert len(selected) == exp_len
+
+    def test_between_time_axis_raises(self, axis):
+        # issue 8839
+        rng = date_range("1/1/2000", periods=100, freq="10min")
+        mask = np.arange(0, len(rng))
+        rand_data = np.random.default_rng(2).standard_normal((len(rng), len(rng)))
+        ts = DataFrame(rand_data, index=rng, columns=rng)
+        stime, etime = ("08:00:00", "09:00:00")
+
+        msg = "Index must be DatetimeIndex"
+        if axis in ["columns", 1]:
+            ts.index = mask
+            with pytest.raises(TypeError, match=msg):
+                ts.between_time(stime, etime)
+            with pytest.raises(TypeError, match=msg):
+                ts.between_time(stime, etime, axis=0)
+
+        if axis in ["index", 0]:
+            ts.columns = mask
+            with pytest.raises(TypeError, match=msg):
+                ts.between_time(stime, etime, axis=1)
+
+    def test_between_time_datetimeindex(self):
+        index = date_range("2012-01-01", "2012-01-05", freq="30min")
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((len(index), 5)), index=index
+        )
+        bkey = slice(time(13, 0, 0), time(14, 0, 0))
+        binds = [26, 27, 28, 74, 75, 76, 122, 123, 124, 170, 171, 172]
+
+        result = df.between_time(bkey.start, bkey.stop)
+        expected = df.loc[bkey]
+        expected2 = df.iloc[binds]
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(result, expected2)
+        assert len(result) == 12
+
+    def test_between_time_incorrect_arg_inclusive(self):
+        # GH40245
+        rng = date_range("1/1/2000", "1/5/2000", freq="5min")
+        ts = DataFrame(
+            np.random.default_rng(2).standard_normal((len(rng), 2)), index=rng
+        )
+
+        stime = time(0, 0)
+        etime = time(1, 0)
+        inclusive = "bad_string"
+        msg = "Inclusive has to be either 'both', 'neither', 'left' or 'right'"
+        with pytest.raises(ValueError, match=msg):
+            ts.between_time(stime, etime, inclusive=inclusive)

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_combine.py

@@ -0,0 +1,47 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+class TestCombine:
+    @pytest.mark.parametrize(
+        "data",
+        [
+            pd.date_range("2000", periods=4),
+            pd.date_range("2000", periods=4, tz="US/Central"),
+            pd.period_range("2000", periods=4),
+            pd.timedelta_range(0, periods=4),
+        ],
+    )
+    def test_combine_datetlike_udf(self, data):
+        # GH#23079
+        df = pd.DataFrame({"A": data})
+        other = df.copy()
+        df.iloc[1, 0] = None
+
+        def combiner(a, b):
+            return b
+
+        result = df.combine(other, combiner)
+        tm.assert_frame_equal(result, other)
+
+    def test_combine_generic(self, float_frame):
+        df1 = float_frame
+        df2 = float_frame.loc[float_frame.index[:-5], ["A", "B", "C"]]
+
+        combined = df1.combine(df2, np.add)
+        combined2 = df2.combine(df1, np.add)
+        assert combined["D"].isna().all()
+        assert combined2["D"].isna().all()
+
+        chunk = combined.loc[combined.index[:-5], ["A", "B", "C"]]
+        chunk2 = combined2.loc[combined2.index[:-5], ["A", "B", "C"]]
+
+        exp = (
+            float_frame.loc[float_frame.index[:-5], ["A", "B", "C"]].reindex_like(chunk)
+            * 2
+        )
+        tm.assert_frame_equal(chunk, exp)
+        tm.assert_frame_equal(chunk2, exp)

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_combine_first.py

@@ -0,0 +1,556 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.cast import find_common_type
+from pandas.core.dtypes.common import is_dtype_equal
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameCombineFirst:
+    def test_combine_first_mixed(self):
+        a = Series(["a", "b"], index=range(2))
+        b = Series(range(2), index=range(2))
+        f = DataFrame({"A": a, "B": b})
+
+        a = Series(["a", "b"], index=range(5, 7))
+        b = Series(range(2), index=range(5, 7))
+        g = DataFrame({"A": a, "B": b})
+
+        exp = DataFrame({"A": list("abab"), "B": [0, 1, 0, 1]}, index=[0, 1, 5, 6])
+        combined = f.combine_first(g)
+        tm.assert_frame_equal(combined, exp)
+
+    def test_combine_first(self, float_frame, using_infer_string):
+        # disjoint
+        head, tail = float_frame[:5], float_frame[5:]
+
+        combined = head.combine_first(tail)
+        reordered_frame = float_frame.reindex(combined.index)
+        tm.assert_frame_equal(combined, reordered_frame)
+        tm.assert_index_equal(combined.columns, float_frame.columns)
+        tm.assert_series_equal(combined["A"], reordered_frame["A"])
+
+        # same index
+        fcopy = float_frame.copy()
+        fcopy["A"] = 1
+        del fcopy["C"]
+
+        fcopy2 = float_frame.copy()
+        fcopy2["B"] = 0
+        del fcopy2["D"]
+
+        combined = fcopy.combine_first(fcopy2)
+
+        assert (combined["A"] == 1).all()
+        tm.assert_series_equal(combined["B"], fcopy["B"])
+        tm.assert_series_equal(combined["C"], fcopy2["C"])
+        tm.assert_series_equal(combined["D"], fcopy["D"])
+
+        # overlap
+        head, tail = reordered_frame[:10].copy(), reordered_frame
+        head["A"] = 1
+
+        combined = head.combine_first(tail)
+        assert (combined["A"][:10] == 1).all()
+
+        # reverse overlap
+        tail.iloc[:10, tail.columns.get_loc("A")] = 0
+        combined = tail.combine_first(head)
+        assert (combined["A"][:10] == 0).all()
+
+        # no overlap
+        f = float_frame[:10]
+        g = float_frame[10:]
+        combined = f.combine_first(g)
+        tm.assert_series_equal(combined["A"].reindex(f.index), f["A"])
+        tm.assert_series_equal(combined["A"].reindex(g.index), g["A"])
+
+        # corner cases
+        warning = FutureWarning if using_infer_string else None
+        with tm.assert_produces_warning(warning, match="empty entries"):
+            comb = float_frame.combine_first(DataFrame())
+        tm.assert_frame_equal(comb, float_frame)
+
+        comb = DataFrame().combine_first(float_frame)
+        tm.assert_frame_equal(comb, float_frame.sort_index())
+
+        comb = float_frame.combine_first(DataFrame(index=["faz", "boo"]))
+        assert "faz" in comb.index
+
+        # #2525
+        df = DataFrame({"a": [1]}, index=[datetime(2012, 1, 1)])
+        df2 = DataFrame(columns=["b"])
+        result = df.combine_first(df2)
+        assert "b" in result
+
+    def test_combine_first_mixed_bug(self):
+        idx = Index(["a", "b", "c", "e"])
+        ser1 = Series([5.0, -9.0, 4.0, 100.0], index=idx)
+        ser2 = Series(["a", "b", "c", "e"], index=idx)
+        ser3 = Series([12, 4, 5, 97], index=idx)
+
+        frame1 = DataFrame({"col0": ser1, "col2": ser2, "col3": ser3})
+
+        idx = Index(["a", "b", "c", "f"])
+        ser1 = Series([5.0, -9.0, 4.0, 100.0], index=idx)
+        ser2 = Series(["a", "b", "c", "f"], index=idx)
+        ser3 = Series([12, 4, 5, 97], index=idx)
+
+        frame2 = DataFrame({"col1": ser1, "col2": ser2, "col5": ser3})
+
+        combined = frame1.combine_first(frame2)
+        assert len(combined.columns) == 5
+
+    def test_combine_first_same_as_in_update(self):
+        # gh 3016 (same as in update)
+        df = DataFrame(
+            [[1.0, 2.0, False, True], [4.0, 5.0, True, False]],
+            columns=["A", "B", "bool1", "bool2"],
+        )
+
+        other = DataFrame([[45, 45]], index=[0], columns=["A", "B"])
+        result = df.combine_first(other)
+        tm.assert_frame_equal(result, df)
+
+        df.loc[0, "A"] = np.nan
+        result = df.combine_first(other)
+        df.loc[0, "A"] = 45
+        tm.assert_frame_equal(result, df)
+
+    def test_combine_first_doc_example(self):
+        # doc example
+        df1 = DataFrame(
+            {"A": [1.0, np.nan, 3.0, 5.0, np.nan], "B": [np.nan, 2.0, 3.0, np.nan, 6.0]}
+        )
+
+        df2 = DataFrame(
+            {
+                "A": [5.0, 2.0, 4.0, np.nan, 3.0, 7.0],
+                "B": [np.nan, np.nan, 3.0, 4.0, 6.0, 8.0],
+            }
+        )
+
+        result = df1.combine_first(df2)
+        expected = DataFrame({"A": [1, 2, 3, 5, 3, 7.0], "B": [np.nan, 2, 3, 4, 6, 8]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_combine_first_return_obj_type_with_bools(self):
+        # GH3552
+
+        df1 = DataFrame(
+            [[np.nan, 3.0, True], [-4.6, np.nan, True], [np.nan, 7.0, False]]
+        )
+        df2 = DataFrame([[-42.6, np.nan, True], [-5.0, 1.6, False]], index=[1, 2])
+
+        expected = Series([True, True, False], name=2, dtype=bool)
+
+        result_12 = df1.combine_first(df2)[2]
+        tm.assert_series_equal(result_12, expected)
+
+        result_21 = df2.combine_first(df1)[2]
+        tm.assert_series_equal(result_21, expected)
+
+    @pytest.mark.parametrize(
+        "data1, data2, data_expected",
+        (
+            (
+                [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
+                [pd.NaT, pd.NaT, pd.NaT],
+                [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
+            ),
+            (
+                [pd.NaT, pd.NaT, pd.NaT],
+                [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
+                [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
+            ),
+            (
+                [datetime(2000, 1, 2), pd.NaT, pd.NaT],
+                [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
+                [datetime(2000, 1, 2), datetime(2000, 1, 2), datetime(2000, 1, 3)],
+            ),
+            (
+                [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
+                [datetime(2000, 1, 2), pd.NaT, pd.NaT],
+                [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
+            ),
+        ),
+    )
+    def test_combine_first_convert_datatime_correctly(
+        self, data1, data2, data_expected
+    ):
+        # GH 3593
+
+        df1, df2 = DataFrame({"a": data1}), DataFrame({"a": data2})
+        result = df1.combine_first(df2)
+        expected = DataFrame({"a": data_expected})
+        tm.assert_frame_equal(result, expected)
+
+    def test_combine_first_align_nan(self):
+        # GH 7509 (not fixed)
+        dfa = DataFrame([[pd.Timestamp("2011-01-01"), 2]], columns=["a", "b"])
+        dfb = DataFrame([[4], [5]], columns=["b"])
+        assert dfa["a"].dtype == "datetime64[ns]"
+        assert dfa["b"].dtype == "int64"
+
+        res = dfa.combine_first(dfb)
+        exp = DataFrame(
+            {"a": [pd.Timestamp("2011-01-01"), pd.NaT], "b": [2, 5]},
+            columns=["a", "b"],
+        )
+        tm.assert_frame_equal(res, exp)
+        assert res["a"].dtype == "datetime64[ns]"
+        # TODO: this must be int64
+        assert res["b"].dtype == "int64"
+
+        res = dfa.iloc[:0].combine_first(dfb)
+        exp = DataFrame({"a": [np.nan, np.nan], "b": [4, 5]}, columns=["a", "b"])
+        tm.assert_frame_equal(res, exp)
+        # TODO: this must be datetime64
+        assert res["a"].dtype == "float64"
+        # TODO: this must be int64
+        assert res["b"].dtype == "int64"
+
+    def test_combine_first_timezone(self, unit):
+        # see gh-7630
+        data1 = pd.to_datetime("20100101 01:01").tz_localize("UTC").as_unit(unit)
+        df1 = DataFrame(
+            columns=["UTCdatetime", "abc"],
+            data=data1,
+            index=pd.date_range("20140627", periods=1),
+        )
+        data2 = pd.to_datetime("20121212 12:12").tz_localize("UTC").as_unit(unit)
+        df2 = DataFrame(
+            columns=["UTCdatetime", "xyz"],
+            data=data2,
+            index=pd.date_range("20140628", periods=1),
+        )
+        res = df2[["UTCdatetime"]].combine_first(df1)
+        exp = DataFrame(
+            {
+                "UTCdatetime": [
+                    pd.Timestamp("2010-01-01 01:01", tz="UTC"),
+                    pd.Timestamp("2012-12-12 12:12", tz="UTC"),
+                ],
+                "abc": [pd.Timestamp("2010-01-01 01:01:00", tz="UTC"), pd.NaT],
+            },
+            columns=["UTCdatetime", "abc"],
+            index=pd.date_range("20140627", periods=2, freq="D"),
+            dtype=f"datetime64[{unit}, UTC]",
+        )
+        assert res["UTCdatetime"].dtype == f"datetime64[{unit}, UTC]"
+        assert res["abc"].dtype == f"datetime64[{unit}, UTC]"
+
+        tm.assert_frame_equal(res, exp)
+
+    def test_combine_first_timezone2(self, unit):
+        # see gh-10567
+        dts1 = pd.date_range("2015-01-01", "2015-01-05", tz="UTC", unit=unit)
+        df1 = DataFrame({"DATE": dts1})
+        dts2 = pd.date_range("2015-01-03", "2015-01-05", tz="UTC", unit=unit)
+        df2 = DataFrame({"DATE": dts2})
+
+        res = df1.combine_first(df2)
+        tm.assert_frame_equal(res, df1)
+        assert res["DATE"].dtype == f"datetime64[{unit}, UTC]"
+
+    def test_combine_first_timezone3(self, unit):
+        dts1 = pd.DatetimeIndex(
+            ["2011-01-01", "NaT", "2011-01-03", "2011-01-04"], tz="US/Eastern"
+        ).as_unit(unit)
+        df1 = DataFrame({"DATE": dts1}, index=[1, 3, 5, 7])
+        dts2 = pd.DatetimeIndex(
+            ["2012-01-01", "2012-01-02", "2012-01-03"], tz="US/Eastern"
+        ).as_unit(unit)
+        df2 = DataFrame({"DATE": dts2}, index=[2, 4, 5])
+
+        res = df1.combine_first(df2)
+        exp_dts = pd.DatetimeIndex(
+            [
+                "2011-01-01",
+                "2012-01-01",
+                "NaT",
+                "2012-01-02",
+                "2011-01-03",
+                "2011-01-04",
+            ],
+            tz="US/Eastern",
+        ).as_unit(unit)
+        exp = DataFrame({"DATE": exp_dts}, index=[1, 2, 3, 4, 5, 7])
+        tm.assert_frame_equal(res, exp)
+
+    # FIXME: parametrizing over unit breaks on non-nano
+    def test_combine_first_timezone4(self):
+        # different tz
+        dts1 = pd.date_range("2015-01-01", "2015-01-05", tz="US/Eastern")
+        df1 = DataFrame({"DATE": dts1})
+        dts2 = pd.date_range("2015-01-03", "2015-01-05")
+        df2 = DataFrame({"DATE": dts2})
+
+        # if df1 doesn't have NaN, keep its dtype
+        res = df1.combine_first(df2)
+        tm.assert_frame_equal(res, df1)
+        assert res["DATE"].dtype == "datetime64[ns, US/Eastern]"
+
+    def test_combine_first_timezone5(self, unit):
+        dts1 = pd.date_range("2015-01-01", "2015-01-02", tz="US/Eastern", unit=unit)
+        df1 = DataFrame({"DATE": dts1})
+        dts2 = pd.date_range("2015-01-01", "2015-01-03", unit=unit)
+        df2 = DataFrame({"DATE": dts2})
+
+        res = df1.combine_first(df2)
+        exp_dts = [
+            pd.Timestamp("2015-01-01", tz="US/Eastern"),
+            pd.Timestamp("2015-01-02", tz="US/Eastern"),
+            pd.Timestamp("2015-01-03"),
+        ]
+        exp = DataFrame({"DATE": exp_dts})
+        tm.assert_frame_equal(res, exp)
+        assert res["DATE"].dtype == "object"
+
+    def test_combine_first_timedelta(self):
+        data1 = pd.TimedeltaIndex(["1 day", "NaT", "3 day", "4day"])
+        df1 = DataFrame({"TD": data1}, index=[1, 3, 5, 7])
+        data2 = pd.TimedeltaIndex(["10 day", "11 day", "12 day"])
+        df2 = DataFrame({"TD": data2}, index=[2, 4, 5])
+
+        res = df1.combine_first(df2)
+        exp_dts = pd.TimedeltaIndex(
+            ["1 day", "10 day", "NaT", "11 day", "3 day", "4 day"]
+        )
+        exp = DataFrame({"TD": exp_dts}, index=[1, 2, 3, 4, 5, 7])
+        tm.assert_frame_equal(res, exp)
+        assert res["TD"].dtype == "timedelta64[ns]"
+
+    def test_combine_first_period(self):
+        data1 = pd.PeriodIndex(["2011-01", "NaT", "2011-03", "2011-04"], freq="M")
+        df1 = DataFrame({"P": data1}, index=[1, 3, 5, 7])
+        data2 = pd.PeriodIndex(["2012-01-01", "2012-02", "2012-03"], freq="M")
+        df2 = DataFrame({"P": data2}, index=[2, 4, 5])
+
+        res = df1.combine_first(df2)
+        exp_dts = pd.PeriodIndex(
+            ["2011-01", "2012-01", "NaT", "2012-02", "2011-03", "2011-04"], freq="M"
+        )
+        exp = DataFrame({"P": exp_dts}, index=[1, 2, 3, 4, 5, 7])
+        tm.assert_frame_equal(res, exp)
+        assert res["P"].dtype == data1.dtype
+
+        # different freq
+        dts2 = pd.PeriodIndex(["2012-01-01", "2012-01-02", "2012-01-03"], freq="D")
+        df2 = DataFrame({"P": dts2}, index=[2, 4, 5])
+
+        res = df1.combine_first(df2)
+        exp_dts = [
+            pd.Period("2011-01", freq="M"),
+            pd.Period("2012-01-01", freq="D"),
+            pd.NaT,
+            pd.Period("2012-01-02", freq="D"),
+            pd.Period("2011-03", freq="M"),
+            pd.Period("2011-04", freq="M"),
+        ]
+        exp = DataFrame({"P": exp_dts}, index=[1, 2, 3, 4, 5, 7])
+        tm.assert_frame_equal(res, exp)
+        assert res["P"].dtype == "object"
+
+    def test_combine_first_int(self):
+        # GH14687 - integer series that do no align exactly
+
+        df1 = DataFrame({"a": [0, 1, 3, 5]}, dtype="int64")
+        df2 = DataFrame({"a": [1, 4]}, dtype="int64")
+
+        result_12 = df1.combine_first(df2)
+        expected_12 = DataFrame({"a": [0, 1, 3, 5]})
+        tm.assert_frame_equal(result_12, expected_12)
+
+        result_21 = df2.combine_first(df1)
+        expected_21 = DataFrame({"a": [1, 4, 3, 5]})
+        tm.assert_frame_equal(result_21, expected_21)
+
+    @pytest.mark.parametrize("val", [1, 1.0])
+    def test_combine_first_with_asymmetric_other(self, val):
+        # see gh-20699
+        df1 = DataFrame({"isNum": [val]})
+        df2 = DataFrame({"isBool": [True]})
+
+        res = df1.combine_first(df2)
+        exp = DataFrame({"isBool": [True], "isNum": [val]})
+
+        tm.assert_frame_equal(res, exp)
+
+    def test_combine_first_string_dtype_only_na(self, nullable_string_dtype):
+        # GH: 37519
+        df = DataFrame(
+            {"a": ["962", "85"], "b": [pd.NA] * 2}, dtype=nullable_string_dtype
+        )
+        df2 = DataFrame({"a": ["85"], "b": [pd.NA]}, dtype=nullable_string_dtype)
+        df.set_index(["a", "b"], inplace=True)
+        df2.set_index(["a", "b"], inplace=True)
+        result = df.combine_first(df2)
+        expected = DataFrame(
+            {"a": ["962", "85"], "b": [pd.NA] * 2}, dtype=nullable_string_dtype
+        ).set_index(["a", "b"])
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "scalar1, scalar2",
+    [
+        (datetime(2020, 1, 1), datetime(2020, 1, 2)),
+        (pd.Period("2020-01-01", "D"), pd.Period("2020-01-02", "D")),
+        (pd.Timedelta("89 days"), pd.Timedelta("60 min")),
+        (pd.Interval(left=0, right=1), pd.Interval(left=2, right=3, closed="left")),
+    ],
+)
+def test_combine_first_timestamp_bug(scalar1, scalar2, nulls_fixture):
+    # GH28481
+    na_value = nulls_fixture
+
+    frame = DataFrame([[na_value, na_value]], columns=["a", "b"])
+    other = DataFrame([[scalar1, scalar2]], columns=["b", "c"])
+
+    common_dtype = find_common_type([frame.dtypes["b"], other.dtypes["b"]])
+
+    if is_dtype_equal(common_dtype, "object") or frame.dtypes["b"] == other.dtypes["b"]:
+        val = scalar1
+    else:
+        val = na_value
+
+    result = frame.combine_first(other)
+
+    expected = DataFrame([[na_value, val, scalar2]], columns=["a", "b", "c"])
+
+    expected["b"] = expected["b"].astype(common_dtype)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_combine_first_timestamp_bug_NaT():
+    # GH28481
+    frame = DataFrame([[pd.NaT, pd.NaT]], columns=["a", "b"])
+    other = DataFrame(
+        [[datetime(2020, 1, 1), datetime(2020, 1, 2)]], columns=["b", "c"]
+    )
+
+    result = frame.combine_first(other)
+    expected = DataFrame(
+        [[pd.NaT, datetime(2020, 1, 1), datetime(2020, 1, 2)]], columns=["a", "b", "c"]
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_combine_first_with_nan_multiindex():
+    # gh-36562
+
+    mi1 = MultiIndex.from_arrays(
+        [["b", "b", "c", "a", "b", np.nan], [1, 2, 3, 4, 5, 6]], names=["a", "b"]
+    )
+    df = DataFrame({"c": [1, 1, 1, 1, 1, 1]}, index=mi1)
+    mi2 = MultiIndex.from_arrays(
+        [["a", "b", "c", "a", "b", "d"], [1, 1, 1, 1, 1, 1]], names=["a", "b"]
+    )
+    s = Series([1, 2, 3, 4, 5, 6], index=mi2)
+    res = df.combine_first(DataFrame({"d": s}))
+    mi_expected = MultiIndex.from_arrays(
+        [
+            ["a", "a", "a", "b", "b", "b", "b", "c", "c", "d", np.nan],
+            [1, 1, 4, 1, 1, 2, 5, 1, 3, 1, 6],
+        ],
+        names=["a", "b"],
+    )
+    expected = DataFrame(
+        {
+            "c": [np.nan, np.nan, 1, 1, 1, 1, 1, np.nan, 1, np.nan, 1],
+            "d": [1.0, 4.0, np.nan, 2.0, 5.0, np.nan, np.nan, 3.0, np.nan, 6.0, np.nan],
+        },
+        index=mi_expected,
+    )
+    tm.assert_frame_equal(res, expected)
+
+
+def test_combine_preserve_dtypes():
+    # GH7509
+    a_column = Series(["a", "b"], index=range(2))
+    b_column = Series(range(2), index=range(2))
+    df1 = DataFrame({"A": a_column, "B": b_column})
+
+    c_column = Series(["a", "b"], index=range(5, 7))
+    b_column = Series(range(-1, 1), index=range(5, 7))
+    df2 = DataFrame({"B": b_column, "C": c_column})
+
+    expected = DataFrame(
+        {
+            "A": ["a", "b", np.nan, np.nan],
+            "B": [0, 1, -1, 0],
+            "C": [np.nan, np.nan, "a", "b"],
+        },
+        index=[0, 1, 5, 6],
+    )
+    combined = df1.combine_first(df2)
+    tm.assert_frame_equal(combined, expected)
+
+
+def test_combine_first_duplicates_rows_for_nan_index_values():
+    # GH39881
+    df1 = DataFrame(
+        {"x": [9, 10, 11]},
+        index=MultiIndex.from_arrays([[1, 2, 3], [np.nan, 5, 6]], names=["a", "b"]),
+    )
+
+    df2 = DataFrame(
+        {"y": [12, 13, 14]},
+        index=MultiIndex.from_arrays([[1, 2, 4], [np.nan, 5, 7]], names=["a", "b"]),
+    )
+
+    expected = DataFrame(
+        {
+            "x": [9.0, 10.0, 11.0, np.nan],
+            "y": [12.0, 13.0, np.nan, 14.0],
+        },
+        index=MultiIndex.from_arrays(
+            [[1, 2, 3, 4], [np.nan, 5, 6, 7]], names=["a", "b"]
+        ),
+    )
+    combined = df1.combine_first(df2)
+    tm.assert_frame_equal(combined, expected)
+
+
+def test_combine_first_int64_not_cast_to_float64():
+    # GH 28613
+    df_1 = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+    df_2 = DataFrame({"A": [1, 20, 30], "B": [40, 50, 60], "C": [12, 34, 65]})
+    result = df_1.combine_first(df_2)
+    expected = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [12, 34, 65]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_midx_losing_dtype():
+    # GH#49830
+    midx = MultiIndex.from_arrays([[0, 0], [np.nan, np.nan]])
+    midx2 = MultiIndex.from_arrays([[1, 1], [np.nan, np.nan]])
+    df1 = DataFrame({"a": [None, 4]}, index=midx)
+    df2 = DataFrame({"a": [3, 3]}, index=midx2)
+    result = df1.combine_first(df2)
+    expected_midx = MultiIndex.from_arrays(
+        [[0, 0, 1, 1], [np.nan, np.nan, np.nan, np.nan]]
+    )
+    expected = DataFrame({"a": [np.nan, 4, 3, 3]}, index=expected_midx)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_combine_first_empty_columns():
+    left = DataFrame(columns=["a", "b"])
+    right = DataFrame(columns=["a", "c"])
+    result = left.combine_first(right)
+    expected = DataFrame(columns=["a", "b", "c"])
+    tm.assert_frame_equal(result, expected)

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_droplevel.py

@@ -0,0 +1,36 @@
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+class TestDropLevel:
+    def test_droplevel(self, frame_or_series):
+        # GH#20342
+        cols = MultiIndex.from_tuples(
+            [("c", "e"), ("d", "f")], names=["level_1", "level_2"]
+        )
+        mi = MultiIndex.from_tuples([(1, 2), (5, 6), (9, 10)], names=["a", "b"])
+        df = DataFrame([[3, 4], [7, 8], [11, 12]], index=mi, columns=cols)
+        if frame_or_series is not DataFrame:
+            df = df.iloc[:, 0]
+
+        # test that dropping of a level in index works
+        expected = df.reset_index("a", drop=True)
+        result = df.droplevel("a", axis="index")
+        tm.assert_equal(result, expected)
+
+        if frame_or_series is DataFrame:
+            # test that dropping of a level in columns works
+            expected = df.copy()
+            expected.columns = Index(["c", "d"], name="level_1")
+            result = df.droplevel("level_2", axis="columns")
+            tm.assert_equal(result, expected)
+        else:
+            # test that droplevel raises ValueError on axis != 0
+            with pytest.raises(ValueError, match="No axis named columns"):
+                df.droplevel(1, axis="columns")

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_fillna.py

@@ -0,0 +1,916 @@
+import numpy as np
+import pytest
+
+from pandas.compat import WARNING_CHECK_DISABLED
+import pandas.util._test_decorators as td
+
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    NaT,
+    PeriodIndex,
+    Series,
+    TimedeltaIndex,
+    Timestamp,
+    date_range,
+    to_datetime,
+)
+import pandas._testing as tm
+from pandas.tests.frame.common import _check_mixed_float
+
+
+class TestFillNA:
+    def test_fillna_dict_inplace_nonunique_columns(
+        self, using_copy_on_write, warn_copy_on_write
+    ):
+        df = DataFrame(
+            {"A": [np.nan] * 3, "B": [NaT, Timestamp(1), NaT], "C": [np.nan, "foo", 2]}
+        )
+        df.columns = ["A", "A", "A"]
+        orig = df[:]
+
+        # TODO(CoW-warn) better warning message
+        with tm.assert_cow_warning(warn_copy_on_write):
+            df.fillna({"A": 2}, inplace=True)
+        # The first and third columns can be set inplace, while the second cannot.
+
+        expected = DataFrame(
+            {"A": [2.0] * 3, "B": [2, Timestamp(1), 2], "C": [2, "foo", 2]}
+        )
+        expected.columns = ["A", "A", "A"]
+        tm.assert_frame_equal(df, expected)
+
+        # TODO: what's the expected/desired behavior with CoW?
+        if not using_copy_on_write:
+            assert tm.shares_memory(df.iloc[:, 0], orig.iloc[:, 0])
+        assert not tm.shares_memory(df.iloc[:, 1], orig.iloc[:, 1])
+        if not using_copy_on_write:
+            assert tm.shares_memory(df.iloc[:, 2], orig.iloc[:, 2])
+
+    @td.skip_array_manager_not_yet_implemented
+    def test_fillna_on_column_view(self, using_copy_on_write):
+        # GH#46149 avoid unnecessary copies
+        arr = np.full((40, 50), np.nan)
+        df = DataFrame(arr, copy=False)
+
+        if using_copy_on_write:
+            with tm.raises_chained_assignment_error():
+                df[0].fillna(-1, inplace=True)
+            assert np.isnan(arr[:, 0]).all()
+        else:
+            with tm.assert_produces_warning(
+                FutureWarning if not WARNING_CHECK_DISABLED else None,
+                match="inplace method",
+            ):
+                df[0].fillna(-1, inplace=True)
+            assert (arr[:, 0] == -1).all()
+
+        # i.e. we didn't create a new 49-column block
+        assert len(df._mgr.arrays) == 1
+        assert np.shares_memory(df.values, arr)
+
+    def test_fillna_datetime(self, datetime_frame):
+        tf = datetime_frame
+        tf.loc[tf.index[:5], "A"] = np.nan
+        tf.loc[tf.index[-5:], "A"] = np.nan
+
+        zero_filled = datetime_frame.fillna(0)
+        assert (zero_filled.loc[zero_filled.index[:5], "A"] == 0).all()
+
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            padded = datetime_frame.fillna(method="pad")
+        assert np.isnan(padded.loc[padded.index[:5], "A"]).all()
+        assert (
+            padded.loc[padded.index[-5:], "A"] == padded.loc[padded.index[-5], "A"]
+        ).all()
+
+        msg = "Must specify a fill 'value' or 'method'"
+        with pytest.raises(ValueError, match=msg):
+            datetime_frame.fillna()
+        msg = "Cannot specify both 'value' and 'method'"
+        with pytest.raises(ValueError, match=msg):
+            datetime_frame.fillna(5, method="ffill")
+
+    def test_fillna_mixed_type(self, float_string_frame):
+        mf = float_string_frame
+        mf.loc[mf.index[5:20], "foo"] = np.nan
+        mf.loc[mf.index[-10:], "A"] = np.nan
+        # TODO: make stronger assertion here, GH 25640
+        mf.fillna(value=0)
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            mf.fillna(method="pad")
+
+    def test_fillna_mixed_float(self, mixed_float_frame):
+        # mixed numeric (but no float16)
+        mf = mixed_float_frame.reindex(columns=["A", "B", "D"])
+        mf.loc[mf.index[-10:], "A"] = np.nan
+        result = mf.fillna(value=0)
+        _check_mixed_float(result, dtype={"C": None})
+
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = mf.fillna(method="pad")
+        _check_mixed_float(result, dtype={"C": None})
+
+    def test_fillna_empty(self, using_copy_on_write):
+        if using_copy_on_write:
+            pytest.skip("condition is unnecessary complex and is deprecated anyway")
+        # empty frame (GH#2778)
+        df = DataFrame(columns=["x"])
+        for m in ["pad", "backfill"]:
+            msg = "Series.fillna with 'method' is deprecated"
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                df.x.fillna(method=m, inplace=True)
+                df.x.fillna(method=m)
+
+    def test_fillna_different_dtype(self):
+        # with different dtype (GH#3386)
+        df = DataFrame(
+            [["a", "a", np.nan, "a"], ["b", "b", np.nan, "b"], ["c", "c", np.nan, "c"]]
+        )
+
+        result = df.fillna({2: "foo"})
+        expected = DataFrame(
+            [["a", "a", "foo", "a"], ["b", "b", "foo", "b"], ["c", "c", "foo", "c"]]
+        )
+        # column is originally float (all-NaN) -> filling with string gives object dtype
+        expected[2] = expected[2].astype("object")
+        tm.assert_frame_equal(result, expected)
+
+        return_value = df.fillna({2: "foo"}, inplace=True)
+        tm.assert_frame_equal(df, expected)
+        assert return_value is None
+
+    def test_fillna_limit_and_value(self):
+        # limit and value
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 3)))
+        df.iloc[2:7, 0] = np.nan
+        df.iloc[3:5, 2] = np.nan
+
+        expected = df.copy()
+        expected.iloc[2, 0] = 999
+        expected.iloc[3, 2] = 999
+        result = df.fillna(999, limit=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_fillna_datelike(self):
+        # with datelike
+        # GH#6344
+        df = DataFrame(
+            {
+                "Date": [NaT, Timestamp("2014-1-1")],
+                "Date2": [Timestamp("2013-1-1"), NaT],
+            }
+        )
+
+        expected = df.copy()
+        expected["Date"] = expected["Date"].fillna(df.loc[df.index[0], "Date2"])
+        result = df.fillna(value={"Date": df["Date2"]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_fillna_tzaware(self):
+        # with timezone
+        # GH#15855
+        df = DataFrame({"A": [Timestamp("2012-11-11 00:00:00+01:00"), NaT]})
+        exp = DataFrame(
+            {
+                "A": [
+                    Timestamp("2012-11-11 00:00:00+01:00"),
+                    Timestamp("2012-11-11 00:00:00+01:00"),
+                ]
+            }
+        )
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = df.fillna(method="pad")
+        tm.assert_frame_equal(res, exp)
+
+        df = DataFrame({"A": [NaT, Timestamp("2012-11-11 00:00:00+01:00")]})
+        exp = DataFrame(
+            {
+                "A": [
+                    Timestamp("2012-11-11 00:00:00+01:00"),
+                    Timestamp("2012-11-11 00:00:00+01:00"),
+                ]
+            }
+        )
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = df.fillna(method="bfill")
+        tm.assert_frame_equal(res, exp)
+
+    def test_fillna_tzaware_different_column(self):
+        # with timezone in another column
+        # GH#15522
+        df = DataFrame(
+            {
+                "A": date_range("20130101", periods=4, tz="US/Eastern"),
+                "B": [1, 2, np.nan, np.nan],
+            }
+        )
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.fillna(method="pad")
+        expected = DataFrame(
+            {
+                "A": date_range("20130101", periods=4, tz="US/Eastern"),
+                "B": [1.0, 2.0, 2.0, 2.0],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_na_actions_categorical(self):
+        cat = Categorical([1, 2, 3, np.nan], categories=[1, 2, 3])
+        vals = ["a", "b", np.nan, "d"]
+        df = DataFrame({"cats": cat, "vals": vals})
+        cat2 = Categorical([1, 2, 3, 3], categories=[1, 2, 3])
+        vals2 = ["a", "b", "b", "d"]
+        df_exp_fill = DataFrame({"cats": cat2, "vals": vals2})
+        cat3 = Categorical([1, 2, 3], categories=[1, 2, 3])
+        vals3 = ["a", "b", np.nan]
+        df_exp_drop_cats = DataFrame({"cats": cat3, "vals": vals3})
+        cat4 = Categorical([1, 2], categories=[1, 2, 3])
+        vals4 = ["a", "b"]
+        df_exp_drop_all = DataFrame({"cats": cat4, "vals": vals4})
+
+        # fillna
+        res = df.fillna(value={"cats": 3, "vals": "b"})
+        tm.assert_frame_equal(res, df_exp_fill)
+
+        msg = "Cannot setitem on a Categorical with a new category"
+        with pytest.raises(TypeError, match=msg):
+            df.fillna(value={"cats": 4, "vals": "c"})
+
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = df.fillna(method="pad")
+        tm.assert_frame_equal(res, df_exp_fill)
+
+        # dropna
+        res = df.dropna(subset=["cats"])
+        tm.assert_frame_equal(res, df_exp_drop_cats)
+
+        res = df.dropna()
+        tm.assert_frame_equal(res, df_exp_drop_all)
+
+        # make sure that fillna takes missing values into account
+        c = Categorical([np.nan, "b", np.nan], categories=["a", "b"])
+        df = DataFrame({"cats": c, "vals": [1, 2, 3]})
+
+        cat_exp = Categorical(["a", "b", "a"], categories=["a", "b"])
+        df_exp = DataFrame({"cats": cat_exp, "vals": [1, 2, 3]})
+
+        res = df.fillna("a")
+        tm.assert_frame_equal(res, df_exp)
+
+    def test_fillna_categorical_nan(self):
+        # GH#14021
+        # np.nan should always be a valid filler
+        cat = Categorical([np.nan, 2, np.nan])
+        val = Categorical([np.nan, np.nan, np.nan])
+        df = DataFrame({"cats": cat, "vals": val})
+
+        # GH#32950 df.median() is poorly behaved because there is no
+        #  Categorical.median
+        median = Series({"cats": 2.0, "vals": np.nan})
+
+        res = df.fillna(median)
+        v_exp = [np.nan, np.nan, np.nan]
+        df_exp = DataFrame({"cats": [2, 2, 2], "vals": v_exp}, dtype="category")
+        tm.assert_frame_equal(res, df_exp)
+
+        result = df.cats.fillna(np.nan)
+        tm.assert_series_equal(result, df.cats)
+
+        result = df.vals.fillna(np.nan)
+        tm.assert_series_equal(result, df.vals)
+
+        idx = DatetimeIndex(
+            ["2011-01-01 09:00", "2016-01-01 23:45", "2011-01-01 09:00", NaT, NaT]
+        )
+        df = DataFrame({"a": Categorical(idx)})
+        tm.assert_frame_equal(df.fillna(value=NaT), df)
+
+        idx = PeriodIndex(["2011-01", "2011-01", "2011-01", NaT, NaT], freq="M")
+        df = DataFrame({"a": Categorical(idx)})
+        tm.assert_frame_equal(df.fillna(value=NaT), df)
+
+        idx = TimedeltaIndex(["1 days", "2 days", "1 days", NaT, NaT])
+        df = DataFrame({"a": Categorical(idx)})
+        tm.assert_frame_equal(df.fillna(value=NaT), df)
+
+    def test_fillna_downcast(self):
+        # GH#15277
+        # infer int64 from float64
+        df = DataFrame({"a": [1.0, np.nan]})
+        msg = "The 'downcast' keyword in fillna is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.fillna(0, downcast="infer")
+        expected = DataFrame({"a": [1, 0]})
+        tm.assert_frame_equal(result, expected)
+
+        # infer int64 from float64 when fillna value is a dict
+        df = DataFrame({"a": [1.0, np.nan]})
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.fillna({"a": 0}, downcast="infer")
+        expected = DataFrame({"a": [1, 0]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_fillna_downcast_false(self, frame_or_series):
+        # GH#45603 preserve object dtype with downcast=False
+        obj = frame_or_series([1, 2, 3], dtype="object")
+        msg = "The 'downcast' keyword in fillna"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = obj.fillna("", downcast=False)
+        tm.assert_equal(result, obj)
+
+    def test_fillna_downcast_noop(self, frame_or_series):
+        # GH#45423
+        # Two relevant paths:
+        #  1) not _can_hold_na (e.g. integer)
+        #  2) _can_hold_na + noop + not can_hold_element
+
+        obj = frame_or_series([1, 2, 3], dtype=np.int64)
+
+        msg = "The 'downcast' keyword in fillna"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            # GH#40988
+            res = obj.fillna("foo", downcast=np.dtype(np.int32))
+        expected = obj.astype(np.int32)
+        tm.assert_equal(res, expected)
+
+        obj2 = obj.astype(np.float64)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res2 = obj2.fillna("foo", downcast="infer")
+        expected2 = obj  # get back int64
+        tm.assert_equal(res2, expected2)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            # GH#40988
+            res3 = obj2.fillna("foo", downcast=np.dtype(np.int32))
+        tm.assert_equal(res3, expected)
+
+    @pytest.mark.parametrize("columns", [["A", "A", "B"], ["A", "A"]])
+    def test_fillna_dictlike_value_duplicate_colnames(self, columns):
+        # GH#43476
+        df = DataFrame(np.nan, index=[0, 1], columns=columns)
+        with tm.assert_produces_warning(None):
+            result = df.fillna({"A": 0})
+
+        expected = df.copy()
+        expected["A"] = 0.0
+        tm.assert_frame_equal(result, expected)
+
+    def test_fillna_dtype_conversion(self, using_infer_string):
+        # make sure that fillna on an empty frame works
+        df = DataFrame(index=["A", "B", "C"], columns=[1, 2, 3, 4, 5])
+        result = df.dtypes
+        expected = Series([np.dtype("object")] * 5, index=[1, 2, 3, 4, 5])
+        tm.assert_series_equal(result, expected)
+
+        msg = "Downcasting object dtype arrays"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.fillna(1)
+        expected = DataFrame(1, index=["A", "B", "C"], columns=[1, 2, 3, 4, 5])
+        tm.assert_frame_equal(result, expected)
+
+        # empty block
+        df = DataFrame(index=range(3), columns=["A", "B"], dtype="float64")
+        result = df.fillna("nan")
+        expected = DataFrame("nan", index=range(3), columns=["A", "B"], dtype=object)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("val", ["", 1, np.nan, 1.0])
+    def test_fillna_dtype_conversion_equiv_replace(self, val):
+        df = DataFrame({"A": [1, np.nan], "B": [1.0, 2.0]})
+        expected = df.replace(np.nan, val)
+        result = df.fillna(val)
+        tm.assert_frame_equal(result, expected)
+
+    def test_fillna_datetime_columns(self):
+        # GH#7095
+        df = DataFrame(
+            {
+                "A": [-1, -2, np.nan],
+                "B": date_range("20130101", periods=3),
+                "C": ["foo", "bar", None],
+                "D": ["foo2", "bar2", None],
+            },
+            index=date_range("20130110", periods=3),
+        )
+        result = df.fillna("?")
+        expected = DataFrame(
+            {
+                "A": [-1, -2, "?"],
+                "B": date_range("20130101", periods=3),
+                "C": ["foo", "bar", "?"],
+                "D": ["foo2", "bar2", "?"],
+            },
+            index=date_range("20130110", periods=3),
+        )
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            {
+                "A": [-1, -2, np.nan],
+                "B": [Timestamp("2013-01-01"), Timestamp("2013-01-02"), NaT],
+                "C": ["foo", "bar", None],
+                "D": ["foo2", "bar2", None],
+            },
+            index=date_range("20130110", periods=3),
+        )
+        result = df.fillna("?")
+        expected = DataFrame(
+            {
+                "A": [-1, -2, "?"],
+                "B": [Timestamp("2013-01-01"), Timestamp("2013-01-02"), "?"],
+                "C": ["foo", "bar", "?"],
+                "D": ["foo2", "bar2", "?"],
+            },
+            index=date_range("20130110", periods=3),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_ffill(self, datetime_frame):
+        datetime_frame.loc[datetime_frame.index[:5], "A"] = np.nan
+        datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan
+
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            alt = datetime_frame.fillna(method="ffill")
+        tm.assert_frame_equal(datetime_frame.ffill(), alt)
+
+    def test_bfill(self, datetime_frame):
+        datetime_frame.loc[datetime_frame.index[:5], "A"] = np.nan
+        datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan
+
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            alt = datetime_frame.fillna(method="bfill")
+
+        tm.assert_frame_equal(datetime_frame.bfill(), alt)
+
+    def test_frame_pad_backfill_limit(self):
+        index = np.arange(10)
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)), index=index)
+
+        result = df[:2].reindex(index, method="pad", limit=5)
+
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            expected = df[:2].reindex(index).fillna(method="pad")
+        expected.iloc[-3:] = np.nan
+        tm.assert_frame_equal(result, expected)
+
+        result = df[-2:].reindex(index, method="backfill", limit=5)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            expected = df[-2:].reindex(index).fillna(method="backfill")
+        expected.iloc[:3] = np.nan
+        tm.assert_frame_equal(result, expected)
+
+    def test_frame_fillna_limit(self):
+        index = np.arange(10)
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)), index=index)
+
+        result = df[:2].reindex(index)
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = result.fillna(method="pad", limit=5)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            expected = df[:2].reindex(index).fillna(method="pad")
+        expected.iloc[-3:] = np.nan
+        tm.assert_frame_equal(result, expected)
+
+        result = df[-2:].reindex(index)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = result.fillna(method="backfill", limit=5)
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            expected = df[-2:].reindex(index).fillna(method="backfill")
+        expected.iloc[:3] = np.nan
+        tm.assert_frame_equal(result, expected)
+
+    def test_fillna_skip_certain_blocks(self):
+        # don't try to fill boolean, int blocks
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)).astype(int))
+
+        # it works!
+        df.fillna(np.nan)
+
+    @pytest.mark.parametrize("type", [int, float])
+    def test_fillna_positive_limit(self, type):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4))).astype(type)
+
+        msg = "Limit must be greater than 0"
+        with pytest.raises(ValueError, match=msg):
+            df.fillna(0, limit=-5)
+
+    @pytest.mark.parametrize("type", [int, float])
+    def test_fillna_integer_limit(self, type):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4))).astype(type)
+
+        msg = "Limit must be an integer"
+        with pytest.raises(ValueError, match=msg):
+            df.fillna(0, limit=0.5)
+
+    def test_fillna_inplace(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
+        df.loc[:4, 1] = np.nan
+        df.loc[-4:, 3] = np.nan
+
+        expected = df.fillna(value=0)
+        assert expected is not df
+
+        df.fillna(value=0, inplace=True)
+        tm.assert_frame_equal(df, expected)
+
+        expected = df.fillna(value={0: 0}, inplace=True)
+        assert expected is None
+
+        df.loc[:4, 1] = np.nan
+        df.loc[-4:, 3] = np.nan
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            expected = df.fillna(method="ffill")
+        assert expected is not df
+
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            df.fillna(method="ffill", inplace=True)
+        tm.assert_frame_equal(df, expected)
+
+    def test_fillna_dict_series(self):
+        df = DataFrame(
+            {
+                "a": [np.nan, 1, 2, np.nan, np.nan],
+                "b": [1, 2, 3, np.nan, np.nan],
+                "c": [np.nan, 1, 2, 3, 4],
+            }
+        )
+
+        result = df.fillna({"a": 0, "b": 5})
+
+        expected = df.copy()
+        expected["a"] = expected["a"].fillna(0)
+        expected["b"] = expected["b"].fillna(5)
+        tm.assert_frame_equal(result, expected)
+
+        # it works
+        result = df.fillna({"a": 0, "b": 5, "d": 7})
+
+        # Series treated same as dict
+        result = df.fillna(df.max())
+        expected = df.fillna(df.max().to_dict())
+        tm.assert_frame_equal(result, expected)
+
+        # disable this for now
+        with pytest.raises(NotImplementedError, match="column by column"):
+            df.fillna(df.max(1), axis=1)
+
+    def test_fillna_dataframe(self):
+        # GH#8377
+        df = DataFrame(
+            {
+                "a": [np.nan, 1, 2, np.nan, np.nan],
+                "b": [1, 2, 3, np.nan, np.nan],
+                "c": [np.nan, 1, 2, 3, 4],
+            },
+            index=list("VWXYZ"),
+        )
+
+        # df2 may have different index and columns
+        df2 = DataFrame(
+            {
+                "a": [np.nan, 10, 20, 30, 40],
+                "b": [50, 60, 70, 80, 90],
+                "foo": ["bar"] * 5,
+            },
+            index=list("VWXuZ"),
+        )
+
+        result = df.fillna(df2)
+
+        # only those columns and indices which are shared get filled
+        expected = DataFrame(
+            {
+                "a": [np.nan, 1, 2, np.nan, 40],
+                "b": [1, 2, 3, np.nan, 90],
+                "c": [np.nan, 1, 2, 3, 4],
+            },
+            index=list("VWXYZ"),
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_fillna_columns(self):
+        arr = np.random.default_rng(2).standard_normal((10, 10))
+        arr[:, ::2] = np.nan
+        df = DataFrame(arr)
+
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.fillna(method="ffill", axis=1)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            expected = df.T.fillna(method="pad").T
+        tm.assert_frame_equal(result, expected)
+
+        df.insert(6, "foo", 5)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.fillna(method="ffill", axis=1)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            expected = df.astype(float).fillna(method="ffill", axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_fillna_invalid_method(self, float_frame):
+        with pytest.raises(ValueError, match="ffil"):
+            float_frame.fillna(method="ffil")
+
+    def test_fillna_invalid_value(self, float_frame):
+        # list
+        msg = '"value" parameter must be a scalar or dict, but you passed a "{}"'
+        with pytest.raises(TypeError, match=msg.format("list")):
+            float_frame.fillna([1, 2])
+        # tuple
+        with pytest.raises(TypeError, match=msg.format("tuple")):
+            float_frame.fillna((1, 2))
+        # frame with series
+        msg = (
+            '"value" parameter must be a scalar, dict or Series, but you '
+            'passed a "DataFrame"'
+        )
+        with pytest.raises(TypeError, match=msg):
+            float_frame.iloc[:, 0].fillna(float_frame)
+
+    def test_fillna_col_reordering(self):
+        cols = ["COL." + str(i) for i in range(5, 0, -1)]
+        data = np.random.default_rng(2).random((20, 5))
+        df = DataFrame(index=range(20), columns=cols, data=data)
+        msg = "DataFrame.fillna with 'method' is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            filled = df.fillna(method="ffill")
+        assert df.columns.tolist() == filled.columns.tolist()
+
+    def test_fill_empty(self, float_frame):
+        df = float_frame.reindex(columns=[])
+        result = df.fillna(value=0)
+        tm.assert_frame_equal(result, df)
+
+    def test_fillna_downcast_dict(self):
+        # GH#40809
+        df = DataFrame({"col1": [1, np.nan]})
+
+        msg = "The 'downcast' keyword in fillna"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = df.fillna({"col1": 2}, downcast={"col1": "int64"})
+        expected = DataFrame({"col1": [1, 2]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_fillna_with_columns_and_limit(self):
+        # GH40989
+        df = DataFrame(
+            [
+                [np.nan, 2, np.nan, 0],
+                [3, 4, np.nan, 1],
+                [np.nan, np.nan, np.nan, 5],
+                [np.nan, 3, np.nan, 4],
+            ],
+            columns=list("ABCD"),
+        )
+        result = df.fillna(axis=1, value=100, limit=1)
+        result2 = df.fillna(axis=1, value=100, limit=2)
+
+        expected = DataFrame(
+            {
+                "A": Series([100, 3, 100, 100], dtype="float64"),
+                "B": [2, 4, np.nan, 3],
+                "C": [np.nan, 100, np.nan, np.nan],
+                "D": Series([0, 1, 5, 4], dtype="float64"),
+            },
+            index=[0, 1, 2, 3],
+        )
+        expected2 = DataFrame(
+            {
+                "A": Series([100, 3, 100, 100], dtype="float64"),
+                "B": Series([2, 4, 100, 3], dtype="float64"),
+                "C": [100, 100, np.nan, 100],
+                "D": Series([0, 1, 5, 4], dtype="float64"),
+            },
+            index=[0, 1, 2, 3],
+        )
+
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(result2, expected2)
+
+    def test_fillna_datetime_inplace(self):
+        # GH#48863
+        df = DataFrame(
+            {
+                "date1": to_datetime(["2018-05-30", None]),
+                "date2": to_datetime(["2018-09-30", None]),
+            }
+        )
+        expected = df.copy()
+        df.fillna(np.nan, inplace=True)
+        tm.assert_frame_equal(df, expected)
+
+    def test_fillna_inplace_with_columns_limit_and_value(self):
+        # GH40989
+        df = DataFrame(
+            [
+                [np.nan, 2, np.nan, 0],
+                [3, 4, np.nan, 1],
+                [np.nan, np.nan, np.nan, 5],
+                [np.nan, 3, np.nan, 4],
+            ],
+            columns=list("ABCD"),
+        )
+
+        expected = df.fillna(axis=1, value=100, limit=1)
+        assert expected is not df
+
+        df.fillna(axis=1, value=100, limit=1, inplace=True)
+        tm.assert_frame_equal(df, expected)
+
+    @td.skip_array_manager_invalid_test
+    @pytest.mark.parametrize("val", [-1, {"x": -1, "y": -1}])
+    def test_inplace_dict_update_view(
+        self, val, using_copy_on_write, warn_copy_on_write
+    ):
+        # GH#47188
+        df = DataFrame({"x": [np.nan, 2], "y": [np.nan, 2]})
+        df_orig = df.copy()
+        result_view = df[:]
+        with tm.assert_cow_warning(warn_copy_on_write):
+            df.fillna(val, inplace=True)
+        expected = DataFrame({"x": [-1, 2.0], "y": [-1.0, 2]})
+        tm.assert_frame_equal(df, expected)
+        if using_copy_on_write:
+            tm.assert_frame_equal(result_view, df_orig)
+        else:
+            tm.assert_frame_equal(result_view, expected)
+
+    def test_single_block_df_with_horizontal_axis(self):
+        # GH 47713
+        df = DataFrame(
+            {
+                "col1": [5, 0, np.nan, 10, np.nan],
+                "col2": [7, np.nan, np.nan, 5, 3],
+                "col3": [12, np.nan, 1, 2, 0],
+                "col4": [np.nan, 1, 1, np.nan, 18],
+            }
+        )
+        result = df.fillna(50, limit=1, axis=1)
+        expected = DataFrame(
+            [
+                [5.0, 7.0, 12.0, 50.0],
+                [0.0, 50.0, np.nan, 1.0],
+                [50.0, np.nan, 1.0, 1.0],
+                [10.0, 5.0, 2.0, 50.0],
+                [50.0, 3.0, 0.0, 18.0],
+            ],
+            columns=["col1", "col2", "col3", "col4"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_fillna_with_multi_index_frame(self):
+        # GH 47649
+        pdf = DataFrame(
+            {
+                ("x", "a"): [np.nan, 2.0, 3.0],
+                ("x", "b"): [1.0, 2.0, np.nan],
+                ("y", "c"): [1.0, 2.0, np.nan],
+            }
+        )
+        expected = DataFrame(
+            {
+                ("x", "a"): [-1.0, 2.0, 3.0],
+                ("x", "b"): [1.0, 2.0, -1.0],
+                ("y", "c"): [1.0, 2.0, np.nan],
+            }
+        )
+        tm.assert_frame_equal(pdf.fillna({"x": -1}), expected)
+        tm.assert_frame_equal(pdf.fillna({"x": -1, ("x", "b"): -2}), expected)
+
+        expected = DataFrame(
+            {
+                ("x", "a"): [-1.0, 2.0, 3.0],
+                ("x", "b"): [1.0, 2.0, -2.0],
+                ("y", "c"): [1.0, 2.0, np.nan],
+            }
+        )
+        tm.assert_frame_equal(pdf.fillna({("x", "b"): -2, "x": -1}), expected)
+
+
+def test_fillna_nonconsolidated_frame():
+    # https://github.com/pandas-dev/pandas/issues/36495
+    df = DataFrame(
+        [
+            [1, 1, 1, 1.0],
+            [2, 2, 2, 2.0],
+            [3, 3, 3, 3.0],
+        ],
+        columns=["i1", "i2", "i3", "f1"],
+    )
+    df_nonconsol = df.pivot(index="i1", columns="i2")
+    result = df_nonconsol.fillna(0)
+    assert result.isna().sum().sum() == 0
+
+
+def test_fillna_nones_inplace():
+    # GH 48480
+    df = DataFrame(
+        [[None, None], [None, None]],
+        columns=["A", "B"],
+    )
+    msg = "Downcasting object dtype arrays"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.fillna(value={"A": 1, "B": 2}, inplace=True)
+
+    expected = DataFrame([[1, 2], [1, 2]], columns=["A", "B"])
+    tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.parametrize("func", ["pad", "backfill"])
+def test_pad_backfill_deprecated(func):
+    # GH#33396
+    df = DataFrame({"a": [1, 2, 3]})
+    with tm.assert_produces_warning(FutureWarning):
+        getattr(df, func)()
+
+
+@pytest.mark.parametrize(
+    "data, expected_data, method, kwargs",
+    (
+        (
+            [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+            [np.nan, np.nan, 3.0, 3.0, 3.0, 3.0, 7.0, np.nan, np.nan],
+            "ffill",
+            {"limit_area": "inside"},
+        ),
+        (
+            [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+            [np.nan, np.nan, 3.0, 3.0, np.nan, np.nan, 7.0, np.nan, np.nan],
+            "ffill",
+            {"limit_area": "inside", "limit": 1},
+        ),
+        (
+            [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+            [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0],
+            "ffill",
+            {"limit_area": "outside"},
+        ),
+        (
+            [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+            [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan],
+            "ffill",
+            {"limit_area": "outside", "limit": 1},
+        ),
+        (
+            [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+            [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+            "ffill",
+            {"limit_area": "outside", "limit": 1},
+        ),
+        (
+            range(5),
+            range(5),
+            "ffill",
+            {"limit_area": "outside", "limit": 1},
+        ),
+        (
+            [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+            [np.nan, np.nan, 3.0, 7.0, 7.0, 7.0, 7.0, np.nan, np.nan],
+            "bfill",
+            {"limit_area": "inside"},
+        ),
+        (
+            [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+            [np.nan, np.nan, 3.0, np.nan, np.nan, 7.0, 7.0, np.nan, np.nan],
+            "bfill",
+            {"limit_area": "inside", "limit": 1},
+        ),
+        (
+            [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+            [3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan],
+            "bfill",
+            {"limit_area": "outside"},
+        ),
+        (
+            [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
+            [np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan],
+            "bfill",
+            {"limit_area": "outside", "limit": 1},
+        ),
+    ),
+)
+def test_ffill_bfill_limit_area(data, expected_data, method, kwargs):
+    # GH#56492
+    df = DataFrame(data)
+    expected = DataFrame(expected_data)
+    result = getattr(df, method)(**kwargs)
+    tm.assert_frame_equal(result, expected)

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_filter.py

@@ -0,0 +1,153 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import DataFrame
+import pandas._testing as tm
+
+
+class TestDataFrameFilter:
+    def test_filter(self, float_frame, float_string_frame):
+        # Items
+        filtered = float_frame.filter(["A", "B", "E"])
+        assert len(filtered.columns) == 2
+        assert "E" not in filtered
+
+        filtered = float_frame.filter(["A", "B", "E"], axis="columns")
+        assert len(filtered.columns) == 2
+        assert "E" not in filtered
+
+        # Other axis
+        idx = float_frame.index[0:4]
+        filtered = float_frame.filter(idx, axis="index")
+        expected = float_frame.reindex(index=idx)
+        tm.assert_frame_equal(filtered, expected)
+
+        # like
+        fcopy = float_frame.copy()
+        fcopy["AA"] = 1
+
+        filtered = fcopy.filter(like="A")
+        assert len(filtered.columns) == 2
+        assert "AA" in filtered
+
+        # like with ints in column names
+        df = DataFrame(0.0, index=[0, 1, 2], columns=[0, 1, "_A", "_B"])
+        filtered = df.filter(like="_")
+        assert len(filtered.columns) == 2
+
+        # regex with ints in column names
+        # from PR #10384
+        df = DataFrame(0.0, index=[0, 1, 2], columns=["A1", 1, "B", 2, "C"])
+        expected = DataFrame(
+            0.0, index=[0, 1, 2], columns=pd.Index([1, 2], dtype=object)
+        )
+        filtered = df.filter(regex="^[0-9]+$")
+        tm.assert_frame_equal(filtered, expected)
+
+        expected = DataFrame(0.0, index=[0, 1, 2], columns=[0, "0", 1, "1"])
+        # shouldn't remove anything
+        filtered = expected.filter(regex="^[0-9]+$")
+        tm.assert_frame_equal(filtered, expected)
+
+        # pass in None
+        with pytest.raises(TypeError, match="Must pass"):
+            float_frame.filter()
+        with pytest.raises(TypeError, match="Must pass"):
+            float_frame.filter(items=None)
+        with pytest.raises(TypeError, match="Must pass"):
+            float_frame.filter(axis=1)
+
+        # test mutually exclusive arguments
+        with pytest.raises(TypeError, match="mutually exclusive"):
+            float_frame.filter(items=["one", "three"], regex="e$", like="bbi")
+        with pytest.raises(TypeError, match="mutually exclusive"):
+            float_frame.filter(items=["one", "three"], regex="e$", axis=1)
+        with pytest.raises(TypeError, match="mutually exclusive"):
+            float_frame.filter(items=["one", "three"], regex="e$")
+        with pytest.raises(TypeError, match="mutually exclusive"):
+            float_frame.filter(items=["one", "three"], like="bbi", axis=0)
+        with pytest.raises(TypeError, match="mutually exclusive"):
+            float_frame.filter(items=["one", "three"], like="bbi")
+
+        # objects
+        filtered = float_string_frame.filter(like="foo")
+        assert "foo" in filtered
+
+        # unicode columns, won't ascii-encode
+        df = float_frame.rename(columns={"B": "\u2202"})
+        filtered = df.filter(like="C")
+        assert "C" in filtered
+
+    def test_filter_regex_search(self, float_frame):
+        fcopy = float_frame.copy()
+        fcopy["AA"] = 1
+
+        # regex
+        filtered = fcopy.filter(regex="[A]+")
+        assert len(filtered.columns) == 2
+        assert "AA" in filtered
+
+        # doesn't have to be at beginning
+        df = DataFrame(
+            {"aBBa": [1, 2], "BBaBB": [1, 2], "aCCa": [1, 2], "aCCaBB": [1, 2]}
+        )
+
+        result = df.filter(regex="BB")
+        exp = df[[x for x in df.columns if "BB" in x]]
+        tm.assert_frame_equal(result, exp)
+
+    @pytest.mark.parametrize(
+        "name,expected",
+        [
+            ("a", DataFrame({"a": [1, 2]})),
+            ("a", DataFrame({"a": [1, 2]})),
+            ("あ", DataFrame({"あ": [3, 4]})),
+        ],
+    )
+    def test_filter_unicode(self, name, expected):
+        # GH13101
+        df = DataFrame({"a": [1, 2], "あ": [3, 4]})
+
+        tm.assert_frame_equal(df.filter(like=name), expected)
+        tm.assert_frame_equal(df.filter(regex=name), expected)
+
+    @pytest.mark.parametrize("name", ["a", "a"])
+    def test_filter_bytestring(self, name):
+        # GH13101
+        df = DataFrame({b"a": [1, 2], b"b": [3, 4]})
+        expected = DataFrame({b"a": [1, 2]})
+
+        tm.assert_frame_equal(df.filter(like=name), expected)
+        tm.assert_frame_equal(df.filter(regex=name), expected)
+
+    def test_filter_corner(self):
+        empty = DataFrame()
+
+        result = empty.filter([])
+        tm.assert_frame_equal(result, empty)
+
+        result = empty.filter(like="foo")
+        tm.assert_frame_equal(result, empty)
+
+    def test_filter_regex_non_string(self):
+        # GH#5798 trying to filter on non-string columns should drop,
+        #  not raise
+        df = DataFrame(np.random.default_rng(2).random((3, 2)), columns=["STRING", 123])
+        result = df.filter(regex="STRING")
+        expected = df[["STRING"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_filter_keep_order(self):
+        # GH#54980
+        df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+        result = df.filter(items=["B", "A"])
+        expected = df[["B", "A"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_filter_different_dtype(self):
+        # GH#54980
+        df = DataFrame({1: [1, 2, 3], 2: [4, 5, 6]})
+        result = df.filter(items=["B", "A"])
+        expected = df[[]]
+        tm.assert_frame_equal(result, expected)

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_iterrows.py

@@ -0,0 +1,16 @@
+from pandas import (
+    DataFrame,
+    Timedelta,
+)
+
+
+def test_no_overflow_of_freq_and_time_in_dataframe():
+    # GH 35665
+    df = DataFrame(
+        {
+            "some_string": ["2222Y3"],
+            "time": [Timedelta("0 days 00:00:00.990000")],
+        }
+    )
+    for _, row in df.iterrows():
+        assert row.dtype == "object"

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_join.py

@@ -0,0 +1,576 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+from pandas.errors import MergeError
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+from pandas.core.reshape.concat import concat
+
+
+@pytest.fixture
+def frame_with_period_index():
+    return DataFrame(
+        data=np.arange(20).reshape(4, 5),
+        columns=list("abcde"),
+        index=period_range(start="2000", freq="Y", periods=4),
+    )
+
+
+@pytest.fixture
+def left():
+    return DataFrame({"a": [20, 10, 0]}, index=[2, 1, 0])
+
+
+@pytest.fixture
+def right():
+    return DataFrame({"b": [300, 100, 200]}, index=[3, 1, 2])
+
+
+@pytest.fixture
+def left_no_dup():
+    return DataFrame(
+        {"a": ["a", "b", "c", "d"], "b": ["cat", "dog", "weasel", "horse"]},
+        index=range(4),
+    )
+
+
+@pytest.fixture
+def right_no_dup():
+    return DataFrame(
+        {
+            "a": ["a", "b", "c", "d", "e"],
+            "c": ["meow", "bark", "um... weasel noise?", "nay", "chirp"],
+        },
+        index=range(5),
+    ).set_index("a")
+
+
+@pytest.fixture
+def left_w_dups(left_no_dup):
+    return concat(
+        [left_no_dup, DataFrame({"a": ["a"], "b": ["cow"]}, index=[3])], sort=True
+    )
+
+
+@pytest.fixture
+def right_w_dups(right_no_dup):
+    return concat(
+        [right_no_dup, DataFrame({"a": ["e"], "c": ["moo"]}, index=[3])]
+    ).set_index("a")
+
+
+@pytest.mark.parametrize(
+    "how, sort, expected",
+    [
+        ("inner", False, DataFrame({"a": [20, 10], "b": [200, 100]}, index=[2, 1])),
+        ("inner", True, DataFrame({"a": [10, 20], "b": [100, 200]}, index=[1, 2])),
+        (
+            "left",
+            False,
+            DataFrame({"a": [20, 10, 0], "b": [200, 100, np.nan]}, index=[2, 1, 0]),
+        ),
+        (
+            "left",
+            True,
+            DataFrame({"a": [0, 10, 20], "b": [np.nan, 100, 200]}, index=[0, 1, 2]),
+        ),
+        (
+            "right",
+            False,
+            DataFrame({"a": [np.nan, 10, 20], "b": [300, 100, 200]}, index=[3, 1, 2]),
+        ),
+        (
+            "right",
+            True,
+            DataFrame({"a": [10, 20, np.nan], "b": [100, 200, 300]}, index=[1, 2, 3]),
+        ),
+        (
+            "outer",
+            False,
+            DataFrame(
+                {"a": [0, 10, 20, np.nan], "b": [np.nan, 100, 200, 300]},
+                index=[0, 1, 2, 3],
+            ),
+        ),
+        (
+            "outer",
+            True,
+            DataFrame(
+                {"a": [0, 10, 20, np.nan], "b": [np.nan, 100, 200, 300]},
+                index=[0, 1, 2, 3],
+            ),
+        ),
+    ],
+)
+def test_join(left, right, how, sort, expected):
+    result = left.join(right, how=how, sort=sort, validate="1:1")
+    tm.assert_frame_equal(result, expected)
+
+
+def test_suffix_on_list_join():
+    first = DataFrame({"key": [1, 2, 3, 4, 5]})
+    second = DataFrame({"key": [1, 8, 3, 2, 5], "v1": [1, 2, 3, 4, 5]})
+    third = DataFrame({"keys": [5, 2, 3, 4, 1], "v2": [1, 2, 3, 4, 5]})
+
+    # check proper errors are raised
+    msg = "Suffixes not supported when joining multiple DataFrames"
+    with pytest.raises(ValueError, match=msg):
+        first.join([second], lsuffix="y")
+    with pytest.raises(ValueError, match=msg):
+        first.join([second, third], rsuffix="x")
+    with pytest.raises(ValueError, match=msg):
+        first.join([second, third], lsuffix="y", rsuffix="x")
+    with pytest.raises(ValueError, match="Indexes have overlapping values"):
+        first.join([second, third])
+
+    # no errors should be raised
+    arr_joined = first.join([third])
+    norm_joined = first.join(third)
+    tm.assert_frame_equal(arr_joined, norm_joined)
+
+
+def test_join_invalid_validate(left_no_dup, right_no_dup):
+    # GH 46622
+    # Check invalid arguments
+    msg = (
+        '"invalid" is not a valid argument. '
+        "Valid arguments are:\n"
+        '- "1:1"\n'
+        '- "1:m"\n'
+        '- "m:1"\n'
+        '- "m:m"\n'
+        '- "one_to_one"\n'
+        '- "one_to_many"\n'
+        '- "many_to_one"\n'
+        '- "many_to_many"'
+    )
+    with pytest.raises(ValueError, match=msg):
+        left_no_dup.merge(right_no_dup, on="a", validate="invalid")
+
+
+@pytest.mark.parametrize("dtype", ["object", "string[pyarrow]"])
+def test_join_on_single_col_dup_on_right(left_no_dup, right_w_dups, dtype):
+    # GH 46622
+    # Dups on right allowed by one_to_many constraint
+    if dtype == "string[pyarrow]":
+        pytest.importorskip("pyarrow")
+    left_no_dup = left_no_dup.astype(dtype)
+    right_w_dups.index = right_w_dups.index.astype(dtype)
+    left_no_dup.join(
+        right_w_dups,
+        on="a",
+        validate="one_to_many",
+    )
+
+    # Dups on right not allowed by one_to_one constraint
+    msg = "Merge keys are not unique in right dataset; not a one-to-one merge"
+    with pytest.raises(MergeError, match=msg):
+        left_no_dup.join(
+            right_w_dups,
+            on="a",
+            validate="one_to_one",
+        )
+
+
+def test_join_on_single_col_dup_on_left(left_w_dups, right_no_dup):
+    # GH 46622
+    # Dups on left allowed by many_to_one constraint
+    left_w_dups.join(
+        right_no_dup,
+        on="a",
+        validate="many_to_one",
+    )
+
+    # Dups on left not allowed by one_to_one constraint
+    msg = "Merge keys are not unique in left dataset; not a one-to-one merge"
+    with pytest.raises(MergeError, match=msg):
+        left_w_dups.join(
+            right_no_dup,
+            on="a",
+            validate="one_to_one",
+        )
+
+
+def test_join_on_single_col_dup_on_both(left_w_dups, right_w_dups):
+    # GH 46622
+    # Dups on both allowed by many_to_many constraint
+    left_w_dups.join(right_w_dups, on="a", validate="many_to_many")
+
+    # Dups on both not allowed by many_to_one constraint
+    msg = "Merge keys are not unique in right dataset; not a many-to-one merge"
+    with pytest.raises(MergeError, match=msg):
+        left_w_dups.join(
+            right_w_dups,
+            on="a",
+            validate="many_to_one",
+        )
+
+    # Dups on both not allowed by one_to_many constraint
+    msg = "Merge keys are not unique in left dataset; not a one-to-many merge"
+    with pytest.raises(MergeError, match=msg):
+        left_w_dups.join(
+            right_w_dups,
+            on="a",
+            validate="one_to_many",
+        )
+
+
+def test_join_on_multi_col_check_dup():
+    # GH 46622
+    # Two column join, dups in both, but jointly no dups
+    left = DataFrame(
+        {
+            "a": ["a", "a", "b", "b"],
+            "b": [0, 1, 0, 1],
+            "c": ["cat", "dog", "weasel", "horse"],
+        },
+        index=range(4),
+    ).set_index(["a", "b"])
+
+    right = DataFrame(
+        {
+            "a": ["a", "a", "b"],
+            "b": [0, 1, 0],
+            "d": ["meow", "bark", "um... weasel noise?"],
+        },
+        index=range(3),
+    ).set_index(["a", "b"])
+
+    expected_multi = DataFrame(
+        {
+            "a": ["a", "a", "b"],
+            "b": [0, 1, 0],
+            "c": ["cat", "dog", "weasel"],
+            "d": ["meow", "bark", "um... weasel noise?"],
+        },
+        index=range(3),
+    ).set_index(["a", "b"])
+
+    # Jointly no dups allowed by one_to_one constraint
+    result = left.join(right, how="inner", validate="1:1")
+    tm.assert_frame_equal(result, expected_multi)
+
+
+def test_join_index(float_frame):
+    # left / right
+
+    f = float_frame.loc[float_frame.index[:10], ["A", "B"]]
+    f2 = float_frame.loc[float_frame.index[5:], ["C", "D"]].iloc[::-1]
+
+    joined = f.join(f2)
+    tm.assert_index_equal(f.index, joined.index)
+    expected_columns = Index(["A", "B", "C", "D"])
+    tm.assert_index_equal(joined.columns, expected_columns)
+
+    joined = f.join(f2, how="left")
+    tm.assert_index_equal(joined.index, f.index)
+    tm.assert_index_equal(joined.columns, expected_columns)
+
+    joined = f.join(f2, how="right")
+    tm.assert_index_equal(joined.index, f2.index)
+    tm.assert_index_equal(joined.columns, expected_columns)
+
+    # inner
+
+    joined = f.join(f2, how="inner")
+    tm.assert_index_equal(joined.index, f.index[5:10])
+    tm.assert_index_equal(joined.columns, expected_columns)
+
+    # outer
+
+    joined = f.join(f2, how="outer")
+    tm.assert_index_equal(joined.index, float_frame.index.sort_values())
+    tm.assert_index_equal(joined.columns, expected_columns)
+
+    with pytest.raises(ValueError, match="join method"):
+        f.join(f2, how="foo")
+
+    # corner case - overlapping columns
+    msg = "columns overlap but no suffix"
+    for how in ("outer", "left", "inner"):
+        with pytest.raises(ValueError, match=msg):
+            float_frame.join(float_frame, how=how)
+
+
+def test_join_index_more(float_frame):
+    af = float_frame.loc[:, ["A", "B"]]
+    bf = float_frame.loc[::2, ["C", "D"]]
+
+    expected = af.copy()
+    expected["C"] = float_frame["C"][::2]
+    expected["D"] = float_frame["D"][::2]
+
+    result = af.join(bf)
+    tm.assert_frame_equal(result, expected)
+
+    result = af.join(bf, how="right")
+    tm.assert_frame_equal(result, expected[::2])
+
+    result = bf.join(af, how="right")
+    tm.assert_frame_equal(result, expected.loc[:, result.columns])
+
+
+def test_join_index_series(float_frame):
+    df = float_frame.copy()
+    ser = df.pop(float_frame.columns[-1])
+    joined = df.join(ser)
+
+    tm.assert_frame_equal(joined, float_frame)
+
+    ser.name = None
+    with pytest.raises(ValueError, match="must have a name"):
+        df.join(ser)
+
+
+def test_join_overlap(float_frame):
+    df1 = float_frame.loc[:, ["A", "B", "C"]]
+    df2 = float_frame.loc[:, ["B", "C", "D"]]
+
+    joined = df1.join(df2, lsuffix="_df1", rsuffix="_df2")
+    df1_suf = df1.loc[:, ["B", "C"]].add_suffix("_df1")
+    df2_suf = df2.loc[:, ["B", "C"]].add_suffix("_df2")
+
+    no_overlap = float_frame.loc[:, ["A", "D"]]
+    expected = df1_suf.join(df2_suf).join(no_overlap)
+
+    # column order not necessarily sorted
+    tm.assert_frame_equal(joined, expected.loc[:, joined.columns])
+
+
+def test_join_period_index(frame_with_period_index):
+    other = frame_with_period_index.rename(columns=lambda key: f"{key}{key}")
+
+    joined_values = np.concatenate([frame_with_period_index.values] * 2, axis=1)
+
+    joined_cols = frame_with_period_index.columns.append(other.columns)
+
+    joined = frame_with_period_index.join(other)
+    expected = DataFrame(
+        data=joined_values, columns=joined_cols, index=frame_with_period_index.index
+    )
+
+    tm.assert_frame_equal(joined, expected)
+
+
+def test_join_left_sequence_non_unique_index():
+    # https://github.com/pandas-dev/pandas/issues/19607
+    df1 = DataFrame({"a": [0, 10, 20]}, index=[1, 2, 3])
+    df2 = DataFrame({"b": [100, 200, 300]}, index=[4, 3, 2])
+    df3 = DataFrame({"c": [400, 500, 600]}, index=[2, 2, 4])
+
+    joined = df1.join([df2, df3], how="left")
+
+    expected = DataFrame(
+        {
+            "a": [0, 10, 10, 20],
+            "b": [np.nan, 300, 300, 200],
+            "c": [np.nan, 400, 500, np.nan],
+        },
+        index=[1, 2, 2, 3],
+    )
+
+    tm.assert_frame_equal(joined, expected)
+
+
+def test_join_list_series(float_frame):
+    # GH#46850
+    # Join a DataFrame with a list containing both a Series and a DataFrame
+    left = float_frame.A.to_frame()
+    right = [float_frame.B, float_frame[["C", "D"]]]
+    result = left.join(right)
+    tm.assert_frame_equal(result, float_frame)
+
+
+@pytest.mark.parametrize("sort_kw", [True, False])
+def test_suppress_future_warning_with_sort_kw(sort_kw):
+    a = DataFrame({"col1": [1, 2]}, index=["c", "a"])
+
+    b = DataFrame({"col2": [4, 5]}, index=["b", "a"])
+
+    c = DataFrame({"col3": [7, 8]}, index=["a", "b"])
+
+    expected = DataFrame(
+        {
+            "col1": {"a": 2.0, "b": float("nan"), "c": 1.0},
+            "col2": {"a": 5.0, "b": 4.0, "c": float("nan")},
+            "col3": {"a": 7.0, "b": 8.0, "c": float("nan")},
+        }
+    )
+    if sort_kw is False:
+        expected = expected.reindex(index=["c", "a", "b"])
+
+    with tm.assert_produces_warning(None):
+        result = a.join([b, c], how="outer", sort=sort_kw)
+    tm.assert_frame_equal(result, expected)
+
+
+class TestDataFrameJoin:
+    def test_join(self, multiindex_dataframe_random_data):
+        frame = multiindex_dataframe_random_data
+
+        a = frame.loc[frame.index[:5], ["A"]]
+        b = frame.loc[frame.index[2:], ["B", "C"]]
+
+        joined = a.join(b, how="outer").reindex(frame.index)
+        expected = frame.copy().values.copy()
+        expected[np.isnan(joined.values)] = np.nan
+        expected = DataFrame(expected, index=frame.index, columns=frame.columns)
+
+        assert not np.isnan(joined.values).all()
+
+        tm.assert_frame_equal(joined, expected)
+
+    def test_join_segfault(self):
+        # GH#1532
+        df1 = DataFrame({"a": [1, 1], "b": [1, 2], "x": [1, 2]})
+        df2 = DataFrame({"a": [2, 2], "b": [1, 2], "y": [1, 2]})
+        df1 = df1.set_index(["a", "b"])
+        df2 = df2.set_index(["a", "b"])
+        # it works!
+        for how in ["left", "right", "outer"]:
+            df1.join(df2, how=how)
+
+    def test_join_str_datetime(self):
+        str_dates = ["20120209", "20120222"]
+        dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)]
+
+        A = DataFrame(str_dates, index=range(2), columns=["aa"])
+        C = DataFrame([[1, 2], [3, 4]], index=str_dates, columns=dt_dates)
+
+        tst = A.join(C, on="aa")
+
+        assert len(tst.columns) == 3
+
+    def test_join_multiindex_leftright(self):
+        # GH 10741
+        df1 = DataFrame(
+            [
+                ["a", "x", 0.471780],
+                ["a", "y", 0.774908],
+                ["a", "z", 0.563634],
+                ["b", "x", -0.353756],
+                ["b", "y", 0.368062],
+                ["b", "z", -1.721840],
+                ["c", "x", 1],
+                ["c", "y", 2],
+                ["c", "z", 3],
+            ],
+            columns=["first", "second", "value1"],
+        ).set_index(["first", "second"])
+
+        df2 = DataFrame([["a", 10], ["b", 20]], columns=["first", "value2"]).set_index(
+            ["first"]
+        )
+
+        exp = DataFrame(
+            [
+                [0.471780, 10],
+                [0.774908, 10],
+                [0.563634, 10],
+                [-0.353756, 20],
+                [0.368062, 20],
+                [-1.721840, 20],
+                [1.000000, np.nan],
+                [2.000000, np.nan],
+                [3.000000, np.nan],
+            ],
+            index=df1.index,
+            columns=["value1", "value2"],
+        )
+
+        # these must be the same results (but columns are flipped)
+        tm.assert_frame_equal(df1.join(df2, how="left"), exp)
+        tm.assert_frame_equal(df2.join(df1, how="right"), exp[["value2", "value1"]])
+
+        exp_idx = MultiIndex.from_product(
+            [["a", "b"], ["x", "y", "z"]], names=["first", "second"]
+        )
+        exp = DataFrame(
+            [
+                [0.471780, 10],
+                [0.774908, 10],
+                [0.563634, 10],
+                [-0.353756, 20],
+                [0.368062, 20],
+                [-1.721840, 20],
+            ],
+            index=exp_idx,
+            columns=["value1", "value2"],
+        )
+
+        tm.assert_frame_equal(df1.join(df2, how="right"), exp)
+        tm.assert_frame_equal(df2.join(df1, how="left"), exp[["value2", "value1"]])
+
+    def test_join_multiindex_dates(self):
+        # GH 33692
+        date = pd.Timestamp(2000, 1, 1).date()
+
+        df1_index = MultiIndex.from_tuples([(0, date)], names=["index_0", "date"])
+        df1 = DataFrame({"col1": [0]}, index=df1_index)
+        df2_index = MultiIndex.from_tuples([(0, date)], names=["index_0", "date"])
+        df2 = DataFrame({"col2": [0]}, index=df2_index)
+        df3_index = MultiIndex.from_tuples([(0, date)], names=["index_0", "date"])
+        df3 = DataFrame({"col3": [0]}, index=df3_index)
+
+        result = df1.join([df2, df3])
+
+        expected_index = MultiIndex.from_tuples([(0, date)], names=["index_0", "date"])
+        expected = DataFrame(
+            {"col1": [0], "col2": [0], "col3": [0]}, index=expected_index
+        )
+
+        tm.assert_equal(result, expected)
+
+    def test_merge_join_different_levels_raises(self):
+        # GH#9455
+        # GH 40993: For raising, enforced in 2.0
+
+        # first dataframe
+        df1 = DataFrame(columns=["a", "b"], data=[[1, 11], [0, 22]])
+
+        # second dataframe
+        columns = MultiIndex.from_tuples([("a", ""), ("c", "c1")])
+        df2 = DataFrame(columns=columns, data=[[1, 33], [0, 44]])
+
+        # merge
+        with pytest.raises(
+            MergeError, match="Not allowed to merge between different levels"
+        ):
+            pd.merge(df1, df2, on="a")
+
+        # join, see discussion in GH#12219
+        with pytest.raises(
+            MergeError, match="Not allowed to merge between different levels"
+        ):
+            df1.join(df2, on="a")
+
+    def test_frame_join_tzaware(self):
+        test1 = DataFrame(
+            np.zeros((6, 3)),
+            index=date_range(
+                "2012-11-15 00:00:00", periods=6, freq="100ms", tz="US/Central"
+            ),
+        )
+        test2 = DataFrame(
+            np.zeros((3, 3)),
+            index=date_range(
+                "2012-11-15 00:00:00", periods=3, freq="250ms", tz="US/Central"
+            ),
+            columns=range(3, 6),
+        )
+
+        result = test1.join(test2, how="outer")
+        expected = test1.index.union(test2.index)
+
+        tm.assert_index_equal(result.index, expected)
+        assert result.index.tz.zone == "US/Central"

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_reindex.py

@@ -0,0 +1,1327 @@
+from datetime import (
+    datetime,
+    timedelta,
+)
+import inspect
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs.timezones import dateutil_gettz as gettz
+from pandas.compat import (
+    IS64,
+    is_platform_windows,
+)
+from pandas.compat.numpy import np_version_gt2
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    date_range,
+    isna,
+)
+import pandas._testing as tm
+from pandas.api.types import CategoricalDtype
+
+
+class TestReindexSetIndex:
+    # Tests that check both reindex and set_index
+
+    def test_dti_set_index_reindex_datetimeindex(self):
+        # GH#6631
+        df = DataFrame(np.random.default_rng(2).random(6))
+        idx1 = date_range("2011/01/01", periods=6, freq="ME", tz="US/Eastern")
+        idx2 = date_range("2013", periods=6, freq="YE", tz="Asia/Tokyo")
+
+        df = df.set_index(idx1)
+        tm.assert_index_equal(df.index, idx1)
+        df = df.reindex(idx2)
+        tm.assert_index_equal(df.index, idx2)
+
+    def test_dti_set_index_reindex_freq_with_tz(self):
+        # GH#11314 with tz
+        index = date_range(
+            datetime(2015, 10, 1), datetime(2015, 10, 1, 23), freq="h", tz="US/Eastern"
+        )
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((24, 1)),
+            columns=["a"],
+            index=index,
+        )
+        new_index = date_range(
+            datetime(2015, 10, 2), datetime(2015, 10, 2, 23), freq="h", tz="US/Eastern"
+        )
+
+        result = df.set_index(new_index)
+        assert result.index.freq == index.freq
+
+    def test_set_reset_index_intervalindex(self):
+        df = DataFrame({"A": range(10)})
+        ser = pd.cut(df.A, 5)
+        df["B"] = ser
+        df = df.set_index("B")
+
+        df = df.reset_index()
+
+    def test_setitem_reset_index_dtypes(self):
+        # GH 22060
+        df = DataFrame(columns=["a", "b", "c"]).astype(
+            {"a": "datetime64[ns]", "b": np.int64, "c": np.float64}
+        )
+        df1 = df.set_index(["a"])
+        df1["d"] = []
+        result = df1.reset_index()
+        expected = DataFrame(columns=["a", "b", "c", "d"], index=range(0)).astype(
+            {"a": "datetime64[ns]", "b": np.int64, "c": np.float64, "d": np.float64}
+        )
+        tm.assert_frame_equal(result, expected)
+
+        df2 = df.set_index(["a", "b"])
+        df2["d"] = []
+        result = df2.reset_index()
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "timezone, year, month, day, hour",
+        [["America/Chicago", 2013, 11, 3, 1], ["America/Santiago", 2021, 4, 3, 23]],
+    )
+    def test_reindex_timestamp_with_fold(self, timezone, year, month, day, hour):
+        # see gh-40817
+        test_timezone = gettz(timezone)
+        transition_1 = pd.Timestamp(
+            year=year,
+            month=month,
+            day=day,
+            hour=hour,
+            minute=0,
+            fold=0,
+            tzinfo=test_timezone,
+        )
+        transition_2 = pd.Timestamp(
+            year=year,
+            month=month,
+            day=day,
+            hour=hour,
+            minute=0,
+            fold=1,
+            tzinfo=test_timezone,
+        )
+        df = (
+            DataFrame({"index": [transition_1, transition_2], "vals": ["a", "b"]})
+            .set_index("index")
+            .reindex(["1", "2"])
+        )
+        exp = DataFrame({"index": ["1", "2"], "vals": [np.nan, np.nan]}).set_index(
+            "index"
+        )
+        exp = exp.astype(df.vals.dtype)
+        tm.assert_frame_equal(
+            df,
+            exp,
+        )
+
+
+class TestDataFrameSelectReindex:
+    # These are specific reindex-based tests; other indexing tests should go in
+    # test_indexing
+
+    @pytest.mark.xfail(
+        not IS64 or (is_platform_windows() and not np_version_gt2),
+        reason="Passes int32 values to DatetimeArray in make_na_array on "
+        "windows, 32bit linux builds",
+    )
+    @td.skip_array_manager_not_yet_implemented
+    def test_reindex_tzaware_fill_value(self):
+        # GH#52586
+        df = DataFrame([[1]])
+
+        ts = pd.Timestamp("2023-04-10 17:32", tz="US/Pacific")
+        res = df.reindex([0, 1], axis=1, fill_value=ts)
+        assert res.dtypes[1] == pd.DatetimeTZDtype(unit="s", tz="US/Pacific")
+        expected = DataFrame({0: [1], 1: [ts]})
+        expected[1] = expected[1].astype(res.dtypes[1])
+        tm.assert_frame_equal(res, expected)
+
+        per = ts.tz_localize(None).to_period("s")
+        res = df.reindex([0, 1], axis=1, fill_value=per)
+        assert res.dtypes[1] == pd.PeriodDtype("s")
+        expected = DataFrame({0: [1], 1: [per]})
+        tm.assert_frame_equal(res, expected)
+
+        interval = pd.Interval(ts, ts + pd.Timedelta(seconds=1))
+        res = df.reindex([0, 1], axis=1, fill_value=interval)
+        assert res.dtypes[1] == pd.IntervalDtype("datetime64[s, US/Pacific]", "right")
+        expected = DataFrame({0: [1], 1: [interval]})
+        expected[1] = expected[1].astype(res.dtypes[1])
+        tm.assert_frame_equal(res, expected)
+
+    def test_reindex_copies(self):
+        # based on asv time_reindex_axis1
+        N = 10
+        df = DataFrame(np.random.default_rng(2).standard_normal((N * 10, N)))
+        cols = np.arange(N)
+        np.random.default_rng(2).shuffle(cols)
+
+        result = df.reindex(columns=cols, copy=True)
+        assert not np.shares_memory(result[0]._values, df[0]._values)
+
+        # pass both columns and index
+        result2 = df.reindex(columns=cols, index=df.index, copy=True)
+        assert not np.shares_memory(result2[0]._values, df[0]._values)
+
+    def test_reindex_copies_ea(self, using_copy_on_write):
+        # https://github.com/pandas-dev/pandas/pull/51197
+        # also ensure to honor copy keyword for ExtensionDtypes
+        N = 10
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((N * 10, N)), dtype="Float64"
+        )
+        cols = np.arange(N)
+        np.random.default_rng(2).shuffle(cols)
+
+        result = df.reindex(columns=cols, copy=True)
+        if using_copy_on_write:
+            assert np.shares_memory(result[0].array._data, df[0].array._data)
+        else:
+            assert not np.shares_memory(result[0].array._data, df[0].array._data)
+
+        # pass both columns and index
+        result2 = df.reindex(columns=cols, index=df.index, copy=True)
+        if using_copy_on_write:
+            assert np.shares_memory(result2[0].array._data, df[0].array._data)
+        else:
+            assert not np.shares_memory(result2[0].array._data, df[0].array._data)
+
+    @td.skip_array_manager_not_yet_implemented
+    def test_reindex_date_fill_value(self):
+        # passing date to dt64 is deprecated; enforced in 2.0 to cast to object
+        arr = date_range("2016-01-01", periods=6).values.reshape(3, 2)
+        df = DataFrame(arr, columns=["A", "B"], index=range(3))
+
+        ts = df.iloc[0, 0]
+        fv = ts.date()
+
+        res = df.reindex(index=range(4), columns=["A", "B", "C"], fill_value=fv)
+
+        expected = DataFrame(
+            {"A": df["A"].tolist() + [fv], "B": df["B"].tolist() + [fv], "C": [fv] * 4},
+            dtype=object,
+        )
+        tm.assert_frame_equal(res, expected)
+
+        # only reindexing rows
+        res = df.reindex(index=range(4), fill_value=fv)
+        tm.assert_frame_equal(res, expected[["A", "B"]])
+
+        # same with a datetime-castable str
+        res = df.reindex(
+            index=range(4), columns=["A", "B", "C"], fill_value="2016-01-01"
+        )
+        expected = DataFrame(
+            {"A": df["A"].tolist() + [ts], "B": df["B"].tolist() + [ts], "C": [ts] * 4},
+        )
+        tm.assert_frame_equal(res, expected)
+
+    def test_reindex_with_multi_index(self):
+        # https://github.com/pandas-dev/pandas/issues/29896
+        # tests for reindexing a multi-indexed DataFrame with a new MultiIndex
+        #
+        # confirms that we can reindex a multi-indexed DataFrame with a new
+        # MultiIndex object correctly when using no filling, backfilling, and
+        # padding
+        #
+        # The DataFrame, `df`, used in this test is:
+        #       c
+        #  a b
+        # -1 0  A
+        #    1  B
+        #    2  C
+        #    3  D
+        #    4  E
+        #    5  F
+        #    6  G
+        #  0 0  A
+        #    1  B
+        #    2  C
+        #    3  D
+        #    4  E
+        #    5  F
+        #    6  G
+        #  1 0  A
+        #    1  B
+        #    2  C
+        #    3  D
+        #    4  E
+        #    5  F
+        #    6  G
+        #
+        # and the other MultiIndex, `new_multi_index`, is:
+        # 0: 0 0.5
+        # 1:   2.0
+        # 2:   5.0
+        # 3:   5.8
+        df = DataFrame(
+            {
+                "a": [-1] * 7 + [0] * 7 + [1] * 7,
+                "b": list(range(7)) * 3,
+                "c": ["A", "B", "C", "D", "E", "F", "G"] * 3,
+            }
+        ).set_index(["a", "b"])
+        new_index = [0.5, 2.0, 5.0, 5.8]
+        new_multi_index = MultiIndex.from_product([[0], new_index], names=["a", "b"])
+
+        # reindexing w/o a `method` value
+        reindexed = df.reindex(new_multi_index)
+        expected = DataFrame(
+            {"a": [0] * 4, "b": new_index, "c": [np.nan, "C", "F", np.nan]}
+        ).set_index(["a", "b"])
+        tm.assert_frame_equal(expected, reindexed)
+
+        # reindexing with backfilling
+        expected = DataFrame(
+            {"a": [0] * 4, "b": new_index, "c": ["B", "C", "F", "G"]}
+        ).set_index(["a", "b"])
+        reindexed_with_backfilling = df.reindex(new_multi_index, method="bfill")
+        tm.assert_frame_equal(expected, reindexed_with_backfilling)
+
+        reindexed_with_backfilling = df.reindex(new_multi_index, method="backfill")
+        tm.assert_frame_equal(expected, reindexed_with_backfilling)
+
+        # reindexing with padding
+        expected = DataFrame(
+            {"a": [0] * 4, "b": new_index, "c": ["A", "C", "F", "F"]}
+        ).set_index(["a", "b"])
+        reindexed_with_padding = df.reindex(new_multi_index, method="pad")
+        tm.assert_frame_equal(expected, reindexed_with_padding)
+
+        reindexed_with_padding = df.reindex(new_multi_index, method="ffill")
+        tm.assert_frame_equal(expected, reindexed_with_padding)
+
+    @pytest.mark.parametrize(
+        "method,expected_values",
+        [
+            ("nearest", [0, 1, 1, 2]),
+            ("pad", [np.nan, 0, 1, 1]),
+            ("backfill", [0, 1, 2, 2]),
+        ],
+    )
+    def test_reindex_methods(self, method, expected_values):
+        df = DataFrame({"x": list(range(5))})
+        target = np.array([-0.1, 0.9, 1.1, 1.5])
+
+        expected = DataFrame({"x": expected_values}, index=target)
+        actual = df.reindex(target, method=method)
+        tm.assert_frame_equal(expected, actual)
+
+        actual = df.reindex(target, method=method, tolerance=1)
+        tm.assert_frame_equal(expected, actual)
+        actual = df.reindex(target, method=method, tolerance=[1, 1, 1, 1])
+        tm.assert_frame_equal(expected, actual)
+
+        e2 = expected[::-1]
+        actual = df.reindex(target[::-1], method=method)
+        tm.assert_frame_equal(e2, actual)
+
+        new_order = [3, 0, 2, 1]
+        e2 = expected.iloc[new_order]
+        actual = df.reindex(target[new_order], method=method)
+        tm.assert_frame_equal(e2, actual)
+
+        switched_method = (
+            "pad" if method == "backfill" else "backfill" if method == "pad" else method
+        )
+        actual = df[::-1].reindex(target, method=switched_method)
+        tm.assert_frame_equal(expected, actual)
+
+    def test_reindex_methods_nearest_special(self):
+        df = DataFrame({"x": list(range(5))})
+        target = np.array([-0.1, 0.9, 1.1, 1.5])
+
+        expected = DataFrame({"x": [0, 1, 1, np.nan]}, index=target)
+        actual = df.reindex(target, method="nearest", tolerance=0.2)
+        tm.assert_frame_equal(expected, actual)
+
+        expected = DataFrame({"x": [0, np.nan, 1, np.nan]}, index=target)
+        actual = df.reindex(target, method="nearest", tolerance=[0.5, 0.01, 0.4, 0.1])
+        tm.assert_frame_equal(expected, actual)
+
+    def test_reindex_nearest_tz(self, tz_aware_fixture):
+        # GH26683
+        tz = tz_aware_fixture
+        idx = date_range("2019-01-01", periods=5, tz=tz)
+        df = DataFrame({"x": list(range(5))}, index=idx)
+
+        expected = df.head(3)
+        actual = df.reindex(idx[:3], method="nearest")
+        tm.assert_frame_equal(expected, actual)
+
+    def test_reindex_nearest_tz_empty_frame(self):
+        # https://github.com/pandas-dev/pandas/issues/31964
+        dti = pd.DatetimeIndex(["2016-06-26 14:27:26+00:00"])
+        df = DataFrame(index=pd.DatetimeIndex(["2016-07-04 14:00:59+00:00"]))
+        expected = DataFrame(index=dti)
+        result = df.reindex(dti, method="nearest")
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex_frame_add_nat(self):
+        rng = date_range("1/1/2000 00:00:00", periods=10, freq="10s")
+        df = DataFrame(
+            {"A": np.random.default_rng(2).standard_normal(len(rng)), "B": rng}
+        )
+
+        result = df.reindex(range(15))
+        assert np.issubdtype(result["B"].dtype, np.dtype("M8[ns]"))
+
+        mask = isna(result)["B"]
+        assert mask[-5:].all()
+        assert not mask[:-5].any()
+
+    @pytest.mark.parametrize(
+        "method, exp_values",
+        [("ffill", [0, 1, 2, 3]), ("bfill", [1.0, 2.0, 3.0, np.nan])],
+    )
+    def test_reindex_frame_tz_ffill_bfill(self, frame_or_series, method, exp_values):
+        # GH#38566
+        obj = frame_or_series(
+            [0, 1, 2, 3],
+            index=date_range("2020-01-01 00:00:00", periods=4, freq="h", tz="UTC"),
+        )
+        new_index = date_range("2020-01-01 00:01:00", periods=4, freq="h", tz="UTC")
+        result = obj.reindex(new_index, method=method, tolerance=pd.Timedelta("1 hour"))
+        expected = frame_or_series(exp_values, index=new_index)
+        tm.assert_equal(result, expected)
+
+    def test_reindex_limit(self):
+        # GH 28631
+        data = [["A", "A", "A"], ["B", "B", "B"], ["C", "C", "C"], ["D", "D", "D"]]
+        exp_data = [
+            ["A", "A", "A"],
+            ["B", "B", "B"],
+            ["C", "C", "C"],
+            ["D", "D", "D"],
+            ["D", "D", "D"],
+            [np.nan, np.nan, np.nan],
+        ]
+        df = DataFrame(data)
+        result = df.reindex([0, 1, 2, 3, 4, 5], method="ffill", limit=1)
+        expected = DataFrame(exp_data)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "idx, check_index_type",
+        [
+            [["C", "B", "A"], True],
+            [["F", "C", "A", "D"], True],
+            [["A"], True],
+            [["A", "B", "C"], True],
+            [["C", "A", "B"], True],
+            [["C", "B"], True],
+            [["C", "A"], True],
+            [["A", "B"], True],
+            [["B", "A", "C"], True],
+            # reindex by these causes different MultiIndex levels
+            [["D", "F"], False],
+            [["A", "C", "B"], False],
+        ],
+    )
+    def test_reindex_level_verify_first_level(self, idx, check_index_type):
+        df = DataFrame(
+            {
+                "jim": list("B" * 4 + "A" * 2 + "C" * 3),
+                "joe": list("abcdeabcd")[::-1],
+                "jolie": [10, 20, 30] * 3,
+                "joline": np.random.default_rng(2).integers(0, 1000, 9),
+            }
+        )
+        icol = ["jim", "joe", "jolie"]
+
+        def f(val):
+            return np.nonzero((df["jim"] == val).to_numpy())[0]
+
+        i = np.concatenate(list(map(f, idx)))
+        left = df.set_index(icol).reindex(idx, level="jim")
+        right = df.iloc[i].set_index(icol)
+        tm.assert_frame_equal(left, right, check_index_type=check_index_type)
+
+    @pytest.mark.parametrize(
+        "idx",
+        [
+            ("mid",),
+            ("mid", "btm"),
+            ("mid", "btm", "top"),
+            ("mid",),
+            ("mid", "top"),
+            ("mid", "top", "btm"),
+            ("btm",),
+            ("btm", "mid"),
+            ("btm", "mid", "top"),
+            ("btm",),
+            ("btm", "top"),
+            ("btm", "top", "mid"),
+            ("top",),
+            ("top", "mid"),
+            ("top", "mid", "btm"),
+            ("top",),
+            ("top", "btm"),
+            ("top", "btm", "mid"),
+        ],
+    )
+    def test_reindex_level_verify_first_level_repeats(self, idx):
+        df = DataFrame(
+            {
+                "jim": ["mid"] * 5 + ["btm"] * 8 + ["top"] * 7,
+                "joe": ["3rd"] * 2
+                + ["1st"] * 3
+                + ["2nd"] * 3
+                + ["1st"] * 2
+                + ["3rd"] * 3
+                + ["1st"] * 2
+                + ["3rd"] * 3
+                + ["2nd"] * 2,
+                # this needs to be jointly unique with jim and joe or
+                # reindexing will fail ~1.5% of the time, this works
+                # out to needing unique groups of same size as joe
+                "jolie": np.concatenate(
+                    [
+                        np.random.default_rng(2).choice(1000, x, replace=False)
+                        for x in [2, 3, 3, 2, 3, 2, 3, 2]
+                    ]
+                ),
+                "joline": np.random.default_rng(2).standard_normal(20).round(3) * 10,
+            }
+        )
+        icol = ["jim", "joe", "jolie"]
+
+        def f(val):
+            return np.nonzero((df["jim"] == val).to_numpy())[0]
+
+        i = np.concatenate(list(map(f, idx)))
+        left = df.set_index(icol).reindex(idx, level="jim")
+        right = df.iloc[i].set_index(icol)
+        tm.assert_frame_equal(left, right)
+
+    @pytest.mark.parametrize(
+        "idx, indexer",
+        [
+            [
+                ["1st", "2nd", "3rd"],
+                [2, 3, 4, 0, 1, 8, 9, 5, 6, 7, 10, 11, 12, 13, 14, 18, 19, 15, 16, 17],
+            ],
+            [
+                ["3rd", "2nd", "1st"],
+                [0, 1, 2, 3, 4, 10, 11, 12, 5, 6, 7, 8, 9, 15, 16, 17, 18, 19, 13, 14],
+            ],
+            [["2nd", "3rd"], [0, 1, 5, 6, 7, 10, 11, 12, 18, 19, 15, 16, 17]],
+            [["3rd", "1st"], [0, 1, 2, 3, 4, 10, 11, 12, 8, 9, 15, 16, 17, 13, 14]],
+        ],
+    )
+    def test_reindex_level_verify_repeats(self, idx, indexer):
+        df = DataFrame(
+            {
+                "jim": ["mid"] * 5 + ["btm"] * 8 + ["top"] * 7,
+                "joe": ["3rd"] * 2
+                + ["1st"] * 3
+                + ["2nd"] * 3
+                + ["1st"] * 2
+                + ["3rd"] * 3
+                + ["1st"] * 2
+                + ["3rd"] * 3
+                + ["2nd"] * 2,
+                # this needs to be jointly unique with jim and joe or
+                # reindexing will fail ~1.5% of the time, this works
+                # out to needing unique groups of same size as joe
+                "jolie": np.concatenate(
+                    [
+                        np.random.default_rng(2).choice(1000, x, replace=False)
+                        for x in [2, 3, 3, 2, 3, 2, 3, 2]
+                    ]
+                ),
+                "joline": np.random.default_rng(2).standard_normal(20).round(3) * 10,
+            }
+        )
+        icol = ["jim", "joe", "jolie"]
+        left = df.set_index(icol).reindex(idx, level="joe")
+        right = df.iloc[indexer].set_index(icol)
+        tm.assert_frame_equal(left, right)
+
+    @pytest.mark.parametrize(
+        "idx, indexer, check_index_type",
+        [
+            [list("abcde"), [3, 2, 1, 0, 5, 4, 8, 7, 6], True],
+            [list("abcd"), [3, 2, 1, 0, 5, 8, 7, 6], True],
+            [list("abc"), [3, 2, 1, 8, 7, 6], True],
+            [list("eca"), [1, 3, 4, 6, 8], True],
+            [list("edc"), [0, 1, 4, 5, 6], True],
+            [list("eadbc"), [3, 0, 2, 1, 4, 5, 8, 7, 6], True],
+            [list("edwq"), [0, 4, 5], True],
+            [list("wq"), [], False],
+        ],
+    )
+    def test_reindex_level_verify(self, idx, indexer, check_index_type):
+        df = DataFrame(
+            {
+                "jim": list("B" * 4 + "A" * 2 + "C" * 3),
+                "joe": list("abcdeabcd")[::-1],
+                "jolie": [10, 20, 30] * 3,
+                "joline": np.random.default_rng(2).integers(0, 1000, 9),
+            }
+        )
+        icol = ["jim", "joe", "jolie"]
+        left = df.set_index(icol).reindex(idx, level="joe")
+        right = df.iloc[indexer].set_index(icol)
+        tm.assert_frame_equal(left, right, check_index_type=check_index_type)
+
+    def test_non_monotonic_reindex_methods(self):
+        dr = date_range("2013-08-01", periods=6, freq="B")
+        data = np.random.default_rng(2).standard_normal((6, 1))
+        df = DataFrame(data, index=dr, columns=list("A"))
+        df_rev = DataFrame(data, index=dr[[3, 4, 5] + [0, 1, 2]], columns=list("A"))
+        # index is not monotonic increasing or decreasing
+        msg = "index must be monotonic increasing or decreasing"
+        with pytest.raises(ValueError, match=msg):
+            df_rev.reindex(df.index, method="pad")
+        with pytest.raises(ValueError, match=msg):
+            df_rev.reindex(df.index, method="ffill")
+        with pytest.raises(ValueError, match=msg):
+            df_rev.reindex(df.index, method="bfill")
+        with pytest.raises(ValueError, match=msg):
+            df_rev.reindex(df.index, method="nearest")
+
+    def test_reindex_sparse(self):
+        # https://github.com/pandas-dev/pandas/issues/35286
+        df = DataFrame(
+            {"A": [0, 1], "B": pd.array([0, 1], dtype=pd.SparseDtype("int64", 0))}
+        )
+        result = df.reindex([0, 2])
+        expected = DataFrame(
+            {
+                "A": [0.0, np.nan],
+                "B": pd.array([0.0, np.nan], dtype=pd.SparseDtype("float64", 0.0)),
+            },
+            index=[0, 2],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex(self, float_frame, using_copy_on_write):
+        datetime_series = Series(
+            np.arange(30, dtype=np.float64), index=date_range("2020-01-01", periods=30)
+        )
+
+        newFrame = float_frame.reindex(datetime_series.index)
+
+        for col in newFrame.columns:
+            for idx, val in newFrame[col].items():
+                if idx in float_frame.index:
+                    if np.isnan(val):
+                        assert np.isnan(float_frame[col][idx])
+                    else:
+                        assert val == float_frame[col][idx]
+                else:
+                    assert np.isnan(val)
+
+        for col, series in newFrame.items():
+            tm.assert_index_equal(series.index, newFrame.index)
+        emptyFrame = float_frame.reindex(Index([]))
+        assert len(emptyFrame.index) == 0
+
+        # Cython code should be unit-tested directly
+        nonContigFrame = float_frame.reindex(datetime_series.index[::2])
+
+        for col in nonContigFrame.columns:
+            for idx, val in nonContigFrame[col].items():
+                if idx in float_frame.index:
+                    if np.isnan(val):
+                        assert np.isnan(float_frame[col][idx])
+                    else:
+                        assert val == float_frame[col][idx]
+                else:
+                    assert np.isnan(val)
+
+        for col, series in nonContigFrame.items():
+            tm.assert_index_equal(series.index, nonContigFrame.index)
+
+        # corner cases
+
+        # Same index, copies values but not index if copy=False
+        newFrame = float_frame.reindex(float_frame.index, copy=False)
+        if using_copy_on_write:
+            assert newFrame.index.is_(float_frame.index)
+        else:
+            assert newFrame.index is float_frame.index
+
+        # length zero
+        newFrame = float_frame.reindex([])
+        assert newFrame.empty
+        assert len(newFrame.columns) == len(float_frame.columns)
+
+        # length zero with columns reindexed with non-empty index
+        newFrame = float_frame.reindex([])
+        newFrame = newFrame.reindex(float_frame.index)
+        assert len(newFrame.index) == len(float_frame.index)
+        assert len(newFrame.columns) == len(float_frame.columns)
+
+        # pass non-Index
+        newFrame = float_frame.reindex(list(datetime_series.index))
+        expected = datetime_series.index._with_freq(None)
+        tm.assert_index_equal(newFrame.index, expected)
+
+        # copy with no axes
+        result = float_frame.reindex()
+        tm.assert_frame_equal(result, float_frame)
+        assert result is not float_frame
+
+    def test_reindex_nan(self):
+        df = DataFrame(
+            [[1, 2], [3, 5], [7, 11], [9, 23]],
+            index=[2, np.nan, 1, 5],
+            columns=["joe", "jim"],
+        )
+
+        i, j = [np.nan, 5, 5, np.nan, 1, 2, np.nan], [1, 3, 3, 1, 2, 0, 1]
+        tm.assert_frame_equal(df.reindex(i), df.iloc[j])
+
+        df.index = df.index.astype("object")
+        tm.assert_frame_equal(df.reindex(i), df.iloc[j], check_index_type=False)
+
+        # GH10388
+        df = DataFrame(
+            {
+                "other": ["a", "b", np.nan, "c"],
+                "date": ["2015-03-22", np.nan, "2012-01-08", np.nan],
+                "amount": [2, 3, 4, 5],
+            }
+        )
+
+        df["date"] = pd.to_datetime(df.date)
+        df["delta"] = (pd.to_datetime("2015-06-18") - df["date"]).shift(1)
+
+        left = df.set_index(["delta", "other", "date"]).reset_index()
+        right = df.reindex(columns=["delta", "other", "date", "amount"])
+        tm.assert_frame_equal(left, right)
+
+    def test_reindex_name_remains(self):
+        s = Series(np.random.default_rng(2).random(10))
+        df = DataFrame(s, index=np.arange(len(s)))
+        i = Series(np.arange(10), name="iname")
+
+        df = df.reindex(i)
+        assert df.index.name == "iname"
+
+        df = df.reindex(Index(np.arange(10), name="tmpname"))
+        assert df.index.name == "tmpname"
+
+        s = Series(np.random.default_rng(2).random(10))
+        df = DataFrame(s.T, index=np.arange(len(s)))
+        i = Series(np.arange(10), name="iname")
+        df = df.reindex(columns=i)
+        assert df.columns.name == "iname"
+
+    def test_reindex_int(self, int_frame):
+        smaller = int_frame.reindex(int_frame.index[::2])
+
+        assert smaller["A"].dtype == np.int64
+
+        bigger = smaller.reindex(int_frame.index)
+        assert bigger["A"].dtype == np.float64
+
+        smaller = int_frame.reindex(columns=["A", "B"])
+        assert smaller["A"].dtype == np.int64
+
+    def test_reindex_columns(self, float_frame):
+        new_frame = float_frame.reindex(columns=["A", "B", "E"])
+
+        tm.assert_series_equal(new_frame["B"], float_frame["B"])
+        assert np.isnan(new_frame["E"]).all()
+        assert "C" not in new_frame
+
+        # Length zero
+        new_frame = float_frame.reindex(columns=[])
+        assert new_frame.empty
+
+    def test_reindex_columns_method(self):
+        # GH 14992, reindexing over columns ignored method
+        df = DataFrame(
+            data=[[11, 12, 13], [21, 22, 23], [31, 32, 33]],
+            index=[1, 2, 4],
+            columns=[1, 2, 4],
+            dtype=float,
+        )
+
+        # default method
+        result = df.reindex(columns=range(6))
+        expected = DataFrame(
+            data=[
+                [np.nan, 11, 12, np.nan, 13, np.nan],
+                [np.nan, 21, 22, np.nan, 23, np.nan],
+                [np.nan, 31, 32, np.nan, 33, np.nan],
+            ],
+            index=[1, 2, 4],
+            columns=range(6),
+            dtype=float,
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # method='ffill'
+        result = df.reindex(columns=range(6), method="ffill")
+        expected = DataFrame(
+            data=[
+                [np.nan, 11, 12, 12, 13, 13],
+                [np.nan, 21, 22, 22, 23, 23],
+                [np.nan, 31, 32, 32, 33, 33],
+            ],
+            index=[1, 2, 4],
+            columns=range(6),
+            dtype=float,
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # method='bfill'
+        result = df.reindex(columns=range(6), method="bfill")
+        expected = DataFrame(
+            data=[
+                [11, 11, 12, 13, 13, np.nan],
+                [21, 21, 22, 23, 23, np.nan],
+                [31, 31, 32, 33, 33, np.nan],
+            ],
+            index=[1, 2, 4],
+            columns=range(6),
+            dtype=float,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex_axes(self):
+        # GH 3317, reindexing by both axes loses freq of the index
+        df = DataFrame(
+            np.ones((3, 3)),
+            index=[datetime(2012, 1, 1), datetime(2012, 1, 2), datetime(2012, 1, 3)],
+            columns=["a", "b", "c"],
+        )
+        time_freq = date_range("2012-01-01", "2012-01-03", freq="d")
+        some_cols = ["a", "b"]
+
+        index_freq = df.reindex(index=time_freq).index.freq
+        both_freq = df.reindex(index=time_freq, columns=some_cols).index.freq
+        seq_freq = df.reindex(index=time_freq).reindex(columns=some_cols).index.freq
+        assert index_freq == both_freq
+        assert index_freq == seq_freq
+
+    def test_reindex_fill_value(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
+
+        # axis=0
+        result = df.reindex(list(range(15)))
+        assert np.isnan(result.values[-5:]).all()
+
+        result = df.reindex(range(15), fill_value=0)
+        expected = df.reindex(range(15)).fillna(0)
+        tm.assert_frame_equal(result, expected)
+
+        # axis=1
+        result = df.reindex(columns=range(5), fill_value=0.0)
+        expected = df.copy()
+        expected[4] = 0.0
+        tm.assert_frame_equal(result, expected)
+
+        result = df.reindex(columns=range(5), fill_value=0)
+        expected = df.copy()
+        expected[4] = 0
+        tm.assert_frame_equal(result, expected)
+
+        result = df.reindex(columns=range(5), fill_value="foo")
+        expected = df.copy()
+        expected[4] = "foo"
+        tm.assert_frame_equal(result, expected)
+
+        # other dtypes
+        df["foo"] = "foo"
+        result = df.reindex(range(15), fill_value="0")
+        expected = df.reindex(range(15)).fillna("0")
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex_uint_dtypes_fill_value(self, any_unsigned_int_numpy_dtype):
+        # GH#48184
+        df = DataFrame({"a": [1, 2], "b": [1, 2]}, dtype=any_unsigned_int_numpy_dtype)
+        result = df.reindex(columns=list("abcd"), index=[0, 1, 2, 3], fill_value=10)
+        expected = DataFrame(
+            {"a": [1, 2, 10, 10], "b": [1, 2, 10, 10], "c": 10, "d": 10},
+            dtype=any_unsigned_int_numpy_dtype,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex_single_column_ea_index_and_columns(self, any_numeric_ea_dtype):
+        # GH#48190
+        df = DataFrame({"a": [1, 2]}, dtype=any_numeric_ea_dtype)
+        result = df.reindex(columns=list("ab"), index=[0, 1, 2], fill_value=10)
+        expected = DataFrame(
+            {"a": Series([1, 2, 10], dtype=any_numeric_ea_dtype), "b": 10}
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex_dups(self):
+        # GH4746, reindex on duplicate index error messages
+        arr = np.random.default_rng(2).standard_normal(10)
+        df = DataFrame(arr, index=[1, 2, 3, 4, 5, 1, 2, 3, 4, 5])
+
+        # set index is ok
+        result = df.copy()
+        result.index = list(range(len(df)))
+        expected = DataFrame(arr, index=list(range(len(df))))
+        tm.assert_frame_equal(result, expected)
+
+        # reindex fails
+        msg = "cannot reindex on an axis with duplicate labels"
+        with pytest.raises(ValueError, match=msg):
+            df.reindex(index=list(range(len(df))))
+
+    def test_reindex_with_duplicate_columns(self):
+        # reindex is invalid!
+        df = DataFrame(
+            [[1, 5, 7.0], [1, 5, 7.0], [1, 5, 7.0]], columns=["bar", "a", "a"]
+        )
+        msg = "cannot reindex on an axis with duplicate labels"
+        with pytest.raises(ValueError, match=msg):
+            df.reindex(columns=["bar"])
+        with pytest.raises(ValueError, match=msg):
+            df.reindex(columns=["bar", "foo"])
+
+    def test_reindex_axis_style(self):
+        # https://github.com/pandas-dev/pandas/issues/12392
+        df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+        expected = DataFrame(
+            {"A": [1, 2, np.nan], "B": [4, 5, np.nan]}, index=[0, 1, 3]
+        )
+        result = df.reindex([0, 1, 3])
+        tm.assert_frame_equal(result, expected)
+
+        result = df.reindex([0, 1, 3], axis=0)
+        tm.assert_frame_equal(result, expected)
+
+        result = df.reindex([0, 1, 3], axis="index")
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex_positional_raises(self):
+        # https://github.com/pandas-dev/pandas/issues/12392
+        # Enforced in 2.0
+        df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+        msg = r"reindex\(\) takes from 1 to 2 positional arguments but 3 were given"
+        with pytest.raises(TypeError, match=msg):
+            df.reindex([0, 1], ["A", "B", "C"])
+
+    def test_reindex_axis_style_raises(self):
+        # https://github.com/pandas-dev/pandas/issues/12392
+        df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
+            df.reindex([0, 1], columns=["A"], axis=1)
+
+        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
+            df.reindex([0, 1], columns=["A"], axis="index")
+
+        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
+            df.reindex(index=[0, 1], axis="index")
+
+        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
+            df.reindex(index=[0, 1], axis="columns")
+
+        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
+            df.reindex(columns=[0, 1], axis="columns")
+
+        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
+            df.reindex(index=[0, 1], columns=[0, 1], axis="columns")
+
+        with pytest.raises(TypeError, match="Cannot specify all"):
+            df.reindex(labels=[0, 1], index=[0], columns=["A"])
+
+        # Mixing styles
+        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
+            df.reindex(index=[0, 1], axis="index")
+
+        with pytest.raises(TypeError, match="Cannot specify both 'axis'"):
+            df.reindex(index=[0, 1], axis="columns")
+
+        # Duplicates
+        with pytest.raises(TypeError, match="multiple values"):
+            df.reindex([0, 1], labels=[0, 1])
+
+    def test_reindex_single_named_indexer(self):
+        # https://github.com/pandas-dev/pandas/issues/12392
+        df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3]})
+        result = df.reindex([0, 1], columns=["A"])
+        expected = DataFrame({"A": [1, 2]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex_api_equivalence(self):
+        # https://github.com/pandas-dev/pandas/issues/12392
+        # equivalence of the labels/axis and index/columns API's
+        df = DataFrame(
+            [[1, 2, 3], [3, 4, 5], [5, 6, 7]],
+            index=["a", "b", "c"],
+            columns=["d", "e", "f"],
+        )
+
+        res1 = df.reindex(["b", "a"])
+        res2 = df.reindex(index=["b", "a"])
+        res3 = df.reindex(labels=["b", "a"])
+        res4 = df.reindex(labels=["b", "a"], axis=0)
+        res5 = df.reindex(["b", "a"], axis=0)
+        for res in [res2, res3, res4, res5]:
+            tm.assert_frame_equal(res1, res)
+
+        res1 = df.reindex(columns=["e", "d"])
+        res2 = df.reindex(["e", "d"], axis=1)
+        res3 = df.reindex(labels=["e", "d"], axis=1)
+        for res in [res2, res3]:
+            tm.assert_frame_equal(res1, res)
+
+        res1 = df.reindex(index=["b", "a"], columns=["e", "d"])
+        res2 = df.reindex(columns=["e", "d"], index=["b", "a"])
+        res3 = df.reindex(labels=["b", "a"], axis=0).reindex(labels=["e", "d"], axis=1)
+        for res in [res2, res3]:
+            tm.assert_frame_equal(res1, res)
+
+    def test_reindex_boolean(self):
+        frame = DataFrame(
+            np.ones((10, 2), dtype=bool), index=np.arange(0, 20, 2), columns=[0, 2]
+        )
+
+        reindexed = frame.reindex(np.arange(10))
+        assert reindexed.values.dtype == np.object_
+        assert isna(reindexed[0][1])
+
+        reindexed = frame.reindex(columns=range(3))
+        assert reindexed.values.dtype == np.object_
+        assert isna(reindexed[1]).all()
+
+    def test_reindex_objects(self, float_string_frame):
+        reindexed = float_string_frame.reindex(columns=["foo", "A", "B"])
+        assert "foo" in reindexed
+
+        reindexed = float_string_frame.reindex(columns=["A", "B"])
+        assert "foo" not in reindexed
+
+    def test_reindex_corner(self, int_frame):
+        index = Index(["a", "b", "c"])
+        dm = DataFrame({}).reindex(index=[1, 2, 3])
+        reindexed = dm.reindex(columns=index)
+        tm.assert_index_equal(reindexed.columns, index)
+
+        # ints are weird
+        smaller = int_frame.reindex(columns=["A", "B", "E"])
+        assert smaller["E"].dtype == np.float64
+
+    def test_reindex_with_nans(self):
+        df = DataFrame(
+            [[1, 2], [3, 4], [np.nan, np.nan], [7, 8], [9, 10]],
+            columns=["a", "b"],
+            index=[100.0, 101.0, np.nan, 102.0, 103.0],
+        )
+
+        result = df.reindex(index=[101.0, 102.0, 103.0])
+        expected = df.iloc[[1, 3, 4]]
+        tm.assert_frame_equal(result, expected)
+
+        result = df.reindex(index=[103.0])
+        expected = df.iloc[[4]]
+        tm.assert_frame_equal(result, expected)
+
+        result = df.reindex(index=[101.0])
+        expected = df.iloc[[1]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex_multi(self):
+        df = DataFrame(np.random.default_rng(2).standard_normal((3, 3)))
+
+        result = df.reindex(index=range(4), columns=range(4))
+        expected = df.reindex(list(range(4))).reindex(columns=range(4))
+
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(np.random.default_rng(2).integers(0, 10, (3, 3)))
+
+        result = df.reindex(index=range(4), columns=range(4))
+        expected = df.reindex(list(range(4))).reindex(columns=range(4))
+
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(np.random.default_rng(2).integers(0, 10, (3, 3)))
+
+        result = df.reindex(index=range(2), columns=range(2))
+        expected = df.reindex(range(2)).reindex(columns=range(2))
+
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 3)) + 1j,
+            columns=["a", "b", "c"],
+        )
+
+        result = df.reindex(index=[0, 1], columns=["a", "b"])
+        expected = df.reindex([0, 1]).reindex(columns=["a", "b"])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex_multi_categorical_time(self):
+        # https://github.com/pandas-dev/pandas/issues/21390
+        midx = MultiIndex.from_product(
+            [
+                Categorical(["a", "b", "c"]),
+                Categorical(date_range("2012-01-01", periods=3, freq="h")),
+            ]
+        )
+        df = DataFrame({"a": range(len(midx))}, index=midx)
+        df2 = df.iloc[[0, 1, 2, 3, 4, 5, 6, 8]]
+
+        result = df2.reindex(midx)
+        expected = DataFrame({"a": [0, 1, 2, 3, 4, 5, 6, np.nan, 8]}, index=midx)
+        tm.assert_frame_equal(result, expected)
+
+    def test_reindex_with_categoricalindex(self):
+        df = DataFrame(
+            {
+                "A": np.arange(3, dtype="int64"),
+            },
+            index=CategoricalIndex(
+                list("abc"), dtype=CategoricalDtype(list("cabe")), name="B"
+            ),
+        )
+
+        # reindexing
+        # convert to a regular index
+        result = df.reindex(["a", "b", "e"])
+        expected = DataFrame({"A": [0, 1, np.nan], "B": Series(list("abe"))}).set_index(
+            "B"
+        )
+        tm.assert_frame_equal(result, expected, check_index_type=True)
+
+        result = df.reindex(["a", "b"])
+        expected = DataFrame({"A": [0, 1], "B": Series(list("ab"))}).set_index("B")
+        tm.assert_frame_equal(result, expected, check_index_type=True)
+
+        result = df.reindex(["e"])
+        expected = DataFrame({"A": [np.nan], "B": Series(["e"])}).set_index("B")
+        tm.assert_frame_equal(result, expected, check_index_type=True)
+
+        result = df.reindex(["d"])
+        expected = DataFrame({"A": [np.nan], "B": Series(["d"])}).set_index("B")
+        tm.assert_frame_equal(result, expected, check_index_type=True)
+
+        # since we are actually reindexing with a Categorical
+        # then return a Categorical
+        cats = list("cabe")
+
+        result = df.reindex(Categorical(["a", "e"], categories=cats))
+        expected = DataFrame(
+            {"A": [0, np.nan], "B": Series(list("ae")).astype(CategoricalDtype(cats))}
+        ).set_index("B")
+        tm.assert_frame_equal(result, expected, check_index_type=True)
+
+        result = df.reindex(Categorical(["a"], categories=cats))
+        expected = DataFrame(
+            {"A": [0], "B": Series(list("a")).astype(CategoricalDtype(cats))}
+        ).set_index("B")
+        tm.assert_frame_equal(result, expected, check_index_type=True)
+
+        result = df.reindex(["a", "b", "e"])
+        expected = DataFrame({"A": [0, 1, np.nan], "B": Series(list("abe"))}).set_index(
+            "B"
+        )
+        tm.assert_frame_equal(result, expected, check_index_type=True)
+
+        result = df.reindex(["a", "b"])
+        expected = DataFrame({"A": [0, 1], "B": Series(list("ab"))}).set_index("B")
+        tm.assert_frame_equal(result, expected, check_index_type=True)
+
+        result = df.reindex(["e"])
+        expected = DataFrame({"A": [np.nan], "B": Series(["e"])}).set_index("B")
+        tm.assert_frame_equal(result, expected, check_index_type=True)
+
+        # give back the type of categorical that we received
+        result = df.reindex(Categorical(["a", "e"], categories=cats, ordered=True))
+        expected = DataFrame(
+            {
+                "A": [0, np.nan],
+                "B": Series(list("ae")).astype(CategoricalDtype(cats, ordered=True)),
+            }
+        ).set_index("B")
+        tm.assert_frame_equal(result, expected, check_index_type=True)
+
+        result = df.reindex(Categorical(["a", "d"], categories=["a", "d"]))
+        expected = DataFrame(
+            {
+                "A": [0, np.nan],
+                "B": Series(list("ad")).astype(CategoricalDtype(["a", "d"])),
+            }
+        ).set_index("B")
+        tm.assert_frame_equal(result, expected, check_index_type=True)
+
+        df2 = DataFrame(
+            {
+                "A": np.arange(6, dtype="int64"),
+            },
+            index=CategoricalIndex(
+                list("aabbca"), dtype=CategoricalDtype(list("cabe")), name="B"
+            ),
+        )
+        # passed duplicate indexers are not allowed
+        msg = "cannot reindex on an axis with duplicate labels"
+        with pytest.raises(ValueError, match=msg):
+            df2.reindex(["a", "b"])
+
+        # args NotImplemented ATM
+        msg = r"argument {} is not implemented for CategoricalIndex\.reindex"
+        with pytest.raises(NotImplementedError, match=msg.format("method")):
+            df.reindex(["a"], method="ffill")
+        with pytest.raises(NotImplementedError, match=msg.format("level")):
+            df.reindex(["a"], level=1)
+        with pytest.raises(NotImplementedError, match=msg.format("limit")):
+            df.reindex(["a"], limit=2)
+
+    def test_reindex_signature(self):
+        sig = inspect.signature(DataFrame.reindex)
+        parameters = set(sig.parameters)
+        assert parameters == {
+            "self",
+            "labels",
+            "index",
+            "columns",
+            "axis",
+            "limit",
+            "copy",
+            "level",
+            "method",
+            "fill_value",
+            "tolerance",
+        }
+
+    def test_reindex_multiindex_ffill_added_rows(self):
+        # GH#23693
+        # reindex added rows with nan values even when fill method was specified
+        mi = MultiIndex.from_tuples([("a", "b"), ("d", "e")])
+        df = DataFrame([[0, 7], [3, 4]], index=mi, columns=["x", "y"])
+        mi2 = MultiIndex.from_tuples([("a", "b"), ("d", "e"), ("h", "i")])
+        result = df.reindex(mi2, axis=0, method="ffill")
+        expected = DataFrame([[0, 7], [3, 4], [3, 4]], index=mi2, columns=["x", "y"])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "kwargs",
+        [
+            {"method": "pad", "tolerance": timedelta(seconds=9)},
+            {"method": "backfill", "tolerance": timedelta(seconds=9)},
+            {"method": "nearest"},
+            {"method": None},
+        ],
+    )
+    def test_reindex_empty_frame(self, kwargs):
+        # GH#27315
+        idx = date_range(start="2020", freq="30s", periods=3)
+        df = DataFrame([], index=Index([], name="time"), columns=["a"])
+        result = df.reindex(idx, **kwargs)
+        expected = DataFrame({"a": [np.nan] * 3}, index=idx, dtype=object)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "src_idx",
+        [
+            Index([]),
+            CategoricalIndex([]),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "cat_idx",
+        [
+            # No duplicates
+            Index([]),
+            CategoricalIndex([]),
+            Index(["A", "B"]),
+            CategoricalIndex(["A", "B"]),
+            # Duplicates: GH#38906
+            Index(["A", "A"]),
+            CategoricalIndex(["A", "A"]),
+        ],
+    )
+    def test_reindex_empty(self, src_idx, cat_idx):
+        df = DataFrame(columns=src_idx, index=["K"], dtype="f8")
+
+        result = df.reindex(columns=cat_idx)
+        expected = DataFrame(index=["K"], columns=cat_idx, dtype="f8")
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["m8[ns]", "M8[ns]"])
+    def test_reindex_datetimelike_to_object(self, dtype):
+        # GH#39755 dont cast dt64/td64 to ints
+        mi = MultiIndex.from_product([list("ABCDE"), range(2)])
+
+        dti = date_range("2016-01-01", periods=10)
+        fv = np.timedelta64("NaT", "ns")
+        if dtype == "m8[ns]":
+            dti = dti - dti[0]
+            fv = np.datetime64("NaT", "ns")
+
+        ser = Series(dti, index=mi)
+        ser[::3] = pd.NaT
+
+        df = ser.unstack()
+
+        index = df.index.append(Index([1]))
+        columns = df.columns.append(Index(["foo"]))
+
+        res = df.reindex(index=index, columns=columns, fill_value=fv)
+
+        expected = DataFrame(
+            {
+                0: df[0].tolist() + [fv],
+                1: df[1].tolist() + [fv],
+                "foo": np.array(["NaT"] * 6, dtype=fv.dtype),
+            },
+            index=index,
+        )
+        assert (res.dtypes[[0, 1]] == object).all()
+        assert res.iloc[0, 0] is pd.NaT
+        assert res.iloc[-1, 0] is fv
+        assert res.iloc[-1, 1] is fv
+        tm.assert_frame_equal(res, expected)
+
+    @pytest.mark.parametrize(
+        "index_df,index_res,index_exp",
+        [
+            (
+                CategoricalIndex([], categories=["A"]),
+                Index(["A"]),
+                Index(["A"]),
+            ),
+            (
+                CategoricalIndex([], categories=["A"]),
+                Index(["B"]),
+                Index(["B"]),
+            ),
+            (
+                CategoricalIndex([], categories=["A"]),
+                CategoricalIndex(["A"]),
+                CategoricalIndex(["A"]),
+            ),
+            (
+                CategoricalIndex([], categories=["A"]),
+                CategoricalIndex(["B"]),
+                CategoricalIndex(["B"]),
+            ),
+        ],
+    )
+    def test_reindex_not_category(self, index_df, index_res, index_exp):
+        # GH#28690
+        df = DataFrame(index=index_df)
+        result = df.reindex(index=index_res)
+        expected = DataFrame(index=index_exp)
+        tm.assert_frame_equal(result, expected)
+
+    def test_invalid_method(self):
+        df = DataFrame({"A": [1, np.nan, 2]})
+
+        msg = "Invalid fill method"
+        with pytest.raises(ValueError, match=msg):
+            df.reindex([1, 0, 2], method="asfreq")

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_rename.py

@@ -0,0 +1,415 @@
+from collections import ChainMap
+import inspect
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    merge,
+)
+import pandas._testing as tm
+
+
+class TestRename:
+    def test_rename_signature(self):
+        sig = inspect.signature(DataFrame.rename)
+        parameters = set(sig.parameters)
+        assert parameters == {
+            "self",
+            "mapper",
+            "index",
+            "columns",
+            "axis",
+            "inplace",
+            "copy",
+            "level",
+            "errors",
+        }
+
+    def test_rename_mi(self, frame_or_series):
+        obj = frame_or_series(
+            [11, 21, 31],
+            index=MultiIndex.from_tuples([("A", x) for x in ["a", "B", "c"]]),
+        )
+        obj.rename(str.lower)
+
+    def test_rename(self, float_frame):
+        mapping = {"A": "a", "B": "b", "C": "c", "D": "d"}
+
+        renamed = float_frame.rename(columns=mapping)
+        renamed2 = float_frame.rename(columns=str.lower)
+
+        tm.assert_frame_equal(renamed, renamed2)
+        tm.assert_frame_equal(
+            renamed2.rename(columns=str.upper), float_frame, check_names=False
+        )
+
+        # index
+        data = {"A": {"foo": 0, "bar": 1}}
+
+        df = DataFrame(data)
+        renamed = df.rename(index={"foo": "bar", "bar": "foo"})
+        tm.assert_index_equal(renamed.index, Index(["bar", "foo"]))
+
+        renamed = df.rename(index=str.upper)
+        tm.assert_index_equal(renamed.index, Index(["FOO", "BAR"]))
+
+        # have to pass something
+        with pytest.raises(TypeError, match="must pass an index to rename"):
+            float_frame.rename()
+
+        # partial columns
+        renamed = float_frame.rename(columns={"C": "foo", "D": "bar"})
+        tm.assert_index_equal(renamed.columns, Index(["A", "B", "foo", "bar"]))
+
+        # other axis
+        renamed = float_frame.T.rename(index={"C": "foo", "D": "bar"})
+        tm.assert_index_equal(renamed.index, Index(["A", "B", "foo", "bar"]))
+
+        # index with name
+        index = Index(["foo", "bar"], name="name")
+        renamer = DataFrame(data, index=index)
+        renamed = renamer.rename(index={"foo": "bar", "bar": "foo"})
+        tm.assert_index_equal(renamed.index, Index(["bar", "foo"], name="name"))
+        assert renamed.index.name == renamer.index.name
+
+    @pytest.mark.parametrize(
+        "args,kwargs",
+        [
+            ((ChainMap({"A": "a"}, {"B": "b"}),), {"axis": "columns"}),
+            ((), {"columns": ChainMap({"A": "a"}, {"B": "b"})}),
+        ],
+    )
+    def test_rename_chainmap(self, args, kwargs):
+        # see gh-23859
+        colAData = range(1, 11)
+        colBdata = np.random.default_rng(2).standard_normal(10)
+
+        df = DataFrame({"A": colAData, "B": colBdata})
+        result = df.rename(*args, **kwargs)
+
+        expected = DataFrame({"a": colAData, "b": colBdata})
+        tm.assert_frame_equal(result, expected)
+
+    def test_rename_multiindex(self):
+        tuples_index = [("foo1", "bar1"), ("foo2", "bar2")]
+        tuples_columns = [("fizz1", "buzz1"), ("fizz2", "buzz2")]
+        index = MultiIndex.from_tuples(tuples_index, names=["foo", "bar"])
+        columns = MultiIndex.from_tuples(tuples_columns, names=["fizz", "buzz"])
+        df = DataFrame([(0, 0), (1, 1)], index=index, columns=columns)
+
+        #
+        # without specifying level -> across all levels
+
+        renamed = df.rename(
+            index={"foo1": "foo3", "bar2": "bar3"},
+            columns={"fizz1": "fizz3", "buzz2": "buzz3"},
+        )
+        new_index = MultiIndex.from_tuples(
+            [("foo3", "bar1"), ("foo2", "bar3")], names=["foo", "bar"]
+        )
+        new_columns = MultiIndex.from_tuples(
+            [("fizz3", "buzz1"), ("fizz2", "buzz3")], names=["fizz", "buzz"]
+        )
+        tm.assert_index_equal(renamed.index, new_index)
+        tm.assert_index_equal(renamed.columns, new_columns)
+        assert renamed.index.names == df.index.names
+        assert renamed.columns.names == df.columns.names
+
+        #
+        # with specifying a level (GH13766)
+
+        # dict
+        new_columns = MultiIndex.from_tuples(
+            [("fizz3", "buzz1"), ("fizz2", "buzz2")], names=["fizz", "buzz"]
+        )
+        renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level=0)
+        tm.assert_index_equal(renamed.columns, new_columns)
+        renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level="fizz")
+        tm.assert_index_equal(renamed.columns, new_columns)
+
+        new_columns = MultiIndex.from_tuples(
+            [("fizz1", "buzz1"), ("fizz2", "buzz3")], names=["fizz", "buzz"]
+        )
+        renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level=1)
+        tm.assert_index_equal(renamed.columns, new_columns)
+        renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level="buzz")
+        tm.assert_index_equal(renamed.columns, new_columns)
+
+        # function
+        func = str.upper
+        new_columns = MultiIndex.from_tuples(
+            [("FIZZ1", "buzz1"), ("FIZZ2", "buzz2")], names=["fizz", "buzz"]
+        )
+        renamed = df.rename(columns=func, level=0)
+        tm.assert_index_equal(renamed.columns, new_columns)
+        renamed = df.rename(columns=func, level="fizz")
+        tm.assert_index_equal(renamed.columns, new_columns)
+
+        new_columns = MultiIndex.from_tuples(
+            [("fizz1", "BUZZ1"), ("fizz2", "BUZZ2")], names=["fizz", "buzz"]
+        )
+        renamed = df.rename(columns=func, level=1)
+        tm.assert_index_equal(renamed.columns, new_columns)
+        renamed = df.rename(columns=func, level="buzz")
+        tm.assert_index_equal(renamed.columns, new_columns)
+
+        # index
+        new_index = MultiIndex.from_tuples(
+            [("foo3", "bar1"), ("foo2", "bar2")], names=["foo", "bar"]
+        )
+        renamed = df.rename(index={"foo1": "foo3", "bar2": "bar3"}, level=0)
+        tm.assert_index_equal(renamed.index, new_index)
+
+    def test_rename_nocopy(self, float_frame, using_copy_on_write, warn_copy_on_write):
+        renamed = float_frame.rename(columns={"C": "foo"}, copy=False)
+
+        assert np.shares_memory(renamed["foo"]._values, float_frame["C"]._values)
+
+        with tm.assert_cow_warning(warn_copy_on_write):
+            renamed.loc[:, "foo"] = 1.0
+        if using_copy_on_write:
+            assert not (float_frame["C"] == 1.0).all()
+        else:
+            assert (float_frame["C"] == 1.0).all()
+
+    def test_rename_inplace(self, float_frame):
+        float_frame.rename(columns={"C": "foo"})
+        assert "C" in float_frame
+        assert "foo" not in float_frame
+
+        c_values = float_frame["C"]
+        float_frame = float_frame.copy()
+        return_value = float_frame.rename(columns={"C": "foo"}, inplace=True)
+        assert return_value is None
+
+        assert "C" not in float_frame
+        assert "foo" in float_frame
+        # GH 44153
+        # Used to be id(float_frame["foo"]) != c_id, but flaky in the CI
+        assert float_frame["foo"] is not c_values
+
+    def test_rename_bug(self):
+        # GH 5344
+        # rename set ref_locs, and set_index was not resetting
+        df = DataFrame({0: ["foo", "bar"], 1: ["bah", "bas"], 2: [1, 2]})
+        df = df.rename(columns={0: "a"})
+        df = df.rename(columns={1: "b"})
+        df = df.set_index(["a", "b"])
+        df.columns = ["2001-01-01"]
+        expected = DataFrame(
+            [[1], [2]],
+            index=MultiIndex.from_tuples(
+                [("foo", "bah"), ("bar", "bas")], names=["a", "b"]
+            ),
+            columns=["2001-01-01"],
+        )
+        tm.assert_frame_equal(df, expected)
+
+    def test_rename_bug2(self):
+        # GH 19497
+        # rename was changing Index to MultiIndex if Index contained tuples
+
+        df = DataFrame(data=np.arange(3), index=[(0, 0), (1, 1), (2, 2)], columns=["a"])
+        df = df.rename({(1, 1): (5, 4)}, axis="index")
+        expected = DataFrame(
+            data=np.arange(3), index=[(0, 0), (5, 4), (2, 2)], columns=["a"]
+        )
+        tm.assert_frame_equal(df, expected)
+
+    def test_rename_errors_raises(self):
+        df = DataFrame(columns=["A", "B", "C", "D"])
+        with pytest.raises(KeyError, match="'E'] not found in axis"):
+            df.rename(columns={"A": "a", "E": "e"}, errors="raise")
+
+    @pytest.mark.parametrize(
+        "mapper, errors, expected_columns",
+        [
+            ({"A": "a", "E": "e"}, "ignore", ["a", "B", "C", "D"]),
+            ({"A": "a"}, "raise", ["a", "B", "C", "D"]),
+            (str.lower, "raise", ["a", "b", "c", "d"]),
+        ],
+    )
+    def test_rename_errors(self, mapper, errors, expected_columns):
+        # GH 13473
+        # rename now works with errors parameter
+        df = DataFrame(columns=["A", "B", "C", "D"])
+        result = df.rename(columns=mapper, errors=errors)
+        expected = DataFrame(columns=expected_columns)
+        tm.assert_frame_equal(result, expected)
+
+    def test_rename_objects(self, float_string_frame):
+        renamed = float_string_frame.rename(columns=str.upper)
+
+        assert "FOO" in renamed
+        assert "foo" not in renamed
+
+    def test_rename_axis_style(self):
+        # https://github.com/pandas-dev/pandas/issues/12392
+        df = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["X", "Y"])
+        expected = DataFrame({"a": [1, 2], "b": [1, 2]}, index=["X", "Y"])
+
+        result = df.rename(str.lower, axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        result = df.rename(str.lower, axis="columns")
+        tm.assert_frame_equal(result, expected)
+
+        result = df.rename({"A": "a", "B": "b"}, axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        result = df.rename({"A": "a", "B": "b"}, axis="columns")
+        tm.assert_frame_equal(result, expected)
+
+        # Index
+        expected = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["x", "y"])
+        result = df.rename(str.lower, axis=0)
+        tm.assert_frame_equal(result, expected)
+
+        result = df.rename(str.lower, axis="index")
+        tm.assert_frame_equal(result, expected)
+
+        result = df.rename({"X": "x", "Y": "y"}, axis=0)
+        tm.assert_frame_equal(result, expected)
+
+        result = df.rename({"X": "x", "Y": "y"}, axis="index")
+        tm.assert_frame_equal(result, expected)
+
+        result = df.rename(mapper=str.lower, axis="index")
+        tm.assert_frame_equal(result, expected)
+
+    def test_rename_mapper_multi(self):
+        df = DataFrame({"A": ["a", "b"], "B": ["c", "d"], "C": [1, 2]}).set_index(
+            ["A", "B"]
+        )
+        result = df.rename(str.upper)
+        expected = df.rename(index=str.upper)
+        tm.assert_frame_equal(result, expected)
+
+    def test_rename_positional_named(self):
+        # https://github.com/pandas-dev/pandas/issues/12392
+        df = DataFrame({"a": [1, 2], "b": [1, 2]}, index=["X", "Y"])
+        result = df.rename(index=str.lower, columns=str.upper)
+        expected = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["x", "y"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_rename_axis_style_raises(self):
+        # see gh-12392
+        df = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["0", "1"])
+
+        # Named target and axis
+        over_spec_msg = "Cannot specify both 'axis' and any of 'index' or 'columns'"
+        with pytest.raises(TypeError, match=over_spec_msg):
+            df.rename(index=str.lower, axis=1)
+
+        with pytest.raises(TypeError, match=over_spec_msg):
+            df.rename(index=str.lower, axis="columns")
+
+        with pytest.raises(TypeError, match=over_spec_msg):
+            df.rename(columns=str.lower, axis="columns")
+
+        with pytest.raises(TypeError, match=over_spec_msg):
+            df.rename(index=str.lower, axis=0)
+
+        # Multiple targets and axis
+        with pytest.raises(TypeError, match=over_spec_msg):
+            df.rename(str.lower, index=str.lower, axis="columns")
+
+        # Too many targets
+        over_spec_msg = "Cannot specify both 'mapper' and any of 'index' or 'columns'"
+        with pytest.raises(TypeError, match=over_spec_msg):
+            df.rename(str.lower, index=str.lower, columns=str.lower)
+
+        # Duplicates
+        with pytest.raises(TypeError, match="multiple values"):
+            df.rename(id, mapper=id)
+
+    def test_rename_positional_raises(self):
+        # GH 29136
+        df = DataFrame(columns=["A", "B"])
+        msg = r"rename\(\) takes from 1 to 2 positional arguments"
+
+        with pytest.raises(TypeError, match=msg):
+            df.rename(None, str.lower)
+
+    def test_rename_no_mappings_raises(self):
+        # GH 29136
+        df = DataFrame([[1]])
+        msg = "must pass an index to rename"
+        with pytest.raises(TypeError, match=msg):
+            df.rename()
+
+        with pytest.raises(TypeError, match=msg):
+            df.rename(None, index=None)
+
+        with pytest.raises(TypeError, match=msg):
+            df.rename(None, columns=None)
+
+        with pytest.raises(TypeError, match=msg):
+            df.rename(None, columns=None, index=None)
+
+    def test_rename_mapper_and_positional_arguments_raises(self):
+        # GH 29136
+        df = DataFrame([[1]])
+        msg = "Cannot specify both 'mapper' and any of 'index' or 'columns'"
+        with pytest.raises(TypeError, match=msg):
+            df.rename({}, index={})
+
+        with pytest.raises(TypeError, match=msg):
+            df.rename({}, columns={})
+
+        with pytest.raises(TypeError, match=msg):
+            df.rename({}, columns={}, index={})
+
+    def test_rename_with_duplicate_columns(self):
+        # GH#4403
+        df4 = DataFrame(
+            {"RT": [0.0454], "TClose": [22.02], "TExg": [0.0422]},
+            index=MultiIndex.from_tuples(
+                [(600809, 20130331)], names=["STK_ID", "RPT_Date"]
+            ),
+        )
+
+        df5 = DataFrame(
+            {
+                "RPT_Date": [20120930, 20121231, 20130331],
+                "STK_ID": [600809] * 3,
+                "STK_Name": ["饡驦", "饡驦", "饡驦"],
+                "TClose": [38.05, 41.66, 30.01],
+            },
+            index=MultiIndex.from_tuples(
+                [(600809, 20120930), (600809, 20121231), (600809, 20130331)],
+                names=["STK_ID", "RPT_Date"],
+            ),
+        )
+        # TODO: can we construct this without merge?
+        k = merge(df4, df5, how="inner", left_index=True, right_index=True)
+        result = k.rename(columns={"TClose_x": "TClose", "TClose_y": "QT_Close"})
+
+        expected = DataFrame(
+            [[0.0454, 22.02, 0.0422, 20130331, 600809, "饡驦", 30.01]],
+            columns=[
+                "RT",
+                "TClose",
+                "TExg",
+                "RPT_Date",
+                "STK_ID",
+                "STK_Name",
+                "QT_Close",
+            ],
+        ).set_index(["STK_ID", "RPT_Date"], drop=False)
+        tm.assert_frame_equal(result, expected)
+
+    def test_rename_boolean_index(self):
+        df = DataFrame(np.arange(15).reshape(3, 5), columns=[False, True, 2, 3, 4])
+        mapper = {0: "foo", 1: "bar", 2: "bah"}
+        res = df.rename(index=mapper)
+        exp = DataFrame(
+            np.arange(15).reshape(3, 5),
+            columns=[False, True, 2, 3, 4],
+            index=["foo", "bar", "bah"],
+        )
+        tm.assert_frame_equal(res, exp)

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_reorder_levels.py

@@ -0,0 +1,74 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+class TestReorderLevels:
+    def test_reorder_levels(self, frame_or_series):
+        index = MultiIndex(
+            levels=[["bar"], ["one", "two", "three"], [0, 1]],
+            codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
+            names=["L0", "L1", "L2"],
+        )
+        df = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=index)
+        obj = tm.get_obj(df, frame_or_series)
+
+        # no change, position
+        result = obj.reorder_levels([0, 1, 2])
+        tm.assert_equal(obj, result)
+
+        # no change, labels
+        result = obj.reorder_levels(["L0", "L1", "L2"])
+        tm.assert_equal(obj, result)
+
+        # rotate, position
+        result = obj.reorder_levels([1, 2, 0])
+        e_idx = MultiIndex(
+            levels=[["one", "two", "three"], [0, 1], ["bar"]],
+            codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0]],
+            names=["L1", "L2", "L0"],
+        )
+        expected = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=e_idx)
+        expected = tm.get_obj(expected, frame_or_series)
+        tm.assert_equal(result, expected)
+
+        result = obj.reorder_levels([0, 0, 0])
+        e_idx = MultiIndex(
+            levels=[["bar"], ["bar"], ["bar"]],
+            codes=[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]],
+            names=["L0", "L0", "L0"],
+        )
+        expected = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=e_idx)
+        expected = tm.get_obj(expected, frame_or_series)
+        tm.assert_equal(result, expected)
+
+        result = obj.reorder_levels(["L0", "L0", "L0"])
+        tm.assert_equal(result, expected)
+
+    def test_reorder_levels_swaplevel_equivalence(
+        self, multiindex_year_month_day_dataframe_random_data
+    ):
+        ymd = multiindex_year_month_day_dataframe_random_data
+
+        result = ymd.reorder_levels(["month", "day", "year"])
+        expected = ymd.swaplevel(0, 1).swaplevel(1, 2)
+        tm.assert_frame_equal(result, expected)
+
+        result = ymd["A"].reorder_levels(["month", "day", "year"])
+        expected = ymd["A"].swaplevel(0, 1).swaplevel(1, 2)
+        tm.assert_series_equal(result, expected)
+
+        result = ymd.T.reorder_levels(["month", "day", "year"], axis=1)
+        expected = ymd.T.swaplevel(0, 1, axis=1).swaplevel(1, 2, axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        with pytest.raises(TypeError, match="hierarchical axis"):
+            ymd.reorder_levels([1, 2], axis=1)
+
+        with pytest.raises(IndexError, match="Too many levels"):
+            ymd.index.reorder_levels([1, 2, 3])

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_shift.py

@@ -0,0 +1,764 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    DataFrame,
+    Index,
+    NaT,
+    Series,
+    date_range,
+    offsets,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameShift:
+    def test_shift_axis1_with_valid_fill_value_one_array(self):
+        # Case with axis=1 that does not go through the "len(arrays)>1" path
+        #  in DataFrame.shift
+        data = np.random.default_rng(2).standard_normal((5, 3))
+        df = DataFrame(data)
+        res = df.shift(axis=1, periods=1, fill_value=12345)
+        expected = df.T.shift(periods=1, fill_value=12345).T
+        tm.assert_frame_equal(res, expected)
+
+        # same but with an 1D ExtensionArray backing it
+        df2 = df[[0]].astype("Float64")
+        res2 = df2.shift(axis=1, periods=1, fill_value=12345)
+        expected2 = DataFrame([12345] * 5, dtype="Float64")
+        tm.assert_frame_equal(res2, expected2)
+
+    def test_shift_deprecate_freq_and_fill_value(self, frame_or_series):
+        # Can't pass both!
+        obj = frame_or_series(
+            np.random.default_rng(2).standard_normal(5),
+            index=date_range("1/1/2000", periods=5, freq="h"),
+        )
+
+        msg = (
+            "Passing a 'freq' together with a 'fill_value' silently ignores the "
+            "fill_value"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            obj.shift(1, fill_value=1, freq="h")
+
+        if frame_or_series is DataFrame:
+            obj.columns = date_range("1/1/2000", periods=1, freq="h")
+            with tm.assert_produces_warning(FutureWarning, match=msg):
+                obj.shift(1, axis=1, fill_value=1, freq="h")
+
+    @pytest.mark.parametrize(
+        "input_data, output_data",
+        [(np.empty(shape=(0,)), []), (np.ones(shape=(2,)), [np.nan, 1.0])],
+    )
+    def test_shift_non_writable_array(self, input_data, output_data, frame_or_series):
+        # GH21049 Verify whether non writable numpy array is shiftable
+        input_data.setflags(write=False)
+
+        result = frame_or_series(input_data).shift(1)
+        if frame_or_series is not Series:
+            # need to explicitly specify columns in the empty case
+            expected = frame_or_series(
+                output_data,
+                index=range(len(output_data)),
+                columns=range(1),
+                dtype="float64",
+            )
+        else:
+            expected = frame_or_series(output_data, dtype="float64")
+
+        tm.assert_equal(result, expected)
+
+    def test_shift_mismatched_freq(self, frame_or_series):
+        ts = frame_or_series(
+            np.random.default_rng(2).standard_normal(5),
+            index=date_range("1/1/2000", periods=5, freq="h"),
+        )
+
+        result = ts.shift(1, freq="5min")
+        exp_index = ts.index.shift(1, freq="5min")
+        tm.assert_index_equal(result.index, exp_index)
+
+        # GH#1063, multiple of same base
+        result = ts.shift(1, freq="4h")
+        exp_index = ts.index + offsets.Hour(4)
+        tm.assert_index_equal(result.index, exp_index)
+
+    @pytest.mark.parametrize(
+        "obj",
+        [
+            Series([np.arange(5)]),
+            date_range("1/1/2011", periods=24, freq="h"),
+            Series(range(5), index=date_range("2017", periods=5)),
+        ],
+    )
+    @pytest.mark.parametrize("shift_size", [0, 1, 2])
+    def test_shift_always_copy(self, obj, shift_size, frame_or_series):
+        # GH#22397
+        if frame_or_series is not Series:
+            obj = obj.to_frame()
+        assert obj.shift(shift_size) is not obj
+
+    def test_shift_object_non_scalar_fill(self):
+        # shift requires scalar fill_value except for object dtype
+        ser = Series(range(3))
+        with pytest.raises(ValueError, match="fill_value must be a scalar"):
+            ser.shift(1, fill_value=[])
+
+        df = ser.to_frame()
+        with pytest.raises(ValueError, match="fill_value must be a scalar"):
+            df.shift(1, fill_value=np.arange(3))
+
+        obj_ser = ser.astype(object)
+        result = obj_ser.shift(1, fill_value={})
+        assert result[0] == {}
+
+        obj_df = obj_ser.to_frame()
+        result = obj_df.shift(1, fill_value={})
+        assert result.iloc[0, 0] == {}
+
+    def test_shift_int(self, datetime_frame, frame_or_series):
+        ts = tm.get_obj(datetime_frame, frame_or_series).astype(int)
+        shifted = ts.shift(1)
+        expected = ts.astype(float).shift(1)
+        tm.assert_equal(shifted, expected)
+
+    @pytest.mark.parametrize("dtype", ["int32", "int64"])
+    def test_shift_32bit_take(self, frame_or_series, dtype):
+        # 32-bit taking
+        # GH#8129
+        index = date_range("2000-01-01", periods=5)
+        arr = np.arange(5, dtype=dtype)
+        s1 = frame_or_series(arr, index=index)
+        p = arr[1]
+        result = s1.shift(periods=p)
+        expected = frame_or_series([np.nan, 0, 1, 2, 3], index=index)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("periods", [1, 2, 3, 4])
+    def test_shift_preserve_freqstr(self, periods, frame_or_series):
+        # GH#21275
+        obj = frame_or_series(
+            range(periods),
+            index=date_range("2016-1-1 00:00:00", periods=periods, freq="h"),
+        )
+
+        result = obj.shift(1, "2h")
+
+        expected = frame_or_series(
+            range(periods),
+            index=date_range("2016-1-1 02:00:00", periods=periods, freq="h"),
+        )
+        tm.assert_equal(result, expected)
+
+    def test_shift_dst(self, frame_or_series):
+        # GH#13926
+        dates = date_range("2016-11-06", freq="h", periods=10, tz="US/Eastern")
+        obj = frame_or_series(dates)
+
+        res = obj.shift(0)
+        tm.assert_equal(res, obj)
+        assert tm.get_dtype(res) == "datetime64[ns, US/Eastern]"
+
+        res = obj.shift(1)
+        exp_vals = [NaT] + dates.astype(object).values.tolist()[:9]
+        exp = frame_or_series(exp_vals)
+        tm.assert_equal(res, exp)
+        assert tm.get_dtype(res) == "datetime64[ns, US/Eastern]"
+
+        res = obj.shift(-2)
+        exp_vals = dates.astype(object).values.tolist()[2:] + [NaT, NaT]
+        exp = frame_or_series(exp_vals)
+        tm.assert_equal(res, exp)
+        assert tm.get_dtype(res) == "datetime64[ns, US/Eastern]"
+
+    @pytest.mark.parametrize("ex", [10, -10, 20, -20])
+    def test_shift_dst_beyond(self, frame_or_series, ex):
+        # GH#13926
+        dates = date_range("2016-11-06", freq="h", periods=10, tz="US/Eastern")
+        obj = frame_or_series(dates)
+        res = obj.shift(ex)
+        exp = frame_or_series([NaT] * 10, dtype="datetime64[ns, US/Eastern]")
+        tm.assert_equal(res, exp)
+        assert tm.get_dtype(res) == "datetime64[ns, US/Eastern]"
+
+    def test_shift_by_zero(self, datetime_frame, frame_or_series):
+        # shift by 0
+        obj = tm.get_obj(datetime_frame, frame_or_series)
+        unshifted = obj.shift(0)
+        tm.assert_equal(unshifted, obj)
+
+    def test_shift(self, datetime_frame):
+        # naive shift
+        ser = datetime_frame["A"]
+
+        shifted = datetime_frame.shift(5)
+        tm.assert_index_equal(shifted.index, datetime_frame.index)
+
+        shifted_ser = ser.shift(5)
+        tm.assert_series_equal(shifted["A"], shifted_ser)
+
+        shifted = datetime_frame.shift(-5)
+        tm.assert_index_equal(shifted.index, datetime_frame.index)
+
+        shifted_ser = ser.shift(-5)
+        tm.assert_series_equal(shifted["A"], shifted_ser)
+
+        unshifted = datetime_frame.shift(5).shift(-5)
+        tm.assert_numpy_array_equal(
+            unshifted.dropna().values, datetime_frame.values[:-5]
+        )
+
+        unshifted_ser = ser.shift(5).shift(-5)
+        tm.assert_numpy_array_equal(unshifted_ser.dropna().values, ser.values[:-5])
+
+    def test_shift_by_offset(self, datetime_frame, frame_or_series):
+        # shift by DateOffset
+        obj = tm.get_obj(datetime_frame, frame_or_series)
+        offset = offsets.BDay()
+
+        shifted = obj.shift(5, freq=offset)
+        assert len(shifted) == len(obj)
+        unshifted = shifted.shift(-5, freq=offset)
+        tm.assert_equal(unshifted, obj)
+
+        shifted2 = obj.shift(5, freq="B")
+        tm.assert_equal(shifted, shifted2)
+
+        unshifted = obj.shift(0, freq=offset)
+        tm.assert_equal(unshifted, obj)
+
+        d = obj.index[0]
+        shifted_d = d + offset * 5
+        if frame_or_series is DataFrame:
+            tm.assert_series_equal(obj.xs(d), shifted.xs(shifted_d), check_names=False)
+        else:
+            tm.assert_almost_equal(obj.at[d], shifted.at[shifted_d])
+
+    def test_shift_with_periodindex(self, frame_or_series):
+        # Shifting with PeriodIndex
+        ps = DataFrame(
+            np.arange(4, dtype=float), index=pd.period_range("2020-01-01", periods=4)
+        )
+        ps = tm.get_obj(ps, frame_or_series)
+
+        shifted = ps.shift(1)
+        unshifted = shifted.shift(-1)
+        tm.assert_index_equal(shifted.index, ps.index)
+        tm.assert_index_equal(unshifted.index, ps.index)
+        if frame_or_series is DataFrame:
+            tm.assert_numpy_array_equal(
+                unshifted.iloc[:, 0].dropna().values, ps.iloc[:-1, 0].values
+            )
+        else:
+            tm.assert_numpy_array_equal(unshifted.dropna().values, ps.values[:-1])
+
+        shifted2 = ps.shift(1, "D")
+        shifted3 = ps.shift(1, offsets.Day())
+        tm.assert_equal(shifted2, shifted3)
+        tm.assert_equal(ps, shifted2.shift(-1, "D"))
+
+        msg = "does not match PeriodIndex freq"
+        with pytest.raises(ValueError, match=msg):
+            ps.shift(freq="W")
+
+        # legacy support
+        shifted4 = ps.shift(1, freq="D")
+        tm.assert_equal(shifted2, shifted4)
+
+        shifted5 = ps.shift(1, freq=offsets.Day())
+        tm.assert_equal(shifted5, shifted4)
+
+    def test_shift_other_axis(self):
+        # shift other axis
+        # GH#6371
+        df = DataFrame(np.random.default_rng(2).random((10, 5)))
+        expected = pd.concat(
+            [DataFrame(np.nan, index=df.index, columns=[0]), df.iloc[:, 0:-1]],
+            ignore_index=True,
+            axis=1,
+        )
+        result = df.shift(1, axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_shift_named_axis(self):
+        # shift named axis
+        df = DataFrame(np.random.default_rng(2).random((10, 5)))
+        expected = pd.concat(
+            [DataFrame(np.nan, index=df.index, columns=[0]), df.iloc[:, 0:-1]],
+            ignore_index=True,
+            axis=1,
+        )
+        result = df.shift(1, axis="columns")
+        tm.assert_frame_equal(result, expected)
+
+    def test_shift_other_axis_with_freq(self, datetime_frame):
+        obj = datetime_frame.T
+        offset = offsets.BDay()
+
+        # GH#47039
+        shifted = obj.shift(5, freq=offset, axis=1)
+        assert len(shifted) == len(obj)
+        unshifted = shifted.shift(-5, freq=offset, axis=1)
+        tm.assert_equal(unshifted, obj)
+
+    def test_shift_bool(self):
+        df = DataFrame({"high": [True, False], "low": [False, False]})
+        rs = df.shift(1)
+        xp = DataFrame(
+            np.array([[np.nan, np.nan], [True, False]], dtype=object),
+            columns=["high", "low"],
+        )
+        tm.assert_frame_equal(rs, xp)
+
+    def test_shift_categorical1(self, frame_or_series):
+        # GH#9416
+        obj = frame_or_series(["a", "b", "c", "d"], dtype="category")
+
+        rt = obj.shift(1).shift(-1)
+        tm.assert_equal(obj.iloc[:-1], rt.dropna())
+
+        def get_cat_values(ndframe):
+            # For Series we could just do ._values; for DataFrame
+            #  we may be able to do this if we ever have 2D Categoricals
+            return ndframe._mgr.arrays[0]
+
+        cat = get_cat_values(obj)
+
+        sp1 = obj.shift(1)
+        tm.assert_index_equal(obj.index, sp1.index)
+        assert np.all(get_cat_values(sp1).codes[:1] == -1)
+        assert np.all(cat.codes[:-1] == get_cat_values(sp1).codes[1:])
+
+        sn2 = obj.shift(-2)
+        tm.assert_index_equal(obj.index, sn2.index)
+        assert np.all(get_cat_values(sn2).codes[-2:] == -1)
+        assert np.all(cat.codes[2:] == get_cat_values(sn2).codes[:-2])
+
+        tm.assert_index_equal(cat.categories, get_cat_values(sp1).categories)
+        tm.assert_index_equal(cat.categories, get_cat_values(sn2).categories)
+
+    def test_shift_categorical(self):
+        # GH#9416
+        s1 = Series(["a", "b", "c"], dtype="category")
+        s2 = Series(["A", "B", "C"], dtype="category")
+        df = DataFrame({"one": s1, "two": s2})
+        rs = df.shift(1)
+        xp = DataFrame({"one": s1.shift(1), "two": s2.shift(1)})
+        tm.assert_frame_equal(rs, xp)
+
+    def test_shift_categorical_fill_value(self, frame_or_series):
+        ts = frame_or_series(["a", "b", "c", "d"], dtype="category")
+        res = ts.shift(1, fill_value="a")
+        expected = frame_or_series(
+            pd.Categorical(
+                ["a", "a", "b", "c"], categories=["a", "b", "c", "d"], ordered=False
+            )
+        )
+        tm.assert_equal(res, expected)
+
+        # check for incorrect fill_value
+        msg = r"Cannot setitem on a Categorical with a new category \(f\)"
+        with pytest.raises(TypeError, match=msg):
+            ts.shift(1, fill_value="f")
+
+    def test_shift_fill_value(self, frame_or_series):
+        # GH#24128
+        dti = date_range("1/1/2000", periods=5, freq="h")
+
+        ts = frame_or_series([1.0, 2.0, 3.0, 4.0, 5.0], index=dti)
+        exp = frame_or_series([0.0, 1.0, 2.0, 3.0, 4.0], index=dti)
+        # check that fill value works
+        result = ts.shift(1, fill_value=0.0)
+        tm.assert_equal(result, exp)
+
+        exp = frame_or_series([0.0, 0.0, 1.0, 2.0, 3.0], index=dti)
+        result = ts.shift(2, fill_value=0.0)
+        tm.assert_equal(result, exp)
+
+        ts = frame_or_series([1, 2, 3])
+        res = ts.shift(2, fill_value=0)
+        assert tm.get_dtype(res) == tm.get_dtype(ts)
+
+        # retain integer dtype
+        obj = frame_or_series([1, 2, 3, 4, 5], index=dti)
+        exp = frame_or_series([0, 1, 2, 3, 4], index=dti)
+        result = obj.shift(1, fill_value=0)
+        tm.assert_equal(result, exp)
+
+        exp = frame_or_series([0, 0, 1, 2, 3], index=dti)
+        result = obj.shift(2, fill_value=0)
+        tm.assert_equal(result, exp)
+
+    def test_shift_empty(self):
+        # Regression test for GH#8019
+        df = DataFrame({"foo": []})
+        rs = df.shift(-1)
+
+        tm.assert_frame_equal(df, rs)
+
+    def test_shift_duplicate_columns(self):
+        # GH#9092; verify that position-based shifting works
+        # in the presence of duplicate columns
+        column_lists = [list(range(5)), [1] * 5, [1, 1, 2, 2, 1]]
+        data = np.random.default_rng(2).standard_normal((20, 5))
+
+        shifted = []
+        for columns in column_lists:
+            df = DataFrame(data.copy(), columns=columns)
+            for s in range(5):
+                df.iloc[:, s] = df.iloc[:, s].shift(s + 1)
+            df.columns = range(5)
+            shifted.append(df)
+
+        # sanity check the base case
+        nulls = shifted[0].isna().sum()
+        tm.assert_series_equal(nulls, Series(range(1, 6), dtype="int64"))
+
+        # check all answers are the same
+        tm.assert_frame_equal(shifted[0], shifted[1])
+        tm.assert_frame_equal(shifted[0], shifted[2])
+
+    def test_shift_axis1_multiple_blocks(self, using_array_manager):
+        # GH#35488
+        df1 = DataFrame(np.random.default_rng(2).integers(1000, size=(5, 3)))
+        df2 = DataFrame(np.random.default_rng(2).integers(1000, size=(5, 2)))
+        df3 = pd.concat([df1, df2], axis=1)
+        if not using_array_manager:
+            assert len(df3._mgr.blocks) == 2
+
+        result = df3.shift(2, axis=1)
+
+        expected = df3.take([-1, -1, 0, 1, 2], axis=1)
+        # Explicit cast to float to avoid implicit cast when setting nan.
+        # Column names aren't unique, so directly calling `expected.astype` won't work.
+        expected = expected.pipe(
+            lambda df: df.set_axis(range(df.shape[1]), axis=1)
+            .astype({0: "float", 1: "float"})
+            .set_axis(df.columns, axis=1)
+        )
+        expected.iloc[:, :2] = np.nan
+        expected.columns = df3.columns
+
+        tm.assert_frame_equal(result, expected)
+
+        # Case with periods < 0
+        # rebuild df3 because `take` call above consolidated
+        df3 = pd.concat([df1, df2], axis=1)
+        if not using_array_manager:
+            assert len(df3._mgr.blocks) == 2
+        result = df3.shift(-2, axis=1)
+
+        expected = df3.take([2, 3, 4, -1, -1], axis=1)
+        # Explicit cast to float to avoid implicit cast when setting nan.
+        # Column names aren't unique, so directly calling `expected.astype` won't work.
+        expected = expected.pipe(
+            lambda df: df.set_axis(range(df.shape[1]), axis=1)
+            .astype({3: "float", 4: "float"})
+            .set_axis(df.columns, axis=1)
+        )
+        expected.iloc[:, -2:] = np.nan
+        expected.columns = df3.columns
+
+        tm.assert_frame_equal(result, expected)
+
+    @td.skip_array_manager_not_yet_implemented  # TODO(ArrayManager) axis=1 support
+    def test_shift_axis1_multiple_blocks_with_int_fill(self):
+        # GH#42719
+        rng = np.random.default_rng(2)
+        df1 = DataFrame(rng.integers(1000, size=(5, 3), dtype=int))
+        df2 = DataFrame(rng.integers(1000, size=(5, 2), dtype=int))
+        df3 = pd.concat([df1.iloc[:4, 1:3], df2.iloc[:4, :]], axis=1)
+        result = df3.shift(2, axis=1, fill_value=np.int_(0))
+        assert len(df3._mgr.blocks) == 2
+
+        expected = df3.take([-1, -1, 0, 1], axis=1)
+        expected.iloc[:, :2] = np.int_(0)
+        expected.columns = df3.columns
+
+        tm.assert_frame_equal(result, expected)
+
+        # Case with periods < 0
+        df3 = pd.concat([df1.iloc[:4, 1:3], df2.iloc[:4, :]], axis=1)
+        result = df3.shift(-2, axis=1, fill_value=np.int_(0))
+        assert len(df3._mgr.blocks) == 2
+
+        expected = df3.take([2, 3, -1, -1], axis=1)
+        expected.iloc[:, -2:] = np.int_(0)
+        expected.columns = df3.columns
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_period_index_frame_shift_with_freq(self, frame_or_series):
+        ps = DataFrame(range(4), index=pd.period_range("2020-01-01", periods=4))
+        ps = tm.get_obj(ps, frame_or_series)
+
+        shifted = ps.shift(1, freq="infer")
+        unshifted = shifted.shift(-1, freq="infer")
+        tm.assert_equal(unshifted, ps)
+
+        shifted2 = ps.shift(freq="D")
+        tm.assert_equal(shifted, shifted2)
+
+        shifted3 = ps.shift(freq=offsets.Day())
+        tm.assert_equal(shifted, shifted3)
+
+    def test_datetime_frame_shift_with_freq(self, datetime_frame, frame_or_series):
+        dtobj = tm.get_obj(datetime_frame, frame_or_series)
+        shifted = dtobj.shift(1, freq="infer")
+        unshifted = shifted.shift(-1, freq="infer")
+        tm.assert_equal(dtobj, unshifted)
+
+        shifted2 = dtobj.shift(freq=dtobj.index.freq)
+        tm.assert_equal(shifted, shifted2)
+
+        inferred_ts = DataFrame(
+            datetime_frame.values,
+            Index(np.asarray(datetime_frame.index)),
+            columns=datetime_frame.columns,
+        )
+        inferred_ts = tm.get_obj(inferred_ts, frame_or_series)
+        shifted = inferred_ts.shift(1, freq="infer")
+        expected = dtobj.shift(1, freq="infer")
+        expected.index = expected.index._with_freq(None)
+        tm.assert_equal(shifted, expected)
+
+        unshifted = shifted.shift(-1, freq="infer")
+        tm.assert_equal(unshifted, inferred_ts)
+
+    def test_period_index_frame_shift_with_freq_error(self, frame_or_series):
+        ps = DataFrame(range(4), index=pd.period_range("2020-01-01", periods=4))
+        ps = tm.get_obj(ps, frame_or_series)
+        msg = "Given freq M does not match PeriodIndex freq D"
+        with pytest.raises(ValueError, match=msg):
+            ps.shift(freq="M")
+
+    def test_datetime_frame_shift_with_freq_error(
+        self, datetime_frame, frame_or_series
+    ):
+        dtobj = tm.get_obj(datetime_frame, frame_or_series)
+        no_freq = dtobj.iloc[[0, 5, 7]]
+        msg = "Freq was not set in the index hence cannot be inferred"
+        with pytest.raises(ValueError, match=msg):
+            no_freq.shift(freq="infer")
+
+    def test_shift_dt64values_int_fill_deprecated(self):
+        # GH#31971
+        ser = Series([pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02")])
+
+        with pytest.raises(TypeError, match="value should be a"):
+            ser.shift(1, fill_value=0)
+
+        df = ser.to_frame()
+        with pytest.raises(TypeError, match="value should be a"):
+            df.shift(1, fill_value=0)
+
+        # axis = 1
+        df2 = DataFrame({"A": ser, "B": ser})
+        df2._consolidate_inplace()
+
+        result = df2.shift(1, axis=1, fill_value=0)
+        expected = DataFrame({"A": [0, 0], "B": df2["A"]})
+        tm.assert_frame_equal(result, expected)
+
+        # same thing but not consolidated; pre-2.0 we got different behavior
+        df3 = DataFrame({"A": ser})
+        df3["B"] = ser
+        assert len(df3._mgr.arrays) == 2
+        result = df3.shift(1, axis=1, fill_value=0)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "as_cat",
+        [
+            pytest.param(
+                True,
+                marks=pytest.mark.xfail(
+                    reason="_can_hold_element incorrectly always returns True"
+                ),
+            ),
+            False,
+        ],
+    )
+    @pytest.mark.parametrize(
+        "vals",
+        [
+            date_range("2020-01-01", periods=2),
+            date_range("2020-01-01", periods=2, tz="US/Pacific"),
+            pd.period_range("2020-01-01", periods=2, freq="D"),
+            pd.timedelta_range("2020 Days", periods=2, freq="D"),
+            pd.interval_range(0, 3, periods=2),
+            pytest.param(
+                pd.array([1, 2], dtype="Int64"),
+                marks=pytest.mark.xfail(
+                    reason="_can_hold_element incorrectly always returns True"
+                ),
+            ),
+            pytest.param(
+                pd.array([1, 2], dtype="Float32"),
+                marks=pytest.mark.xfail(
+                    reason="_can_hold_element incorrectly always returns True"
+                ),
+            ),
+        ],
+        ids=lambda x: str(x.dtype),
+    )
+    def test_shift_dt64values_axis1_invalid_fill(self, vals, as_cat):
+        # GH#44564
+        ser = Series(vals)
+        if as_cat:
+            ser = ser.astype("category")
+
+        df = DataFrame({"A": ser})
+        result = df.shift(-1, axis=1, fill_value="foo")
+        expected = DataFrame({"A": ["foo", "foo"]})
+        tm.assert_frame_equal(result, expected)
+
+        # same thing but multiple blocks
+        df2 = DataFrame({"A": ser, "B": ser})
+        df2._consolidate_inplace()
+
+        result = df2.shift(-1, axis=1, fill_value="foo")
+        expected = DataFrame({"A": df2["B"], "B": ["foo", "foo"]})
+        tm.assert_frame_equal(result, expected)
+
+        # same thing but not consolidated
+        df3 = DataFrame({"A": ser})
+        df3["B"] = ser
+        assert len(df3._mgr.arrays) == 2
+        result = df3.shift(-1, axis=1, fill_value="foo")
+        tm.assert_frame_equal(result, expected)
+
+    def test_shift_axis1_categorical_columns(self):
+        # GH#38434
+        ci = CategoricalIndex(["a", "b", "c"])
+        df = DataFrame(
+            {"a": [1, 3], "b": [2, 4], "c": [5, 6]}, index=ci[:-1], columns=ci
+        )
+        result = df.shift(axis=1)
+
+        expected = DataFrame(
+            {"a": [np.nan, np.nan], "b": [1, 3], "c": [2, 4]}, index=ci[:-1], columns=ci
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # periods != 1
+        result = df.shift(2, axis=1)
+        expected = DataFrame(
+            {"a": [np.nan, np.nan], "b": [np.nan, np.nan], "c": [1, 3]},
+            index=ci[:-1],
+            columns=ci,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_shift_axis1_many_periods(self):
+        # GH#44978 periods > len(columns)
+        df = DataFrame(np.random.default_rng(2).random((5, 3)))
+        shifted = df.shift(6, axis=1, fill_value=None)
+
+        expected = df * np.nan
+        tm.assert_frame_equal(shifted, expected)
+
+        shifted2 = df.shift(-6, axis=1, fill_value=None)
+        tm.assert_frame_equal(shifted2, expected)
+
+    def test_shift_with_offsets_freq(self):
+        df = DataFrame({"x": [1, 2, 3]}, index=date_range("2000", periods=3))
+        shifted = df.shift(freq="1MS")
+        expected = DataFrame(
+            {"x": [1, 2, 3]},
+            index=date_range(start="02/01/2000", end="02/01/2000", periods=3),
+        )
+        tm.assert_frame_equal(shifted, expected)
+
+    def test_shift_with_iterable_basic_functionality(self):
+        # GH#44424
+        data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+        shifts = [0, 1, 2]
+
+        df = DataFrame(data)
+        shifted = df.shift(shifts)
+
+        expected = DataFrame(
+            {
+                "a_0": [1, 2, 3],
+                "b_0": [4, 5, 6],
+                "a_1": [np.nan, 1.0, 2.0],
+                "b_1": [np.nan, 4.0, 5.0],
+                "a_2": [np.nan, np.nan, 1.0],
+                "b_2": [np.nan, np.nan, 4.0],
+            }
+        )
+        tm.assert_frame_equal(expected, shifted)
+
+    def test_shift_with_iterable_series(self):
+        # GH#44424
+        data = {"a": [1, 2, 3]}
+        shifts = [0, 1, 2]
+
+        df = DataFrame(data)
+        s = df["a"]
+        tm.assert_frame_equal(s.shift(shifts), df.shift(shifts))
+
+    def test_shift_with_iterable_freq_and_fill_value(self):
+        # GH#44424
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal(5),
+            index=date_range("1/1/2000", periods=5, freq="h"),
+        )
+
+        tm.assert_frame_equal(
+            # rename because shift with an iterable leads to str column names
+            df.shift([1], fill_value=1).rename(columns=lambda x: int(x[0])),
+            df.shift(1, fill_value=1),
+        )
+
+        tm.assert_frame_equal(
+            df.shift([1], freq="h").rename(columns=lambda x: int(x[0])),
+            df.shift(1, freq="h"),
+        )
+
+        msg = (
+            "Passing a 'freq' together with a 'fill_value' silently ignores the "
+            "fill_value"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            df.shift([1, 2], fill_value=1, freq="h")
+
+    def test_shift_with_iterable_check_other_arguments(self):
+        # GH#44424
+        data = {"a": [1, 2], "b": [4, 5]}
+        shifts = [0, 1]
+        df = DataFrame(data)
+
+        # test suffix
+        shifted = df[["a"]].shift(shifts, suffix="_suffix")
+        expected = DataFrame({"a_suffix_0": [1, 2], "a_suffix_1": [np.nan, 1.0]})
+        tm.assert_frame_equal(shifted, expected)
+
+        # check bad inputs when doing multiple shifts
+        msg = "If `periods` contains multiple shifts, `axis` cannot be 1."
+        with pytest.raises(ValueError, match=msg):
+            df.shift(shifts, axis=1)
+
+        msg = "Periods must be integer, but s is <class 'str'>."
+        with pytest.raises(TypeError, match=msg):
+            df.shift(["s"])
+
+        msg = "If `periods` is an iterable, it cannot be empty."
+        with pytest.raises(ValueError, match=msg):
+            df.shift([])
+
+        msg = "Cannot specify `suffix` if `periods` is an int."
+        with pytest.raises(ValueError, match=msg):
+            df.shift(1, suffix="fails")
+
+    def test_shift_axis_one_empty(self):
+        # GH#57301
+        df = DataFrame()
+        result = df.shift(1, axis=1)
+        tm.assert_frame_equal(result, df)

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_swaplevel.py

@@ -0,0 +1,36 @@
+import pytest
+
+from pandas import DataFrame
+import pandas._testing as tm
+
+
+class TestSwaplevel:
+    def test_swaplevel(self, multiindex_dataframe_random_data):
+        frame = multiindex_dataframe_random_data
+
+        swapped = frame["A"].swaplevel()
+        swapped2 = frame["A"].swaplevel(0)
+        swapped3 = frame["A"].swaplevel(0, 1)
+        swapped4 = frame["A"].swaplevel("first", "second")
+        assert not swapped.index.equals(frame.index)
+        tm.assert_series_equal(swapped, swapped2)
+        tm.assert_series_equal(swapped, swapped3)
+        tm.assert_series_equal(swapped, swapped4)
+
+        back = swapped.swaplevel()
+        back2 = swapped.swaplevel(0)
+        back3 = swapped.swaplevel(0, 1)
+        back4 = swapped.swaplevel("second", "first")
+        assert back.index.equals(frame.index)
+        tm.assert_series_equal(back, back2)
+        tm.assert_series_equal(back, back3)
+        tm.assert_series_equal(back, back4)
+
+        ft = frame.T
+        swapped = ft.swaplevel("first", "second", axis=1)
+        exp = frame.swaplevel("first", "second").T
+        tm.assert_frame_equal(swapped, exp)
+
+        msg = "Can only swap levels on a hierarchical axis."
+        with pytest.raises(TypeError, match=msg):
+            DataFrame(range(3)).swaplevel()

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_to_dict_of_blocks.py

@@ -0,0 +1,79 @@
+import numpy as np
+import pytest
+
+from pandas._config import using_string_dtype
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    DataFrame,
+    MultiIndex,
+)
+import pandas._testing as tm
+from pandas.core.arrays import NumpyExtensionArray
+
+pytestmark = td.skip_array_manager_invalid_test
+
+
+class TestToDictOfBlocks:
+    @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning")
+    def test_no_copy_blocks(self, float_frame, using_copy_on_write):
+        # GH#9607
+        df = DataFrame(float_frame, copy=True)
+        column = df.columns[0]
+
+        _last_df = None
+        # use the copy=False, change a column
+        blocks = df._to_dict_of_blocks()
+        for _df in blocks.values():
+            _last_df = _df
+            if column in _df:
+                _df.loc[:, column] = _df[column] + 1
+
+        if not using_copy_on_write:
+            # make sure we did change the original DataFrame
+            assert _last_df is not None and _last_df[column].equals(df[column])
+        else:
+            assert _last_df is not None and not _last_df[column].equals(df[column])
+
+
+@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)")
+def test_to_dict_of_blocks_item_cache(using_copy_on_write, warn_copy_on_write):
+    # Calling to_dict_of_blocks should not poison item_cache
+    df = DataFrame({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"]})
+    df["c"] = NumpyExtensionArray(np.array([1, 2, None, 3], dtype=object))
+    mgr = df._mgr
+    assert len(mgr.blocks) == 3  # i.e. not consolidated
+
+    ser = df["b"]  # populations item_cache["b"]
+
+    df._to_dict_of_blocks()
+
+    if using_copy_on_write:
+        with pytest.raises(ValueError, match="read-only"):
+            ser.values[0] = "foo"
+    elif warn_copy_on_write:
+        ser.values[0] = "foo"
+        assert df.loc[0, "b"] == "foo"
+        # with warning mode, the item cache is disabled
+        assert df["b"] is not ser
+    else:
+        # Check that the to_dict_of_blocks didn't break link between ser and df
+        ser.values[0] = "foo"
+        assert df.loc[0, "b"] == "foo"
+
+        assert df["b"] is ser
+
+
+def test_set_change_dtype_slice():
+    # GH#8850
+    cols = MultiIndex.from_tuples([("1st", "a"), ("2nd", "b"), ("3rd", "c")])
+    df = DataFrame([[1.0, 2, 3], [4.0, 5, 6]], columns=cols)
+    df["2nd"] = df["2nd"] * 2.0
+
+    blocks = df._to_dict_of_blocks()
+    assert sorted(blocks.keys()) == ["float64", "int64"]
+    tm.assert_frame_equal(
+        blocks["float64"], DataFrame([[1.0, 4.0], [4.0, 10.0]], columns=cols[:2])
+    )
+    tm.assert_frame_equal(blocks["int64"], DataFrame([[3], [6]], columns=cols[2:]))

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_tz_convert.py

@@ -0,0 +1,131 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestTZConvert:
+    def test_tz_convert(self, frame_or_series):
+        rng = date_range("1/1/2011", periods=200, freq="D", tz="US/Eastern")
+
+        obj = DataFrame({"a": 1}, index=rng)
+        obj = tm.get_obj(obj, frame_or_series)
+
+        result = obj.tz_convert("Europe/Berlin")
+        expected = DataFrame({"a": 1}, rng.tz_convert("Europe/Berlin"))
+        expected = tm.get_obj(expected, frame_or_series)
+
+        assert result.index.tz.zone == "Europe/Berlin"
+        tm.assert_equal(result, expected)
+
+    def test_tz_convert_axis1(self):
+        rng = date_range("1/1/2011", periods=200, freq="D", tz="US/Eastern")
+
+        obj = DataFrame({"a": 1}, index=rng)
+
+        obj = obj.T
+        result = obj.tz_convert("Europe/Berlin", axis=1)
+        assert result.columns.tz.zone == "Europe/Berlin"
+
+        expected = DataFrame({"a": 1}, rng.tz_convert("Europe/Berlin"))
+
+        tm.assert_equal(result, expected.T)
+
+    def test_tz_convert_naive(self, frame_or_series):
+        # can't convert tz-naive
+        rng = date_range("1/1/2011", periods=200, freq="D")
+        ts = Series(1, index=rng)
+        ts = frame_or_series(ts)
+
+        with pytest.raises(TypeError, match="Cannot convert tz-naive"):
+            ts.tz_convert("US/Eastern")
+
+    @pytest.mark.parametrize("fn", ["tz_localize", "tz_convert"])
+    def test_tz_convert_and_localize(self, fn):
+        l0 = date_range("20140701", periods=5, freq="D")
+        l1 = date_range("20140701", periods=5, freq="D")
+
+        int_idx = Index(range(5))
+
+        if fn == "tz_convert":
+            l0 = l0.tz_localize("UTC")
+            l1 = l1.tz_localize("UTC")
+
+        for idx in [l0, l1]:
+            l0_expected = getattr(idx, fn)("US/Pacific")
+            l1_expected = getattr(idx, fn)("US/Pacific")
+
+            df1 = DataFrame(np.ones(5), index=l0)
+            df1 = getattr(df1, fn)("US/Pacific")
+            tm.assert_index_equal(df1.index, l0_expected)
+
+            # MultiIndex
+            # GH7846
+            df2 = DataFrame(np.ones(5), MultiIndex.from_arrays([l0, l1]))
+
+            # freq is not preserved in MultiIndex construction
+            l1_expected = l1_expected._with_freq(None)
+            l0_expected = l0_expected._with_freq(None)
+            l1 = l1._with_freq(None)
+            l0 = l0._with_freq(None)
+
+            df3 = getattr(df2, fn)("US/Pacific", level=0)
+            assert not df3.index.levels[0].equals(l0)
+            tm.assert_index_equal(df3.index.levels[0], l0_expected)
+            tm.assert_index_equal(df3.index.levels[1], l1)
+            assert not df3.index.levels[1].equals(l1_expected)
+
+            df3 = getattr(df2, fn)("US/Pacific", level=1)
+            tm.assert_index_equal(df3.index.levels[0], l0)
+            assert not df3.index.levels[0].equals(l0_expected)
+            tm.assert_index_equal(df3.index.levels[1], l1_expected)
+            assert not df3.index.levels[1].equals(l1)
+
+            df4 = DataFrame(np.ones(5), MultiIndex.from_arrays([int_idx, l0]))
+
+            # TODO: untested
+            getattr(df4, fn)("US/Pacific", level=1)
+
+            tm.assert_index_equal(df3.index.levels[0], l0)
+            assert not df3.index.levels[0].equals(l0_expected)
+            tm.assert_index_equal(df3.index.levels[1], l1_expected)
+            assert not df3.index.levels[1].equals(l1)
+
+        # Bad Inputs
+
+        # Not DatetimeIndex / PeriodIndex
+        with pytest.raises(TypeError, match="DatetimeIndex"):
+            df = DataFrame(index=int_idx)
+            getattr(df, fn)("US/Pacific")
+
+        # Not DatetimeIndex / PeriodIndex
+        with pytest.raises(TypeError, match="DatetimeIndex"):
+            df = DataFrame(np.ones(5), MultiIndex.from_arrays([int_idx, l0]))
+            getattr(df, fn)("US/Pacific", level=0)
+
+        # Invalid level
+        with pytest.raises(ValueError, match="not valid"):
+            df = DataFrame(index=l0)
+            getattr(df, fn)("US/Pacific", level=1)
+
+    @pytest.mark.parametrize("copy", [True, False])
+    def test_tz_convert_copy_inplace_mutate(self, copy, frame_or_series):
+        # GH#6326
+        obj = frame_or_series(
+            np.arange(0, 5),
+            index=date_range("20131027", periods=5, freq="h", tz="Europe/Berlin"),
+        )
+        orig = obj.copy()
+        result = obj.tz_convert("UTC", copy=copy)
+        expected = frame_or_series(np.arange(0, 5), index=obj.index.tz_convert("UTC"))
+        tm.assert_equal(result, expected)
+        tm.assert_equal(obj, orig)
+        assert result.index is not obj.index
+        assert result is not obj

py311/lib/python3.11/site-packages/pandas/tests/frame/methods/test_values.py

@@ -0,0 +1,280 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    DataFrame,
+    NaT,
+    Series,
+    Timestamp,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameValues:
+    @td.skip_array_manager_invalid_test
+    def test_values(self, float_frame, using_copy_on_write):
+        if using_copy_on_write:
+            with pytest.raises(ValueError, match="read-only"):
+                float_frame.values[:, 0] = 5.0
+            assert (float_frame.values[:, 0] != 5).all()
+        else:
+            float_frame.values[:, 0] = 5.0
+            assert (float_frame.values[:, 0] == 5).all()
+
+    def test_more_values(self, float_string_frame):
+        values = float_string_frame.values
+        assert values.shape[1] == len(float_string_frame.columns)
+
+    def test_values_mixed_dtypes(self, float_frame, float_string_frame):
+        frame = float_frame
+        arr = frame.values
+
+        frame_cols = frame.columns
+        for i, row in enumerate(arr):
+            for j, value in enumerate(row):
+                col = frame_cols[j]
+                if np.isnan(value):
+                    assert np.isnan(frame[col].iloc[i])
+                else:
+                    assert value == frame[col].iloc[i]
+
+        # mixed type
+        arr = float_string_frame[["foo", "A"]].values
+        assert arr[0, 0] == "bar"
+
+        df = DataFrame({"complex": [1j, 2j, 3j], "real": [1, 2, 3]})
+        arr = df.values
+        assert arr[0, 0] == 1j
+
+    def test_values_duplicates(self):
+        df = DataFrame(
+            [[1, 2, "a", "b"], [1, 2, "a", "b"]], columns=["one", "one", "two", "two"]
+        )
+
+        result = df.values
+        expected = np.array([[1, 2, "a", "b"], [1, 2, "a", "b"]], dtype=object)
+
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_values_with_duplicate_columns(self):
+        df = DataFrame([[1, 2.5], [3, 4.5]], index=[1, 2], columns=["x", "x"])
+        result = df.values
+        expected = np.array([[1, 2.5], [3, 4.5]])
+        assert (result == expected).all().all()
+
+    @pytest.mark.parametrize("constructor", [date_range, period_range])
+    def test_values_casts_datetimelike_to_object(self, constructor):
+        series = Series(constructor("2000-01-01", periods=10, freq="D"))
+
+        expected = series.astype("object")
+
+        df = DataFrame(
+            {"a": series, "b": np.random.default_rng(2).standard_normal(len(series))}
+        )
+
+        result = df.values.squeeze()
+        assert (result[:, 0] == expected.values).all()
+
+        df = DataFrame({"a": series, "b": ["foo"] * len(series)})
+
+        result = df.values.squeeze()
+        assert (result[:, 0] == expected.values).all()
+
+    def test_frame_values_with_tz(self):
+        tz = "US/Central"
+        df = DataFrame({"A": date_range("2000", periods=4, tz=tz)})
+        result = df.values
+        expected = np.array(
+            [
+                [Timestamp("2000-01-01", tz=tz)],
+                [Timestamp("2000-01-02", tz=tz)],
+                [Timestamp("2000-01-03", tz=tz)],
+                [Timestamp("2000-01-04", tz=tz)],
+            ]
+        )
+        tm.assert_numpy_array_equal(result, expected)
+
+        # two columns, homogeneous
+
+        df["B"] = df["A"]
+        result = df.values
+        expected = np.concatenate([expected, expected], axis=1)
+        tm.assert_numpy_array_equal(result, expected)
+
+        # three columns, heterogeneous
+        est = "US/Eastern"
+        df["C"] = df["A"].dt.tz_convert(est)
+
+        new = np.array(
+            [
+                [Timestamp("2000-01-01T01:00:00", tz=est)],
+                [Timestamp("2000-01-02T01:00:00", tz=est)],
+                [Timestamp("2000-01-03T01:00:00", tz=est)],
+                [Timestamp("2000-01-04T01:00:00", tz=est)],
+            ]
+        )
+        expected = np.concatenate([expected, new], axis=1)
+        result = df.values
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_interleave_with_tzaware(self, timezone_frame):
+        # interleave with object
+        result = timezone_frame.assign(D="foo").values
+        expected = np.array(
+            [
+                [
+                    Timestamp("2013-01-01 00:00:00"),
+                    Timestamp("2013-01-02 00:00:00"),
+                    Timestamp("2013-01-03 00:00:00"),
+                ],
+                [
+                    Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern"),
+                    NaT,
+                    Timestamp("2013-01-03 00:00:00-0500", tz="US/Eastern"),
+                ],
+                [
+                    Timestamp("2013-01-01 00:00:00+0100", tz="CET"),
+                    NaT,
+                    Timestamp("2013-01-03 00:00:00+0100", tz="CET"),
+                ],
+                ["foo", "foo", "foo"],
+            ],
+            dtype=object,
+        ).T
+        tm.assert_numpy_array_equal(result, expected)
+
+        # interleave with only datetime64[ns]
+        result = timezone_frame.values
+        expected = np.array(
+            [
+                [
+                    Timestamp("2013-01-01 00:00:00"),
+                    Timestamp("2013-01-02 00:00:00"),
+                    Timestamp("2013-01-03 00:00:00"),
+                ],
+                [
+                    Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern"),
+                    NaT,
+                    Timestamp("2013-01-03 00:00:00-0500", tz="US/Eastern"),
+                ],
+                [
+                    Timestamp("2013-01-01 00:00:00+0100", tz="CET"),
+                    NaT,
+                    Timestamp("2013-01-03 00:00:00+0100", tz="CET"),
+                ],
+            ],
+            dtype=object,
+        ).T
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_values_interleave_non_unique_cols(self):
+        df = DataFrame(
+            [[Timestamp("20130101"), 3.5], [Timestamp("20130102"), 4.5]],
+            columns=["x", "x"],
+            index=[1, 2],
+        )
+
+        df_unique = df.copy()
+        df_unique.columns = ["x", "y"]
+        assert df_unique.values.shape == df.values.shape
+        tm.assert_numpy_array_equal(df_unique.values[0], df.values[0])
+        tm.assert_numpy_array_equal(df_unique.values[1], df.values[1])
+
+    def test_values_numeric_cols(self, float_frame):
+        float_frame["foo"] = "bar"
+
+        values = float_frame[["A", "B", "C", "D"]].values
+        assert values.dtype == np.float64
+
+    def test_values_lcd(self, mixed_float_frame, mixed_int_frame):
+        # mixed lcd
+        values = mixed_float_frame[["A", "B", "C", "D"]].values
+        assert values.dtype == np.float64
+
+        values = mixed_float_frame[["A", "B", "C"]].values
+        assert values.dtype == np.float32
+
+        values = mixed_float_frame[["C"]].values
+        assert values.dtype == np.float16
+
+        # GH#10364
+        # B uint64 forces float because there are other signed int types
+        values = mixed_int_frame[["A", "B", "C", "D"]].values
+        assert values.dtype == np.float64
+
+        values = mixed_int_frame[["A", "D"]].values
+        assert values.dtype == np.int64
+
+        # B uint64 forces float because there are other signed int types
+        values = mixed_int_frame[["A", "B", "C"]].values
+        assert values.dtype == np.float64
+
+        # as B and C are both unsigned, no forcing to float is needed
+        values = mixed_int_frame[["B", "C"]].values
+        assert values.dtype == np.uint64
+
+        values = mixed_int_frame[["A", "C"]].values
+        assert values.dtype == np.int32
+
+        values = mixed_int_frame[["C", "D"]].values
+        assert values.dtype == np.int64
+
+        values = mixed_int_frame[["A"]].values
+        assert values.dtype == np.int32
+
+        values = mixed_int_frame[["C"]].values
+        assert values.dtype == np.uint8
+
+
+class TestPrivateValues:
+    @td.skip_array_manager_invalid_test
+    def test_private_values_dt64tz(self, using_copy_on_write):
+        dta = date_range("2000", periods=4, tz="US/Central")._data.reshape(-1, 1)
+
+        df = DataFrame(dta, columns=["A"])
+        tm.assert_equal(df._values, dta)
+
+        if using_copy_on_write:
+            assert not np.shares_memory(df._values._ndarray, dta._ndarray)
+        else:
+            # we have a view
+            assert np.shares_memory(df._values._ndarray, dta._ndarray)
+
+        # TimedeltaArray
+        tda = dta - dta
+        df2 = df - df
+        tm.assert_equal(df2._values, tda)
+
+    @td.skip_array_manager_invalid_test
+    def test_private_values_dt64tz_multicol(self, using_copy_on_write):
+        dta = date_range("2000", periods=8, tz="US/Central")._data.reshape(-1, 2)
+
+        df = DataFrame(dta, columns=["A", "B"])
+        tm.assert_equal(df._values, dta)
+
+        if using_copy_on_write:
+            assert not np.shares_memory(df._values._ndarray, dta._ndarray)
+        else:
+            # we have a view
+            assert np.shares_memory(df._values._ndarray, dta._ndarray)
+
+        # TimedeltaArray
+        tda = dta - dta
+        df2 = df - df
+        tm.assert_equal(df2._values, tda)
+
+    def test_private_values_dt64_multiblock(self):
+        dta = date_range("2000", periods=8)._data
+
+        df = DataFrame({"A": dta[:4]}, copy=False)
+        df["B"] = dta[4:]
+
+        assert len(df._mgr.arrays) == 2
+
+        result = df._values
+        expected = dta.reshape(2, 4).T
+        tm.assert_equal(result, expected)

py311/lib/python3.11/site-packages/pandas/tests/scalar/interval/test_arithmetic.py

@@ -0,0 +1,192 @@
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Interval,
+    Timedelta,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestIntervalArithmetic:
+    def test_interval_add(self, closed):
+        interval = Interval(0, 1, closed=closed)
+        expected = Interval(1, 2, closed=closed)
+
+        result = interval + 1
+        assert result == expected
+
+        result = 1 + interval
+        assert result == expected
+
+        result = interval
+        result += 1
+        assert result == expected
+
+        msg = r"unsupported operand type\(s\) for \+"
+        with pytest.raises(TypeError, match=msg):
+            interval + interval
+
+        with pytest.raises(TypeError, match=msg):
+            interval + "foo"
+
+    def test_interval_sub(self, closed):
+        interval = Interval(0, 1, closed=closed)
+        expected = Interval(-1, 0, closed=closed)
+
+        result = interval - 1
+        assert result == expected
+
+        result = interval
+        result -= 1
+        assert result == expected
+
+        msg = r"unsupported operand type\(s\) for -"
+        with pytest.raises(TypeError, match=msg):
+            interval - interval
+
+        with pytest.raises(TypeError, match=msg):
+            interval - "foo"
+
+    def test_interval_mult(self, closed):
+        interval = Interval(0, 1, closed=closed)
+        expected = Interval(0, 2, closed=closed)
+
+        result = interval * 2
+        assert result == expected
+
+        result = 2 * interval
+        assert result == expected
+
+        result = interval
+        result *= 2
+        assert result == expected
+
+        msg = r"unsupported operand type\(s\) for \*"
+        with pytest.raises(TypeError, match=msg):
+            interval * interval
+
+        msg = r"can\'t multiply sequence by non-int"
+        with pytest.raises(TypeError, match=msg):
+            interval * "foo"
+
+    def test_interval_div(self, closed):
+        interval = Interval(0, 1, closed=closed)
+        expected = Interval(0, 0.5, closed=closed)
+
+        result = interval / 2.0
+        assert result == expected
+
+        result = interval
+        result /= 2.0
+        assert result == expected
+
+        msg = r"unsupported operand type\(s\) for /"
+        with pytest.raises(TypeError, match=msg):
+            interval / interval
+
+        with pytest.raises(TypeError, match=msg):
+            interval / "foo"
+
+    def test_interval_floordiv(self, closed):
+        interval = Interval(1, 2, closed=closed)
+        expected = Interval(0, 1, closed=closed)
+
+        result = interval // 2
+        assert result == expected
+
+        result = interval
+        result //= 2
+        assert result == expected
+
+        msg = r"unsupported operand type\(s\) for //"
+        with pytest.raises(TypeError, match=msg):
+            interval // interval
+
+        with pytest.raises(TypeError, match=msg):
+            interval // "foo"
+
+    @pytest.mark.parametrize("method", ["__add__", "__sub__"])
+    @pytest.mark.parametrize(
+        "interval",
+        [
+            Interval(
+                Timestamp("2017-01-01 00:00:00"), Timestamp("2018-01-01 00:00:00")
+            ),
+            Interval(Timedelta(days=7), Timedelta(days=14)),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "delta", [Timedelta(days=7), timedelta(7), np.timedelta64(7, "D")]
+    )
+    def test_time_interval_add_subtract_timedelta(self, interval, delta, method):
+        # https://github.com/pandas-dev/pandas/issues/32023
+        result = getattr(interval, method)(delta)
+        left = getattr(interval.left, method)(delta)
+        right = getattr(interval.right, method)(delta)
+        expected = Interval(left, right)
+
+        assert result == expected
+
+    @pytest.mark.parametrize("interval", [Interval(1, 2), Interval(1.0, 2.0)])
+    @pytest.mark.parametrize(
+        "delta", [Timedelta(days=7), timedelta(7), np.timedelta64(7, "D")]
+    )
+    def test_numeric_interval_add_timedelta_raises(self, interval, delta):
+        # https://github.com/pandas-dev/pandas/issues/32023
+        msg = "|".join(
+            [
+                "unsupported operand",
+                "cannot use operands",
+                "Only numeric, Timestamp and Timedelta endpoints are allowed",
+            ]
+        )
+        with pytest.raises((TypeError, ValueError), match=msg):
+            interval + delta
+
+        with pytest.raises((TypeError, ValueError), match=msg):
+            delta + interval
+
+    @pytest.mark.parametrize("klass", [timedelta, np.timedelta64, Timedelta])
+    def test_timedelta_add_timestamp_interval(self, klass):
+        delta = klass(0)
+        expected = Interval(Timestamp("2020-01-01"), Timestamp("2020-02-01"))
+
+        result = delta + expected
+        assert result == expected
+
+        result = expected + delta
+        assert result == expected
+
+
+class TestIntervalComparisons:
+    def test_interval_equal(self):
+        assert Interval(0, 1) == Interval(0, 1, closed="right")
+        assert Interval(0, 1) != Interval(0, 1, closed="left")
+        assert Interval(0, 1) != 0
+
+    def test_interval_comparison(self):
+        msg = (
+            "'<' not supported between instances of "
+            "'pandas._libs.interval.Interval' and 'int'"
+        )
+        with pytest.raises(TypeError, match=msg):
+            Interval(0, 1) < 2
+
+        assert Interval(0, 1) < Interval(1, 2)
+        assert Interval(0, 1) < Interval(0, 2)
+        assert Interval(0, 1) < Interval(0.5, 1.5)
+        assert Interval(0, 1) <= Interval(0, 1)
+        assert Interval(0, 1) > Interval(-1, 2)
+        assert Interval(0, 1) >= Interval(0, 1)
+
+    def test_equality_comparison_broadcasts_over_array(self):
+        # https://github.com/pandas-dev/pandas/issues/35931
+        interval = Interval(0, 1)
+        arr = np.array([interval, interval])
+        result = interval == arr
+        expected = np.array([True, True])
+        tm.assert_numpy_array_equal(result, expected)

py311/lib/python3.11/site-packages/pandas/tests/scalar/period/test_arithmetic.py

@@ -0,0 +1,486 @@
+from datetime import timedelta
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs.period import IncompatibleFrequency
+
+from pandas import (
+    NaT,
+    Period,
+    Timedelta,
+    Timestamp,
+    offsets,
+)
+
+
+class TestPeriodArithmetic:
+    def test_add_overflow_raises(self):
+        # GH#55503
+        per = Timestamp.max.to_period("ns")
+
+        msg = "|".join(
+            [
+                "Python int too large to convert to C long",
+                # windows, 32bit linux builds
+                "int too big to convert",
+            ]
+        )
+        with pytest.raises(OverflowError, match=msg):
+            per + 1
+
+        msg = "value too large"
+        with pytest.raises(OverflowError, match=msg):
+            per + Timedelta(1)
+        with pytest.raises(OverflowError, match=msg):
+            per + offsets.Nano(1)
+
+    def test_period_add_integer(self):
+        per1 = Period(freq="D", year=2008, month=1, day=1)
+        per2 = Period(freq="D", year=2008, month=1, day=2)
+        assert per1 + 1 == per2
+        assert 1 + per1 == per2
+
+    def test_period_add_invalid(self):
+        # GH#4731
+        per1 = Period(freq="D", year=2008, month=1, day=1)
+        per2 = Period(freq="D", year=2008, month=1, day=2)
+
+        msg = "|".join(
+            [
+                r"unsupported operand type\(s\)",
+                "can only concatenate str",
+                "must be str, not Period",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            per1 + "str"
+        with pytest.raises(TypeError, match=msg):
+            "str" + per1
+        with pytest.raises(TypeError, match=msg):
+            per1 + per2
+
+    def test_period_sub_period_annual(self):
+        left, right = Period("2011", freq="Y"), Period("2007", freq="Y")
+        result = left - right
+        assert result == 4 * right.freq
+
+        msg = r"Input has different freq=M from Period\(freq=Y-DEC\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            left - Period("2007-01", freq="M")
+
+    def test_period_sub_period(self):
+        per1 = Period("2011-01-01", freq="D")
+        per2 = Period("2011-01-15", freq="D")
+
+        off = per1.freq
+        assert per1 - per2 == -14 * off
+        assert per2 - per1 == 14 * off
+
+        msg = r"Input has different freq=M from Period\(freq=D\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            per1 - Period("2011-02", freq="M")
+
+    @pytest.mark.parametrize("n", [1, 2, 3, 4])
+    def test_sub_n_gt_1_ticks(self, tick_classes, n):
+        # GH#23878
+        p1 = Period("19910905", freq=tick_classes(n))
+        p2 = Period("19920406", freq=tick_classes(n))
+
+        expected = Period(str(p2), freq=p2.freq.base) - Period(
+            str(p1), freq=p1.freq.base
+        )
+
+        assert (p2 - p1) == expected
+
+    @pytest.mark.parametrize("normalize", [True, False])
+    @pytest.mark.parametrize("n", [1, 2, 3, 4])
+    @pytest.mark.parametrize(
+        "offset, kwd_name",
+        [
+            (offsets.YearEnd, "month"),
+            (offsets.QuarterEnd, "startingMonth"),
+            (offsets.MonthEnd, None),
+            (offsets.Week, "weekday"),
+        ],
+    )
+    def test_sub_n_gt_1_offsets(self, offset, kwd_name, n, normalize):
+        # GH#23878
+        kwds = {kwd_name: 3} if kwd_name is not None else {}
+        p1_d = "19910905"
+        p2_d = "19920406"
+        p1 = Period(p1_d, freq=offset(n, normalize, **kwds))
+        p2 = Period(p2_d, freq=offset(n, normalize, **kwds))
+
+        expected = Period(p2_d, freq=p2.freq.base) - Period(p1_d, freq=p1.freq.base)
+
+        assert (p2 - p1) == expected
+
+    def test_period_add_offset(self):
+        # freq is DateOffset
+        for freq in ["Y", "2Y", "3Y"]:
+            per = Period("2011", freq=freq)
+            exp = Period("2013", freq=freq)
+            assert per + offsets.YearEnd(2) == exp
+            assert offsets.YearEnd(2) + per == exp
+
+            for off in [
+                offsets.YearBegin(2),
+                offsets.MonthBegin(1),
+                offsets.Minute(),
+                np.timedelta64(365, "D"),
+                timedelta(365),
+            ]:
+                msg = "Input has different freq|Input cannot be converted to Period"
+                with pytest.raises(IncompatibleFrequency, match=msg):
+                    per + off
+                with pytest.raises(IncompatibleFrequency, match=msg):
+                    off + per
+
+        for freq in ["M", "2M", "3M"]:
+            per = Period("2011-03", freq=freq)
+            exp = Period("2011-05", freq=freq)
+            assert per + offsets.MonthEnd(2) == exp
+            assert offsets.MonthEnd(2) + per == exp
+
+            exp = Period("2012-03", freq=freq)
+            assert per + offsets.MonthEnd(12) == exp
+            assert offsets.MonthEnd(12) + per == exp
+
+            msg = "|".join(
+                [
+                    "Input has different freq",
+                    "Input cannot be converted to Period",
+                ]
+            )
+
+            for off in [
+                offsets.YearBegin(2),
+                offsets.MonthBegin(1),
+                offsets.Minute(),
+                np.timedelta64(365, "D"),
+                timedelta(365),
+            ]:
+                with pytest.raises(IncompatibleFrequency, match=msg):
+                    per + off
+                with pytest.raises(IncompatibleFrequency, match=msg):
+                    off + per
+
+        # freq is Tick
+        for freq in ["D", "2D", "3D"]:
+            per = Period("2011-04-01", freq=freq)
+
+            exp = Period("2011-04-06", freq=freq)
+            assert per + offsets.Day(5) == exp
+            assert offsets.Day(5) + per == exp
+
+            exp = Period("2011-04-02", freq=freq)
+            assert per + offsets.Hour(24) == exp
+            assert offsets.Hour(24) + per == exp
+
+            exp = Period("2011-04-03", freq=freq)
+            assert per + np.timedelta64(2, "D") == exp
+            assert np.timedelta64(2, "D") + per == exp
+
+            exp = Period("2011-04-02", freq=freq)
+            assert per + np.timedelta64(3600 * 24, "s") == exp
+            assert np.timedelta64(3600 * 24, "s") + per == exp
+
+            exp = Period("2011-03-30", freq=freq)
+            assert per + timedelta(-2) == exp
+            assert timedelta(-2) + per == exp
+
+            exp = Period("2011-04-03", freq=freq)
+            assert per + timedelta(hours=48) == exp
+            assert timedelta(hours=48) + per == exp
+
+            msg = "|".join(
+                [
+                    "Input has different freq",
+                    "Input cannot be converted to Period",
+                ]
+            )
+
+            for off in [
+                offsets.YearBegin(2),
+                offsets.MonthBegin(1),
+                offsets.Minute(),
+                np.timedelta64(4, "h"),
+                timedelta(hours=23),
+            ]:
+                with pytest.raises(IncompatibleFrequency, match=msg):
+                    per + off
+                with pytest.raises(IncompatibleFrequency, match=msg):
+                    off + per
+
+        for freq in ["h", "2h", "3h"]:
+            per = Period("2011-04-01 09:00", freq=freq)
+
+            exp = Period("2011-04-03 09:00", freq=freq)
+            assert per + offsets.Day(2) == exp
+            assert offsets.Day(2) + per == exp
+
+            exp = Period("2011-04-01 12:00", freq=freq)
+            assert per + offsets.Hour(3) == exp
+            assert offsets.Hour(3) + per == exp
+
+            msg = "cannot use operands with types"
+            exp = Period("2011-04-01 12:00", freq=freq)
+            assert per + np.timedelta64(3, "h") == exp
+            assert np.timedelta64(3, "h") + per == exp
+
+            exp = Period("2011-04-01 10:00", freq=freq)
+            assert per + np.timedelta64(3600, "s") == exp
+            assert np.timedelta64(3600, "s") + per == exp
+
+            exp = Period("2011-04-01 11:00", freq=freq)
+            assert per + timedelta(minutes=120) == exp
+            assert timedelta(minutes=120) + per == exp
+
+            exp = Period("2011-04-05 12:00", freq=freq)
+            assert per + timedelta(days=4, minutes=180) == exp
+            assert timedelta(days=4, minutes=180) + per == exp
+
+            msg = "|".join(
+                [
+                    "Input has different freq",
+                    "Input cannot be converted to Period",
+                ]
+            )
+
+            for off in [
+                offsets.YearBegin(2),
+                offsets.MonthBegin(1),
+                offsets.Minute(),
+                np.timedelta64(3200, "s"),
+                timedelta(hours=23, minutes=30),
+            ]:
+                with pytest.raises(IncompatibleFrequency, match=msg):
+                    per + off
+                with pytest.raises(IncompatibleFrequency, match=msg):
+                    off + per
+
+    def test_period_sub_offset(self):
+        # freq is DateOffset
+        msg = "|".join(
+            [
+                "Input has different freq",
+                "Input cannot be converted to Period",
+            ]
+        )
+
+        for freq in ["Y", "2Y", "3Y"]:
+            per = Period("2011", freq=freq)
+            assert per - offsets.YearEnd(2) == Period("2009", freq=freq)
+
+            for off in [
+                offsets.YearBegin(2),
+                offsets.MonthBegin(1),
+                offsets.Minute(),
+                np.timedelta64(365, "D"),
+                timedelta(365),
+            ]:
+                with pytest.raises(IncompatibleFrequency, match=msg):
+                    per - off
+
+        for freq in ["M", "2M", "3M"]:
+            per = Period("2011-03", freq=freq)
+            assert per - offsets.MonthEnd(2) == Period("2011-01", freq=freq)
+            assert per - offsets.MonthEnd(12) == Period("2010-03", freq=freq)
+
+            for off in [
+                offsets.YearBegin(2),
+                offsets.MonthBegin(1),
+                offsets.Minute(),
+                np.timedelta64(365, "D"),
+                timedelta(365),
+            ]:
+                with pytest.raises(IncompatibleFrequency, match=msg):
+                    per - off
+
+        # freq is Tick
+        for freq in ["D", "2D", "3D"]:
+            per = Period("2011-04-01", freq=freq)
+            assert per - offsets.Day(5) == Period("2011-03-27", freq=freq)
+            assert per - offsets.Hour(24) == Period("2011-03-31", freq=freq)
+            assert per - np.timedelta64(2, "D") == Period("2011-03-30", freq=freq)
+            assert per - np.timedelta64(3600 * 24, "s") == Period(
+                "2011-03-31", freq=freq
+            )
+            assert per - timedelta(-2) == Period("2011-04-03", freq=freq)
+            assert per - timedelta(hours=48) == Period("2011-03-30", freq=freq)
+
+            for off in [
+                offsets.YearBegin(2),
+                offsets.MonthBegin(1),
+                offsets.Minute(),
+                np.timedelta64(4, "h"),
+                timedelta(hours=23),
+            ]:
+                with pytest.raises(IncompatibleFrequency, match=msg):
+                    per - off
+
+        for freq in ["h", "2h", "3h"]:
+            per = Period("2011-04-01 09:00", freq=freq)
+            assert per - offsets.Day(2) == Period("2011-03-30 09:00", freq=freq)
+            assert per - offsets.Hour(3) == Period("2011-04-01 06:00", freq=freq)
+            assert per - np.timedelta64(3, "h") == Period("2011-04-01 06:00", freq=freq)
+            assert per - np.timedelta64(3600, "s") == Period(
+                "2011-04-01 08:00", freq=freq
+            )
+            assert per - timedelta(minutes=120) == Period("2011-04-01 07:00", freq=freq)
+            assert per - timedelta(days=4, minutes=180) == Period(
+                "2011-03-28 06:00", freq=freq
+            )
+
+            for off in [
+                offsets.YearBegin(2),
+                offsets.MonthBegin(1),
+                offsets.Minute(),
+                np.timedelta64(3200, "s"),
+                timedelta(hours=23, minutes=30),
+            ]:
+                with pytest.raises(IncompatibleFrequency, match=msg):
+                    per - off
+
+    @pytest.mark.parametrize("freq", ["M", "2M", "3M"])
+    def test_period_addsub_nat(self, freq):
+        # GH#13071
+        per = Period("2011-01", freq=freq)
+
+        # For subtraction, NaT is treated as another Period object
+        assert NaT - per is NaT
+        assert per - NaT is NaT
+
+        # For addition, NaT is treated as offset-like
+        assert NaT + per is NaT
+        assert per + NaT is NaT
+
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "m"])
+    def test_period_add_sub_td64_nat(self, unit):
+        # GH#47196
+        per = Period("2022-06-01", "D")
+        nat = np.timedelta64("NaT", unit)
+
+        assert per + nat is NaT
+        assert nat + per is NaT
+        assert per - nat is NaT
+
+        with pytest.raises(TypeError, match="unsupported operand"):
+            nat - per
+
+    def test_period_ops_offset(self):
+        per = Period("2011-04-01", freq="D")
+        result = per + offsets.Day()
+        exp = Period("2011-04-02", freq="D")
+        assert result == exp
+
+        result = per - offsets.Day(2)
+        exp = Period("2011-03-30", freq="D")
+        assert result == exp
+
+        msg = r"Input cannot be converted to Period\(freq=D\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            per + offsets.Hour(2)
+
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            per - offsets.Hour(2)
+
+    def test_period_add_timestamp_raises(self):
+        # GH#17983
+        ts = Timestamp("2017")
+        per = Period("2017", freq="M")
+
+        msg = r"unsupported operand type\(s\) for \+: 'Timestamp' and 'Period'"
+        with pytest.raises(TypeError, match=msg):
+            ts + per
+
+        msg = r"unsupported operand type\(s\) for \+: 'Period' and 'Timestamp'"
+        with pytest.raises(TypeError, match=msg):
+            per + ts
+
+
+class TestPeriodComparisons:
+    def test_period_comparison_same_freq(self):
+        jan = Period("2000-01", "M")
+        feb = Period("2000-02", "M")
+
+        assert not jan == feb
+        assert jan != feb
+        assert jan < feb
+        assert jan <= feb
+        assert not jan > feb
+        assert not jan >= feb
+
+    def test_period_comparison_same_period_different_object(self):
+        # Separate Period objects for the same period
+        left = Period("2000-01", "M")
+        right = Period("2000-01", "M")
+
+        assert left == right
+        assert left >= right
+        assert left <= right
+        assert not left < right
+        assert not left > right
+
+    def test_period_comparison_mismatched_freq(self):
+        jan = Period("2000-01", "M")
+        day = Period("2012-01-01", "D")
+
+        assert not jan == day
+        assert jan != day
+        msg = r"Input has different freq=D from Period\(freq=M\)"
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            jan < day
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            jan <= day
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            jan > day
+        with pytest.raises(IncompatibleFrequency, match=msg):
+            jan >= day
+
+    def test_period_comparison_invalid_type(self):
+        jan = Period("2000-01", "M")
+
+        assert not jan == 1
+        assert jan != 1
+
+        int_or_per = "'(Period|int)'"
+        msg = f"not supported between instances of {int_or_per} and {int_or_per}"
+        for left, right in [(jan, 1), (1, jan)]:
+            with pytest.raises(TypeError, match=msg):
+                left > right
+            with pytest.raises(TypeError, match=msg):
+                left >= right
+            with pytest.raises(TypeError, match=msg):
+                left < right
+            with pytest.raises(TypeError, match=msg):
+                left <= right
+
+    def test_period_comparison_nat(self):
+        per = Period("2011-01-01", freq="D")
+
+        ts = Timestamp("2011-01-01")
+        # confirm Period('NaT') work identical with Timestamp('NaT')
+        for left, right in [
+            (NaT, per),
+            (per, NaT),
+            (NaT, ts),
+            (ts, NaT),
+        ]:
+            assert not left < right
+            assert not left > right
+            assert not left == right
+            assert left != right
+            assert not left <= right
+            assert not left >= right
+
+    @pytest.mark.parametrize(
+        "zerodim_arr, expected",
+        ((np.array(0), False), (np.array(Period("2000-01", "M")), True)),
+    )
+    def test_period_comparison_numpy_zerodim_arr(self, zerodim_arr, expected):
+        per = Period("2000-01", "M")
+
+        assert (per == zerodim_arr) is expected
+        assert (zerodim_arr == per) is expected

py311/lib/python3.11/site-packages/pandas/tests/scalar/period/test_asfreq.py

@@ -0,0 +1,828 @@
+import pytest
+
+from pandas._libs.tslibs.period import INVALID_FREQ_ERR_MSG
+from pandas.errors import OutOfBoundsDatetime
+
+from pandas import (
+    Period,
+    Timestamp,
+    offsets,
+)
+import pandas._testing as tm
+
+bday_msg = "Period with BDay freq is deprecated"
+
+
+class TestFreqConversion:
+    """Test frequency conversion of date objects"""
+
+    @pytest.mark.filterwarnings("ignore:Period with BDay:FutureWarning")
+    @pytest.mark.parametrize("freq", ["Y", "Q", "M", "W", "B", "D"])
+    def test_asfreq_near_zero(self, freq):
+        # GH#19643, GH#19650
+        per = Period("0001-01-01", freq=freq)
+        tup1 = (per.year, per.hour, per.day)
+
+        prev = per - 1
+        assert prev.ordinal == per.ordinal - 1
+        tup2 = (prev.year, prev.month, prev.day)
+        assert tup2 < tup1
+
+    def test_asfreq_near_zero_weekly(self):
+        # GH#19834
+        per1 = Period("0001-01-01", "D") + 6
+        per2 = Period("0001-01-01", "D") - 6
+        week1 = per1.asfreq("W")
+        week2 = per2.asfreq("W")
+        assert week1 != week2
+        assert week1.asfreq("D", "E") >= per1
+        assert week2.asfreq("D", "S") <= per2
+
+    def test_to_timestamp_out_of_bounds(self):
+        # GH#19643, used to incorrectly give Timestamp in 1754
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            per = Period("0001-01-01", freq="B")
+        msg = "Out of bounds nanosecond timestamp"
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+                per.to_timestamp()
+
+    def test_asfreq_corner(self):
+        val = Period(freq="Y", year=2007)
+        result1 = val.asfreq("5min")
+        result2 = val.asfreq("min")
+        expected = Period("2007-12-31 23:59", freq="min")
+        assert result1.ordinal == expected.ordinal
+        assert result1.freqstr == "5min"
+        assert result2.ordinal == expected.ordinal
+        assert result2.freqstr == "min"
+
+    def test_conv_annual(self):
+        # frequency conversion tests: from Annual Frequency
+
+        ival_A = Period(freq="Y", year=2007)
+
+        ival_AJAN = Period(freq="Y-JAN", year=2007)
+        ival_AJUN = Period(freq="Y-JUN", year=2007)
+        ival_ANOV = Period(freq="Y-NOV", year=2007)
+
+        ival_A_to_Q_start = Period(freq="Q", year=2007, quarter=1)
+        ival_A_to_Q_end = Period(freq="Q", year=2007, quarter=4)
+        ival_A_to_M_start = Period(freq="M", year=2007, month=1)
+        ival_A_to_M_end = Period(freq="M", year=2007, month=12)
+        ival_A_to_W_start = Period(freq="W", year=2007, month=1, day=1)
+        ival_A_to_W_end = Period(freq="W", year=2007, month=12, day=31)
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            ival_A_to_B_start = Period(freq="B", year=2007, month=1, day=1)
+            ival_A_to_B_end = Period(freq="B", year=2007, month=12, day=31)
+        ival_A_to_D_start = Period(freq="D", year=2007, month=1, day=1)
+        ival_A_to_D_end = Period(freq="D", year=2007, month=12, day=31)
+        ival_A_to_H_start = Period(freq="h", year=2007, month=1, day=1, hour=0)
+        ival_A_to_H_end = Period(freq="h", year=2007, month=12, day=31, hour=23)
+        ival_A_to_T_start = Period(
+            freq="Min", year=2007, month=1, day=1, hour=0, minute=0
+        )
+        ival_A_to_T_end = Period(
+            freq="Min", year=2007, month=12, day=31, hour=23, minute=59
+        )
+        ival_A_to_S_start = Period(
+            freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0
+        )
+        ival_A_to_S_end = Period(
+            freq="s", year=2007, month=12, day=31, hour=23, minute=59, second=59
+        )
+
+        ival_AJAN_to_D_end = Period(freq="D", year=2007, month=1, day=31)
+        ival_AJAN_to_D_start = Period(freq="D", year=2006, month=2, day=1)
+        ival_AJUN_to_D_end = Period(freq="D", year=2007, month=6, day=30)
+        ival_AJUN_to_D_start = Period(freq="D", year=2006, month=7, day=1)
+        ival_ANOV_to_D_end = Period(freq="D", year=2007, month=11, day=30)
+        ival_ANOV_to_D_start = Period(freq="D", year=2006, month=12, day=1)
+
+        assert ival_A.asfreq("Q", "s") == ival_A_to_Q_start
+        assert ival_A.asfreq("Q", "e") == ival_A_to_Q_end
+        assert ival_A.asfreq("M", "s") == ival_A_to_M_start
+        assert ival_A.asfreq("M", "E") == ival_A_to_M_end
+        assert ival_A.asfreq("W", "s") == ival_A_to_W_start
+        assert ival_A.asfreq("W", "E") == ival_A_to_W_end
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert ival_A.asfreq("B", "s") == ival_A_to_B_start
+            assert ival_A.asfreq("B", "E") == ival_A_to_B_end
+        assert ival_A.asfreq("D", "s") == ival_A_to_D_start
+        assert ival_A.asfreq("D", "E") == ival_A_to_D_end
+        msg = "'H' is deprecated and will be removed in a future version."
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert ival_A.asfreq("H", "s") == ival_A_to_H_start
+            assert ival_A.asfreq("H", "E") == ival_A_to_H_end
+        assert ival_A.asfreq("min", "s") == ival_A_to_T_start
+        assert ival_A.asfreq("min", "E") == ival_A_to_T_end
+        msg = "'T' is deprecated and will be removed in a future version."
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert ival_A.asfreq("T", "s") == ival_A_to_T_start
+            assert ival_A.asfreq("T", "E") == ival_A_to_T_end
+        msg = "'S' is deprecated and will be removed in a future version."
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert ival_A.asfreq("S", "S") == ival_A_to_S_start
+            assert ival_A.asfreq("S", "E") == ival_A_to_S_end
+
+        assert ival_AJAN.asfreq("D", "s") == ival_AJAN_to_D_start
+        assert ival_AJAN.asfreq("D", "E") == ival_AJAN_to_D_end
+
+        assert ival_AJUN.asfreq("D", "s") == ival_AJUN_to_D_start
+        assert ival_AJUN.asfreq("D", "E") == ival_AJUN_to_D_end
+
+        assert ival_ANOV.asfreq("D", "s") == ival_ANOV_to_D_start
+        assert ival_ANOV.asfreq("D", "E") == ival_ANOV_to_D_end
+
+        assert ival_A.asfreq("Y") == ival_A
+
+    def test_conv_quarterly(self):
+        # frequency conversion tests: from Quarterly Frequency
+
+        ival_Q = Period(freq="Q", year=2007, quarter=1)
+        ival_Q_end_of_year = Period(freq="Q", year=2007, quarter=4)
+
+        ival_QEJAN = Period(freq="Q-JAN", year=2007, quarter=1)
+        ival_QEJUN = Period(freq="Q-JUN", year=2007, quarter=1)
+
+        ival_Q_to_A = Period(freq="Y", year=2007)
+        ival_Q_to_M_start = Period(freq="M", year=2007, month=1)
+        ival_Q_to_M_end = Period(freq="M", year=2007, month=3)
+        ival_Q_to_W_start = Period(freq="W", year=2007, month=1, day=1)
+        ival_Q_to_W_end = Period(freq="W", year=2007, month=3, day=31)
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            ival_Q_to_B_start = Period(freq="B", year=2007, month=1, day=1)
+            ival_Q_to_B_end = Period(freq="B", year=2007, month=3, day=30)
+        ival_Q_to_D_start = Period(freq="D", year=2007, month=1, day=1)
+        ival_Q_to_D_end = Period(freq="D", year=2007, month=3, day=31)
+        ival_Q_to_H_start = Period(freq="h", year=2007, month=1, day=1, hour=0)
+        ival_Q_to_H_end = Period(freq="h", year=2007, month=3, day=31, hour=23)
+        ival_Q_to_T_start = Period(
+            freq="Min", year=2007, month=1, day=1, hour=0, minute=0
+        )
+        ival_Q_to_T_end = Period(
+            freq="Min", year=2007, month=3, day=31, hour=23, minute=59
+        )
+        ival_Q_to_S_start = Period(
+            freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0
+        )
+        ival_Q_to_S_end = Period(
+            freq="s", year=2007, month=3, day=31, hour=23, minute=59, second=59
+        )
+
+        ival_QEJAN_to_D_start = Period(freq="D", year=2006, month=2, day=1)
+        ival_QEJAN_to_D_end = Period(freq="D", year=2006, month=4, day=30)
+
+        ival_QEJUN_to_D_start = Period(freq="D", year=2006, month=7, day=1)
+        ival_QEJUN_to_D_end = Period(freq="D", year=2006, month=9, day=30)
+
+        assert ival_Q.asfreq("Y") == ival_Q_to_A
+        assert ival_Q_end_of_year.asfreq("Y") == ival_Q_to_A
+
+        assert ival_Q.asfreq("M", "s") == ival_Q_to_M_start
+        assert ival_Q.asfreq("M", "E") == ival_Q_to_M_end
+        assert ival_Q.asfreq("W", "s") == ival_Q_to_W_start
+        assert ival_Q.asfreq("W", "E") == ival_Q_to_W_end
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert ival_Q.asfreq("B", "s") == ival_Q_to_B_start
+            assert ival_Q.asfreq("B", "E") == ival_Q_to_B_end
+        assert ival_Q.asfreq("D", "s") == ival_Q_to_D_start
+        assert ival_Q.asfreq("D", "E") == ival_Q_to_D_end
+        assert ival_Q.asfreq("h", "s") == ival_Q_to_H_start
+        assert ival_Q.asfreq("h", "E") == ival_Q_to_H_end
+        assert ival_Q.asfreq("Min", "s") == ival_Q_to_T_start
+        assert ival_Q.asfreq("Min", "E") == ival_Q_to_T_end
+        assert ival_Q.asfreq("s", "s") == ival_Q_to_S_start
+        assert ival_Q.asfreq("s", "E") == ival_Q_to_S_end
+
+        assert ival_QEJAN.asfreq("D", "s") == ival_QEJAN_to_D_start
+        assert ival_QEJAN.asfreq("D", "E") == ival_QEJAN_to_D_end
+        assert ival_QEJUN.asfreq("D", "s") == ival_QEJUN_to_D_start
+        assert ival_QEJUN.asfreq("D", "E") == ival_QEJUN_to_D_end
+
+        assert ival_Q.asfreq("Q") == ival_Q
+
+    def test_conv_monthly(self):
+        # frequency conversion tests: from Monthly Frequency
+
+        ival_M = Period(freq="M", year=2007, month=1)
+        ival_M_end_of_year = Period(freq="M", year=2007, month=12)
+        ival_M_end_of_quarter = Period(freq="M", year=2007, month=3)
+        ival_M_to_A = Period(freq="Y", year=2007)
+        ival_M_to_Q = Period(freq="Q", year=2007, quarter=1)
+        ival_M_to_W_start = Period(freq="W", year=2007, month=1, day=1)
+        ival_M_to_W_end = Period(freq="W", year=2007, month=1, day=31)
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            ival_M_to_B_start = Period(freq="B", year=2007, month=1, day=1)
+            ival_M_to_B_end = Period(freq="B", year=2007, month=1, day=31)
+        ival_M_to_D_start = Period(freq="D", year=2007, month=1, day=1)
+        ival_M_to_D_end = Period(freq="D", year=2007, month=1, day=31)
+        ival_M_to_H_start = Period(freq="h", year=2007, month=1, day=1, hour=0)
+        ival_M_to_H_end = Period(freq="h", year=2007, month=1, day=31, hour=23)
+        ival_M_to_T_start = Period(
+            freq="Min", year=2007, month=1, day=1, hour=0, minute=0
+        )
+        ival_M_to_T_end = Period(
+            freq="Min", year=2007, month=1, day=31, hour=23, minute=59
+        )
+        ival_M_to_S_start = Period(
+            freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0
+        )
+        ival_M_to_S_end = Period(
+            freq="s", year=2007, month=1, day=31, hour=23, minute=59, second=59
+        )
+
+        assert ival_M.asfreq("Y") == ival_M_to_A
+        assert ival_M_end_of_year.asfreq("Y") == ival_M_to_A
+        assert ival_M.asfreq("Q") == ival_M_to_Q
+        assert ival_M_end_of_quarter.asfreq("Q") == ival_M_to_Q
+
+        assert ival_M.asfreq("W", "s") == ival_M_to_W_start
+        assert ival_M.asfreq("W", "E") == ival_M_to_W_end
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert ival_M.asfreq("B", "s") == ival_M_to_B_start
+            assert ival_M.asfreq("B", "E") == ival_M_to_B_end
+        assert ival_M.asfreq("D", "s") == ival_M_to_D_start
+        assert ival_M.asfreq("D", "E") == ival_M_to_D_end
+        assert ival_M.asfreq("h", "s") == ival_M_to_H_start
+        assert ival_M.asfreq("h", "E") == ival_M_to_H_end
+        assert ival_M.asfreq("Min", "s") == ival_M_to_T_start
+        assert ival_M.asfreq("Min", "E") == ival_M_to_T_end
+        assert ival_M.asfreq("s", "s") == ival_M_to_S_start
+        assert ival_M.asfreq("s", "E") == ival_M_to_S_end
+
+        assert ival_M.asfreq("M") == ival_M
+
+    def test_conv_weekly(self):
+        # frequency conversion tests: from Weekly Frequency
+        ival_W = Period(freq="W", year=2007, month=1, day=1)
+
+        ival_WSUN = Period(freq="W", year=2007, month=1, day=7)
+        ival_WSAT = Period(freq="W-SAT", year=2007, month=1, day=6)
+        ival_WFRI = Period(freq="W-FRI", year=2007, month=1, day=5)
+        ival_WTHU = Period(freq="W-THU", year=2007, month=1, day=4)
+        ival_WWED = Period(freq="W-WED", year=2007, month=1, day=3)
+        ival_WTUE = Period(freq="W-TUE", year=2007, month=1, day=2)
+        ival_WMON = Period(freq="W-MON", year=2007, month=1, day=1)
+
+        ival_WSUN_to_D_start = Period(freq="D", year=2007, month=1, day=1)
+        ival_WSUN_to_D_end = Period(freq="D", year=2007, month=1, day=7)
+        ival_WSAT_to_D_start = Period(freq="D", year=2006, month=12, day=31)
+        ival_WSAT_to_D_end = Period(freq="D", year=2007, month=1, day=6)
+        ival_WFRI_to_D_start = Period(freq="D", year=2006, month=12, day=30)
+        ival_WFRI_to_D_end = Period(freq="D", year=2007, month=1, day=5)
+        ival_WTHU_to_D_start = Period(freq="D", year=2006, month=12, day=29)
+        ival_WTHU_to_D_end = Period(freq="D", year=2007, month=1, day=4)
+        ival_WWED_to_D_start = Period(freq="D", year=2006, month=12, day=28)
+        ival_WWED_to_D_end = Period(freq="D", year=2007, month=1, day=3)
+        ival_WTUE_to_D_start = Period(freq="D", year=2006, month=12, day=27)
+        ival_WTUE_to_D_end = Period(freq="D", year=2007, month=1, day=2)
+        ival_WMON_to_D_start = Period(freq="D", year=2006, month=12, day=26)
+        ival_WMON_to_D_end = Period(freq="D", year=2007, month=1, day=1)
+
+        ival_W_end_of_year = Period(freq="W", year=2007, month=12, day=31)
+        ival_W_end_of_quarter = Period(freq="W", year=2007, month=3, day=31)
+        ival_W_end_of_month = Period(freq="W", year=2007, month=1, day=31)
+        ival_W_to_A = Period(freq="Y", year=2007)
+        ival_W_to_Q = Period(freq="Q", year=2007, quarter=1)
+        ival_W_to_M = Period(freq="M", year=2007, month=1)
+
+        if Period(freq="D", year=2007, month=12, day=31).weekday == 6:
+            ival_W_to_A_end_of_year = Period(freq="Y", year=2007)
+        else:
+            ival_W_to_A_end_of_year = Period(freq="Y", year=2008)
+
+        if Period(freq="D", year=2007, month=3, day=31).weekday == 6:
+            ival_W_to_Q_end_of_quarter = Period(freq="Q", year=2007, quarter=1)
+        else:
+            ival_W_to_Q_end_of_quarter = Period(freq="Q", year=2007, quarter=2)
+
+        if Period(freq="D", year=2007, month=1, day=31).weekday == 6:
+            ival_W_to_M_end_of_month = Period(freq="M", year=2007, month=1)
+        else:
+            ival_W_to_M_end_of_month = Period(freq="M", year=2007, month=2)
+
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            ival_W_to_B_start = Period(freq="B", year=2007, month=1, day=1)
+            ival_W_to_B_end = Period(freq="B", year=2007, month=1, day=5)
+        ival_W_to_D_start = Period(freq="D", year=2007, month=1, day=1)
+        ival_W_to_D_end = Period(freq="D", year=2007, month=1, day=7)
+        ival_W_to_H_start = Period(freq="h", year=2007, month=1, day=1, hour=0)
+        ival_W_to_H_end = Period(freq="h", year=2007, month=1, day=7, hour=23)
+        ival_W_to_T_start = Period(
+            freq="Min", year=2007, month=1, day=1, hour=0, minute=0
+        )
+        ival_W_to_T_end = Period(
+            freq="Min", year=2007, month=1, day=7, hour=23, minute=59
+        )
+        ival_W_to_S_start = Period(
+            freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0
+        )
+        ival_W_to_S_end = Period(
+            freq="s", year=2007, month=1, day=7, hour=23, minute=59, second=59
+        )
+
+        assert ival_W.asfreq("Y") == ival_W_to_A
+        assert ival_W_end_of_year.asfreq("Y") == ival_W_to_A_end_of_year
+
+        assert ival_W.asfreq("Q") == ival_W_to_Q
+        assert ival_W_end_of_quarter.asfreq("Q") == ival_W_to_Q_end_of_quarter
+
+        assert ival_W.asfreq("M") == ival_W_to_M
+        assert ival_W_end_of_month.asfreq("M") == ival_W_to_M_end_of_month
+
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert ival_W.asfreq("B", "s") == ival_W_to_B_start
+            assert ival_W.asfreq("B", "E") == ival_W_to_B_end
+
+        assert ival_W.asfreq("D", "s") == ival_W_to_D_start
+        assert ival_W.asfreq("D", "E") == ival_W_to_D_end
+
+        assert ival_WSUN.asfreq("D", "s") == ival_WSUN_to_D_start
+        assert ival_WSUN.asfreq("D", "E") == ival_WSUN_to_D_end
+        assert ival_WSAT.asfreq("D", "s") == ival_WSAT_to_D_start
+        assert ival_WSAT.asfreq("D", "E") == ival_WSAT_to_D_end
+        assert ival_WFRI.asfreq("D", "s") == ival_WFRI_to_D_start
+        assert ival_WFRI.asfreq("D", "E") == ival_WFRI_to_D_end
+        assert ival_WTHU.asfreq("D", "s") == ival_WTHU_to_D_start
+        assert ival_WTHU.asfreq("D", "E") == ival_WTHU_to_D_end
+        assert ival_WWED.asfreq("D", "s") == ival_WWED_to_D_start
+        assert ival_WWED.asfreq("D", "E") == ival_WWED_to_D_end
+        assert ival_WTUE.asfreq("D", "s") == ival_WTUE_to_D_start
+        assert ival_WTUE.asfreq("D", "E") == ival_WTUE_to_D_end
+        assert ival_WMON.asfreq("D", "s") == ival_WMON_to_D_start
+        assert ival_WMON.asfreq("D", "E") == ival_WMON_to_D_end
+
+        assert ival_W.asfreq("h", "s") == ival_W_to_H_start
+        assert ival_W.asfreq("h", "E") == ival_W_to_H_end
+        assert ival_W.asfreq("Min", "s") == ival_W_to_T_start
+        assert ival_W.asfreq("Min", "E") == ival_W_to_T_end
+        assert ival_W.asfreq("s", "s") == ival_W_to_S_start
+        assert ival_W.asfreq("s", "E") == ival_W_to_S_end
+
+        assert ival_W.asfreq("W") == ival_W
+
+        msg = INVALID_FREQ_ERR_MSG
+        with pytest.raises(ValueError, match=msg):
+            ival_W.asfreq("WK")
+
+    def test_conv_weekly_legacy(self):
+        # frequency conversion tests: from Weekly Frequency
+        msg = INVALID_FREQ_ERR_MSG
+        with pytest.raises(ValueError, match=msg):
+            Period(freq="WK", year=2007, month=1, day=1)
+
+        with pytest.raises(ValueError, match=msg):
+            Period(freq="WK-SAT", year=2007, month=1, day=6)
+        with pytest.raises(ValueError, match=msg):
+            Period(freq="WK-FRI", year=2007, month=1, day=5)
+        with pytest.raises(ValueError, match=msg):
+            Period(freq="WK-THU", year=2007, month=1, day=4)
+        with pytest.raises(ValueError, match=msg):
+            Period(freq="WK-WED", year=2007, month=1, day=3)
+        with pytest.raises(ValueError, match=msg):
+            Period(freq="WK-TUE", year=2007, month=1, day=2)
+        with pytest.raises(ValueError, match=msg):
+            Period(freq="WK-MON", year=2007, month=1, day=1)
+
+    def test_conv_business(self):
+        # frequency conversion tests: from Business Frequency"
+
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            ival_B = Period(freq="B", year=2007, month=1, day=1)
+            ival_B_end_of_year = Period(freq="B", year=2007, month=12, day=31)
+            ival_B_end_of_quarter = Period(freq="B", year=2007, month=3, day=30)
+            ival_B_end_of_month = Period(freq="B", year=2007, month=1, day=31)
+            ival_B_end_of_week = Period(freq="B", year=2007, month=1, day=5)
+
+        ival_B_to_A = Period(freq="Y", year=2007)
+        ival_B_to_Q = Period(freq="Q", year=2007, quarter=1)
+        ival_B_to_M = Period(freq="M", year=2007, month=1)
+        ival_B_to_W = Period(freq="W", year=2007, month=1, day=7)
+        ival_B_to_D = Period(freq="D", year=2007, month=1, day=1)
+        ival_B_to_H_start = Period(freq="h", year=2007, month=1, day=1, hour=0)
+        ival_B_to_H_end = Period(freq="h", year=2007, month=1, day=1, hour=23)
+        ival_B_to_T_start = Period(
+            freq="Min", year=2007, month=1, day=1, hour=0, minute=0
+        )
+        ival_B_to_T_end = Period(
+            freq="Min", year=2007, month=1, day=1, hour=23, minute=59
+        )
+        ival_B_to_S_start = Period(
+            freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0
+        )
+        ival_B_to_S_end = Period(
+            freq="s", year=2007, month=1, day=1, hour=23, minute=59, second=59
+        )
+
+        assert ival_B.asfreq("Y") == ival_B_to_A
+        assert ival_B_end_of_year.asfreq("Y") == ival_B_to_A
+        assert ival_B.asfreq("Q") == ival_B_to_Q
+        assert ival_B_end_of_quarter.asfreq("Q") == ival_B_to_Q
+        assert ival_B.asfreq("M") == ival_B_to_M
+        assert ival_B_end_of_month.asfreq("M") == ival_B_to_M
+        assert ival_B.asfreq("W") == ival_B_to_W
+        assert ival_B_end_of_week.asfreq("W") == ival_B_to_W
+
+        assert ival_B.asfreq("D") == ival_B_to_D
+
+        assert ival_B.asfreq("h", "s") == ival_B_to_H_start
+        assert ival_B.asfreq("h", "E") == ival_B_to_H_end
+        assert ival_B.asfreq("Min", "s") == ival_B_to_T_start
+        assert ival_B.asfreq("Min", "E") == ival_B_to_T_end
+        assert ival_B.asfreq("s", "s") == ival_B_to_S_start
+        assert ival_B.asfreq("s", "E") == ival_B_to_S_end
+
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert ival_B.asfreq("B") == ival_B
+
+    def test_conv_daily(self):
+        # frequency conversion tests: from Business Frequency"
+
+        ival_D = Period(freq="D", year=2007, month=1, day=1)
+        ival_D_end_of_year = Period(freq="D", year=2007, month=12, day=31)
+        ival_D_end_of_quarter = Period(freq="D", year=2007, month=3, day=31)
+        ival_D_end_of_month = Period(freq="D", year=2007, month=1, day=31)
+        ival_D_end_of_week = Period(freq="D", year=2007, month=1, day=7)
+
+        ival_D_friday = Period(freq="D", year=2007, month=1, day=5)
+        ival_D_saturday = Period(freq="D", year=2007, month=1, day=6)
+        ival_D_sunday = Period(freq="D", year=2007, month=1, day=7)
+
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            ival_B_friday = Period(freq="B", year=2007, month=1, day=5)
+            ival_B_monday = Period(freq="B", year=2007, month=1, day=8)
+
+        ival_D_to_A = Period(freq="Y", year=2007)
+
+        ival_Deoq_to_AJAN = Period(freq="Y-JAN", year=2008)
+        ival_Deoq_to_AJUN = Period(freq="Y-JUN", year=2007)
+        ival_Deoq_to_ADEC = Period(freq="Y-DEC", year=2007)
+
+        ival_D_to_QEJAN = Period(freq="Q-JAN", year=2007, quarter=4)
+        ival_D_to_QEJUN = Period(freq="Q-JUN", year=2007, quarter=3)
+        ival_D_to_QEDEC = Period(freq="Q-DEC", year=2007, quarter=1)
+
+        ival_D_to_M = Period(freq="M", year=2007, month=1)
+        ival_D_to_W = Period(freq="W", year=2007, month=1, day=7)
+
+        ival_D_to_H_start = Period(freq="h", year=2007, month=1, day=1, hour=0)
+        ival_D_to_H_end = Period(freq="h", year=2007, month=1, day=1, hour=23)
+        ival_D_to_T_start = Period(
+            freq="Min", year=2007, month=1, day=1, hour=0, minute=0
+        )
+        ival_D_to_T_end = Period(
+            freq="Min", year=2007, month=1, day=1, hour=23, minute=59
+        )
+        ival_D_to_S_start = Period(
+            freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0
+        )
+        ival_D_to_S_end = Period(
+            freq="s", year=2007, month=1, day=1, hour=23, minute=59, second=59
+        )
+
+        assert ival_D.asfreq("Y") == ival_D_to_A
+
+        assert ival_D_end_of_quarter.asfreq("Y-JAN") == ival_Deoq_to_AJAN
+        assert ival_D_end_of_quarter.asfreq("Y-JUN") == ival_Deoq_to_AJUN
+        assert ival_D_end_of_quarter.asfreq("Y-DEC") == ival_Deoq_to_ADEC
+
+        assert ival_D_end_of_year.asfreq("Y") == ival_D_to_A
+        assert ival_D_end_of_quarter.asfreq("Q") == ival_D_to_QEDEC
+        assert ival_D.asfreq("Q-JAN") == ival_D_to_QEJAN
+        assert ival_D.asfreq("Q-JUN") == ival_D_to_QEJUN
+        assert ival_D.asfreq("Q-DEC") == ival_D_to_QEDEC
+        assert ival_D.asfreq("M") == ival_D_to_M
+        assert ival_D_end_of_month.asfreq("M") == ival_D_to_M
+        assert ival_D.asfreq("W") == ival_D_to_W
+        assert ival_D_end_of_week.asfreq("W") == ival_D_to_W
+
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert ival_D_friday.asfreq("B") == ival_B_friday
+            assert ival_D_saturday.asfreq("B", "s") == ival_B_friday
+            assert ival_D_saturday.asfreq("B", "E") == ival_B_monday
+            assert ival_D_sunday.asfreq("B", "s") == ival_B_friday
+            assert ival_D_sunday.asfreq("B", "E") == ival_B_monday
+
+        assert ival_D.asfreq("h", "s") == ival_D_to_H_start
+        assert ival_D.asfreq("h", "E") == ival_D_to_H_end
+        assert ival_D.asfreq("Min", "s") == ival_D_to_T_start
+        assert ival_D.asfreq("Min", "E") == ival_D_to_T_end
+        assert ival_D.asfreq("s", "s") == ival_D_to_S_start
+        assert ival_D.asfreq("s", "E") == ival_D_to_S_end
+
+        assert ival_D.asfreq("D") == ival_D
+
+    def test_conv_hourly(self):
+        # frequency conversion tests: from Hourly Frequency"
+
+        ival_H = Period(freq="h", year=2007, month=1, day=1, hour=0)
+        ival_H_end_of_year = Period(freq="h", year=2007, month=12, day=31, hour=23)
+        ival_H_end_of_quarter = Period(freq="h", year=2007, month=3, day=31, hour=23)
+        ival_H_end_of_month = Period(freq="h", year=2007, month=1, day=31, hour=23)
+        ival_H_end_of_week = Period(freq="h", year=2007, month=1, day=7, hour=23)
+        ival_H_end_of_day = Period(freq="h", year=2007, month=1, day=1, hour=23)
+        ival_H_end_of_bus = Period(freq="h", year=2007, month=1, day=1, hour=23)
+
+        ival_H_to_A = Period(freq="Y", year=2007)
+        ival_H_to_Q = Period(freq="Q", year=2007, quarter=1)
+        ival_H_to_M = Period(freq="M", year=2007, month=1)
+        ival_H_to_W = Period(freq="W", year=2007, month=1, day=7)
+        ival_H_to_D = Period(freq="D", year=2007, month=1, day=1)
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            ival_H_to_B = Period(freq="B", year=2007, month=1, day=1)
+
+        ival_H_to_T_start = Period(
+            freq="Min", year=2007, month=1, day=1, hour=0, minute=0
+        )
+        ival_H_to_T_end = Period(
+            freq="Min", year=2007, month=1, day=1, hour=0, minute=59
+        )
+        ival_H_to_S_start = Period(
+            freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0
+        )
+        ival_H_to_S_end = Period(
+            freq="s", year=2007, month=1, day=1, hour=0, minute=59, second=59
+        )
+
+        assert ival_H.asfreq("Y") == ival_H_to_A
+        assert ival_H_end_of_year.asfreq("Y") == ival_H_to_A
+        assert ival_H.asfreq("Q") == ival_H_to_Q
+        assert ival_H_end_of_quarter.asfreq("Q") == ival_H_to_Q
+        assert ival_H.asfreq("M") == ival_H_to_M
+        assert ival_H_end_of_month.asfreq("M") == ival_H_to_M
+        assert ival_H.asfreq("W") == ival_H_to_W
+        assert ival_H_end_of_week.asfreq("W") == ival_H_to_W
+        assert ival_H.asfreq("D") == ival_H_to_D
+        assert ival_H_end_of_day.asfreq("D") == ival_H_to_D
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert ival_H.asfreq("B") == ival_H_to_B
+            assert ival_H_end_of_bus.asfreq("B") == ival_H_to_B
+
+        assert ival_H.asfreq("Min", "s") == ival_H_to_T_start
+        assert ival_H.asfreq("Min", "E") == ival_H_to_T_end
+        assert ival_H.asfreq("s", "s") == ival_H_to_S_start
+        assert ival_H.asfreq("s", "E") == ival_H_to_S_end
+
+        assert ival_H.asfreq("h") == ival_H
+
+    def test_conv_minutely(self):
+        # frequency conversion tests: from Minutely Frequency"
+
+        ival_T = Period(freq="Min", year=2007, month=1, day=1, hour=0, minute=0)
+        ival_T_end_of_year = Period(
+            freq="Min", year=2007, month=12, day=31, hour=23, minute=59
+        )
+        ival_T_end_of_quarter = Period(
+            freq="Min", year=2007, month=3, day=31, hour=23, minute=59
+        )
+        ival_T_end_of_month = Period(
+            freq="Min", year=2007, month=1, day=31, hour=23, minute=59
+        )
+        ival_T_end_of_week = Period(
+            freq="Min", year=2007, month=1, day=7, hour=23, minute=59
+        )
+        ival_T_end_of_day = Period(
+            freq="Min", year=2007, month=1, day=1, hour=23, minute=59
+        )
+        ival_T_end_of_bus = Period(
+            freq="Min", year=2007, month=1, day=1, hour=23, minute=59
+        )
+        ival_T_end_of_hour = Period(
+            freq="Min", year=2007, month=1, day=1, hour=0, minute=59
+        )
+
+        ival_T_to_A = Period(freq="Y", year=2007)
+        ival_T_to_Q = Period(freq="Q", year=2007, quarter=1)
+        ival_T_to_M = Period(freq="M", year=2007, month=1)
+        ival_T_to_W = Period(freq="W", year=2007, month=1, day=7)
+        ival_T_to_D = Period(freq="D", year=2007, month=1, day=1)
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            ival_T_to_B = Period(freq="B", year=2007, month=1, day=1)
+        ival_T_to_H = Period(freq="h", year=2007, month=1, day=1, hour=0)
+
+        ival_T_to_S_start = Period(
+            freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0
+        )
+        ival_T_to_S_end = Period(
+            freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=59
+        )
+
+        assert ival_T.asfreq("Y") == ival_T_to_A
+        assert ival_T_end_of_year.asfreq("Y") == ival_T_to_A
+        assert ival_T.asfreq("Q") == ival_T_to_Q
+        assert ival_T_end_of_quarter.asfreq("Q") == ival_T_to_Q
+        assert ival_T.asfreq("M") == ival_T_to_M
+        assert ival_T_end_of_month.asfreq("M") == ival_T_to_M
+        assert ival_T.asfreq("W") == ival_T_to_W
+        assert ival_T_end_of_week.asfreq("W") == ival_T_to_W
+        assert ival_T.asfreq("D") == ival_T_to_D
+        assert ival_T_end_of_day.asfreq("D") == ival_T_to_D
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert ival_T.asfreq("B") == ival_T_to_B
+            assert ival_T_end_of_bus.asfreq("B") == ival_T_to_B
+        assert ival_T.asfreq("h") == ival_T_to_H
+        assert ival_T_end_of_hour.asfreq("h") == ival_T_to_H
+
+        assert ival_T.asfreq("s", "s") == ival_T_to_S_start
+        assert ival_T.asfreq("s", "E") == ival_T_to_S_end
+
+        assert ival_T.asfreq("Min") == ival_T
+
+    def test_conv_secondly(self):
+        # frequency conversion tests: from Secondly Frequency"
+
+        ival_S = Period(freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0)
+        ival_S_end_of_year = Period(
+            freq="s", year=2007, month=12, day=31, hour=23, minute=59, second=59
+        )
+        ival_S_end_of_quarter = Period(
+            freq="s", year=2007, month=3, day=31, hour=23, minute=59, second=59
+        )
+        ival_S_end_of_month = Period(
+            freq="s", year=2007, month=1, day=31, hour=23, minute=59, second=59
+        )
+        ival_S_end_of_week = Period(
+            freq="s", year=2007, month=1, day=7, hour=23, minute=59, second=59
+        )
+        ival_S_end_of_day = Period(
+            freq="s", year=2007, month=1, day=1, hour=23, minute=59, second=59
+        )
+        ival_S_end_of_bus = Period(
+            freq="s", year=2007, month=1, day=1, hour=23, minute=59, second=59
+        )
+        ival_S_end_of_hour = Period(
+            freq="s", year=2007, month=1, day=1, hour=0, minute=59, second=59
+        )
+        ival_S_end_of_minute = Period(
+            freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=59
+        )
+
+        ival_S_to_A = Period(freq="Y", year=2007)
+        ival_S_to_Q = Period(freq="Q", year=2007, quarter=1)
+        ival_S_to_M = Period(freq="M", year=2007, month=1)
+        ival_S_to_W = Period(freq="W", year=2007, month=1, day=7)
+        ival_S_to_D = Period(freq="D", year=2007, month=1, day=1)
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            ival_S_to_B = Period(freq="B", year=2007, month=1, day=1)
+        ival_S_to_H = Period(freq="h", year=2007, month=1, day=1, hour=0)
+        ival_S_to_T = Period(freq="Min", year=2007, month=1, day=1, hour=0, minute=0)
+
+        assert ival_S.asfreq("Y") == ival_S_to_A
+        assert ival_S_end_of_year.asfreq("Y") == ival_S_to_A
+        assert ival_S.asfreq("Q") == ival_S_to_Q
+        assert ival_S_end_of_quarter.asfreq("Q") == ival_S_to_Q
+        assert ival_S.asfreq("M") == ival_S_to_M
+        assert ival_S_end_of_month.asfreq("M") == ival_S_to_M
+        assert ival_S.asfreq("W") == ival_S_to_W
+        assert ival_S_end_of_week.asfreq("W") == ival_S_to_W
+        assert ival_S.asfreq("D") == ival_S_to_D
+        assert ival_S_end_of_day.asfreq("D") == ival_S_to_D
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert ival_S.asfreq("B") == ival_S_to_B
+            assert ival_S_end_of_bus.asfreq("B") == ival_S_to_B
+        assert ival_S.asfreq("h") == ival_S_to_H
+        assert ival_S_end_of_hour.asfreq("h") == ival_S_to_H
+        assert ival_S.asfreq("Min") == ival_S_to_T
+        assert ival_S_end_of_minute.asfreq("Min") == ival_S_to_T
+
+        assert ival_S.asfreq("s") == ival_S
+
+    def test_conv_microsecond(self):
+        # GH#31475 Avoid floating point errors dropping the start_time to
+        #  before the beginning of the Period
+        per = Period("2020-01-30 15:57:27.576166", freq="us")
+        assert per.ordinal == 1580399847576166
+
+        start = per.start_time
+        expected = Timestamp("2020-01-30 15:57:27.576166")
+        assert start == expected
+        assert start._value == per.ordinal * 1000
+
+        per2 = Period("2300-01-01", "us")
+        msg = "2300-01-01"
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            per2.start_time
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            per2.end_time
+
+    def test_asfreq_mult(self):
+        # normal freq to mult freq
+        p = Period(freq="Y", year=2007)
+        # ordinal will not change
+        for freq in ["3Y", offsets.YearEnd(3)]:
+            result = p.asfreq(freq)
+            expected = Period("2007", freq="3Y")
+
+            assert result == expected
+            assert result.ordinal == expected.ordinal
+            assert result.freq == expected.freq
+        # ordinal will not change
+        for freq in ["3Y", offsets.YearEnd(3)]:
+            result = p.asfreq(freq, how="S")
+            expected = Period("2007", freq="3Y")
+
+            assert result == expected
+            assert result.ordinal == expected.ordinal
+            assert result.freq == expected.freq
+
+        # mult freq to normal freq
+        p = Period(freq="3Y", year=2007)
+        # ordinal will change because how=E is the default
+        for freq in ["Y", offsets.YearEnd()]:
+            result = p.asfreq(freq)
+            expected = Period("2009", freq="Y")
+
+            assert result == expected
+            assert result.ordinal == expected.ordinal
+            assert result.freq == expected.freq
+        # ordinal will not change
+        for freq in ["Y", offsets.YearEnd()]:
+            result = p.asfreq(freq, how="s")
+            expected = Period("2007", freq="Y")
+
+            assert result == expected
+            assert result.ordinal == expected.ordinal
+            assert result.freq == expected.freq
+
+        p = Period(freq="Y", year=2007)
+        for freq in ["2M", offsets.MonthEnd(2)]:
+            result = p.asfreq(freq)
+            expected = Period("2007-12", freq="2M")
+
+            assert result == expected
+            assert result.ordinal == expected.ordinal
+            assert result.freq == expected.freq
+        for freq in ["2M", offsets.MonthEnd(2)]:
+            result = p.asfreq(freq, how="s")
+            expected = Period("2007-01", freq="2M")
+
+            assert result == expected
+            assert result.ordinal == expected.ordinal
+            assert result.freq == expected.freq
+
+        p = Period(freq="3Y", year=2007)
+        for freq in ["2M", offsets.MonthEnd(2)]:
+            result = p.asfreq(freq)
+            expected = Period("2009-12", freq="2M")
+
+            assert result == expected
+            assert result.ordinal == expected.ordinal
+            assert result.freq == expected.freq
+        for freq in ["2M", offsets.MonthEnd(2)]:
+            result = p.asfreq(freq, how="s")
+            expected = Period("2007-01", freq="2M")
+
+            assert result == expected
+            assert result.ordinal == expected.ordinal
+            assert result.freq == expected.freq
+
+    def test_asfreq_combined(self):
+        # normal freq to combined freq
+        p = Period("2007", freq="h")
+
+        # ordinal will not change
+        expected = Period("2007", freq="25h")
+        for freq, how in zip(["1D1h", "1h1D"], ["E", "S"]):
+            result = p.asfreq(freq, how=how)
+            assert result == expected
+            assert result.ordinal == expected.ordinal
+            assert result.freq == expected.freq
+
+        # combined freq to normal freq
+        p1 = Period(freq="1D1h", year=2007)
+        p2 = Period(freq="1h1D", year=2007)
+
+        # ordinal will change because how=E is the default
+        result1 = p1.asfreq("h")
+        result2 = p2.asfreq("h")
+        expected = Period("2007-01-02", freq="h")
+        assert result1 == expected
+        assert result1.ordinal == expected.ordinal
+        assert result1.freq == expected.freq
+        assert result2 == expected
+        assert result2.ordinal == expected.ordinal
+        assert result2.freq == expected.freq
+
+        # ordinal will not change
+        result1 = p1.asfreq("h", how="S")
+        result2 = p2.asfreq("h", how="S")
+        expected = Period("2007-01-01", freq="h")
+        assert result1 == expected
+        assert result1.ordinal == expected.ordinal
+        assert result1.freq == expected.freq
+        assert result2 == expected
+        assert result2.ordinal == expected.ordinal
+        assert result2.freq == expected.freq
+
+    def test_asfreq_MS(self):
+        initial = Period("2013")
+
+        assert initial.asfreq(freq="M", how="S") == Period("2013-01", "M")
+
+        msg = "MS is not supported as period frequency"
+        with pytest.raises(ValueError, match=msg):
+            initial.asfreq(freq="MS", how="S")
+
+        with pytest.raises(ValueError, match=msg):
+            Period("2013-01", "MS")

py311/lib/python3.11/site-packages/pandas/tests/scalar/period/test_period.py

@@ -0,0 +1,1158 @@
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+)
+import re
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import iNaT
+from pandas._libs.tslibs.ccalendar import (
+    DAYS,
+    MONTHS,
+)
+from pandas._libs.tslibs.np_datetime import OutOfBoundsDatetime
+from pandas._libs.tslibs.parsing import DateParseError
+from pandas._libs.tslibs.period import INVALID_FREQ_ERR_MSG
+from pandas.compat import PY314
+
+from pandas import (
+    NaT,
+    Period,
+    Timedelta,
+    Timestamp,
+    offsets,
+)
+import pandas._testing as tm
+
+bday_msg = "Period with BDay freq is deprecated"
+
+
+class TestPeriodDisallowedFreqs:
+    @pytest.mark.parametrize(
+        "freq, freq_msg",
+        [
+            (offsets.BYearBegin(), "BYearBegin"),
+            (offsets.YearBegin(2), "YearBegin"),
+            (offsets.QuarterBegin(startingMonth=12), "QuarterBegin"),
+            (offsets.BusinessMonthEnd(2), "BusinessMonthEnd"),
+        ],
+    )
+    def test_offsets_not_supported(self, freq, freq_msg):
+        # GH#55785
+        msg = re.escape(f"{freq} is not supported as period frequency")
+        with pytest.raises(ValueError, match=msg):
+            Period(year=2014, freq=freq)
+
+    def test_custom_business_day_freq_raises(self):
+        # GH#52534
+        msg = "C is not supported as period frequency"
+        with pytest.raises(ValueError, match=msg):
+            Period("2023-04-10", freq="C")
+        msg = f"{offsets.CustomBusinessDay().base} is not supported as period frequency"
+        with pytest.raises(ValueError, match=msg):
+            Period("2023-04-10", freq=offsets.CustomBusinessDay())
+
+    def test_invalid_frequency_error_message(self):
+        msg = "WOM-1MON is not supported as period frequency"
+        with pytest.raises(ValueError, match=msg):
+            Period("2012-01-02", freq="WOM-1MON")
+
+    def test_invalid_frequency_period_error_message(self):
+        msg = "for Period, please use 'M' instead of 'ME'"
+        with pytest.raises(ValueError, match=msg):
+            Period("2012-01-02", freq="ME")
+
+
+class TestPeriodConstruction:
+    def test_from_td64nat_raises(self):
+        # GH#44507
+        td = NaT.to_numpy("m8[ns]")
+
+        msg = "Value must be Period, string, integer, or datetime"
+        with pytest.raises(ValueError, match=msg):
+            Period(td)
+
+        with pytest.raises(ValueError, match=msg):
+            Period(td, freq="D")
+
+    def test_construction(self):
+        i1 = Period("1/1/2005", freq="M")
+        i2 = Period("Jan 2005")
+
+        assert i1 == i2
+
+        # GH#54105 - Period can be confusingly instantiated with lowercase freq
+        # TODO: raise in the future an error when passing lowercase freq
+        i1 = Period("2005", freq="Y")
+        i2 = Period("2005")
+
+        assert i1 == i2
+
+        i4 = Period("2005", freq="M")
+        assert i1 != i4
+
+        i1 = Period.now(freq="Q")
+        i2 = Period(datetime.now(), freq="Q")
+
+        assert i1 == i2
+
+        # Pass in freq as a keyword argument sometimes as a test for
+        # https://github.com/pandas-dev/pandas/issues/53369
+        i1 = Period.now(freq="D")
+        i2 = Period(datetime.now(), freq="D")
+        i3 = Period.now(offsets.Day())
+
+        assert i1 == i2
+        assert i1 == i3
+
+        i1 = Period("1982", freq="min")
+        msg = "'MIN' is deprecated and will be removed in a future version."
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            i2 = Period("1982", freq="MIN")
+        assert i1 == i2
+
+        i1 = Period(year=2005, month=3, day=1, freq="D")
+        i2 = Period("3/1/2005", freq="D")
+        assert i1 == i2
+
+        i3 = Period(year=2005, month=3, day=1, freq="d")
+        assert i1 == i3
+
+        i1 = Period("2007-01-01 09:00:00.001")
+        expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq="ms")
+        assert i1 == expected
+
+        expected = Period("2007-01-01 09:00:00.001", freq="ms")
+        assert i1 == expected
+
+        i1 = Period("2007-01-01 09:00:00.00101")
+        expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq="us")
+        assert i1 == expected
+
+        expected = Period("2007-01-01 09:00:00.00101", freq="us")
+        assert i1 == expected
+
+        msg = "Must supply freq for ordinal value"
+        with pytest.raises(ValueError, match=msg):
+            Period(ordinal=200701)
+
+        msg = "Invalid frequency: X"
+        with pytest.raises(ValueError, match=msg):
+            Period("2007-1-1", freq="X")
+
+    def test_tuple_freq_disallowed(self):
+        # GH#34703 tuple freq disallowed
+        with pytest.raises(TypeError, match="pass as a string instead"):
+            Period("1982", freq=("Min", 1))
+
+        with pytest.raises(TypeError, match="pass as a string instead"):
+            Period("2006-12-31", ("w", 1))
+
+    def test_construction_from_timestamp_nanos(self):
+        # GH#46811 don't drop nanos from Timestamp
+        ts = Timestamp("2022-04-20 09:23:24.123456789")
+        per = Period(ts, freq="ns")
+
+        # should losslessly round-trip, not lose the 789
+        rt = per.to_timestamp()
+        assert rt == ts
+
+        # same thing but from a datetime64 object
+        dt64 = ts.asm8
+        per2 = Period(dt64, freq="ns")
+        rt2 = per2.to_timestamp()
+        assert rt2.asm8 == dt64
+
+    def test_construction_bday(self):
+        # Biz day construction, roll forward if non-weekday
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            i1 = Period("3/10/12", freq="B")
+            i2 = Period("3/10/12", freq="D")
+            assert i1 == i2.asfreq("B")
+            i2 = Period("3/11/12", freq="D")
+            assert i1 == i2.asfreq("B")
+            i2 = Period("3/12/12", freq="D")
+            assert i1 == i2.asfreq("B")
+
+            i3 = Period("3/10/12", freq="b")
+            assert i1 == i3
+
+            i1 = Period(year=2012, month=3, day=10, freq="B")
+            i2 = Period("3/12/12", freq="B")
+            assert i1 == i2
+
+    def test_construction_quarter(self):
+        i1 = Period(year=2005, quarter=1, freq="Q")
+        i2 = Period("1/1/2005", freq="Q")
+        assert i1 == i2
+
+        i1 = Period(year=2005, quarter=3, freq="Q")
+        i2 = Period("9/1/2005", freq="Q")
+        assert i1 == i2
+
+        i1 = Period("2005Q1")
+        i2 = Period(year=2005, quarter=1, freq="Q")
+        i3 = Period("2005q1")
+        assert i1 == i2
+        assert i1 == i3
+
+        i1 = Period("05Q1")
+        assert i1 == i2
+        lower = Period("05q1")
+        assert i1 == lower
+
+        i1 = Period("1Q2005")
+        assert i1 == i2
+        lower = Period("1q2005")
+        assert i1 == lower
+
+        i1 = Period("1Q05")
+        assert i1 == i2
+        lower = Period("1q05")
+        assert i1 == lower
+
+        i1 = Period("4Q1984")
+        assert i1.year == 1984
+        lower = Period("4q1984")
+        assert i1 == lower
+
+    def test_construction_month(self):
+        expected = Period("2007-01", freq="M")
+        i1 = Period("200701", freq="M")
+        assert i1 == expected
+
+        i1 = Period("200701", freq="M")
+        assert i1 == expected
+
+        i1 = Period(200701, freq="M")
+        assert i1 == expected
+
+        i1 = Period(ordinal=200701, freq="M")
+        assert i1.year == 18695
+
+        i1 = Period(datetime(2007, 1, 1), freq="M")
+        i2 = Period("200701", freq="M")
+        assert i1 == i2
+
+        i1 = Period(date(2007, 1, 1), freq="M")
+        i2 = Period(datetime(2007, 1, 1), freq="M")
+        i3 = Period(np.datetime64("2007-01-01"), freq="M")
+        i4 = Period("2007-01-01 00:00:00", freq="M")
+        i5 = Period("2007-01-01 00:00:00.000", freq="M")
+        assert i1 == i2
+        assert i1 == i3
+        assert i1 == i4
+        assert i1 == i5
+
+    def test_period_constructor_offsets(self):
+        assert Period("1/1/2005", freq=offsets.MonthEnd()) == Period(
+            "1/1/2005", freq="M"
+        )
+        assert Period("2005", freq=offsets.YearEnd()) == Period("2005", freq="Y")
+        assert Period("2005", freq=offsets.MonthEnd()) == Period("2005", freq="M")
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert Period("3/10/12", freq=offsets.BusinessDay()) == Period(
+                "3/10/12", freq="B"
+            )
+        assert Period("3/10/12", freq=offsets.Day()) == Period("3/10/12", freq="D")
+
+        assert Period(
+            year=2005, quarter=1, freq=offsets.QuarterEnd(startingMonth=12)
+        ) == Period(year=2005, quarter=1, freq="Q")
+        assert Period(
+            year=2005, quarter=2, freq=offsets.QuarterEnd(startingMonth=12)
+        ) == Period(year=2005, quarter=2, freq="Q")
+
+        assert Period(year=2005, month=3, day=1, freq=offsets.Day()) == Period(
+            year=2005, month=3, day=1, freq="D"
+        )
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert Period(year=2012, month=3, day=10, freq=offsets.BDay()) == Period(
+                year=2012, month=3, day=10, freq="B"
+            )
+
+        expected = Period("2005-03-01", freq="3D")
+        assert Period(year=2005, month=3, day=1, freq=offsets.Day(3)) == expected
+        assert Period(year=2005, month=3, day=1, freq="3D") == expected
+
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert Period(year=2012, month=3, day=10, freq=offsets.BDay(3)) == Period(
+                year=2012, month=3, day=10, freq="3B"
+            )
+
+        assert Period(200701, freq=offsets.MonthEnd()) == Period(200701, freq="M")
+
+        i1 = Period(ordinal=200701, freq=offsets.MonthEnd())
+        i2 = Period(ordinal=200701, freq="M")
+        assert i1 == i2
+        assert i1.year == 18695
+        assert i2.year == 18695
+
+        i1 = Period(datetime(2007, 1, 1), freq="M")
+        i2 = Period("200701", freq="M")
+        assert i1 == i2
+
+        i1 = Period(date(2007, 1, 1), freq="M")
+        i2 = Period(datetime(2007, 1, 1), freq="M")
+        i3 = Period(np.datetime64("2007-01-01"), freq="M")
+        i4 = Period("2007-01-01 00:00:00", freq="M")
+        i5 = Period("2007-01-01 00:00:00.000", freq="M")
+        assert i1 == i2
+        assert i1 == i3
+        assert i1 == i4
+        assert i1 == i5
+
+        i1 = Period("2007-01-01 09:00:00.001")
+        expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq="ms")
+        assert i1 == expected
+
+        expected = Period("2007-01-01 09:00:00.001", freq="ms")
+        assert i1 == expected
+
+        i1 = Period("2007-01-01 09:00:00.00101")
+        expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq="us")
+        assert i1 == expected
+
+        expected = Period("2007-01-01 09:00:00.00101", freq="us")
+        assert i1 == expected
+
+    def test_invalid_arguments(self):
+        msg = "Must supply freq for datetime value"
+        with pytest.raises(ValueError, match=msg):
+            Period(datetime.now())
+        with pytest.raises(ValueError, match=msg):
+            Period(datetime.now().date())
+
+        msg = "Value must be Period, string, integer, or datetime"
+        with pytest.raises(ValueError, match=msg):
+            Period(1.6, freq="D")
+        msg = "Ordinal must be an integer"
+        with pytest.raises(ValueError, match=msg):
+            Period(ordinal=1.6, freq="D")
+        msg = "Only value or ordinal but not both should be given but not both"
+        with pytest.raises(ValueError, match=msg):
+            Period(ordinal=2, value=1, freq="D")
+
+        msg = "If value is None, freq cannot be None"
+        with pytest.raises(ValueError, match=msg):
+            Period(month=1)
+
+        msg = '^Given date string "-2000" not likely a datetime$'
+        with pytest.raises(ValueError, match=msg):
+            Period("-2000", "Y")
+        if PY314:
+            msg = "day 0 must be in range 1..31 for month 1 in year 1: 0"
+        else:
+            msg = "day is out of range for month"
+        with pytest.raises(DateParseError, match=msg):
+            Period("0", "Y")
+        msg = "Unknown datetime string format, unable to parse"
+        with pytest.raises(DateParseError, match=msg):
+            Period("1/1/-2000", "Y")
+
+    def test_constructor_corner(self):
+        expected = Period("2007-01", freq="2M")
+        assert Period(year=2007, month=1, freq="2M") == expected
+
+        assert Period(None) is NaT
+
+        p = Period("2007-01-01", freq="D")
+
+        result = Period(p, freq="Y")
+        exp = Period("2007", freq="Y")
+        assert result == exp
+
+    def test_constructor_infer_freq(self):
+        p = Period("2007-01-01")
+        assert p.freq == "D"
+
+        p = Period("2007-01-01 07")
+        assert p.freq == "h"
+
+        p = Period("2007-01-01 07:10")
+        assert p.freq == "min"
+
+        p = Period("2007-01-01 07:10:15")
+        assert p.freq == "s"
+
+        p = Period("2007-01-01 07:10:15.123")
+        assert p.freq == "ms"
+
+        # We see that there are 6 digits after the decimal, so get microsecond
+        #  even though they are all zeros.
+        p = Period("2007-01-01 07:10:15.123000")
+        assert p.freq == "us"
+
+        p = Period("2007-01-01 07:10:15.123400")
+        assert p.freq == "us"
+
+    def test_multiples(self):
+        result1 = Period("1989", freq="2Y")
+        result2 = Period("1989", freq="Y")
+        assert result1.ordinal == result2.ordinal
+        assert result1.freqstr == "2Y-DEC"
+        assert result2.freqstr == "Y-DEC"
+        assert result1.freq == offsets.YearEnd(2)
+        assert result2.freq == offsets.YearEnd()
+
+        assert (result1 + 1).ordinal == result1.ordinal + 2
+        assert (1 + result1).ordinal == result1.ordinal + 2
+        assert (result1 - 1).ordinal == result2.ordinal - 2
+        assert (-1 + result1).ordinal == result2.ordinal - 2
+
+    @pytest.mark.parametrize("month", MONTHS)
+    def test_period_cons_quarterly(self, month):
+        # bugs in scikits.timeseries
+        freq = f"Q-{month}"
+        exp = Period("1989Q3", freq=freq)
+        assert "1989Q3" in str(exp)
+        stamp = exp.to_timestamp("D", how="end")
+        p = Period(stamp, freq=freq)
+        assert p == exp
+
+        stamp = exp.to_timestamp("3D", how="end")
+        p = Period(stamp, freq=freq)
+        assert p == exp
+
+    @pytest.mark.parametrize("month", MONTHS)
+    def test_period_cons_annual(self, month):
+        # bugs in scikits.timeseries
+        freq = f"Y-{month}"
+        exp = Period("1989", freq=freq)
+        stamp = exp.to_timestamp("D", how="end") + timedelta(days=30)
+        p = Period(stamp, freq=freq)
+
+        assert p == exp + 1
+        assert isinstance(p, Period)
+
+    @pytest.mark.parametrize("day", DAYS)
+    @pytest.mark.parametrize("num", range(10, 17))
+    def test_period_cons_weekly(self, num, day):
+        daystr = f"2011-02-{num}"
+        freq = f"W-{day}"
+
+        result = Period(daystr, freq=freq)
+        expected = Period(daystr, freq="D").asfreq(freq)
+        assert result == expected
+        assert isinstance(result, Period)
+
+    def test_parse_week_str_roundstrip(self):
+        # GH#50803
+        per = Period("2017-01-23/2017-01-29")
+        assert per.freq.freqstr == "W-SUN"
+
+        per = Period("2017-01-24/2017-01-30")
+        assert per.freq.freqstr == "W-MON"
+
+        msg = "Could not parse as weekly-freq Period"
+        with pytest.raises(ValueError, match=msg):
+            # not 6 days apart
+            Period("2016-01-23/2017-01-29")
+
+    def test_period_from_ordinal(self):
+        p = Period("2011-01", freq="M")
+        res = Period._from_ordinal(p.ordinal, freq=p.freq)
+        assert p == res
+        assert isinstance(res, Period)
+
+    @pytest.mark.parametrize("freq", ["Y", "M", "D", "h"])
+    def test_construct_from_nat_string_and_freq(self, freq):
+        per = Period("NaT", freq=freq)
+        assert per is NaT
+
+        per = Period("NaT", freq="2" + freq)
+        assert per is NaT
+
+        per = Period("NaT", freq="3" + freq)
+        assert per is NaT
+
+    def test_period_cons_nat(self):
+        p = Period("nat", freq="W-SUN")
+        assert p is NaT
+
+        p = Period(iNaT, freq="D")
+        assert p is NaT
+
+        p = Period(iNaT, freq="3D")
+        assert p is NaT
+
+        p = Period(iNaT, freq="1D1h")
+        assert p is NaT
+
+        p = Period("NaT")
+        assert p is NaT
+
+        p = Period(iNaT)
+        assert p is NaT
+
+    def test_period_cons_mult(self):
+        p1 = Period("2011-01", freq="3M")
+        p2 = Period("2011-01", freq="M")
+        assert p1.ordinal == p2.ordinal
+
+        assert p1.freq == offsets.MonthEnd(3)
+        assert p1.freqstr == "3M"
+
+        assert p2.freq == offsets.MonthEnd()
+        assert p2.freqstr == "M"
+
+        result = p1 + 1
+        assert result.ordinal == (p2 + 3).ordinal
+
+        assert result.freq == p1.freq
+        assert result.freqstr == "3M"
+
+        result = p1 - 1
+        assert result.ordinal == (p2 - 3).ordinal
+        assert result.freq == p1.freq
+        assert result.freqstr == "3M"
+
+        msg = "Frequency must be positive, because it represents span: -3M"
+        with pytest.raises(ValueError, match=msg):
+            Period("2011-01", freq="-3M")
+
+        msg = "Frequency must be positive, because it represents span: 0M"
+        with pytest.raises(ValueError, match=msg):
+            Period("2011-01", freq="0M")
+
+    def test_period_cons_combined(self):
+        p = [
+            (
+                Period("2011-01", freq="1D1h"),
+                Period("2011-01", freq="1h1D"),
+                Period("2011-01", freq="h"),
+            ),
+            (
+                Period(ordinal=1, freq="1D1h"),
+                Period(ordinal=1, freq="1h1D"),
+                Period(ordinal=1, freq="h"),
+            ),
+        ]
+
+        for p1, p2, p3 in p:
+            assert p1.ordinal == p3.ordinal
+            assert p2.ordinal == p3.ordinal
+
+            assert p1.freq == offsets.Hour(25)
+            assert p1.freqstr == "25h"
+
+            assert p2.freq == offsets.Hour(25)
+            assert p2.freqstr == "25h"
+
+            assert p3.freq == offsets.Hour()
+            assert p3.freqstr == "h"
+
+            result = p1 + 1
+            assert result.ordinal == (p3 + 25).ordinal
+            assert result.freq == p1.freq
+            assert result.freqstr == "25h"
+
+            result = p2 + 1
+            assert result.ordinal == (p3 + 25).ordinal
+            assert result.freq == p2.freq
+            assert result.freqstr == "25h"
+
+            result = p1 - 1
+            assert result.ordinal == (p3 - 25).ordinal
+            assert result.freq == p1.freq
+            assert result.freqstr == "25h"
+
+            result = p2 - 1
+            assert result.ordinal == (p3 - 25).ordinal
+            assert result.freq == p2.freq
+            assert result.freqstr == "25h"
+
+        msg = "Frequency must be positive, because it represents span: -25h"
+        with pytest.raises(ValueError, match=msg):
+            Period("2011-01", freq="-1D1h")
+        with pytest.raises(ValueError, match=msg):
+            Period("2011-01", freq="-1h1D")
+        with pytest.raises(ValueError, match=msg):
+            Period(ordinal=1, freq="-1D1h")
+        with pytest.raises(ValueError, match=msg):
+            Period(ordinal=1, freq="-1h1D")
+
+        msg = "Frequency must be positive, because it represents span: 0D"
+        with pytest.raises(ValueError, match=msg):
+            Period("2011-01", freq="0D0h")
+        with pytest.raises(ValueError, match=msg):
+            Period(ordinal=1, freq="0D0h")
+
+        # You can only combine together day and intraday offsets
+        msg = "Invalid frequency: 1W1D"
+        with pytest.raises(ValueError, match=msg):
+            Period("2011-01", freq="1W1D")
+        msg = "Invalid frequency: 1D1W"
+        with pytest.raises(ValueError, match=msg):
+            Period("2011-01", freq="1D1W")
+
+    @pytest.mark.parametrize("day", ["1970/01/01 ", "2020-12-31 ", "1981/09/13 "])
+    @pytest.mark.parametrize("hour", ["00:00:00", "00:00:01", "23:59:59", "12:00:59"])
+    @pytest.mark.parametrize(
+        "sec_float, expected",
+        [
+            (".000000001", 1),
+            (".000000999", 999),
+            (".123456789", 789),
+            (".999999999", 999),
+            (".999999000", 0),
+            # Test femtoseconds, attoseconds, picoseconds are dropped like Timestamp
+            (".999999001123", 1),
+            (".999999001123456", 1),
+            (".999999001123456789", 1),
+        ],
+    )
+    def test_period_constructor_nanosecond(self, day, hour, sec_float, expected):
+        # GH 34621
+
+        assert Period(day + hour + sec_float).start_time.nanosecond == expected
+
+    @pytest.mark.parametrize("hour", range(24))
+    def test_period_large_ordinal(self, hour):
+        # Issue #36430
+        # Integer overflow for Period over the maximum timestamp
+        p = Period(ordinal=2562048 + hour, freq="1h")
+        assert p.hour == hour
+
+
+class TestPeriodMethods:
+    def test_round_trip(self):
+        p = Period("2000Q1")
+        new_p = tm.round_trip_pickle(p)
+        assert new_p == p
+
+    def test_hash(self):
+        assert hash(Period("2011-01", freq="M")) == hash(Period("2011-01", freq="M"))
+
+        assert hash(Period("2011-01-01", freq="D")) != hash(Period("2011-01", freq="M"))
+
+        assert hash(Period("2011-01", freq="3M")) != hash(Period("2011-01", freq="2M"))
+
+        assert hash(Period("2011-01", freq="M")) != hash(Period("2011-02", freq="M"))
+
+    # --------------------------------------------------------------
+    # to_timestamp
+
+    def test_to_timestamp_mult(self):
+        p = Period("2011-01", freq="M")
+        assert p.to_timestamp(how="S") == Timestamp("2011-01-01")
+        expected = Timestamp("2011-02-01") - Timedelta(1, "ns")
+        assert p.to_timestamp(how="E") == expected
+
+        p = Period("2011-01", freq="3M")
+        assert p.to_timestamp(how="S") == Timestamp("2011-01-01")
+        expected = Timestamp("2011-04-01") - Timedelta(1, "ns")
+        assert p.to_timestamp(how="E") == expected
+
+    @pytest.mark.filterwarnings(
+        "ignore:Period with BDay freq is deprecated:FutureWarning"
+    )
+    def test_to_timestamp(self):
+        p = Period("1982", freq="Y")
+        start_ts = p.to_timestamp(how="S")
+        aliases = ["s", "StarT", "BEGIn"]
+        for a in aliases:
+            assert start_ts == p.to_timestamp("D", how=a)
+            # freq with mult should not affect to the result
+            assert start_ts == p.to_timestamp("3D", how=a)
+
+        end_ts = p.to_timestamp(how="E")
+        aliases = ["e", "end", "FINIsH"]
+        for a in aliases:
+            assert end_ts == p.to_timestamp("D", how=a)
+            assert end_ts == p.to_timestamp("3D", how=a)
+
+        from_lst = ["Y", "Q", "M", "W", "B", "D", "h", "Min", "s"]
+
+        def _ex(p):
+            if p.freq == "B":
+                return p.start_time + Timedelta(days=1, nanoseconds=-1)
+            return Timestamp((p + p.freq).start_time._value - 1)
+
+        for fcode in from_lst:
+            p = Period("1982", freq=fcode)
+            result = p.to_timestamp().to_period(fcode)
+            assert result == p
+
+            assert p.start_time == p.to_timestamp(how="S")
+
+            assert p.end_time == _ex(p)
+
+        # Frequency other than daily
+
+        p = Period("1985", freq="Y")
+
+        result = p.to_timestamp("h", how="end")
+        expected = Timestamp(1986, 1, 1) - Timedelta(1, "ns")
+        assert result == expected
+        result = p.to_timestamp("3h", how="end")
+        assert result == expected
+
+        result = p.to_timestamp("min", how="end")
+        expected = Timestamp(1986, 1, 1) - Timedelta(1, "ns")
+        assert result == expected
+        result = p.to_timestamp("2min", how="end")
+        assert result == expected
+
+        result = p.to_timestamp(how="end")
+        expected = Timestamp(1986, 1, 1) - Timedelta(1, "ns")
+        assert result == expected
+
+        expected = datetime(1985, 1, 1)
+        result = p.to_timestamp("h", how="start")
+        assert result == expected
+        result = p.to_timestamp("min", how="start")
+        assert result == expected
+        result = p.to_timestamp("s", how="start")
+        assert result == expected
+        result = p.to_timestamp("3h", how="start")
+        assert result == expected
+        result = p.to_timestamp("5s", how="start")
+        assert result == expected
+
+    def test_to_timestamp_business_end(self):
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            per = Period("1990-01-05", "B")  # Friday
+            result = per.to_timestamp("B", how="E")
+
+        expected = Timestamp("1990-01-06") - Timedelta(nanoseconds=1)
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "ts, expected",
+        [
+            ("1970-01-01 00:00:00", 0),
+            ("1970-01-01 00:00:00.000001", 1),
+            ("1970-01-01 00:00:00.00001", 10),
+            ("1970-01-01 00:00:00.499", 499000),
+            ("1999-12-31 23:59:59.999", 999000),
+            ("1999-12-31 23:59:59.999999", 999999),
+            ("2050-12-31 23:59:59.5", 500000),
+            ("2050-12-31 23:59:59.500001", 500001),
+            ("2050-12-31 23:59:59.123456", 123456),
+        ],
+    )
+    @pytest.mark.parametrize("freq", [None, "us", "ns"])
+    def test_to_timestamp_microsecond(self, ts, expected, freq):
+        # GH 24444
+        result = Period(ts).to_timestamp(freq=freq).microsecond
+        assert result == expected
+
+    # --------------------------------------------------------------
+    # Rendering: __repr__, strftime, etc
+
+    @pytest.mark.parametrize(
+        "str_ts,freq,str_res,str_freq",
+        (
+            ("Jan-2000", None, "2000-01", "M"),
+            ("2000-12-15", None, "2000-12-15", "D"),
+            (
+                "2000-12-15 13:45:26.123456789",
+                "ns",
+                "2000-12-15 13:45:26.123456789",
+                "ns",
+            ),
+            ("2000-12-15 13:45:26.123456789", "us", "2000-12-15 13:45:26.123456", "us"),
+            ("2000-12-15 13:45:26.123456", None, "2000-12-15 13:45:26.123456", "us"),
+            ("2000-12-15 13:45:26.123456789", "ms", "2000-12-15 13:45:26.123", "ms"),
+            ("2000-12-15 13:45:26.123", None, "2000-12-15 13:45:26.123", "ms"),
+            ("2000-12-15 13:45:26", "s", "2000-12-15 13:45:26", "s"),
+            ("2000-12-15 13:45:26", "min", "2000-12-15 13:45", "min"),
+            ("2000-12-15 13:45:26", "h", "2000-12-15 13:00", "h"),
+            ("2000-12-15", "Y", "2000", "Y-DEC"),
+            ("2000-12-15", "Q", "2000Q4", "Q-DEC"),
+            ("2000-12-15", "M", "2000-12", "M"),
+            ("2000-12-15", "W", "2000-12-11/2000-12-17", "W-SUN"),
+            ("2000-12-15", "D", "2000-12-15", "D"),
+            ("2000-12-15", "B", "2000-12-15", "B"),
+        ),
+    )
+    @pytest.mark.filterwarnings(
+        "ignore:Period with BDay freq is deprecated:FutureWarning"
+    )
+    def test_repr(self, str_ts, freq, str_res, str_freq):
+        p = Period(str_ts, freq=freq)
+        assert str(p) == str_res
+        assert repr(p) == f"Period('{str_res}', '{str_freq}')"
+
+    def test_repr_nat(self):
+        p = Period("nat", freq="M")
+        assert repr(NaT) in repr(p)
+
+    def test_strftime(self):
+        # GH#3363
+        p = Period("2000-1-1 12:34:12", freq="s")
+        res = p.strftime("%Y-%m-%d %H:%M:%S")
+        assert res == "2000-01-01 12:34:12"
+        assert isinstance(res, str)
+
+
+class TestPeriodProperties:
+    """Test properties such as year, month, weekday, etc...."""
+
+    @pytest.mark.parametrize("freq", ["Y", "M", "D", "h"])
+    def test_is_leap_year(self, freq):
+        # GH 13727
+        p = Period("2000-01-01 00:00:00", freq=freq)
+        assert p.is_leap_year
+        assert isinstance(p.is_leap_year, bool)
+
+        p = Period("1999-01-01 00:00:00", freq=freq)
+        assert not p.is_leap_year
+
+        p = Period("2004-01-01 00:00:00", freq=freq)
+        assert p.is_leap_year
+
+        p = Period("2100-01-01 00:00:00", freq=freq)
+        assert not p.is_leap_year
+
+    def test_quarterly_negative_ordinals(self):
+        p = Period(ordinal=-1, freq="Q-DEC")
+        assert p.year == 1969
+        assert p.quarter == 4
+        assert isinstance(p, Period)
+
+        p = Period(ordinal=-2, freq="Q-DEC")
+        assert p.year == 1969
+        assert p.quarter == 3
+        assert isinstance(p, Period)
+
+        p = Period(ordinal=-2, freq="M")
+        assert p.year == 1969
+        assert p.month == 11
+        assert isinstance(p, Period)
+
+    def test_freq_str(self):
+        i1 = Period("1982", freq="Min")
+        assert i1.freq == offsets.Minute()
+        assert i1.freqstr == "min"
+
+    @pytest.mark.filterwarnings(
+        "ignore:Period with BDay freq is deprecated:FutureWarning"
+    )
+    def test_period_deprecated_freq(self):
+        cases = {
+            "M": ["MTH", "MONTH", "MONTHLY", "Mth", "month", "monthly"],
+            "B": ["BUS", "BUSINESS", "BUSINESSLY", "WEEKDAY", "bus"],
+            "D": ["DAY", "DLY", "DAILY", "Day", "Dly", "Daily"],
+            "h": ["HR", "HOUR", "HRLY", "HOURLY", "hr", "Hour", "HRly"],
+            "min": ["minute", "MINUTE", "MINUTELY", "minutely"],
+            "s": ["sec", "SEC", "SECOND", "SECONDLY", "second"],
+            "ms": ["MILLISECOND", "MILLISECONDLY", "millisecond"],
+            "us": ["MICROSECOND", "MICROSECONDLY", "microsecond"],
+            "ns": ["NANOSECOND", "NANOSECONDLY", "nanosecond"],
+        }
+
+        msg = INVALID_FREQ_ERR_MSG
+        for exp, freqs in cases.items():
+            for freq in freqs:
+                with pytest.raises(ValueError, match=msg):
+                    Period("2016-03-01 09:00", freq=freq)
+                with pytest.raises(ValueError, match=msg):
+                    Period(ordinal=1, freq=freq)
+
+            # check supported freq-aliases still works
+            p1 = Period("2016-03-01 09:00", freq=exp)
+            p2 = Period(ordinal=1, freq=exp)
+            assert isinstance(p1, Period)
+            assert isinstance(p2, Period)
+
+    @staticmethod
+    def _period_constructor(bound, offset):
+        return Period(
+            year=bound.year,
+            month=bound.month,
+            day=bound.day,
+            hour=bound.hour,
+            minute=bound.minute,
+            second=bound.second + offset,
+            freq="us",
+        )
+
+    @pytest.mark.parametrize("bound, offset", [(Timestamp.min, -1), (Timestamp.max, 1)])
+    @pytest.mark.parametrize("period_property", ["start_time", "end_time"])
+    def test_outer_bounds_start_and_end_time(self, bound, offset, period_property):
+        # GH #13346
+        period = TestPeriodProperties._period_constructor(bound, offset)
+        with pytest.raises(OutOfBoundsDatetime, match="Out of bounds nanosecond"):
+            getattr(period, period_property)
+
+    @pytest.mark.parametrize("bound, offset", [(Timestamp.min, -1), (Timestamp.max, 1)])
+    @pytest.mark.parametrize("period_property", ["start_time", "end_time"])
+    def test_inner_bounds_start_and_end_time(self, bound, offset, period_property):
+        # GH #13346
+        period = TestPeriodProperties._period_constructor(bound, -offset)
+        expected = period.to_timestamp().round(freq="s")
+        assert getattr(period, period_property).round(freq="s") == expected
+        expected = (bound - offset * Timedelta(1, unit="s")).floor("s")
+        assert getattr(period, period_property).floor("s") == expected
+
+    def test_start_time(self):
+        freq_lst = ["Y", "Q", "M", "D", "h", "min", "s"]
+        xp = datetime(2012, 1, 1)
+        for f in freq_lst:
+            p = Period("2012", freq=f)
+            assert p.start_time == xp
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert Period("2012", freq="B").start_time == datetime(2012, 1, 2)
+        assert Period("2012", freq="W").start_time == datetime(2011, 12, 26)
+
+    def test_end_time(self):
+        p = Period("2012", freq="Y")
+
+        def _ex(*args):
+            return Timestamp(Timestamp(datetime(*args)).as_unit("ns")._value - 1)
+
+        xp = _ex(2013, 1, 1)
+        assert xp == p.end_time
+
+        p = Period("2012", freq="Q")
+        xp = _ex(2012, 4, 1)
+        assert xp == p.end_time
+
+        p = Period("2012", freq="M")
+        xp = _ex(2012, 2, 1)
+        assert xp == p.end_time
+
+        p = Period("2012", freq="D")
+        xp = _ex(2012, 1, 2)
+        assert xp == p.end_time
+
+        p = Period("2012", freq="h")
+        xp = _ex(2012, 1, 1, 1)
+        assert xp == p.end_time
+
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            p = Period("2012", freq="B")
+            xp = _ex(2012, 1, 3)
+            assert xp == p.end_time
+
+        p = Period("2012", freq="W")
+        xp = _ex(2012, 1, 2)
+        assert xp == p.end_time
+
+        # Test for GH 11738
+        p = Period("2012", freq="15D")
+        xp = _ex(2012, 1, 16)
+        assert xp == p.end_time
+
+        p = Period("2012", freq="1D1h")
+        xp = _ex(2012, 1, 2, 1)
+        assert xp == p.end_time
+
+        p = Period("2012", freq="1h1D")
+        xp = _ex(2012, 1, 2, 1)
+        assert xp == p.end_time
+
+    def test_end_time_business_friday(self):
+        # GH#34449
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            per = Period("1990-01-05", "B")
+            result = per.end_time
+
+        expected = Timestamp("1990-01-06") - Timedelta(nanoseconds=1)
+        assert result == expected
+
+    def test_anchor_week_end_time(self):
+        def _ex(*args):
+            return Timestamp(Timestamp(datetime(*args)).as_unit("ns")._value - 1)
+
+        p = Period("2013-1-1", "W-SAT")
+        xp = _ex(2013, 1, 6)
+        assert p.end_time == xp
+
+    def test_properties_annually(self):
+        # Test properties on Periods with annually frequency.
+        a_date = Period(freq="Y", year=2007)
+        assert a_date.year == 2007
+
+    def test_properties_quarterly(self):
+        # Test properties on Periods with daily frequency.
+        qedec_date = Period(freq="Q-DEC", year=2007, quarter=1)
+        qejan_date = Period(freq="Q-JAN", year=2007, quarter=1)
+        qejun_date = Period(freq="Q-JUN", year=2007, quarter=1)
+        #
+        for x in range(3):
+            for qd in (qedec_date, qejan_date, qejun_date):
+                assert (qd + x).qyear == 2007
+                assert (qd + x).quarter == x + 1
+
+    def test_properties_monthly(self):
+        # Test properties on Periods with daily frequency.
+        m_date = Period(freq="M", year=2007, month=1)
+        for x in range(11):
+            m_ival_x = m_date + x
+            assert m_ival_x.year == 2007
+            if 1 <= x + 1 <= 3:
+                assert m_ival_x.quarter == 1
+            elif 4 <= x + 1 <= 6:
+                assert m_ival_x.quarter == 2
+            elif 7 <= x + 1 <= 9:
+                assert m_ival_x.quarter == 3
+            elif 10 <= x + 1 <= 12:
+                assert m_ival_x.quarter == 4
+            assert m_ival_x.month == x + 1
+
+    def test_properties_weekly(self):
+        # Test properties on Periods with daily frequency.
+        w_date = Period(freq="W", year=2007, month=1, day=7)
+        #
+        assert w_date.year == 2007
+        assert w_date.quarter == 1
+        assert w_date.month == 1
+        assert w_date.week == 1
+        assert (w_date - 1).week == 52
+        assert w_date.days_in_month == 31
+        assert Period(freq="W", year=2012, month=2, day=1).days_in_month == 29
+
+    def test_properties_weekly_legacy(self):
+        # Test properties on Periods with daily frequency.
+        w_date = Period(freq="W", year=2007, month=1, day=7)
+        assert w_date.year == 2007
+        assert w_date.quarter == 1
+        assert w_date.month == 1
+        assert w_date.week == 1
+        assert (w_date - 1).week == 52
+        assert w_date.days_in_month == 31
+
+        exp = Period(freq="W", year=2012, month=2, day=1)
+        assert exp.days_in_month == 29
+
+        msg = INVALID_FREQ_ERR_MSG
+        with pytest.raises(ValueError, match=msg):
+            Period(freq="WK", year=2007, month=1, day=7)
+
+    def test_properties_daily(self):
+        # Test properties on Periods with daily frequency.
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            b_date = Period(freq="B", year=2007, month=1, day=1)
+        #
+        assert b_date.year == 2007
+        assert b_date.quarter == 1
+        assert b_date.month == 1
+        assert b_date.day == 1
+        assert b_date.weekday == 0
+        assert b_date.dayofyear == 1
+        assert b_date.days_in_month == 31
+        with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+            assert Period(freq="B", year=2012, month=2, day=1).days_in_month == 29
+
+        d_date = Period(freq="D", year=2007, month=1, day=1)
+
+        assert d_date.year == 2007
+        assert d_date.quarter == 1
+        assert d_date.month == 1
+        assert d_date.day == 1
+        assert d_date.weekday == 0
+        assert d_date.dayofyear == 1
+        assert d_date.days_in_month == 31
+        assert Period(freq="D", year=2012, month=2, day=1).days_in_month == 29
+
+    def test_properties_hourly(self):
+        # Test properties on Periods with hourly frequency.
+        h_date1 = Period(freq="h", year=2007, month=1, day=1, hour=0)
+        h_date2 = Period(freq="2h", year=2007, month=1, day=1, hour=0)
+
+        for h_date in [h_date1, h_date2]:
+            assert h_date.year == 2007
+            assert h_date.quarter == 1
+            assert h_date.month == 1
+            assert h_date.day == 1
+            assert h_date.weekday == 0
+            assert h_date.dayofyear == 1
+            assert h_date.hour == 0
+            assert h_date.days_in_month == 31
+            assert (
+                Period(freq="h", year=2012, month=2, day=1, hour=0).days_in_month == 29
+            )
+
+    def test_properties_minutely(self):
+        # Test properties on Periods with minutely frequency.
+        t_date = Period(freq="Min", year=2007, month=1, day=1, hour=0, minute=0)
+        #
+        assert t_date.quarter == 1
+        assert t_date.month == 1
+        assert t_date.day == 1
+        assert t_date.weekday == 0
+        assert t_date.dayofyear == 1
+        assert t_date.hour == 0
+        assert t_date.minute == 0
+        assert t_date.days_in_month == 31
+        assert (
+            Period(freq="D", year=2012, month=2, day=1, hour=0, minute=0).days_in_month
+            == 29
+        )
+
+    def test_properties_secondly(self):
+        # Test properties on Periods with secondly frequency.
+        s_date = Period(
+            freq="Min", year=2007, month=1, day=1, hour=0, minute=0, second=0
+        )
+        #
+        assert s_date.year == 2007
+        assert s_date.quarter == 1
+        assert s_date.month == 1
+        assert s_date.day == 1
+        assert s_date.weekday == 0
+        assert s_date.dayofyear == 1
+        assert s_date.hour == 0
+        assert s_date.minute == 0
+        assert s_date.second == 0
+        assert s_date.days_in_month == 31
+        assert (
+            Period(
+                freq="Min", year=2012, month=2, day=1, hour=0, minute=0, second=0
+            ).days_in_month
+            == 29
+        )
+
+
+class TestPeriodComparisons:
+    def test_sort_periods(self):
+        jan = Period("2000-01", "M")
+        feb = Period("2000-02", "M")
+        mar = Period("2000-03", "M")
+        periods = [mar, jan, feb]
+        correctPeriods = [jan, feb, mar]
+        assert sorted(periods) == correctPeriods
+
+
+def test_period_immutable():
+    # see gh-17116
+    msg = "not writable"
+
+    per = Period("2014Q1")
+    with pytest.raises(AttributeError, match=msg):
+        per.ordinal = 14
+
+    freq = per.freq
+    with pytest.raises(AttributeError, match=msg):
+        per.freq = 2 * freq
+
+
+def test_small_year_parsing():
+    per1 = Period("0001-01-07", "D")
+    assert per1.year == 1
+    assert per1.day == 7
+
+
+def test_negone_ordinals():
+    freqs = ["Y", "M", "Q", "D", "h", "min", "s"]
+
+    period = Period(ordinal=-1, freq="D")
+    for freq in freqs:
+        repr(period.asfreq(freq))
+
+    for freq in freqs:
+        period = Period(ordinal=-1, freq=freq)
+        repr(period)
+        assert period.year == 1969
+
+    with tm.assert_produces_warning(FutureWarning, match=bday_msg):
+        period = Period(ordinal=-1, freq="B")
+    repr(period)
+    period = Period(ordinal=-1, freq="W")
+    repr(period)

py311/lib/python3.11/site-packages/pandas/tests/scalar/timedelta/methods/test_as_unit.py

@@ -0,0 +1,80 @@
+import pytest
+
+from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+from pandas.errors import OutOfBoundsTimedelta
+
+from pandas import Timedelta
+
+
+class TestAsUnit:
+    def test_as_unit(self):
+        td = Timedelta(days=1)
+
+        assert td.as_unit("ns") is td
+
+        res = td.as_unit("us")
+        assert res._value == td._value // 1000
+        assert res._creso == NpyDatetimeUnit.NPY_FR_us.value
+
+        rt = res.as_unit("ns")
+        assert rt._value == td._value
+        assert rt._creso == td._creso
+
+        res = td.as_unit("ms")
+        assert res._value == td._value // 1_000_000
+        assert res._creso == NpyDatetimeUnit.NPY_FR_ms.value
+
+        rt = res.as_unit("ns")
+        assert rt._value == td._value
+        assert rt._creso == td._creso
+
+        res = td.as_unit("s")
+        assert res._value == td._value // 1_000_000_000
+        assert res._creso == NpyDatetimeUnit.NPY_FR_s.value
+
+        rt = res.as_unit("ns")
+        assert rt._value == td._value
+        assert rt._creso == td._creso
+
+    def test_as_unit_overflows(self):
+        # microsecond that would be just out of bounds for nano
+        us = 9223372800000000
+        td = Timedelta._from_value_and_reso(us, NpyDatetimeUnit.NPY_FR_us.value)
+
+        msg = "Cannot cast 106752 days 00:00:00 to unit='ns' without overflow"
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            td.as_unit("ns")
+
+        res = td.as_unit("ms")
+        assert res._value == us // 1000
+        assert res._creso == NpyDatetimeUnit.NPY_FR_ms.value
+
+    def test_as_unit_rounding(self):
+        td = Timedelta(microseconds=1500)
+        res = td.as_unit("ms")
+
+        expected = Timedelta(milliseconds=1)
+        assert res == expected
+
+        assert res._creso == NpyDatetimeUnit.NPY_FR_ms.value
+        assert res._value == 1
+
+        with pytest.raises(ValueError, match="Cannot losslessly convert units"):
+            td.as_unit("ms", round_ok=False)
+
+    def test_as_unit_non_nano(self):
+        # case where we are going neither to nor from nano
+        td = Timedelta(days=1).as_unit("ms")
+        assert td.days == 1
+        assert td._value == 86_400_000
+        assert td.components.days == 1
+        assert td._d == 1
+        assert td.total_seconds() == 86400
+
+        res = td.as_unit("us")
+        assert res._value == 86_400_000_000
+        assert res.components.days == 1
+        assert res.components.hours == 0
+        assert res._d == 1
+        assert res._h == 0
+        assert res.total_seconds() == 86400

py311/lib/python3.11/site-packages/pandas/tests/scalar/timedelta/methods/test_round.py

@@ -0,0 +1,187 @@
+from hypothesis import (
+    given,
+    strategies as st,
+)
+import numpy as np
+import pytest
+
+from pandas._libs import lib
+from pandas._libs.tslibs import iNaT
+from pandas.errors import OutOfBoundsTimedelta
+
+from pandas import Timedelta
+
+
+class TestTimedeltaRound:
+    @pytest.mark.parametrize(
+        "freq,s1,s2",
+        [
+            # This first case has s1, s2 being the same as t1,t2 below
+            (
+                "ns",
+                Timedelta("1 days 02:34:56.789123456"),
+                Timedelta("-1 days 02:34:56.789123456"),
+            ),
+            (
+                "us",
+                Timedelta("1 days 02:34:56.789123000"),
+                Timedelta("-1 days 02:34:56.789123000"),
+            ),
+            (
+                "ms",
+                Timedelta("1 days 02:34:56.789000000"),
+                Timedelta("-1 days 02:34:56.789000000"),
+            ),
+            ("s", Timedelta("1 days 02:34:57"), Timedelta("-1 days 02:34:57")),
+            ("2s", Timedelta("1 days 02:34:56"), Timedelta("-1 days 02:34:56")),
+            ("5s", Timedelta("1 days 02:34:55"), Timedelta("-1 days 02:34:55")),
+            ("min", Timedelta("1 days 02:35:00"), Timedelta("-1 days 02:35:00")),
+            ("12min", Timedelta("1 days 02:36:00"), Timedelta("-1 days 02:36:00")),
+            ("h", Timedelta("1 days 03:00:00"), Timedelta("-1 days 03:00:00")),
+            ("d", Timedelta("1 days"), Timedelta("-1 days")),
+        ],
+    )
+    def test_round(self, freq, s1, s2):
+        t1 = Timedelta("1 days 02:34:56.789123456")
+        t2 = Timedelta("-1 days 02:34:56.789123456")
+
+        r1 = t1.round(freq)
+        assert r1 == s1
+        r2 = t2.round(freq)
+        assert r2 == s2
+
+    def test_round_invalid(self):
+        t1 = Timedelta("1 days 02:34:56.789123456")
+
+        for freq, msg in [
+            ("YE", "<YearEnd: month=12> is a non-fixed frequency"),
+            ("ME", "<MonthEnd> is a non-fixed frequency"),
+            ("foobar", "Invalid frequency: foobar"),
+        ]:
+            with pytest.raises(ValueError, match=msg):
+                t1.round(freq)
+
+    @pytest.mark.skip_ubsan
+    def test_round_implementation_bounds(self):
+        # See also: analogous test for Timestamp
+        # GH#38964
+        result = Timedelta.min.ceil("s")
+        expected = Timedelta.min + Timedelta(seconds=1) - Timedelta(145224193)
+        assert result == expected
+
+        result = Timedelta.max.floor("s")
+        expected = Timedelta.max - Timedelta(854775807)
+        assert result == expected
+
+        msg = (
+            r"Cannot round -106752 days \+00:12:43.145224193 to freq=s without overflow"
+        )
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            Timedelta.min.floor("s")
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            Timedelta.min.round("s")
+
+        msg = "Cannot round 106751 days 23:47:16.854775807 to freq=s without overflow"
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            Timedelta.max.ceil("s")
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            Timedelta.max.round("s")
+
+    @pytest.mark.skip_ubsan
+    @given(val=st.integers(min_value=iNaT + 1, max_value=lib.i8max))
+    @pytest.mark.parametrize(
+        "method", [Timedelta.round, Timedelta.floor, Timedelta.ceil]
+    )
+    def test_round_sanity(self, val, method):
+        cls = Timedelta
+        err_cls = OutOfBoundsTimedelta
+
+        val = np.int64(val)
+        td = cls(val)
+
+        def checker(ts, nanos, unit):
+            # First check that we do raise in cases where we should
+            if nanos == 1:
+                pass
+            else:
+                div, mod = divmod(ts._value, nanos)
+                diff = int(nanos - mod)
+                lb = ts._value - mod
+                assert lb <= ts._value  # i.e. no overflows with python ints
+                ub = ts._value + diff
+                assert ub > ts._value  # i.e. no overflows with python ints
+
+                msg = "without overflow"
+                if mod == 0:
+                    # We should never be raising in this
+                    pass
+                elif method is cls.ceil:
+                    if ub > cls.max._value:
+                        with pytest.raises(err_cls, match=msg):
+                            method(ts, unit)
+                        return
+                elif method is cls.floor:
+                    if lb < cls.min._value:
+                        with pytest.raises(err_cls, match=msg):
+                            method(ts, unit)
+                        return
+                elif mod >= diff:
+                    if ub > cls.max._value:
+                        with pytest.raises(err_cls, match=msg):
+                            method(ts, unit)
+                        return
+                elif lb < cls.min._value:
+                    with pytest.raises(err_cls, match=msg):
+                        method(ts, unit)
+                    return
+
+            res = method(ts, unit)
+
+            td = res - ts
+            diff = abs(td._value)
+            assert diff < nanos
+            assert res._value % nanos == 0
+
+            if method is cls.round:
+                assert diff <= nanos / 2
+            elif method is cls.floor:
+                assert res <= ts
+            elif method is cls.ceil:
+                assert res >= ts
+
+        nanos = 1
+        checker(td, nanos, "ns")
+
+        nanos = 1000
+        checker(td, nanos, "us")
+
+        nanos = 1_000_000
+        checker(td, nanos, "ms")
+
+        nanos = 1_000_000_000
+        checker(td, nanos, "s")
+
+        nanos = 60 * 1_000_000_000
+        checker(td, nanos, "min")
+
+        nanos = 60 * 60 * 1_000_000_000
+        checker(td, nanos, "h")
+
+        nanos = 24 * 60 * 60 * 1_000_000_000
+        checker(td, nanos, "D")
+
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"])
+    def test_round_non_nano(self, unit):
+        td = Timedelta("1 days 02:34:57").as_unit(unit)
+
+        res = td.round("min")
+        assert res == Timedelta("1 days 02:35:00")
+        assert res._creso == td._creso
+
+        res = td.floor("min")
+        assert res == Timedelta("1 days 02:34:00")
+        assert res._creso == td._creso
+
+        res = td.ceil("min")
+        assert res == Timedelta("1 days 02:35:00")
+        assert res._creso == td._creso

py311/lib/python3.11/site-packages/pandas/tests/scalar/timedelta/test_arithmetic.py

@@ -0,0 +1,1183 @@
+"""
+Tests for scalar Timedelta arithmetic ops
+"""
+from datetime import (
+    datetime,
+    timedelta,
+)
+import operator
+
+import numpy as np
+import pytest
+
+from pandas.errors import OutOfBoundsTimedelta
+
+import pandas as pd
+from pandas import (
+    NaT,
+    Timedelta,
+    Timestamp,
+    offsets,
+)
+import pandas._testing as tm
+from pandas.core import ops
+
+
+class TestTimedeltaAdditionSubtraction:
+    """
+    Tests for Timedelta methods:
+
+        __add__, __radd__,
+        __sub__, __rsub__
+    """
+
+    @pytest.mark.parametrize(
+        "ten_seconds",
+        [
+            Timedelta(10, unit="s"),
+            timedelta(seconds=10),
+            np.timedelta64(10, "s"),
+            np.timedelta64(10000000000, "ns"),
+            offsets.Second(10),
+        ],
+    )
+    def test_td_add_sub_ten_seconds(self, ten_seconds):
+        # GH#6808
+        base = Timestamp("20130101 09:01:12.123456")
+        expected_add = Timestamp("20130101 09:01:22.123456")
+        expected_sub = Timestamp("20130101 09:01:02.123456")
+
+        result = base + ten_seconds
+        assert result == expected_add
+
+        result = base - ten_seconds
+        assert result == expected_sub
+
+    @pytest.mark.parametrize(
+        "one_day_ten_secs",
+        [
+            Timedelta("1 day, 00:00:10"),
+            Timedelta("1 days, 00:00:10"),
+            timedelta(days=1, seconds=10),
+            np.timedelta64(1, "D") + np.timedelta64(10, "s"),
+            offsets.Day() + offsets.Second(10),
+        ],
+    )
+    def test_td_add_sub_one_day_ten_seconds(self, one_day_ten_secs):
+        # GH#6808
+        base = Timestamp("20130102 09:01:12.123456")
+        expected_add = Timestamp("20130103 09:01:22.123456")
+        expected_sub = Timestamp("20130101 09:01:02.123456")
+
+        result = base + one_day_ten_secs
+        assert result == expected_add
+
+        result = base - one_day_ten_secs
+        assert result == expected_sub
+
+    @pytest.mark.parametrize("op", [operator.add, ops.radd])
+    def test_td_add_datetimelike_scalar(self, op):
+        # GH#19738
+        td = Timedelta(10, unit="d")
+
+        result = op(td, datetime(2016, 1, 1))
+        if op is operator.add:
+            # datetime + Timedelta does _not_ call Timedelta.__radd__,
+            # so we get a datetime back instead of a Timestamp
+            assert isinstance(result, Timestamp)
+        assert result == Timestamp(2016, 1, 11)
+
+        result = op(td, Timestamp("2018-01-12 18:09"))
+        assert isinstance(result, Timestamp)
+        assert result == Timestamp("2018-01-22 18:09")
+
+        result = op(td, np.datetime64("2018-01-12"))
+        assert isinstance(result, Timestamp)
+        assert result == Timestamp("2018-01-22")
+
+        result = op(td, NaT)
+        assert result is NaT
+
+    def test_td_add_timestamp_overflow(self):
+        ts = Timestamp("1700-01-01").as_unit("ns")
+        msg = "Cannot cast 259987 from D to 'ns' without overflow."
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            ts + Timedelta(13 * 19999, unit="D")
+
+        msg = "Cannot cast 259987 days 00:00:00 to unit='ns' without overflow"
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            ts + timedelta(days=13 * 19999)
+
+    @pytest.mark.parametrize("op", [operator.add, ops.radd])
+    def test_td_add_td(self, op):
+        td = Timedelta(10, unit="d")
+
+        result = op(td, Timedelta(days=10))
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(days=20)
+
+    @pytest.mark.parametrize("op", [operator.add, ops.radd])
+    def test_td_add_pytimedelta(self, op):
+        td = Timedelta(10, unit="d")
+        result = op(td, timedelta(days=9))
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(days=19)
+
+    @pytest.mark.parametrize("op", [operator.add, ops.radd])
+    def test_td_add_timedelta64(self, op):
+        td = Timedelta(10, unit="d")
+        result = op(td, np.timedelta64(-4, "D"))
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(days=6)
+
+    @pytest.mark.parametrize("op", [operator.add, ops.radd])
+    def test_td_add_offset(self, op):
+        td = Timedelta(10, unit="d")
+
+        result = op(td, offsets.Hour(6))
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(days=10, hours=6)
+
+    def test_td_sub_td(self):
+        td = Timedelta(10, unit="d")
+        expected = Timedelta(0, unit="ns")
+        result = td - td
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+    def test_td_sub_pytimedelta(self):
+        td = Timedelta(10, unit="d")
+        expected = Timedelta(0, unit="ns")
+
+        result = td - td.to_pytimedelta()
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+        result = td.to_pytimedelta() - td
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+    def test_td_sub_timedelta64(self):
+        td = Timedelta(10, unit="d")
+        expected = Timedelta(0, unit="ns")
+
+        result = td - td.to_timedelta64()
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+        result = td.to_timedelta64() - td
+        assert isinstance(result, Timedelta)
+        assert result == expected
+
+    def test_td_sub_nat(self):
+        # In this context pd.NaT is treated as timedelta-like
+        td = Timedelta(10, unit="d")
+        result = td - NaT
+        assert result is NaT
+
+    def test_td_sub_td64_nat(self):
+        td = Timedelta(10, unit="d")
+        td_nat = np.timedelta64("NaT")
+
+        result = td - td_nat
+        assert result is NaT
+
+        result = td_nat - td
+        assert result is NaT
+
+    def test_td_sub_offset(self):
+        td = Timedelta(10, unit="d")
+        result = td - offsets.Hour(1)
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(239, unit="h")
+
+    def test_td_add_sub_numeric_raises(self):
+        td = Timedelta(10, unit="d")
+        msg = "unsupported operand type"
+        for other in [2, 2.0, np.int64(2), np.float64(2)]:
+            with pytest.raises(TypeError, match=msg):
+                td + other
+            with pytest.raises(TypeError, match=msg):
+                other + td
+            with pytest.raises(TypeError, match=msg):
+                td - other
+            with pytest.raises(TypeError, match=msg):
+                other - td
+
+    def test_td_add_sub_int_ndarray(self):
+        td = Timedelta("1 day")
+        other = np.array([1])
+
+        msg = r"unsupported operand type\(s\) for \+: 'Timedelta' and 'int'"
+        with pytest.raises(TypeError, match=msg):
+            td + np.array([1])
+
+        msg = "|".join(
+            [
+                (
+                    r"unsupported operand type\(s\) for \+: 'numpy.ndarray' "
+                    "and 'Timedelta'"
+                ),
+                # This message goes on to say "Please do not rely on this error;
+                #  it may not be given on all Python implementations"
+                "Concatenation operation is not implemented for NumPy arrays",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            other + td
+        msg = r"unsupported operand type\(s\) for -: 'Timedelta' and 'int'"
+        with pytest.raises(TypeError, match=msg):
+            td - other
+        msg = r"unsupported operand type\(s\) for -: 'numpy.ndarray' and 'Timedelta'"
+        with pytest.raises(TypeError, match=msg):
+            other - td
+
+    def test_td_rsub_nat(self):
+        td = Timedelta(10, unit="d")
+        result = NaT - td
+        assert result is NaT
+
+        result = np.datetime64("NaT") - td
+        assert result is NaT
+
+    def test_td_rsub_offset(self):
+        result = offsets.Hour(1) - Timedelta(10, unit="d")
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(-239, unit="h")
+
+    def test_td_sub_timedeltalike_object_dtype_array(self):
+        # GH#21980
+        arr = np.array([Timestamp("20130101 9:01"), Timestamp("20121230 9:02")])
+        exp = np.array([Timestamp("20121231 9:01"), Timestamp("20121229 9:02")])
+        res = arr - Timedelta("1D")
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_td_sub_mixed_most_timedeltalike_object_dtype_array(self):
+        # GH#21980
+        now = Timestamp("2021-11-09 09:54:00")
+        arr = np.array([now, Timedelta("1D"), np.timedelta64(2, "h")])
+        exp = np.array(
+            [
+                now - Timedelta("1D"),
+                Timedelta("0D"),
+                np.timedelta64(2, "h") - Timedelta("1D"),
+            ]
+        )
+        res = arr - Timedelta("1D")
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_td_rsub_mixed_most_timedeltalike_object_dtype_array(self):
+        # GH#21980
+        now = Timestamp("2021-11-09 09:54:00")
+        arr = np.array([now, Timedelta("1D"), np.timedelta64(2, "h")])
+        msg = r"unsupported operand type\(s\) for \-: 'Timedelta' and 'Timestamp'"
+        with pytest.raises(TypeError, match=msg):
+            Timedelta("1D") - arr
+
+    @pytest.mark.parametrize("op", [operator.add, ops.radd])
+    def test_td_add_timedeltalike_object_dtype_array(self, op):
+        # GH#21980
+        arr = np.array([Timestamp("20130101 9:01"), Timestamp("20121230 9:02")])
+        exp = np.array([Timestamp("20130102 9:01"), Timestamp("20121231 9:02")])
+        res = op(arr, Timedelta("1D"))
+        tm.assert_numpy_array_equal(res, exp)
+
+    @pytest.mark.parametrize("op", [operator.add, ops.radd])
+    def test_td_add_mixed_timedeltalike_object_dtype_array(self, op):
+        # GH#21980
+        now = Timestamp("2021-11-09 09:54:00")
+        arr = np.array([now, Timedelta("1D")])
+        exp = np.array([now + Timedelta("1D"), Timedelta("2D")])
+        res = op(arr, Timedelta("1D"))
+        tm.assert_numpy_array_equal(res, exp)
+
+    def test_td_add_sub_td64_ndarray(self):
+        td = Timedelta("1 day")
+
+        other = np.array([td.to_timedelta64()])
+        expected = np.array([Timedelta("2 Days").to_timedelta64()])
+
+        result = td + other
+        tm.assert_numpy_array_equal(result, expected)
+        result = other + td
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = td - other
+        tm.assert_numpy_array_equal(result, expected * 0)
+        result = other - td
+        tm.assert_numpy_array_equal(result, expected * 0)
+
+    def test_td_add_sub_dt64_ndarray(self):
+        td = Timedelta("1 day")
+        other = np.array(["2000-01-01"], dtype="M8[ns]")
+
+        expected = np.array(["2000-01-02"], dtype="M8[ns]")
+        tm.assert_numpy_array_equal(td + other, expected)
+        tm.assert_numpy_array_equal(other + td, expected)
+
+        expected = np.array(["1999-12-31"], dtype="M8[ns]")
+        tm.assert_numpy_array_equal(-td + other, expected)
+        tm.assert_numpy_array_equal(other - td, expected)
+
+    def test_td_add_sub_ndarray_0d(self):
+        td = Timedelta("1 day")
+        other = np.array(td.asm8)
+
+        result = td + other
+        assert isinstance(result, Timedelta)
+        assert result == 2 * td
+
+        result = other + td
+        assert isinstance(result, Timedelta)
+        assert result == 2 * td
+
+        result = other - td
+        assert isinstance(result, Timedelta)
+        assert result == 0 * td
+
+        result = td - other
+        assert isinstance(result, Timedelta)
+        assert result == 0 * td
+
+
+class TestTimedeltaMultiplicationDivision:
+    """
+    Tests for Timedelta methods:
+
+        __mul__, __rmul__,
+        __div__, __rdiv__,
+        __truediv__, __rtruediv__,
+        __floordiv__, __rfloordiv__,
+        __mod__, __rmod__,
+        __divmod__, __rdivmod__
+    """
+
+    # ---------------------------------------------------------------
+    # Timedelta.__mul__, __rmul__
+
+    @pytest.mark.parametrize(
+        "td_nat", [NaT, np.timedelta64("NaT", "ns"), np.timedelta64("NaT")]
+    )
+    @pytest.mark.parametrize("op", [operator.mul, ops.rmul])
+    def test_td_mul_nat(self, op, td_nat):
+        # GH#19819
+        td = Timedelta(10, unit="d")
+        typs = "|".join(["numpy.timedelta64", "NaTType", "Timedelta"])
+        msg = "|".join(
+            [
+                rf"unsupported operand type\(s\) for \*: '{typs}' and '{typs}'",
+                r"ufunc '?multiply'? cannot use operands with types",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            op(td, td_nat)
+
+    @pytest.mark.parametrize("nan", [np.nan, np.float64("NaN"), float("nan")])
+    @pytest.mark.parametrize("op", [operator.mul, ops.rmul])
+    def test_td_mul_nan(self, op, nan):
+        # np.float64('NaN') has a 'dtype' attr, avoid treating as array
+        td = Timedelta(10, unit="d")
+        result = op(td, nan)
+        assert result is NaT
+
+    @pytest.mark.parametrize("op", [operator.mul, ops.rmul])
+    def test_td_mul_scalar(self, op):
+        # GH#19738
+        td = Timedelta(minutes=3)
+
+        result = op(td, 2)
+        assert result == Timedelta(minutes=6)
+
+        result = op(td, 1.5)
+        assert result == Timedelta(minutes=4, seconds=30)
+
+        assert op(td, np.nan) is NaT
+
+        assert op(-1, td)._value == -1 * td._value
+        assert op(-1.0, td)._value == -1.0 * td._value
+
+        msg = "unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            # timedelta * datetime is gibberish
+            op(td, Timestamp(2016, 1, 2))
+
+        with pytest.raises(TypeError, match=msg):
+            # invalid multiply with another timedelta
+            op(td, td)
+
+    def test_td_mul_numeric_ndarray(self):
+        td = Timedelta("1 day")
+        other = np.array([2])
+        expected = np.array([Timedelta("2 Days").to_timedelta64()])
+
+        result = td * other
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = other * td
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_td_mul_numeric_ndarray_0d(self):
+        td = Timedelta("1 day")
+        other = np.array(2, dtype=np.int64)
+        assert other.ndim == 0
+        expected = Timedelta("2 days")
+
+        res = td * other
+        assert type(res) is Timedelta
+        assert res == expected
+
+        res = other * td
+        assert type(res) is Timedelta
+        assert res == expected
+
+    def test_td_mul_td64_ndarray_invalid(self):
+        td = Timedelta("1 day")
+        other = np.array([Timedelta("2 Days").to_timedelta64()])
+
+        msg = (
+            "ufunc '?multiply'? cannot use operands with types "
+            rf"dtype\('{tm.ENDIAN}m8\[ns\]'\) and dtype\('{tm.ENDIAN}m8\[ns\]'\)"
+        )
+        with pytest.raises(TypeError, match=msg):
+            td * other
+        with pytest.raises(TypeError, match=msg):
+            other * td
+
+    # ---------------------------------------------------------------
+    # Timedelta.__div__, __truediv__
+
+    def test_td_div_timedeltalike_scalar(self):
+        # GH#19738
+        td = Timedelta(10, unit="d")
+
+        result = td / offsets.Hour(1)
+        assert result == 240
+
+        assert td / td == 1
+        assert td / np.timedelta64(60, "h") == 4
+
+        assert np.isnan(td / NaT)
+
+    def test_td_div_td64_non_nano(self):
+        # truediv
+        td = Timedelta("1 days 2 hours 3 ns")
+        result = td / np.timedelta64(1, "D")
+        assert result == td._value / (86400 * 10**9)
+        result = td / np.timedelta64(1, "s")
+        assert result == td._value / 10**9
+        result = td / np.timedelta64(1, "ns")
+        assert result == td._value
+
+        # floordiv
+        td = Timedelta("1 days 2 hours 3 ns")
+        result = td // np.timedelta64(1, "D")
+        assert result == 1
+        result = td // np.timedelta64(1, "s")
+        assert result == 93600
+        result = td // np.timedelta64(1, "ns")
+        assert result == td._value
+
+    def test_td_div_numeric_scalar(self):
+        # GH#19738
+        td = Timedelta(10, unit="d")
+
+        result = td / 2
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(days=5)
+
+        result = td / 5
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(days=2)
+
+    @pytest.mark.parametrize(
+        "nan",
+        [
+            np.nan,
+            np.float64("NaN"),
+            float("nan"),
+        ],
+    )
+    def test_td_div_nan(self, nan):
+        # np.float64('NaN') has a 'dtype' attr, avoid treating as array
+        td = Timedelta(10, unit="d")
+        result = td / nan
+        assert result is NaT
+
+        result = td // nan
+        assert result is NaT
+
+    def test_td_div_td64_ndarray(self):
+        td = Timedelta("1 day")
+
+        other = np.array([Timedelta("2 Days").to_timedelta64()])
+        expected = np.array([0.5])
+
+        result = td / other
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = other / td
+        tm.assert_numpy_array_equal(result, expected * 4)
+
+    def test_td_div_ndarray_0d(self):
+        td = Timedelta("1 day")
+
+        other = np.array(1)
+        res = td / other
+        assert isinstance(res, Timedelta)
+        assert res == td
+
+    # ---------------------------------------------------------------
+    # Timedelta.__rdiv__
+
+    def test_td_rdiv_timedeltalike_scalar(self):
+        # GH#19738
+        td = Timedelta(10, unit="d")
+        result = offsets.Hour(1) / td
+        assert result == 1 / 240.0
+
+        assert np.timedelta64(60, "h") / td == 0.25
+
+    def test_td_rdiv_na_scalar(self):
+        # GH#31869 None gets cast to NaT
+        td = Timedelta(10, unit="d")
+
+        result = NaT / td
+        assert np.isnan(result)
+
+        result = None / td
+        assert np.isnan(result)
+
+        result = np.timedelta64("NaT") / td
+        assert np.isnan(result)
+
+        msg = r"unsupported operand type\(s\) for /: 'numpy.datetime64' and 'Timedelta'"
+        with pytest.raises(TypeError, match=msg):
+            np.datetime64("NaT") / td
+
+        msg = r"unsupported operand type\(s\) for /: 'float' and 'Timedelta'"
+        with pytest.raises(TypeError, match=msg):
+            np.nan / td
+
+    def test_td_rdiv_ndarray(self):
+        td = Timedelta(10, unit="d")
+
+        arr = np.array([td], dtype=object)
+        result = arr / td
+        expected = np.array([1], dtype=np.float64)
+        tm.assert_numpy_array_equal(result, expected)
+
+        arr = np.array([None])
+        result = arr / td
+        expected = np.array([np.nan])
+        tm.assert_numpy_array_equal(result, expected)
+
+        arr = np.array([np.nan], dtype=object)
+        msg = r"unsupported operand type\(s\) for /: 'float' and 'Timedelta'"
+        with pytest.raises(TypeError, match=msg):
+            arr / td
+
+        arr = np.array([np.nan], dtype=np.float64)
+        msg = "cannot use operands with types dtype"
+        with pytest.raises(TypeError, match=msg):
+            arr / td
+
+    def test_td_rdiv_ndarray_0d(self):
+        td = Timedelta(10, unit="d")
+
+        arr = np.array(td.asm8)
+
+        assert arr / td == 1
+
+    # ---------------------------------------------------------------
+    # Timedelta.__floordiv__
+
+    def test_td_floordiv_timedeltalike_scalar(self):
+        # GH#18846
+        td = Timedelta(hours=3, minutes=4)
+        scalar = Timedelta(hours=3, minutes=3)
+
+        assert td // scalar == 1
+        assert -td // scalar.to_pytimedelta() == -2
+        assert (2 * td) // scalar.to_timedelta64() == 2
+
+    def test_td_floordiv_null_scalar(self):
+        # GH#18846
+        td = Timedelta(hours=3, minutes=4)
+
+        assert td // np.nan is NaT
+        assert np.isnan(td // NaT)
+        assert np.isnan(td // np.timedelta64("NaT"))
+
+    def test_td_floordiv_offsets(self):
+        # GH#19738
+        td = Timedelta(hours=3, minutes=4)
+        assert td // offsets.Hour(1) == 3
+        assert td // offsets.Minute(2) == 92
+
+    def test_td_floordiv_invalid_scalar(self):
+        # GH#18846
+        td = Timedelta(hours=3, minutes=4)
+
+        msg = "|".join(
+            [
+                r"Invalid dtype datetime64\[D\] for __floordiv__",
+                "'dtype' is an invalid keyword argument for this function",
+                "this function got an unexpected keyword argument 'dtype'",
+                r"ufunc '?floor_divide'? cannot use operands with types",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            td // np.datetime64("2016-01-01", dtype="datetime64[us]")
+
+    def test_td_floordiv_numeric_scalar(self):
+        # GH#18846
+        td = Timedelta(hours=3, minutes=4)
+
+        expected = Timedelta(hours=1, minutes=32)
+        assert td // 2 == expected
+        assert td // 2.0 == expected
+        assert td // np.float64(2.0) == expected
+        assert td // np.int32(2.0) == expected
+        assert td // np.uint8(2.0) == expected
+
+    def test_td_floordiv_timedeltalike_array(self):
+        # GH#18846
+        td = Timedelta(hours=3, minutes=4)
+        scalar = Timedelta(hours=3, minutes=3)
+
+        # Array-like others
+        assert td // np.array(scalar.to_timedelta64()) == 1
+
+        res = (3 * td) // np.array([scalar.to_timedelta64()])
+        expected = np.array([3], dtype=np.int64)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = (10 * td) // np.array([scalar.to_timedelta64(), np.timedelta64("NaT")])
+        expected = np.array([10, np.nan])
+        tm.assert_numpy_array_equal(res, expected)
+
+    def test_td_floordiv_numeric_series(self):
+        # GH#18846
+        td = Timedelta(hours=3, minutes=4)
+        ser = pd.Series([1], dtype=np.int64)
+        res = td // ser
+        assert res.dtype.kind == "m"
+
+    # ---------------------------------------------------------------
+    # Timedelta.__rfloordiv__
+
+    def test_td_rfloordiv_timedeltalike_scalar(self):
+        # GH#18846
+        td = Timedelta(hours=3, minutes=3)
+        scalar = Timedelta(hours=3, minutes=4)
+
+        # scalar others
+        # x // Timedelta is defined only for timedelta-like x. int-like,
+        # float-like, and date-like, in particular, should all either
+        # a) raise TypeError directly or
+        # b) return NotImplemented, following which the reversed
+        #    operation will raise TypeError.
+        assert td.__rfloordiv__(scalar) == 1
+        assert (-td).__rfloordiv__(scalar.to_pytimedelta()) == -2
+        assert (2 * td).__rfloordiv__(scalar.to_timedelta64()) == 0
+
+    def test_td_rfloordiv_null_scalar(self):
+        # GH#18846
+        td = Timedelta(hours=3, minutes=3)
+
+        assert np.isnan(td.__rfloordiv__(NaT))
+        assert np.isnan(td.__rfloordiv__(np.timedelta64("NaT")))
+
+    def test_td_rfloordiv_offsets(self):
+        # GH#19738
+        assert offsets.Hour(1) // Timedelta(minutes=25) == 2
+
+    def test_td_rfloordiv_invalid_scalar(self):
+        # GH#18846
+        td = Timedelta(hours=3, minutes=3)
+
+        dt64 = np.datetime64("2016-01-01", "us")
+
+        assert td.__rfloordiv__(dt64) is NotImplemented
+
+        msg = (
+            r"unsupported operand type\(s\) for //: 'numpy.datetime64' and 'Timedelta'"
+        )
+        with pytest.raises(TypeError, match=msg):
+            dt64 // td
+
+    def test_td_rfloordiv_numeric_scalar(self):
+        # GH#18846
+        td = Timedelta(hours=3, minutes=3)
+
+        assert td.__rfloordiv__(np.nan) is NotImplemented
+        assert td.__rfloordiv__(3.5) is NotImplemented
+        assert td.__rfloordiv__(2) is NotImplemented
+        assert td.__rfloordiv__(np.float64(2.0)) is NotImplemented
+        assert td.__rfloordiv__(np.uint8(9)) is NotImplemented
+        assert td.__rfloordiv__(np.int32(2.0)) is NotImplemented
+
+        msg = r"unsupported operand type\(s\) for //: '.*' and 'Timedelta"
+        with pytest.raises(TypeError, match=msg):
+            np.float64(2.0) // td
+        with pytest.raises(TypeError, match=msg):
+            np.uint8(9) // td
+        with pytest.raises(TypeError, match=msg):
+            # deprecated GH#19761, enforced GH#29797
+            np.int32(2.0) // td
+
+    def test_td_rfloordiv_timedeltalike_array(self):
+        # GH#18846
+        td = Timedelta(hours=3, minutes=3)
+        scalar = Timedelta(hours=3, minutes=4)
+
+        # Array-like others
+        assert td.__rfloordiv__(np.array(scalar.to_timedelta64())) == 1
+
+        res = td.__rfloordiv__(np.array([(3 * scalar).to_timedelta64()]))
+        expected = np.array([3], dtype=np.int64)
+        tm.assert_numpy_array_equal(res, expected)
+
+        arr = np.array([(10 * scalar).to_timedelta64(), np.timedelta64("NaT")])
+        res = td.__rfloordiv__(arr)
+        expected = np.array([10, np.nan])
+        tm.assert_numpy_array_equal(res, expected)
+
+    def test_td_rfloordiv_intarray(self):
+        # deprecated GH#19761, enforced GH#29797
+        ints = np.array([1349654400, 1349740800, 1349827200, 1349913600]) * 10**9
+
+        msg = "Invalid dtype"
+        with pytest.raises(TypeError, match=msg):
+            ints // Timedelta(1, unit="s")
+
+    def test_td_rfloordiv_numeric_series(self):
+        # GH#18846
+        td = Timedelta(hours=3, minutes=3)
+        ser = pd.Series([1], dtype=np.int64)
+        res = td.__rfloordiv__(ser)
+        assert res is NotImplemented
+
+        msg = "Invalid dtype"
+        with pytest.raises(TypeError, match=msg):
+            # Deprecated GH#19761, enforced GH#29797
+            ser // td
+
+    # ----------------------------------------------------------------
+    # Timedelta.__mod__, __rmod__
+
+    def test_mod_timedeltalike(self):
+        # GH#19365
+        td = Timedelta(hours=37)
+
+        # Timedelta-like others
+        result = td % Timedelta(hours=6)
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(hours=1)
+
+        result = td % timedelta(minutes=60)
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(0)
+
+        result = td % NaT
+        assert result is NaT
+
+    def test_mod_timedelta64_nat(self):
+        # GH#19365
+        td = Timedelta(hours=37)
+
+        result = td % np.timedelta64("NaT", "ns")
+        assert result is NaT
+
+    def test_mod_timedelta64(self):
+        # GH#19365
+        td = Timedelta(hours=37)
+
+        result = td % np.timedelta64(2, "h")
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(hours=1)
+
+    def test_mod_offset(self):
+        # GH#19365
+        td = Timedelta(hours=37)
+
+        result = td % offsets.Hour(5)
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(hours=2)
+
+    def test_mod_numeric(self):
+        # GH#19365
+        td = Timedelta(hours=37)
+
+        # Numeric Others
+        result = td % 2
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(0)
+
+        result = td % 1e12
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(minutes=3, seconds=20)
+
+        result = td % int(1e12)
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(minutes=3, seconds=20)
+
+    def test_mod_invalid(self):
+        # GH#19365
+        td = Timedelta(hours=37)
+        msg = "unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            td % Timestamp("2018-01-22")
+
+        with pytest.raises(TypeError, match=msg):
+            td % []
+
+    def test_rmod_pytimedelta(self):
+        # GH#19365
+        td = Timedelta(minutes=3)
+
+        result = timedelta(minutes=4) % td
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(minutes=1)
+
+    def test_rmod_timedelta64(self):
+        # GH#19365
+        td = Timedelta(minutes=3)
+        result = np.timedelta64(5, "m") % td
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta(minutes=2)
+
+    def test_rmod_invalid(self):
+        # GH#19365
+        td = Timedelta(minutes=3)
+
+        msg = "unsupported operand"
+        with pytest.raises(TypeError, match=msg):
+            Timestamp("2018-01-22") % td
+
+        with pytest.raises(TypeError, match=msg):
+            15 % td
+
+        with pytest.raises(TypeError, match=msg):
+            16.0 % td
+
+        msg = "Invalid dtype int"
+        with pytest.raises(TypeError, match=msg):
+            np.array([22, 24]) % td
+
+    # ----------------------------------------------------------------
+    # Timedelta.__divmod__, __rdivmod__
+
+    def test_divmod_numeric(self):
+        # GH#19365
+        td = Timedelta(days=2, hours=6)
+
+        result = divmod(td, 53 * 3600 * 1e9)
+        assert result[0] == Timedelta(1, unit="ns")
+        assert isinstance(result[1], Timedelta)
+        assert result[1] == Timedelta(hours=1)
+
+        assert result
+        result = divmod(td, np.nan)
+        assert result[0] is NaT
+        assert result[1] is NaT
+
+    def test_divmod(self):
+        # GH#19365
+        td = Timedelta(days=2, hours=6)
+
+        result = divmod(td, timedelta(days=1))
+        assert result[0] == 2
+        assert isinstance(result[1], Timedelta)
+        assert result[1] == Timedelta(hours=6)
+
+        result = divmod(td, 54)
+        assert result[0] == Timedelta(hours=1)
+        assert isinstance(result[1], Timedelta)
+        assert result[1] == Timedelta(0)
+
+        result = divmod(td, NaT)
+        assert np.isnan(result[0])
+        assert result[1] is NaT
+
+    def test_divmod_offset(self):
+        # GH#19365
+        td = Timedelta(days=2, hours=6)
+
+        result = divmod(td, offsets.Hour(-4))
+        assert result[0] == -14
+        assert isinstance(result[1], Timedelta)
+        assert result[1] == Timedelta(hours=-2)
+
+    def test_divmod_invalid(self):
+        # GH#19365
+        td = Timedelta(days=2, hours=6)
+
+        msg = r"unsupported operand type\(s\) for //: 'Timedelta' and 'Timestamp'"
+        with pytest.raises(TypeError, match=msg):
+            divmod(td, Timestamp("2018-01-22"))
+
+    def test_rdivmod_pytimedelta(self):
+        # GH#19365
+        result = divmod(timedelta(days=2, hours=6), Timedelta(days=1))
+        assert result[0] == 2
+        assert isinstance(result[1], Timedelta)
+        assert result[1] == Timedelta(hours=6)
+
+    def test_rdivmod_offset(self):
+        result = divmod(offsets.Hour(54), Timedelta(hours=-4))
+        assert result[0] == -14
+        assert isinstance(result[1], Timedelta)
+        assert result[1] == Timedelta(hours=-2)
+
+    def test_rdivmod_invalid(self):
+        # GH#19365
+        td = Timedelta(minutes=3)
+        msg = "unsupported operand type"
+
+        with pytest.raises(TypeError, match=msg):
+            divmod(Timestamp("2018-01-22"), td)
+
+        with pytest.raises(TypeError, match=msg):
+            divmod(15, td)
+
+        with pytest.raises(TypeError, match=msg):
+            divmod(16.0, td)
+
+        msg = "Invalid dtype int"
+        with pytest.raises(TypeError, match=msg):
+            divmod(np.array([22, 24]), td)
+
+    # ----------------------------------------------------------------
+
+    @pytest.mark.parametrize(
+        "op", [operator.mul, ops.rmul, operator.truediv, ops.rdiv, ops.rsub]
+    )
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            np.array([Timestamp("20130101 9:01"), Timestamp("20121230 9:02")]),
+            np.array([Timestamp("2021-11-09 09:54:00"), Timedelta("1D")]),
+        ],
+    )
+    def test_td_op_timedelta_timedeltalike_array(self, op, arr):
+        msg = "unsupported operand type|cannot use operands with types"
+        with pytest.raises(TypeError, match=msg):
+            op(arr, Timedelta("1D"))
+
+
+class TestTimedeltaComparison:
+    @pytest.mark.skip_ubsan
+    def test_compare_pytimedelta_bounds(self):
+        # GH#49021 don't overflow on comparison with very large pytimedeltas
+
+        for unit in ["ns", "us"]:
+            tdmax = Timedelta.max.as_unit(unit).max
+            tdmin = Timedelta.min.as_unit(unit).min
+
+            assert tdmax < timedelta.max
+            assert tdmax <= timedelta.max
+            assert not tdmax > timedelta.max
+            assert not tdmax >= timedelta.max
+            assert tdmax != timedelta.max
+            assert not tdmax == timedelta.max
+
+            assert tdmin > timedelta.min
+            assert tdmin >= timedelta.min
+            assert not tdmin < timedelta.min
+            assert not tdmin <= timedelta.min
+            assert tdmin != timedelta.min
+            assert not tdmin == timedelta.min
+
+        # But the "ms" and "s"-reso bounds extend pass pytimedelta
+        for unit in ["ms", "s"]:
+            tdmax = Timedelta.max.as_unit(unit).max
+            tdmin = Timedelta.min.as_unit(unit).min
+
+            assert tdmax > timedelta.max
+            assert tdmax >= timedelta.max
+            assert not tdmax < timedelta.max
+            assert not tdmax <= timedelta.max
+            assert tdmax != timedelta.max
+            assert not tdmax == timedelta.max
+
+            assert tdmin < timedelta.min
+            assert tdmin <= timedelta.min
+            assert not tdmin > timedelta.min
+            assert not tdmin >= timedelta.min
+            assert tdmin != timedelta.min
+            assert not tdmin == timedelta.min
+
+    def test_compare_pytimedelta_bounds2(self):
+        # a pytimedelta outside the microsecond bounds
+        pytd = timedelta(days=999999999, seconds=86399)
+        # NB: np.timedelta64(td, "s"") incorrectly overflows
+        td64 = np.timedelta64(pytd.days, "D") + np.timedelta64(pytd.seconds, "s")
+        td = Timedelta(td64)
+        assert td.days == pytd.days
+        assert td.seconds == pytd.seconds
+
+        assert td == pytd
+        assert not td != pytd
+        assert not td < pytd
+        assert not td > pytd
+        assert td <= pytd
+        assert td >= pytd
+
+        td2 = td - Timedelta(seconds=1).as_unit("s")
+        assert td2 != pytd
+        assert not td2 == pytd
+        assert td2 < pytd
+        assert td2 <= pytd
+        assert not td2 > pytd
+        assert not td2 >= pytd
+
+    def test_compare_tick(self, tick_classes):
+        cls = tick_classes
+
+        off = cls(4)
+        td = off._as_pd_timedelta
+        assert isinstance(td, Timedelta)
+
+        assert td == off
+        assert not td != off
+        assert td <= off
+        assert td >= off
+        assert not td < off
+        assert not td > off
+
+        assert not td == 2 * off
+        assert td != 2 * off
+        assert td <= 2 * off
+        assert td < 2 * off
+        assert not td >= 2 * off
+        assert not td > 2 * off
+
+    def test_comparison_object_array(self):
+        # analogous to GH#15183
+        td = Timedelta("2 days")
+        other = Timedelta("3 hours")
+
+        arr = np.array([other, td], dtype=object)
+        res = arr == td
+        expected = np.array([False, True], dtype=bool)
+        assert (res == expected).all()
+
+        # 2D case
+        arr = np.array([[other, td], [td, other]], dtype=object)
+        res = arr != td
+        expected = np.array([[True, False], [False, True]], dtype=bool)
+        assert res.shape == expected.shape
+        assert (res == expected).all()
+
+    def test_compare_timedelta_ndarray(self):
+        # GH#11835
+        periods = [Timedelta("0 days 01:00:00"), Timedelta("0 days 01:00:00")]
+        arr = np.array(periods)
+        result = arr[0] > arr
+        expected = np.array([False, False])
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_compare_td64_ndarray(self):
+        # GG#33441
+        arr = np.arange(5).astype("timedelta64[ns]")
+        td = Timedelta(arr[1])
+
+        expected = np.array([False, True, False, False, False], dtype=bool)
+
+        result = td == arr
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = arr == td
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = td != arr
+        tm.assert_numpy_array_equal(result, ~expected)
+
+        result = arr != td
+        tm.assert_numpy_array_equal(result, ~expected)
+
+    def test_compare_custom_object(self):
+        """
+        Make sure non supported operations on Timedelta returns NonImplemented
+        and yields to other operand (GH#20829).
+        """
+
+        class CustomClass:
+            def __init__(self, cmp_result=None) -> None:
+                self.cmp_result = cmp_result
+
+            def generic_result(self):
+                if self.cmp_result is None:
+                    return NotImplemented
+                else:
+                    return self.cmp_result
+
+            def __eq__(self, other):
+                return self.generic_result()
+
+            def __gt__(self, other):
+                return self.generic_result()
+
+        t = Timedelta("1s")
+
+        assert t != "string"
+        assert t != 1
+        assert t != CustomClass()
+        assert t != CustomClass(cmp_result=False)
+
+        assert t < CustomClass(cmp_result=True)
+        assert not t < CustomClass(cmp_result=False)
+
+        assert t == CustomClass(cmp_result=True)
+
+    @pytest.mark.parametrize("val", ["string", 1])
+    def test_compare_unknown_type(self, val):
+        # GH#20829
+        t = Timedelta("1s")
+        msg = "not supported between instances of 'Timedelta' and '(int|str)'"
+        with pytest.raises(TypeError, match=msg):
+            t >= val
+        with pytest.raises(TypeError, match=msg):
+            t > val
+        with pytest.raises(TypeError, match=msg):
+            t <= val
+        with pytest.raises(TypeError, match=msg):
+            t < val
+
+
+def test_ops_notimplemented():
+    class Other:
+        pass
+
+    other = Other()
+
+    td = Timedelta("1 day")
+    assert td.__add__(other) is NotImplemented
+    assert td.__sub__(other) is NotImplemented
+    assert td.__truediv__(other) is NotImplemented
+    assert td.__mul__(other) is NotImplemented
+    assert td.__floordiv__(other) is NotImplemented
+
+
+def test_ops_error_str():
+    # GH#13624
+    td = Timedelta("1 day")
+
+    for left, right in [(td, "a"), ("a", td)]:
+        msg = "|".join(
+            [
+                "unsupported operand type",
+                r'can only concatenate str \(not "Timedelta"\) to str',
+                "must be str, not Timedelta",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            left + right
+
+        msg = "not supported between instances of"
+        with pytest.raises(TypeError, match=msg):
+            left > right
+
+        assert not left == right  # pylint: disable=unneeded-not
+        assert left != right

py311/lib/python3.11/site-packages/pandas/tests/scalar/timedelta/test_constructors.py

@@ -0,0 +1,698 @@
+from datetime import timedelta
+from itertools import product
+
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import OutOfBoundsTimedelta
+from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+
+from pandas import (
+    Index,
+    NaT,
+    Timedelta,
+    TimedeltaIndex,
+    offsets,
+    to_timedelta,
+)
+import pandas._testing as tm
+
+
+class TestTimedeltaConstructorUnitKeyword:
+    @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):
+            Timedelta(10, unit)
+
+        with pytest.raises(ValueError, match=msg):
+            to_timedelta(10, unit)
+
+        with pytest.raises(ValueError, match=msg):
+            to_timedelta([1, 2], unit)
+
+    @pytest.mark.parametrize(
+        "unit,unit_depr",
+        [
+            ("h", "H"),
+            ("min", "T"),
+            ("s", "S"),
+            ("ms", "L"),
+            ("ns", "N"),
+            ("us", "U"),
+        ],
+    )
+    def test_units_H_T_S_L_N_U_deprecated(self, unit, unit_depr):
+        # GH#52536
+        msg = f"'{unit_depr}' is deprecated and will be removed in a future version."
+
+        expected = Timedelta(1, unit=unit)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            result = Timedelta(1, unit=unit_depr)
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "unit, np_unit",
+        [(value, "W") for value in ["W", "w"]]
+        + [(value, "D") for value in ["D", "d", "days", "day", "Days", "Day"]]
+        + [
+            (value, "m")
+            for value in [
+                "m",
+                "minute",
+                "min",
+                "minutes",
+                "Minute",
+                "Min",
+                "Minutes",
+            ]
+        ]
+        + [
+            (value, "s")
+            for value in [
+                "s",
+                "seconds",
+                "sec",
+                "second",
+                "Seconds",
+                "Sec",
+                "Second",
+            ]
+        ]
+        + [
+            (value, "ms")
+            for value in [
+                "ms",
+                "milliseconds",
+                "millisecond",
+                "milli",
+                "millis",
+                "MS",
+                "Milliseconds",
+                "Millisecond",
+                "Milli",
+                "Millis",
+            ]
+        ]
+        + [
+            (value, "us")
+            for value in [
+                "us",
+                "microseconds",
+                "microsecond",
+                "micro",
+                "micros",
+                "u",
+                "US",
+                "Microseconds",
+                "Microsecond",
+                "Micro",
+                "Micros",
+                "U",
+            ]
+        ]
+        + [
+            (value, "ns")
+            for value in [
+                "ns",
+                "nanoseconds",
+                "nanosecond",
+                "nano",
+                "nanos",
+                "n",
+                "NS",
+                "Nanoseconds",
+                "Nanosecond",
+                "Nano",
+                "Nanos",
+                "N",
+            ]
+        ],
+    )
+    @pytest.mark.parametrize("wrapper", [np.array, list, Index])
+    def test_unit_parser(self, unit, np_unit, wrapper):
+        # validate all units, GH 6855, GH 21762
+        # array-likes
+        expected = TimedeltaIndex(
+            [np.timedelta64(i, np_unit) for i in np.arange(5).tolist()],
+            dtype="m8[ns]",
+        )
+        # TODO(2.0): the desired output dtype may have non-nano resolution
+        msg = f"'{unit}' is deprecated and will be removed in a future version."
+
+        if (unit, np_unit) in (("u", "us"), ("U", "us"), ("n", "ns"), ("N", "ns")):
+            warn = FutureWarning
+        else:
+            warn = FutureWarning
+            msg = "The 'unit' keyword in TimedeltaIndex construction is deprecated"
+        with tm.assert_produces_warning(warn, match=msg):
+            result = to_timedelta(wrapper(range(5)), unit=unit)
+            tm.assert_index_equal(result, expected)
+            result = TimedeltaIndex(wrapper(range(5)), unit=unit)
+            tm.assert_index_equal(result, expected)
+
+            str_repr = [f"{x}{unit}" for x in np.arange(5)]
+            result = to_timedelta(wrapper(str_repr))
+            tm.assert_index_equal(result, expected)
+            result = to_timedelta(wrapper(str_repr))
+            tm.assert_index_equal(result, expected)
+
+            # scalar
+            expected = Timedelta(np.timedelta64(2, np_unit).astype("timedelta64[ns]"))
+            result = to_timedelta(2, unit=unit)
+            assert result == expected
+            result = Timedelta(2, unit=unit)
+            assert result == expected
+
+            result = to_timedelta(f"2{unit}")
+            assert result == expected
+            result = Timedelta(f"2{unit}")
+            assert result == expected
+
+
+def test_construct_from_kwargs_overflow():
+    # GH#55503
+    msg = "seconds=86400000000000000000, milliseconds=0, microseconds=0, nanoseconds=0"
+    with pytest.raises(OutOfBoundsTimedelta, match=msg):
+        Timedelta(days=10**6)
+    msg = "seconds=60000000000000000000, milliseconds=0, microseconds=0, nanoseconds=0"
+    with pytest.raises(OutOfBoundsTimedelta, match=msg):
+        Timedelta(minutes=10**9)
+
+
+def test_construct_with_weeks_unit_overflow():
+    # GH#47268 don't silently wrap around
+    with pytest.raises(OutOfBoundsTimedelta, match="without overflow"):
+        Timedelta(1000000000000000000, unit="W")
+
+    with pytest.raises(OutOfBoundsTimedelta, match="without overflow"):
+        Timedelta(1000000000000000000.0, unit="W")
+
+
+def test_construct_from_td64_with_unit():
+    # ignore the unit, as it may cause silently overflows leading to incorrect
+    #  results, and in non-overflow cases is irrelevant GH#46827
+    obj = np.timedelta64(123456789000000000, "h")
+
+    with pytest.raises(OutOfBoundsTimedelta, match="123456789000000000 hours"):
+        Timedelta(obj, unit="ps")
+
+    with pytest.raises(OutOfBoundsTimedelta, match="123456789000000000 hours"):
+        Timedelta(obj, unit="ns")
+
+    with pytest.raises(OutOfBoundsTimedelta, match="123456789000000000 hours"):
+        Timedelta(obj)
+
+
+def test_from_td64_retain_resolution():
+    # case where we retain millisecond resolution
+    obj = np.timedelta64(12345, "ms")
+
+    td = Timedelta(obj)
+    assert td._value == obj.view("i8")
+    assert td._creso == NpyDatetimeUnit.NPY_FR_ms.value
+
+    # Case where we cast to nearest-supported reso
+    obj2 = np.timedelta64(1234, "D")
+    td2 = Timedelta(obj2)
+    assert td2._creso == NpyDatetimeUnit.NPY_FR_s.value
+    assert td2 == obj2
+    assert td2.days == 1234
+
+    # Case that _would_ overflow if we didn't support non-nano
+    obj3 = np.timedelta64(1000000000000000000, "us")
+    td3 = Timedelta(obj3)
+    assert td3.total_seconds() == 1000000000000
+    assert td3._creso == NpyDatetimeUnit.NPY_FR_us.value
+
+
+def test_from_pytimedelta_us_reso():
+    # pytimedelta has microsecond resolution, so Timedelta(pytd) inherits that
+    td = timedelta(days=4, minutes=3)
+    result = Timedelta(td)
+    assert result.to_pytimedelta() == td
+    assert result._creso == NpyDatetimeUnit.NPY_FR_us.value
+
+
+def test_from_tick_reso():
+    tick = offsets.Nano()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_ns.value
+
+    tick = offsets.Micro()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_us.value
+
+    tick = offsets.Milli()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_ms.value
+
+    tick = offsets.Second()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_s.value
+
+    # everything above Second gets cast to the closest supported reso: second
+    tick = offsets.Minute()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_s.value
+
+    tick = offsets.Hour()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_s.value
+
+    tick = offsets.Day()
+    assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_s.value
+
+
+def test_construction():
+    expected = np.timedelta64(10, "D").astype("m8[ns]").view("i8")
+    assert Timedelta(10, unit="d")._value == expected
+    assert Timedelta(10.0, unit="d")._value == expected
+    assert Timedelta("10 days")._value == expected
+    assert Timedelta(days=10)._value == expected
+    assert Timedelta(days=10.0)._value == expected
+
+    expected += np.timedelta64(10, "s").astype("m8[ns]").view("i8")
+    assert Timedelta("10 days 00:00:10")._value == expected
+    assert Timedelta(days=10, seconds=10)._value == expected
+    assert Timedelta(days=10, milliseconds=10 * 1000)._value == expected
+    assert Timedelta(days=10, microseconds=10 * 1000 * 1000)._value == expected
+
+    # rounding cases
+    assert Timedelta(82739999850000)._value == 82739999850000
+    assert "0 days 22:58:59.999850" in str(Timedelta(82739999850000))
+    assert Timedelta(123072001000000)._value == 123072001000000
+    assert "1 days 10:11:12.001" in str(Timedelta(123072001000000))
+
+    # string conversion with/without leading zero
+    # GH#9570
+    assert Timedelta("0:00:00") == timedelta(hours=0)
+    assert Timedelta("00:00:00") == timedelta(hours=0)
+    assert Timedelta("-1:00:00") == -timedelta(hours=1)
+    assert Timedelta("-01:00:00") == -timedelta(hours=1)
+
+    # more strings & abbrevs
+    # GH#8190
+    assert Timedelta("1 h") == timedelta(hours=1)
+    assert Timedelta("1 hour") == timedelta(hours=1)
+    assert Timedelta("1 hr") == timedelta(hours=1)
+    assert Timedelta("1 hours") == timedelta(hours=1)
+    assert Timedelta("-1 hours") == -timedelta(hours=1)
+    assert Timedelta("1 m") == timedelta(minutes=1)
+    assert Timedelta("1.5 m") == timedelta(seconds=90)
+    assert Timedelta("1 minute") == timedelta(minutes=1)
+    assert Timedelta("1 minutes") == timedelta(minutes=1)
+    assert Timedelta("1 s") == timedelta(seconds=1)
+    assert Timedelta("1 second") == timedelta(seconds=1)
+    assert Timedelta("1 seconds") == timedelta(seconds=1)
+    assert Timedelta("1 ms") == timedelta(milliseconds=1)
+    assert Timedelta("1 milli") == timedelta(milliseconds=1)
+    assert Timedelta("1 millisecond") == timedelta(milliseconds=1)
+    assert Timedelta("1 us") == timedelta(microseconds=1)
+    assert Timedelta("1 µs") == timedelta(microseconds=1)
+    assert Timedelta("1 micros") == timedelta(microseconds=1)
+    assert Timedelta("1 microsecond") == timedelta(microseconds=1)
+    assert Timedelta("1.5 microsecond") == Timedelta("00:00:00.000001500")
+    assert Timedelta("1 ns") == Timedelta("00:00:00.000000001")
+    assert Timedelta("1 nano") == Timedelta("00:00:00.000000001")
+    assert Timedelta("1 nanosecond") == Timedelta("00:00:00.000000001")
+
+    # combos
+    assert Timedelta("10 days 1 hour") == timedelta(days=10, hours=1)
+    assert Timedelta("10 days 1 h") == timedelta(days=10, hours=1)
+    assert Timedelta("10 days 1 h 1m 1s") == timedelta(
+        days=10, hours=1, minutes=1, seconds=1
+    )
+    assert Timedelta("-10 days 1 h 1m 1s") == -timedelta(
+        days=10, hours=1, minutes=1, seconds=1
+    )
+    assert Timedelta("-10 days 1 h 1m 1s") == -timedelta(
+        days=10, hours=1, minutes=1, seconds=1
+    )
+    assert Timedelta("-10 days 1 h 1m 1s 3us") == -timedelta(
+        days=10, hours=1, minutes=1, seconds=1, microseconds=3
+    )
+    assert Timedelta("-10 days 1 h 1.5m 1s 3us") == -timedelta(
+        days=10, hours=1, minutes=1, seconds=31, microseconds=3
+    )
+
+    # Currently invalid as it has a - on the hh:mm:dd part
+    # (only allowed on the days)
+    msg = "only leading negative signs are allowed"
+    with pytest.raises(ValueError, match=msg):
+        Timedelta("-10 days -1 h 1.5m 1s 3us")
+
+    # only leading neg signs are allowed
+    with pytest.raises(ValueError, match=msg):
+        Timedelta("10 days -1 h 1.5m 1s 3us")
+
+    # no units specified
+    msg = "no units specified"
+    with pytest.raises(ValueError, match=msg):
+        Timedelta("3.1415")
+
+    # invalid construction
+    msg = "cannot construct a Timedelta"
+    with pytest.raises(ValueError, match=msg):
+        Timedelta()
+
+    msg = "unit abbreviation w/o a number"
+    with pytest.raises(ValueError, match=msg):
+        Timedelta("foo")
+
+    msg = (
+        "cannot construct a Timedelta from "
+        "the passed arguments, allowed keywords are "
+    )
+    with pytest.raises(ValueError, match=msg):
+        Timedelta(day=10)
+
+    # floats
+    expected = np.timedelta64(10, "s").astype("m8[ns]").view("i8") + np.timedelta64(
+        500, "ms"
+    ).astype("m8[ns]").view("i8")
+    assert Timedelta(10.5, unit="s")._value == expected
+
+    # offset
+    assert to_timedelta(offsets.Hour(2)) == Timedelta(hours=2)
+    assert Timedelta(offsets.Hour(2)) == Timedelta(hours=2)
+    assert Timedelta(offsets.Second(2)) == Timedelta(seconds=2)
+
+    # GH#11995: unicode
+    expected = Timedelta("1h")
+    result = Timedelta("1h")
+    assert result == expected
+    assert to_timedelta(offsets.Hour(2)) == Timedelta("0 days, 02:00:00")
+
+    msg = "unit abbreviation w/o a number"
+    with pytest.raises(ValueError, match=msg):
+        Timedelta("foo bar")
+
+
+@pytest.mark.parametrize(
+    "item",
+    list(
+        {
+            "days": "D",
+            "seconds": "s",
+            "microseconds": "us",
+            "milliseconds": "ms",
+            "minutes": "m",
+            "hours": "h",
+            "weeks": "W",
+        }.items()
+    ),
+)
+@pytest.mark.parametrize(
+    "npdtype", [np.int64, np.int32, np.int16, np.float64, np.float32, np.float16]
+)
+def test_td_construction_with_np_dtypes(npdtype, item):
+    # GH#8757: test construction with np dtypes
+    pykwarg, npkwarg = item
+    expected = np.timedelta64(1, npkwarg).astype("m8[ns]").view("i8")
+    assert Timedelta(**{pykwarg: npdtype(1)})._value == expected
+
+
+@pytest.mark.parametrize(
+    "val",
+    [
+        "1s",
+        "-1s",
+        "1us",
+        "-1us",
+        "1 day",
+        "-1 day",
+        "-23:59:59.999999",
+        "-1 days +23:59:59.999999",
+        "-1ns",
+        "1ns",
+        "-23:59:59.999999999",
+    ],
+)
+def test_td_from_repr_roundtrip(val):
+    # round-trip both for string and value
+    td = Timedelta(val)
+    assert Timedelta(td._value) == td
+
+    assert Timedelta(str(td)) == td
+    assert Timedelta(td._repr_base(format="all")) == td
+    assert Timedelta(td._repr_base()) == td
+
+
+def test_overflow_on_construction():
+    # GH#3374
+    value = Timedelta("1day")._value * 20169940
+    msg = "Cannot cast 1742682816000000000000 from ns to 'ns' without overflow"
+    with pytest.raises(OutOfBoundsTimedelta, match=msg):
+        Timedelta(value)
+
+    # xref GH#17637
+    msg = "Cannot cast 139993 from D to 'ns' without overflow"
+    with pytest.raises(OutOfBoundsTimedelta, match=msg):
+        Timedelta(7 * 19999, unit="D")
+
+    # used to overflow before non-ns support
+    td = Timedelta(timedelta(days=13 * 19999))
+    assert td._creso == NpyDatetimeUnit.NPY_FR_us.value
+    assert td.days == 13 * 19999
+
+
+@pytest.mark.parametrize(
+    "val, unit",
+    [
+        (15251, "W"),  # 1
+        (106752, "D"),  # change from previous:
+        (2562048, "h"),  # 0 hours
+        (153722868, "m"),  # 13 minutes
+        (9223372037, "s"),  # 44 seconds
+    ],
+)
+def test_construction_out_of_bounds_td64ns(val, unit):
+    # TODO: parametrize over units just above/below the implementation bounds
+    #  once GH#38964 is resolved
+
+    # Timedelta.max is just under 106752 days
+    td64 = np.timedelta64(val, unit)
+    assert td64.astype("m8[ns]").view("i8") < 0  # i.e. naive astype will be wrong
+
+    td = Timedelta(td64)
+    if unit != "M":
+        # with unit="M" the conversion to "s" is poorly defined
+        #  (and numpy issues DeprecationWarning)
+        assert td.asm8 == td64
+    assert td.asm8.dtype == "m8[s]"
+    msg = r"Cannot cast 1067\d\d days .* to unit='ns' without overflow"
+    with pytest.raises(OutOfBoundsTimedelta, match=msg):
+        td.as_unit("ns")
+
+    # But just back in bounds and we are OK
+    assert Timedelta(td64 - 1) == td64 - 1
+
+    td64 *= -1
+    assert td64.astype("m8[ns]").view("i8") > 0  # i.e. naive astype will be wrong
+
+    td2 = Timedelta(td64)
+    msg = r"Cannot cast -1067\d\d days .* to unit='ns' without overflow"
+    with pytest.raises(OutOfBoundsTimedelta, match=msg):
+        td2.as_unit("ns")
+
+    # But just back in bounds and we are OK
+    assert Timedelta(td64 + 1) == td64 + 1
+
+
+@pytest.mark.parametrize(
+    "val, unit",
+    [
+        (15251 * 10**9, "W"),
+        (106752 * 10**9, "D"),
+        (2562048 * 10**9, "h"),
+        (153722868 * 10**9, "m"),
+    ],
+)
+def test_construction_out_of_bounds_td64s(val, unit):
+    td64 = np.timedelta64(val, unit)
+    with pytest.raises(OutOfBoundsTimedelta, match=str(td64)):
+        Timedelta(td64)
+
+    # But just back in bounds and we are OK
+    assert Timedelta(td64 - 10**9) == td64 - 10**9
+
+
+@pytest.mark.parametrize(
+    "fmt,exp",
+    [
+        (
+            "P6DT0H50M3.010010012S",
+            Timedelta(
+                days=6,
+                minutes=50,
+                seconds=3,
+                milliseconds=10,
+                microseconds=10,
+                nanoseconds=12,
+            ),
+        ),
+        (
+            "P-6DT0H50M3.010010012S",
+            Timedelta(
+                days=-6,
+                minutes=50,
+                seconds=3,
+                milliseconds=10,
+                microseconds=10,
+                nanoseconds=12,
+            ),
+        ),
+        ("P4DT12H30M5S", Timedelta(days=4, hours=12, minutes=30, seconds=5)),
+        ("P0DT0H0M0.000000123S", Timedelta(nanoseconds=123)),
+        ("P0DT0H0M0.00001S", Timedelta(microseconds=10)),
+        ("P0DT0H0M0.001S", Timedelta(milliseconds=1)),
+        ("P0DT0H1M0S", Timedelta(minutes=1)),
+        ("P1DT25H61M61S", Timedelta(days=1, hours=25, minutes=61, seconds=61)),
+        ("PT1S", Timedelta(seconds=1)),
+        ("PT0S", Timedelta(seconds=0)),
+        ("P1WT0S", Timedelta(days=7, seconds=0)),
+        ("P1D", Timedelta(days=1)),
+        ("P1DT1H", Timedelta(days=1, hours=1)),
+        ("P1W", Timedelta(days=7)),
+        ("PT300S", Timedelta(seconds=300)),
+        ("P1DT0H0M00000000000S", Timedelta(days=1)),
+        ("PT-6H3M", Timedelta(hours=-6, minutes=3)),
+        ("-PT6H3M", Timedelta(hours=-6, minutes=-3)),
+        ("-PT-6H+3M", Timedelta(hours=6, minutes=-3)),
+    ],
+)
+def test_iso_constructor(fmt, exp):
+    assert Timedelta(fmt) == exp
+
+
+@pytest.mark.parametrize(
+    "fmt",
+    [
+        "PPPPPPPPPPPP",
+        "PDTHMS",
+        "P0DT999H999M999S",
+        "P1DT0H0M0.0000000000000S",
+        "P1DT0H0M0.S",
+        "P",
+        "-P",
+    ],
+)
+def test_iso_constructor_raises(fmt):
+    msg = f"Invalid ISO 8601 Duration format - {fmt}"
+    with pytest.raises(ValueError, match=msg):
+        Timedelta(fmt)
+
+
+@pytest.mark.parametrize(
+    "constructed_td, conversion",
+    [
+        (Timedelta(nanoseconds=100), "100ns"),
+        (
+            Timedelta(
+                days=1,
+                hours=1,
+                minutes=1,
+                weeks=1,
+                seconds=1,
+                milliseconds=1,
+                microseconds=1,
+                nanoseconds=1,
+            ),
+            694861001001001,
+        ),
+        (Timedelta(microseconds=1) + Timedelta(nanoseconds=1), "1us1ns"),
+        (Timedelta(microseconds=1) - Timedelta(nanoseconds=1), "999ns"),
+        (Timedelta(microseconds=1) + 5 * Timedelta(nanoseconds=-2), "990ns"),
+    ],
+)
+def test_td_constructor_on_nanoseconds(constructed_td, conversion):
+    # GH#9273
+    assert constructed_td == Timedelta(conversion)
+
+
+def test_td_constructor_value_error():
+    msg = "Invalid type <class 'str'>. Must be int or float."
+    with pytest.raises(TypeError, match=msg):
+        Timedelta(nanoseconds="abc")
+
+
+def test_timedelta_constructor_identity():
+    # Test for #30543
+    expected = Timedelta(np.timedelta64(1, "s"))
+    result = Timedelta(expected)
+    assert result is expected
+
+
+def test_timedelta_pass_td_and_kwargs_raises():
+    # don't silently ignore the kwargs GH#48898
+    td = Timedelta(days=1)
+    msg = (
+        "Cannot pass both a Timedelta input and timedelta keyword arguments, "
+        r"got \['days'\]"
+    )
+    with pytest.raises(ValueError, match=msg):
+        Timedelta(td, days=2)
+
+
+@pytest.mark.parametrize(
+    "constructor, value, unit, expectation",
+    [
+        (Timedelta, "10s", "ms", (ValueError, "unit must not be specified")),
+        (to_timedelta, "10s", "ms", (ValueError, "unit must not be specified")),
+        (to_timedelta, ["1", 2, 3], "s", (ValueError, "unit must not be specified")),
+    ],
+)
+def test_string_with_unit(constructor, value, unit, expectation):
+    exp, match = expectation
+    with pytest.raises(exp, match=match):
+        _ = constructor(value, unit=unit)
+
+
+@pytest.mark.parametrize(
+    "value",
+    [
+        "".join(elements)
+        for repetition in (1, 2)
+        for elements in product("+-, ", repeat=repetition)
+    ],
+)
+def test_string_without_numbers(value):
+    # GH39710 Timedelta input string with only symbols and no digits raises an error
+    msg = (
+        "symbols w/o a number"
+        if value != "--"
+        else "only leading negative signs are allowed"
+    )
+    with pytest.raises(ValueError, match=msg):
+        Timedelta(value)
+
+
+def test_timedelta_new_npnat():
+    # GH#48898
+    nat = np.timedelta64("NaT", "h")
+    assert Timedelta(nat) is NaT
+
+
+def test_subclass_respected():
+    # GH#49579
+    class MyCustomTimedelta(Timedelta):
+        pass
+
+    td = MyCustomTimedelta("1 minute")
+    assert isinstance(td, MyCustomTimedelta)
+
+
+def test_non_nano_value():
+    # https://github.com/pandas-dev/pandas/issues/49076
+    result = Timedelta(10, unit="D").as_unit("s").value
+    # `.value` shows nanoseconds, even though unit is 's'
+    assert result == 864000000000000
+
+    # out-of-nanoseconds-bounds `.value` raises informative message
+    msg = (
+        r"Cannot convert Timedelta to nanoseconds without overflow. "
+        r"Use `.asm8.view\('i8'\)` to cast represent Timedelta in its "
+        r"own unit \(here, s\).$"
+    )
+    td = Timedelta(1_000, "D").as_unit("s") * 1_000
+    with pytest.raises(OverflowError, match=msg):
+        td.value
+    # check that the suggested workaround actually works
+    result = td.asm8.view("i8")
+    assert result == 86400000000

py311/lib/python3.11/site-packages/pandas/tests/scalar/timedelta/test_formats.py

@@ -0,0 +1,109 @@
+import pytest
+
+from pandas import Timedelta
+
+
+@pytest.mark.parametrize(
+    "td, expected_repr",
+    [
+        (Timedelta(10, unit="d"), "Timedelta('10 days 00:00:00')"),
+        (Timedelta(10, unit="s"), "Timedelta('0 days 00:00:10')"),
+        (Timedelta(10, unit="ms"), "Timedelta('0 days 00:00:00.010000')"),
+        (Timedelta(-10, unit="ms"), "Timedelta('-1 days +23:59:59.990000')"),
+    ],
+)
+def test_repr(td, expected_repr):
+    assert repr(td) == expected_repr
+
+
+@pytest.mark.parametrize(
+    "td, expected_iso",
+    [
+        (
+            Timedelta(
+                days=6,
+                minutes=50,
+                seconds=3,
+                milliseconds=10,
+                microseconds=10,
+                nanoseconds=12,
+            ),
+            "P6DT0H50M3.010010012S",
+        ),
+        (Timedelta(days=4, hours=12, minutes=30, seconds=5), "P4DT12H30M5S"),
+        (Timedelta(nanoseconds=123), "P0DT0H0M0.000000123S"),
+        # trim nano
+        (Timedelta(microseconds=10), "P0DT0H0M0.00001S"),
+        # trim micro
+        (Timedelta(milliseconds=1), "P0DT0H0M0.001S"),
+        # don't strip every 0
+        (Timedelta(minutes=1), "P0DT0H1M0S"),
+    ],
+)
+def test_isoformat(td, expected_iso):
+    assert td.isoformat() == expected_iso
+
+
+class TestReprBase:
+    def test_none(self):
+        delta_1d = Timedelta(1, unit="D")
+        delta_0d = Timedelta(0, unit="D")
+        delta_1s = Timedelta(1, unit="s")
+        delta_500ms = Timedelta(500, unit="ms")
+
+        drepr = lambda x: x._repr_base()
+        assert drepr(delta_1d) == "1 days"
+        assert drepr(-delta_1d) == "-1 days"
+        assert drepr(delta_0d) == "0 days"
+        assert drepr(delta_1s) == "0 days 00:00:01"
+        assert drepr(delta_500ms) == "0 days 00:00:00.500000"
+        assert drepr(delta_1d + delta_1s) == "1 days 00:00:01"
+        assert drepr(-delta_1d + delta_1s) == "-1 days +00:00:01"
+        assert drepr(delta_1d + delta_500ms) == "1 days 00:00:00.500000"
+        assert drepr(-delta_1d + delta_500ms) == "-1 days +00:00:00.500000"
+
+    def test_sub_day(self):
+        delta_1d = Timedelta(1, unit="D")
+        delta_0d = Timedelta(0, unit="D")
+        delta_1s = Timedelta(1, unit="s")
+        delta_500ms = Timedelta(500, unit="ms")
+
+        drepr = lambda x: x._repr_base(format="sub_day")
+        assert drepr(delta_1d) == "1 days"
+        assert drepr(-delta_1d) == "-1 days"
+        assert drepr(delta_0d) == "00:00:00"
+        assert drepr(delta_1s) == "00:00:01"
+        assert drepr(delta_500ms) == "00:00:00.500000"
+        assert drepr(delta_1d + delta_1s) == "1 days 00:00:01"
+        assert drepr(-delta_1d + delta_1s) == "-1 days +00:00:01"
+        assert drepr(delta_1d + delta_500ms) == "1 days 00:00:00.500000"
+        assert drepr(-delta_1d + delta_500ms) == "-1 days +00:00:00.500000"
+
+    def test_long(self):
+        delta_1d = Timedelta(1, unit="D")
+        delta_0d = Timedelta(0, unit="D")
+        delta_1s = Timedelta(1, unit="s")
+        delta_500ms = Timedelta(500, unit="ms")
+
+        drepr = lambda x: x._repr_base(format="long")
+        assert drepr(delta_1d) == "1 days 00:00:00"
+        assert drepr(-delta_1d) == "-1 days +00:00:00"
+        assert drepr(delta_0d) == "0 days 00:00:00"
+        assert drepr(delta_1s) == "0 days 00:00:01"
+        assert drepr(delta_500ms) == "0 days 00:00:00.500000"
+        assert drepr(delta_1d + delta_1s) == "1 days 00:00:01"
+        assert drepr(-delta_1d + delta_1s) == "-1 days +00:00:01"
+        assert drepr(delta_1d + delta_500ms) == "1 days 00:00:00.500000"
+        assert drepr(-delta_1d + delta_500ms) == "-1 days +00:00:00.500000"
+
+    def test_all(self):
+        delta_1d = Timedelta(1, unit="D")
+        delta_0d = Timedelta(0, unit="D")
+        delta_1ns = Timedelta(1, unit="ns")
+
+        drepr = lambda x: x._repr_base(format="all")
+        assert drepr(delta_1d) == "1 days 00:00:00.000000000"
+        assert drepr(-delta_1d) == "-1 days +00:00:00.000000000"
+        assert drepr(delta_0d) == "0 days 00:00:00.000000000"
+        assert drepr(delta_1ns) == "0 days 00:00:00.000000001"
+        assert drepr(-delta_1d + delta_1ns) == "-1 days +00:00:00.000000001"

py311/lib/python3.11/site-packages/pandas/tests/scalar/timedelta/test_timedelta.py

@@ -0,0 +1,666 @@
+""" test the scalar Timedelta """
+from datetime import timedelta
+import sys
+
+from hypothesis import (
+    given,
+    strategies as st,
+)
+import numpy as np
+import pytest
+
+from pandas._libs import lib
+from pandas._libs.tslibs import (
+    NaT,
+    iNaT,
+)
+from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+from pandas.errors import OutOfBoundsTimedelta
+
+from pandas import (
+    Timedelta,
+    to_timedelta,
+)
+import pandas._testing as tm
+
+
+class TestNonNano:
+    @pytest.fixture(params=["s", "ms", "us"])
+    def unit_str(self, request):
+        return request.param
+
+    @pytest.fixture
+    def unit(self, unit_str):
+        # 7, 8, 9 correspond to second, millisecond, and microsecond, respectively
+        attr = f"NPY_FR_{unit_str}"
+        return getattr(NpyDatetimeUnit, attr).value
+
+    @pytest.fixture
+    def val(self, unit):
+        # microsecond that would be just out of bounds for nano
+        us = 9223372800000000
+        if unit == NpyDatetimeUnit.NPY_FR_us.value:
+            value = us
+        elif unit == NpyDatetimeUnit.NPY_FR_ms.value:
+            value = us // 1000
+        else:
+            value = us // 1_000_000
+        return value
+
+    @pytest.fixture
+    def td(self, unit, val):
+        return Timedelta._from_value_and_reso(val, unit)
+
+    def test_from_value_and_reso(self, unit, val):
+        # Just checking that the fixture is giving us what we asked for
+        td = Timedelta._from_value_and_reso(val, unit)
+        assert td._value == val
+        assert td._creso == unit
+        assert td.days == 106752
+
+    def test_unary_non_nano(self, td, unit):
+        assert abs(td)._creso == unit
+        assert (-td)._creso == unit
+        assert (+td)._creso == unit
+
+    def test_sub_preserves_reso(self, td, unit):
+        res = td - td
+        expected = Timedelta._from_value_and_reso(0, unit)
+        assert res == expected
+        assert res._creso == unit
+
+    def test_mul_preserves_reso(self, td, unit):
+        # The td fixture should always be far from the implementation
+        #  bound, so doubling does not risk overflow.
+        res = td * 2
+        assert res._value == td._value * 2
+        assert res._creso == unit
+
+    def test_cmp_cross_reso(self, td):
+        # numpy gets this wrong because of silent overflow
+        other = Timedelta(days=106751, unit="ns")
+        assert other < td
+        assert td > other
+        assert not other == td
+        assert td != other
+
+    def test_to_pytimedelta(self, td):
+        res = td.to_pytimedelta()
+        expected = timedelta(days=106752)
+        assert type(res) is timedelta
+        assert res == expected
+
+    def test_to_timedelta64(self, td, unit):
+        for res in [td.to_timedelta64(), td.to_numpy(), td.asm8]:
+            assert isinstance(res, np.timedelta64)
+            assert res.view("i8") == td._value
+            if unit == NpyDatetimeUnit.NPY_FR_s.value:
+                assert res.dtype == "m8[s]"
+            elif unit == NpyDatetimeUnit.NPY_FR_ms.value:
+                assert res.dtype == "m8[ms]"
+            elif unit == NpyDatetimeUnit.NPY_FR_us.value:
+                assert res.dtype == "m8[us]"
+
+    def test_truediv_timedeltalike(self, td):
+        assert td / td == 1
+        assert (2.5 * td) / td == 2.5
+
+        other = Timedelta(td._value)
+        msg = "Cannot cast 106752 days 00:00:00 to unit='ns' without overflow."
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            td / other
+
+        # Timedelta(other.to_pytimedelta()) has microsecond resolution,
+        #  so the division doesn't require casting all the way to nanos,
+        #  so succeeds
+        res = other.to_pytimedelta() / td
+        expected = other.to_pytimedelta() / td.to_pytimedelta()
+        assert res == expected
+
+        # if there's no overflow, we cast to the higher reso
+        left = Timedelta._from_value_and_reso(50, NpyDatetimeUnit.NPY_FR_us.value)
+        right = Timedelta._from_value_and_reso(50, NpyDatetimeUnit.NPY_FR_ms.value)
+        result = left / right
+        assert result == 0.001
+
+        result = right / left
+        assert result == 1000
+
+    def test_truediv_numeric(self, td):
+        assert td / np.nan is NaT
+
+        res = td / 2
+        assert res._value == td._value / 2
+        assert res._creso == td._creso
+
+        res = td / 2.0
+        assert res._value == td._value / 2
+        assert res._creso == td._creso
+
+    def test_floordiv_timedeltalike(self, td):
+        assert td // td == 1
+        assert (2.5 * td) // td == 2
+
+        other = Timedelta(td._value)
+        msg = "Cannot cast 106752 days 00:00:00 to unit='ns' without overflow"
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            td // other
+
+        # Timedelta(other.to_pytimedelta()) has microsecond resolution,
+        #  so the floordiv doesn't require casting all the way to nanos,
+        #  so succeeds
+        res = other.to_pytimedelta() // td
+        assert res == 0
+
+        # if there's no overflow, we cast to the higher reso
+        left = Timedelta._from_value_and_reso(50050, NpyDatetimeUnit.NPY_FR_us.value)
+        right = Timedelta._from_value_and_reso(50, NpyDatetimeUnit.NPY_FR_ms.value)
+        result = left // right
+        assert result == 1
+        result = right // left
+        assert result == 0
+
+    def test_floordiv_numeric(self, td):
+        assert td // np.nan is NaT
+
+        res = td // 2
+        assert res._value == td._value // 2
+        assert res._creso == td._creso
+
+        res = td // 2.0
+        assert res._value == td._value // 2
+        assert res._creso == td._creso
+
+        assert td // np.array(np.nan) is NaT
+
+        res = td // np.array(2)
+        assert res._value == td._value // 2
+        assert res._creso == td._creso
+
+        res = td // np.array(2.0)
+        assert res._value == td._value // 2
+        assert res._creso == td._creso
+
+    def test_addsub_mismatched_reso(self, td):
+        # need to cast to since td is out of bounds for ns, so
+        #  so we would raise OverflowError without casting
+        other = Timedelta(days=1).as_unit("us")
+
+        # td is out of bounds for ns
+        result = td + other
+        assert result._creso == other._creso
+        assert result.days == td.days + 1
+
+        result = other + td
+        assert result._creso == other._creso
+        assert result.days == td.days + 1
+
+        result = td - other
+        assert result._creso == other._creso
+        assert result.days == td.days - 1
+
+        result = other - td
+        assert result._creso == other._creso
+        assert result.days == 1 - td.days
+
+        other2 = Timedelta(500)
+        msg = "Cannot cast 106752 days 00:00:00 to unit='ns' without overflow"
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            td + other2
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            other2 + td
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            td - other2
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            other2 - td
+
+    def test_min(self, td):
+        assert td.min <= td
+        assert td.min._creso == td._creso
+        assert td.min._value == NaT._value + 1
+
+    def test_max(self, td):
+        assert td.max >= td
+        assert td.max._creso == td._creso
+        assert td.max._value == np.iinfo(np.int64).max
+
+    def test_resolution(self, td):
+        expected = Timedelta._from_value_and_reso(1, td._creso)
+        result = td.resolution
+        assert result == expected
+        assert result._creso == expected._creso
+
+    def test_hash(self) -> None:
+        # GH#54037
+        second_resolution_max = Timedelta(0).as_unit("s").max
+
+        assert hash(second_resolution_max)
+
+
+def test_timedelta_class_min_max_resolution():
+    # when accessed on the class (as opposed to an instance), we default
+    #  to nanoseconds
+    assert Timedelta.min == Timedelta(NaT._value + 1)
+    assert Timedelta.min._creso == NpyDatetimeUnit.NPY_FR_ns.value
+
+    assert Timedelta.max == Timedelta(np.iinfo(np.int64).max)
+    assert Timedelta.max._creso == NpyDatetimeUnit.NPY_FR_ns.value
+
+    assert Timedelta.resolution == Timedelta(1)
+    assert Timedelta.resolution._creso == NpyDatetimeUnit.NPY_FR_ns.value
+
+
+class TestTimedeltaUnaryOps:
+    def test_invert(self):
+        td = Timedelta(10, unit="d")
+
+        msg = "bad operand type for unary ~"
+        with pytest.raises(TypeError, match=msg):
+            ~td
+
+        # check this matches pytimedelta and timedelta64
+        with pytest.raises(TypeError, match=msg):
+            ~(td.to_pytimedelta())
+
+        umsg = "ufunc 'invert' not supported for the input types"
+        with pytest.raises(TypeError, match=umsg):
+            ~(td.to_timedelta64())
+
+    def test_unary_ops(self):
+        td = Timedelta(10, unit="d")
+
+        # __neg__, __pos__
+        assert -td == Timedelta(-10, unit="d")
+        assert -td == Timedelta("-10d")
+        assert +td == Timedelta(10, unit="d")
+
+        # __abs__, __abs__(__neg__)
+        assert abs(td) == td
+        assert abs(-td) == td
+        assert abs(-td) == Timedelta("10d")
+
+
+class TestTimedeltas:
+    @pytest.mark.parametrize(
+        "unit, value, expected",
+        [
+            ("us", 9.999, 9999),
+            ("ms", 9.999999, 9999999),
+            ("s", 9.999999999, 9999999999),
+        ],
+    )
+    def test_rounding_on_int_unit_construction(self, unit, value, expected):
+        # GH 12690
+        result = Timedelta(value, unit=unit)
+        assert result._value == expected
+        result = Timedelta(str(value) + unit)
+        assert result._value == expected
+
+    def test_total_seconds_scalar(self):
+        # see gh-10939
+        rng = Timedelta("1 days, 10:11:12.100123456")
+        expt = 1 * 86400 + 10 * 3600 + 11 * 60 + 12 + 100123456.0 / 1e9
+        tm.assert_almost_equal(rng.total_seconds(), expt)
+
+        rng = Timedelta(np.nan)
+        assert np.isnan(rng.total_seconds())
+
+    def test_conversion(self):
+        for td in [Timedelta(10, unit="d"), Timedelta("1 days, 10:11:12.012345")]:
+            pydt = td.to_pytimedelta()
+            assert td == Timedelta(pydt)
+            assert td == pydt
+            assert isinstance(pydt, timedelta) and not isinstance(pydt, Timedelta)
+
+            assert td == np.timedelta64(td._value, "ns")
+            td64 = td.to_timedelta64()
+
+            assert td64 == np.timedelta64(td._value, "ns")
+            assert td == td64
+
+            assert isinstance(td64, np.timedelta64)
+
+        # this is NOT equal and cannot be roundtripped (because of the nanos)
+        td = Timedelta("1 days, 10:11:12.012345678")
+        assert td != td.to_pytimedelta()
+
+    def test_fields(self):
+        def check(value):
+            # that we are int
+            assert isinstance(value, int)
+
+        # compat to datetime.timedelta
+        rng = to_timedelta("1 days, 10:11:12")
+        assert rng.days == 1
+        assert rng.seconds == 10 * 3600 + 11 * 60 + 12
+        assert rng.microseconds == 0
+        assert rng.nanoseconds == 0
+
+        msg = "'Timedelta' 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
+
+        # GH 10050
+        check(rng.days)
+        check(rng.seconds)
+        check(rng.microseconds)
+        check(rng.nanoseconds)
+
+        td = Timedelta("-1 days, 10:11:12")
+        assert abs(td) == Timedelta("13:48:48")
+        assert str(td) == "-1 days +10:11:12"
+        assert -td == Timedelta("0 days 13:48:48")
+        assert -Timedelta("-1 days, 10:11:12")._value == 49728000000000
+        assert Timedelta("-1 days, 10:11:12")._value == -49728000000000
+
+        rng = to_timedelta("-1 days, 10:11:12.100123456")
+        assert rng.days == -1
+        assert rng.seconds == 10 * 3600 + 11 * 60 + 12
+        assert rng.microseconds == 100 * 1000 + 123
+        assert rng.nanoseconds == 456
+        msg = "'Timedelta' 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
+
+        # components
+        tup = to_timedelta(-1, "us").components
+        assert tup.days == -1
+        assert tup.hours == 23
+        assert tup.minutes == 59
+        assert tup.seconds == 59
+        assert tup.milliseconds == 999
+        assert tup.microseconds == 999
+        assert tup.nanoseconds == 0
+
+        # GH 10050
+        check(tup.days)
+        check(tup.hours)
+        check(tup.minutes)
+        check(tup.seconds)
+        check(tup.milliseconds)
+        check(tup.microseconds)
+        check(tup.nanoseconds)
+
+        tup = Timedelta("-1 days 1 us").components
+        assert tup.days == -2
+        assert tup.hours == 23
+        assert tup.minutes == 59
+        assert tup.seconds == 59
+        assert tup.milliseconds == 999
+        assert tup.microseconds == 999
+        assert tup.nanoseconds == 0
+
+    # TODO: this is a test of to_timedelta string parsing
+    def test_iso_conversion(self):
+        # GH #21877
+        expected = Timedelta(1, unit="s")
+        assert to_timedelta("P0DT0H0M1S") == expected
+
+    # TODO: this is a test of to_timedelta returning NaT
+    def test_nat_converters(self):
+        result = to_timedelta("nat").to_numpy()
+        assert result.dtype.kind == "M"
+        assert result.astype("int64") == iNaT
+
+        result = to_timedelta("nan").to_numpy()
+        assert result.dtype.kind == "M"
+        assert result.astype("int64") == iNaT
+
+    def test_numeric_conversions(self):
+        assert Timedelta(0) == np.timedelta64(0, "ns")
+        assert Timedelta(10) == np.timedelta64(10, "ns")
+        assert Timedelta(10, unit="ns") == np.timedelta64(10, "ns")
+
+        assert Timedelta(10, unit="us") == np.timedelta64(10, "us")
+        assert Timedelta(10, unit="ms") == np.timedelta64(10, "ms")
+        assert Timedelta(10, unit="s") == np.timedelta64(10, "s")
+        assert Timedelta(10, unit="d") == np.timedelta64(10, "D")
+
+    def test_timedelta_conversions(self):
+        assert Timedelta(timedelta(seconds=1)) == np.timedelta64(1, "s").astype(
+            "m8[ns]"
+        )
+        assert Timedelta(timedelta(microseconds=1)) == np.timedelta64(1, "us").astype(
+            "m8[ns]"
+        )
+        assert Timedelta(timedelta(days=1)) == np.timedelta64(1, "D").astype("m8[ns]")
+
+    def test_to_numpy_alias(self):
+        # GH 24653: alias .to_numpy() for scalars
+        td = Timedelta("10m7s")
+        assert td.to_timedelta64() == td.to_numpy()
+
+        # GH#44460
+        msg = "dtype and copy arguments are ignored"
+        with pytest.raises(ValueError, match=msg):
+            td.to_numpy("m8[s]")
+        with pytest.raises(ValueError, match=msg):
+            td.to_numpy(copy=True)
+
+    def test_identity(self):
+        td = Timedelta(10, unit="d")
+        assert isinstance(td, Timedelta)
+        assert isinstance(td, timedelta)
+
+    def test_short_format_converters(self):
+        def conv(v):
+            return v.astype("m8[ns]")
+
+        assert Timedelta("10") == np.timedelta64(10, "ns")
+        assert Timedelta("10ns") == np.timedelta64(10, "ns")
+        assert Timedelta("100") == np.timedelta64(100, "ns")
+        assert Timedelta("100ns") == np.timedelta64(100, "ns")
+
+        assert Timedelta("1000") == np.timedelta64(1000, "ns")
+        assert Timedelta("1000ns") == np.timedelta64(1000, "ns")
+        assert Timedelta("1000NS") == np.timedelta64(1000, "ns")
+
+        assert Timedelta("10us") == np.timedelta64(10000, "ns")
+        assert Timedelta("100us") == np.timedelta64(100000, "ns")
+        assert Timedelta("1000us") == np.timedelta64(1000000, "ns")
+        assert Timedelta("1000Us") == np.timedelta64(1000000, "ns")
+        assert Timedelta("1000uS") == np.timedelta64(1000000, "ns")
+
+        assert Timedelta("1ms") == np.timedelta64(1000000, "ns")
+        assert Timedelta("10ms") == np.timedelta64(10000000, "ns")
+        assert Timedelta("100ms") == np.timedelta64(100000000, "ns")
+        assert Timedelta("1000ms") == np.timedelta64(1000000000, "ns")
+
+        assert Timedelta("-1s") == -np.timedelta64(1000000000, "ns")
+        assert Timedelta("1s") == np.timedelta64(1000000000, "ns")
+        assert Timedelta("10s") == np.timedelta64(10000000000, "ns")
+        assert Timedelta("100s") == np.timedelta64(100000000000, "ns")
+        assert Timedelta("1000s") == np.timedelta64(1000000000000, "ns")
+
+        assert Timedelta("1d") == conv(np.timedelta64(1, "D"))
+        assert Timedelta("-1d") == -conv(np.timedelta64(1, "D"))
+        assert Timedelta("1D") == conv(np.timedelta64(1, "D"))
+        assert Timedelta("10D") == conv(np.timedelta64(10, "D"))
+        assert Timedelta("100D") == conv(np.timedelta64(100, "D"))
+        assert Timedelta("1000D") == conv(np.timedelta64(1000, "D"))
+        assert Timedelta("10000D") == conv(np.timedelta64(10000, "D"))
+
+        # space
+        assert Timedelta(" 10000D ") == conv(np.timedelta64(10000, "D"))
+        assert Timedelta(" - 10000D ") == -conv(np.timedelta64(10000, "D"))
+
+        # invalid
+        msg = "invalid unit abbreviation"
+        with pytest.raises(ValueError, match=msg):
+            Timedelta("1foo")
+        msg = "unit abbreviation w/o a number"
+        with pytest.raises(ValueError, match=msg):
+            Timedelta("foo")
+
+    def test_full_format_converters(self):
+        def conv(v):
+            return v.astype("m8[ns]")
+
+        d1 = np.timedelta64(1, "D")
+
+        assert Timedelta("1days") == conv(d1)
+        assert Timedelta("1days,") == conv(d1)
+        assert Timedelta("- 1days,") == -conv(d1)
+
+        assert Timedelta("00:00:01") == conv(np.timedelta64(1, "s"))
+        assert Timedelta("06:00:01") == conv(np.timedelta64(6 * 3600 + 1, "s"))
+        assert Timedelta("06:00:01.0") == conv(np.timedelta64(6 * 3600 + 1, "s"))
+        assert Timedelta("06:00:01.01") == conv(
+            np.timedelta64(1000 * (6 * 3600 + 1) + 10, "ms")
+        )
+
+        assert Timedelta("- 1days, 00:00:01") == conv(-d1 + np.timedelta64(1, "s"))
+        assert Timedelta("1days, 06:00:01") == conv(
+            d1 + np.timedelta64(6 * 3600 + 1, "s")
+        )
+        assert Timedelta("1days, 06:00:01.01") == conv(
+            d1 + np.timedelta64(1000 * (6 * 3600 + 1) + 10, "ms")
+        )
+
+        # invalid
+        msg = "have leftover units"
+        with pytest.raises(ValueError, match=msg):
+            Timedelta("- 1days, 00")
+
+    def test_pickle(self):
+        v = Timedelta("1 days 10:11:12.0123456")
+        v_p = tm.round_trip_pickle(v)
+        assert v == v_p
+
+    def test_timedelta_hash_equality(self):
+        # GH 11129
+        v = Timedelta(1, "D")
+        td = timedelta(days=1)
+        assert hash(v) == hash(td)
+
+        d = {td: 2}
+        assert d[v] == 2
+
+        tds = [Timedelta(seconds=1) + Timedelta(days=n) for n in range(20)]
+        assert all(hash(td) == hash(td.to_pytimedelta()) for td in tds)
+
+        # python timedeltas drop ns resolution
+        ns_td = Timedelta(1, "ns")
+        assert hash(ns_td) != hash(ns_td.to_pytimedelta())
+
+    @pytest.mark.skip_ubsan
+    @pytest.mark.xfail(
+        reason="pd.Timedelta violates the Python hash invariant (GH#44504).",
+    )
+    @given(
+        st.integers(
+            min_value=(-sys.maxsize - 1) // 500,
+            max_value=sys.maxsize // 500,
+        )
+    )
+    def test_hash_equality_invariance(self, half_microseconds: int) -> None:
+        # GH#44504
+
+        nanoseconds = half_microseconds * 500
+
+        pandas_timedelta = Timedelta(nanoseconds)
+        numpy_timedelta = np.timedelta64(nanoseconds)
+
+        # See: https://docs.python.org/3/glossary.html#term-hashable
+        # Hashable objects which compare equal must have the same hash value.
+        assert pandas_timedelta != numpy_timedelta or hash(pandas_timedelta) == hash(
+            numpy_timedelta
+        )
+
+    def test_implementation_limits(self):
+        min_td = Timedelta(Timedelta.min)
+        max_td = Timedelta(Timedelta.max)
+
+        # GH 12727
+        # timedelta limits correspond to int64 boundaries
+        assert min_td._value == iNaT + 1
+        assert max_td._value == lib.i8max
+
+        # Beyond lower limit, a NAT before the Overflow
+        assert (min_td - Timedelta(1, "ns")) is NaT
+
+        msg = "int too (large|big) to convert"
+        with pytest.raises(OverflowError, match=msg):
+            min_td - Timedelta(2, "ns")
+
+        with pytest.raises(OverflowError, match=msg):
+            max_td + Timedelta(1, "ns")
+
+        # Same tests using the internal nanosecond values
+        td = Timedelta(min_td._value - 1, "ns")
+        assert td is NaT
+
+        msg = "Cannot cast -9223372036854775809 from ns to 'ns' without overflow"
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            Timedelta(min_td._value - 2, "ns")
+
+        msg = "Cannot cast 9223372036854775808 from ns to 'ns' without overflow"
+        with pytest.raises(OutOfBoundsTimedelta, match=msg):
+            Timedelta(max_td._value + 1, "ns")
+
+    def test_total_seconds_precision(self):
+        # GH 19458
+        assert Timedelta("30s").total_seconds() == 30.0
+        assert Timedelta("0").total_seconds() == 0.0
+        assert Timedelta("-2s").total_seconds() == -2.0
+        assert Timedelta("5.324s").total_seconds() == 5.324
+        assert (Timedelta("30s").total_seconds() - 30.0) < 1e-20
+        assert (30.0 - Timedelta("30s").total_seconds()) < 1e-20
+
+    def test_resolution_string(self):
+        assert Timedelta(days=1).resolution_string == "D"
+        assert Timedelta(days=1, hours=6).resolution_string == "h"
+        assert Timedelta(days=1, minutes=6).resolution_string == "min"
+        assert Timedelta(days=1, seconds=6).resolution_string == "s"
+        assert Timedelta(days=1, milliseconds=6).resolution_string == "ms"
+        assert Timedelta(days=1, microseconds=6).resolution_string == "us"
+        assert Timedelta(days=1, nanoseconds=6).resolution_string == "ns"
+
+    def test_resolution_deprecated(self):
+        # GH#21344
+        td = Timedelta(days=4, hours=3)
+        result = td.resolution
+        assert result == Timedelta(nanoseconds=1)
+
+        # Check that the attribute is available on the class, mirroring
+        #  the stdlib timedelta behavior
+        result = Timedelta.resolution
+        assert result == Timedelta(nanoseconds=1)
+
+
+@pytest.mark.parametrize(
+    "value, expected",
+    [
+        (Timedelta("10s"), True),
+        (Timedelta("-10s"), True),
+        (Timedelta(10, unit="ns"), True),
+        (Timedelta(0, unit="ns"), False),
+        (Timedelta(-10, unit="ns"), True),
+        (Timedelta(None), True),
+        (NaT, True),
+    ],
+)
+def test_truthiness(value, expected):
+    # https://github.com/pandas-dev/pandas/issues/21484
+    assert bool(value) is expected
+
+
+def test_timedelta_attribute_precision():
+    # GH 31354
+    td = Timedelta(1552211999999999872, unit="ns")
+    result = td.days * 86400
+    result += td.seconds
+    result *= 1000000
+    result += td.microseconds
+    result *= 1000
+    result += td.nanoseconds
+    expected = td._value
+    assert result == expected

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/methods/test_as_unit.py

@@ -0,0 +1,86 @@
+import pytest
+
+from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+from pandas.errors import OutOfBoundsDatetime
+
+from pandas import Timestamp
+
+
+class TestTimestampAsUnit:
+    def test_as_unit(self):
+        ts = Timestamp("1970-01-01").as_unit("ns")
+        assert ts.unit == "ns"
+
+        assert ts.as_unit("ns") is ts
+
+        res = ts.as_unit("us")
+        assert res._value == ts._value // 1000
+        assert res._creso == NpyDatetimeUnit.NPY_FR_us.value
+
+        rt = res.as_unit("ns")
+        assert rt._value == ts._value
+        assert rt._creso == ts._creso
+
+        res = ts.as_unit("ms")
+        assert res._value == ts._value // 1_000_000
+        assert res._creso == NpyDatetimeUnit.NPY_FR_ms.value
+
+        rt = res.as_unit("ns")
+        assert rt._value == ts._value
+        assert rt._creso == ts._creso
+
+        res = ts.as_unit("s")
+        assert res._value == ts._value // 1_000_000_000
+        assert res._creso == NpyDatetimeUnit.NPY_FR_s.value
+
+        rt = res.as_unit("ns")
+        assert rt._value == ts._value
+        assert rt._creso == ts._creso
+
+    def test_as_unit_overflows(self):
+        # microsecond that would be just out of bounds for nano
+        us = 9223372800000000
+        ts = Timestamp._from_value_and_reso(us, NpyDatetimeUnit.NPY_FR_us.value, None)
+
+        msg = "Cannot cast 2262-04-12 00:00:00 to unit='ns' without overflow"
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            ts.as_unit("ns")
+
+        res = ts.as_unit("ms")
+        assert res._value == us // 1000
+        assert res._creso == NpyDatetimeUnit.NPY_FR_ms.value
+
+    def test_as_unit_rounding(self):
+        ts = Timestamp(1_500_000)  # i.e. 1500 microseconds
+        res = ts.as_unit("ms")
+
+        expected = Timestamp(1_000_000)  # i.e. 1 millisecond
+        assert res == expected
+
+        assert res._creso == NpyDatetimeUnit.NPY_FR_ms.value
+        assert res._value == 1
+
+        with pytest.raises(ValueError, match="Cannot losslessly convert units"):
+            ts.as_unit("ms", round_ok=False)
+
+    def test_as_unit_non_nano(self):
+        # case where we are going neither to nor from nano
+        ts = Timestamp("1970-01-02").as_unit("ms")
+        assert ts.year == 1970
+        assert ts.month == 1
+        assert ts.day == 2
+        assert ts.hour == ts.minute == ts.second == ts.microsecond == ts.nanosecond == 0
+
+        res = ts.as_unit("s")
+        assert res._value == 24 * 3600
+        assert res.year == 1970
+        assert res.month == 1
+        assert res.day == 2
+        assert (
+            res.hour
+            == res.minute
+            == res.second
+            == res.microsecond
+            == res.nanosecond
+            == 0
+        )

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/methods/test_normalize.py

@@ -0,0 +1,22 @@
+import pytest
+
+from pandas._libs.tslibs import Timestamp
+from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+
+
+class TestTimestampNormalize:
+    @pytest.mark.parametrize("arg", ["2013-11-30", "2013-11-30 12:00:00"])
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"])
+    def test_normalize(self, tz_naive_fixture, arg, unit):
+        tz = tz_naive_fixture
+        ts = Timestamp(arg, tz=tz).as_unit(unit)
+        result = ts.normalize()
+        expected = Timestamp("2013-11-30", tz=tz)
+        assert result == expected
+        assert result._creso == getattr(NpyDatetimeUnit, f"NPY_FR_{unit}").value
+
+    def test_normalize_pre_epoch_dates(self):
+        # GH: 36294
+        result = Timestamp("1969-01-01 09:00:00").normalize()
+        expected = Timestamp("1969-01-01 00:00:00")
+        assert result == expected

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/methods/test_replace.py

@@ -0,0 +1,193 @@
+from datetime import datetime
+
+from dateutil.tz import gettz
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs.tslibs import (
+    OutOfBoundsDatetime,
+    Timestamp,
+    conversion,
+)
+from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+import pandas.util._test_decorators as td
+
+import pandas._testing as tm
+
+
+class TestTimestampReplace:
+    def test_replace_out_of_pydatetime_bounds(self):
+        # GH#50348
+        ts = Timestamp("2016-01-01").as_unit("ns")
+
+        msg = "Out of bounds timestamp: 99999-01-01 00:00:00 with frequency 'ns'"
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            ts.replace(year=99_999)
+
+        ts = ts.as_unit("ms")
+        result = ts.replace(year=99_999)
+        assert result.year == 99_999
+        assert result._value == Timestamp(np.datetime64("99999-01-01", "ms"))._value
+
+    def test_replace_non_nano(self):
+        ts = Timestamp._from_value_and_reso(
+            91514880000000000, NpyDatetimeUnit.NPY_FR_us.value, None
+        )
+        assert ts.to_pydatetime() == datetime(4869, 12, 28)
+
+        result = ts.replace(year=4900)
+        assert result._creso == ts._creso
+        assert result.to_pydatetime() == datetime(4900, 12, 28)
+
+    def test_replace_naive(self):
+        # GH#14621, GH#7825
+        ts = Timestamp("2016-01-01 09:00:00")
+        result = ts.replace(hour=0)
+        expected = Timestamp("2016-01-01 00:00:00")
+        assert result == expected
+
+    def test_replace_aware(self, tz_aware_fixture):
+        tz = tz_aware_fixture
+        # GH#14621, GH#7825
+        # replacing datetime components with and w/o presence of a timezone
+        ts = Timestamp("2016-01-01 09:00:00", tz=tz)
+        result = ts.replace(hour=0)
+        expected = Timestamp("2016-01-01 00:00:00", tz=tz)
+        assert result == expected
+
+    def test_replace_preserves_nanos(self, tz_aware_fixture):
+        tz = tz_aware_fixture
+        # GH#14621, GH#7825
+        ts = Timestamp("2016-01-01 09:00:00.000000123", tz=tz)
+        result = ts.replace(hour=0)
+        expected = Timestamp("2016-01-01 00:00:00.000000123", tz=tz)
+        assert result == expected
+
+    def test_replace_multiple(self, tz_aware_fixture):
+        tz = tz_aware_fixture
+        # GH#14621, GH#7825
+        # replacing datetime components with and w/o presence of a timezone
+        # test all
+        ts = Timestamp("2016-01-01 09:00:00.000000123", tz=tz)
+        result = ts.replace(
+            year=2015,
+            month=2,
+            day=2,
+            hour=0,
+            minute=5,
+            second=5,
+            microsecond=5,
+            nanosecond=5,
+        )
+        expected = Timestamp("2015-02-02 00:05:05.000005005", tz=tz)
+        assert result == expected
+
+    def test_replace_invalid_kwarg(self, tz_aware_fixture):
+        tz = tz_aware_fixture
+        # GH#14621, GH#7825
+        ts = Timestamp("2016-01-01 09:00:00.000000123", tz=tz)
+        msg = r"replace\(\) got an unexpected keyword argument"
+        with pytest.raises(TypeError, match=msg):
+            ts.replace(foo=5)
+
+    def test_replace_integer_args(self, tz_aware_fixture):
+        tz = tz_aware_fixture
+        # GH#14621, GH#7825
+        ts = Timestamp("2016-01-01 09:00:00.000000123", tz=tz)
+        msg = "value must be an integer, received <class 'float'> for hour"
+        with pytest.raises(ValueError, match=msg):
+            ts.replace(hour=0.1)
+
+    def test_replace_tzinfo_equiv_tz_localize_none(self):
+        # GH#14621, GH#7825
+        # assert conversion to naive is the same as replacing tzinfo with None
+        ts = Timestamp("2013-11-03 01:59:59.999999-0400", tz="US/Eastern")
+        assert ts.tz_localize(None) == ts.replace(tzinfo=None)
+
+    @td.skip_if_windows
+    def test_replace_tzinfo(self):
+        # GH#15683
+        dt = datetime(2016, 3, 27, 1)
+        tzinfo = pytz.timezone("CET").localize(dt, is_dst=False).tzinfo
+
+        result_dt = dt.replace(tzinfo=tzinfo)
+        result_pd = Timestamp(dt).replace(tzinfo=tzinfo)
+
+        # datetime.timestamp() converts in the local timezone
+        with tm.set_timezone("UTC"):
+            assert result_dt.timestamp() == result_pd.timestamp()
+
+        assert result_dt == result_pd
+        assert result_dt == result_pd.to_pydatetime()
+
+        result_dt = dt.replace(tzinfo=tzinfo).replace(tzinfo=None)
+        result_pd = Timestamp(dt).replace(tzinfo=tzinfo).replace(tzinfo=None)
+
+        # datetime.timestamp() converts in the local timezone
+        with tm.set_timezone("UTC"):
+            assert result_dt.timestamp() == result_pd.timestamp()
+
+        assert result_dt == result_pd
+        assert result_dt == result_pd.to_pydatetime()
+
+    @pytest.mark.parametrize(
+        "tz, normalize",
+        [
+            (pytz.timezone("US/Eastern"), lambda x: x.tzinfo.normalize(x)),
+            (gettz("US/Eastern"), lambda x: x),
+        ],
+    )
+    def test_replace_across_dst(self, tz, normalize):
+        # GH#18319 check that 1) timezone is correctly normalized and
+        # 2) that hour is not incorrectly changed by this normalization
+        ts_naive = Timestamp("2017-12-03 16:03:30")
+        ts_aware = conversion.localize_pydatetime(ts_naive, tz)
+
+        # Preliminary sanity-check
+        assert ts_aware == normalize(ts_aware)
+
+        # Replace across DST boundary
+        ts2 = ts_aware.replace(month=6)
+
+        # Check that `replace` preserves hour literal
+        assert (ts2.hour, ts2.minute) == (ts_aware.hour, ts_aware.minute)
+
+        # Check that post-replace object is appropriately normalized
+        ts2b = normalize(ts2)
+        assert ts2 == ts2b
+
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"])
+    def test_replace_dst_border(self, unit):
+        # Gh 7825
+        t = Timestamp("2013-11-3", tz="America/Chicago").as_unit(unit)
+        result = t.replace(hour=3)
+        expected = Timestamp("2013-11-3 03:00:00", tz="America/Chicago")
+        assert result == expected
+        assert result._creso == getattr(NpyDatetimeUnit, f"NPY_FR_{unit}").value
+
+    @pytest.mark.parametrize("fold", [0, 1])
+    @pytest.mark.parametrize("tz", ["dateutil/Europe/London", "Europe/London"])
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"])
+    def test_replace_dst_fold(self, fold, tz, unit):
+        # GH 25017
+        d = datetime(2019, 10, 27, 2, 30)
+        ts = Timestamp(d, tz=tz).as_unit(unit)
+        result = ts.replace(hour=1, fold=fold)
+        expected = Timestamp(datetime(2019, 10, 27, 1, 30)).tz_localize(
+            tz, ambiguous=not fold
+        )
+        assert result == expected
+        assert result._creso == getattr(NpyDatetimeUnit, f"NPY_FR_{unit}").value
+
+    @pytest.mark.parametrize("fold", [0, 1])
+    def test_replace_preserves_fold(self, fold):
+        # GH#37610. Check that replace preserves Timestamp fold property
+        tz = gettz("Europe/Moscow")
+
+        ts = Timestamp(
+            year=2009, month=10, day=25, hour=2, minute=30, fold=fold, tzinfo=tz
+        )
+        ts_replaced = ts.replace(second=1)
+
+        assert ts_replaced.fold == fold

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/methods/test_round.py

@@ -0,0 +1,383 @@
+from hypothesis import (
+    given,
+    strategies as st,
+)
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs import lib
+from pandas._libs.tslibs import (
+    NaT,
+    OutOfBoundsDatetime,
+    Timedelta,
+    Timestamp,
+    iNaT,
+    to_offset,
+)
+from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+from pandas._libs.tslibs.period import INVALID_FREQ_ERR_MSG
+
+import pandas._testing as tm
+
+
+class TestTimestampRound:
+    def test_round_division_by_zero_raises(self):
+        ts = Timestamp("2016-01-01")
+
+        msg = "Division by zero in rounding"
+        with pytest.raises(ValueError, match=msg):
+            ts.round("0ns")
+
+    @pytest.mark.parametrize(
+        "timestamp, freq, expected",
+        [
+            ("20130101 09:10:11", "D", "20130101"),
+            ("20130101 19:10:11", "D", "20130102"),
+            ("20130201 12:00:00", "D", "20130202"),
+            ("20130104 12:00:00", "D", "20130105"),
+            ("2000-01-05 05:09:15.13", "D", "2000-01-05 00:00:00"),
+            ("2000-01-05 05:09:15.13", "h", "2000-01-05 05:00:00"),
+            ("2000-01-05 05:09:15.13", "s", "2000-01-05 05:09:15"),
+        ],
+    )
+    def test_round_frequencies(self, timestamp, freq, expected):
+        dt = Timestamp(timestamp)
+        result = dt.round(freq)
+        expected = Timestamp(expected)
+        assert result == expected
+
+    def test_round_tzaware(self):
+        dt = Timestamp("20130101 09:10:11", tz="US/Eastern")
+        result = dt.round("D")
+        expected = Timestamp("20130101", tz="US/Eastern")
+        assert result == expected
+
+        dt = Timestamp("20130101 09:10:11", tz="US/Eastern")
+        result = dt.round("s")
+        assert result == dt
+
+    def test_round_30min(self):
+        # round
+        dt = Timestamp("20130104 12:32:00")
+        result = dt.round("30Min")
+        expected = Timestamp("20130104 12:30:00")
+        assert result == expected
+
+    def test_round_subsecond(self):
+        # GH#14440 & GH#15578
+        result = Timestamp("2016-10-17 12:00:00.0015").round("ms")
+        expected = Timestamp("2016-10-17 12:00:00.002000")
+        assert result == expected
+
+        result = Timestamp("2016-10-17 12:00:00.00149").round("ms")
+        expected = Timestamp("2016-10-17 12:00:00.001000")
+        assert result == expected
+
+        ts = Timestamp("2016-10-17 12:00:00.0015")
+        for freq in ["us", "ns"]:
+            assert ts == ts.round(freq)
+
+        result = Timestamp("2016-10-17 12:00:00.001501031").round("10ns")
+        expected = Timestamp("2016-10-17 12:00:00.001501030")
+        assert result == expected
+
+    def test_round_nonstandard_freq(self):
+        with tm.assert_produces_warning(False):
+            Timestamp("2016-10-17 12:00:00.001501031").round("1010ns")
+
+    def test_round_invalid_arg(self):
+        stamp = Timestamp("2000-01-05 05:09:15.13")
+        with pytest.raises(ValueError, match=INVALID_FREQ_ERR_MSG):
+            stamp.round("foo")
+
+    @pytest.mark.parametrize(
+        "test_input, rounder, freq, expected",
+        [
+            ("2117-01-01 00:00:45", "floor", "15s", "2117-01-01 00:00:45"),
+            ("2117-01-01 00:00:45", "ceil", "15s", "2117-01-01 00:00:45"),
+            (
+                "2117-01-01 00:00:45.000000012",
+                "floor",
+                "10ns",
+                "2117-01-01 00:00:45.000000010",
+            ),
+            (
+                "1823-01-01 00:00:01.000000012",
+                "ceil",
+                "10ns",
+                "1823-01-01 00:00:01.000000020",
+            ),
+            ("1823-01-01 00:00:01", "floor", "1s", "1823-01-01 00:00:01"),
+            ("1823-01-01 00:00:01", "ceil", "1s", "1823-01-01 00:00:01"),
+            ("NaT", "floor", "1s", "NaT"),
+            ("NaT", "ceil", "1s", "NaT"),
+        ],
+    )
+    def test_ceil_floor_edge(self, test_input, rounder, freq, expected):
+        dt = Timestamp(test_input)
+        func = getattr(dt, rounder)
+        result = func(freq)
+
+        if dt is NaT:
+            assert result is NaT
+        else:
+            expected = Timestamp(expected)
+            assert result == expected
+
+    @pytest.mark.parametrize(
+        "test_input, freq, expected",
+        [
+            ("2018-01-01 00:02:06", "2s", "2018-01-01 00:02:06"),
+            ("2018-01-01 00:02:00", "2min", "2018-01-01 00:02:00"),
+            ("2018-01-01 00:04:00", "4min", "2018-01-01 00:04:00"),
+            ("2018-01-01 00:15:00", "15min", "2018-01-01 00:15:00"),
+            ("2018-01-01 00:20:00", "20min", "2018-01-01 00:20:00"),
+            ("2018-01-01 03:00:00", "3h", "2018-01-01 03:00:00"),
+        ],
+    )
+    @pytest.mark.parametrize("rounder", ["ceil", "floor", "round"])
+    def test_round_minute_freq(self, test_input, freq, expected, rounder):
+        # Ensure timestamps that shouldn't round dont!
+        # GH#21262
+
+        dt = Timestamp(test_input)
+        expected = Timestamp(expected)
+        func = getattr(dt, rounder)
+        result = func(freq)
+        assert result == expected
+
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"])
+    def test_ceil(self, unit):
+        dt = Timestamp("20130101 09:10:11").as_unit(unit)
+        result = dt.ceil("D")
+        expected = Timestamp("20130102")
+        assert result == expected
+        assert result._creso == dt._creso
+
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"])
+    def test_floor(self, unit):
+        dt = Timestamp("20130101 09:10:11").as_unit(unit)
+        result = dt.floor("D")
+        expected = Timestamp("20130101")
+        assert result == expected
+        assert result._creso == dt._creso
+
+    @pytest.mark.parametrize("method", ["ceil", "round", "floor"])
+    @pytest.mark.parametrize(
+        "unit",
+        ["ns", "us", "ms", "s"],
+    )
+    def test_round_dst_border_ambiguous(self, method, unit):
+        # GH 18946 round near "fall back" DST
+        ts = Timestamp("2017-10-29 00:00:00", tz="UTC").tz_convert("Europe/Madrid")
+        ts = ts.as_unit(unit)
+        #
+        result = getattr(ts, method)("h", ambiguous=True)
+        assert result == ts
+        assert result._creso == getattr(NpyDatetimeUnit, f"NPY_FR_{unit}").value
+
+        result = getattr(ts, method)("h", ambiguous=False)
+        expected = Timestamp("2017-10-29 01:00:00", tz="UTC").tz_convert(
+            "Europe/Madrid"
+        )
+        assert result == expected
+        assert result._creso == getattr(NpyDatetimeUnit, f"NPY_FR_{unit}").value
+
+        result = getattr(ts, method)("h", ambiguous="NaT")
+        assert result is NaT
+
+        msg = "Cannot infer dst time"
+        with pytest.raises(pytz.AmbiguousTimeError, match=msg):
+            getattr(ts, method)("h", ambiguous="raise")
+
+    @pytest.mark.parametrize(
+        "method, ts_str, freq",
+        [
+            ["ceil", "2018-03-11 01:59:00-0600", "5min"],
+            ["round", "2018-03-11 01:59:00-0600", "5min"],
+            ["floor", "2018-03-11 03:01:00-0500", "2h"],
+        ],
+    )
+    @pytest.mark.parametrize(
+        "unit",
+        ["ns", "us", "ms", "s"],
+    )
+    def test_round_dst_border_nonexistent(self, method, ts_str, freq, unit):
+        # GH 23324 round near "spring forward" DST
+        ts = Timestamp(ts_str, tz="America/Chicago").as_unit(unit)
+        result = getattr(ts, method)(freq, nonexistent="shift_forward")
+        expected = Timestamp("2018-03-11 03:00:00", tz="America/Chicago")
+        assert result == expected
+        assert result._creso == getattr(NpyDatetimeUnit, f"NPY_FR_{unit}").value
+
+        result = getattr(ts, method)(freq, nonexistent="NaT")
+        assert result is NaT
+
+        msg = "2018-03-11 02:00:00"
+        with pytest.raises(pytz.NonExistentTimeError, match=msg):
+            getattr(ts, method)(freq, nonexistent="raise")
+
+    @pytest.mark.parametrize(
+        "timestamp",
+        [
+            "2018-01-01 0:0:0.124999360",
+            "2018-01-01 0:0:0.125000367",
+            "2018-01-01 0:0:0.125500",
+            "2018-01-01 0:0:0.126500",
+            "2018-01-01 12:00:00",
+            "2019-01-01 12:00:00",
+        ],
+    )
+    @pytest.mark.parametrize(
+        "freq",
+        [
+            "2ns",
+            "3ns",
+            "4ns",
+            "5ns",
+            "6ns",
+            "7ns",
+            "250ns",
+            "500ns",
+            "750ns",
+            "1us",
+            "19us",
+            "250us",
+            "500us",
+            "750us",
+            "1s",
+            "2s",
+            "3s",
+            "1D",
+        ],
+    )
+    def test_round_int64(self, timestamp, freq):
+        # check that all rounding modes are accurate to int64 precision
+        # see GH#22591
+        dt = Timestamp(timestamp).as_unit("ns")
+        unit = to_offset(freq).nanos
+
+        # test floor
+        result = dt.floor(freq)
+        assert result._value % unit == 0, f"floor not a {freq} multiple"
+        assert 0 <= dt._value - result._value < unit, "floor error"
+
+        # test ceil
+        result = dt.ceil(freq)
+        assert result._value % unit == 0, f"ceil not a {freq} multiple"
+        assert 0 <= result._value - dt._value < unit, "ceil error"
+
+        # test round
+        result = dt.round(freq)
+        assert result._value % unit == 0, f"round not a {freq} multiple"
+        assert abs(result._value - dt._value) <= unit // 2, "round error"
+        if unit % 2 == 0 and abs(result._value - dt._value) == unit // 2:
+            # round half to even
+            assert result._value // unit % 2 == 0, "round half to even error"
+
+    def test_round_implementation_bounds(self):
+        # See also: analogous test for Timedelta
+        result = Timestamp.min.ceil("s")
+        expected = Timestamp(1677, 9, 21, 0, 12, 44)
+        assert result == expected
+
+        result = Timestamp.max.floor("s")
+        expected = Timestamp.max - Timedelta(854775807)
+        assert result == expected
+
+        msg = "Cannot round 1677-09-21 00:12:43.145224193 to freq=<Second>"
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            Timestamp.min.floor("s")
+
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            Timestamp.min.round("s")
+
+        msg = "Cannot round 2262-04-11 23:47:16.854775807 to freq=<Second>"
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            Timestamp.max.ceil("s")
+
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            Timestamp.max.round("s")
+
+    @given(val=st.integers(iNaT + 1, lib.i8max))
+    @pytest.mark.parametrize(
+        "method", [Timestamp.round, Timestamp.floor, Timestamp.ceil]
+    )
+    def test_round_sanity(self, val, method):
+        cls = Timestamp
+        err_cls = OutOfBoundsDatetime
+
+        val = np.int64(val)
+        ts = cls(val)
+
+        def checker(ts, nanos, unit):
+            # First check that we do raise in cases where we should
+            if nanos == 1:
+                pass
+            else:
+                div, mod = divmod(ts._value, nanos)
+                diff = int(nanos - mod)
+                lb = ts._value - mod
+                assert lb <= ts._value  # i.e. no overflows with python ints
+                ub = ts._value + diff
+                assert ub > ts._value  # i.e. no overflows with python ints
+
+                msg = "without overflow"
+                if mod == 0:
+                    # We should never be raising in this
+                    pass
+                elif method is cls.ceil:
+                    if ub > cls.max._value:
+                        with pytest.raises(err_cls, match=msg):
+                            method(ts, unit)
+                        return
+                elif method is cls.floor:
+                    if lb < cls.min._value:
+                        with pytest.raises(err_cls, match=msg):
+                            method(ts, unit)
+                        return
+                elif mod >= diff:
+                    if ub > cls.max._value:
+                        with pytest.raises(err_cls, match=msg):
+                            method(ts, unit)
+                        return
+                elif lb < cls.min._value:
+                    with pytest.raises(err_cls, match=msg):
+                        method(ts, unit)
+                    return
+
+            res = method(ts, unit)
+
+            td = res - ts
+            diff = abs(td._value)
+            assert diff < nanos
+            assert res._value % nanos == 0
+
+            if method is cls.round:
+                assert diff <= nanos / 2
+            elif method is cls.floor:
+                assert res <= ts
+            elif method is cls.ceil:
+                assert res >= ts
+
+        nanos = 1
+        checker(ts, nanos, "ns")
+
+        nanos = 1000
+        checker(ts, nanos, "us")
+
+        nanos = 1_000_000
+        checker(ts, nanos, "ms")
+
+        nanos = 1_000_000_000
+        checker(ts, nanos, "s")
+
+        nanos = 60 * 1_000_000_000
+        checker(ts, nanos, "min")
+
+        nanos = 60 * 60 * 1_000_000_000
+        checker(ts, nanos, "h")
+
+        nanos = 24 * 60 * 60 * 1_000_000_000
+        checker(ts, nanos, "D")

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/methods/test_timestamp_method.py

@@ -0,0 +1,31 @@
+# NB: This is for the Timestamp.timestamp *method* specifically, not
+# the Timestamp class in general.
+
+from pytz import utc
+
+from pandas._libs.tslibs import Timestamp
+import pandas.util._test_decorators as td
+
+import pandas._testing as tm
+
+
+class TestTimestampMethod:
+    @td.skip_if_windows
+    def test_timestamp(self, fixed_now_ts):
+        # GH#17329
+        # tz-naive --> treat it as if it were UTC for purposes of timestamp()
+        ts = fixed_now_ts
+        uts = ts.replace(tzinfo=utc)
+        assert ts.timestamp() == uts.timestamp()
+
+        tsc = Timestamp("2014-10-11 11:00:01.12345678", tz="US/Central")
+        utsc = tsc.tz_convert("UTC")
+
+        # utsc is a different representation of the same time
+        assert tsc.timestamp() == utsc.timestamp()
+
+        # datetime.timestamp() converts in the local timezone
+        with tm.set_timezone("UTC"):
+            # should agree with datetime.timestamp method
+            dt = ts.to_pydatetime()
+            assert dt.timestamp() == ts.timestamp()

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/methods/test_to_julian_date.py

@@ -0,0 +1,28 @@
+from pandas import Timestamp
+
+
+class TestTimestampToJulianDate:
+    def test_compare_1700(self):
+        ts = Timestamp("1700-06-23")
+        res = ts.to_julian_date()
+        assert res == 2_342_145.5
+
+    def test_compare_2000(self):
+        ts = Timestamp("2000-04-12")
+        res = ts.to_julian_date()
+        assert res == 2_451_646.5
+
+    def test_compare_2100(self):
+        ts = Timestamp("2100-08-12")
+        res = ts.to_julian_date()
+        assert res == 2_488_292.5
+
+    def test_compare_hour01(self):
+        ts = Timestamp("2000-08-12T01:00:00")
+        res = ts.to_julian_date()
+        assert res == 2_451_768.5416666666666666
+
+    def test_compare_hour13(self):
+        ts = Timestamp("2000-08-12T13:00:00")
+        res = ts.to_julian_date()
+        assert res == 2_451_769.0416666666666666

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/methods/test_to_pydatetime.py

@@ -0,0 +1,81 @@
+from datetime import (
+    datetime,
+    timedelta,
+)
+
+import pytz
+
+from pandas._libs.tslibs.timezones import dateutil_gettz as gettz
+import pandas.util._test_decorators as td
+
+from pandas import Timestamp
+import pandas._testing as tm
+
+
+class TestTimestampToPyDatetime:
+    def test_to_pydatetime_fold(self):
+        # GH#45087
+        tzstr = "dateutil/usr/share/zoneinfo/America/Chicago"
+        ts = Timestamp(year=2013, month=11, day=3, hour=1, minute=0, fold=1, tz=tzstr)
+        dt = ts.to_pydatetime()
+        assert dt.fold == 1
+
+    def test_to_pydatetime_nonzero_nano(self):
+        ts = Timestamp("2011-01-01 9:00:00.123456789")
+
+        # Warn the user of data loss (nanoseconds).
+        with tm.assert_produces_warning(UserWarning):
+            expected = datetime(2011, 1, 1, 9, 0, 0, 123456)
+            result = ts.to_pydatetime()
+            assert result == expected
+
+    def test_timestamp_to_datetime(self):
+        stamp = Timestamp("20090415", tz="US/Eastern")
+        dtval = stamp.to_pydatetime()
+        assert stamp == dtval
+        assert stamp.tzinfo == dtval.tzinfo
+
+    def test_timestamp_to_pydatetime_dateutil(self):
+        stamp = Timestamp("20090415", tz="dateutil/US/Eastern")
+        dtval = stamp.to_pydatetime()
+        assert stamp == dtval
+        assert stamp.tzinfo == dtval.tzinfo
+
+    def test_timestamp_to_pydatetime_explicit_pytz(self):
+        stamp = Timestamp("20090415", tz=pytz.timezone("US/Eastern"))
+        dtval = stamp.to_pydatetime()
+        assert stamp == dtval
+        assert stamp.tzinfo == dtval.tzinfo
+
+    @td.skip_if_windows
+    def test_timestamp_to_pydatetime_explicit_dateutil(self):
+        stamp = Timestamp("20090415", tz=gettz("US/Eastern"))
+        dtval = stamp.to_pydatetime()
+        assert stamp == dtval
+        assert stamp.tzinfo == dtval.tzinfo
+
+    def test_to_pydatetime_bijective(self):
+        # Ensure that converting to datetime and back only loses precision
+        # by going from nanoseconds to microseconds.
+        exp_warning = None if Timestamp.max.nanosecond == 0 else UserWarning
+        with tm.assert_produces_warning(exp_warning):
+            pydt_max = Timestamp.max.to_pydatetime()
+
+        assert (
+            Timestamp(pydt_max).as_unit("ns")._value / 1000
+            == Timestamp.max._value / 1000
+        )
+
+        exp_warning = None if Timestamp.min.nanosecond == 0 else UserWarning
+        with tm.assert_produces_warning(exp_warning):
+            pydt_min = Timestamp.min.to_pydatetime()
+
+        # The next assertion can be enabled once GH#39221 is merged
+        #  assert pydt_min < Timestamp.min  # this is bc nanos are dropped
+        tdus = timedelta(microseconds=1)
+        assert pydt_min + tdus > Timestamp.min
+
+        assert (
+            Timestamp(pydt_min + tdus).as_unit("ns")._value / 1000
+            == Timestamp.min._value / 1000
+        )

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/methods/test_tz_convert.py

@@ -0,0 +1,51 @@
+import dateutil
+import pytest
+
+from pandas._libs.tslibs import timezones
+import pandas.util._test_decorators as td
+
+from pandas import Timestamp
+
+
+class TestTimestampTZConvert:
+    @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"])
+    def test_astimezone(self, tzstr):
+        # astimezone is an alias for tz_convert, so keep it with
+        # the tz_convert tests
+        utcdate = Timestamp("3/11/2012 22:00", tz="UTC")
+        expected = utcdate.tz_convert(tzstr)
+        result = utcdate.astimezone(tzstr)
+        assert expected == result
+        assert isinstance(result, Timestamp)
+
+    @pytest.mark.parametrize(
+        "stamp",
+        [
+            "2014-02-01 09:00",
+            "2014-07-08 09:00",
+            "2014-11-01 17:00",
+            "2014-11-05 00:00",
+        ],
+    )
+    def test_tz_convert_roundtrip(self, stamp, tz_aware_fixture):
+        tz = tz_aware_fixture
+
+        ts = Timestamp(stamp, tz="UTC")
+        converted = ts.tz_convert(tz)
+
+        reset = converted.tz_convert(None)
+        assert reset == Timestamp(stamp)
+        assert reset.tzinfo is None
+        assert reset == converted.tz_convert("UTC").tz_localize(None)
+
+    @td.skip_if_windows
+    def test_tz_convert_utc_with_system_utc(self):
+        # from system utc to real utc
+        ts = Timestamp("2001-01-05 11:56", tz=timezones.maybe_get_tz("dateutil/UTC"))
+        # check that the time hasn't changed.
+        assert ts == ts.tz_convert(dateutil.tz.tzutc())
+
+        # from system utc to real utc
+        ts = Timestamp("2001-01-05 11:56", tz=timezones.maybe_get_tz("dateutil/UTC"))
+        # check that the time hasn't changed.
+        assert ts == ts.tz_convert(dateutil.tz.tzutc())

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/methods/test_tz_localize.py

@@ -0,0 +1,351 @@
+from datetime import timedelta
+import re
+
+from dateutil.tz import gettz
+import pytest
+import pytz
+from pytz.exceptions import (
+    AmbiguousTimeError,
+    NonExistentTimeError,
+)
+
+from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+from pandas.errors import OutOfBoundsDatetime
+
+from pandas import (
+    NaT,
+    Timestamp,
+)
+
+try:
+    from zoneinfo import ZoneInfo
+except ImportError:
+    # Cannot assign to a type
+    ZoneInfo = None  # type: ignore[misc, assignment]
+
+
+class TestTimestampTZLocalize:
+    @pytest.mark.skip_ubsan
+    def test_tz_localize_pushes_out_of_bounds(self):
+        # GH#12677
+        # tz_localize that pushes away from the boundary is OK
+        msg = (
+            f"Converting {Timestamp.min.strftime('%Y-%m-%d %H:%M:%S')} "
+            f"underflows past {Timestamp.min}"
+        )
+        pac = Timestamp.min.tz_localize("US/Pacific")
+        assert pac._value > Timestamp.min._value
+        pac.tz_convert("Asia/Tokyo")  # tz_convert doesn't change value
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            Timestamp.min.tz_localize("Asia/Tokyo")
+
+        # tz_localize that pushes away from the boundary is OK
+        msg = (
+            f"Converting {Timestamp.max.strftime('%Y-%m-%d %H:%M:%S')} "
+            f"overflows past {Timestamp.max}"
+        )
+        tokyo = Timestamp.max.tz_localize("Asia/Tokyo")
+        assert tokyo._value < Timestamp.max._value
+        tokyo.tz_convert("US/Pacific")  # tz_convert doesn't change value
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            Timestamp.max.tz_localize("US/Pacific")
+
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"])
+    def test_tz_localize_ambiguous_bool(self, unit):
+        # make sure that we are correctly accepting bool values as ambiguous
+        # GH#14402
+        ts = Timestamp("2015-11-01 01:00:03").as_unit(unit)
+        expected0 = Timestamp("2015-11-01 01:00:03-0500", tz="US/Central")
+        expected1 = Timestamp("2015-11-01 01:00:03-0600", tz="US/Central")
+
+        msg = "Cannot infer dst time from 2015-11-01 01:00:03"
+        with pytest.raises(pytz.AmbiguousTimeError, match=msg):
+            ts.tz_localize("US/Central")
+
+        with pytest.raises(pytz.AmbiguousTimeError, match=msg):
+            ts.tz_localize("dateutil/US/Central")
+
+        if ZoneInfo is not None:
+            try:
+                tz = ZoneInfo("US/Central")
+            except KeyError:
+                # no tzdata
+                pass
+            else:
+                with pytest.raises(pytz.AmbiguousTimeError, match=msg):
+                    ts.tz_localize(tz)
+
+        result = ts.tz_localize("US/Central", ambiguous=True)
+        assert result == expected0
+        assert result._creso == getattr(NpyDatetimeUnit, f"NPY_FR_{unit}").value
+
+        result = ts.tz_localize("US/Central", ambiguous=False)
+        assert result == expected1
+        assert result._creso == getattr(NpyDatetimeUnit, f"NPY_FR_{unit}").value
+
+    def test_tz_localize_ambiguous(self):
+        ts = Timestamp("2014-11-02 01:00")
+        ts_dst = ts.tz_localize("US/Eastern", ambiguous=True)
+        ts_no_dst = ts.tz_localize("US/Eastern", ambiguous=False)
+
+        assert ts_no_dst._value - ts_dst._value == 3600
+        msg = re.escape(
+            "'ambiguous' parameter must be one of: "
+            "True, False, 'NaT', 'raise' (default)"
+        )
+        with pytest.raises(ValueError, match=msg):
+            ts.tz_localize("US/Eastern", ambiguous="infer")
+
+        # GH#8025
+        msg = "Cannot localize tz-aware Timestamp, use tz_convert for conversions"
+        with pytest.raises(TypeError, match=msg):
+            Timestamp("2011-01-01", tz="US/Eastern").tz_localize("Asia/Tokyo")
+
+        msg = "Cannot convert tz-naive Timestamp, use tz_localize to localize"
+        with pytest.raises(TypeError, match=msg):
+            Timestamp("2011-01-01").tz_convert("Asia/Tokyo")
+
+    @pytest.mark.parametrize(
+        "stamp, tz",
+        [
+            ("2015-03-08 02:00", "US/Eastern"),
+            ("2015-03-08 02:30", "US/Pacific"),
+            ("2015-03-29 02:00", "Europe/Paris"),
+            ("2015-03-29 02:30", "Europe/Belgrade"),
+        ],
+    )
+    def test_tz_localize_nonexistent(self, stamp, tz):
+        # GH#13057
+        ts = Timestamp(stamp)
+        with pytest.raises(NonExistentTimeError, match=stamp):
+            ts.tz_localize(tz)
+        # GH 22644
+        with pytest.raises(NonExistentTimeError, match=stamp):
+            ts.tz_localize(tz, nonexistent="raise")
+        assert ts.tz_localize(tz, nonexistent="NaT") is NaT
+
+    @pytest.mark.parametrize(
+        "stamp, tz, forward_expected, backward_expected",
+        [
+            (
+                "2015-03-29 02:00:00",
+                "Europe/Warsaw",
+                "2015-03-29 03:00:00",
+                "2015-03-29 01:59:59",
+            ),  # utc+1 -> utc+2
+            (
+                "2023-03-12 02:00:00",
+                "America/Los_Angeles",
+                "2023-03-12 03:00:00",
+                "2023-03-12 01:59:59",
+            ),  # utc-8 -> utc-7
+            (
+                "2023-03-26 01:00:00",
+                "Europe/London",
+                "2023-03-26 02:00:00",
+                "2023-03-26 00:59:59",
+            ),  # utc+0 -> utc+1
+            (
+                "2023-03-26 00:00:00",
+                "Atlantic/Azores",
+                "2023-03-26 01:00:00",
+                "2023-03-25 23:59:59",
+            ),  # utc-1 -> utc+0
+        ],
+    )
+    def test_tz_localize_nonexistent_shift(
+        self, stamp, tz, forward_expected, backward_expected
+    ):
+        ts = Timestamp(stamp)
+        forward_ts = ts.tz_localize(tz, nonexistent="shift_forward")
+        assert forward_ts == Timestamp(forward_expected, tz=tz)
+        backward_ts = ts.tz_localize(tz, nonexistent="shift_backward")
+        assert backward_ts == Timestamp(backward_expected, tz=tz)
+
+    def test_tz_localize_ambiguous_raise(self):
+        # GH#13057
+        ts = Timestamp("2015-11-1 01:00")
+        msg = "Cannot infer dst time from 2015-11-01 01:00:00,"
+        with pytest.raises(AmbiguousTimeError, match=msg):
+            ts.tz_localize("US/Pacific", ambiguous="raise")
+
+    def test_tz_localize_nonexistent_invalid_arg(self, warsaw):
+        # GH 22644
+        tz = warsaw
+        ts = Timestamp("2015-03-29 02:00:00")
+        msg = (
+            "The nonexistent argument must be one of 'raise', 'NaT', "
+            "'shift_forward', 'shift_backward' or a timedelta object"
+        )
+        with pytest.raises(ValueError, match=msg):
+            ts.tz_localize(tz, nonexistent="foo")
+
+    @pytest.mark.parametrize(
+        "stamp",
+        [
+            "2014-02-01 09:00",
+            "2014-07-08 09:00",
+            "2014-11-01 17:00",
+            "2014-11-05 00:00",
+        ],
+    )
+    def test_tz_localize_roundtrip(self, stamp, tz_aware_fixture):
+        tz = tz_aware_fixture
+        ts = Timestamp(stamp)
+        localized = ts.tz_localize(tz)
+        assert localized == Timestamp(stamp, tz=tz)
+
+        msg = "Cannot localize tz-aware Timestamp"
+        with pytest.raises(TypeError, match=msg):
+            localized.tz_localize(tz)
+
+        reset = localized.tz_localize(None)
+        assert reset == ts
+        assert reset.tzinfo is None
+
+    def test_tz_localize_ambiguous_compat(self):
+        # validate that pytz and dateutil are compat for dst
+        # when the transition happens
+        naive = Timestamp("2013-10-27 01:00:00")
+
+        pytz_zone = "Europe/London"
+        dateutil_zone = "dateutil/Europe/London"
+        result_pytz = naive.tz_localize(pytz_zone, ambiguous=False)
+        result_dateutil = naive.tz_localize(dateutil_zone, ambiguous=False)
+        assert result_pytz._value == result_dateutil._value
+        assert result_pytz._value == 1382835600
+
+        # fixed ambiguous behavior
+        # see gh-14621, GH#45087
+        assert result_pytz.to_pydatetime().tzname() == "GMT"
+        assert result_dateutil.to_pydatetime().tzname() == "GMT"
+        assert str(result_pytz) == str(result_dateutil)
+
+        # 1 hour difference
+        result_pytz = naive.tz_localize(pytz_zone, ambiguous=True)
+        result_dateutil = naive.tz_localize(dateutil_zone, ambiguous=True)
+        assert result_pytz._value == result_dateutil._value
+        assert result_pytz._value == 1382832000
+
+        # see gh-14621
+        assert str(result_pytz) == str(result_dateutil)
+        assert (
+            result_pytz.to_pydatetime().tzname()
+            == result_dateutil.to_pydatetime().tzname()
+        )
+
+    @pytest.mark.parametrize(
+        "tz",
+        [
+            pytz.timezone("US/Eastern"),
+            gettz("US/Eastern"),
+            "US/Eastern",
+            "dateutil/US/Eastern",
+        ],
+    )
+    def test_timestamp_tz_localize(self, tz):
+        stamp = Timestamp("3/11/2012 04:00")
+
+        result = stamp.tz_localize(tz)
+        expected = Timestamp("3/11/2012 04:00", tz=tz)
+        assert result.hour == expected.hour
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "start_ts, tz, end_ts, shift",
+        [
+            ["2015-03-29 02:20:00", "Europe/Warsaw", "2015-03-29 03:00:00", "forward"],
+            [
+                "2015-03-29 02:20:00",
+                "Europe/Warsaw",
+                "2015-03-29 01:59:59.999999999",
+                "backward",
+            ],
+            [
+                "2015-03-29 02:20:00",
+                "Europe/Warsaw",
+                "2015-03-29 03:20:00",
+                timedelta(hours=1),
+            ],
+            [
+                "2015-03-29 02:20:00",
+                "Europe/Warsaw",
+                "2015-03-29 01:20:00",
+                timedelta(hours=-1),
+            ],
+            ["2018-03-11 02:33:00", "US/Pacific", "2018-03-11 03:00:00", "forward"],
+            [
+                "2018-03-11 02:33:00",
+                "US/Pacific",
+                "2018-03-11 01:59:59.999999999",
+                "backward",
+            ],
+            [
+                "2018-03-11 02:33:00",
+                "US/Pacific",
+                "2018-03-11 03:33:00",
+                timedelta(hours=1),
+            ],
+            [
+                "2018-03-11 02:33:00",
+                "US/Pacific",
+                "2018-03-11 01:33:00",
+                timedelta(hours=-1),
+            ],
+        ],
+    )
+    @pytest.mark.parametrize("tz_type", ["", "dateutil/"])
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"])
+    def test_timestamp_tz_localize_nonexistent_shift(
+        self, start_ts, tz, end_ts, shift, tz_type, unit
+    ):
+        # GH 8917, 24466
+        tz = tz_type + tz
+        if isinstance(shift, str):
+            shift = "shift_" + shift
+        ts = Timestamp(start_ts).as_unit(unit)
+        result = ts.tz_localize(tz, nonexistent=shift)
+        expected = Timestamp(end_ts).tz_localize(tz)
+
+        if unit == "us":
+            assert result == expected.replace(nanosecond=0)
+        elif unit == "ms":
+            micros = expected.microsecond - expected.microsecond % 1000
+            assert result == expected.replace(microsecond=micros, nanosecond=0)
+        elif unit == "s":
+            assert result == expected.replace(microsecond=0, nanosecond=0)
+        else:
+            assert result == expected
+        assert result._creso == getattr(NpyDatetimeUnit, f"NPY_FR_{unit}").value
+
+    @pytest.mark.parametrize("offset", [-1, 1])
+    def test_timestamp_tz_localize_nonexistent_shift_invalid(self, offset, warsaw):
+        # GH 8917, 24466
+        tz = warsaw
+        ts = Timestamp("2015-03-29 02:20:00")
+        msg = "The provided timedelta will relocalize on a nonexistent time"
+        with pytest.raises(ValueError, match=msg):
+            ts.tz_localize(tz, nonexistent=timedelta(seconds=offset))
+
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"])
+    def test_timestamp_tz_localize_nonexistent_NaT(self, warsaw, unit):
+        # GH 8917
+        tz = warsaw
+        ts = Timestamp("2015-03-29 02:20:00").as_unit(unit)
+        result = ts.tz_localize(tz, nonexistent="NaT")
+        assert result is NaT
+
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"])
+    def test_timestamp_tz_localize_nonexistent_raise(self, warsaw, unit):
+        # GH 8917
+        tz = warsaw
+        ts = Timestamp("2015-03-29 02:20:00").as_unit(unit)
+        msg = "2015-03-29 02:20:00"
+        with pytest.raises(pytz.NonExistentTimeError, match=msg):
+            ts.tz_localize(tz, nonexistent="raise")
+        msg = (
+            "The nonexistent argument must be one of 'raise', 'NaT', "
+            "'shift_forward', 'shift_backward' or a timedelta object"
+        )
+        with pytest.raises(ValueError, match=msg):
+            ts.tz_localize(tz, nonexistent="foo")

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/test_arithmetic.py

@@ -0,0 +1,334 @@
+from datetime import (
+    datetime,
+    timedelta,
+    timezone,
+)
+
+from dateutil.tz import gettz
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs.tslibs import (
+    OutOfBoundsDatetime,
+    OutOfBoundsTimedelta,
+    Timedelta,
+    Timestamp,
+    offsets,
+    to_offset,
+)
+
+import pandas._testing as tm
+
+
+class TestTimestampArithmetic:
+    def test_overflow_offset(self):
+        # no overflow expected
+
+        stamp = Timestamp("2000/1/1")
+        offset_no_overflow = to_offset("D") * 100
+
+        expected = Timestamp("2000/04/10")
+        assert stamp + offset_no_overflow == expected
+
+        assert offset_no_overflow + stamp == expected
+
+        expected = Timestamp("1999/09/23")
+        assert stamp - offset_no_overflow == expected
+
+    def test_overflow_offset_raises(self):
+        # xref https://github.com/statsmodels/statsmodels/issues/3374
+        # ends up multiplying really large numbers which overflow
+
+        stamp = Timestamp("2017-01-13 00:00:00").as_unit("ns")
+        offset_overflow = 20169940 * offsets.Day(1)
+        lmsg2 = r"Cannot cast -?20169940 days \+?00:00:00 to unit='ns' without overflow"
+
+        with pytest.raises(OutOfBoundsTimedelta, match=lmsg2):
+            stamp + offset_overflow
+
+        with pytest.raises(OutOfBoundsTimedelta, match=lmsg2):
+            offset_overflow + stamp
+
+        with pytest.raises(OutOfBoundsTimedelta, match=lmsg2):
+            stamp - offset_overflow
+
+        # xref https://github.com/pandas-dev/pandas/issues/14080
+        # used to crash, so check for proper overflow exception
+
+        stamp = Timestamp("2000/1/1").as_unit("ns")
+        offset_overflow = to_offset("D") * 100**5
+
+        lmsg3 = (
+            r"Cannot cast -?10000000000 days \+?00:00:00 to unit='ns' without overflow"
+        )
+        with pytest.raises(OutOfBoundsTimedelta, match=lmsg3):
+            stamp + offset_overflow
+
+        with pytest.raises(OutOfBoundsTimedelta, match=lmsg3):
+            offset_overflow + stamp
+
+        with pytest.raises(OutOfBoundsTimedelta, match=lmsg3):
+            stamp - offset_overflow
+
+    def test_overflow_timestamp_raises(self):
+        # https://github.com/pandas-dev/pandas/issues/31774
+        msg = "Result is too large"
+        a = Timestamp("2101-01-01 00:00:00").as_unit("ns")
+        b = Timestamp("1688-01-01 00:00:00").as_unit("ns")
+
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            a - b
+
+        # but we're OK for timestamp and datetime.datetime
+        assert (a - b.to_pydatetime()) == (a.to_pydatetime() - b)
+
+    def test_delta_preserve_nanos(self):
+        val = Timestamp(1337299200000000123)
+        result = val + timedelta(1)
+        assert result.nanosecond == val.nanosecond
+
+    def test_rsub_dtscalars(self, tz_naive_fixture):
+        # In particular, check that datetime64 - Timestamp works GH#28286
+        td = Timedelta(1235345642000)
+        ts = Timestamp("2021-01-01", tz=tz_naive_fixture)
+        other = ts + td
+
+        assert other - ts == td
+        assert other.to_pydatetime() - ts == td
+        if tz_naive_fixture is None:
+            assert other.to_datetime64() - ts == td
+        else:
+            msg = "Cannot subtract tz-naive and tz-aware datetime-like objects"
+            with pytest.raises(TypeError, match=msg):
+                other.to_datetime64() - ts
+
+    def test_timestamp_sub_datetime(self):
+        dt = datetime(2013, 10, 12)
+        ts = Timestamp(datetime(2013, 10, 13))
+        assert (ts - dt).days == 1
+        assert (dt - ts).days == -1
+
+    def test_subtract_tzaware_datetime(self):
+        t1 = Timestamp("2020-10-22T22:00:00+00:00")
+        t2 = datetime(2020, 10, 22, 22, tzinfo=timezone.utc)
+
+        result = t1 - t2
+
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta("0 days")
+
+    def test_subtract_timestamp_from_different_timezone(self):
+        t1 = Timestamp("20130101").tz_localize("US/Eastern")
+        t2 = Timestamp("20130101").tz_localize("CET")
+
+        result = t1 - t2
+
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta("0 days 06:00:00")
+
+    def test_subtracting_involving_datetime_with_different_tz(self):
+        t1 = datetime(2013, 1, 1, tzinfo=timezone(timedelta(hours=-5)))
+        t2 = Timestamp("20130101").tz_localize("CET")
+
+        result = t1 - t2
+
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta("0 days 06:00:00")
+
+        result = t2 - t1
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta("-1 days +18:00:00")
+
+    def test_subtracting_different_timezones(self, tz_aware_fixture):
+        t_raw = Timestamp("20130101")
+        t_UTC = t_raw.tz_localize("UTC")
+        t_diff = t_UTC.tz_convert(tz_aware_fixture) + Timedelta("0 days 05:00:00")
+
+        result = t_diff - t_UTC
+
+        assert isinstance(result, Timedelta)
+        assert result == Timedelta("0 days 05:00:00")
+
+    def test_addition_subtraction_types(self):
+        # Assert on the types resulting from Timestamp +/- various date/time
+        # objects
+        dt = datetime(2014, 3, 4)
+        td = timedelta(seconds=1)
+        ts = Timestamp(dt)
+
+        msg = "Addition/subtraction of integers"
+        with pytest.raises(TypeError, match=msg):
+            # GH#22535 add/sub with integers is deprecated
+            ts + 1
+        with pytest.raises(TypeError, match=msg):
+            ts - 1
+
+        # Timestamp + datetime not supported, though subtraction is supported
+        # and yields timedelta more tests in tseries/base/tests/test_base.py
+        assert type(ts - dt) == Timedelta
+        assert type(ts + td) == Timestamp
+        assert type(ts - td) == Timestamp
+
+        # Timestamp +/- datetime64 not supported, so not tested (could possibly
+        # assert error raised?)
+        td64 = np.timedelta64(1, "D")
+        assert type(ts + td64) == Timestamp
+        assert type(ts - td64) == Timestamp
+
+    @pytest.mark.parametrize(
+        "td", [Timedelta(hours=3), np.timedelta64(3, "h"), timedelta(hours=3)]
+    )
+    def test_radd_tdscalar(self, td, fixed_now_ts):
+        # GH#24775 timedelta64+Timestamp should not raise
+        ts = fixed_now_ts
+        assert td + ts == ts + td
+
+    @pytest.mark.parametrize(
+        "other,expected_difference",
+        [
+            (np.timedelta64(-123, "ns"), -123),
+            (np.timedelta64(1234567898, "ns"), 1234567898),
+            (np.timedelta64(-123, "us"), -123000),
+            (np.timedelta64(-123, "ms"), -123000000),
+        ],
+    )
+    def test_timestamp_add_timedelta64_unit(self, other, expected_difference):
+        now = datetime.now(timezone.utc)
+        ts = Timestamp(now).as_unit("ns")
+        result = ts + other
+        valdiff = result._value - ts._value
+        assert valdiff == expected_difference
+
+        ts2 = Timestamp(now)
+        assert ts2 + other == result
+
+    @pytest.mark.parametrize(
+        "ts",
+        [
+            Timestamp("1776-07-04"),
+            Timestamp("1776-07-04", tz="UTC"),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "other",
+        [
+            1,
+            np.int64(1),
+            np.array([1, 2], dtype=np.int32),
+            np.array([3, 4], dtype=np.uint64),
+        ],
+    )
+    def test_add_int_with_freq(self, ts, other):
+        msg = "Addition/subtraction of integers and integer-arrays"
+        with pytest.raises(TypeError, match=msg):
+            ts + other
+        with pytest.raises(TypeError, match=msg):
+            other + ts
+
+        with pytest.raises(TypeError, match=msg):
+            ts - other
+
+        msg = "unsupported operand type"
+        with pytest.raises(TypeError, match=msg):
+            other - ts
+
+    @pytest.mark.parametrize("shape", [(6,), (2, 3)])
+    def test_addsub_m8ndarray(self, shape):
+        # GH#33296
+        ts = Timestamp("2020-04-04 15:45").as_unit("ns")
+        other = np.arange(6).astype("m8[h]").reshape(shape)
+
+        result = ts + other
+
+        ex_stamps = [ts + Timedelta(hours=n) for n in range(6)]
+        expected = np.array([x.asm8 for x in ex_stamps], dtype="M8[ns]").reshape(shape)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = other + ts
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = ts - other
+        ex_stamps = [ts - Timedelta(hours=n) for n in range(6)]
+        expected = np.array([x.asm8 for x in ex_stamps], dtype="M8[ns]").reshape(shape)
+        tm.assert_numpy_array_equal(result, expected)
+
+        msg = r"unsupported operand type\(s\) for -: 'numpy.ndarray' and 'Timestamp'"
+        with pytest.raises(TypeError, match=msg):
+            other - ts
+
+    @pytest.mark.parametrize("shape", [(6,), (2, 3)])
+    def test_addsub_m8ndarray_tzaware(self, shape):
+        # GH#33296
+        ts = Timestamp("2020-04-04 15:45", tz="US/Pacific")
+
+        other = np.arange(6).astype("m8[h]").reshape(shape)
+
+        result = ts + other
+
+        ex_stamps = [ts + Timedelta(hours=n) for n in range(6)]
+        expected = np.array(ex_stamps).reshape(shape)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = other + ts
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = ts - other
+        ex_stamps = [ts - Timedelta(hours=n) for n in range(6)]
+        expected = np.array(ex_stamps).reshape(shape)
+        tm.assert_numpy_array_equal(result, expected)
+
+        msg = r"unsupported operand type\(s\) for -: 'numpy.ndarray' and 'Timestamp'"
+        with pytest.raises(TypeError, match=msg):
+            other - ts
+
+    def test_subtract_different_utc_objects(self, utc_fixture, utc_fixture2):
+        # GH 32619
+        dt = datetime(2021, 1, 1)
+        ts1 = Timestamp(dt, tz=utc_fixture)
+        ts2 = Timestamp(dt, tz=utc_fixture2)
+        result = ts1 - ts2
+        expected = Timedelta(0)
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "tz",
+        [
+            pytz.timezone("US/Eastern"),
+            gettz("US/Eastern"),
+            "US/Eastern",
+            "dateutil/US/Eastern",
+        ],
+    )
+    def test_timestamp_add_timedelta_push_over_dst_boundary(self, tz):
+        # GH#1389
+
+        # 4 hours before DST transition
+        stamp = Timestamp("3/10/2012 22:00", tz=tz)
+
+        result = stamp + timedelta(hours=6)
+
+        # spring forward, + "7" hours
+        expected = Timestamp("3/11/2012 05:00", tz=tz)
+
+        assert result == expected
+
+
+class SubDatetime(datetime):
+    pass
+
+
+@pytest.mark.parametrize(
+    "lh,rh",
+    [
+        (SubDatetime(2000, 1, 1), Timedelta(hours=1)),
+        (Timedelta(hours=1), SubDatetime(2000, 1, 1)),
+    ],
+)
+def test_dt_subclass_add_timedelta(lh, rh):
+    # GH#25851
+    # ensure that subclassed datetime works for
+    # Timedelta operations
+    result = lh + rh
+    expected = SubDatetime(2000, 1, 1, 1)
+    assert result == expected

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/test_comparisons.py

@@ -0,0 +1,313 @@
+from datetime import (
+    datetime,
+    timedelta,
+)
+import operator
+
+import numpy as np
+import pytest
+
+from pandas import Timestamp
+import pandas._testing as tm
+
+
+class TestTimestampComparison:
+    def test_compare_non_nano_dt64(self):
+        # don't raise when converting dt64 to Timestamp in __richcmp__
+        dt = np.datetime64("1066-10-14")
+        ts = Timestamp(dt)
+
+        assert dt == ts
+
+    def test_comparison_dt64_ndarray(self):
+        ts = Timestamp("2021-01-01")
+        ts2 = Timestamp("2019-04-05")
+        arr = np.array([[ts.asm8, ts2.asm8]], dtype="M8[ns]")
+
+        result = ts == arr
+        expected = np.array([[True, False]], dtype=bool)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = arr == ts
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = ts != arr
+        tm.assert_numpy_array_equal(result, ~expected)
+
+        result = arr != ts
+        tm.assert_numpy_array_equal(result, ~expected)
+
+        result = ts2 < arr
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = arr < ts2
+        tm.assert_numpy_array_equal(result, np.array([[False, False]], dtype=bool))
+
+        result = ts2 <= arr
+        tm.assert_numpy_array_equal(result, np.array([[True, True]], dtype=bool))
+
+        result = arr <= ts2
+        tm.assert_numpy_array_equal(result, ~expected)
+
+        result = ts >= arr
+        tm.assert_numpy_array_equal(result, np.array([[True, True]], dtype=bool))
+
+        result = arr >= ts
+        tm.assert_numpy_array_equal(result, np.array([[True, False]], dtype=bool))
+
+    @pytest.mark.parametrize("reverse", [True, False])
+    def test_comparison_dt64_ndarray_tzaware(self, reverse, comparison_op):
+        ts = Timestamp("2021-01-01 00:00:00.00000", tz="UTC")
+        arr = np.array([ts.asm8, ts.asm8], dtype="M8[ns]")
+
+        left, right = ts, arr
+        if reverse:
+            left, right = arr, ts
+
+        if comparison_op is operator.eq:
+            expected = np.array([False, False], dtype=bool)
+            result = comparison_op(left, right)
+            tm.assert_numpy_array_equal(result, expected)
+        elif comparison_op is operator.ne:
+            expected = np.array([True, True], dtype=bool)
+            result = comparison_op(left, right)
+            tm.assert_numpy_array_equal(result, expected)
+        else:
+            msg = "Cannot compare tz-naive and tz-aware timestamps"
+            with pytest.raises(TypeError, match=msg):
+                comparison_op(left, right)
+
+    def test_comparison_object_array(self):
+        # GH#15183
+        ts = Timestamp("2011-01-03 00:00:00-0500", tz="US/Eastern")
+        other = Timestamp("2011-01-01 00:00:00-0500", tz="US/Eastern")
+        naive = Timestamp("2011-01-01 00:00:00")
+
+        arr = np.array([other, ts], dtype=object)
+        res = arr == ts
+        expected = np.array([False, True], dtype=bool)
+        assert (res == expected).all()
+
+        # 2D case
+        arr = np.array([[other, ts], [ts, other]], dtype=object)
+        res = arr != ts
+        expected = np.array([[True, False], [False, True]], dtype=bool)
+        assert res.shape == expected.shape
+        assert (res == expected).all()
+
+        # tzaware mismatch
+        arr = np.array([naive], dtype=object)
+        msg = "Cannot compare tz-naive and tz-aware timestamps"
+        with pytest.raises(TypeError, match=msg):
+            arr < ts
+
+    def test_comparison(self):
+        # 5-18-2012 00:00:00.000
+        stamp = 1337299200000000000
+
+        val = Timestamp(stamp)
+
+        assert val == val
+        assert not val != val
+        assert not val < val
+        assert val <= val
+        assert not val > val
+        assert val >= val
+
+        other = datetime(2012, 5, 18)
+        assert val == other
+        assert not val != other
+        assert not val < other
+        assert val <= other
+        assert not val > other
+        assert val >= other
+
+        other = Timestamp(stamp + 100)
+
+        assert val != other
+        assert val != other
+        assert val < other
+        assert val <= other
+        assert other > val
+        assert other >= val
+
+    def test_compare_invalid(self):
+        # GH#8058
+        val = Timestamp("20130101 12:01:02")
+        assert not val == "foo"
+        assert not val == 10.0
+        assert not val == 1
+        assert not val == []
+        assert not val == {"foo": 1}
+        assert not val == np.float64(1)
+        assert not val == np.int64(1)
+
+        assert val != "foo"
+        assert val != 10.0
+        assert val != 1
+        assert val != []
+        assert val != {"foo": 1}
+        assert val != np.float64(1)
+        assert val != np.int64(1)
+
+    @pytest.mark.parametrize("tz", [None, "US/Pacific"])
+    def test_compare_date(self, tz):
+        # GH#36131 comparing Timestamp with date object is deprecated
+        ts = Timestamp("2021-01-01 00:00:00.00000", tz=tz)
+        dt = ts.to_pydatetime().date()
+        # in 2.0 we disallow comparing pydate objects with Timestamps,
+        #  following the stdlib datetime behavior.
+
+        msg = "Cannot compare Timestamp with datetime.date"
+        for left, right in [(ts, dt), (dt, ts)]:
+            assert not left == right
+            assert left != right
+
+            with pytest.raises(TypeError, match=msg):
+                left < right
+            with pytest.raises(TypeError, match=msg):
+                left <= right
+            with pytest.raises(TypeError, match=msg):
+                left > right
+            with pytest.raises(TypeError, match=msg):
+                left >= right
+
+    def test_cant_compare_tz_naive_w_aware(self, utc_fixture):
+        # see GH#1404
+        a = Timestamp("3/12/2012")
+        b = Timestamp("3/12/2012", tz=utc_fixture)
+
+        msg = "Cannot compare tz-naive and tz-aware timestamps"
+        assert not a == b
+        assert a != b
+        with pytest.raises(TypeError, match=msg):
+            a < b
+        with pytest.raises(TypeError, match=msg):
+            a <= b
+        with pytest.raises(TypeError, match=msg):
+            a > b
+        with pytest.raises(TypeError, match=msg):
+            a >= b
+
+        assert not b == a
+        assert b != a
+        with pytest.raises(TypeError, match=msg):
+            b < a
+        with pytest.raises(TypeError, match=msg):
+            b <= a
+        with pytest.raises(TypeError, match=msg):
+            b > a
+        with pytest.raises(TypeError, match=msg):
+            b >= a
+
+        assert not a == b.to_pydatetime()
+        assert not a.to_pydatetime() == b
+
+    def test_timestamp_compare_scalars(self):
+        # case where ndim == 0
+        lhs = np.datetime64(datetime(2013, 12, 6))
+        rhs = Timestamp("now")
+        nat = Timestamp("nat")
+
+        ops = {"gt": "lt", "lt": "gt", "ge": "le", "le": "ge", "eq": "eq", "ne": "ne"}
+
+        for left, right in ops.items():
+            left_f = getattr(operator, left)
+            right_f = getattr(operator, right)
+            expected = left_f(lhs, rhs)
+
+            result = right_f(rhs, lhs)
+            assert result == expected
+
+            expected = left_f(rhs, nat)
+            result = right_f(nat, rhs)
+            assert result == expected
+
+    def test_timestamp_compare_with_early_datetime(self):
+        # e.g. datetime.min
+        stamp = Timestamp("2012-01-01")
+
+        assert not stamp == datetime.min
+        assert not stamp == datetime(1600, 1, 1)
+        assert not stamp == datetime(2700, 1, 1)
+        assert stamp != datetime.min
+        assert stamp != datetime(1600, 1, 1)
+        assert stamp != datetime(2700, 1, 1)
+        assert stamp > datetime(1600, 1, 1)
+        assert stamp >= datetime(1600, 1, 1)
+        assert stamp < datetime(2700, 1, 1)
+        assert stamp <= datetime(2700, 1, 1)
+
+        other = Timestamp.min.to_pydatetime(warn=False)
+        assert other - timedelta(microseconds=1) < Timestamp.min
+
+    def test_timestamp_compare_oob_dt64(self):
+        us = np.timedelta64(1, "us")
+        other = np.datetime64(Timestamp.min).astype("M8[us]")
+
+        # This may change if the implementation bound is dropped to match
+        #  DatetimeArray/DatetimeIndex GH#24124
+        assert Timestamp.min > other
+        # Note: numpy gets the reversed comparison wrong
+
+        other = np.datetime64(Timestamp.max).astype("M8[us]")
+        assert Timestamp.max > other  # not actually OOB
+        assert other < Timestamp.max
+
+        assert Timestamp.max < other + us
+        # Note: numpy gets the reversed comparison wrong
+
+        # GH-42794
+        other = datetime(9999, 9, 9)
+        assert Timestamp.min < other
+        assert other > Timestamp.min
+        assert Timestamp.max < other
+        assert other > Timestamp.max
+
+        other = datetime(1, 1, 1)
+        assert Timestamp.max > other
+        assert other < Timestamp.max
+        assert Timestamp.min > other
+        assert other < Timestamp.min
+
+    def test_compare_zerodim_array(self, fixed_now_ts):
+        # GH#26916
+        ts = fixed_now_ts
+        dt64 = np.datetime64("2016-01-01", "ns")
+        arr = np.array(dt64)
+        assert arr.ndim == 0
+
+        result = arr < ts
+        assert result is np.bool_(True)
+        result = arr > ts
+        assert result is np.bool_(False)
+
+
+def test_rich_comparison_with_unsupported_type():
+    # Comparisons with unsupported objects should return NotImplemented
+    # (it previously raised TypeError, see #24011)
+
+    class Inf:
+        def __lt__(self, o):
+            return False
+
+        def __le__(self, o):
+            return isinstance(o, Inf)
+
+        def __gt__(self, o):
+            return not isinstance(o, Inf)
+
+        def __ge__(self, o):
+            return True
+
+        def __eq__(self, other) -> bool:
+            return isinstance(other, Inf)
+
+    inf = Inf()
+    timestamp = Timestamp("2018-11-30")
+
+    for left, right in [(inf, timestamp), (timestamp, inf)]:
+        assert left > right or left < right
+        assert left >= right or left <= right
+        assert not left == right  # pylint: disable=unneeded-not
+        assert left != right

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/test_constructors.py

@@ -0,0 +1,1077 @@
+import calendar
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+    timezone,
+)
+import zoneinfo
+
+import dateutil.tz
+from dateutil.tz import (
+    gettz,
+    tzoffset,
+    tzutc,
+)
+import numpy as np
+import pytest
+import pytz
+
+from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+from pandas.compat import (
+    PY310,
+    PY314,
+)
+from pandas.errors import OutOfBoundsDatetime
+
+from pandas import (
+    NA,
+    NaT,
+    Period,
+    Timedelta,
+    Timestamp,
+)
+
+
+class TestTimestampConstructorUnitKeyword:
+    @pytest.mark.parametrize("typ", [int, float])
+    def test_constructor_int_float_with_YM_unit(self, typ):
+        # GH#47266 avoid the conversions in cast_from_unit
+        val = typ(150)
+
+        ts = Timestamp(val, unit="Y")
+        expected = Timestamp("2120-01-01")
+        assert ts == expected
+
+        ts = Timestamp(val, unit="M")
+        expected = Timestamp("1982-07-01")
+        assert ts == expected
+
+    @pytest.mark.parametrize("typ", [int, float])
+    def test_construct_from_int_float_with_unit_out_of_bound_raises(self, typ):
+        # GH#50870  make sure we get a OutOfBoundsDatetime instead of OverflowError
+        val = typ(150000000000000)
+
+        msg = f"cannot convert input {val} with the unit 'D'"
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            Timestamp(val, unit="D")
+
+    def test_constructor_float_not_round_with_YM_unit_raises(self):
+        # GH#47267 avoid the conversions in cast_from-unit
+
+        msg = "Conversion of non-round float with unit=[MY] is ambiguous"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(150.5, unit="Y")
+
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(150.5, unit="M")
+
+    @pytest.mark.parametrize(
+        "value, check_kwargs",
+        [
+            [946688461000000000, {}],
+            [946688461000000000 / 1000, {"unit": "us"}],
+            [946688461000000000 / 1_000_000, {"unit": "ms"}],
+            [946688461000000000 / 1_000_000_000, {"unit": "s"}],
+            [10957, {"unit": "D", "h": 0}],
+            [
+                (946688461000000000 + 500000) / 1000000000,
+                {"unit": "s", "us": 499, "ns": 964},
+            ],
+            [
+                (946688461000000000 + 500000000) / 1000000000,
+                {"unit": "s", "us": 500000},
+            ],
+            [(946688461000000000 + 500000) / 1000000, {"unit": "ms", "us": 500}],
+            [(946688461000000000 + 500000) / 1000, {"unit": "us", "us": 500}],
+            [(946688461000000000 + 500000000) / 1000000, {"unit": "ms", "us": 500000}],
+            [946688461000000000 / 1000.0 + 5, {"unit": "us", "us": 5}],
+            [946688461000000000 / 1000.0 + 5000, {"unit": "us", "us": 5000}],
+            [946688461000000000 / 1000000.0 + 0.5, {"unit": "ms", "us": 500}],
+            [946688461000000000 / 1000000.0 + 0.005, {"unit": "ms", "us": 5, "ns": 5}],
+            [946688461000000000 / 1000000000.0 + 0.5, {"unit": "s", "us": 500000}],
+            [10957 + 0.5, {"unit": "D", "h": 12}],
+        ],
+    )
+    def test_construct_with_unit(self, value, check_kwargs):
+        def check(value, unit=None, h=1, s=1, us=0, ns=0):
+            stamp = Timestamp(value, unit=unit)
+            assert stamp.year == 2000
+            assert stamp.month == 1
+            assert stamp.day == 1
+            assert stamp.hour == h
+            if unit != "D":
+                assert stamp.minute == 1
+                assert stamp.second == s
+                assert stamp.microsecond == us
+            else:
+                assert stamp.minute == 0
+                assert stamp.second == 0
+                assert stamp.microsecond == 0
+            assert stamp.nanosecond == ns
+
+        check(value, **check_kwargs)
+
+
+class TestTimestampConstructorFoldKeyword:
+    def test_timestamp_constructor_invalid_fold_raise(self):
+        # Test for GH#25057
+        # Valid fold values are only [None, 0, 1]
+        msg = "Valid values for the fold argument are None, 0, or 1."
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(123, fold=2)
+
+    def test_timestamp_constructor_pytz_fold_raise(self):
+        # Test for GH#25057
+        # pytz doesn't support fold. Check that we raise
+        # if fold is passed with pytz
+        msg = "pytz timezones do not support fold. Please use dateutil timezones."
+        tz = pytz.timezone("Europe/London")
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(datetime(2019, 10, 27, 0, 30, 0, 0), tz=tz, fold=0)
+
+    @pytest.mark.parametrize("fold", [0, 1])
+    @pytest.mark.parametrize(
+        "ts_input",
+        [
+            1572136200000000000,
+            1572136200000000000.0,
+            np.datetime64(1572136200000000000, "ns"),
+            "2019-10-27 01:30:00+01:00",
+            datetime(2019, 10, 27, 0, 30, 0, 0, tzinfo=timezone.utc),
+        ],
+    )
+    def test_timestamp_constructor_fold_conflict(self, ts_input, fold):
+        # Test for GH#25057
+        # Check that we raise on fold conflict
+        msg = (
+            "Cannot pass fold with possibly unambiguous input: int, float, "
+            "numpy.datetime64, str, or timezone-aware datetime-like. "
+            "Pass naive datetime-like or build Timestamp from components."
+        )
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(ts_input=ts_input, fold=fold)
+
+    @pytest.mark.parametrize("tz", ["dateutil/Europe/London", None])
+    @pytest.mark.parametrize("fold", [0, 1])
+    def test_timestamp_constructor_retain_fold(self, tz, fold):
+        # Test for GH#25057
+        # Check that we retain fold
+        ts = Timestamp(year=2019, month=10, day=27, hour=1, minute=30, tz=tz, fold=fold)
+        result = ts.fold
+        expected = fold
+        assert result == expected
+
+    try:
+        _tzs = [
+            "dateutil/Europe/London",
+            zoneinfo.ZoneInfo("Europe/London"),
+        ]
+    except zoneinfo.ZoneInfoNotFoundError:
+        _tzs = ["dateutil/Europe/London"]
+
+    @pytest.mark.parametrize("tz", _tzs)
+    @pytest.mark.parametrize(
+        "ts_input,fold_out",
+        [
+            (1572136200000000000, 0),
+            (1572139800000000000, 1),
+            ("2019-10-27 01:30:00+01:00", 0),
+            ("2019-10-27 01:30:00+00:00", 1),
+            (datetime(2019, 10, 27, 1, 30, 0, 0, fold=0), 0),
+            (datetime(2019, 10, 27, 1, 30, 0, 0, fold=1), 1),
+        ],
+    )
+    def test_timestamp_constructor_infer_fold_from_value(self, tz, ts_input, fold_out):
+        # Test for GH#25057
+        # Check that we infer fold correctly based on timestamps since utc
+        # or strings
+        ts = Timestamp(ts_input, tz=tz)
+        result = ts.fold
+        expected = fold_out
+        assert result == expected
+
+    @pytest.mark.parametrize("tz", ["dateutil/Europe/London"])
+    @pytest.mark.parametrize(
+        "ts_input,fold,value_out",
+        [
+            (datetime(2019, 10, 27, 1, 30, 0, 0), 0, 1572136200000000),
+            (datetime(2019, 10, 27, 1, 30, 0, 0), 1, 1572139800000000),
+        ],
+    )
+    def test_timestamp_constructor_adjust_value_for_fold(
+        self, tz, ts_input, fold, value_out
+    ):
+        # Test for GH#25057
+        # Check that we adjust value for fold correctly
+        # based on timestamps since utc
+        ts = Timestamp(ts_input, tz=tz, fold=fold)
+        result = ts._value
+        expected = value_out
+        assert result == expected
+
+
+class TestTimestampConstructorPositionalAndKeywordSupport:
+    def test_constructor_positional(self):
+        # see GH#10758
+        msg = (
+            "'NoneType' object cannot be interpreted as an integer"
+            if PY310
+            else "an integer is required"
+        )
+        with pytest.raises(TypeError, match=msg):
+            Timestamp(2000, 1)
+
+        msg = "month must be in 1..12"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(2000, 0, 1)
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(2000, 13, 1)
+
+        if PY314:
+            msg = "must be in range 1..31 for month 1 in year 2000"
+        else:
+            msg = "day is out of range for month"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(2000, 1, 0)
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(2000, 1, 32)
+
+        # see gh-11630
+        assert repr(Timestamp(2015, 11, 12)) == repr(Timestamp("20151112"))
+        assert repr(Timestamp(2015, 11, 12, 1, 2, 3, 999999)) == repr(
+            Timestamp("2015-11-12 01:02:03.999999")
+        )
+
+    def test_constructor_keyword(self):
+        # GH#10758
+        msg = "function missing required argument 'day'|Required argument 'day'"
+        with pytest.raises(TypeError, match=msg):
+            Timestamp(year=2000, month=1)
+
+        msg = "month must be in 1..12"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(year=2000, month=0, day=1)
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(year=2000, month=13, day=1)
+
+        if PY314:
+            msg = "must be in range 1..31 for month 1 in year 2000"
+        else:
+            msg = "day is out of range for month"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(year=2000, month=1, day=0)
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(year=2000, month=1, day=32)
+
+        assert repr(Timestamp(year=2015, month=11, day=12)) == repr(
+            Timestamp("20151112")
+        )
+
+        assert repr(
+            Timestamp(
+                year=2015,
+                month=11,
+                day=12,
+                hour=1,
+                minute=2,
+                second=3,
+                microsecond=999999,
+            )
+        ) == repr(Timestamp("2015-11-12 01:02:03.999999"))
+
+    @pytest.mark.parametrize(
+        "arg",
+        [
+            "year",
+            "month",
+            "day",
+            "hour",
+            "minute",
+            "second",
+            "microsecond",
+            "nanosecond",
+        ],
+    )
+    def test_invalid_date_kwarg_with_string_input(self, arg):
+        kwarg = {arg: 1}
+        msg = "Cannot pass a date attribute keyword argument"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp("2010-10-10 12:59:59.999999999", **kwarg)
+
+    @pytest.mark.parametrize("kwargs", [{}, {"year": 2020}, {"year": 2020, "month": 1}])
+    def test_constructor_missing_keyword(self, kwargs):
+        # GH#31200
+
+        # The exact error message of datetime() depends on its version
+        msg1 = r"function missing required argument '(year|month|day)' \(pos [123]\)"
+        msg2 = r"Required argument '(year|month|day)' \(pos [123]\) not found"
+        msg = "|".join([msg1, msg2])
+
+        with pytest.raises(TypeError, match=msg):
+            Timestamp(**kwargs)
+
+    def test_constructor_positional_with_tzinfo(self):
+        # GH#31929
+        ts = Timestamp(2020, 12, 31, tzinfo=timezone.utc)
+        expected = Timestamp("2020-12-31", tzinfo=timezone.utc)
+        assert ts == expected
+
+    @pytest.mark.parametrize("kwd", ["nanosecond", "microsecond", "second", "minute"])
+    def test_constructor_positional_keyword_mixed_with_tzinfo(self, kwd, request):
+        # TODO: if we passed microsecond with a keyword we would mess up
+        #  xref GH#45307
+        if kwd != "nanosecond":
+            # nanosecond is keyword-only as of 2.0, others are not
+            mark = pytest.mark.xfail(reason="GH#45307")
+            request.applymarker(mark)
+
+        kwargs = {kwd: 4}
+        ts = Timestamp(2020, 12, 31, tzinfo=timezone.utc, **kwargs)
+
+        td_kwargs = {kwd + "s": 4}
+        td = Timedelta(**td_kwargs)
+        expected = Timestamp("2020-12-31", tz=timezone.utc) + td
+        assert ts == expected
+
+
+class TestTimestampClassMethodConstructors:
+    # Timestamp constructors other than __new__
+
+    def test_constructor_strptime(self):
+        # GH#25016
+        # Test support for Timestamp.strptime
+        fmt = "%Y%m%d-%H%M%S-%f%z"
+        ts = "20190129-235348-000001+0000"
+        msg = r"Timestamp.strptime\(\) is not implemented"
+        with pytest.raises(NotImplementedError, match=msg):
+            Timestamp.strptime(ts, fmt)
+
+    def test_constructor_fromisocalendar(self):
+        # GH#30395
+        expected_timestamp = Timestamp("2000-01-03 00:00:00")
+        expected_stdlib = datetime.fromisocalendar(2000, 1, 1)
+        result = Timestamp.fromisocalendar(2000, 1, 1)
+        assert result == expected_timestamp
+        assert result == expected_stdlib
+        assert isinstance(result, Timestamp)
+
+    def test_constructor_fromordinal(self):
+        base = datetime(2000, 1, 1)
+
+        ts = Timestamp.fromordinal(base.toordinal())
+        assert base == ts
+        assert base.toordinal() == ts.toordinal()
+
+        ts = Timestamp.fromordinal(base.toordinal(), tz="US/Eastern")
+        assert Timestamp("2000-01-01", tz="US/Eastern") == ts
+        assert base.toordinal() == ts.toordinal()
+
+        # GH#3042
+        dt = datetime(2011, 4, 16, 0, 0)
+        ts = Timestamp.fromordinal(dt.toordinal())
+        assert ts.to_pydatetime() == dt
+
+        # with a tzinfo
+        stamp = Timestamp("2011-4-16", tz="US/Eastern")
+        dt_tz = stamp.to_pydatetime()
+        ts = Timestamp.fromordinal(dt_tz.toordinal(), tz="US/Eastern")
+        assert ts.to_pydatetime() == dt_tz
+
+    def test_now(self):
+        # GH#9000
+        ts_from_string = Timestamp("now")
+        ts_from_method = Timestamp.now()
+        ts_datetime = datetime.now()
+
+        ts_from_string_tz = Timestamp("now", tz="US/Eastern")
+        ts_from_method_tz = Timestamp.now(tz="US/Eastern")
+
+        # Check that the delta between the times is less than 1s (arbitrarily
+        # small)
+        delta = Timedelta(seconds=1)
+        assert abs(ts_from_method - ts_from_string) < delta
+        assert abs(ts_datetime - ts_from_method) < delta
+        assert abs(ts_from_method_tz - ts_from_string_tz) < delta
+        assert (
+            abs(
+                ts_from_string_tz.tz_localize(None)
+                - ts_from_method_tz.tz_localize(None)
+            )
+            < delta
+        )
+
+    def test_today(self):
+        ts_from_string = Timestamp("today")
+        ts_from_method = Timestamp.today()
+        ts_datetime = datetime.today()
+
+        ts_from_string_tz = Timestamp("today", tz="US/Eastern")
+        ts_from_method_tz = Timestamp.today(tz="US/Eastern")
+
+        # Check that the delta between the times is less than 1s (arbitrarily
+        # small)
+        delta = Timedelta(seconds=1)
+        assert abs(ts_from_method - ts_from_string) < delta
+        assert abs(ts_datetime - ts_from_method) < delta
+        assert abs(ts_from_method_tz - ts_from_string_tz) < delta
+        assert (
+            abs(
+                ts_from_string_tz.tz_localize(None)
+                - ts_from_method_tz.tz_localize(None)
+            )
+            < delta
+        )
+
+
+class TestTimestampResolutionInference:
+    def test_construct_from_time_unit(self):
+        # GH#54097 only passing a time component, no date
+        ts = Timestamp("01:01:01.111")
+        assert ts.unit == "ms"
+
+    def test_constructor_str_infer_reso(self):
+        # non-iso8601 path
+
+        # _parse_delimited_date path
+        ts = Timestamp("01/30/2023")
+        assert ts.unit == "s"
+
+        # _parse_dateabbr_string path
+        ts = Timestamp("2015Q1")
+        assert ts.unit == "s"
+
+        # dateutil_parse path
+        ts = Timestamp("2016-01-01 1:30:01 PM")
+        assert ts.unit == "s"
+
+        ts = Timestamp("2016 June 3 15:25:01.345")
+        assert ts.unit == "ms"
+
+        ts = Timestamp("300-01-01")
+        assert ts.unit == "s"
+
+        ts = Timestamp("300 June 1:30:01.300")
+        assert ts.unit == "ms"
+
+        # dateutil path -> don't drop trailing zeros
+        ts = Timestamp("01-01-2013T00:00:00.000000000+0000")
+        assert ts.unit == "ns"
+
+        ts = Timestamp("2016/01/02 03:04:05.001000 UTC")
+        assert ts.unit == "us"
+
+        # higher-than-nanosecond -> we drop the trailing bits
+        ts = Timestamp("01-01-2013T00:00:00.000000002100+0000")
+        assert ts == Timestamp("01-01-2013T00:00:00.000000002+0000")
+        assert ts.unit == "ns"
+
+        # GH#56208 minute reso through the ISO8601 path with tz offset
+        ts = Timestamp("2020-01-01 00:00+00:00")
+        assert ts.unit == "s"
+
+        ts = Timestamp("2020-01-01 00+00:00")
+        assert ts.unit == "s"
+
+    @pytest.mark.parametrize("method", ["now", "today"])
+    def test_now_today_unit(self, method):
+        # GH#55879
+        ts_from_method = getattr(Timestamp, method)()
+        ts_from_string = Timestamp(method)
+        assert ts_from_method.unit == ts_from_string.unit == "us"
+
+
+class TestTimestampConstructors:
+    def test_weekday_but_no_day_raises(self):
+        # GH#52659
+        msg = "Parsing datetimes with weekday but no day information is not supported"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp("2023 Sept Thu")
+
+    def test_construct_from_string_invalid_raises(self):
+        # dateutil (weirdly) parses "200622-12-31" as
+        #  datetime(2022, 6, 20, 12, 0, tzinfo=tzoffset(None, -111600)
+        #  which besides being mis-parsed, is a tzoffset that will cause
+        #  str(ts) to raise ValueError.  Ensure we raise in the constructor
+        #  instead.
+        # see test_to_datetime_malformed_raise for analogous to_datetime test
+        with pytest.raises(ValueError, match="gives an invalid tzoffset"):
+            Timestamp("200622-12-31")
+
+    def test_constructor_from_iso8601_str_with_offset_reso(self):
+        # GH#49737
+        ts = Timestamp("2016-01-01 04:05:06-01:00")
+        assert ts.unit == "s"
+
+        ts = Timestamp("2016-01-01 04:05:06.000-01:00")
+        assert ts.unit == "ms"
+
+        ts = Timestamp("2016-01-01 04:05:06.000000-01:00")
+        assert ts.unit == "us"
+
+        ts = Timestamp("2016-01-01 04:05:06.000000001-01:00")
+        assert ts.unit == "ns"
+
+    def test_constructor_from_date_second_reso(self):
+        # GH#49034 constructing from a pydate object gets lowest supported
+        #  reso, i.e. seconds
+        obj = date(2012, 9, 1)
+        ts = Timestamp(obj)
+        assert ts.unit == "s"
+
+    def test_constructor_datetime64_with_tz(self):
+        # GH#42288, GH#24559
+        dt = np.datetime64("1970-01-01 05:00:00")
+        tzstr = "UTC+05:00"
+
+        # pre-2.0 this interpreted dt as a UTC time. in 2.0 this is treated
+        #  as a wall-time, consistent with DatetimeIndex behavior
+        ts = Timestamp(dt, tz=tzstr)
+
+        alt = Timestamp(dt).tz_localize(tzstr)
+        assert ts == alt
+        assert ts.hour == 5
+
+    def test_constructor(self):
+        base_str = "2014-07-01 09:00"
+        base_dt = datetime(2014, 7, 1, 9)
+        base_expected = 1_404_205_200_000_000_000
+
+        # confirm base representation is correct
+        assert calendar.timegm(base_dt.timetuple()) * 1_000_000_000 == base_expected
+
+        tests = [
+            (base_str, base_dt, base_expected),
+            (
+                "2014-07-01 10:00",
+                datetime(2014, 7, 1, 10),
+                base_expected + 3600 * 1_000_000_000,
+            ),
+            (
+                "2014-07-01 09:00:00.000008000",
+                datetime(2014, 7, 1, 9, 0, 0, 8),
+                base_expected + 8000,
+            ),
+            (
+                "2014-07-01 09:00:00.000000005",
+                Timestamp("2014-07-01 09:00:00.000000005"),
+                base_expected + 5,
+            ),
+        ]
+
+        timezones = [
+            (None, 0),
+            ("UTC", 0),
+            (pytz.utc, 0),
+            ("Asia/Tokyo", 9),
+            ("US/Eastern", -4),
+            ("dateutil/US/Pacific", -7),
+            (pytz.FixedOffset(-180), -3),
+            (dateutil.tz.tzoffset(None, 18000), 5),
+        ]
+
+        for date_str, date_obj, expected in tests:
+            for result in [Timestamp(date_str), Timestamp(date_obj)]:
+                result = result.as_unit("ns")  # test originally written before non-nano
+                # only with timestring
+                assert result.as_unit("ns")._value == expected
+
+                # re-creation shouldn't affect to internal value
+                result = Timestamp(result)
+                assert result.as_unit("ns")._value == expected
+
+            # with timezone
+            for tz, offset in timezones:
+                for result in [Timestamp(date_str, tz=tz), Timestamp(date_obj, tz=tz)]:
+                    result = result.as_unit(
+                        "ns"
+                    )  # test originally written before non-nano
+                    expected_tz = expected - offset * 3600 * 1_000_000_000
+                    assert result.as_unit("ns")._value == expected_tz
+
+                    # should preserve tz
+                    result = Timestamp(result)
+                    assert result.as_unit("ns")._value == expected_tz
+
+                    # should convert to UTC
+                    if tz is not None:
+                        result = Timestamp(result).tz_convert("UTC")
+                    else:
+                        result = Timestamp(result, tz="UTC")
+                    expected_utc = expected - offset * 3600 * 1_000_000_000
+                    assert result.as_unit("ns")._value == expected_utc
+
+    def test_constructor_with_stringoffset(self):
+        # GH 7833
+        base_str = "2014-07-01 11:00:00+02:00"
+        base_dt = datetime(2014, 7, 1, 9)
+        base_expected = 1_404_205_200_000_000_000
+
+        # confirm base representation is correct
+        assert calendar.timegm(base_dt.timetuple()) * 1_000_000_000 == base_expected
+
+        tests = [
+            (base_str, base_expected),
+            ("2014-07-01 12:00:00+02:00", base_expected + 3600 * 1_000_000_000),
+            ("2014-07-01 11:00:00.000008000+02:00", base_expected + 8000),
+            ("2014-07-01 11:00:00.000000005+02:00", base_expected + 5),
+        ]
+
+        timezones = [
+            (None, 0),
+            ("UTC", 0),
+            (pytz.utc, 0),
+            ("Asia/Tokyo", 9),
+            ("US/Eastern", -4),
+            ("dateutil/US/Pacific", -7),
+            (pytz.FixedOffset(-180), -3),
+            (dateutil.tz.tzoffset(None, 18000), 5),
+        ]
+
+        for date_str, expected in tests:
+            for result in [Timestamp(date_str)]:
+                # only with timestring
+                assert result.as_unit("ns")._value == expected
+
+                # re-creation shouldn't affect to internal value
+                result = Timestamp(result)
+                assert result.as_unit("ns")._value == expected
+
+            # with timezone
+            for tz, offset in timezones:
+                result = Timestamp(date_str, tz=tz)
+                expected_tz = expected
+                assert result.as_unit("ns")._value == expected_tz
+
+                # should preserve tz
+                result = Timestamp(result)
+                assert result.as_unit("ns")._value == expected_tz
+
+                # should convert to UTC
+                result = Timestamp(result).tz_convert("UTC")
+                expected_utc = expected
+                assert result.as_unit("ns")._value == expected_utc
+
+        # This should be 2013-11-01 05:00 in UTC
+        # converted to Chicago tz
+        result = Timestamp("2013-11-01 00:00:00-0500", tz="America/Chicago")
+        assert result._value == Timestamp("2013-11-01 05:00")._value
+        expected = "Timestamp('2013-11-01 00:00:00-0500', tz='America/Chicago')"
+        assert repr(result) == expected
+        assert result == eval(repr(result))
+
+        # This should be 2013-11-01 05:00 in UTC
+        # converted to Tokyo tz (+09:00)
+        result = Timestamp("2013-11-01 00:00:00-0500", tz="Asia/Tokyo")
+        assert result._value == Timestamp("2013-11-01 05:00")._value
+        expected = "Timestamp('2013-11-01 14:00:00+0900', tz='Asia/Tokyo')"
+        assert repr(result) == expected
+        assert result == eval(repr(result))
+
+        # GH11708
+        # This should be 2015-11-18 10:00 in UTC
+        # converted to Asia/Katmandu
+        result = Timestamp("2015-11-18 15:45:00+05:45", tz="Asia/Katmandu")
+        assert result._value == Timestamp("2015-11-18 10:00")._value
+        expected = "Timestamp('2015-11-18 15:45:00+0545', tz='Asia/Katmandu')"
+        assert repr(result) == expected
+        assert result == eval(repr(result))
+
+        # This should be 2015-11-18 10:00 in UTC
+        # converted to Asia/Kolkata
+        result = Timestamp("2015-11-18 15:30:00+05:30", tz="Asia/Kolkata")
+        assert result._value == Timestamp("2015-11-18 10:00")._value
+        expected = "Timestamp('2015-11-18 15:30:00+0530', tz='Asia/Kolkata')"
+        assert repr(result) == expected
+        assert result == eval(repr(result))
+
+    def test_constructor_invalid(self):
+        msg = "Cannot convert input"
+        with pytest.raises(TypeError, match=msg):
+            Timestamp(slice(2))
+        msg = "Cannot convert Period"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(Period("1000-01-01"))
+
+    def test_constructor_invalid_tz(self):
+        # GH#17690
+        msg = (
+            "Argument 'tzinfo' has incorrect type "
+            r"\(expected datetime.tzinfo, got str\)"
+        )
+        with pytest.raises(TypeError, match=msg):
+            Timestamp("2017-10-22", tzinfo="US/Eastern")
+
+        msg = "at most one of"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp("2017-10-22", tzinfo=pytz.utc, tz="UTC")
+
+        msg = "Cannot pass a date attribute keyword argument when passing a date string"
+        with pytest.raises(ValueError, match=msg):
+            # GH#5168
+            # case where user tries to pass tz as an arg, not kwarg, gets
+            # interpreted as `year`
+            Timestamp("2012-01-01", "US/Pacific")
+
+    def test_constructor_tz_or_tzinfo(self):
+        # GH#17943, GH#17690, GH#5168
+        stamps = [
+            Timestamp(year=2017, month=10, day=22, tz="UTC"),
+            Timestamp(year=2017, month=10, day=22, tzinfo=pytz.utc),
+            Timestamp(year=2017, month=10, day=22, tz=pytz.utc),
+            Timestamp(datetime(2017, 10, 22), tzinfo=pytz.utc),
+            Timestamp(datetime(2017, 10, 22), tz="UTC"),
+            Timestamp(datetime(2017, 10, 22), tz=pytz.utc),
+        ]
+        assert all(ts == stamps[0] for ts in stamps)
+
+    @pytest.mark.parametrize(
+        "result",
+        [
+            Timestamp(datetime(2000, 1, 2, 3, 4, 5, 6), nanosecond=1),
+            Timestamp(
+                year=2000,
+                month=1,
+                day=2,
+                hour=3,
+                minute=4,
+                second=5,
+                microsecond=6,
+                nanosecond=1,
+            ),
+            Timestamp(
+                year=2000,
+                month=1,
+                day=2,
+                hour=3,
+                minute=4,
+                second=5,
+                microsecond=6,
+                nanosecond=1,
+                tz="UTC",
+            ),
+            Timestamp(2000, 1, 2, 3, 4, 5, 6, None, nanosecond=1),
+            Timestamp(2000, 1, 2, 3, 4, 5, 6, tz=pytz.UTC, nanosecond=1),
+        ],
+    )
+    def test_constructor_nanosecond(self, result):
+        # GH 18898
+        # As of 2.0 (GH 49416), nanosecond should not be accepted positionally
+        expected = Timestamp(datetime(2000, 1, 2, 3, 4, 5, 6), tz=result.tz)
+        expected = expected + Timedelta(nanoseconds=1)
+        assert result == expected
+
+    @pytest.mark.parametrize("z", ["Z0", "Z00"])
+    def test_constructor_invalid_Z0_isostring(self, z):
+        # GH 8910
+        msg = f"Unknown datetime string format, unable to parse: 2014-11-02 01:00{z}"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(f"2014-11-02 01:00{z}")
+
+    def test_out_of_bounds_integer_value(self):
+        # GH#26651 check that we raise OutOfBoundsDatetime, not OverflowError
+        msg = str(Timestamp.max._value * 2)
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            Timestamp(Timestamp.max._value * 2)
+        msg = str(Timestamp.min._value * 2)
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            Timestamp(Timestamp.min._value * 2)
+
+    def test_out_of_bounds_value(self):
+        one_us = np.timedelta64(1).astype("timedelta64[us]")
+
+        # By definition we can't go out of bounds in [ns], so we
+        # convert the datetime64s to [us] so we can go out of bounds
+        min_ts_us = np.datetime64(Timestamp.min).astype("M8[us]") + one_us
+        max_ts_us = np.datetime64(Timestamp.max).astype("M8[us]")
+
+        # No error for the min/max datetimes
+        Timestamp(min_ts_us)
+        Timestamp(max_ts_us)
+
+        # We used to raise on these before supporting non-nano
+        us_val = NpyDatetimeUnit.NPY_FR_us.value
+        assert Timestamp(min_ts_us - one_us)._creso == us_val
+        assert Timestamp(max_ts_us + one_us)._creso == us_val
+
+        # https://github.com/numpy/numpy/issues/22346 for why
+        #  we can't use the same construction as above with minute resolution
+
+        # too_low, too_high are the _just_ outside the range of M8[s]
+        too_low = np.datetime64("-292277022657-01-27T08:29", "m")
+        too_high = np.datetime64("292277026596-12-04T15:31", "m")
+
+        msg = "Out of bounds"
+        # One us less than the minimum is an error
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(too_low)
+
+        # One us more than the maximum is an error
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(too_high)
+
+    def test_out_of_bounds_string(self):
+        msg = "Cannot cast .* to unit='ns' without overflow"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp("1676-01-01").as_unit("ns")
+        with pytest.raises(ValueError, match=msg):
+            Timestamp("2263-01-01").as_unit("ns")
+
+        ts = Timestamp("2263-01-01")
+        assert ts.unit == "s"
+
+        ts = Timestamp("1676-01-01")
+        assert ts.unit == "s"
+
+    def test_barely_out_of_bounds(self):
+        # GH#19529
+        # GH#19382 close enough to bounds that dropping nanos would result
+        # in an in-bounds datetime
+        msg = "Out of bounds nanosecond timestamp: 2262-04-11 23:47:16"
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            Timestamp("2262-04-11 23:47:16.854775808")
+
+    @pytest.mark.skip_ubsan
+    def test_bounds_with_different_units(self):
+        out_of_bounds_dates = ("1677-09-21", "2262-04-12")
+
+        time_units = ("D", "h", "m", "s", "ms", "us")
+
+        for date_string in out_of_bounds_dates:
+            for unit in time_units:
+                dt64 = np.datetime64(date_string, unit)
+                ts = Timestamp(dt64)
+                if unit in ["s", "ms", "us"]:
+                    # We can preserve the input unit
+                    assert ts._value == dt64.view("i8")
+                else:
+                    # we chose the closest unit that we _do_ support
+                    assert ts._creso == NpyDatetimeUnit.NPY_FR_s.value
+
+        # With more extreme cases, we can't even fit inside second resolution
+        info = np.iinfo(np.int64)
+        msg = "Out of bounds second timestamp:"
+        for value in [info.min + 1, info.max]:
+            for unit in ["D", "h", "m"]:
+                dt64 = np.datetime64(value, unit)
+                with pytest.raises(OutOfBoundsDatetime, match=msg):
+                    Timestamp(dt64)
+
+        in_bounds_dates = ("1677-09-23", "2262-04-11")
+
+        for date_string in in_bounds_dates:
+            for unit in time_units:
+                dt64 = np.datetime64(date_string, unit)
+                Timestamp(dt64)
+
+    @pytest.mark.parametrize("arg", ["001-01-01", "0001-01-01"])
+    def test_out_of_bounds_string_consistency(self, arg):
+        # GH 15829
+        msg = "Cannot cast 0001-01-01 00:00:00 to unit='ns' without overflow"
+        with pytest.raises(OutOfBoundsDatetime, match=msg):
+            Timestamp(arg).as_unit("ns")
+
+        ts = Timestamp(arg)
+        assert ts.unit == "s"
+        assert ts.year == ts.month == ts.day == 1
+
+    def test_min_valid(self):
+        # Ensure that Timestamp.min is a valid Timestamp
+        Timestamp(Timestamp.min)
+
+    def test_max_valid(self):
+        # Ensure that Timestamp.max is a valid Timestamp
+        Timestamp(Timestamp.max)
+
+    @pytest.mark.parametrize("offset", ["+0300", "+0200"])
+    def test_construct_timestamp_near_dst(self, offset):
+        # GH 20854
+        expected = Timestamp(f"2016-10-30 03:00:00{offset}", tz="Europe/Helsinki")
+        result = Timestamp(expected).tz_convert("Europe/Helsinki")
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "arg", ["2013/01/01 00:00:00+09:00", "2013-01-01 00:00:00+09:00"]
+    )
+    def test_construct_with_different_string_format(self, arg):
+        # GH 12064
+        result = Timestamp(arg)
+        expected = Timestamp(datetime(2013, 1, 1), tz=pytz.FixedOffset(540))
+        assert result == expected
+
+    @pytest.mark.parametrize("box", [datetime, Timestamp])
+    def test_raise_tz_and_tzinfo_in_datetime_input(self, box):
+        # GH 23579
+        kwargs = {"year": 2018, "month": 1, "day": 1, "tzinfo": pytz.utc}
+        msg = "Cannot pass a datetime or Timestamp"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(box(**kwargs), tz="US/Pacific")
+        msg = "Cannot pass a datetime or Timestamp"
+        with pytest.raises(ValueError, match=msg):
+            Timestamp(box(**kwargs), tzinfo=pytz.timezone("US/Pacific"))
+
+    def test_dont_convert_dateutil_utc_to_pytz_utc(self):
+        result = Timestamp(datetime(2018, 1, 1), tz=tzutc())
+        expected = Timestamp(datetime(2018, 1, 1)).tz_localize(tzutc())
+        assert result == expected
+
+    def test_constructor_subclassed_datetime(self):
+        # GH 25851
+        # ensure that subclassed datetime works for
+        # Timestamp creation
+        class SubDatetime(datetime):
+            pass
+
+        data = SubDatetime(2000, 1, 1)
+        result = Timestamp(data)
+        expected = Timestamp(2000, 1, 1)
+        assert result == expected
+
+    def test_timestamp_constructor_tz_utc(self):
+        utc_stamp = Timestamp("3/11/2012 05:00", tz="utc")
+        assert utc_stamp.tzinfo is timezone.utc
+        assert utc_stamp.hour == 5
+
+        utc_stamp = Timestamp("3/11/2012 05:00").tz_localize("utc")
+        assert utc_stamp.hour == 5
+
+    def test_timestamp_to_datetime_tzoffset(self):
+        tzinfo = tzoffset(None, 7200)
+        expected = Timestamp("3/11/2012 04:00", tz=tzinfo)
+        result = Timestamp(expected.to_pydatetime())
+        assert expected == result
+
+    def test_timestamp_constructor_near_dst_boundary(self):
+        # GH#11481 & GH#15777
+        # Naive string timestamps were being localized incorrectly
+        # with tz_convert_from_utc_single instead of tz_localize_to_utc
+
+        for tz in ["Europe/Brussels", "Europe/Prague"]:
+            result = Timestamp("2015-10-25 01:00", tz=tz)
+            expected = Timestamp("2015-10-25 01:00").tz_localize(tz)
+            assert result == expected
+
+            msg = "Cannot infer dst time from 2015-10-25 02:00:00"
+            with pytest.raises(pytz.AmbiguousTimeError, match=msg):
+                Timestamp("2015-10-25 02:00", tz=tz)
+
+        result = Timestamp("2017-03-26 01:00", tz="Europe/Paris")
+        expected = Timestamp("2017-03-26 01:00").tz_localize("Europe/Paris")
+        assert result == expected
+
+        msg = "2017-03-26 02:00"
+        with pytest.raises(pytz.NonExistentTimeError, match=msg):
+            Timestamp("2017-03-26 02:00", tz="Europe/Paris")
+
+        # GH#11708
+        naive = Timestamp("2015-11-18 10:00:00")
+        result = naive.tz_localize("UTC").tz_convert("Asia/Kolkata")
+        expected = Timestamp("2015-11-18 15:30:00+0530", tz="Asia/Kolkata")
+        assert result == expected
+
+        # GH#15823
+        result = Timestamp("2017-03-26 00:00", tz="Europe/Paris")
+        expected = Timestamp("2017-03-26 00:00:00+0100", tz="Europe/Paris")
+        assert result == expected
+
+        result = Timestamp("2017-03-26 01:00", tz="Europe/Paris")
+        expected = Timestamp("2017-03-26 01:00:00+0100", tz="Europe/Paris")
+        assert result == expected
+
+        msg = "2017-03-26 02:00"
+        with pytest.raises(pytz.NonExistentTimeError, match=msg):
+            Timestamp("2017-03-26 02:00", tz="Europe/Paris")
+
+        result = Timestamp("2017-03-26 02:00:00+0100", tz="Europe/Paris")
+        naive = Timestamp(result.as_unit("ns")._value)
+        expected = naive.tz_localize("UTC").tz_convert("Europe/Paris")
+        assert result == expected
+
+        result = Timestamp("2017-03-26 03:00", tz="Europe/Paris")
+        expected = Timestamp("2017-03-26 03:00:00+0200", tz="Europe/Paris")
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "tz",
+        [
+            pytz.timezone("US/Eastern"),
+            gettz("US/Eastern"),
+            "US/Eastern",
+            "dateutil/US/Eastern",
+        ],
+    )
+    def test_timestamp_constructed_by_date_and_tz(self, tz):
+        # GH#2993, Timestamp cannot be constructed by datetime.date
+        # and tz correctly
+
+        result = Timestamp(date(2012, 3, 11), tz=tz)
+
+        expected = Timestamp("3/11/2012", tz=tz)
+        assert result.hour == expected.hour
+        assert result == expected
+
+
+def test_constructor_ambiguous_dst():
+    # GH 24329
+    # Make sure that calling Timestamp constructor
+    # on Timestamp created from ambiguous time
+    # doesn't change Timestamp.value
+    ts = Timestamp(1382835600000000000, tz="dateutil/Europe/London")
+    expected = ts._value
+    result = Timestamp(ts)._value
+    assert result == expected
+
+
+@pytest.mark.parametrize("epoch", [1552211999999999872, 1552211999999999999])
+def test_constructor_before_dst_switch(epoch):
+    # GH 31043
+    # Make sure that calling Timestamp constructor
+    # on time just before DST switch doesn't lead to
+    # nonexistent time or value change
+    ts = Timestamp(epoch, tz="dateutil/America/Los_Angeles")
+    result = ts.tz.dst(ts)
+    expected = timedelta(seconds=0)
+    assert Timestamp(ts)._value == epoch
+    assert result == expected
+
+
+def test_timestamp_constructor_identity():
+    # Test for #30543
+    expected = Timestamp("2017-01-01T12")
+    result = Timestamp(expected)
+    assert result is expected
+
+
+@pytest.mark.parametrize("nano", [-1, 1000])
+def test_timestamp_nano_range(nano):
+    # GH 48255
+    with pytest.raises(ValueError, match="nanosecond must be in 0..999"):
+        Timestamp(year=2022, month=1, day=1, nanosecond=nano)
+
+
+def test_non_nano_value():
+    # https://github.com/pandas-dev/pandas/issues/49076
+    result = Timestamp("1800-01-01", unit="s").value
+    # `.value` shows nanoseconds, even though unit is 's'
+    assert result == -5364662400000000000
+
+    # out-of-nanoseconds-bounds `.value` raises informative message
+    msg = (
+        r"Cannot convert Timestamp to nanoseconds without overflow. "
+        r"Use `.asm8.view\('i8'\)` to cast represent Timestamp in its "
+        r"own unit \(here, s\).$"
+    )
+    ts = Timestamp("0300-01-01")
+    with pytest.raises(OverflowError, match=msg):
+        ts.value
+    # check that the suggested workaround actually works
+    result = ts.asm8.view("i8")
+    assert result == -52700112000
+
+
+@pytest.mark.parametrize("na_value", [None, np.nan, np.datetime64("NaT"), NaT, NA])
+def test_timestamp_constructor_na_value(na_value):
+    # GH45481
+    result = Timestamp(na_value)
+    expected = NaT
+    assert result is expected

py311/lib/python3.11/site-packages/pandas/tests/scalar/timestamp/test_timestamp.py

@@ -0,0 +1,928 @@
+""" test the scalar Timestamp """
+
+import calendar
+from datetime import (
+    datetime,
+    timedelta,
+    timezone,
+)
+import locale
+import time
+import unicodedata
+
+from dateutil.tz import (
+    tzlocal,
+    tzutc,
+)
+from hypothesis import (
+    given,
+    strategies as st,
+)
+import numpy as np
+import pytest
+import pytz
+from pytz import utc
+
+from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+from pandas._libs.tslibs.timezones import (
+    dateutil_gettz as gettz,
+    get_timezone,
+    maybe_get_tz,
+    tz_compare,
+)
+from pandas.compat import IS64
+
+from pandas import (
+    NaT,
+    Timedelta,
+    Timestamp,
+)
+import pandas._testing as tm
+
+from pandas.tseries import offsets
+from pandas.tseries.frequencies import to_offset
+
+
+class TestTimestampProperties:
+    def test_properties_business(self):
+        freq = to_offset("B")
+
+        ts = Timestamp("2017-10-01")
+        assert ts.dayofweek == 6
+        assert ts.day_of_week == 6
+        assert ts.is_month_start  # not a weekday
+        assert not freq.is_month_start(ts)
+        assert freq.is_month_start(ts + Timedelta(days=1))
+        assert not freq.is_quarter_start(ts)
+        assert freq.is_quarter_start(ts + Timedelta(days=1))
+
+        ts = Timestamp("2017-09-30")
+        assert ts.dayofweek == 5
+        assert ts.day_of_week == 5
+        assert ts.is_month_end
+        assert not freq.is_month_end(ts)
+        assert freq.is_month_end(ts - Timedelta(days=1))
+        assert ts.is_quarter_end
+        assert not freq.is_quarter_end(ts)
+        assert freq.is_quarter_end(ts - Timedelta(days=1))
+
+    @pytest.mark.parametrize(
+        "attr, expected",
+        [
+            ["year", 2014],
+            ["month", 12],
+            ["day", 31],
+            ["hour", 23],
+            ["minute", 59],
+            ["second", 0],
+            ["microsecond", 0],
+            ["nanosecond", 0],
+            ["dayofweek", 2],
+            ["day_of_week", 2],
+            ["quarter", 4],
+            ["dayofyear", 365],
+            ["day_of_year", 365],
+            ["week", 1],
+            ["daysinmonth", 31],
+        ],
+    )
+    @pytest.mark.parametrize("tz", [None, "US/Eastern"])
+    def test_fields(self, attr, expected, tz):
+        # GH 10050
+        # GH 13303
+        ts = Timestamp("2014-12-31 23:59:00", tz=tz)
+        result = getattr(ts, attr)
+        # that we are int like
+        assert isinstance(result, int)
+        assert result == expected
+
+    @pytest.mark.parametrize("tz", [None, "US/Eastern"])
+    def test_millisecond_raises(self, tz):
+        ts = Timestamp("2014-12-31 23:59:00", tz=tz)
+        msg = "'Timestamp' object has no attribute 'millisecond'"
+        with pytest.raises(AttributeError, match=msg):
+            ts.millisecond
+
+    @pytest.mark.parametrize(
+        "start", ["is_month_start", "is_quarter_start", "is_year_start"]
+    )
+    @pytest.mark.parametrize("tz", [None, "US/Eastern"])
+    def test_is_start(self, start, tz):
+        ts = Timestamp("2014-01-01 00:00:00", tz=tz)
+        assert getattr(ts, start)
+
+    @pytest.mark.parametrize("end", ["is_month_end", "is_year_end", "is_quarter_end"])
+    @pytest.mark.parametrize("tz", [None, "US/Eastern"])
+    def test_is_end(self, end, tz):
+        ts = Timestamp("2014-12-31 23:59:59", tz=tz)
+        assert getattr(ts, end)
+
+    # GH 12806
+    @pytest.mark.parametrize(
+        "data",
+        [Timestamp("2017-08-28 23:00:00"), Timestamp("2017-08-28 23:00:00", tz="EST")],
+    )
+    # error: Unsupported operand types for + ("List[None]" and "List[str]")
+    @pytest.mark.parametrize(
+        "time_locale", [None] + tm.get_locales()  # type: ignore[operator]
+    )
+    def test_names(self, data, time_locale):
+        # GH 17354
+        # Test .day_name(), .month_name
+        if time_locale is None:
+            expected_day = "Monday"
+            expected_month = "August"
+        else:
+            with tm.set_locale(time_locale, locale.LC_TIME):
+                expected_day = calendar.day_name[0].capitalize()
+                expected_month = calendar.month_name[8].capitalize()
+
+        result_day = data.day_name(time_locale)
+        result_month = data.month_name(time_locale)
+
+        # Work around https://github.com/pandas-dev/pandas/issues/22342
+        # different normalizations
+        expected_day = unicodedata.normalize("NFD", expected_day)
+        expected_month = unicodedata.normalize("NFD", expected_month)
+
+        result_day = unicodedata.normalize("NFD", result_day)
+        result_month = unicodedata.normalize("NFD", result_month)
+
+        assert result_day == expected_day
+        assert result_month == expected_month
+
+        # Test NaT
+        nan_ts = Timestamp(NaT)
+        assert np.isnan(nan_ts.day_name(time_locale))
+        assert np.isnan(nan_ts.month_name(time_locale))
+
+    def test_is_leap_year(self, tz_naive_fixture):
+        tz = tz_naive_fixture
+        if not IS64 and tz == tzlocal():
+            # https://github.com/dateutil/dateutil/issues/197
+            pytest.skip(
+                "tzlocal() on a 32 bit platform causes internal overflow errors"
+            )
+        # GH 13727
+        dt = Timestamp("2000-01-01 00:00:00", tz=tz)
+        assert dt.is_leap_year
+        assert isinstance(dt.is_leap_year, bool)
+
+        dt = Timestamp("1999-01-01 00:00:00", tz=tz)
+        assert not dt.is_leap_year
+
+        dt = Timestamp("2004-01-01 00:00:00", tz=tz)
+        assert dt.is_leap_year
+
+        dt = Timestamp("2100-01-01 00:00:00", tz=tz)
+        assert not dt.is_leap_year
+
+    def test_woy_boundary(self):
+        # make sure weeks at year boundaries are correct
+        d = datetime(2013, 12, 31)
+        result = Timestamp(d).week
+        expected = 1  # ISO standard
+        assert result == expected
+
+        d = datetime(2008, 12, 28)
+        result = Timestamp(d).week
+        expected = 52  # ISO standard
+        assert result == expected
+
+        d = datetime(2009, 12, 31)
+        result = Timestamp(d).week
+        expected = 53  # ISO standard
+        assert result == expected
+
+        d = datetime(2010, 1, 1)
+        result = Timestamp(d).week
+        expected = 53  # ISO standard
+        assert result == expected
+
+        d = datetime(2010, 1, 3)
+        result = Timestamp(d).week
+        expected = 53  # ISO standard
+        assert result == expected
+
+        result = np.array(
+            [
+                Timestamp(datetime(*args)).week
+                for args in [(2000, 1, 1), (2000, 1, 2), (2005, 1, 1), (2005, 1, 2)]
+            ]
+        )
+        assert (result == [52, 52, 53, 53]).all()
+
+    def test_resolution(self):
+        # GH#21336, GH#21365
+        dt = Timestamp("2100-01-01 00:00:00.000000000")
+        assert dt.resolution == Timedelta(nanoseconds=1)
+
+        # Check that the attribute is available on the class, mirroring
+        #  the stdlib datetime behavior
+        assert Timestamp.resolution == Timedelta(nanoseconds=1)
+
+        assert dt.as_unit("us").resolution == Timedelta(microseconds=1)
+        assert dt.as_unit("ms").resolution == Timedelta(milliseconds=1)
+        assert dt.as_unit("s").resolution == Timedelta(seconds=1)
+
+    @pytest.mark.parametrize(
+        "date_string, expected",
+        [
+            ("0000-2-29", 1),
+            ("0000-3-1", 2),
+            ("1582-10-14", 3),
+            ("-0040-1-1", 4),
+            ("2023-06-18", 6),
+        ],
+    )
+    def test_dow_historic(self, date_string, expected):
+        # GH 53738
+        ts = Timestamp(date_string)
+        dow = ts.weekday()
+        assert dow == expected
+
+    @given(
+        ts=st.datetimes(),
+        sign=st.sampled_from(["-", ""]),
+    )
+    def test_dow_parametric(self, ts, sign):
+        # GH 53738
+        ts = (
+            f"{sign}{str(ts.year).zfill(4)}"
+            f"-{str(ts.month).zfill(2)}"
+            f"-{str(ts.day).zfill(2)}"
+        )
+        result = Timestamp(ts).weekday()
+        expected = (
+            (np.datetime64(ts) - np.datetime64("1970-01-01")).astype("int64") - 4
+        ) % 7
+        assert result == expected
+
+
+class TestTimestamp:
+    @pytest.mark.parametrize("tz", [None, pytz.timezone("US/Pacific")])
+    def test_disallow_setting_tz(self, tz):
+        # GH#3746
+        ts = Timestamp("2010")
+        msg = "Cannot directly set timezone"
+        with pytest.raises(AttributeError, match=msg):
+            ts.tz = tz
+
+    def test_default_to_stdlib_utc(self):
+        assert Timestamp.utcnow().tz is timezone.utc
+        assert Timestamp.now("UTC").tz is timezone.utc
+        assert Timestamp("2016-01-01", tz="UTC").tz is timezone.utc
+
+    def test_tz(self):
+        tstr = "2014-02-01 09:00"
+        ts = Timestamp(tstr)
+        local = ts.tz_localize("Asia/Tokyo")
+        assert local.hour == 9
+        assert local == Timestamp(tstr, tz="Asia/Tokyo")
+        conv = local.tz_convert("US/Eastern")
+        assert conv == Timestamp("2014-01-31 19:00", tz="US/Eastern")
+        assert conv.hour == 19
+
+        # preserves nanosecond
+        ts = Timestamp(tstr) + offsets.Nano(5)
+        local = ts.tz_localize("Asia/Tokyo")
+        assert local.hour == 9
+        assert local.nanosecond == 5
+        conv = local.tz_convert("US/Eastern")
+        assert conv.nanosecond == 5
+        assert conv.hour == 19
+
+    def test_utc_z_designator(self):
+        assert get_timezone(Timestamp("2014-11-02 01:00Z").tzinfo) is timezone.utc
+
+    def test_asm8(self):
+        ns = [Timestamp.min._value, Timestamp.max._value, 1000]
+
+        for n in ns:
+            assert (
+                Timestamp(n).asm8.view("i8") == np.datetime64(n, "ns").view("i8") == n
+            )
+
+        assert Timestamp("nat").asm8.view("i8") == np.datetime64("nat", "ns").view("i8")
+
+    def test_class_ops(self):
+        def compare(x, y):
+            assert int((Timestamp(x)._value - Timestamp(y)._value) / 1e9) == 0
+
+        compare(Timestamp.now(), datetime.now())
+        compare(Timestamp.now("UTC"), datetime.now(pytz.timezone("UTC")))
+        compare(Timestamp.now("UTC"), datetime.now(tzutc()))
+        compare(Timestamp.utcnow(), datetime.now(timezone.utc))
+        compare(Timestamp.today(), datetime.today())
+        current_time = calendar.timegm(datetime.now().utctimetuple())
+
+        ts_utc = Timestamp.utcfromtimestamp(current_time)
+        assert ts_utc.timestamp() == current_time
+        compare(
+            Timestamp.fromtimestamp(current_time), datetime.fromtimestamp(current_time)
+        )
+        compare(
+            # Support tz kwarg in Timestamp.fromtimestamp
+            Timestamp.fromtimestamp(current_time, "UTC"),
+            datetime.fromtimestamp(current_time, utc),
+        )
+        compare(
+            # Support tz kwarg in Timestamp.fromtimestamp
+            Timestamp.fromtimestamp(current_time, tz="UTC"),
+            datetime.fromtimestamp(current_time, utc),
+        )
+
+        date_component = datetime.now(timezone.utc)
+        time_component = (date_component + timedelta(minutes=10)).time()
+        compare(
+            Timestamp.combine(date_component, time_component),
+            datetime.combine(date_component, time_component),
+        )
+
+    def test_basics_nanos(self):
+        val = np.int64(946_684_800_000_000_000).view("M8[ns]")
+        stamp = Timestamp(val.view("i8") + 500)
+        assert stamp.year == 2000
+        assert stamp.month == 1
+        assert stamp.microsecond == 0
+        assert stamp.nanosecond == 500
+
+        # GH 14415
+        val = np.iinfo(np.int64).min + 80_000_000_000_000
+        stamp = Timestamp(val)
+        assert stamp.year == 1677
+        assert stamp.month == 9
+        assert stamp.day == 21
+        assert stamp.microsecond == 145224
+        assert stamp.nanosecond == 192
+
+    def test_roundtrip(self):
+        # test value to string and back conversions
+        # further test accessors
+        base = Timestamp("20140101 00:00:00").as_unit("ns")
+
+        result = Timestamp(base._value + Timedelta("5ms")._value)
+        assert result == Timestamp(f"{base}.005000")
+        assert result.microsecond == 5000
+
+        result = Timestamp(base._value + Timedelta("5us")._value)
+        assert result == Timestamp(f"{base}.000005")
+        assert result.microsecond == 5
+
+        result = Timestamp(base._value + Timedelta("5ns")._value)
+        assert result == Timestamp(f"{base}.000000005")
+        assert result.nanosecond == 5
+        assert result.microsecond == 0
+
+        result = Timestamp(base._value + Timedelta("6ms 5us")._value)
+        assert result == Timestamp(f"{base}.006005")
+        assert result.microsecond == 5 + 6 * 1000
+
+        result = Timestamp(base._value + Timedelta("200ms 5us")._value)
+        assert result == Timestamp(f"{base}.200005")
+        assert result.microsecond == 5 + 200 * 1000
+
+    def test_hash_equivalent(self):
+        d = {datetime(2011, 1, 1): 5}
+        stamp = Timestamp(datetime(2011, 1, 1))
+        assert d[stamp] == 5
+
+    @pytest.mark.parametrize(
+        "timezone, year, month, day, hour",
+        [["America/Chicago", 2013, 11, 3, 1], ["America/Santiago", 2021, 4, 3, 23]],
+    )
+    def test_hash_timestamp_with_fold(self, timezone, year, month, day, hour):
+        # see gh-33931
+        test_timezone = gettz(timezone)
+        transition_1 = Timestamp(
+            year=year,
+            month=month,
+            day=day,
+            hour=hour,
+            minute=0,
+            fold=0,
+            tzinfo=test_timezone,
+        )
+        transition_2 = Timestamp(
+            year=year,
+            month=month,
+            day=day,
+            hour=hour,
+            minute=0,
+            fold=1,
+            tzinfo=test_timezone,
+        )
+        assert hash(transition_1) == hash(transition_2)
+
+
+class TestTimestampNsOperations:
+    def test_nanosecond_string_parsing(self):
+        ts = Timestamp("2013-05-01 07:15:45.123456789")
+        # GH 7878
+        expected_repr = "2013-05-01 07:15:45.123456789"
+        expected_value = 1_367_392_545_123_456_789
+        assert ts._value == expected_value
+        assert expected_repr in repr(ts)
+
+        ts = Timestamp("2013-05-01 07:15:45.123456789+09:00", tz="Asia/Tokyo")
+        assert ts._value == expected_value - 9 * 3600 * 1_000_000_000
+        assert expected_repr in repr(ts)
+
+        ts = Timestamp("2013-05-01 07:15:45.123456789", tz="UTC")
+        assert ts._value == expected_value
+        assert expected_repr in repr(ts)
+
+        ts = Timestamp("2013-05-01 07:15:45.123456789", tz="US/Eastern")
+        assert ts._value == expected_value + 4 * 3600 * 1_000_000_000
+        assert expected_repr in repr(ts)
+
+        # GH 10041
+        ts = Timestamp("20130501T071545.123456789")
+        assert ts._value == expected_value
+        assert expected_repr in repr(ts)
+
+    def test_nanosecond_timestamp(self):
+        # GH 7610
+        expected = 1_293_840_000_000_000_005
+        t = Timestamp("2011-01-01") + offsets.Nano(5)
+        assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000005')"
+        assert t._value == expected
+        assert t.nanosecond == 5
+
+        t = Timestamp(t)
+        assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000005')"
+        assert t._value == expected
+        assert t.nanosecond == 5
+
+        t = Timestamp("2011-01-01 00:00:00.000000005")
+        assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000005')"
+        assert t._value == expected
+        assert t.nanosecond == 5
+
+        expected = 1_293_840_000_000_000_010
+        t = t + offsets.Nano(5)
+        assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000010')"
+        assert t._value == expected
+        assert t.nanosecond == 10
+
+        t = Timestamp(t)
+        assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000010')"
+        assert t._value == expected
+        assert t.nanosecond == 10
+
+        t = Timestamp("2011-01-01 00:00:00.000000010")
+        assert repr(t) == "Timestamp('2011-01-01 00:00:00.000000010')"
+        assert t._value == expected
+        assert t.nanosecond == 10
+
+
+class TestTimestampConversion:
+    def test_conversion(self):
+        # GH#9255
+        ts = Timestamp("2000-01-01").as_unit("ns")
+
+        result = ts.to_pydatetime()
+        expected = datetime(2000, 1, 1)
+        assert result == expected
+        assert type(result) == type(expected)
+
+        result = ts.to_datetime64()
+        expected = np.datetime64(ts._value, "ns")
+        assert result == expected
+        assert type(result) == type(expected)
+        assert result.dtype == expected.dtype
+
+    def test_to_period_tz_warning(self):
+        # GH#21333 make sure a warning is issued when timezone
+        # info is lost
+        ts = Timestamp("2009-04-15 16:17:18", tz="US/Eastern")
+        with tm.assert_produces_warning(UserWarning):
+            # warning that timezone info will be lost
+            ts.to_period("D")
+
+    def test_to_numpy_alias(self):
+        # GH 24653: alias .to_numpy() for scalars
+        ts = Timestamp(datetime.now())
+        assert ts.to_datetime64() == ts.to_numpy()
+
+        # GH#44460
+        msg = "dtype and copy arguments are ignored"
+        with pytest.raises(ValueError, match=msg):
+            ts.to_numpy("M8[s]")
+        with pytest.raises(ValueError, match=msg):
+            ts.to_numpy(copy=True)
+
+
+class TestNonNano:
+    @pytest.fixture(params=["s", "ms", "us"])
+    def reso(self, request):
+        return request.param
+
+    @pytest.fixture
+    def dt64(self, reso):
+        # cases that are in-bounds for nanosecond, so we can compare against
+        #  the existing implementation.
+        return np.datetime64("2016-01-01", reso)
+
+    @pytest.fixture
+    def ts(self, dt64):
+        return Timestamp._from_dt64(dt64)
+
+    @pytest.fixture
+    def ts_tz(self, ts, tz_aware_fixture):
+        tz = maybe_get_tz(tz_aware_fixture)
+        return Timestamp._from_value_and_reso(ts._value, ts._creso, tz)
+
+    def test_non_nano_construction(self, dt64, ts, reso):
+        assert ts._value == dt64.view("i8")
+
+        if reso == "s":
+            assert ts._creso == NpyDatetimeUnit.NPY_FR_s.value
+        elif reso == "ms":
+            assert ts._creso == NpyDatetimeUnit.NPY_FR_ms.value
+        elif reso == "us":
+            assert ts._creso == NpyDatetimeUnit.NPY_FR_us.value
+
+    def test_non_nano_fields(self, dt64, ts):
+        alt = Timestamp(dt64)
+
+        assert ts.year == alt.year
+        assert ts.month == alt.month
+        assert ts.day == alt.day
+        assert ts.hour == ts.minute == ts.second == ts.microsecond == 0
+        assert ts.nanosecond == 0
+
+        assert ts.to_julian_date() == alt.to_julian_date()
+        assert ts.weekday() == alt.weekday()
+        assert ts.isoweekday() == alt.isoweekday()
+
+    def test_start_end_fields(self, ts):
+        assert ts.is_year_start
+        assert ts.is_quarter_start
+        assert ts.is_month_start
+        assert not ts.is_year_end
+        assert not ts.is_month_end
+        assert not ts.is_month_end
+
+        # 2016-01-01 is a Friday, so is year/quarter/month start with this freq
+        assert ts.is_year_start
+        assert ts.is_quarter_start
+        assert ts.is_month_start
+        assert not ts.is_year_end
+        assert not ts.is_month_end
+        assert not ts.is_month_end
+
+    def test_day_name(self, dt64, ts):
+        alt = Timestamp(dt64)
+        assert ts.day_name() == alt.day_name()
+
+    def test_month_name(self, dt64, ts):
+        alt = Timestamp(dt64)
+        assert ts.month_name() == alt.month_name()
+
+    def test_tz_convert(self, ts):
+        ts = Timestamp._from_value_and_reso(ts._value, ts._creso, utc)
+
+        tz = pytz.timezone("US/Pacific")
+        result = ts.tz_convert(tz)
+
+        assert isinstance(result, Timestamp)
+        assert result._creso == ts._creso
+        assert tz_compare(result.tz, tz)
+
+    def test_repr(self, dt64, ts):
+        alt = Timestamp(dt64)
+
+        assert str(ts) == str(alt)
+        assert repr(ts) == repr(alt)
+
+    def test_comparison(self, dt64, ts):
+        alt = Timestamp(dt64)
+
+        assert ts == dt64
+        assert dt64 == ts
+        assert ts == alt
+        assert alt == ts
+
+        assert not ts != dt64
+        assert not dt64 != ts
+        assert not ts != alt
+        assert not alt != ts
+
+        assert not ts < dt64
+        assert not dt64 < ts
+        assert not ts < alt
+        assert not alt < ts
+
+        assert not ts > dt64
+        assert not dt64 > ts
+        assert not ts > alt
+        assert not alt > ts
+
+        assert ts >= dt64
+        assert dt64 >= ts
+        assert ts >= alt
+        assert alt >= ts
+
+        assert ts <= dt64
+        assert dt64 <= ts
+        assert ts <= alt
+        assert alt <= ts
+
+    def test_cmp_cross_reso(self):
+        # numpy gets this wrong because of silent overflow
+        dt64 = np.datetime64(9223372800, "s")  # won't fit in M8[ns]
+        ts = Timestamp._from_dt64(dt64)
+
+        # subtracting 3600*24 gives a datetime64 that _can_ fit inside the
+        #  nanosecond implementation bounds.
+        other = Timestamp(dt64 - 3600 * 24).as_unit("ns")
+        assert other < ts
+        assert other.asm8 > ts.asm8  # <- numpy gets this wrong
+        assert ts > other
+        assert ts.asm8 < other.asm8  # <- numpy gets this wrong
+        assert not other == ts
+        assert ts != other
+
+    @pytest.mark.xfail(reason="Dispatches to np.datetime64 which is wrong")
+    def test_cmp_cross_reso_reversed_dt64(self):
+        dt64 = np.datetime64(106752, "D")  # won't fit in M8[ns]
+        ts = Timestamp._from_dt64(dt64)
+        other = Timestamp(dt64 - 1)
+
+        assert other.asm8 < ts
+
+    def test_pickle(self, ts, tz_aware_fixture):
+        tz = tz_aware_fixture
+        tz = maybe_get_tz(tz)
+        ts = Timestamp._from_value_and_reso(ts._value, ts._creso, tz)
+        rt = tm.round_trip_pickle(ts)
+        assert rt._creso == ts._creso
+        assert rt == ts
+
+    def test_normalize(self, dt64, ts):
+        alt = Timestamp(dt64)
+        result = ts.normalize()
+        assert result._creso == ts._creso
+        assert result == alt.normalize()
+
+    def test_asm8(self, dt64, ts):
+        rt = ts.asm8
+        assert rt == dt64
+        assert rt.dtype == dt64.dtype
+
+    def test_to_numpy(self, dt64, ts):
+        res = ts.to_numpy()
+        assert res == dt64
+        assert res.dtype == dt64.dtype
+
+    def test_to_datetime64(self, dt64, ts):
+        res = ts.to_datetime64()
+        assert res == dt64
+        assert res.dtype == dt64.dtype
+
+    def test_timestamp(self, dt64, ts):
+        alt = Timestamp(dt64)
+        assert ts.timestamp() == alt.timestamp()
+
+    def test_to_period(self, dt64, ts):
+        alt = Timestamp(dt64)
+        assert ts.to_period("D") == alt.to_period("D")
+
+    @pytest.mark.parametrize(
+        "td", [timedelta(days=4), Timedelta(days=4), np.timedelta64(4, "D")]
+    )
+    def test_addsub_timedeltalike_non_nano(self, dt64, ts, td):
+        exp_reso = max(ts._creso, Timedelta(td)._creso)
+
+        result = ts - td
+        expected = Timestamp(dt64) - td
+        assert isinstance(result, Timestamp)
+        assert result._creso == exp_reso
+        assert result == expected
+
+        result = ts + td
+        expected = Timestamp(dt64) + td
+        assert isinstance(result, Timestamp)
+        assert result._creso == exp_reso
+        assert result == expected
+
+        result = td + ts
+        expected = td + Timestamp(dt64)
+        assert isinstance(result, Timestamp)
+        assert result._creso == exp_reso
+        assert result == expected
+
+    def test_addsub_offset(self, ts_tz):
+        # specifically non-Tick offset
+        off = offsets.YearEnd(1)
+        result = ts_tz + off
+
+        assert isinstance(result, Timestamp)
+        assert result._creso == ts_tz._creso
+        if ts_tz.month == 12 and ts_tz.day == 31:
+            assert result.year == ts_tz.year + 1
+        else:
+            assert result.year == ts_tz.year
+        assert result.day == 31
+        assert result.month == 12
+        assert tz_compare(result.tz, ts_tz.tz)
+
+        result = ts_tz - off
+
+        assert isinstance(result, Timestamp)
+        assert result._creso == ts_tz._creso
+        assert result.year == ts_tz.year - 1
+        assert result.day == 31
+        assert result.month == 12
+        assert tz_compare(result.tz, ts_tz.tz)
+
+    def test_sub_datetimelike_mismatched_reso(self, ts_tz):
+        # case with non-lossy rounding
+        ts = ts_tz
+
+        # choose a unit for `other` that doesn't match ts_tz's;
+        #  this construction ensures we get cases with other._creso < ts._creso
+        #  and cases with other._creso > ts._creso
+        unit = {
+            NpyDatetimeUnit.NPY_FR_us.value: "ms",
+            NpyDatetimeUnit.NPY_FR_ms.value: "s",
+            NpyDatetimeUnit.NPY_FR_s.value: "us",
+        }[ts._creso]
+        other = ts.as_unit(unit)
+        assert other._creso != ts._creso
+
+        result = ts - other
+        assert isinstance(result, Timedelta)
+        assert result._value == 0
+        assert result._creso == max(ts._creso, other._creso)
+
+        result = other - ts
+        assert isinstance(result, Timedelta)
+        assert result._value == 0
+        assert result._creso == max(ts._creso, other._creso)
+
+        if ts._creso < other._creso:
+            # Case where rounding is lossy
+            other2 = other + Timedelta._from_value_and_reso(1, other._creso)
+            exp = ts.as_unit(other.unit) - other2
+
+            res = ts - other2
+            assert res == exp
+            assert res._creso == max(ts._creso, other._creso)
+
+            res = other2 - ts
+            assert res == -exp
+            assert res._creso == max(ts._creso, other._creso)
+        else:
+            ts2 = ts + Timedelta._from_value_and_reso(1, ts._creso)
+            exp = ts2 - other.as_unit(ts2.unit)
+
+            res = ts2 - other
+            assert res == exp
+            assert res._creso == max(ts._creso, other._creso)
+            res = other - ts2
+            assert res == -exp
+            assert res._creso == max(ts._creso, other._creso)
+
+    def test_sub_timedeltalike_mismatched_reso(self, ts_tz):
+        # case with non-lossy rounding
+        ts = ts_tz
+
+        # choose a unit for `other` that doesn't match ts_tz's;
+        #  this construction ensures we get cases with other._creso < ts._creso
+        #  and cases with other._creso > ts._creso
+        unit = {
+            NpyDatetimeUnit.NPY_FR_us.value: "ms",
+            NpyDatetimeUnit.NPY_FR_ms.value: "s",
+            NpyDatetimeUnit.NPY_FR_s.value: "us",
+        }[ts._creso]
+        other = Timedelta(0).as_unit(unit)
+        assert other._creso != ts._creso
+
+        result = ts + other
+        assert isinstance(result, Timestamp)
+        assert result == ts
+        assert result._creso == max(ts._creso, other._creso)
+
+        result = other + ts
+        assert isinstance(result, Timestamp)
+        assert result == ts
+        assert result._creso == max(ts._creso, other._creso)
+
+        if ts._creso < other._creso:
+            # Case where rounding is lossy
+            other2 = other + Timedelta._from_value_and_reso(1, other._creso)
+            exp = ts.as_unit(other.unit) + other2
+            res = ts + other2
+            assert res == exp
+            assert res._creso == max(ts._creso, other._creso)
+            res = other2 + ts
+            assert res == exp
+            assert res._creso == max(ts._creso, other._creso)
+        else:
+            ts2 = ts + Timedelta._from_value_and_reso(1, ts._creso)
+            exp = ts2 + other.as_unit(ts2.unit)
+
+            res = ts2 + other
+            assert res == exp
+            assert res._creso == max(ts._creso, other._creso)
+            res = other + ts2
+            assert res == exp
+            assert res._creso == max(ts._creso, other._creso)
+
+    def test_addition_doesnt_downcast_reso(self):
+        # https://github.com/pandas-dev/pandas/pull/48748#pullrequestreview-1122635413
+        ts = Timestamp(year=2022, month=1, day=1, microsecond=999999).as_unit("us")
+        td = Timedelta(microseconds=1).as_unit("us")
+        res = ts + td
+        assert res._creso == ts._creso
+
+    def test_sub_timedelta64_mismatched_reso(self, ts_tz):
+        ts = ts_tz
+
+        res = ts + np.timedelta64(1, "ns")
+        exp = ts.as_unit("ns") + np.timedelta64(1, "ns")
+        assert exp == res
+        assert exp._creso == NpyDatetimeUnit.NPY_FR_ns.value
+
+    def test_min(self, ts):
+        assert ts.min <= ts
+        assert ts.min._creso == ts._creso
+        assert ts.min._value == NaT._value + 1
+
+    def test_max(self, ts):
+        assert ts.max >= ts
+        assert ts.max._creso == ts._creso
+        assert ts.max._value == np.iinfo(np.int64).max
+
+    def test_resolution(self, ts):
+        expected = Timedelta._from_value_and_reso(1, ts._creso)
+        result = ts.resolution
+        assert result == expected
+        assert result._creso == expected._creso
+
+    def test_out_of_ns_bounds(self):
+        # https://github.com/pandas-dev/pandas/issues/51060
+        result = Timestamp(-52700112000, unit="s")
+        assert result == Timestamp("0300-01-01")
+        assert result.to_numpy() == np.datetime64("0300-01-01T00:00:00", "s")
+
+
+def test_timestamp_class_min_max_resolution():
+    # when accessed on the class (as opposed to an instance), we default
+    #  to nanoseconds
+    assert Timestamp.min == Timestamp(NaT._value + 1)
+    assert Timestamp.min._creso == NpyDatetimeUnit.NPY_FR_ns.value
+
+    assert Timestamp.max == Timestamp(np.iinfo(np.int64).max)
+    assert Timestamp.max._creso == NpyDatetimeUnit.NPY_FR_ns.value
+
+    assert Timestamp.resolution == Timedelta(1)
+    assert Timestamp.resolution._creso == NpyDatetimeUnit.NPY_FR_ns.value
+
+
+def test_delimited_date():
+    # https://github.com/pandas-dev/pandas/issues/50231
+    with tm.assert_produces_warning(None):
+        result = Timestamp("13-01-2000")
+    expected = Timestamp(2000, 1, 13)
+    assert result == expected
+
+
+def test_utctimetuple():
+    # GH 32174
+    ts = Timestamp("2000-01-01", tz="UTC")
+    result = ts.utctimetuple()
+    expected = time.struct_time((2000, 1, 1, 0, 0, 0, 5, 1, 0))
+    assert result == expected
+
+
+def test_negative_dates():
+    # https://github.com/pandas-dev/pandas/issues/50787
+    ts = Timestamp("-2000-01-01")
+    msg = (
+        " not yet supported on Timestamps which are outside the range of "
+        "Python's standard library. For now, please call the components you need "
+        r"\(such as `.year` and `.month`\) and construct your string from there.$"
+    )
+    func = "^strftime"
+    with pytest.raises(NotImplementedError, match=func + msg):
+        ts.strftime("%Y")
+
+    msg = (
+        " not yet supported on Timestamps which "
+        "are outside the range of Python's standard library. "
+    )
+    func = "^date"
+    with pytest.raises(NotImplementedError, match=func + msg):
+        ts.date()
+    func = "^isocalendar"
+    with pytest.raises(NotImplementedError, match=func + msg):
+        ts.isocalendar()
+    func = "^timetuple"
+    with pytest.raises(NotImplementedError, match=func + msg):
+        ts.timetuple()
+    func = "^toordinal"
+    with pytest.raises(NotImplementedError, match=func + msg):
+        ts.toordinal()