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@@ -1284,3 +1284,4 @@ videochat2/lib/python3.10/site-packages/pandas/io/formats/__pycache__/style.cpyt
 videochat2/lib/python3.10/site-packages/pandas/io/sas/_sas.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 videochat2/lib/python3.10/site-packages/pandas/io/__pycache__/pytables.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
 videochat2/lib/python3.10/site-packages/pandas/io/__pycache__/stata.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+videochat2/lib/python3.10/site-packages/pandas/tests/frame/__pycache__/test_constructors.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text

videochat2/lib/python3.10/site-packages/pandas/tests/frame/__pycache__/test_constructors.cpython-310.pyc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c5945616be3a4d0d714c99dc9e697c7083ec978885c7332374d77c8f60f15e1
+size 108834

videochat2/lib/python3.10/site-packages/pandas/tests/internals/test_api.py

@@ -0,0 +1,55 @@
+"""
+Tests for the pseudo-public API implemented in internals/api.py and exposed
+in core.internals
+"""
+
+import pandas as pd
+from pandas.core import internals
+from pandas.core.internals import api
+
+
+def test_internals_api():
+    assert internals.make_block is api.make_block
+
+
+def test_namespace():
+    # SUBJECT TO CHANGE
+
+    modules = [
+        "blocks",
+        "concat",
+        "managers",
+        "construction",
+        "array_manager",
+        "base",
+        "api",
+        "ops",
+    ]
+    expected = [
+        "Block",
+        "NumericBlock",
+        "DatetimeTZBlock",
+        "ExtensionBlock",
+        "ObjectBlock",
+        "make_block",
+        "DataManager",
+        "ArrayManager",
+        "BlockManager",
+        "SingleDataManager",
+        "SingleBlockManager",
+        "SingleArrayManager",
+        "concatenate_managers",
+        "create_block_manager_from_blocks",
+    ]
+
+    result = [x for x in dir(internals) if not x.startswith("__")]
+    assert set(result) == set(expected + modules)
+
+
+def test_make_block_2d_with_dti():
+    # GH#41168
+    dti = pd.date_range("2012", periods=3, tz="UTC")
+    blk = api.make_block(dti, placement=[0])
+
+    assert blk.shape == (1, 3)
+    assert blk.values.shape == (1, 3)

videochat2/lib/python3.10/site-packages/pandas/tests/internals/test_internals.py

@@ -0,0 +1,1437 @@
+from datetime import (
+    date,
+    datetime,
+)
+import itertools
+import re
+
+import numpy as np
+import pytest
+
+from pandas._libs.internals import BlockPlacement
+from pandas.compat import IS64
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.common import is_scalar
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+    period_range,
+)
+import pandas._testing as tm
+import pandas.core.algorithms as algos
+from pandas.core.arrays import (
+    DatetimeArray,
+    SparseArray,
+    TimedeltaArray,
+)
+from pandas.core.internals import (
+    BlockManager,
+    SingleBlockManager,
+    make_block,
+)
+from pandas.core.internals.blocks import (
+    ensure_block_shape,
+    new_block,
+)
+
+# this file contains BlockManager specific tests
+# TODO(ArrayManager) factor out interleave_dtype tests
+pytestmark = td.skip_array_manager_invalid_test
+
+
+@pytest.fixture(params=[new_block, make_block])
+def block_maker(request):
+    """
+    Fixture to test both the internal new_block and pseudo-public make_block.
+    """
+    return request.param
+
+
+@pytest.fixture
+def mgr():
+    return create_mgr(
+        "a: f8; b: object; c: f8; d: object; e: f8;"
+        "f: bool; g: i8; h: complex; i: datetime-1; j: datetime-2;"
+        "k: M8[ns, US/Eastern]; l: M8[ns, CET];"
+    )
+
+
+def assert_block_equal(left, right):
+    tm.assert_numpy_array_equal(left.values, right.values)
+    assert left.dtype == right.dtype
+    assert isinstance(left.mgr_locs, BlockPlacement)
+    assert isinstance(right.mgr_locs, BlockPlacement)
+    tm.assert_numpy_array_equal(left.mgr_locs.as_array, right.mgr_locs.as_array)
+
+
+def get_numeric_mat(shape):
+    arr = np.arange(shape[0])
+    return np.lib.stride_tricks.as_strided(
+        x=arr, shape=shape, strides=(arr.itemsize,) + (0,) * (len(shape) - 1)
+    ).copy()
+
+
+N = 10
+
+
+def create_block(typestr, placement, item_shape=None, num_offset=0, maker=new_block):
+    """
+    Supported typestr:
+
+        * float, f8, f4, f2
+        * int, i8, i4, i2, i1
+        * uint, u8, u4, u2, u1
+        * complex, c16, c8
+        * bool
+        * object, string, O
+        * datetime, dt, M8[ns], M8[ns, tz]
+        * timedelta, td, m8[ns]
+        * sparse (SparseArray with fill_value=0.0)
+        * sparse_na (SparseArray with fill_value=np.nan)
+        * category, category2
+
+    """
+    placement = BlockPlacement(placement)
+    num_items = len(placement)
+
+    if item_shape is None:
+        item_shape = (N,)
+
+    shape = (num_items,) + item_shape
+
+    mat = get_numeric_mat(shape)
+
+    if typestr in (
+        "float",
+        "f8",
+        "f4",
+        "f2",
+        "int",
+        "i8",
+        "i4",
+        "i2",
+        "i1",
+        "uint",
+        "u8",
+        "u4",
+        "u2",
+        "u1",
+    ):
+        values = mat.astype(typestr) + num_offset
+    elif typestr in ("complex", "c16", "c8"):
+        values = 1.0j * (mat.astype(typestr) + num_offset)
+    elif typestr in ("object", "string", "O"):
+        values = np.reshape([f"A{i:d}" for i in mat.ravel() + num_offset], shape)
+    elif typestr in ("b", "bool"):
+        values = np.ones(shape, dtype=np.bool_)
+    elif typestr in ("datetime", "dt", "M8[ns]"):
+        values = (mat * 1e9).astype("M8[ns]")
+    elif typestr.startswith("M8[ns"):
+        # datetime with tz
+        m = re.search(r"M8\[ns,\s*(\w+\/?\w*)\]", typestr)
+        assert m is not None, f"incompatible typestr -> {typestr}"
+        tz = m.groups()[0]
+        assert num_items == 1, "must have only 1 num items for a tz-aware"
+        values = DatetimeIndex(np.arange(N) * 10**9, tz=tz)._data
+        values = ensure_block_shape(values, ndim=len(shape))
+    elif typestr in ("timedelta", "td", "m8[ns]"):
+        values = (mat * 1).astype("m8[ns]")
+    elif typestr in ("category",):
+        values = Categorical([1, 1, 2, 2, 3, 3, 3, 3, 4, 4])
+    elif typestr in ("category2",):
+        values = Categorical(["a", "a", "a", "a", "b", "b", "c", "c", "c", "d"])
+    elif typestr in ("sparse", "sparse_na"):
+        if shape[-1] != 10:
+            # We also are implicitly assuming this in the category cases above
+            raise NotImplementedError
+
+        assert all(s == 1 for s in shape[:-1])
+        if typestr.endswith("_na"):
+            fill_value = np.nan
+        else:
+            fill_value = 0.0
+        values = SparseArray(
+            [fill_value, fill_value, 1, 2, 3, fill_value, 4, 5, fill_value, 6],
+            fill_value=fill_value,
+        )
+        arr = values.sp_values.view()
+        arr += num_offset - 1
+    else:
+        raise ValueError(f'Unsupported typestr: "{typestr}"')
+
+    return maker(values, placement=placement, ndim=len(shape))
+
+
+def create_single_mgr(typestr, num_rows=None):
+    if num_rows is None:
+        num_rows = N
+
+    return SingleBlockManager(
+        create_block(typestr, placement=slice(0, num_rows), item_shape=()),
+        Index(np.arange(num_rows)),
+    )
+
+
+def create_mgr(descr, item_shape=None):
+    """
+    Construct BlockManager from string description.
+
+    String description syntax looks similar to np.matrix initializer.  It looks
+    like this::
+
+        a,b,c: f8; d,e,f: i8
+
+    Rules are rather simple:
+
+    * see list of supported datatypes in `create_block` method
+    * components are semicolon-separated
+    * each component is `NAME,NAME,NAME: DTYPE_ID`
+    * whitespace around colons & semicolons are removed
+    * components with same DTYPE_ID are combined into single block
+    * to force multiple blocks with same dtype, use '-SUFFIX'::
+
+        'a:f8-1; b:f8-2; c:f8-foobar'
+
+    """
+    if item_shape is None:
+        item_shape = (N,)
+
+    offset = 0
+    mgr_items = []
+    block_placements = {}
+    for d in descr.split(";"):
+        d = d.strip()
+        if not len(d):
+            continue
+        names, blockstr = d.partition(":")[::2]
+        blockstr = blockstr.strip()
+        names = names.strip().split(",")
+
+        mgr_items.extend(names)
+        placement = list(np.arange(len(names)) + offset)
+        try:
+            block_placements[blockstr].extend(placement)
+        except KeyError:
+            block_placements[blockstr] = placement
+        offset += len(names)
+
+    mgr_items = Index(mgr_items)
+
+    blocks = []
+    num_offset = 0
+    for blockstr, placement in block_placements.items():
+        typestr = blockstr.split("-")[0]
+        blocks.append(
+            create_block(
+                typestr, placement, item_shape=item_shape, num_offset=num_offset
+            )
+        )
+        num_offset += len(placement)
+
+    sblocks = sorted(blocks, key=lambda b: b.mgr_locs[0])
+    return BlockManager(
+        tuple(sblocks),
+        [mgr_items] + [Index(np.arange(n)) for n in item_shape],
+    )
+
+
+@pytest.fixture
+def fblock():
+    return create_block("float", [0, 2, 4])
+
+
+class TestBlock:
+    def test_constructor(self):
+        int32block = create_block("i4", [0])
+        assert int32block.dtype == np.int32
+
+    @pytest.mark.parametrize(
+        "typ, data",
+        [
+            ["float", [0, 2, 4]],
+            ["complex", [7]],
+            ["object", [1, 3]],
+            ["bool", [5]],
+        ],
+    )
+    def test_pickle(self, typ, data):
+        blk = create_block(typ, data)
+        assert_block_equal(tm.round_trip_pickle(blk), blk)
+
+    def test_mgr_locs(self, fblock):
+        assert isinstance(fblock.mgr_locs, BlockPlacement)
+        tm.assert_numpy_array_equal(
+            fblock.mgr_locs.as_array, np.array([0, 2, 4], dtype=np.intp)
+        )
+
+    def test_attrs(self, fblock):
+        assert fblock.shape == fblock.values.shape
+        assert fblock.dtype == fblock.values.dtype
+        assert len(fblock) == len(fblock.values)
+
+    def test_copy(self, fblock):
+        cop = fblock.copy()
+        assert cop is not fblock
+        assert_block_equal(fblock, cop)
+
+    def test_delete(self, fblock):
+        newb = fblock.copy()
+        locs = newb.mgr_locs
+        nb = newb.delete(0)[0]
+        assert newb.mgr_locs is locs
+
+        assert nb is not newb
+
+        tm.assert_numpy_array_equal(
+            nb.mgr_locs.as_array, np.array([2, 4], dtype=np.intp)
+        )
+        assert not (newb.values[0] == 1).all()
+        assert (nb.values[0] == 1).all()
+
+        newb = fblock.copy()
+        locs = newb.mgr_locs
+        nb = newb.delete(1)
+        assert len(nb) == 2
+        assert newb.mgr_locs is locs
+
+        tm.assert_numpy_array_equal(
+            nb[0].mgr_locs.as_array, np.array([0], dtype=np.intp)
+        )
+        tm.assert_numpy_array_equal(
+            nb[1].mgr_locs.as_array, np.array([4], dtype=np.intp)
+        )
+        assert not (newb.values[1] == 2).all()
+        assert (nb[1].values[0] == 2).all()
+
+        newb = fblock.copy()
+        nb = newb.delete(2)
+        assert len(nb) == 1
+        tm.assert_numpy_array_equal(
+            nb[0].mgr_locs.as_array, np.array([0, 2], dtype=np.intp)
+        )
+        assert (nb[0].values[1] == 1).all()
+
+        newb = fblock.copy()
+
+        with pytest.raises(IndexError, match=None):
+            newb.delete(3)
+
+    def test_delete_datetimelike(self):
+        # dont use np.delete on values, as that will coerce from DTA/TDA to ndarray
+        arr = np.arange(20, dtype="i8").reshape(5, 4).view("m8[ns]")
+        df = DataFrame(arr)
+        blk = df._mgr.blocks[0]
+        assert isinstance(blk.values, TimedeltaArray)
+
+        nb = blk.delete(1)
+        assert len(nb) == 2
+        assert isinstance(nb[0].values, TimedeltaArray)
+        assert isinstance(nb[1].values, TimedeltaArray)
+
+        df = DataFrame(arr.view("M8[ns]"))
+        blk = df._mgr.blocks[0]
+        assert isinstance(blk.values, DatetimeArray)
+
+        nb = blk.delete([1, 3])
+        assert len(nb) == 2
+        assert isinstance(nb[0].values, DatetimeArray)
+        assert isinstance(nb[1].values, DatetimeArray)
+
+    def test_split(self):
+        # GH#37799
+        values = np.random.randn(3, 4)
+        blk = new_block(values, placement=[3, 1, 6], ndim=2)
+        result = blk._split()
+
+        # check that we get views, not copies
+        values[:] = -9999
+        assert (blk.values == -9999).all()
+
+        assert len(result) == 3
+        expected = [
+            new_block(values[[0]], placement=[3], ndim=2),
+            new_block(values[[1]], placement=[1], ndim=2),
+            new_block(values[[2]], placement=[6], ndim=2),
+        ]
+        for res, exp in zip(result, expected):
+            assert_block_equal(res, exp)
+
+
+class TestBlockManager:
+    def test_attrs(self):
+        mgr = create_mgr("a,b,c: f8-1; d,e,f: f8-2")
+        assert mgr.nblocks == 2
+        assert len(mgr) == 6
+
+    def test_duplicate_ref_loc_failure(self):
+        tmp_mgr = create_mgr("a:bool; a: f8")
+
+        axes, blocks = tmp_mgr.axes, tmp_mgr.blocks
+
+        blocks[0].mgr_locs = BlockPlacement(np.array([0]))
+        blocks[1].mgr_locs = BlockPlacement(np.array([0]))
+
+        # test trying to create block manager with overlapping ref locs
+
+        msg = "Gaps in blk ref_locs"
+
+        with pytest.raises(AssertionError, match=msg):
+            mgr = BlockManager(blocks, axes)
+            mgr._rebuild_blknos_and_blklocs()
+
+        blocks[0].mgr_locs = BlockPlacement(np.array([0]))
+        blocks[1].mgr_locs = BlockPlacement(np.array([1]))
+        mgr = BlockManager(blocks, axes)
+        mgr.iget(1)
+
+    def test_pickle(self, mgr):
+        mgr2 = tm.round_trip_pickle(mgr)
+        tm.assert_frame_equal(DataFrame(mgr), DataFrame(mgr2))
+
+        # GH2431
+        assert hasattr(mgr2, "_is_consolidated")
+        assert hasattr(mgr2, "_known_consolidated")
+
+        # reset to False on load
+        assert not mgr2._is_consolidated
+        assert not mgr2._known_consolidated
+
+    @pytest.mark.parametrize("mgr_string", ["a,a,a:f8", "a: f8; a: i8"])
+    def test_non_unique_pickle(self, mgr_string):
+        mgr = create_mgr(mgr_string)
+        mgr2 = tm.round_trip_pickle(mgr)
+        tm.assert_frame_equal(DataFrame(mgr), DataFrame(mgr2))
+
+    def test_categorical_block_pickle(self):
+        mgr = create_mgr("a: category")
+        mgr2 = tm.round_trip_pickle(mgr)
+        tm.assert_frame_equal(DataFrame(mgr), DataFrame(mgr2))
+
+        smgr = create_single_mgr("category")
+        smgr2 = tm.round_trip_pickle(smgr)
+        tm.assert_series_equal(Series(smgr), Series(smgr2))
+
+    def test_iget(self):
+        cols = Index(list("abc"))
+        values = np.random.rand(3, 3)
+        block = new_block(
+            values=values.copy(),
+            placement=np.arange(3, dtype=np.intp),
+            ndim=values.ndim,
+        )
+        mgr = BlockManager(blocks=(block,), axes=[cols, Index(np.arange(3))])
+
+        tm.assert_almost_equal(mgr.iget(0).internal_values(), values[0])
+        tm.assert_almost_equal(mgr.iget(1).internal_values(), values[1])
+        tm.assert_almost_equal(mgr.iget(2).internal_values(), values[2])
+
+    def test_set(self):
+        mgr = create_mgr("a,b,c: int", item_shape=(3,))
+
+        mgr.insert(len(mgr.items), "d", np.array(["foo"] * 3))
+        mgr.iset(1, np.array(["bar"] * 3))
+        tm.assert_numpy_array_equal(mgr.iget(0).internal_values(), np.array([0] * 3))
+        tm.assert_numpy_array_equal(
+            mgr.iget(1).internal_values(), np.array(["bar"] * 3, dtype=np.object_)
+        )
+        tm.assert_numpy_array_equal(mgr.iget(2).internal_values(), np.array([2] * 3))
+        tm.assert_numpy_array_equal(
+            mgr.iget(3).internal_values(), np.array(["foo"] * 3, dtype=np.object_)
+        )
+
+    def test_set_change_dtype(self, mgr):
+        mgr.insert(len(mgr.items), "baz", np.zeros(N, dtype=bool))
+
+        mgr.iset(mgr.items.get_loc("baz"), np.repeat("foo", N))
+        idx = mgr.items.get_loc("baz")
+        assert mgr.iget(idx).dtype == np.object_
+
+        mgr2 = mgr.consolidate()
+        mgr2.iset(mgr2.items.get_loc("baz"), np.repeat("foo", N))
+        idx = mgr2.items.get_loc("baz")
+        assert mgr2.iget(idx).dtype == np.object_
+
+        mgr2.insert(len(mgr2.items), "quux", np.random.randn(N).astype(int))
+        idx = mgr2.items.get_loc("quux")
+        assert mgr2.iget(idx).dtype == np.int_
+
+        mgr2.iset(mgr2.items.get_loc("quux"), np.random.randn(N))
+        assert mgr2.iget(idx).dtype == np.float_
+
+    def test_copy(self, mgr):
+        cp = mgr.copy(deep=False)
+        for blk, cp_blk in zip(mgr.blocks, cp.blocks):
+            # view assertion
+            tm.assert_equal(cp_blk.values, blk.values)
+            if isinstance(blk.values, np.ndarray):
+                assert cp_blk.values.base is blk.values.base
+            else:
+                # DatetimeTZBlock has DatetimeIndex values
+                assert cp_blk.values._ndarray.base is blk.values._ndarray.base
+
+        # copy(deep=True) consolidates, so the block-wise assertions will
+        #  fail is mgr is not consolidated
+        mgr._consolidate_inplace()
+        cp = mgr.copy(deep=True)
+        for blk, cp_blk in zip(mgr.blocks, cp.blocks):
+            bvals = blk.values
+            cpvals = cp_blk.values
+
+            tm.assert_equal(cpvals, bvals)
+
+            if isinstance(cpvals, np.ndarray):
+                lbase = cpvals.base
+                rbase = bvals.base
+            else:
+                lbase = cpvals._ndarray.base
+                rbase = bvals._ndarray.base
+
+            # copy assertion we either have a None for a base or in case of
+            # some blocks it is an array (e.g. datetimetz), but was copied
+            if isinstance(cpvals, DatetimeArray):
+                assert (lbase is None and rbase is None) or (lbase is not rbase)
+            elif not isinstance(cpvals, np.ndarray):
+                assert lbase is not rbase
+            else:
+                assert lbase is None and rbase is None
+
+    def test_sparse(self):
+        mgr = create_mgr("a: sparse-1; b: sparse-2")
+        assert mgr.as_array().dtype == np.float64
+
+    def test_sparse_mixed(self):
+        mgr = create_mgr("a: sparse-1; b: sparse-2; c: f8")
+        assert len(mgr.blocks) == 3
+        assert isinstance(mgr, BlockManager)
+
+    @pytest.mark.parametrize(
+        "mgr_string, dtype",
+        [("c: f4; d: f2", np.float32), ("c: f4; d: f2; e: f8", np.float64)],
+    )
+    def test_as_array_float(self, mgr_string, dtype):
+        mgr = create_mgr(mgr_string)
+        assert mgr.as_array().dtype == dtype
+
+    @pytest.mark.parametrize(
+        "mgr_string, dtype",
+        [
+            ("a: bool-1; b: bool-2", np.bool_),
+            ("a: i8-1; b: i8-2; c: i4; d: i2; e: u1", np.int64),
+            ("c: i4; d: i2; e: u1", np.int32),
+        ],
+    )
+    def test_as_array_int_bool(self, mgr_string, dtype):
+        mgr = create_mgr(mgr_string)
+        assert mgr.as_array().dtype == dtype
+
+    def test_as_array_datetime(self):
+        mgr = create_mgr("h: datetime-1; g: datetime-2")
+        assert mgr.as_array().dtype == "M8[ns]"
+
+    def test_as_array_datetime_tz(self):
+        mgr = create_mgr("h: M8[ns, US/Eastern]; g: M8[ns, CET]")
+        assert mgr.iget(0).dtype == "datetime64[ns, US/Eastern]"
+        assert mgr.iget(1).dtype == "datetime64[ns, CET]"
+        assert mgr.as_array().dtype == "object"
+
+    @pytest.mark.parametrize("t", ["float16", "float32", "float64", "int32", "int64"])
+    def test_astype(self, t):
+        # coerce all
+        mgr = create_mgr("c: f4; d: f2; e: f8")
+
+        t = np.dtype(t)
+        tmgr = mgr.astype(t)
+        assert tmgr.iget(0).dtype.type == t
+        assert tmgr.iget(1).dtype.type == t
+        assert tmgr.iget(2).dtype.type == t
+
+        # mixed
+        mgr = create_mgr("a,b: object; c: bool; d: datetime; e: f4; f: f2; g: f8")
+
+        t = np.dtype(t)
+        tmgr = mgr.astype(t, errors="ignore")
+        assert tmgr.iget(2).dtype.type == t
+        assert tmgr.iget(4).dtype.type == t
+        assert tmgr.iget(5).dtype.type == t
+        assert tmgr.iget(6).dtype.type == t
+
+        assert tmgr.iget(0).dtype.type == np.object_
+        assert tmgr.iget(1).dtype.type == np.object_
+        if t != np.int64:
+            assert tmgr.iget(3).dtype.type == np.datetime64
+        else:
+            assert tmgr.iget(3).dtype.type == t
+
+    def test_convert(self):
+        def _compare(old_mgr, new_mgr):
+            """compare the blocks, numeric compare ==, object don't"""
+            old_blocks = set(old_mgr.blocks)
+            new_blocks = set(new_mgr.blocks)
+            assert len(old_blocks) == len(new_blocks)
+
+            # compare non-numeric
+            for b in old_blocks:
+                found = False
+                for nb in new_blocks:
+                    if (b.values == nb.values).all():
+                        found = True
+                        break
+                assert found
+
+            for b in new_blocks:
+                found = False
+                for ob in old_blocks:
+                    if (b.values == ob.values).all():
+                        found = True
+                        break
+                assert found
+
+        # noops
+        mgr = create_mgr("f: i8; g: f8")
+        new_mgr = mgr.convert(copy=True)
+        _compare(mgr, new_mgr)
+
+        # convert
+        mgr = create_mgr("a,b,foo: object; f: i8; g: f8")
+        mgr.iset(0, np.array(["1"] * N, dtype=np.object_))
+        mgr.iset(1, np.array(["2."] * N, dtype=np.object_))
+        mgr.iset(2, np.array(["foo."] * N, dtype=np.object_))
+        new_mgr = mgr.convert(copy=True)
+        assert new_mgr.iget(0).dtype == np.object_
+        assert new_mgr.iget(1).dtype == np.object_
+        assert new_mgr.iget(2).dtype == np.object_
+        assert new_mgr.iget(3).dtype == np.int64
+        assert new_mgr.iget(4).dtype == np.float64
+
+        mgr = create_mgr(
+            "a,b,foo: object; f: i4; bool: bool; dt: datetime; i: i8; g: f8; h: f2"
+        )
+        mgr.iset(0, np.array(["1"] * N, dtype=np.object_))
+        mgr.iset(1, np.array(["2."] * N, dtype=np.object_))
+        mgr.iset(2, np.array(["foo."] * N, dtype=np.object_))
+        new_mgr = mgr.convert(copy=True)
+        assert new_mgr.iget(0).dtype == np.object_
+        assert new_mgr.iget(1).dtype == np.object_
+        assert new_mgr.iget(2).dtype == np.object_
+        assert new_mgr.iget(3).dtype == np.int32
+        assert new_mgr.iget(4).dtype == np.bool_
+        assert new_mgr.iget(5).dtype.type, np.datetime64
+        assert new_mgr.iget(6).dtype == np.int64
+        assert new_mgr.iget(7).dtype == np.float64
+        assert new_mgr.iget(8).dtype == np.float16
+
+    def test_invalid_ea_block(self):
+        with pytest.raises(ValueError, match="need to split"):
+            create_mgr("a: category; b: category")
+
+        with pytest.raises(ValueError, match="need to split"):
+            create_mgr("a: category2; b: category2")
+
+    def test_interleave(self):
+        # self
+        for dtype in ["f8", "i8", "object", "bool", "complex", "M8[ns]", "m8[ns]"]:
+            mgr = create_mgr(f"a: {dtype}")
+            assert mgr.as_array().dtype == dtype
+            mgr = create_mgr(f"a: {dtype}; b: {dtype}")
+            assert mgr.as_array().dtype == dtype
+
+    @pytest.mark.parametrize(
+        "mgr_string, dtype",
+        [
+            ("a: category", "i8"),
+            ("a: category; b: category", "i8"),
+            ("a: category; b: category2", "object"),
+            ("a: category2", "object"),
+            ("a: category2; b: category2", "object"),
+            ("a: f8", "f8"),
+            ("a: f8; b: i8", "f8"),
+            ("a: f4; b: i8", "f8"),
+            ("a: f4; b: i8; d: object", "object"),
+            ("a: bool; b: i8", "object"),
+            ("a: complex", "complex"),
+            ("a: f8; b: category", "object"),
+            ("a: M8[ns]; b: category", "object"),
+            ("a: M8[ns]; b: bool", "object"),
+            ("a: M8[ns]; b: i8", "object"),
+            ("a: m8[ns]; b: bool", "object"),
+            ("a: m8[ns]; b: i8", "object"),
+            ("a: M8[ns]; b: m8[ns]", "object"),
+        ],
+    )
+    def test_interleave_dtype(self, mgr_string, dtype):
+        # will be converted according the actual dtype of the underlying
+        mgr = create_mgr("a: category")
+        assert mgr.as_array().dtype == "i8"
+        mgr = create_mgr("a: category; b: category2")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: category2")
+        assert mgr.as_array().dtype == "object"
+
+        # combinations
+        mgr = create_mgr("a: f8")
+        assert mgr.as_array().dtype == "f8"
+        mgr = create_mgr("a: f8; b: i8")
+        assert mgr.as_array().dtype == "f8"
+        mgr = create_mgr("a: f4; b: i8")
+        assert mgr.as_array().dtype == "f8"
+        mgr = create_mgr("a: f4; b: i8; d: object")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: bool; b: i8")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: complex")
+        assert mgr.as_array().dtype == "complex"
+        mgr = create_mgr("a: f8; b: category")
+        assert mgr.as_array().dtype == "f8"
+        mgr = create_mgr("a: M8[ns]; b: category")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: M8[ns]; b: bool")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: M8[ns]; b: i8")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: m8[ns]; b: bool")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: m8[ns]; b: i8")
+        assert mgr.as_array().dtype == "object"
+        mgr = create_mgr("a: M8[ns]; b: m8[ns]")
+        assert mgr.as_array().dtype == "object"
+
+    def test_consolidate_ordering_issues(self, mgr):
+        mgr.iset(mgr.items.get_loc("f"), np.random.randn(N))
+        mgr.iset(mgr.items.get_loc("d"), np.random.randn(N))
+        mgr.iset(mgr.items.get_loc("b"), np.random.randn(N))
+        mgr.iset(mgr.items.get_loc("g"), np.random.randn(N))
+        mgr.iset(mgr.items.get_loc("h"), np.random.randn(N))
+
+        # we have datetime/tz blocks in mgr
+        cons = mgr.consolidate()
+        assert cons.nblocks == 4
+        cons = mgr.consolidate().get_numeric_data()
+        assert cons.nblocks == 1
+        assert isinstance(cons.blocks[0].mgr_locs, BlockPlacement)
+        tm.assert_numpy_array_equal(
+            cons.blocks[0].mgr_locs.as_array, np.arange(len(cons.items), dtype=np.intp)
+        )
+
+    def test_reindex_items(self):
+        # mgr is not consolidated, f8 & f8-2 blocks
+        mgr = create_mgr("a: f8; b: i8; c: f8; d: i8; e: f8; f: bool; g: f8-2")
+
+        reindexed = mgr.reindex_axis(["g", "c", "a", "d"], axis=0)
+        # reindex_axis does not consolidate_inplace, as that risks failing to
+        #  invalidate _item_cache
+        assert not reindexed.is_consolidated()
+
+        tm.assert_index_equal(reindexed.items, Index(["g", "c", "a", "d"]))
+        tm.assert_almost_equal(
+            mgr.iget(6).internal_values(), reindexed.iget(0).internal_values()
+        )
+        tm.assert_almost_equal(
+            mgr.iget(2).internal_values(), reindexed.iget(1).internal_values()
+        )
+        tm.assert_almost_equal(
+            mgr.iget(0).internal_values(), reindexed.iget(2).internal_values()
+        )
+        tm.assert_almost_equal(
+            mgr.iget(3).internal_values(), reindexed.iget(3).internal_values()
+        )
+
+    def test_get_numeric_data(self, using_copy_on_write):
+        mgr = create_mgr(
+            "int: int; float: float; complex: complex;"
+            "str: object; bool: bool; obj: object; dt: datetime",
+            item_shape=(3,),
+        )
+        mgr.iset(5, np.array([1, 2, 3], dtype=np.object_))
+
+        numeric = mgr.get_numeric_data()
+        tm.assert_index_equal(numeric.items, Index(["int", "float", "complex", "bool"]))
+        tm.assert_almost_equal(
+            mgr.iget(mgr.items.get_loc("float")).internal_values(),
+            numeric.iget(numeric.items.get_loc("float")).internal_values(),
+        )
+
+        # Check sharing
+        numeric.iset(
+            numeric.items.get_loc("float"),
+            np.array([100.0, 200.0, 300.0]),
+            inplace=True,
+        )
+        if using_copy_on_write:
+            tm.assert_almost_equal(
+                mgr.iget(mgr.items.get_loc("float")).internal_values(),
+                np.array([1.0, 1.0, 1.0]),
+            )
+        else:
+            tm.assert_almost_equal(
+                mgr.iget(mgr.items.get_loc("float")).internal_values(),
+                np.array([100.0, 200.0, 300.0]),
+            )
+
+        numeric2 = mgr.get_numeric_data(copy=True)
+        tm.assert_index_equal(numeric.items, Index(["int", "float", "complex", "bool"]))
+        numeric2.iset(
+            numeric2.items.get_loc("float"),
+            np.array([1000.0, 2000.0, 3000.0]),
+            inplace=True,
+        )
+        if using_copy_on_write:
+            tm.assert_almost_equal(
+                mgr.iget(mgr.items.get_loc("float")).internal_values(),
+                np.array([1.0, 1.0, 1.0]),
+            )
+        else:
+            tm.assert_almost_equal(
+                mgr.iget(mgr.items.get_loc("float")).internal_values(),
+                np.array([100.0, 200.0, 300.0]),
+            )
+
+    def test_get_bool_data(self, using_copy_on_write):
+        mgr = create_mgr(
+            "int: int; float: float; complex: complex;"
+            "str: object; bool: bool; obj: object; dt: datetime",
+            item_shape=(3,),
+        )
+        mgr.iset(6, np.array([True, False, True], dtype=np.object_))
+
+        bools = mgr.get_bool_data()
+        tm.assert_index_equal(bools.items, Index(["bool"]))
+        tm.assert_almost_equal(
+            mgr.iget(mgr.items.get_loc("bool")).internal_values(),
+            bools.iget(bools.items.get_loc("bool")).internal_values(),
+        )
+
+        bools.iset(0, np.array([True, False, True]), inplace=True)
+        if using_copy_on_write:
+            tm.assert_numpy_array_equal(
+                mgr.iget(mgr.items.get_loc("bool")).internal_values(),
+                np.array([True, True, True]),
+            )
+        else:
+            tm.assert_numpy_array_equal(
+                mgr.iget(mgr.items.get_loc("bool")).internal_values(),
+                np.array([True, False, True]),
+            )
+
+        # Check sharing
+        bools2 = mgr.get_bool_data(copy=True)
+        bools2.iset(0, np.array([False, True, False]))
+        if using_copy_on_write:
+            tm.assert_numpy_array_equal(
+                mgr.iget(mgr.items.get_loc("bool")).internal_values(),
+                np.array([True, True, True]),
+            )
+        else:
+            tm.assert_numpy_array_equal(
+                mgr.iget(mgr.items.get_loc("bool")).internal_values(),
+                np.array([True, False, True]),
+            )
+
+    def test_unicode_repr_doesnt_raise(self):
+        repr(create_mgr("b,\u05d0: object"))
+
+    @pytest.mark.parametrize(
+        "mgr_string", ["a,b,c: i8-1; d,e,f: i8-2", "a,a,a: i8-1; b,b,b: i8-2"]
+    )
+    def test_equals(self, mgr_string):
+        # unique items
+        bm1 = create_mgr(mgr_string)
+        bm2 = BlockManager(bm1.blocks[::-1], bm1.axes)
+        assert bm1.equals(bm2)
+
+    @pytest.mark.parametrize(
+        "mgr_string",
+        [
+            "a:i8;b:f8",  # basic case
+            "a:i8;b:f8;c:c8;d:b",  # many types
+            "a:i8;e:dt;f:td;g:string",  # more types
+            "a:i8;b:category;c:category2",  # categories
+            "c:sparse;d:sparse_na;b:f8",  # sparse
+        ],
+    )
+    def test_equals_block_order_different_dtypes(self, mgr_string):
+        # GH 9330
+        bm = create_mgr(mgr_string)
+        block_perms = itertools.permutations(bm.blocks)
+        for bm_perm in block_perms:
+            bm_this = BlockManager(tuple(bm_perm), bm.axes)
+            assert bm.equals(bm_this)
+            assert bm_this.equals(bm)
+
+    def test_single_mgr_ctor(self):
+        mgr = create_single_mgr("f8", num_rows=5)
+        assert mgr.external_values().tolist() == [0.0, 1.0, 2.0, 3.0, 4.0]
+
+    @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0])
+    def test_validate_bool_args(self, value):
+        bm1 = create_mgr("a,b,c: i8-1; d,e,f: i8-2")
+
+        msg = (
+            'For argument "inplace" expected type bool, '
+            f"received type {type(value).__name__}."
+        )
+        with pytest.raises(ValueError, match=msg):
+            bm1.replace_list([1], [2], inplace=value)
+
+    def test_iset_split_block(self):
+        bm = create_mgr("a,b,c: i8; d: f8")
+        bm._iset_split_block(0, np.array([0]))
+        tm.assert_numpy_array_equal(
+            bm.blklocs, np.array([0, 0, 1, 0], dtype="int64" if IS64 else "int32")
+        )
+        # First indexer currently does not have a block associated with it in case
+        tm.assert_numpy_array_equal(
+            bm.blknos, np.array([0, 0, 0, 1], dtype="int64" if IS64 else "int32")
+        )
+        assert len(bm.blocks) == 2
+
+    def test_iset_split_block_values(self):
+        bm = create_mgr("a,b,c: i8; d: f8")
+        bm._iset_split_block(0, np.array([0]), np.array([list(range(10))]))
+        tm.assert_numpy_array_equal(
+            bm.blklocs, np.array([0, 0, 1, 0], dtype="int64" if IS64 else "int32")
+        )
+        # First indexer currently does not have a block associated with it in case
+        tm.assert_numpy_array_equal(
+            bm.blknos, np.array([0, 2, 2, 1], dtype="int64" if IS64 else "int32")
+        )
+        assert len(bm.blocks) == 3
+
+
+def _as_array(mgr):
+    if mgr.ndim == 1:
+        return mgr.external_values()
+    return mgr.as_array().T
+
+
+class TestIndexing:
+    # Nosetests-style data-driven tests.
+    #
+    # This test applies different indexing routines to block managers and
+    # compares the outcome to the result of same operations on np.ndarray.
+    #
+    # NOTE: sparse (SparseBlock with fill_value != np.nan) fail a lot of tests
+    #       and are disabled.
+
+    MANAGERS = [
+        create_single_mgr("f8", N),
+        create_single_mgr("i8", N),
+        # 2-dim
+        create_mgr("a,b,c,d,e,f: f8", item_shape=(N,)),
+        create_mgr("a,b,c,d,e,f: i8", item_shape=(N,)),
+        create_mgr("a,b: f8; c,d: i8; e,f: string", item_shape=(N,)),
+        create_mgr("a,b: f8; c,d: i8; e,f: f8", item_shape=(N,)),
+    ]
+
+    @pytest.mark.parametrize("mgr", MANAGERS)
+    def test_get_slice(self, mgr):
+        def assert_slice_ok(mgr, axis, slobj):
+            mat = _as_array(mgr)
+
+            # we maybe using an ndarray to test slicing and
+            # might not be the full length of the axis
+            if isinstance(slobj, np.ndarray):
+                ax = mgr.axes[axis]
+                if len(ax) and len(slobj) and len(slobj) != len(ax):
+                    slobj = np.concatenate(
+                        [slobj, np.zeros(len(ax) - len(slobj), dtype=bool)]
+                    )
+
+            if isinstance(slobj, slice):
+                sliced = mgr.get_slice(slobj, axis=axis)
+            elif mgr.ndim == 1 and axis == 0:
+                sliced = mgr.getitem_mgr(slobj)
+            else:
+                # BlockManager doesn't support non-slice, SingleBlockManager
+                #  doesn't support axis > 0
+                return
+
+            mat_slobj = (slice(None),) * axis + (slobj,)
+            tm.assert_numpy_array_equal(
+                mat[mat_slobj], _as_array(sliced), check_dtype=False
+            )
+            tm.assert_index_equal(mgr.axes[axis][slobj], sliced.axes[axis])
+
+        assert mgr.ndim <= 2, mgr.ndim
+        for ax in range(mgr.ndim):
+            # slice
+            assert_slice_ok(mgr, ax, slice(None))
+            assert_slice_ok(mgr, ax, slice(3))
+            assert_slice_ok(mgr, ax, slice(100))
+            assert_slice_ok(mgr, ax, slice(1, 4))
+            assert_slice_ok(mgr, ax, slice(3, 0, -2))
+
+            if mgr.ndim < 2:
+                # 2D only support slice objects
+
+                # boolean mask
+                assert_slice_ok(mgr, ax, np.array([], dtype=np.bool_))
+                assert_slice_ok(mgr, ax, np.ones(mgr.shape[ax], dtype=np.bool_))
+                assert_slice_ok(mgr, ax, np.zeros(mgr.shape[ax], dtype=np.bool_))
+
+                if mgr.shape[ax] >= 3:
+                    assert_slice_ok(mgr, ax, np.arange(mgr.shape[ax]) % 3 == 0)
+                    assert_slice_ok(
+                        mgr, ax, np.array([True, True, False], dtype=np.bool_)
+                    )
+
+                # fancy indexer
+                assert_slice_ok(mgr, ax, [])
+                assert_slice_ok(mgr, ax, list(range(mgr.shape[ax])))
+
+                if mgr.shape[ax] >= 3:
+                    assert_slice_ok(mgr, ax, [0, 1, 2])
+                    assert_slice_ok(mgr, ax, [-1, -2, -3])
+
+    @pytest.mark.parametrize("mgr", MANAGERS)
+    def test_take(self, mgr):
+        def assert_take_ok(mgr, axis, indexer):
+            mat = _as_array(mgr)
+            taken = mgr.take(indexer, axis)
+            tm.assert_numpy_array_equal(
+                np.take(mat, indexer, axis), _as_array(taken), check_dtype=False
+            )
+            tm.assert_index_equal(mgr.axes[axis].take(indexer), taken.axes[axis])
+
+        for ax in range(mgr.ndim):
+            # take/fancy indexer
+            assert_take_ok(mgr, ax, indexer=[])
+            assert_take_ok(mgr, ax, indexer=[0, 0, 0])
+            assert_take_ok(mgr, ax, indexer=list(range(mgr.shape[ax])))
+
+            if mgr.shape[ax] >= 3:
+                assert_take_ok(mgr, ax, indexer=[0, 1, 2])
+                assert_take_ok(mgr, ax, indexer=[-1, -2, -3])
+
+    @pytest.mark.parametrize("mgr", MANAGERS)
+    @pytest.mark.parametrize("fill_value", [None, np.nan, 100.0])
+    def test_reindex_axis(self, fill_value, mgr):
+        def assert_reindex_axis_is_ok(mgr, axis, new_labels, fill_value):
+            mat = _as_array(mgr)
+            indexer = mgr.axes[axis].get_indexer_for(new_labels)
+
+            reindexed = mgr.reindex_axis(new_labels, axis, fill_value=fill_value)
+            tm.assert_numpy_array_equal(
+                algos.take_nd(mat, indexer, axis, fill_value=fill_value),
+                _as_array(reindexed),
+                check_dtype=False,
+            )
+            tm.assert_index_equal(reindexed.axes[axis], new_labels)
+
+        for ax in range(mgr.ndim):
+            assert_reindex_axis_is_ok(mgr, ax, Index([]), fill_value)
+            assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax], fill_value)
+            assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax][[0, 0, 0]], fill_value)
+            assert_reindex_axis_is_ok(mgr, ax, Index(["foo", "bar", "baz"]), fill_value)
+            assert_reindex_axis_is_ok(
+                mgr, ax, Index(["foo", mgr.axes[ax][0], "baz"]), fill_value
+            )
+
+            if mgr.shape[ax] >= 3:
+                assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax][:-3], fill_value)
+                assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax][-3::-1], fill_value)
+                assert_reindex_axis_is_ok(
+                    mgr, ax, mgr.axes[ax][[0, 1, 2, 0, 1, 2]], fill_value
+                )
+
+    @pytest.mark.parametrize("mgr", MANAGERS)
+    @pytest.mark.parametrize("fill_value", [None, np.nan, 100.0])
+    def test_reindex_indexer(self, fill_value, mgr):
+        def assert_reindex_indexer_is_ok(mgr, axis, new_labels, indexer, fill_value):
+            mat = _as_array(mgr)
+            reindexed_mat = algos.take_nd(mat, indexer, axis, fill_value=fill_value)
+            reindexed = mgr.reindex_indexer(
+                new_labels, indexer, axis, fill_value=fill_value
+            )
+            tm.assert_numpy_array_equal(
+                reindexed_mat, _as_array(reindexed), check_dtype=False
+            )
+            tm.assert_index_equal(reindexed.axes[axis], new_labels)
+
+        for ax in range(mgr.ndim):
+            assert_reindex_indexer_is_ok(
+                mgr, ax, Index([]), np.array([], dtype=np.intp), fill_value
+            )
+            assert_reindex_indexer_is_ok(
+                mgr, ax, mgr.axes[ax], np.arange(mgr.shape[ax]), fill_value
+            )
+            assert_reindex_indexer_is_ok(
+                mgr,
+                ax,
+                Index(["foo"] * mgr.shape[ax]),
+                np.arange(mgr.shape[ax]),
+                fill_value,
+            )
+            assert_reindex_indexer_is_ok(
+                mgr, ax, mgr.axes[ax][::-1], np.arange(mgr.shape[ax]), fill_value
+            )
+            assert_reindex_indexer_is_ok(
+                mgr, ax, mgr.axes[ax], np.arange(mgr.shape[ax])[::-1], fill_value
+            )
+            assert_reindex_indexer_is_ok(
+                mgr, ax, Index(["foo", "bar", "baz"]), np.array([0, 0, 0]), fill_value
+            )
+            assert_reindex_indexer_is_ok(
+                mgr, ax, Index(["foo", "bar", "baz"]), np.array([-1, 0, -1]), fill_value
+            )
+            assert_reindex_indexer_is_ok(
+                mgr,
+                ax,
+                Index(["foo", mgr.axes[ax][0], "baz"]),
+                np.array([-1, -1, -1]),
+                fill_value,
+            )
+
+            if mgr.shape[ax] >= 3:
+                assert_reindex_indexer_is_ok(
+                    mgr,
+                    ax,
+                    Index(["foo", "bar", "baz"]),
+                    np.array([0, 1, 2]),
+                    fill_value,
+                )
+
+
+class TestBlockPlacement:
+    @pytest.mark.parametrize(
+        "slc, expected",
+        [
+            (slice(0, 4), 4),
+            (slice(0, 4, 2), 2),
+            (slice(0, 3, 2), 2),
+            (slice(0, 1, 2), 1),
+            (slice(1, 0, -1), 1),
+        ],
+    )
+    def test_slice_len(self, slc, expected):
+        assert len(BlockPlacement(slc)) == expected
+
+    @pytest.mark.parametrize("slc", [slice(1, 1, 0), slice(1, 2, 0)])
+    def test_zero_step_raises(self, slc):
+        msg = "slice step cannot be zero"
+        with pytest.raises(ValueError, match=msg):
+            BlockPlacement(slc)
+
+    def test_slice_canonize_negative_stop(self):
+        # GH#37524 negative stop is OK with negative step and positive start
+        slc = slice(3, -1, -2)
+
+        bp = BlockPlacement(slc)
+        assert bp.indexer == slice(3, None, -2)
+
+    @pytest.mark.parametrize(
+        "slc",
+        [
+            slice(None, None),
+            slice(10, None),
+            slice(None, None, -1),
+            slice(None, 10, -1),
+            # These are "unbounded" because negative index will
+            #  change depending on container shape.
+            slice(-1, None),
+            slice(None, -1),
+            slice(-1, -1),
+            slice(-1, None, -1),
+            slice(None, -1, -1),
+            slice(-1, -1, -1),
+        ],
+    )
+    def test_unbounded_slice_raises(self, slc):
+        msg = "unbounded slice"
+        with pytest.raises(ValueError, match=msg):
+            BlockPlacement(slc)
+
+    @pytest.mark.parametrize(
+        "slc",
+        [
+            slice(0, 0),
+            slice(100, 0),
+            slice(100, 100),
+            slice(100, 100, -1),
+            slice(0, 100, -1),
+        ],
+    )
+    def test_not_slice_like_slices(self, slc):
+        assert not BlockPlacement(slc).is_slice_like
+
+    @pytest.mark.parametrize(
+        "arr, slc",
+        [
+            ([0], slice(0, 1, 1)),
+            ([100], slice(100, 101, 1)),
+            ([0, 1, 2], slice(0, 3, 1)),
+            ([0, 5, 10], slice(0, 15, 5)),
+            ([0, 100], slice(0, 200, 100)),
+            ([2, 1], slice(2, 0, -1)),
+        ],
+    )
+    def test_array_to_slice_conversion(self, arr, slc):
+        assert BlockPlacement(arr).as_slice == slc
+
+    @pytest.mark.parametrize(
+        "arr",
+        [
+            [],
+            [-1],
+            [-1, -2, -3],
+            [-10],
+            [-1],
+            [-1, 0, 1, 2],
+            [-2, 0, 2, 4],
+            [1, 0, -1],
+            [1, 1, 1],
+        ],
+    )
+    def test_not_slice_like_arrays(self, arr):
+        assert not BlockPlacement(arr).is_slice_like
+
+    @pytest.mark.parametrize(
+        "slc, expected",
+        [(slice(0, 3), [0, 1, 2]), (slice(0, 0), []), (slice(3, 0), [])],
+    )
+    def test_slice_iter(self, slc, expected):
+        assert list(BlockPlacement(slc)) == expected
+
+    @pytest.mark.parametrize(
+        "slc, arr",
+        [
+            (slice(0, 3), [0, 1, 2]),
+            (slice(0, 0), []),
+            (slice(3, 0), []),
+            (slice(3, 0, -1), [3, 2, 1]),
+        ],
+    )
+    def test_slice_to_array_conversion(self, slc, arr):
+        tm.assert_numpy_array_equal(
+            BlockPlacement(slc).as_array, np.asarray(arr, dtype=np.intp)
+        )
+
+    def test_blockplacement_add(self):
+        bpl = BlockPlacement(slice(0, 5))
+        assert bpl.add(1).as_slice == slice(1, 6, 1)
+        assert bpl.add(np.arange(5)).as_slice == slice(0, 10, 2)
+        assert list(bpl.add(np.arange(5, 0, -1))) == [5, 5, 5, 5, 5]
+
+    @pytest.mark.parametrize(
+        "val, inc, expected",
+        [
+            (slice(0, 0), 0, []),
+            (slice(1, 4), 0, [1, 2, 3]),
+            (slice(3, 0, -1), 0, [3, 2, 1]),
+            ([1, 2, 4], 0, [1, 2, 4]),
+            (slice(0, 0), 10, []),
+            (slice(1, 4), 10, [11, 12, 13]),
+            (slice(3, 0, -1), 10, [13, 12, 11]),
+            ([1, 2, 4], 10, [11, 12, 14]),
+            (slice(0, 0), -1, []),
+            (slice(1, 4), -1, [0, 1, 2]),
+            ([1, 2, 4], -1, [0, 1, 3]),
+        ],
+    )
+    def test_blockplacement_add_int(self, val, inc, expected):
+        assert list(BlockPlacement(val).add(inc)) == expected
+
+    @pytest.mark.parametrize("val", [slice(1, 4), [1, 2, 4]])
+    def test_blockplacement_add_int_raises(self, val):
+        msg = "iadd causes length change"
+        with pytest.raises(ValueError, match=msg):
+            BlockPlacement(val).add(-10)
+
+
+class TestCanHoldElement:
+    @pytest.fixture(
+        params=[
+            lambda x: x,
+            lambda x: x.to_series(),
+            lambda x: x._data,
+            lambda x: list(x),
+            lambda x: x.astype(object),
+            lambda x: np.asarray(x),
+            lambda x: x[0],
+            lambda x: x[:0],
+        ]
+    )
+    def element(self, request):
+        """
+        Functions that take an Index and return an element that should have
+        blk._can_hold_element(element) for a Block with this index's dtype.
+        """
+        return request.param
+
+    def test_datetime_block_can_hold_element(self):
+        block = create_block("datetime", [0])
+
+        assert block._can_hold_element([])
+
+        # We will check that block._can_hold_element iff arr.__setitem__ works
+        arr = pd.array(block.values.ravel())
+
+        # coerce None
+        assert block._can_hold_element(None)
+        arr[0] = None
+        assert arr[0] is pd.NaT
+
+        # coerce different types of datetime objects
+        vals = [np.datetime64("2010-10-10"), datetime(2010, 10, 10)]
+        for val in vals:
+            assert block._can_hold_element(val)
+            arr[0] = val
+
+        val = date(2010, 10, 10)
+        assert not block._can_hold_element(val)
+
+        msg = (
+            "value should be a 'Timestamp', 'NaT', "
+            "or array of those. Got 'date' instead."
+        )
+        with pytest.raises(TypeError, match=msg):
+            arr[0] = val
+
+    @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64])
+    def test_interval_can_hold_element_emptylist(self, dtype, element):
+        arr = np.array([1, 3, 4], dtype=dtype)
+        ii = IntervalIndex.from_breaks(arr)
+        blk = new_block(ii._data, [1], ndim=2)
+
+        assert blk._can_hold_element([])
+        # TODO: check this holds for all blocks
+
+    @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64])
+    def test_interval_can_hold_element(self, dtype, element):
+        arr = np.array([1, 3, 4, 9], dtype=dtype)
+        ii = IntervalIndex.from_breaks(arr)
+        blk = new_block(ii._data, [1], ndim=2)
+
+        elem = element(ii)
+        self.check_series_setitem(elem, ii, True)
+        assert blk._can_hold_element(elem)
+
+        # Careful: to get the expected Series-inplace behavior we need
+        # `elem` to not have the same length as `arr`
+        ii2 = IntervalIndex.from_breaks(arr[:-1], closed="neither")
+        elem = element(ii2)
+        self.check_series_setitem(elem, ii, False)
+        assert not blk._can_hold_element(elem)
+
+        ii3 = IntervalIndex.from_breaks([Timestamp(1), Timestamp(3), Timestamp(4)])
+        elem = element(ii3)
+        self.check_series_setitem(elem, ii, False)
+        assert not blk._can_hold_element(elem)
+
+        ii4 = IntervalIndex.from_breaks([Timedelta(1), Timedelta(3), Timedelta(4)])
+        elem = element(ii4)
+        self.check_series_setitem(elem, ii, False)
+        assert not blk._can_hold_element(elem)
+
+    def test_period_can_hold_element_emptylist(self):
+        pi = period_range("2016", periods=3, freq="A")
+        blk = new_block(pi._data.reshape(1, 3), [1], ndim=2)
+
+        assert blk._can_hold_element([])
+
+    def test_period_can_hold_element(self, element):
+        pi = period_range("2016", periods=3, freq="A")
+
+        elem = element(pi)
+        self.check_series_setitem(elem, pi, True)
+
+        # Careful: to get the expected Series-inplace behavior we need
+        # `elem` to not have the same length as `arr`
+        pi2 = pi.asfreq("D")[:-1]
+        elem = element(pi2)
+        self.check_series_setitem(elem, pi, False)
+
+        dti = pi.to_timestamp("S")[:-1]
+        elem = element(dti)
+        self.check_series_setitem(elem, pi, False)
+
+    def check_can_hold_element(self, obj, elem, inplace: bool):
+        blk = obj._mgr.blocks[0]
+        if inplace:
+            assert blk._can_hold_element(elem)
+        else:
+            assert not blk._can_hold_element(elem)
+
+    def check_series_setitem(self, elem, index: Index, inplace: bool):
+        arr = index._data.copy()
+        ser = Series(arr, copy=False)
+
+        self.check_can_hold_element(ser, elem, inplace)
+
+        if is_scalar(elem):
+            ser[0] = elem
+        else:
+            ser[: len(elem)] = elem
+
+        if inplace:
+            assert ser.array is arr  # i.e. setting was done inplace
+        else:
+            assert ser.dtype == object
+
+
+class TestShouldStore:
+    def test_should_store_categorical(self):
+        cat = Categorical(["A", "B", "C"])
+        df = DataFrame(cat)
+        blk = df._mgr.blocks[0]
+
+        # matching dtype
+        assert blk.should_store(cat)
+        assert blk.should_store(cat[:-1])
+
+        # different dtype
+        assert not blk.should_store(cat.as_ordered())
+
+        # ndarray instead of Categorical
+        assert not blk.should_store(np.asarray(cat))
+
+
+def test_validate_ndim(block_maker):
+    values = np.array([1.0, 2.0])
+    placement = slice(2)
+    msg = r"Wrong number of dimensions. values.ndim != ndim \[1 != 2\]"
+
+    with pytest.raises(ValueError, match=msg):
+        block_maker(values, placement, ndim=2)
+
+
+def test_block_shape():
+    idx = Index([0, 1, 2, 3, 4])
+    a = Series([1, 2, 3]).reindex(idx)
+    b = Series(Categorical([1, 2, 3])).reindex(idx)
+
+    assert a._mgr.blocks[0].mgr_locs.indexer == b._mgr.blocks[0].mgr_locs.indexer
+
+
+def test_make_block_no_pandas_array(block_maker):
+    # https://github.com/pandas-dev/pandas/pull/24866
+    arr = pd.arrays.PandasArray(np.array([1, 2]))
+
+    # PandasArray, no dtype
+    result = block_maker(arr, slice(len(arr)), ndim=arr.ndim)
+    assert result.dtype.kind in ["i", "u"]
+
+    if block_maker is make_block:
+        # new_block requires caller to unwrap PandasArray
+        assert result.is_extension is False
+
+        # PandasArray, PandasDtype
+        result = block_maker(arr, slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim)
+        assert result.dtype.kind in ["i", "u"]
+        assert result.is_extension is False
+
+        # new_block no longer taked dtype keyword
+        # ndarray, PandasDtype
+        result = block_maker(
+            arr.to_numpy(), slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim
+        )
+        assert result.dtype.kind in ["i", "u"]
+        assert result.is_extension is False

videochat2/lib/python3.10/site-packages/pandas/tests/internals/test_managers.py

@@ -0,0 +1,70 @@
+"""
+Testing interaction between the different managers (BlockManager, ArrayManager)
+"""
+from pandas.core.dtypes.missing import array_equivalent
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.core.internals import (
+    ArrayManager,
+    BlockManager,
+    SingleArrayManager,
+    SingleBlockManager,
+)
+
+
+def test_dataframe_creation():
+    with pd.option_context("mode.data_manager", "block"):
+        df_block = pd.DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3], "c": [4, 5, 6]})
+    assert isinstance(df_block._mgr, BlockManager)
+
+    with pd.option_context("mode.data_manager", "array"):
+        df_array = pd.DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3], "c": [4, 5, 6]})
+    assert isinstance(df_array._mgr, ArrayManager)
+
+    # also ensure both are seen as equal
+    tm.assert_frame_equal(df_block, df_array)
+
+    # conversion from one manager to the other
+    result = df_block._as_manager("block")
+    assert isinstance(result._mgr, BlockManager)
+    result = df_block._as_manager("array")
+    assert isinstance(result._mgr, ArrayManager)
+    tm.assert_frame_equal(result, df_block)
+    assert all(
+        array_equivalent(left, right)
+        for left, right in zip(result._mgr.arrays, df_array._mgr.arrays)
+    )
+
+    result = df_array._as_manager("array")
+    assert isinstance(result._mgr, ArrayManager)
+    result = df_array._as_manager("block")
+    assert isinstance(result._mgr, BlockManager)
+    tm.assert_frame_equal(result, df_array)
+    assert len(result._mgr.blocks) == 2
+
+
+def test_series_creation():
+    with pd.option_context("mode.data_manager", "block"):
+        s_block = pd.Series([1, 2, 3], name="A", index=["a", "b", "c"])
+    assert isinstance(s_block._mgr, SingleBlockManager)
+
+    with pd.option_context("mode.data_manager", "array"):
+        s_array = pd.Series([1, 2, 3], name="A", index=["a", "b", "c"])
+    assert isinstance(s_array._mgr, SingleArrayManager)
+
+    # also ensure both are seen as equal
+    tm.assert_series_equal(s_block, s_array)
+
+    # conversion from one manager to the other
+    result = s_block._as_manager("block")
+    assert isinstance(result._mgr, SingleBlockManager)
+    result = s_block._as_manager("array")
+    assert isinstance(result._mgr, SingleArrayManager)
+    tm.assert_series_equal(result, s_block)
+
+    result = s_array._as_manager("array")
+    assert isinstance(result._mgr, SingleArrayManager)
+    result = s_array._as_manager("block")
+    assert isinstance(result._mgr, SingleBlockManager)
+    tm.assert_series_equal(result, s_array)

videochat2/lib/python3.10/site-packages/pandas/tests/io/data/parquet/simple.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe3c5e35a94667ab6da6076fa6db0b57ce991047108d000518d4a8251f9e5f79
+size 2157

videochat2/lib/python3.10/site-packages/pandas/tests/io/data/pickle/test_mi_py27.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a459bfcc43aebb69e8bf9a7e18a3a4e1ad926cb73b8ce9dba68b53dc81109bd
+size 1395

videochat2/lib/python3.10/site-packages/pandas/tests/io/data/pickle/test_py27.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a4d456262c5e3c687b5cf1f6656cd72d0844f3b0dbe8f00a63c48084718584b
+size 943

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

@@ -0,0 +1,7 @@
+import pytest
+
+
+@pytest.fixture(params=[True, False])
+def sort(request):
+    """Boolean sort keyword for concat and DataFrame.append."""
+    return request.param

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_append.py

@@ -0,0 +1,377 @@
+import datetime as dt
+from itertools import combinations
+
+import dateutil
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    Timestamp,
+    concat,
+    isna,
+)
+import pandas._testing as tm
+
+
+class TestAppend:
+    def test_append(self, sort, float_frame):
+        mixed_frame = float_frame.copy()
+        mixed_frame["foo"] = "bar"
+
+        begin_index = float_frame.index[:5]
+        end_index = float_frame.index[5:]
+
+        begin_frame = float_frame.reindex(begin_index)
+        end_frame = float_frame.reindex(end_index)
+
+        appended = begin_frame._append(end_frame)
+        tm.assert_almost_equal(appended["A"], float_frame["A"])
+
+        del end_frame["A"]
+        partial_appended = begin_frame._append(end_frame, sort=sort)
+        assert "A" in partial_appended
+
+        partial_appended = end_frame._append(begin_frame, sort=sort)
+        assert "A" in partial_appended
+
+        # mixed type handling
+        appended = mixed_frame[:5]._append(mixed_frame[5:])
+        tm.assert_frame_equal(appended, mixed_frame)
+
+        # what to test here
+        mixed_appended = mixed_frame[:5]._append(float_frame[5:], sort=sort)
+        mixed_appended2 = float_frame[:5]._append(mixed_frame[5:], sort=sort)
+
+        # all equal except 'foo' column
+        tm.assert_frame_equal(
+            mixed_appended.reindex(columns=["A", "B", "C", "D"]),
+            mixed_appended2.reindex(columns=["A", "B", "C", "D"]),
+        )
+
+    def test_append_empty(self, float_frame):
+        empty = DataFrame()
+
+        appended = float_frame._append(empty)
+        tm.assert_frame_equal(float_frame, appended)
+        assert appended is not float_frame
+
+        appended = empty._append(float_frame)
+        tm.assert_frame_equal(float_frame, appended)
+        assert appended is not float_frame
+
+    def test_append_overlap_raises(self, float_frame):
+        msg = "Indexes have overlapping values"
+        with pytest.raises(ValueError, match=msg):
+            float_frame._append(float_frame, verify_integrity=True)
+
+    def test_append_new_columns(self):
+        # see gh-6129: new columns
+        df = DataFrame({"a": {"x": 1, "y": 2}, "b": {"x": 3, "y": 4}})
+        row = Series([5, 6, 7], index=["a", "b", "c"], name="z")
+        expected = DataFrame(
+            {
+                "a": {"x": 1, "y": 2, "z": 5},
+                "b": {"x": 3, "y": 4, "z": 6},
+                "c": {"z": 7},
+            }
+        )
+        result = df._append(row)
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_length0_frame(self, sort):
+        df = DataFrame(columns=["A", "B", "C"])
+        df3 = DataFrame(index=[0, 1], columns=["A", "B"])
+        df5 = df._append(df3, sort=sort)
+
+        expected = DataFrame(index=[0, 1], columns=["A", "B", "C"])
+        tm.assert_frame_equal(df5, expected)
+
+    def test_append_records(self):
+        arr1 = np.zeros((2,), dtype=("i4,f4,a10"))
+        arr1[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")]
+
+        arr2 = np.zeros((3,), dtype=("i4,f4,a10"))
+        arr2[:] = [(3, 4.0, "foo"), (5, 6.0, "bar"), (7.0, 8.0, "baz")]
+
+        df1 = DataFrame(arr1)
+        df2 = DataFrame(arr2)
+
+        result = df1._append(df2, ignore_index=True)
+        expected = DataFrame(np.concatenate((arr1, arr2)))
+        tm.assert_frame_equal(result, expected)
+
+    # rewrite sort fixture, since we also want to test default of None
+    def test_append_sorts(self, sort):
+        df1 = DataFrame({"a": [1, 2], "b": [1, 2]}, columns=["b", "a"])
+        df2 = DataFrame({"a": [1, 2], "c": [3, 4]}, index=[2, 3])
+
+        result = df1._append(df2, sort=sort)
+
+        # for None / True
+        expected = DataFrame(
+            {"b": [1, 2, None, None], "a": [1, 2, 1, 2], "c": [None, None, 3, 4]},
+            columns=["a", "b", "c"],
+        )
+        if sort is False:
+            expected = expected[["b", "a", "c"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_different_columns(self, sort):
+        df = DataFrame(
+            {
+                "bools": np.random.randn(10) > 0,
+                "ints": np.random.randint(0, 10, 10),
+                "floats": np.random.randn(10),
+                "strings": ["foo", "bar"] * 5,
+            }
+        )
+
+        a = df[:5].loc[:, ["bools", "ints", "floats"]]
+        b = df[5:].loc[:, ["strings", "ints", "floats"]]
+
+        appended = a._append(b, sort=sort)
+        assert isna(appended["strings"][0:4]).all()
+        assert isna(appended["bools"][5:]).all()
+
+    def test_append_many(self, sort, float_frame):
+        chunks = [
+            float_frame[:5],
+            float_frame[5:10],
+            float_frame[10:15],
+            float_frame[15:],
+        ]
+
+        result = chunks[0]._append(chunks[1:])
+        tm.assert_frame_equal(result, float_frame)
+
+        chunks[-1] = chunks[-1].copy()
+        chunks[-1]["foo"] = "bar"
+        result = chunks[0]._append(chunks[1:], sort=sort)
+        tm.assert_frame_equal(result.loc[:, float_frame.columns], float_frame)
+        assert (result["foo"][15:] == "bar").all()
+        assert result["foo"][:15].isna().all()
+
+    def test_append_preserve_index_name(self):
+        # #980
+        df1 = DataFrame(columns=["A", "B", "C"])
+        df1 = df1.set_index(["A"])
+        df2 = DataFrame(data=[[1, 4, 7], [2, 5, 8], [3, 6, 9]], columns=["A", "B", "C"])
+        df2 = df2.set_index(["A"])
+
+        result = df1._append(df2)
+        assert result.index.name == "A"
+
+    indexes_can_append = [
+        pd.RangeIndex(3),
+        Index([4, 5, 6]),
+        Index([4.5, 5.5, 6.5]),
+        Index(list("abc")),
+        pd.CategoricalIndex("A B C".split()),
+        pd.CategoricalIndex("D E F".split(), ordered=True),
+        pd.IntervalIndex.from_breaks([7, 8, 9, 10]),
+        pd.DatetimeIndex(
+            [
+                dt.datetime(2013, 1, 3, 0, 0),
+                dt.datetime(2013, 1, 3, 6, 10),
+                dt.datetime(2013, 1, 3, 7, 12),
+            ]
+        ),
+        pd.MultiIndex.from_arrays(["A B C".split(), "D E F".split()]),
+    ]
+
+    @pytest.mark.parametrize(
+        "index", indexes_can_append, ids=lambda x: type(x).__name__
+    )
+    def test_append_same_columns_type(self, index):
+        # GH18359
+
+        # df wider than ser
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=index)
+        ser_index = index[:2]
+        ser = Series([7, 8], index=ser_index, name=2)
+        result = df._append(ser)
+        expected = DataFrame(
+            [[1, 2, 3.0], [4, 5, 6], [7, 8, np.nan]], index=[0, 1, 2], columns=index
+        )
+        # integer dtype is preserved for columns present in ser.index
+        assert expected.dtypes.iloc[0].kind == "i"
+        assert expected.dtypes.iloc[1].kind == "i"
+
+        tm.assert_frame_equal(result, expected)
+
+        # ser wider than df
+        ser_index = index
+        index = index[:2]
+        df = DataFrame([[1, 2], [4, 5]], columns=index)
+        ser = Series([7, 8, 9], index=ser_index, name=2)
+        result = df._append(ser)
+        expected = DataFrame(
+            [[1, 2, np.nan], [4, 5, np.nan], [7, 8, 9]],
+            index=[0, 1, 2],
+            columns=ser_index,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "df_columns, series_index",
+        combinations(indexes_can_append, r=2),
+        ids=lambda x: type(x).__name__,
+    )
+    def test_append_different_columns_types(self, df_columns, series_index):
+        # GH18359
+        # See also test 'test_append_different_columns_types_raises' below
+        # for errors raised when appending
+
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=df_columns)
+        ser = Series([7, 8, 9], index=series_index, name=2)
+
+        result = df._append(ser)
+        idx_diff = ser.index.difference(df_columns)
+        combined_columns = Index(df_columns.tolist()).append(idx_diff)
+        expected = DataFrame(
+            [
+                [1.0, 2.0, 3.0, np.nan, np.nan, np.nan],
+                [4, 5, 6, np.nan, np.nan, np.nan],
+                [np.nan, np.nan, np.nan, 7, 8, 9],
+            ],
+            index=[0, 1, 2],
+            columns=combined_columns,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_dtype_coerce(self, sort):
+        # GH 4993
+        # appending with datetime will incorrectly convert datetime64
+
+        df1 = DataFrame(
+            index=[1, 2],
+            data=[dt.datetime(2013, 1, 1, 0, 0), dt.datetime(2013, 1, 2, 0, 0)],
+            columns=["start_time"],
+        )
+        df2 = DataFrame(
+            index=[4, 5],
+            data=[
+                [dt.datetime(2013, 1, 3, 0, 0), dt.datetime(2013, 1, 3, 6, 10)],
+                [dt.datetime(2013, 1, 4, 0, 0), dt.datetime(2013, 1, 4, 7, 10)],
+            ],
+            columns=["start_time", "end_time"],
+        )
+
+        expected = concat(
+            [
+                Series(
+                    [
+                        pd.NaT,
+                        pd.NaT,
+                        dt.datetime(2013, 1, 3, 6, 10),
+                        dt.datetime(2013, 1, 4, 7, 10),
+                    ],
+                    name="end_time",
+                ),
+                Series(
+                    [
+                        dt.datetime(2013, 1, 1, 0, 0),
+                        dt.datetime(2013, 1, 2, 0, 0),
+                        dt.datetime(2013, 1, 3, 0, 0),
+                        dt.datetime(2013, 1, 4, 0, 0),
+                    ],
+                    name="start_time",
+                ),
+            ],
+            axis=1,
+            sort=sort,
+        )
+        result = df1._append(df2, ignore_index=True, sort=sort)
+        if sort:
+            expected = expected[["end_time", "start_time"]]
+        else:
+            expected = expected[["start_time", "end_time"]]
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_missing_column_proper_upcast(self, sort):
+        df1 = DataFrame({"A": np.array([1, 2, 3, 4], dtype="i8")})
+        df2 = DataFrame({"B": np.array([True, False, True, False], dtype=bool)})
+
+        appended = df1._append(df2, ignore_index=True, sort=sort)
+        assert appended["A"].dtype == "f8"
+        assert appended["B"].dtype == "O"
+
+    def test_append_empty_frame_to_series_with_dateutil_tz(self):
+        # GH 23682
+        date = Timestamp("2018-10-24 07:30:00", tz=dateutil.tz.tzutc())
+        ser = Series({"a": 1.0, "b": 2.0, "date": date})
+        df = DataFrame(columns=["c", "d"])
+        result_a = df._append(ser, ignore_index=True)
+        expected = DataFrame(
+            [[np.nan, np.nan, 1.0, 2.0, date]], columns=["c", "d", "a", "b", "date"]
+        )
+        # These columns get cast to object after append
+        expected["c"] = expected["c"].astype(object)
+        expected["d"] = expected["d"].astype(object)
+        tm.assert_frame_equal(result_a, expected)
+
+        expected = DataFrame(
+            [[np.nan, np.nan, 1.0, 2.0, date]] * 2, columns=["c", "d", "a", "b", "date"]
+        )
+        expected["c"] = expected["c"].astype(object)
+        expected["d"] = expected["d"].astype(object)
+        result_b = result_a._append(ser, ignore_index=True)
+        tm.assert_frame_equal(result_b, expected)
+
+        result = df._append([ser, ser], ignore_index=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_append_empty_tz_frame_with_datetime64ns(self):
+        # https://github.com/pandas-dev/pandas/issues/35460
+        df = DataFrame(columns=["a"]).astype("datetime64[ns, UTC]")
+
+        # pd.NaT gets inferred as tz-naive, so append result is tz-naive
+        result = df._append({"a": pd.NaT}, ignore_index=True)
+        expected = DataFrame({"a": [pd.NaT]}).astype(object)
+        tm.assert_frame_equal(result, expected)
+
+        # also test with typed value to append
+        df = DataFrame(columns=["a"]).astype("datetime64[ns, UTC]")
+        other = Series({"a": pd.NaT}, dtype="datetime64[ns]")
+        result = df._append(other, ignore_index=True)
+        expected = DataFrame({"a": [pd.NaT]}).astype(object)
+        tm.assert_frame_equal(result, expected)
+
+        # mismatched tz
+        other = Series({"a": pd.NaT}, dtype="datetime64[ns, US/Pacific]")
+        result = df._append(other, ignore_index=True)
+        expected = DataFrame({"a": [pd.NaT]}).astype(object)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype_str", ["datetime64[ns, UTC]", "datetime64[ns]", "Int64", "int64"]
+    )
+    @pytest.mark.parametrize("val", [1, "NaT"])
+    def test_append_empty_frame_with_timedelta64ns_nat(self, dtype_str, val):
+        # https://github.com/pandas-dev/pandas/issues/35460
+        df = DataFrame(columns=["a"]).astype(dtype_str)
+
+        other = DataFrame({"a": [np.timedelta64(val, "ns")]})
+        result = df._append(other, ignore_index=True)
+
+        expected = other.astype(object)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype_str", ["datetime64[ns, UTC]", "datetime64[ns]", "Int64", "int64"]
+    )
+    @pytest.mark.parametrize("val", [1, "NaT"])
+    def test_append_frame_with_timedelta64ns_nat(self, dtype_str, val):
+        # https://github.com/pandas-dev/pandas/issues/35460
+        df = DataFrame({"a": pd.array([1], dtype=dtype_str)})
+
+        other = DataFrame({"a": [np.timedelta64(val, "ns")]})
+        result = df._append(other, ignore_index=True)
+
+        expected = DataFrame({"a": [df.iloc[0, 0], other.iloc[0, 0]]}, dtype=object)
+        tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_append_common.py

@@ -0,0 +1,749 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+dt_data = [
+    pd.Timestamp("2011-01-01"),
+    pd.Timestamp("2011-01-02"),
+    pd.Timestamp("2011-01-03"),
+]
+tz_data = [
+    pd.Timestamp("2011-01-01", tz="US/Eastern"),
+    pd.Timestamp("2011-01-02", tz="US/Eastern"),
+    pd.Timestamp("2011-01-03", tz="US/Eastern"),
+]
+td_data = [
+    pd.Timedelta("1 days"),
+    pd.Timedelta("2 days"),
+    pd.Timedelta("3 days"),
+]
+period_data = [
+    pd.Period("2011-01", freq="M"),
+    pd.Period("2011-02", freq="M"),
+    pd.Period("2011-03", freq="M"),
+]
+data_dict = {
+    "bool": [True, False, True],
+    "int64": [1, 2, 3],
+    "float64": [1.1, np.nan, 3.3],
+    "category": Categorical(["X", "Y", "Z"]),
+    "object": ["a", "b", "c"],
+    "datetime64[ns]": dt_data,
+    "datetime64[ns, US/Eastern]": tz_data,
+    "timedelta64[ns]": td_data,
+    "period[M]": period_data,
+}
+
+
+class TestConcatAppendCommon:
+    """
+    Test common dtype coercion rules between concat and append.
+    """
+
+    @pytest.fixture(params=sorted(data_dict.keys()))
+    def item(self, request):
+        key = request.param
+        return key, data_dict[key]
+
+    item2 = item
+
+    def test_dtypes(self, item, index_or_series):
+        # to confirm test case covers intended dtypes
+        typ, vals = item
+        obj = index_or_series(vals)
+        if isinstance(obj, Index):
+            assert obj.dtype == typ
+        elif isinstance(obj, Series):
+            if typ.startswith("period"):
+                assert obj.dtype == "Period[M]"
+            else:
+                assert obj.dtype == typ
+
+    def test_concatlike_same_dtypes(self, item):
+        # GH 13660
+        typ1, vals1 = item
+
+        vals2 = vals1
+        vals3 = vals1
+
+        if typ1 == "category":
+            exp_data = Categorical(list(vals1) + list(vals2))
+            exp_data3 = Categorical(list(vals1) + list(vals2) + list(vals3))
+        else:
+            exp_data = vals1 + vals2
+            exp_data3 = vals1 + vals2 + vals3
+
+        # ----- Index ----- #
+
+        # index.append
+        res = Index(vals1).append(Index(vals2))
+        exp = Index(exp_data)
+        tm.assert_index_equal(res, exp)
+
+        # 3 elements
+        res = Index(vals1).append([Index(vals2), Index(vals3)])
+        exp = Index(exp_data3)
+        tm.assert_index_equal(res, exp)
+
+        # index.append name mismatch
+        i1 = Index(vals1, name="x")
+        i2 = Index(vals2, name="y")
+        res = i1.append(i2)
+        exp = Index(exp_data)
+        tm.assert_index_equal(res, exp)
+
+        # index.append name match
+        i1 = Index(vals1, name="x")
+        i2 = Index(vals2, name="x")
+        res = i1.append(i2)
+        exp = Index(exp_data, name="x")
+        tm.assert_index_equal(res, exp)
+
+        # cannot append non-index
+        with pytest.raises(TypeError, match="all inputs must be Index"):
+            Index(vals1).append(vals2)
+
+        with pytest.raises(TypeError, match="all inputs must be Index"):
+            Index(vals1).append([Index(vals2), vals3])
+
+        # ----- Series ----- #
+
+        # series.append
+        res = Series(vals1)._append(Series(vals2), ignore_index=True)
+        exp = Series(exp_data)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # concat
+        res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # 3 elements
+        res = Series(vals1)._append([Series(vals2), Series(vals3)], ignore_index=True)
+        exp = Series(exp_data3)
+        tm.assert_series_equal(res, exp)
+
+        res = pd.concat(
+            [Series(vals1), Series(vals2), Series(vals3)],
+            ignore_index=True,
+        )
+        tm.assert_series_equal(res, exp)
+
+        # name mismatch
+        s1 = Series(vals1, name="x")
+        s2 = Series(vals2, name="y")
+        res = s1._append(s2, ignore_index=True)
+        exp = Series(exp_data)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        res = pd.concat([s1, s2], ignore_index=True)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # name match
+        s1 = Series(vals1, name="x")
+        s2 = Series(vals2, name="x")
+        res = s1._append(s2, ignore_index=True)
+        exp = Series(exp_data, name="x")
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        res = pd.concat([s1, s2], ignore_index=True)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # cannot append non-index
+        msg = (
+            r"cannot concatenate object of type '.+'; "
+            "only Series and DataFrame objs are valid"
+        )
+        with pytest.raises(TypeError, match=msg):
+            Series(vals1)._append(vals2)
+
+        with pytest.raises(TypeError, match=msg):
+            Series(vals1)._append([Series(vals2), vals3])
+
+        with pytest.raises(TypeError, match=msg):
+            pd.concat([Series(vals1), vals2])
+
+        with pytest.raises(TypeError, match=msg):
+            pd.concat([Series(vals1), Series(vals2), vals3])
+
+    def test_concatlike_dtypes_coercion(self, item, item2, request):
+        # GH 13660
+        typ1, vals1 = item
+        typ2, vals2 = item2
+
+        vals3 = vals2
+
+        # basically infer
+        exp_index_dtype = None
+        exp_series_dtype = None
+
+        if typ1 == typ2:
+            # same dtype is tested in test_concatlike_same_dtypes
+            return
+        elif typ1 == "category" or typ2 == "category":
+            # The `vals1 + vals2` below fails bc one of these is a Categorical
+            #  instead of a list; we have separate dedicated tests for categorical
+            return
+
+        # specify expected dtype
+        if typ1 == "bool" and typ2 in ("int64", "float64"):
+            # series coerces to numeric based on numpy rule
+            # index doesn't because bool is object dtype
+            exp_series_dtype = typ2
+            mark = pytest.mark.xfail(reason="GH#39187 casting to object")
+            request.node.add_marker(mark)
+        elif typ2 == "bool" and typ1 in ("int64", "float64"):
+            exp_series_dtype = typ1
+            mark = pytest.mark.xfail(reason="GH#39187 casting to object")
+            request.node.add_marker(mark)
+        elif (
+            typ1 == "datetime64[ns, US/Eastern]"
+            or typ2 == "datetime64[ns, US/Eastern]"
+            or typ1 == "timedelta64[ns]"
+            or typ2 == "timedelta64[ns]"
+        ):
+            exp_index_dtype = object
+            exp_series_dtype = object
+
+        exp_data = vals1 + vals2
+        exp_data3 = vals1 + vals2 + vals3
+
+        # ----- Index ----- #
+
+        # index.append
+        # GH#39817
+        res = Index(vals1).append(Index(vals2))
+        exp = Index(exp_data, dtype=exp_index_dtype)
+        tm.assert_index_equal(res, exp)
+
+        # 3 elements
+        res = Index(vals1).append([Index(vals2), Index(vals3)])
+        exp = Index(exp_data3, dtype=exp_index_dtype)
+        tm.assert_index_equal(res, exp)
+
+        # ----- Series ----- #
+
+        # series._append
+        # GH#39817
+        res = Series(vals1)._append(Series(vals2), ignore_index=True)
+        exp = Series(exp_data, dtype=exp_series_dtype)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # concat
+        # GH#39817
+        res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
+        tm.assert_series_equal(res, exp, check_index_type=True)
+
+        # 3 elements
+        # GH#39817
+        res = Series(vals1)._append([Series(vals2), Series(vals3)], ignore_index=True)
+        exp = Series(exp_data3, dtype=exp_series_dtype)
+        tm.assert_series_equal(res, exp)
+
+        # GH#39817
+        res = pd.concat(
+            [Series(vals1), Series(vals2), Series(vals3)],
+            ignore_index=True,
+        )
+        tm.assert_series_equal(res, exp)
+
+    def test_concatlike_common_coerce_to_pandas_object(self):
+        # GH 13626
+        # result must be Timestamp/Timedelta, not datetime.datetime/timedelta
+        dti = pd.DatetimeIndex(["2011-01-01", "2011-01-02"])
+        tdi = pd.TimedeltaIndex(["1 days", "2 days"])
+
+        exp = Index(
+            [
+                pd.Timestamp("2011-01-01"),
+                pd.Timestamp("2011-01-02"),
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+            ]
+        )
+
+        res = dti.append(tdi)
+        tm.assert_index_equal(res, exp)
+        assert isinstance(res[0], pd.Timestamp)
+        assert isinstance(res[-1], pd.Timedelta)
+
+        dts = Series(dti)
+        tds = Series(tdi)
+        res = dts._append(tds)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+        assert isinstance(res.iloc[0], pd.Timestamp)
+        assert isinstance(res.iloc[-1], pd.Timedelta)
+
+        res = pd.concat([dts, tds])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+        assert isinstance(res.iloc[0], pd.Timestamp)
+        assert isinstance(res.iloc[-1], pd.Timedelta)
+
+    def test_concatlike_datetimetz(self, tz_aware_fixture):
+        tz = tz_aware_fixture
+        # GH 7795
+        dti1 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], tz=tz)
+        dti2 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz=tz)
+
+        exp = pd.DatetimeIndex(
+            ["2011-01-01", "2011-01-02", "2012-01-01", "2012-01-02"], tz=tz
+        )
+
+        res = dti1.append(dti2)
+        tm.assert_index_equal(res, exp)
+
+        dts1 = Series(dti1)
+        dts2 = Series(dti2)
+        res = dts1._append(dts2)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([dts1, dts2])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    @pytest.mark.parametrize("tz", ["UTC", "US/Eastern", "Asia/Tokyo", "EST5EDT"])
+    def test_concatlike_datetimetz_short(self, tz):
+        # GH#7795
+        ix1 = pd.date_range(start="2014-07-15", end="2014-07-17", freq="D", tz=tz)
+        ix2 = pd.DatetimeIndex(["2014-07-11", "2014-07-21"], tz=tz)
+        df1 = DataFrame(0, index=ix1, columns=["A", "B"])
+        df2 = DataFrame(0, index=ix2, columns=["A", "B"])
+
+        exp_idx = pd.DatetimeIndex(
+            ["2014-07-15", "2014-07-16", "2014-07-17", "2014-07-11", "2014-07-21"],
+            tz=tz,
+        )
+        exp = DataFrame(0, index=exp_idx, columns=["A", "B"])
+
+        tm.assert_frame_equal(df1._append(df2), exp)
+        tm.assert_frame_equal(pd.concat([df1, df2]), exp)
+
+    def test_concatlike_datetimetz_to_object(self, tz_aware_fixture):
+        tz = tz_aware_fixture
+        # GH 13660
+
+        # different tz coerces to object
+        dti1 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], tz=tz)
+        dti2 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"])
+
+        exp = Index(
+            [
+                pd.Timestamp("2011-01-01", tz=tz),
+                pd.Timestamp("2011-01-02", tz=tz),
+                pd.Timestamp("2012-01-01"),
+                pd.Timestamp("2012-01-02"),
+            ],
+            dtype=object,
+        )
+
+        res = dti1.append(dti2)
+        tm.assert_index_equal(res, exp)
+
+        dts1 = Series(dti1)
+        dts2 = Series(dti2)
+        res = dts1._append(dts2)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([dts1, dts2])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        # different tz
+        dti3 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz="US/Pacific")
+
+        exp = Index(
+            [
+                pd.Timestamp("2011-01-01", tz=tz),
+                pd.Timestamp("2011-01-02", tz=tz),
+                pd.Timestamp("2012-01-01", tz="US/Pacific"),
+                pd.Timestamp("2012-01-02", tz="US/Pacific"),
+            ],
+            dtype=object,
+        )
+
+        res = dti1.append(dti3)
+        tm.assert_index_equal(res, exp)
+
+        dts1 = Series(dti1)
+        dts3 = Series(dti3)
+        res = dts1._append(dts3)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([dts1, dts3])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    def test_concatlike_common_period(self):
+        # GH 13660
+        pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
+        pi2 = pd.PeriodIndex(["2012-01", "2012-02"], freq="M")
+
+        exp = pd.PeriodIndex(["2011-01", "2011-02", "2012-01", "2012-02"], freq="M")
+
+        res = pi1.append(pi2)
+        tm.assert_index_equal(res, exp)
+
+        ps1 = Series(pi1)
+        ps2 = Series(pi2)
+        res = ps1._append(ps2)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([ps1, ps2])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    def test_concatlike_common_period_diff_freq_to_object(self):
+        # GH 13221
+        pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
+        pi2 = pd.PeriodIndex(["2012-01-01", "2012-02-01"], freq="D")
+
+        exp = Index(
+            [
+                pd.Period("2011-01", freq="M"),
+                pd.Period("2011-02", freq="M"),
+                pd.Period("2012-01-01", freq="D"),
+                pd.Period("2012-02-01", freq="D"),
+            ],
+            dtype=object,
+        )
+
+        res = pi1.append(pi2)
+        tm.assert_index_equal(res, exp)
+
+        ps1 = Series(pi1)
+        ps2 = Series(pi2)
+        res = ps1._append(ps2)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([ps1, ps2])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    def test_concatlike_common_period_mixed_dt_to_object(self):
+        # GH 13221
+        # different datetimelike
+        pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
+        tdi = pd.TimedeltaIndex(["1 days", "2 days"])
+        exp = Index(
+            [
+                pd.Period("2011-01", freq="M"),
+                pd.Period("2011-02", freq="M"),
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+            ],
+            dtype=object,
+        )
+
+        res = pi1.append(tdi)
+        tm.assert_index_equal(res, exp)
+
+        ps1 = Series(pi1)
+        tds = Series(tdi)
+        res = ps1._append(tds)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([ps1, tds])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        # inverse
+        exp = Index(
+            [
+                pd.Timedelta("1 days"),
+                pd.Timedelta("2 days"),
+                pd.Period("2011-01", freq="M"),
+                pd.Period("2011-02", freq="M"),
+            ],
+            dtype=object,
+        )
+
+        res = tdi.append(pi1)
+        tm.assert_index_equal(res, exp)
+
+        ps1 = Series(pi1)
+        tds = Series(tdi)
+        res = tds._append(ps1)
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+        res = pd.concat([tds, ps1])
+        tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
+
+    def test_concat_categorical(self):
+        # GH 13524
+
+        # same categories -> category
+        s1 = Series([1, 2, np.nan], dtype="category")
+        s2 = Series([2, 1, 2], dtype="category")
+
+        exp = Series([1, 2, np.nan, 2, 1, 2], dtype="category")
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        # partially different categories => not-category
+        s1 = Series([3, 2], dtype="category")
+        s2 = Series([2, 1], dtype="category")
+
+        exp = Series([3, 2, 2, 1])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        # completely different categories (same dtype) => not-category
+        s1 = Series([10, 11, np.nan], dtype="category")
+        s2 = Series([np.nan, 1, 3, 2], dtype="category")
+
+        exp = Series([10, 11, np.nan, np.nan, 1, 3, 2], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+    def test_union_categorical_same_categories_different_order(self):
+        # https://github.com/pandas-dev/pandas/issues/19096
+        a = Series(Categorical(["a", "b", "c"], categories=["a", "b", "c"]))
+        b = Series(Categorical(["a", "b", "c"], categories=["b", "a", "c"]))
+        result = pd.concat([a, b], ignore_index=True)
+        expected = Series(
+            Categorical(["a", "b", "c", "a", "b", "c"], categories=["a", "b", "c"])
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_categorical_coercion(self):
+        # GH 13524
+
+        # category + not-category => not-category
+        s1 = Series([1, 2, np.nan], dtype="category")
+        s2 = Series([2, 1, 2])
+
+        exp = Series([1, 2, np.nan, 2, 1, 2], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        # result shouldn't be affected by 1st elem dtype
+        exp = Series([2, 1, 2, 1, 2, np.nan], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # all values are not in category => not-category
+        s1 = Series([3, 2], dtype="category")
+        s2 = Series([2, 1])
+
+        exp = Series([3, 2, 2, 1])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series([2, 1, 3, 2])
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # completely different categories => not-category
+        s1 = Series([10, 11, np.nan], dtype="category")
+        s2 = Series([1, 3, 2])
+
+        exp = Series([10, 11, np.nan, 1, 3, 2], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series([1, 3, 2, 10, 11, np.nan], dtype=np.float64)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # different dtype => not-category
+        s1 = Series([10, 11, np.nan], dtype="category")
+        s2 = Series(["a", "b", "c"])
+
+        exp = Series([10, 11, np.nan, "a", "b", "c"])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series(["a", "b", "c", 10, 11, np.nan])
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # if normal series only contains NaN-likes => not-category
+        s1 = Series([10, 11], dtype="category")
+        s2 = Series([np.nan, np.nan, np.nan])
+
+        exp = Series([10, 11, np.nan, np.nan, np.nan])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series([np.nan, np.nan, np.nan, 10, 11])
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+    def test_concat_categorical_3elem_coercion(self):
+        # GH 13524
+
+        # mixed dtypes => not-category
+        s1 = Series([1, 2, np.nan], dtype="category")
+        s2 = Series([2, 1, 2], dtype="category")
+        s3 = Series([1, 2, 1, 2, np.nan])
+
+        exp = Series([1, 2, np.nan, 2, 1, 2, 1, 2, 1, 2, np.nan], dtype="float")
+        tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
+
+        exp = Series([1, 2, 1, 2, np.nan, 1, 2, np.nan, 2, 1, 2], dtype="float")
+        tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
+
+        # values are all in either category => not-category
+        s1 = Series([4, 5, 6], dtype="category")
+        s2 = Series([1, 2, 3], dtype="category")
+        s3 = Series([1, 3, 4])
+
+        exp = Series([4, 5, 6, 1, 2, 3, 1, 3, 4])
+        tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
+
+        exp = Series([1, 3, 4, 4, 5, 6, 1, 2, 3])
+        tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
+
+        # values are all in either category => not-category
+        s1 = Series([4, 5, 6], dtype="category")
+        s2 = Series([1, 2, 3], dtype="category")
+        s3 = Series([10, 11, 12])
+
+        exp = Series([4, 5, 6, 1, 2, 3, 10, 11, 12])
+        tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
+
+        exp = Series([10, 11, 12, 4, 5, 6, 1, 2, 3])
+        tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
+
+    def test_concat_categorical_multi_coercion(self):
+        # GH 13524
+
+        s1 = Series([1, 3], dtype="category")
+        s2 = Series([3, 4], dtype="category")
+        s3 = Series([2, 3])
+        s4 = Series([2, 2], dtype="category")
+        s5 = Series([1, np.nan])
+        s6 = Series([1, 3, 2], dtype="category")
+
+        # mixed dtype, values are all in categories => not-category
+        exp = Series([1, 3, 3, 4, 2, 3, 2, 2, 1, np.nan, 1, 3, 2])
+        res = pd.concat([s1, s2, s3, s4, s5, s6], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+        res = s1._append([s2, s3, s4, s5, s6], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+
+        exp = Series([1, 3, 2, 1, np.nan, 2, 2, 2, 3, 3, 4, 1, 3])
+        res = pd.concat([s6, s5, s4, s3, s2, s1], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+        res = s6._append([s5, s4, s3, s2, s1], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+
+    def test_concat_categorical_ordered(self):
+        # GH 13524
+
+        s1 = Series(Categorical([1, 2, np.nan], ordered=True))
+        s2 = Series(Categorical([2, 1, 2], ordered=True))
+
+        exp = Series(Categorical([1, 2, np.nan, 2, 1, 2], ordered=True))
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        exp = Series(Categorical([1, 2, np.nan, 2, 1, 2, 1, 2, np.nan], ordered=True))
+        tm.assert_series_equal(pd.concat([s1, s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append([s2, s1], ignore_index=True), exp)
+
+    def test_concat_categorical_coercion_nan(self):
+        # GH 13524
+
+        # some edge cases
+        # category + not-category => not category
+        s1 = Series(np.array([np.nan, np.nan], dtype=np.float64), dtype="category")
+        s2 = Series([np.nan, 1])
+
+        exp = Series([np.nan, np.nan, np.nan, 1])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        s1 = Series([1, np.nan], dtype="category")
+        s2 = Series([np.nan, np.nan])
+
+        exp = Series([1, np.nan, np.nan, np.nan], dtype="float")
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        # mixed dtype, all nan-likes => not-category
+        s1 = Series([np.nan, np.nan], dtype="category")
+        s2 = Series([np.nan, np.nan])
+
+        exp = Series([np.nan, np.nan, np.nan, np.nan])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+        # all category nan-likes => category
+        s1 = Series([np.nan, np.nan], dtype="category")
+        s2 = Series([np.nan, np.nan], dtype="category")
+
+        exp = Series([np.nan, np.nan, np.nan, np.nan], dtype="category")
+
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+    def test_concat_categorical_empty(self):
+        # GH 13524
+
+        s1 = Series([], dtype="category")
+        s2 = Series([1, 2], dtype="category")
+
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
+
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), s2)
+
+        s1 = Series([], dtype="category")
+        s2 = Series([], dtype="category")
+
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
+
+        s1 = Series([], dtype="category")
+        s2 = Series([], dtype="object")
+
+        # different dtype => not-category
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), s2)
+
+        s1 = Series([], dtype="category")
+        s2 = Series([np.nan, np.nan])
+
+        # empty Series is ignored
+        exp = Series([np.nan, np.nan])
+        tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
+        tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
+
+        tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
+        tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
+
+    def test_categorical_concat_append(self):
+        cat = Categorical(["a", "b"], categories=["a", "b"])
+        vals = [1, 2]
+        df = DataFrame({"cats": cat, "vals": vals})
+        cat2 = Categorical(["a", "b", "a", "b"], categories=["a", "b"])
+        vals2 = [1, 2, 1, 2]
+        exp = DataFrame({"cats": cat2, "vals": vals2}, index=Index([0, 1, 0, 1]))
+
+        tm.assert_frame_equal(pd.concat([df, df]), exp)
+        tm.assert_frame_equal(df._append(df), exp)
+
+        # GH 13524 can concat different categories
+        cat3 = Categorical(["a", "b"], categories=["a", "b", "c"])
+        vals3 = [1, 2]
+        df_different_categories = DataFrame({"cats": cat3, "vals": vals3})
+
+        res = pd.concat([df, df_different_categories], ignore_index=True)
+        exp = DataFrame({"cats": list("abab"), "vals": [1, 2, 1, 2]})
+        tm.assert_frame_equal(res, exp)
+
+        res = df._append(df_different_categories, ignore_index=True)
+        tm.assert_frame_equal(res, exp)

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_categorical.py

@@ -0,0 +1,253 @@
+import numpy as np
+
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+
+
+class TestCategoricalConcat:
+    def test_categorical_concat(self, sort):
+        # See GH 10177
+        df1 = DataFrame(
+            np.arange(18, dtype="int64").reshape(6, 3), columns=["a", "b", "c"]
+        )
+
+        df2 = DataFrame(np.arange(14, dtype="int64").reshape(7, 2), columns=["a", "c"])
+
+        cat_values = ["one", "one", "two", "one", "two", "two", "one"]
+        df2["h"] = Series(Categorical(cat_values))
+
+        res = pd.concat((df1, df2), axis=0, ignore_index=True, sort=sort)
+        exp = DataFrame(
+            {
+                "a": [0, 3, 6, 9, 12, 15, 0, 2, 4, 6, 8, 10, 12],
+                "b": [
+                    1,
+                    4,
+                    7,
+                    10,
+                    13,
+                    16,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                    np.nan,
+                ],
+                "c": [2, 5, 8, 11, 14, 17, 1, 3, 5, 7, 9, 11, 13],
+                "h": [None] * 6 + cat_values,
+            }
+        )
+        exp["h"] = exp["h"].astype(df2["h"].dtype)
+        tm.assert_frame_equal(res, exp)
+
+    def test_categorical_concat_dtypes(self):
+        # GH8143
+        index = ["cat", "obj", "num"]
+        cat = Categorical(["a", "b", "c"])
+        obj = Series(["a", "b", "c"])
+        num = Series([1, 2, 3])
+        df = pd.concat([Series(cat), obj, num], axis=1, keys=index)
+
+        result = df.dtypes == "object"
+        expected = Series([False, True, False], index=index)
+        tm.assert_series_equal(result, expected)
+
+        result = df.dtypes == "int64"
+        expected = Series([False, False, True], index=index)
+        tm.assert_series_equal(result, expected)
+
+        result = df.dtypes == "category"
+        expected = Series([True, False, False], index=index)
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_categoricalindex(self):
+        # GH 16111, categories that aren't lexsorted
+        categories = [9, 0, 1, 2, 3]
+
+        a = Series(1, index=pd.CategoricalIndex([9, 0], categories=categories))
+        b = Series(2, index=pd.CategoricalIndex([0, 1], categories=categories))
+        c = Series(3, index=pd.CategoricalIndex([1, 2], categories=categories))
+
+        result = pd.concat([a, b, c], axis=1)
+
+        exp_idx = pd.CategoricalIndex([9, 0, 1, 2], categories=categories)
+        exp = DataFrame(
+            {
+                0: [1, 1, np.nan, np.nan],
+                1: [np.nan, 2, 2, np.nan],
+                2: [np.nan, np.nan, 3, 3],
+            },
+            columns=[0, 1, 2],
+            index=exp_idx,
+        )
+        tm.assert_frame_equal(result, exp)
+
+    def test_categorical_concat_preserve(self):
+        # GH 8641  series concat not preserving category dtype
+        # GH 13524 can concat different categories
+        s = Series(list("abc"), dtype="category")
+        s2 = Series(list("abd"), dtype="category")
+
+        exp = Series(list("abcabd"))
+        res = pd.concat([s, s2], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+
+        exp = Series(list("abcabc"), dtype="category")
+        res = pd.concat([s, s], ignore_index=True)
+        tm.assert_series_equal(res, exp)
+
+        exp = Series(list("abcabc"), index=[0, 1, 2, 0, 1, 2], dtype="category")
+        res = pd.concat([s, s])
+        tm.assert_series_equal(res, exp)
+
+        a = Series(np.arange(6, dtype="int64"))
+        b = Series(list("aabbca"))
+
+        df2 = DataFrame({"A": a, "B": b.astype(CategoricalDtype(list("cab")))})
+        res = pd.concat([df2, df2])
+        exp = DataFrame(
+            {
+                "A": pd.concat([a, a]),
+                "B": pd.concat([b, b]).astype(CategoricalDtype(list("cab"))),
+            }
+        )
+        tm.assert_frame_equal(res, exp)
+
+    def test_categorical_index_preserver(self):
+        a = Series(np.arange(6, dtype="int64"))
+        b = Series(list("aabbca"))
+
+        df2 = DataFrame(
+            {"A": a, "B": b.astype(CategoricalDtype(list("cab")))}
+        ).set_index("B")
+        result = pd.concat([df2, df2])
+        expected = DataFrame(
+            {
+                "A": pd.concat([a, a]),
+                "B": pd.concat([b, b]).astype(CategoricalDtype(list("cab"))),
+            }
+        ).set_index("B")
+        tm.assert_frame_equal(result, expected)
+
+        # wrong categories -> uses concat_compat, which casts to object
+        df3 = DataFrame(
+            {"A": a, "B": Categorical(b, categories=list("abe"))}
+        ).set_index("B")
+        result = pd.concat([df2, df3])
+        expected = pd.concat(
+            [
+                df2.set_axis(df2.index.astype(object), axis=0),
+                df3.set_axis(df3.index.astype(object), axis=0),
+            ]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_categorical_tz(self):
+        # GH-23816
+        a = Series(pd.date_range("2017-01-01", periods=2, tz="US/Pacific"))
+        b = Series(["a", "b"], dtype="category")
+        result = pd.concat([a, b], ignore_index=True)
+        expected = Series(
+            [
+                pd.Timestamp("2017-01-01", tz="US/Pacific"),
+                pd.Timestamp("2017-01-02", tz="US/Pacific"),
+                "a",
+                "b",
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_categorical_unchanged(self):
+        # GH-12007
+        # test fix for when concat on categorical and float
+        # coerces dtype categorical -> float
+        df = DataFrame(Series(["a", "b", "c"], dtype="category", name="A"))
+        ser = Series([0, 1, 2], index=[0, 1, 3], name="B")
+        result = pd.concat([df, ser], axis=1)
+        expected = DataFrame(
+            {
+                "A": Series(["a", "b", "c", np.nan], dtype="category"),
+                "B": Series([0, 1, np.nan, 2], dtype="float"),
+            }
+        )
+        tm.assert_equal(result, expected)
+
+    def test_categorical_concat_gh7864(self):
+        # GH 7864
+        # make sure ordering is preserved
+        df = DataFrame({"id": [1, 2, 3, 4, 5, 6], "raw_grade": list("abbaae")})
+        df["grade"] = Categorical(df["raw_grade"])
+        df["grade"].cat.set_categories(["e", "a", "b"])
+
+        df1 = df[0:3]
+        df2 = df[3:]
+
+        tm.assert_index_equal(df["grade"].cat.categories, df1["grade"].cat.categories)
+        tm.assert_index_equal(df["grade"].cat.categories, df2["grade"].cat.categories)
+
+        dfx = pd.concat([df1, df2])
+        tm.assert_index_equal(df["grade"].cat.categories, dfx["grade"].cat.categories)
+
+        dfa = df1._append(df2)
+        tm.assert_index_equal(df["grade"].cat.categories, dfa["grade"].cat.categories)
+
+    def test_categorical_index_upcast(self):
+        # GH 17629
+        # test upcasting to object when concatinating on categorical indexes
+        # with non-identical categories
+
+        a = DataFrame({"foo": [1, 2]}, index=Categorical(["foo", "bar"]))
+        b = DataFrame({"foo": [4, 3]}, index=Categorical(["baz", "bar"]))
+
+        res = pd.concat([a, b])
+        exp = DataFrame({"foo": [1, 2, 4, 3]}, index=["foo", "bar", "baz", "bar"])
+
+        tm.assert_equal(res, exp)
+
+        a = Series([1, 2], index=Categorical(["foo", "bar"]))
+        b = Series([4, 3], index=Categorical(["baz", "bar"]))
+
+        res = pd.concat([a, b])
+        exp = Series([1, 2, 4, 3], index=["foo", "bar", "baz", "bar"])
+
+        tm.assert_equal(res, exp)
+
+    def test_categorical_missing_from_one_frame(self):
+        # GH 25412
+        df1 = DataFrame({"f1": [1, 2, 3]})
+        df2 = DataFrame({"f1": [2, 3, 1], "f2": Series([4, 4, 4]).astype("category")})
+        result = pd.concat([df1, df2], sort=True)
+        dtype = CategoricalDtype([4])
+        expected = DataFrame(
+            {
+                "f1": [1, 2, 3, 2, 3, 1],
+                "f2": Categorical.from_codes([-1, -1, -1, 0, 0, 0], dtype=dtype),
+            },
+            index=[0, 1, 2, 0, 1, 2],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_categorical_same_categories_different_order(self):
+        # https://github.com/pandas-dev/pandas/issues/24845
+
+        c1 = pd.CategoricalIndex(["a", "a"], categories=["a", "b"], ordered=False)
+        c2 = pd.CategoricalIndex(["b", "b"], categories=["b", "a"], ordered=False)
+        c3 = pd.CategoricalIndex(
+            ["a", "a", "b", "b"], categories=["a", "b"], ordered=False
+        )
+
+        df1 = DataFrame({"A": [1, 2]}, index=c1)
+        df2 = DataFrame({"A": [3, 4]}, index=c2)
+
+        result = pd.concat((df1, df2))
+        expected = DataFrame({"A": [1, 2, 3, 4]}, index=c3)
+        tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_concat.py

@@ -0,0 +1,787 @@
+from collections import (
+    abc,
+    deque,
+)
+from datetime import datetime
+from decimal import Decimal
+from typing import Iterator
+from warnings import (
+    catch_warnings,
+    simplefilter,
+)
+
+import numpy as np
+import pytest
+
+from pandas.errors import (
+    InvalidIndexError,
+    PerformanceWarning,
+)
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    Series,
+    concat,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import SparseArray
+from pandas.tests.extension.decimal import to_decimal
+
+
+class TestConcatenate:
+    def test_append_concat(self):
+        # GH#1815
+        d1 = date_range("12/31/1990", "12/31/1999", freq="A-DEC")
+        d2 = date_range("12/31/2000", "12/31/2009", freq="A-DEC")
+
+        s1 = Series(np.random.randn(10), d1)
+        s2 = Series(np.random.randn(10), d2)
+
+        s1 = s1.to_period()
+        s2 = s2.to_period()
+
+        # drops index
+        result = concat([s1, s2])
+        assert isinstance(result.index, PeriodIndex)
+        assert result.index[0] == s1.index[0]
+
+    def test_concat_copy(self, using_array_manager, using_copy_on_write):
+        df = DataFrame(np.random.randn(4, 3))
+        df2 = DataFrame(np.random.randint(0, 10, size=4).reshape(4, 1))
+        df3 = DataFrame({5: "foo"}, index=range(4))
+
+        # These are actual copies.
+        result = concat([df, df2, df3], axis=1, copy=True)
+
+        if not using_copy_on_write:
+            for arr in result._mgr.arrays:
+                assert arr.base is None
+        else:
+            for arr in result._mgr.arrays:
+                assert arr.base is not None
+
+        # These are the same.
+        result = concat([df, df2, df3], axis=1, copy=False)
+
+        for arr in result._mgr.arrays:
+            if arr.dtype.kind == "f":
+                assert arr.base is df._mgr.arrays[0].base
+            elif arr.dtype.kind in ["i", "u"]:
+                assert arr.base is df2._mgr.arrays[0].base
+            elif arr.dtype == object:
+                if using_array_manager:
+                    # we get the same array object, which has no base
+                    assert arr is df3._mgr.arrays[0]
+                else:
+                    assert arr.base is not None
+
+        # Float block was consolidated.
+        df4 = DataFrame(np.random.randn(4, 1))
+        result = concat([df, df2, df3, df4], axis=1, copy=False)
+        for arr in result._mgr.arrays:
+            if arr.dtype.kind == "f":
+                if using_array_manager or using_copy_on_write:
+                    # this is a view on some array in either df or df4
+                    assert any(
+                        np.shares_memory(arr, other)
+                        for other in df._mgr.arrays + df4._mgr.arrays
+                    )
+                else:
+                    # the block was consolidated, so we got a copy anyway
+                    assert arr.base is None
+            elif arr.dtype.kind in ["i", "u"]:
+                assert arr.base is df2._mgr.arrays[0].base
+            elif arr.dtype == object:
+                # this is a view on df3
+                assert any(np.shares_memory(arr, other) for other in df3._mgr.arrays)
+
+    def test_concat_with_group_keys(self):
+        # axis=0
+        df = DataFrame(np.random.randn(3, 4))
+        df2 = DataFrame(np.random.randn(4, 4))
+
+        result = concat([df, df2], keys=[0, 1])
+        exp_index = MultiIndex.from_arrays(
+            [[0, 0, 0, 1, 1, 1, 1], [0, 1, 2, 0, 1, 2, 3]]
+        )
+        expected = DataFrame(np.r_[df.values, df2.values], index=exp_index)
+        tm.assert_frame_equal(result, expected)
+
+        result = concat([df, df], keys=[0, 1])
+        exp_index2 = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
+        expected = DataFrame(np.r_[df.values, df.values], index=exp_index2)
+        tm.assert_frame_equal(result, expected)
+
+        # axis=1
+        df = DataFrame(np.random.randn(4, 3))
+        df2 = DataFrame(np.random.randn(4, 4))
+
+        result = concat([df, df2], keys=[0, 1], axis=1)
+        expected = DataFrame(np.c_[df.values, df2.values], columns=exp_index)
+        tm.assert_frame_equal(result, expected)
+
+        result = concat([df, df], keys=[0, 1], axis=1)
+        expected = DataFrame(np.c_[df.values, df.values], columns=exp_index2)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_keys_specific_levels(self):
+        df = DataFrame(np.random.randn(10, 4))
+        pieces = [df.iloc[:, [0, 1]], df.iloc[:, [2]], df.iloc[:, [3]]]
+        level = ["three", "two", "one", "zero"]
+        result = concat(
+            pieces,
+            axis=1,
+            keys=["one", "two", "three"],
+            levels=[level],
+            names=["group_key"],
+        )
+
+        tm.assert_index_equal(result.columns.levels[0], Index(level, name="group_key"))
+        tm.assert_index_equal(result.columns.levels[1], Index([0, 1, 2, 3]))
+
+        assert result.columns.names == ["group_key", None]
+
+    @pytest.mark.parametrize("mapping", ["mapping", "dict"])
+    def test_concat_mapping(self, mapping, non_dict_mapping_subclass):
+        constructor = dict if mapping == "dict" else non_dict_mapping_subclass
+        frames = constructor(
+            {
+                "foo": DataFrame(np.random.randn(4, 3)),
+                "bar": DataFrame(np.random.randn(4, 3)),
+                "baz": DataFrame(np.random.randn(4, 3)),
+                "qux": DataFrame(np.random.randn(4, 3)),
+            }
+        )
+
+        sorted_keys = list(frames.keys())
+
+        result = concat(frames)
+        expected = concat([frames[k] for k in sorted_keys], keys=sorted_keys)
+        tm.assert_frame_equal(result, expected)
+
+        result = concat(frames, axis=1)
+        expected = concat([frames[k] for k in sorted_keys], keys=sorted_keys, axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        keys = ["baz", "foo", "bar"]
+        result = concat(frames, keys=keys)
+        expected = concat([frames[k] for k in keys], keys=keys)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_keys_and_levels(self):
+        df = DataFrame(np.random.randn(1, 3))
+        df2 = DataFrame(np.random.randn(1, 4))
+
+        levels = [["foo", "baz"], ["one", "two"]]
+        names = ["first", "second"]
+        result = concat(
+            [df, df2, df, df2],
+            keys=[("foo", "one"), ("foo", "two"), ("baz", "one"), ("baz", "two")],
+            levels=levels,
+            names=names,
+        )
+        expected = concat([df, df2, df, df2])
+        exp_index = MultiIndex(
+            levels=levels + [[0]],
+            codes=[[0, 0, 1, 1], [0, 1, 0, 1], [0, 0, 0, 0]],
+            names=names + [None],
+        )
+        expected.index = exp_index
+
+        tm.assert_frame_equal(result, expected)
+
+        # no names
+        result = concat(
+            [df, df2, df, df2],
+            keys=[("foo", "one"), ("foo", "two"), ("baz", "one"), ("baz", "two")],
+            levels=levels,
+        )
+        assert result.index.names == (None,) * 3
+
+        # no levels
+        result = concat(
+            [df, df2, df, df2],
+            keys=[("foo", "one"), ("foo", "two"), ("baz", "one"), ("baz", "two")],
+            names=["first", "second"],
+        )
+        assert result.index.names == ("first", "second", None)
+        tm.assert_index_equal(
+            result.index.levels[0], Index(["baz", "foo"], name="first")
+        )
+
+    def test_concat_keys_levels_no_overlap(self):
+        # GH #1406
+        df = DataFrame(np.random.randn(1, 3), index=["a"])
+        df2 = DataFrame(np.random.randn(1, 4), index=["b"])
+
+        msg = "Values not found in passed level"
+        with pytest.raises(ValueError, match=msg):
+            concat([df, df], keys=["one", "two"], levels=[["foo", "bar", "baz"]])
+
+        msg = "Key one not in level"
+        with pytest.raises(ValueError, match=msg):
+            concat([df, df2], keys=["one", "two"], levels=[["foo", "bar", "baz"]])
+
+    def test_crossed_dtypes_weird_corner(self):
+        columns = ["A", "B", "C", "D"]
+        df1 = DataFrame(
+            {
+                "A": np.array([1, 2, 3, 4], dtype="f8"),
+                "B": np.array([1, 2, 3, 4], dtype="i8"),
+                "C": np.array([1, 2, 3, 4], dtype="f8"),
+                "D": np.array([1, 2, 3, 4], dtype="i8"),
+            },
+            columns=columns,
+        )
+
+        df2 = DataFrame(
+            {
+                "A": np.array([1, 2, 3, 4], dtype="i8"),
+                "B": np.array([1, 2, 3, 4], dtype="f8"),
+                "C": np.array([1, 2, 3, 4], dtype="i8"),
+                "D": np.array([1, 2, 3, 4], dtype="f8"),
+            },
+            columns=columns,
+        )
+
+        appended = concat([df1, df2], ignore_index=True)
+        expected = DataFrame(
+            np.concatenate([df1.values, df2.values], axis=0), columns=columns
+        )
+        tm.assert_frame_equal(appended, expected)
+
+        df = DataFrame(np.random.randn(1, 3), index=["a"])
+        df2 = DataFrame(np.random.randn(1, 4), index=["b"])
+        result = concat([df, df2], keys=["one", "two"], names=["first", "second"])
+        assert result.index.names == ("first", "second")
+
+    def test_with_mixed_tuples(self, sort):
+        # 10697
+        # columns have mixed tuples, so handle properly
+        df1 = DataFrame({"A": "foo", ("B", 1): "bar"}, index=range(2))
+        df2 = DataFrame({"B": "foo", ("B", 1): "bar"}, index=range(2))
+
+        # it works
+        concat([df1, df2], sort=sort)
+
+    def test_concat_mixed_objs(self):
+        # concat mixed series/frames
+        # G2385
+
+        # axis 1
+        index = date_range("01-Jan-2013", periods=10, freq="H")
+        arr = np.arange(10, dtype="int64")
+        s1 = Series(arr, index=index)
+        s2 = Series(arr, index=index)
+        df = DataFrame(arr.reshape(-1, 1), index=index)
+
+        expected = DataFrame(
+            np.repeat(arr, 2).reshape(-1, 2), index=index, columns=[0, 0]
+        )
+        result = concat([df, df], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame(
+            np.repeat(arr, 2).reshape(-1, 2), index=index, columns=[0, 1]
+        )
+        result = concat([s1, s2], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame(
+            np.repeat(arr, 3).reshape(-1, 3), index=index, columns=[0, 1, 2]
+        )
+        result = concat([s1, s2, s1], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame(
+            np.repeat(arr, 5).reshape(-1, 5), index=index, columns=[0, 0, 1, 2, 3]
+        )
+        result = concat([s1, df, s2, s2, s1], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        # with names
+        s1.name = "foo"
+        expected = DataFrame(
+            np.repeat(arr, 3).reshape(-1, 3), index=index, columns=["foo", 0, 0]
+        )
+        result = concat([s1, df, s2], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        s2.name = "bar"
+        expected = DataFrame(
+            np.repeat(arr, 3).reshape(-1, 3), index=index, columns=["foo", 0, "bar"]
+        )
+        result = concat([s1, df, s2], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+        # ignore index
+        expected = DataFrame(
+            np.repeat(arr, 3).reshape(-1, 3), index=index, columns=[0, 1, 2]
+        )
+        result = concat([s1, df, s2], axis=1, ignore_index=True)
+        tm.assert_frame_equal(result, expected)
+
+        # axis 0
+        expected = DataFrame(
+            np.tile(arr, 3).reshape(-1, 1), index=index.tolist() * 3, columns=[0]
+        )
+        result = concat([s1, df, s2])
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame(np.tile(arr, 3).reshape(-1, 1), columns=[0])
+        result = concat([s1, df, s2], ignore_index=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_dtype_coerceion(self):
+        # 12411
+        df = DataFrame({"date": [pd.Timestamp("20130101").tz_localize("UTC"), pd.NaT]})
+
+        result = concat([df.iloc[[0]], df.iloc[[1]]])
+        tm.assert_series_equal(result.dtypes, df.dtypes)
+
+        # 12045
+        df = DataFrame({"date": [datetime(2012, 1, 1), datetime(1012, 1, 2)]})
+        result = concat([df.iloc[[0]], df.iloc[[1]]])
+        tm.assert_series_equal(result.dtypes, df.dtypes)
+
+        # 11594
+        df = DataFrame({"text": ["some words"] + [None] * 9})
+        result = concat([df.iloc[[0]], df.iloc[[1]]])
+        tm.assert_series_equal(result.dtypes, df.dtypes)
+
+    def test_concat_single_with_key(self):
+        df = DataFrame(np.random.randn(10, 4))
+
+        result = concat([df], keys=["foo"])
+        expected = concat([df, df], keys=["foo", "bar"])
+        tm.assert_frame_equal(result, expected[:10])
+
+    def test_concat_no_items_raises(self):
+        with pytest.raises(ValueError, match="No objects to concatenate"):
+            concat([])
+
+    def test_concat_exclude_none(self):
+        df = DataFrame(np.random.randn(10, 4))
+
+        pieces = [df[:5], None, None, df[5:]]
+        result = concat(pieces)
+        tm.assert_frame_equal(result, df)
+        with pytest.raises(ValueError, match="All objects passed were None"):
+            concat([None, None])
+
+    def test_concat_keys_with_none(self):
+        # #1649
+        df0 = DataFrame([[10, 20, 30], [10, 20, 30], [10, 20, 30]])
+
+        result = concat({"a": None, "b": df0, "c": df0[:2], "d": df0[:1], "e": df0})
+        expected = concat({"b": df0, "c": df0[:2], "d": df0[:1], "e": df0})
+        tm.assert_frame_equal(result, expected)
+
+        result = concat(
+            [None, df0, df0[:2], df0[:1], df0], keys=["a", "b", "c", "d", "e"]
+        )
+        expected = concat([df0, df0[:2], df0[:1], df0], keys=["b", "c", "d", "e"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_bug_1719(self):
+        ts1 = tm.makeTimeSeries()
+        ts2 = tm.makeTimeSeries()[::2]
+
+        # to join with union
+        # these two are of different length!
+        left = concat([ts1, ts2], join="outer", axis=1)
+        right = concat([ts2, ts1], join="outer", axis=1)
+
+        assert len(left) == len(right)
+
+    def test_concat_bug_2972(self):
+        ts0 = Series(np.zeros(5))
+        ts1 = Series(np.ones(5))
+        ts0.name = ts1.name = "same name"
+        result = concat([ts0, ts1], axis=1)
+
+        expected = DataFrame({0: ts0, 1: ts1})
+        expected.columns = ["same name", "same name"]
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_bug_3602(self):
+        # GH 3602, duplicate columns
+        df1 = DataFrame(
+            {
+                "firmNo": [0, 0, 0, 0],
+                "prc": [6, 6, 6, 6],
+                "stringvar": ["rrr", "rrr", "rrr", "rrr"],
+            }
+        )
+        df2 = DataFrame(
+            {"C": [9, 10, 11, 12], "misc": [1, 2, 3, 4], "prc": [6, 6, 6, 6]}
+        )
+        expected = DataFrame(
+            [
+                [0, 6, "rrr", 9, 1, 6],
+                [0, 6, "rrr", 10, 2, 6],
+                [0, 6, "rrr", 11, 3, 6],
+                [0, 6, "rrr", 12, 4, 6],
+            ]
+        )
+        expected.columns = ["firmNo", "prc", "stringvar", "C", "misc", "prc"]
+
+        result = concat([df1, df2], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_iterables(self):
+        # GH8645 check concat works with tuples, list, generators, and weird
+        # stuff like deque and custom iterables
+        df1 = DataFrame([1, 2, 3])
+        df2 = DataFrame([4, 5, 6])
+        expected = DataFrame([1, 2, 3, 4, 5, 6])
+        tm.assert_frame_equal(concat((df1, df2), ignore_index=True), expected)
+        tm.assert_frame_equal(concat([df1, df2], ignore_index=True), expected)
+        tm.assert_frame_equal(
+            concat((df for df in (df1, df2)), ignore_index=True), expected
+        )
+        tm.assert_frame_equal(concat(deque((df1, df2)), ignore_index=True), expected)
+
+        class CustomIterator1:
+            def __len__(self) -> int:
+                return 2
+
+            def __getitem__(self, index):
+                try:
+                    return {0: df1, 1: df2}[index]
+                except KeyError as err:
+                    raise IndexError from err
+
+        tm.assert_frame_equal(concat(CustomIterator1(), ignore_index=True), expected)
+
+        class CustomIterator2(abc.Iterable):
+            def __iter__(self) -> Iterator:
+                yield df1
+                yield df2
+
+        tm.assert_frame_equal(concat(CustomIterator2(), ignore_index=True), expected)
+
+    def test_concat_order(self):
+        # GH 17344, GH#47331
+        dfs = [DataFrame(index=range(3), columns=["a", 1, None])]
+        dfs += [DataFrame(index=range(3), columns=[None, 1, "a"]) for _ in range(100)]
+
+        result = concat(dfs, sort=True).columns
+        expected = Index([1, "a", None])
+        tm.assert_index_equal(result, expected)
+
+    def test_concat_different_extension_dtypes_upcasts(self):
+        a = Series(pd.array([1, 2], dtype="Int64"))
+        b = Series(to_decimal([1, 2]))
+
+        result = concat([a, b], ignore_index=True)
+        expected = Series([1, 2, Decimal(1), Decimal(2)], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_ordered_dict(self):
+        # GH 21510
+        expected = concat(
+            [Series(range(3)), Series(range(4))], keys=["First", "Another"]
+        )
+        result = concat({"First": Series(range(3)), "Another": Series(range(4))})
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_duplicate_indices_raise(self):
+        # GH 45888: test raise for concat DataFrames with duplicate indices
+        # https://github.com/pandas-dev/pandas/issues/36263
+        df1 = DataFrame(np.random.randn(5), index=[0, 1, 2, 3, 3], columns=["a"])
+        df2 = DataFrame(np.random.randn(5), index=[0, 1, 2, 2, 4], columns=["b"])
+        msg = "Reindexing only valid with uniquely valued Index objects"
+        with pytest.raises(InvalidIndexError, match=msg):
+            concat([df1, df2], axis=1)
+
+
+@pytest.mark.parametrize("dt", np.sctypes["float"])
+def test_concat_no_unnecessary_upcast(dt, frame_or_series):
+    # GH 13247
+    dims = frame_or_series(dtype=object).ndim
+
+    dfs = [
+        frame_or_series(np.array([1], dtype=dt, ndmin=dims)),
+        frame_or_series(np.array([np.nan], dtype=dt, ndmin=dims)),
+        frame_or_series(np.array([5], dtype=dt, ndmin=dims)),
+    ]
+    x = concat(dfs)
+    assert x.values.dtype == dt
+
+
+@pytest.mark.parametrize("pdt", [Series, DataFrame])
+@pytest.mark.parametrize("dt", np.sctypes["int"])
+def test_concat_will_upcast(dt, pdt):
+    with catch_warnings(record=True):
+        dims = pdt().ndim
+        dfs = [
+            pdt(np.array([1], dtype=dt, ndmin=dims)),
+            pdt(np.array([np.nan], ndmin=dims)),
+            pdt(np.array([5], dtype=dt, ndmin=dims)),
+        ]
+        x = concat(dfs)
+        assert x.values.dtype == "float64"
+
+
+def test_concat_empty_and_non_empty_frame_regression():
+    # GH 18178 regression test
+    df1 = DataFrame({"foo": [1]})
+    df2 = DataFrame({"foo": []})
+    expected = DataFrame({"foo": [1.0]})
+    result = concat([df1, df2])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_sparse():
+    # GH 23557
+    a = Series(SparseArray([0, 1, 2]))
+    expected = DataFrame(data=[[0, 0], [1, 1], [2, 2]]).astype(
+        pd.SparseDtype(np.int64, 0)
+    )
+    result = concat([a, a], axis=1)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_dense_sparse():
+    # GH 30668
+    dtype = pd.SparseDtype(np.float64, None)
+    a = Series(pd.arrays.SparseArray([1, None]), dtype=dtype)
+    b = Series([1], dtype=float)
+    expected = Series(data=[1, None, 1], index=[0, 1, 0]).astype(dtype)
+    result = concat([a, b], axis=0)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("keys", [["e", "f", "f"], ["f", "e", "f"]])
+def test_duplicate_keys(keys):
+    # GH 33654
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    s1 = Series([7, 8, 9], name="c")
+    s2 = Series([10, 11, 12], name="d")
+    result = concat([df, s1, s2], axis=1, keys=keys)
+    expected_values = [[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]]
+    expected_columns = MultiIndex.from_tuples(
+        [(keys[0], "a"), (keys[0], "b"), (keys[1], "c"), (keys[2], "d")]
+    )
+    expected = DataFrame(expected_values, columns=expected_columns)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_duplicate_keys_same_frame():
+    # GH 43595
+    keys = ["e", "e"]
+    df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+    result = concat([df, df], axis=1, keys=keys)
+    expected_values = [[1, 4, 1, 4], [2, 5, 2, 5], [3, 6, 3, 6]]
+    expected_columns = MultiIndex.from_tuples(
+        [(keys[0], "a"), (keys[0], "b"), (keys[1], "a"), (keys[1], "b")]
+    )
+    expected = DataFrame(expected_values, columns=expected_columns)
+    with catch_warnings():
+        # result.columns not sorted, resulting in performance warning
+        simplefilter("ignore", PerformanceWarning)
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "obj",
+    [
+        tm.SubclassedDataFrame({"A": np.arange(0, 10)}),
+        tm.SubclassedSeries(np.arange(0, 10), name="A"),
+    ],
+)
+def test_concat_preserves_subclass(obj):
+    # GH28330 -- preserve subclass
+
+    result = concat([obj, obj])
+    assert isinstance(result, type(obj))
+
+
+def test_concat_frame_axis0_extension_dtypes():
+    # preserve extension dtype (through common_dtype mechanism)
+    df1 = DataFrame({"a": pd.array([1, 2, 3], dtype="Int64")})
+    df2 = DataFrame({"a": np.array([4, 5, 6])})
+
+    result = concat([df1, df2], ignore_index=True)
+    expected = DataFrame({"a": [1, 2, 3, 4, 5, 6]}, dtype="Int64")
+    tm.assert_frame_equal(result, expected)
+
+    result = concat([df2, df1], ignore_index=True)
+    expected = DataFrame({"a": [4, 5, 6, 1, 2, 3]}, dtype="Int64")
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_preserves_extension_int64_dtype():
+    # GH 24768
+    df_a = DataFrame({"a": [-1]}, dtype="Int64")
+    df_b = DataFrame({"b": [1]}, dtype="Int64")
+    result = concat([df_a, df_b], ignore_index=True)
+    expected = DataFrame({"a": [-1, None], "b": [None, 1]}, dtype="Int64")
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype1,dtype2,expected_dtype",
+    [
+        ("bool", "bool", "bool"),
+        ("boolean", "bool", "boolean"),
+        ("bool", "boolean", "boolean"),
+        ("boolean", "boolean", "boolean"),
+    ],
+)
+def test_concat_bool_types(dtype1, dtype2, expected_dtype):
+    # GH 42800
+    ser1 = Series([True, False], dtype=dtype1)
+    ser2 = Series([False, True], dtype=dtype2)
+    result = concat([ser1, ser2], ignore_index=True)
+    expected = Series([True, False, False, True], dtype=expected_dtype)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("keys", "integrity"),
+    [
+        (["red"] * 3, True),
+        (["red"] * 3, False),
+        (["red", "blue", "red"], False),
+        (["red", "blue", "red"], True),
+    ],
+)
+def test_concat_repeated_keys(keys, integrity):
+    # GH: 20816
+    series_list = [Series({"a": 1}), Series({"b": 2}), Series({"c": 3})]
+    result = concat(series_list, keys=keys, verify_integrity=integrity)
+    tuples = list(zip(keys, ["a", "b", "c"]))
+    expected = Series([1, 2, 3], index=MultiIndex.from_tuples(tuples))
+    tm.assert_series_equal(result, expected)
+
+
+def test_concat_null_object_with_dti():
+    # GH#40841
+    dti = pd.DatetimeIndex(
+        ["2021-04-08 21:21:14+00:00"], dtype="datetime64[ns, UTC]", name="Time (UTC)"
+    )
+    right = DataFrame(data={"C": [0.5274]}, index=dti)
+
+    idx = Index([None], dtype="object", name="Maybe Time (UTC)")
+    left = DataFrame(data={"A": [None], "B": [np.nan]}, index=idx)
+
+    result = concat([left, right], axis="columns")
+
+    exp_index = Index([None, dti[0]], dtype=object)
+    expected = DataFrame(
+        {"A": [None, None], "B": [np.nan, np.nan], "C": [np.nan, 0.5274]},
+        index=exp_index,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_concat_multiindex_with_empty_rangeindex():
+    # GH#41234
+    mi = MultiIndex.from_tuples([("B", 1), ("C", 1)])
+    df1 = DataFrame([[1, 2]], columns=mi)
+    df2 = DataFrame(index=[1], columns=pd.RangeIndex(0))
+
+    result = concat([df1, df2])
+    expected = DataFrame([[1, 2], [np.nan, np.nan]], columns=mi)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        Series(data=[1, 2]),
+        DataFrame(
+            data={
+                "col1": [1, 2],
+            }
+        ),
+        DataFrame(dtype=float),
+        Series(dtype=float),
+    ],
+)
+def test_concat_drop_attrs(data):
+    # GH#41828
+    df1 = data.copy()
+    df1.attrs = {1: 1}
+    df2 = data.copy()
+    df2.attrs = {1: 2}
+    df = concat([df1, df2])
+    assert len(df.attrs) == 0
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        Series(data=[1, 2]),
+        DataFrame(
+            data={
+                "col1": [1, 2],
+            }
+        ),
+        DataFrame(dtype=float),
+        Series(dtype=float),
+    ],
+)
+def test_concat_retain_attrs(data):
+    # GH#41828
+    df1 = data.copy()
+    df1.attrs = {1: 1}
+    df2 = data.copy()
+    df2.attrs = {1: 1}
+    df = concat([df1, df2])
+    assert df.attrs[1] == 1
+
+
+@td.skip_array_manager_invalid_test
+@pytest.mark.parametrize("df_dtype", ["float64", "int64", "datetime64[ns]"])
+@pytest.mark.parametrize("empty_dtype", [None, "float64", "object"])
+def test_concat_ignore_empty_object_float(empty_dtype, df_dtype):
+    # https://github.com/pandas-dev/pandas/issues/45637
+    df = DataFrame({"foo": [1, 2], "bar": [1, 2]}, dtype=df_dtype)
+    empty = DataFrame(columns=["foo", "bar"], dtype=empty_dtype)
+    result = concat([empty, df])
+    expected = df
+    if df_dtype == "int64":
+        # TODO what exact behaviour do we want for integer eventually?
+        if empty_dtype == "float64":
+            expected = df.astype("float64")
+        else:
+            expected = df.astype("object")
+    tm.assert_frame_equal(result, expected)
+
+
+@td.skip_array_manager_invalid_test
+@pytest.mark.parametrize("df_dtype", ["float64", "int64", "datetime64[ns]"])
+@pytest.mark.parametrize("empty_dtype", [None, "float64", "object"])
+def test_concat_ignore_all_na_object_float(empty_dtype, df_dtype):
+    df = DataFrame({"foo": [1, 2], "bar": [1, 2]}, dtype=df_dtype)
+    empty = DataFrame({"foo": [np.nan], "bar": [np.nan]}, dtype=empty_dtype)
+    result = concat([empty, df], ignore_index=True)
+
+    if df_dtype == "int64":
+        # TODO what exact behaviour do we want for integer eventually?
+        if empty_dtype == "object":
+            df_dtype = "object"
+        else:
+            df_dtype = "float64"
+    expected = DataFrame({"foo": [None, 1, 2], "bar": [None, 1, 2]}, dtype=df_dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+@td.skip_array_manager_invalid_test
+def test_concat_ignore_empty_from_reindex():
+    # https://github.com/pandas-dev/pandas/pull/43507#issuecomment-920375856
+    df1 = DataFrame({"a": [1], "b": [pd.Timestamp("2012-01-01")]})
+    df2 = DataFrame({"a": [2]})
+
+    result = concat([df1, df2.reindex(columns=df1.columns)], ignore_index=True)
+    expected = df1 = DataFrame({"a": [1, 2], "b": [pd.Timestamp("2012-01-01"), pd.NaT]})
+    tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_dataframe.py

@@ -0,0 +1,230 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    concat,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameConcat:
+    def test_concat_multiple_frames_dtypes(self):
+        # GH#2759
+        df1 = DataFrame(data=np.ones((10, 2)), columns=["foo", "bar"], dtype=np.float64)
+        df2 = DataFrame(data=np.ones((10, 2)), dtype=np.float32)
+        results = concat((df1, df2), axis=1).dtypes
+        expected = Series(
+            [np.dtype("float64")] * 2 + [np.dtype("float32")] * 2,
+            index=["foo", "bar", 0, 1],
+        )
+        tm.assert_series_equal(results, expected)
+
+    def test_concat_tuple_keys(self):
+        # GH#14438
+        df1 = DataFrame(np.ones((2, 2)), columns=list("AB"))
+        df2 = DataFrame(np.ones((3, 2)) * 2, columns=list("AB"))
+        results = concat((df1, df2), keys=[("bee", "bah"), ("bee", "boo")])
+        expected = DataFrame(
+            {
+                "A": {
+                    ("bee", "bah", 0): 1.0,
+                    ("bee", "bah", 1): 1.0,
+                    ("bee", "boo", 0): 2.0,
+                    ("bee", "boo", 1): 2.0,
+                    ("bee", "boo", 2): 2.0,
+                },
+                "B": {
+                    ("bee", "bah", 0): 1.0,
+                    ("bee", "bah", 1): 1.0,
+                    ("bee", "boo", 0): 2.0,
+                    ("bee", "boo", 1): 2.0,
+                    ("bee", "boo", 2): 2.0,
+                },
+            }
+        )
+        tm.assert_frame_equal(results, expected)
+
+    def test_concat_named_keys(self):
+        # GH#14252
+        df = DataFrame({"foo": [1, 2], "bar": [0.1, 0.2]})
+        index = Index(["a", "b"], name="baz")
+        concatted_named_from_keys = concat([df, df], keys=index)
+        expected_named = DataFrame(
+            {"foo": [1, 2, 1, 2], "bar": [0.1, 0.2, 0.1, 0.2]},
+            index=pd.MultiIndex.from_product((["a", "b"], [0, 1]), names=["baz", None]),
+        )
+        tm.assert_frame_equal(concatted_named_from_keys, expected_named)
+
+        index_no_name = Index(["a", "b"], name=None)
+        concatted_named_from_names = concat([df, df], keys=index_no_name, names=["baz"])
+        tm.assert_frame_equal(concatted_named_from_names, expected_named)
+
+        concatted_unnamed = concat([df, df], keys=index_no_name)
+        expected_unnamed = DataFrame(
+            {"foo": [1, 2, 1, 2], "bar": [0.1, 0.2, 0.1, 0.2]},
+            index=pd.MultiIndex.from_product((["a", "b"], [0, 1]), names=[None, None]),
+        )
+        tm.assert_frame_equal(concatted_unnamed, expected_unnamed)
+
+    def test_concat_axis_parameter(self):
+        # GH#14369
+        df1 = DataFrame({"A": [0.1, 0.2]}, index=range(2))
+        df2 = DataFrame({"A": [0.3, 0.4]}, index=range(2))
+
+        # Index/row/0 DataFrame
+        expected_index = DataFrame({"A": [0.1, 0.2, 0.3, 0.4]}, index=[0, 1, 0, 1])
+
+        concatted_index = concat([df1, df2], axis="index")
+        tm.assert_frame_equal(concatted_index, expected_index)
+
+        concatted_row = concat([df1, df2], axis="rows")
+        tm.assert_frame_equal(concatted_row, expected_index)
+
+        concatted_0 = concat([df1, df2], axis=0)
+        tm.assert_frame_equal(concatted_0, expected_index)
+
+        # Columns/1 DataFrame
+        expected_columns = DataFrame(
+            [[0.1, 0.3], [0.2, 0.4]], index=[0, 1], columns=["A", "A"]
+        )
+
+        concatted_columns = concat([df1, df2], axis="columns")
+        tm.assert_frame_equal(concatted_columns, expected_columns)
+
+        concatted_1 = concat([df1, df2], axis=1)
+        tm.assert_frame_equal(concatted_1, expected_columns)
+
+        series1 = Series([0.1, 0.2])
+        series2 = Series([0.3, 0.4])
+
+        # Index/row/0 Series
+        expected_index_series = Series([0.1, 0.2, 0.3, 0.4], index=[0, 1, 0, 1])
+
+        concatted_index_series = concat([series1, series2], axis="index")
+        tm.assert_series_equal(concatted_index_series, expected_index_series)
+
+        concatted_row_series = concat([series1, series2], axis="rows")
+        tm.assert_series_equal(concatted_row_series, expected_index_series)
+
+        concatted_0_series = concat([series1, series2], axis=0)
+        tm.assert_series_equal(concatted_0_series, expected_index_series)
+
+        # Columns/1 Series
+        expected_columns_series = DataFrame(
+            [[0.1, 0.3], [0.2, 0.4]], index=[0, 1], columns=[0, 1]
+        )
+
+        concatted_columns_series = concat([series1, series2], axis="columns")
+        tm.assert_frame_equal(concatted_columns_series, expected_columns_series)
+
+        concatted_1_series = concat([series1, series2], axis=1)
+        tm.assert_frame_equal(concatted_1_series, expected_columns_series)
+
+        # Testing ValueError
+        with pytest.raises(ValueError, match="No axis named"):
+            concat([series1, series2], axis="something")
+
+    def test_concat_numerical_names(self):
+        # GH#15262, GH#12223
+        df = DataFrame(
+            {"col": range(9)},
+            dtype="int32",
+            index=(
+                pd.MultiIndex.from_product(
+                    [["A0", "A1", "A2"], ["B0", "B1", "B2"]], names=[1, 2]
+                )
+            ),
+        )
+        result = concat((df.iloc[:2, :], df.iloc[-2:, :]))
+        expected = DataFrame(
+            {"col": [0, 1, 7, 8]},
+            dtype="int32",
+            index=pd.MultiIndex.from_tuples(
+                [("A0", "B0"), ("A0", "B1"), ("A2", "B1"), ("A2", "B2")], names=[1, 2]
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_astype_dup_col(self):
+        # GH#23049
+        df = DataFrame([{"a": "b"}])
+        df = concat([df, df], axis=1)
+
+        result = df.astype("category")
+        expected = DataFrame(
+            np.array(["b", "b"]).reshape(1, 2), columns=["a", "a"]
+        ).astype("category")
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_dataframe_keys_bug(self, sort):
+        t1 = DataFrame(
+            {"value": Series([1, 2, 3], index=Index(["a", "b", "c"], name="id"))}
+        )
+        t2 = DataFrame({"value": Series([7, 8], index=Index(["a", "b"], name="id"))})
+
+        # it works
+        result = concat([t1, t2], axis=1, keys=["t1", "t2"], sort=sort)
+        assert list(result.columns) == [("t1", "value"), ("t2", "value")]
+
+    def test_concat_bool_with_int(self):
+        # GH#42092 we may want to change this to return object, but that
+        #  would need a deprecation
+        df1 = DataFrame(Series([True, False, True, True], dtype="bool"))
+        df2 = DataFrame(Series([1, 0, 1], dtype="int64"))
+
+        result = concat([df1, df2])
+        expected = concat([df1.astype("int64"), df2])
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_duplicates_in_index_with_keys(self):
+        # GH#42651
+        index = [1, 1, 3]
+        data = [1, 2, 3]
+
+        df = DataFrame(data=data, index=index)
+        result = concat([df], keys=["A"], names=["ID", "date"])
+        mi = pd.MultiIndex.from_product([["A"], index], names=["ID", "date"])
+        expected = DataFrame(data=data, index=mi)
+        tm.assert_frame_equal(result, expected)
+        tm.assert_index_equal(result.index.levels[1], Index([1, 3], name="date"))
+
+    @pytest.mark.parametrize("ignore_index", [True, False])
+    @pytest.mark.parametrize("order", ["C", "F"])
+    @pytest.mark.parametrize("axis", [0, 1])
+    def test_concat_copies(self, axis, order, ignore_index, using_copy_on_write):
+        # based on asv ConcatDataFrames
+        df = DataFrame(np.zeros((10000, 200), dtype=np.float32, order=order))
+
+        res = concat([df] * 5, axis=axis, ignore_index=ignore_index, copy=True)
+
+        if not using_copy_on_write:
+            for arr in res._iter_column_arrays():
+                for arr2 in df._iter_column_arrays():
+                    assert not np.shares_memory(arr, arr2)
+
+    def test_outer_sort_columns(self):
+        # GH#47127
+        df1 = DataFrame({"A": [0], "B": [1], 0: 1})
+        df2 = DataFrame({"A": [100]})
+        result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
+        expected = DataFrame({0: [1.0, np.nan], "A": [0, 100], "B": [1.0, np.nan]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_inner_sort_columns(self):
+        # GH#47127
+        df1 = DataFrame({"A": [0], "B": [1], 0: 1})
+        df2 = DataFrame({"A": [100], 0: 2})
+        result = concat([df1, df2], ignore_index=True, join="inner", sort=True)
+        expected = DataFrame({0: [1, 2], "A": [0, 100]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_sort_columns_one_df(self):
+        # GH#47127
+        df1 = DataFrame({"A": [100], 0: 2})
+        result = concat([df1], ignore_index=True, join="inner", sort=True)
+        expected = DataFrame({0: [2], "A": [100]})
+        tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_datetimes.py

@@ -0,0 +1,540 @@
+import datetime as dt
+from datetime import datetime
+
+import dateutil
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    Series,
+    Timestamp,
+    concat,
+    date_range,
+    to_timedelta,
+)
+import pandas._testing as tm
+
+
+class TestDatetimeConcat:
+    def test_concat_datetime64_block(self):
+        rng = date_range("1/1/2000", periods=10)
+
+        df = DataFrame({"time": rng})
+
+        result = concat([df, df])
+        assert (result.iloc[:10]["time"] == rng).all()
+        assert (result.iloc[10:]["time"] == rng).all()
+
+    def test_concat_datetime_datetime64_frame(self):
+        # GH#2624
+        rows = []
+        rows.append([datetime(2010, 1, 1), 1])
+        rows.append([datetime(2010, 1, 2), "hi"])
+
+        df2_obj = DataFrame.from_records(rows, columns=["date", "test"])
+
+        ind = date_range(start="2000/1/1", freq="D", periods=10)
+        df1 = DataFrame({"date": ind, "test": range(10)})
+
+        # it works!
+        concat([df1, df2_obj])
+
+    def test_concat_datetime_timezone(self):
+        # GH 18523
+        idx1 = date_range("2011-01-01", periods=3, freq="H", tz="Europe/Paris")
+        idx2 = date_range(start=idx1[0], end=idx1[-1], freq="H")
+        df1 = DataFrame({"a": [1, 2, 3]}, index=idx1)
+        df2 = DataFrame({"b": [1, 2, 3]}, index=idx2)
+        result = concat([df1, df2], axis=1)
+
+        exp_idx = (
+            DatetimeIndex(
+                [
+                    "2011-01-01 00:00:00+01:00",
+                    "2011-01-01 01:00:00+01:00",
+                    "2011-01-01 02:00:00+01:00",
+                ],
+                freq="H",
+            )
+            .tz_convert("UTC")
+            .tz_convert("Europe/Paris")
+        )
+
+        expected = DataFrame(
+            [[1, 1], [2, 2], [3, 3]], index=exp_idx, columns=["a", "b"]
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+        idx3 = date_range("2011-01-01", periods=3, freq="H", tz="Asia/Tokyo")
+        df3 = DataFrame({"b": [1, 2, 3]}, index=idx3)
+        result = concat([df1, df3], axis=1)
+
+        exp_idx = DatetimeIndex(
+            [
+                "2010-12-31 15:00:00+00:00",
+                "2010-12-31 16:00:00+00:00",
+                "2010-12-31 17:00:00+00:00",
+                "2010-12-31 23:00:00+00:00",
+                "2011-01-01 00:00:00+00:00",
+                "2011-01-01 01:00:00+00:00",
+            ]
+        )
+
+        expected = DataFrame(
+            [
+                [np.nan, 1],
+                [np.nan, 2],
+                [np.nan, 3],
+                [1, np.nan],
+                [2, np.nan],
+                [3, np.nan],
+            ],
+            index=exp_idx,
+            columns=["a", "b"],
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+        # GH 13783: Concat after resample
+        result = concat([df1.resample("H").mean(), df2.resample("H").mean()], sort=True)
+        expected = DataFrame(
+            {"a": [1, 2, 3] + [np.nan] * 3, "b": [np.nan] * 3 + [1, 2, 3]},
+            index=idx1.append(idx1),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_datetimeindex_freq(self):
+        # GH 3232
+        # Monotonic index result
+        dr = date_range("01-Jan-2013", periods=100, freq="50L", tz="UTC")
+        data = list(range(100))
+        expected = DataFrame(data, index=dr)
+        result = concat([expected[:50], expected[50:]])
+        tm.assert_frame_equal(result, expected)
+
+        # Non-monotonic index result
+        result = concat([expected[50:], expected[:50]])
+        expected = DataFrame(data[50:] + data[:50], index=dr[50:].append(dr[:50]))
+        expected.index._data.freq = None
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_multiindex_datetime_object_index(self):
+        # https://github.com/pandas-dev/pandas/issues/11058
+        idx = Index(
+            [dt.date(2013, 1, 1), dt.date(2014, 1, 1), dt.date(2015, 1, 1)],
+            dtype="object",
+        )
+
+        s = Series(
+            ["a", "b"],
+            index=MultiIndex.from_arrays(
+                [
+                    [1, 2],
+                    idx[:-1],
+                ],
+                names=["first", "second"],
+            ),
+        )
+        s2 = Series(
+            ["a", "b"],
+            index=MultiIndex.from_arrays(
+                [[1, 2], idx[::2]],
+                names=["first", "second"],
+            ),
+        )
+        mi = MultiIndex.from_arrays(
+            [[1, 2, 2], idx],
+            names=["first", "second"],
+        )
+        assert mi.levels[1].dtype == object
+
+        expected = DataFrame(
+            [["a", "a"], ["b", np.nan], [np.nan, "b"]],
+            index=mi,
+        )
+        result = concat([s, s2], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_NaT_series(self):
+        # GH 11693
+        # test for merging NaT series with datetime series.
+        x = Series(
+            date_range("20151124 08:00", "20151124 09:00", freq="1h", tz="US/Eastern")
+        )
+        y = Series(pd.NaT, index=[0, 1], dtype="datetime64[ns, US/Eastern]")
+        expected = Series([x[0], x[1], pd.NaT, pd.NaT])
+
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+        # all NaT with tz
+        expected = Series(pd.NaT, index=range(4), dtype="datetime64[ns, US/Eastern]")
+        result = concat([y, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+        # without tz
+        x = Series(date_range("20151124 08:00", "20151124 09:00", freq="1h"))
+        y = Series(date_range("20151124 10:00", "20151124 11:00", freq="1h"))
+        y[:] = pd.NaT
+        expected = Series([x[0], x[1], pd.NaT, pd.NaT])
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+        # all NaT without tz
+        x[:] = pd.NaT
+        expected = Series(pd.NaT, index=range(4), dtype="datetime64[ns]")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("tz", [None, "UTC"])
+    def test_concat_NaT_dataframes(self, tz):
+        # GH 12396
+
+        first = DataFrame([[pd.NaT], [pd.NaT]])
+        first = first.apply(lambda x: x.dt.tz_localize(tz))
+        second = DataFrame(
+            [[Timestamp("2015/01/01", tz=tz)], [Timestamp("2016/01/01", tz=tz)]],
+            index=[2, 3],
+        )
+        expected = DataFrame(
+            [
+                pd.NaT,
+                pd.NaT,
+                Timestamp("2015/01/01", tz=tz),
+                Timestamp("2016/01/01", tz=tz),
+            ]
+        )
+
+        result = concat([first, second], axis=0)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("tz1", [None, "UTC"])
+    @pytest.mark.parametrize("tz2", [None, "UTC"])
+    @pytest.mark.parametrize("s", [pd.NaT, Timestamp("20150101")])
+    def test_concat_NaT_dataframes_all_NaT_axis_0(self, tz1, tz2, s):
+        # GH 12396
+
+        # tz-naive
+        first = DataFrame([[pd.NaT], [pd.NaT]]).apply(lambda x: x.dt.tz_localize(tz1))
+        second = DataFrame([s]).apply(lambda x: x.dt.tz_localize(tz2))
+
+        result = concat([first, second], axis=0)
+        expected = DataFrame(Series([pd.NaT, pd.NaT, s], index=[0, 1, 0]))
+        expected = expected.apply(lambda x: x.dt.tz_localize(tz2))
+        if tz1 != tz2:
+            expected = expected.astype(object)
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("tz1", [None, "UTC"])
+    @pytest.mark.parametrize("tz2", [None, "UTC"])
+    def test_concat_NaT_dataframes_all_NaT_axis_1(self, tz1, tz2):
+        # GH 12396
+
+        first = DataFrame(Series([pd.NaT, pd.NaT]).dt.tz_localize(tz1))
+        second = DataFrame(Series([pd.NaT]).dt.tz_localize(tz2), columns=[1])
+        expected = DataFrame(
+            {
+                0: Series([pd.NaT, pd.NaT]).dt.tz_localize(tz1),
+                1: Series([pd.NaT, pd.NaT]).dt.tz_localize(tz2),
+            }
+        )
+        result = concat([first, second], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("tz1", [None, "UTC"])
+    @pytest.mark.parametrize("tz2", [None, "UTC"])
+    def test_concat_NaT_series_dataframe_all_NaT(self, tz1, tz2):
+        # GH 12396
+
+        # tz-naive
+        first = Series([pd.NaT, pd.NaT]).dt.tz_localize(tz1)
+        second = DataFrame(
+            [
+                [Timestamp("2015/01/01", tz=tz2)],
+                [Timestamp("2016/01/01", tz=tz2)],
+            ],
+            index=[2, 3],
+        )
+
+        expected = DataFrame(
+            [
+                pd.NaT,
+                pd.NaT,
+                Timestamp("2015/01/01", tz=tz2),
+                Timestamp("2016/01/01", tz=tz2),
+            ]
+        )
+        if tz1 != tz2:
+            expected = expected.astype(object)
+
+        result = concat([first, second])
+        tm.assert_frame_equal(result, expected)
+
+
+class TestTimezoneConcat:
+    def test_concat_tz_series(self):
+        # gh-11755: tz and no tz
+        x = Series(date_range("20151124 08:00", "20151124 09:00", freq="1h", tz="UTC"))
+        y = Series(date_range("2012-01-01", "2012-01-02"))
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+        # gh-11887: concat tz and object
+        x = Series(date_range("20151124 08:00", "20151124 09:00", freq="1h", tz="UTC"))
+        y = Series(["a", "b"])
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+        # see gh-12217 and gh-12306
+        # Concatenating two UTC times
+        first = DataFrame([[datetime(2016, 1, 1)]])
+        first[0] = first[0].dt.tz_localize("UTC")
+
+        second = DataFrame([[datetime(2016, 1, 2)]])
+        second[0] = second[0].dt.tz_localize("UTC")
+
+        result = concat([first, second])
+        assert result[0].dtype == "datetime64[ns, UTC]"
+
+        # Concatenating two London times
+        first = DataFrame([[datetime(2016, 1, 1)]])
+        first[0] = first[0].dt.tz_localize("Europe/London")
+
+        second = DataFrame([[datetime(2016, 1, 2)]])
+        second[0] = second[0].dt.tz_localize("Europe/London")
+
+        result = concat([first, second])
+        assert result[0].dtype == "datetime64[ns, Europe/London]"
+
+        # Concatenating 2+1 London times
+        first = DataFrame([[datetime(2016, 1, 1)], [datetime(2016, 1, 2)]])
+        first[0] = first[0].dt.tz_localize("Europe/London")
+
+        second = DataFrame([[datetime(2016, 1, 3)]])
+        second[0] = second[0].dt.tz_localize("Europe/London")
+
+        result = concat([first, second])
+        assert result[0].dtype == "datetime64[ns, Europe/London]"
+
+        # Concat'ing 1+2 London times
+        first = DataFrame([[datetime(2016, 1, 1)]])
+        first[0] = first[0].dt.tz_localize("Europe/London")
+
+        second = DataFrame([[datetime(2016, 1, 2)], [datetime(2016, 1, 3)]])
+        second[0] = second[0].dt.tz_localize("Europe/London")
+
+        result = concat([first, second])
+        assert result[0].dtype == "datetime64[ns, Europe/London]"
+
+    def test_concat_tz_series_tzlocal(self):
+        # see gh-13583
+        x = [
+            Timestamp("2011-01-01", tz=dateutil.tz.tzlocal()),
+            Timestamp("2011-02-01", tz=dateutil.tz.tzlocal()),
+        ]
+        y = [
+            Timestamp("2012-01-01", tz=dateutil.tz.tzlocal()),
+            Timestamp("2012-02-01", tz=dateutil.tz.tzlocal()),
+        ]
+
+        result = concat([Series(x), Series(y)], ignore_index=True)
+        tm.assert_series_equal(result, Series(x + y))
+        assert result.dtype == "datetime64[ns, tzlocal()]"
+
+    def test_concat_tz_series_with_datetimelike(self):
+        # see gh-12620: tz and timedelta
+        x = [
+            Timestamp("2011-01-01", tz="US/Eastern"),
+            Timestamp("2011-02-01", tz="US/Eastern"),
+        ]
+        y = [pd.Timedelta("1 day"), pd.Timedelta("2 day")]
+        result = concat([Series(x), Series(y)], ignore_index=True)
+        tm.assert_series_equal(result, Series(x + y, dtype="object"))
+
+        # tz and period
+        y = [pd.Period("2011-03", freq="M"), pd.Period("2011-04", freq="M")]
+        result = concat([Series(x), Series(y)], ignore_index=True)
+        tm.assert_series_equal(result, Series(x + y, dtype="object"))
+
+    def test_concat_tz_frame(self):
+        df2 = DataFrame(
+            {
+                "A": Timestamp("20130102", tz="US/Eastern"),
+                "B": Timestamp("20130603", tz="CET"),
+            },
+            index=range(5),
+        )
+
+        # concat
+        df3 = concat([df2.A.to_frame(), df2.B.to_frame()], axis=1)
+        tm.assert_frame_equal(df2, df3)
+
+    def test_concat_multiple_tzs(self):
+        # GH#12467
+        # combining datetime tz-aware and naive DataFrames
+        ts1 = Timestamp("2015-01-01", tz=None)
+        ts2 = Timestamp("2015-01-01", tz="UTC")
+        ts3 = Timestamp("2015-01-01", tz="EST")
+
+        df1 = DataFrame({"time": [ts1]})
+        df2 = DataFrame({"time": [ts2]})
+        df3 = DataFrame({"time": [ts3]})
+
+        results = concat([df1, df2]).reset_index(drop=True)
+        expected = DataFrame({"time": [ts1, ts2]}, dtype=object)
+        tm.assert_frame_equal(results, expected)
+
+        results = concat([df1, df3]).reset_index(drop=True)
+        expected = DataFrame({"time": [ts1, ts3]}, dtype=object)
+        tm.assert_frame_equal(results, expected)
+
+        results = concat([df2, df3]).reset_index(drop=True)
+        expected = DataFrame({"time": [ts2, ts3]})
+        tm.assert_frame_equal(results, expected)
+
+    def test_concat_multiindex_with_tz(self):
+        # GH 6606
+        df = DataFrame(
+            {
+                "dt": [
+                    datetime(2014, 1, 1),
+                    datetime(2014, 1, 2),
+                    datetime(2014, 1, 3),
+                ],
+                "b": ["A", "B", "C"],
+                "c": [1, 2, 3],
+                "d": [4, 5, 6],
+            }
+        )
+        df["dt"] = df["dt"].apply(lambda d: Timestamp(d, tz="US/Pacific"))
+        df = df.set_index(["dt", "b"])
+
+        exp_idx1 = DatetimeIndex(
+            ["2014-01-01", "2014-01-02", "2014-01-03"] * 2, tz="US/Pacific", name="dt"
+        )
+        exp_idx2 = Index(["A", "B", "C"] * 2, name="b")
+        exp_idx = MultiIndex.from_arrays([exp_idx1, exp_idx2])
+        expected = DataFrame(
+            {"c": [1, 2, 3] * 2, "d": [4, 5, 6] * 2}, index=exp_idx, columns=["c", "d"]
+        )
+
+        result = concat([df, df])
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_tz_not_aligned(self):
+        # GH#22796
+        ts = pd.to_datetime([1, 2]).tz_localize("UTC")
+        a = DataFrame({"A": ts})
+        b = DataFrame({"A": ts, "B": ts})
+        result = concat([a, b], sort=True, ignore_index=True)
+        expected = DataFrame(
+            {"A": list(ts) + list(ts), "B": [pd.NaT, pd.NaT] + list(ts)}
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "t1",
+        [
+            "2015-01-01",
+            pytest.param(
+                pd.NaT,
+                marks=pytest.mark.xfail(
+                    reason="GH23037 incorrect dtype when concatenating"
+                ),
+            ),
+        ],
+    )
+    def test_concat_tz_NaT(self, t1):
+        # GH#22796
+        # Concatenating tz-aware multicolumn DataFrames
+        ts1 = Timestamp(t1, tz="UTC")
+        ts2 = Timestamp("2015-01-01", tz="UTC")
+        ts3 = Timestamp("2015-01-01", tz="UTC")
+
+        df1 = DataFrame([[ts1, ts2]])
+        df2 = DataFrame([[ts3]])
+
+        result = concat([df1, df2])
+        expected = DataFrame([[ts1, ts2], [ts3, pd.NaT]], index=[0, 0])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_tz_with_empty(self):
+        # GH 9188
+        result = concat(
+            [DataFrame(date_range("2000", periods=1, tz="UTC")), DataFrame()]
+        )
+        expected = DataFrame(date_range("2000", periods=1, tz="UTC"))
+        tm.assert_frame_equal(result, expected)
+
+
+class TestPeriodConcat:
+    def test_concat_period_series(self):
+        x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
+        y = Series(pd.PeriodIndex(["2015-10-01", "2016-01-01"], freq="D"))
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="Period[D]")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_period_multiple_freq_series(self):
+        x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
+        y = Series(pd.PeriodIndex(["2015-10-01", "2016-01-01"], freq="M"))
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+        assert result.dtype == "object"
+
+    def test_concat_period_other_series(self):
+        x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
+        y = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="M"))
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+        assert result.dtype == "object"
+
+        # non-period
+        x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
+        y = Series(DatetimeIndex(["2015-11-01", "2015-12-01"]))
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+        assert result.dtype == "object"
+
+        x = Series(pd.PeriodIndex(["2015-11-01", "2015-12-01"], freq="D"))
+        y = Series(["A", "B"])
+        expected = Series([x[0], x[1], y[0], y[1]], dtype="object")
+        result = concat([x, y], ignore_index=True)
+        tm.assert_series_equal(result, expected)
+        assert result.dtype == "object"
+
+
+def test_concat_timedelta64_block():
+    rng = to_timedelta(np.arange(10), unit="s")
+
+    df = DataFrame({"time": rng})
+
+    result = concat([df, df])
+    tm.assert_frame_equal(result.iloc[:10], df)
+    tm.assert_frame_equal(result.iloc[10:], df)
+
+
+def test_concat_multiindex_datetime_nat():
+    # GH#44900
+    left = DataFrame({"a": 1}, index=MultiIndex.from_tuples([(1, pd.NaT)]))
+    right = DataFrame(
+        {"b": 2}, index=MultiIndex.from_tuples([(1, pd.NaT), (2, pd.NaT)])
+    )
+    result = concat([left, right], axis="columns")
+    expected = DataFrame(
+        {"a": [1.0, np.nan], "b": 2}, MultiIndex.from_tuples([(1, pd.NaT), (2, pd.NaT)])
+    )
+    tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_empty.py

@@ -0,0 +1,287 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    RangeIndex,
+    Series,
+    concat,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestEmptyConcat:
+    def test_handle_empty_objects(self, sort):
+        df = DataFrame(np.random.randn(10, 4), columns=list("abcd"))
+
+        dfcopy = df[:5].copy()
+        dfcopy["foo"] = "bar"
+        empty = df[5:5]
+
+        frames = [dfcopy, empty, empty, df[5:]]
+        concatted = concat(frames, axis=0, sort=sort)
+
+        expected = df.reindex(columns=["a", "b", "c", "d", "foo"])
+        expected["foo"] = expected["foo"].astype("O")
+        expected.loc[0:4, "foo"] = "bar"
+
+        tm.assert_frame_equal(concatted, expected)
+
+        # empty as first element with time series
+        # GH3259
+        df = DataFrame(
+            {"A": range(10000)}, index=date_range("20130101", periods=10000, freq="s")
+        )
+        empty = DataFrame()
+        result = concat([df, empty], axis=1)
+        tm.assert_frame_equal(result, df)
+        result = concat([empty, df], axis=1)
+        tm.assert_frame_equal(result, df)
+
+        result = concat([df, empty])
+        tm.assert_frame_equal(result, df)
+        result = concat([empty, df])
+        tm.assert_frame_equal(result, df)
+
+    def test_concat_empty_series(self):
+        # GH 11082
+        s1 = Series([1, 2, 3], name="x")
+        s2 = Series(name="y", dtype="float64")
+        res = concat([s1, s2], axis=1)
+        exp = DataFrame(
+            {"x": [1, 2, 3], "y": [np.nan, np.nan, np.nan]},
+            index=RangeIndex(3),
+        )
+        tm.assert_frame_equal(res, exp)
+
+        s1 = Series([1, 2, 3], name="x")
+        s2 = Series(name="y", dtype="float64")
+        res = concat([s1, s2], axis=0)
+        # name will be reset
+        exp = Series([1, 2, 3])
+        tm.assert_series_equal(res, exp)
+
+        # empty Series with no name
+        s1 = Series([1, 2, 3], name="x")
+        s2 = Series(name=None, dtype="float64")
+        res = concat([s1, s2], axis=1)
+        exp = DataFrame(
+            {"x": [1, 2, 3], 0: [np.nan, np.nan, np.nan]},
+            columns=["x", 0],
+            index=RangeIndex(3),
+        )
+        tm.assert_frame_equal(res, exp)
+
+    @pytest.mark.parametrize("tz", [None, "UTC"])
+    @pytest.mark.parametrize("values", [[], [1, 2, 3]])
+    def test_concat_empty_series_timelike(self, tz, values):
+        # GH 18447
+
+        first = Series([], dtype="M8[ns]").dt.tz_localize(tz)
+        dtype = None if values else np.float64
+        second = Series(values, dtype=dtype)
+
+        expected = DataFrame(
+            {
+                0: Series([pd.NaT] * len(values), dtype="M8[ns]").dt.tz_localize(tz),
+                1: values,
+            }
+        )
+        result = concat([first, second], axis=1)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "left,right,expected",
+        [
+            # booleans
+            (np.bool_, np.int32, np.object_),  # changed from int32 in 2.0 GH#39817
+            (np.bool_, np.float32, np.object_),
+            # datetime-like
+            ("m8[ns]", np.bool_, np.object_),
+            ("m8[ns]", np.int64, np.object_),
+            ("M8[ns]", np.bool_, np.object_),
+            ("M8[ns]", np.int64, np.object_),
+            # categorical
+            ("category", "category", "category"),
+            ("category", "object", "object"),
+        ],
+    )
+    def test_concat_empty_series_dtypes(self, left, right, expected):
+        # GH#39817, GH#45101
+        result = concat([Series(dtype=left), Series(dtype=right)])
+        assert result.dtype == expected
+
+    @pytest.mark.parametrize(
+        "dtype", ["float64", "int8", "uint8", "bool", "m8[ns]", "M8[ns]"]
+    )
+    def test_concat_empty_series_dtypes_match_roundtrips(self, dtype):
+        dtype = np.dtype(dtype)
+
+        result = concat([Series(dtype=dtype)])
+        assert result.dtype == dtype
+
+        result = concat([Series(dtype=dtype), Series(dtype=dtype)])
+        assert result.dtype == dtype
+
+    @pytest.mark.parametrize("dtype", ["float64", "int8", "uint8", "m8[ns]", "M8[ns]"])
+    @pytest.mark.parametrize(
+        "dtype2",
+        ["float64", "int8", "uint8", "m8[ns]", "M8[ns]"],
+    )
+    def test_concat_empty_series_dtypes_roundtrips(self, dtype, dtype2):
+        # round-tripping with self & like self
+        if dtype == dtype2:
+            return
+
+        def int_result_type(dtype, dtype2):
+            typs = {dtype.kind, dtype2.kind}
+            if not len(typs - {"i", "u", "b"}) and (
+                dtype.kind == "i" or dtype2.kind == "i"
+            ):
+                return "i"
+            elif not len(typs - {"u", "b"}) and (
+                dtype.kind == "u" or dtype2.kind == "u"
+            ):
+                return "u"
+            return None
+
+        def float_result_type(dtype, dtype2):
+            typs = {dtype.kind, dtype2.kind}
+            if not len(typs - {"f", "i", "u"}) and (
+                dtype.kind == "f" or dtype2.kind == "f"
+            ):
+                return "f"
+            return None
+
+        def get_result_type(dtype, dtype2):
+            result = float_result_type(dtype, dtype2)
+            if result is not None:
+                return result
+            result = int_result_type(dtype, dtype2)
+            if result is not None:
+                return result
+            return "O"
+
+        dtype = np.dtype(dtype)
+        dtype2 = np.dtype(dtype2)
+        expected = get_result_type(dtype, dtype2)
+        result = concat([Series(dtype=dtype), Series(dtype=dtype2)]).dtype
+        assert result.kind == expected
+
+    def test_concat_empty_series_dtypes_triple(self):
+        assert (
+            concat(
+                [Series(dtype="M8[ns]"), Series(dtype=np.bool_), Series(dtype=np.int64)]
+            ).dtype
+            == np.object_
+        )
+
+    def test_concat_empty_series_dtype_category_with_array(self):
+        # GH#18515
+        assert (
+            concat(
+                [Series(np.array([]), dtype="category"), Series(dtype="float64")]
+            ).dtype
+            == "float64"
+        )
+
+    def test_concat_empty_series_dtypes_sparse(self):
+        result = concat(
+            [
+                Series(dtype="float64").astype("Sparse"),
+                Series(dtype="float64").astype("Sparse"),
+            ]
+        )
+        assert result.dtype == "Sparse[float64]"
+
+        result = concat(
+            [Series(dtype="float64").astype("Sparse"), Series(dtype="float64")]
+        )
+        expected = pd.SparseDtype(np.float64)
+        assert result.dtype == expected
+
+        result = concat(
+            [Series(dtype="float64").astype("Sparse"), Series(dtype="object")]
+        )
+        expected = pd.SparseDtype("object")
+        assert result.dtype == expected
+
+    def test_concat_empty_df_object_dtype(self):
+        # GH 9149
+        df_1 = DataFrame({"Row": [0, 1, 1], "EmptyCol": np.nan, "NumberCol": [1, 2, 3]})
+        df_2 = DataFrame(columns=df_1.columns)
+        result = concat([df_1, df_2], axis=0)
+        expected = df_1.astype(object)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_empty_dataframe_dtypes(self):
+        df = DataFrame(columns=list("abc"))
+        df["a"] = df["a"].astype(np.bool_)
+        df["b"] = df["b"].astype(np.int32)
+        df["c"] = df["c"].astype(np.float64)
+
+        result = concat([df, df])
+        assert result["a"].dtype == np.bool_
+        assert result["b"].dtype == np.int32
+        assert result["c"].dtype == np.float64
+
+        result = concat([df, df.astype(np.float64)])
+        assert result["a"].dtype == np.object_
+        assert result["b"].dtype == np.float64
+        assert result["c"].dtype == np.float64
+
+    def test_concat_inner_join_empty(self):
+        # GH 15328
+        df_empty = DataFrame()
+        df_a = DataFrame({"a": [1, 2]}, index=[0, 1], dtype="int64")
+        df_expected = DataFrame({"a": []}, index=RangeIndex(0), dtype="int64")
+
+        for how, expected in [("inner", df_expected), ("outer", df_a)]:
+            result = concat([df_a, df_empty], axis=1, join=how)
+            tm.assert_frame_equal(result, expected)
+
+    def test_empty_dtype_coerce(self):
+        # xref to #12411
+        # xref to #12045
+        # xref to #11594
+        # see below
+
+        # 10571
+        df1 = DataFrame(data=[[1, None], [2, None]], columns=["a", "b"])
+        df2 = DataFrame(data=[[3, None], [4, None]], columns=["a", "b"])
+        result = concat([df1, df2])
+        expected = df1.dtypes
+        tm.assert_series_equal(result.dtypes, expected)
+
+    def test_concat_empty_dataframe(self):
+        # 39037
+        df1 = DataFrame(columns=["a", "b"])
+        df2 = DataFrame(columns=["b", "c"])
+        result = concat([df1, df2, df1])
+        expected = DataFrame(columns=["a", "b", "c"])
+        tm.assert_frame_equal(result, expected)
+
+        df3 = DataFrame(columns=["a", "b"])
+        df4 = DataFrame(columns=["b"])
+        result = concat([df3, df4])
+        expected = DataFrame(columns=["a", "b"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_empty_dataframe_different_dtypes(self):
+        # 39037
+        df1 = DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]})
+        df2 = DataFrame({"a": [1, 2, 3]})
+
+        result = concat([df1[:0], df2[:0]])
+        assert result["a"].dtype == np.int64
+        assert result["b"].dtype == np.object_
+
+    def test_concat_to_empty_ea(self):
+        """48510 `concat` to an empty EA should maintain type EA dtype."""
+        df_empty = DataFrame({"a": pd.array([], dtype=pd.Int64Dtype())})
+        df_new = DataFrame({"a": pd.array([1, 2, 3], dtype=pd.Int64Dtype())})
+        expected = df_new.copy()
+        result = concat([df_empty, df_new])
+        tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_index.py

@@ -0,0 +1,466 @@
+from copy import deepcopy
+
+import numpy as np
+import pytest
+
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    concat,
+)
+import pandas._testing as tm
+
+
+class TestIndexConcat:
+    def test_concat_ignore_index(self, sort):
+        frame1 = DataFrame(
+            {"test1": ["a", "b", "c"], "test2": [1, 2, 3], "test3": [4.5, 3.2, 1.2]}
+        )
+        frame2 = DataFrame({"test3": [5.2, 2.2, 4.3]})
+        frame1.index = Index(["x", "y", "z"])
+        frame2.index = Index(["x", "y", "q"])
+
+        v1 = concat([frame1, frame2], axis=1, ignore_index=True, sort=sort)
+
+        nan = np.nan
+        expected = DataFrame(
+            [
+                [nan, nan, nan, 4.3],
+                ["a", 1, 4.5, 5.2],
+                ["b", 2, 3.2, 2.2],
+                ["c", 3, 1.2, nan],
+            ],
+            index=Index(["q", "x", "y", "z"]),
+        )
+        if not sort:
+            expected = expected.loc[["x", "y", "z", "q"]]
+
+        tm.assert_frame_equal(v1, expected)
+
+    @pytest.mark.parametrize(
+        "name_in1,name_in2,name_in3,name_out",
+        [
+            ("idx", "idx", "idx", "idx"),
+            ("idx", "idx", None, None),
+            ("idx", None, None, None),
+            ("idx1", "idx2", None, None),
+            ("idx1", "idx1", "idx2", None),
+            ("idx1", "idx2", "idx3", None),
+            (None, None, None, None),
+        ],
+    )
+    def test_concat_same_index_names(self, name_in1, name_in2, name_in3, name_out):
+        # GH13475
+        indices = [
+            Index(["a", "b", "c"], name=name_in1),
+            Index(["b", "c", "d"], name=name_in2),
+            Index(["c", "d", "e"], name=name_in3),
+        ]
+        frames = [
+            DataFrame({c: [0, 1, 2]}, index=i) for i, c in zip(indices, ["x", "y", "z"])
+        ]
+        result = concat(frames, axis=1)
+
+        exp_ind = Index(["a", "b", "c", "d", "e"], name=name_out)
+        expected = DataFrame(
+            {
+                "x": [0, 1, 2, np.nan, np.nan],
+                "y": [np.nan, 0, 1, 2, np.nan],
+                "z": [np.nan, np.nan, 0, 1, 2],
+            },
+            index=exp_ind,
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_rename_index(self):
+        a = DataFrame(
+            np.random.rand(3, 3),
+            columns=list("ABC"),
+            index=Index(list("abc"), name="index_a"),
+        )
+        b = DataFrame(
+            np.random.rand(3, 3),
+            columns=list("ABC"),
+            index=Index(list("abc"), name="index_b"),
+        )
+
+        result = concat([a, b], keys=["key0", "key1"], names=["lvl0", "lvl1"])
+
+        exp = concat([a, b], keys=["key0", "key1"], names=["lvl0"])
+        names = list(exp.index.names)
+        names[1] = "lvl1"
+        exp.index.set_names(names, inplace=True)
+
+        tm.assert_frame_equal(result, exp)
+        assert result.index.names == exp.index.names
+
+    def test_concat_copy_index_series(self, axis, using_copy_on_write):
+        # GH 29879
+        ser = Series([1, 2])
+        comb = concat([ser, ser], axis=axis, copy=True)
+        if not using_copy_on_write or axis in [0, "index"]:
+            assert comb.index is not ser.index
+        else:
+            assert comb.index is ser.index
+
+    def test_concat_copy_index_frame(self, axis, using_copy_on_write):
+        # GH 29879
+        df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
+        comb = concat([df, df], axis=axis, copy=True)
+        if not using_copy_on_write:
+            assert comb.index is not df.index
+            assert comb.columns is not df.columns
+        elif axis in [0, "index"]:
+            assert comb.index is not df.index
+            assert comb.columns is df.columns
+        elif axis in [1, "columns"]:
+            assert comb.index is df.index
+            assert comb.columns is not df.columns
+
+    def test_default_index(self):
+        # is_series and ignore_index
+        s1 = Series([1, 2, 3], name="x")
+        s2 = Series([4, 5, 6], name="y")
+        res = concat([s1, s2], axis=1, ignore_index=True)
+        assert isinstance(res.columns, pd.RangeIndex)
+        exp = DataFrame([[1, 4], [2, 5], [3, 6]])
+        # use check_index_type=True to check the result have
+        # RangeIndex (default index)
+        tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
+
+        # is_series and all inputs have no names
+        s1 = Series([1, 2, 3])
+        s2 = Series([4, 5, 6])
+        res = concat([s1, s2], axis=1, ignore_index=False)
+        assert isinstance(res.columns, pd.RangeIndex)
+        exp = DataFrame([[1, 4], [2, 5], [3, 6]])
+        exp.columns = pd.RangeIndex(2)
+        tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
+
+        # is_dataframe and ignore_index
+        df1 = DataFrame({"A": [1, 2], "B": [5, 6]})
+        df2 = DataFrame({"A": [3, 4], "B": [7, 8]})
+
+        res = concat([df1, df2], axis=0, ignore_index=True)
+        exp = DataFrame([[1, 5], [2, 6], [3, 7], [4, 8]], columns=["A", "B"])
+        tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
+
+        res = concat([df1, df2], axis=1, ignore_index=True)
+        exp = DataFrame([[1, 5, 3, 7], [2, 6, 4, 8]])
+        tm.assert_frame_equal(res, exp, check_index_type=True, check_column_type=True)
+
+    def test_dups_index(self):
+        # GH 4771
+
+        # single dtypes
+        df = DataFrame(
+            np.random.randint(0, 10, size=40).reshape(10, 4),
+            columns=["A", "A", "C", "C"],
+        )
+
+        result = concat([df, df], axis=1)
+        tm.assert_frame_equal(result.iloc[:, :4], df)
+        tm.assert_frame_equal(result.iloc[:, 4:], df)
+
+        result = concat([df, df], axis=0)
+        tm.assert_frame_equal(result.iloc[:10], df)
+        tm.assert_frame_equal(result.iloc[10:], df)
+
+        # multi dtypes
+        df = concat(
+            [
+                DataFrame(np.random.randn(10, 4), columns=["A", "A", "B", "B"]),
+                DataFrame(
+                    np.random.randint(0, 10, size=20).reshape(10, 2), columns=["A", "C"]
+                ),
+            ],
+            axis=1,
+        )
+
+        result = concat([df, df], axis=1)
+        tm.assert_frame_equal(result.iloc[:, :6], df)
+        tm.assert_frame_equal(result.iloc[:, 6:], df)
+
+        result = concat([df, df], axis=0)
+        tm.assert_frame_equal(result.iloc[:10], df)
+        tm.assert_frame_equal(result.iloc[10:], df)
+
+        # append
+        result = df.iloc[0:8, :]._append(df.iloc[8:])
+        tm.assert_frame_equal(result, df)
+
+        result = df.iloc[0:8, :]._append(df.iloc[8:9])._append(df.iloc[9:10])
+        tm.assert_frame_equal(result, df)
+
+        expected = concat([df, df], axis=0)
+        result = df._append(df)
+        tm.assert_frame_equal(result, expected)
+
+
+class TestMultiIndexConcat:
+    def test_concat_multiindex_with_keys(self, multiindex_dataframe_random_data):
+        frame = multiindex_dataframe_random_data
+        index = frame.index
+        result = concat([frame, frame], keys=[0, 1], names=["iteration"])
+
+        assert result.index.names == ("iteration",) + index.names
+        tm.assert_frame_equal(result.loc[0], frame)
+        tm.assert_frame_equal(result.loc[1], frame)
+        assert result.index.nlevels == 3
+
+    def test_concat_multiindex_with_none_in_index_names(self):
+        # GH 15787
+        index = MultiIndex.from_product([[1], range(5)], names=["level1", None])
+        df = DataFrame({"col": range(5)}, index=index, dtype=np.int32)
+
+        result = concat([df, df], keys=[1, 2], names=["level2"])
+        index = MultiIndex.from_product(
+            [[1, 2], [1], range(5)], names=["level2", "level1", None]
+        )
+        expected = DataFrame({"col": list(range(5)) * 2}, index=index, dtype=np.int32)
+        tm.assert_frame_equal(result, expected)
+
+        result = concat([df, df[:2]], keys=[1, 2], names=["level2"])
+        level2 = [1] * 5 + [2] * 2
+        level1 = [1] * 7
+        no_name = list(range(5)) + list(range(2))
+        tuples = list(zip(level2, level1, no_name))
+        index = MultiIndex.from_tuples(tuples, names=["level2", "level1", None])
+        expected = DataFrame({"col": no_name}, index=index, dtype=np.int32)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_multiindex_rangeindex(self):
+        # GH13542
+        # when multi-index levels are RangeIndex objects
+        # there is a bug in concat with objects of len 1
+
+        df = DataFrame(np.random.randn(9, 2))
+        df.index = MultiIndex(
+            levels=[pd.RangeIndex(3), pd.RangeIndex(3)],
+            codes=[np.repeat(np.arange(3), 3), np.tile(np.arange(3), 3)],
+        )
+
+        res = concat([df.iloc[[2, 3, 4], :], df.iloc[[5], :]])
+        exp = df.iloc[[2, 3, 4, 5], :]
+        tm.assert_frame_equal(res, exp)
+
+    def test_concat_multiindex_dfs_with_deepcopy(self):
+        # GH 9967
+        example_multiindex1 = MultiIndex.from_product([["a"], ["b"]])
+        example_dataframe1 = DataFrame([0], index=example_multiindex1)
+
+        example_multiindex2 = MultiIndex.from_product([["a"], ["c"]])
+        example_dataframe2 = DataFrame([1], index=example_multiindex2)
+
+        example_dict = {"s1": example_dataframe1, "s2": example_dataframe2}
+        expected_index = MultiIndex(
+            levels=[["s1", "s2"], ["a"], ["b", "c"]],
+            codes=[[0, 1], [0, 0], [0, 1]],
+            names=["testname", None, None],
+        )
+        expected = DataFrame([[0], [1]], index=expected_index)
+        result_copy = concat(deepcopy(example_dict), names=["testname"])
+        tm.assert_frame_equal(result_copy, expected)
+        result_no_copy = concat(example_dict, names=["testname"])
+        tm.assert_frame_equal(result_no_copy, expected)
+
+    @pytest.mark.parametrize(
+        "mi1_list",
+        [
+            [["a"], range(2)],
+            [["b"], np.arange(2.0, 4.0)],
+            [["c"], ["A", "B"]],
+            [["d"], pd.date_range(start="2017", end="2018", periods=2)],
+        ],
+    )
+    @pytest.mark.parametrize(
+        "mi2_list",
+        [
+            [["a"], range(2)],
+            [["b"], np.arange(2.0, 4.0)],
+            [["c"], ["A", "B"]],
+            [["d"], pd.date_range(start="2017", end="2018", periods=2)],
+        ],
+    )
+    def test_concat_with_various_multiindex_dtypes(
+        self, mi1_list: list, mi2_list: list
+    ):
+        # GitHub #23478
+        mi1 = MultiIndex.from_product(mi1_list)
+        mi2 = MultiIndex.from_product(mi2_list)
+
+        df1 = DataFrame(np.zeros((1, len(mi1))), columns=mi1)
+        df2 = DataFrame(np.zeros((1, len(mi2))), columns=mi2)
+
+        if mi1_list[0] == mi2_list[0]:
+            expected_mi = MultiIndex(
+                levels=[mi1_list[0], list(mi1_list[1])],
+                codes=[[0, 0, 0, 0], [0, 1, 0, 1]],
+            )
+        else:
+            expected_mi = MultiIndex(
+                levels=[
+                    mi1_list[0] + mi2_list[0],
+                    list(mi1_list[1]) + list(mi2_list[1]),
+                ],
+                codes=[[0, 0, 1, 1], [0, 1, 2, 3]],
+            )
+
+        expected_df = DataFrame(np.zeros((1, len(expected_mi))), columns=expected_mi)
+
+        with tm.assert_produces_warning(None):
+            result_df = concat((df1, df2), axis=1)
+
+        tm.assert_frame_equal(expected_df, result_df)
+
+    def test_concat_multiindex_(self):
+        # GitHub #44786
+        df = DataFrame({"col": ["a", "b", "c"]}, index=["1", "2", "2"])
+        df = concat([df], keys=["X"])
+
+        iterables = [["X"], ["1", "2", "2"]]
+        result_index = df.index
+        expected_index = MultiIndex.from_product(iterables)
+
+        tm.assert_index_equal(result_index, expected_index)
+
+        result_df = df
+        expected_df = DataFrame(
+            {"col": ["a", "b", "c"]}, index=MultiIndex.from_product(iterables)
+        )
+        tm.assert_frame_equal(result_df, expected_df)
+
+    def test_concat_with_key_not_unique(self):
+        # GitHub #46519
+        df1 = DataFrame({"name": [1]})
+        df2 = DataFrame({"name": [2]})
+        df3 = DataFrame({"name": [3]})
+        df_a = concat([df1, df2, df3], keys=["x", "y", "x"])
+        # the warning is caused by indexing unsorted multi-index
+        with tm.assert_produces_warning(
+            PerformanceWarning, match="indexing past lexsort depth"
+        ):
+            out_a = df_a.loc[("x", 0), :]
+
+        df_b = DataFrame(
+            {"name": [1, 2, 3]}, index=Index([("x", 0), ("y", 0), ("x", 0)])
+        )
+        with tm.assert_produces_warning(
+            PerformanceWarning, match="indexing past lexsort depth"
+        ):
+            out_b = df_b.loc[("x", 0)]
+
+        tm.assert_frame_equal(out_a, out_b)
+
+        df1 = DataFrame({"name": ["a", "a", "b"]})
+        df2 = DataFrame({"name": ["a", "b"]})
+        df3 = DataFrame({"name": ["c", "d"]})
+        df_a = concat([df1, df2, df3], keys=["x", "y", "x"])
+        with tm.assert_produces_warning(
+            PerformanceWarning, match="indexing past lexsort depth"
+        ):
+            out_a = df_a.loc[("x", 0), :]
+
+        df_b = DataFrame(
+            {
+                "a": ["x", "x", "x", "y", "y", "x", "x"],
+                "b": [0, 1, 2, 0, 1, 0, 1],
+                "name": list("aababcd"),
+            }
+        ).set_index(["a", "b"])
+        df_b.index.names = [None, None]
+        with tm.assert_produces_warning(
+            PerformanceWarning, match="indexing past lexsort depth"
+        ):
+            out_b = df_b.loc[("x", 0), :]
+
+        tm.assert_frame_equal(out_a, out_b)
+
+    def test_concat_with_duplicated_levels(self):
+        # keyword levels should be unique
+        df1 = DataFrame({"A": [1]}, index=["x"])
+        df2 = DataFrame({"A": [1]}, index=["y"])
+        msg = r"Level values not unique: \['x', 'y', 'y'\]"
+        with pytest.raises(ValueError, match=msg):
+            concat([df1, df2], keys=["x", "y"], levels=[["x", "y", "y"]])
+
+    @pytest.mark.parametrize("levels", [[["x", "y"]], [["x", "y", "y"]]])
+    def test_concat_with_levels_with_none_keys(self, levels):
+        df1 = DataFrame({"A": [1]}, index=["x"])
+        df2 = DataFrame({"A": [1]}, index=["y"])
+        msg = "levels supported only when keys is not None"
+        with pytest.raises(ValueError, match=msg):
+            concat([df1, df2], levels=levels)
+
+    def test_concat_range_index_result(self):
+        # GH#47501
+        df1 = DataFrame({"a": [1, 2]})
+        df2 = DataFrame({"b": [1, 2]})
+
+        result = concat([df1, df2], sort=True, axis=1)
+        expected = DataFrame({"a": [1, 2], "b": [1, 2]})
+        tm.assert_frame_equal(result, expected)
+        expected_index = pd.RangeIndex(0, 2)
+        tm.assert_index_equal(result.index, expected_index, exact=True)
+
+    def test_concat_index_keep_dtype(self):
+        # GH#47329
+        df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype="object"))
+        df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype="object"))
+        result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
+        expected = DataFrame(
+            [[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype="object")
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_index_keep_dtype_ea_numeric(self, any_numeric_ea_dtype):
+        # GH#47329
+        df1 = DataFrame(
+            [[0, 1, 1]], columns=Index([1, 2, 3], dtype=any_numeric_ea_dtype)
+        )
+        df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype=any_numeric_ea_dtype))
+        result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
+        expected = DataFrame(
+            [[0, 1, 1.0], [0, 1, np.nan]],
+            columns=Index([1, 2, 3], dtype=any_numeric_ea_dtype),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["Int8", "Int16", "Int32"])
+    def test_concat_index_find_common(self, dtype):
+        # GH#47329
+        df1 = DataFrame([[0, 1, 1]], columns=Index([1, 2, 3], dtype=dtype))
+        df2 = DataFrame([[0, 1]], columns=Index([1, 2], dtype="Int32"))
+        result = concat([df1, df2], ignore_index=True, join="outer", sort=True)
+        expected = DataFrame(
+            [[0, 1, 1.0], [0, 1, np.nan]], columns=Index([1, 2, 3], dtype="Int32")
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_axis_1_sort_false_rangeindex(self):
+        # GH 46675
+        s1 = Series(["a", "b", "c"])
+        s2 = Series(["a", "b"])
+        s3 = Series(["a", "b", "c", "d"])
+        s4 = Series([], dtype=object)
+        result = concat(
+            [s1, s2, s3, s4], sort=False, join="outer", ignore_index=False, axis=1
+        )
+        expected = DataFrame(
+            [
+                ["a"] * 3 + [np.nan],
+                ["b"] * 3 + [np.nan],
+                ["c", np.nan] * 2,
+                [np.nan] * 2 + ["d"] + [np.nan],
+            ],
+            dtype=object,
+        )
+        tm.assert_frame_equal(
+            result, expected, check_index_type=True, check_column_type=True
+        )

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_invalid.py

@@ -0,0 +1,54 @@
+from io import StringIO
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    concat,
+    read_csv,
+)
+import pandas._testing as tm
+
+
+class TestInvalidConcat:
+    def test_concat_invalid(self):
+        # trying to concat a ndframe with a non-ndframe
+        df1 = tm.makeCustomDataframe(10, 2)
+        for obj in [1, {}, [1, 2], (1, 2)]:
+            msg = (
+                f"cannot concatenate object of type '{type(obj)}'; "
+                "only Series and DataFrame objs are valid"
+            )
+            with pytest.raises(TypeError, match=msg):
+                concat([df1, obj])
+
+    def test_concat_invalid_first_argument(self):
+        df1 = tm.makeCustomDataframe(10, 2)
+        msg = (
+            "first argument must be an iterable of pandas "
+            'objects, you passed an object of type "DataFrame"'
+        )
+        with pytest.raises(TypeError, match=msg):
+            concat(df1)
+
+    def test_concat_generator_obj(self):
+        # generator ok though
+        concat(DataFrame(np.random.rand(5, 5)) for _ in range(3))
+
+    def test_concat_textreader_obj(self):
+        # text reader ok
+        # GH6583
+        data = """index,A,B,C,D
+                  foo,2,3,4,5
+                  bar,7,8,9,10
+                  baz,12,13,14,15
+                  qux,12,13,14,15
+                  foo2,12,13,14,15
+                  bar2,12,13,14,15
+               """
+
+        with read_csv(StringIO(data), chunksize=1) as reader:
+            result = concat(reader, ignore_index=True)
+        expected = read_csv(StringIO(data))
+        tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_series.py

@@ -0,0 +1,153 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    MultiIndex,
+    Series,
+    concat,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestSeriesConcat:
+    def test_concat_series(self):
+        ts = tm.makeTimeSeries()
+        ts.name = "foo"
+
+        pieces = [ts[:5], ts[5:15], ts[15:]]
+
+        result = concat(pieces)
+        tm.assert_series_equal(result, ts)
+        assert result.name == ts.name
+
+        result = concat(pieces, keys=[0, 1, 2])
+        expected = ts.copy()
+
+        ts.index = DatetimeIndex(np.array(ts.index.values, dtype="M8[ns]"))
+
+        exp_codes = [np.repeat([0, 1, 2], [len(x) for x in pieces]), np.arange(len(ts))]
+        exp_index = MultiIndex(levels=[[0, 1, 2], ts.index], codes=exp_codes)
+        expected.index = exp_index
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_empty_and_non_empty_series_regression(self):
+        # GH 18187 regression test
+        s1 = Series([1])
+        s2 = Series([], dtype=object)
+
+        expected = s1
+        result = concat([s1, s2])
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_series_axis1(self):
+        ts = tm.makeTimeSeries()
+
+        pieces = [ts[:-2], ts[2:], ts[2:-2]]
+
+        result = concat(pieces, axis=1)
+        expected = DataFrame(pieces).T
+        tm.assert_frame_equal(result, expected)
+
+        result = concat(pieces, keys=["A", "B", "C"], axis=1)
+        expected = DataFrame(pieces, index=["A", "B", "C"]).T
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_series_axis1_preserves_series_names(self):
+        # preserve series names, #2489
+        s = Series(np.random.randn(5), name="A")
+        s2 = Series(np.random.randn(5), name="B")
+
+        result = concat([s, s2], axis=1)
+        expected = DataFrame({"A": s, "B": s2})
+        tm.assert_frame_equal(result, expected)
+
+        s2.name = None
+        result = concat([s, s2], axis=1)
+        tm.assert_index_equal(result.columns, Index(["A", 0], dtype="object"))
+
+    def test_concat_series_axis1_with_reindex(self, sort):
+        # must reindex, #2603
+        s = Series(np.random.randn(3), index=["c", "a", "b"], name="A")
+        s2 = Series(np.random.randn(4), index=["d", "a", "b", "c"], name="B")
+        result = concat([s, s2], axis=1, sort=sort)
+        expected = DataFrame({"A": s, "B": s2}, index=["c", "a", "b", "d"])
+        if sort:
+            expected = expected.sort_index()
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_series_axis1_names_applied(self):
+        # ensure names argument is not ignored on axis=1, #23490
+        s = Series([1, 2, 3])
+        s2 = Series([4, 5, 6])
+        result = concat([s, s2], axis=1, keys=["a", "b"], names=["A"])
+        expected = DataFrame(
+            [[1, 4], [2, 5], [3, 6]], columns=Index(["a", "b"], name="A")
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = concat([s, s2], axis=1, keys=[("a", 1), ("b", 2)], names=["A", "B"])
+        expected = DataFrame(
+            [[1, 4], [2, 5], [3, 6]],
+            columns=MultiIndex.from_tuples([("a", 1), ("b", 2)], names=["A", "B"]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_series_axis1_same_names_ignore_index(self):
+        dates = date_range("01-Jan-2013", "01-Jan-2014", freq="MS")[0:-1]
+        s1 = Series(np.random.randn(len(dates)), index=dates, name="value")
+        s2 = Series(np.random.randn(len(dates)), index=dates, name="value")
+
+        result = concat([s1, s2], axis=1, ignore_index=True)
+        expected = Index(range(2))
+
+        tm.assert_index_equal(result.columns, expected, exact=True)
+
+    @pytest.mark.parametrize(
+        "s1name,s2name", [(np.int64(190), (43, 0)), (190, (43, 0))]
+    )
+    def test_concat_series_name_npscalar_tuple(self, s1name, s2name):
+        # GH21015
+        s1 = Series({"a": 1, "b": 2}, name=s1name)
+        s2 = Series({"c": 5, "d": 6}, name=s2name)
+        result = concat([s1, s2])
+        expected = Series({"a": 1, "b": 2, "c": 5, "d": 6})
+        tm.assert_series_equal(result, expected)
+
+    def test_concat_series_partial_columns_names(self):
+        # GH10698
+        named_series = Series([1, 2], name="foo")
+        unnamed_series1 = Series([1, 2])
+        unnamed_series2 = Series([4, 5])
+
+        result = concat([named_series, unnamed_series1, unnamed_series2], axis=1)
+        expected = DataFrame(
+            {"foo": [1, 2], 0: [1, 2], 1: [4, 5]}, columns=["foo", 0, 1]
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = concat(
+            [named_series, unnamed_series1, unnamed_series2],
+            axis=1,
+            keys=["red", "blue", "yellow"],
+        )
+        expected = DataFrame(
+            {"red": [1, 2], "blue": [1, 2], "yellow": [4, 5]},
+            columns=["red", "blue", "yellow"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = concat(
+            [named_series, unnamed_series1, unnamed_series2], axis=1, ignore_index=True
+        )
+        expected = DataFrame({0: [1, 2], 1: [1, 2], 2: [4, 5]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_series_length_one_reversed(self, frame_or_series):
+        # GH39401
+        obj = frame_or_series([100])
+        result = concat([obj.iloc[::-1]])
+        tm.assert_equal(result, obj)

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/concat/test_sort.py

@@ -0,0 +1,118 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import DataFrame
+import pandas._testing as tm
+
+
+class TestConcatSort:
+    def test_concat_sorts_columns(self, sort):
+        # GH-4588
+        df1 = DataFrame({"a": [1, 2], "b": [1, 2]}, columns=["b", "a"])
+        df2 = DataFrame({"a": [3, 4], "c": [5, 6]})
+
+        # for sort=True/None
+        expected = DataFrame(
+            {"a": [1, 2, 3, 4], "b": [1, 2, None, None], "c": [None, None, 5, 6]},
+            columns=["a", "b", "c"],
+        )
+
+        if sort is False:
+            expected = expected[["b", "a", "c"]]
+
+        # default
+        with tm.assert_produces_warning(None):
+            result = pd.concat([df1, df2], ignore_index=True, sort=sort)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_sorts_index(self, sort):
+        df1 = DataFrame({"a": [1, 2, 3]}, index=["c", "a", "b"])
+        df2 = DataFrame({"b": [1, 2]}, index=["a", "b"])
+
+        # For True/None
+        expected = DataFrame(
+            {"a": [2, 3, 1], "b": [1, 2, None]},
+            index=["a", "b", "c"],
+            columns=["a", "b"],
+        )
+        if sort is False:
+            expected = expected.loc[["c", "a", "b"]]
+
+        # Warn and sort by default
+        with tm.assert_produces_warning(None):
+            result = pd.concat([df1, df2], axis=1, sort=sort)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_inner_sort(self, sort):
+        # https://github.com/pandas-dev/pandas/pull/20613
+        df1 = DataFrame(
+            {"a": [1, 2], "b": [1, 2], "c": [1, 2]}, columns=["b", "a", "c"]
+        )
+        df2 = DataFrame({"a": [1, 2], "b": [3, 4]}, index=[3, 4])
+
+        with tm.assert_produces_warning(None):
+            # unset sort should *not* warn for inner join
+            # since that never sorted
+            result = pd.concat([df1, df2], sort=sort, join="inner", ignore_index=True)
+
+        expected = DataFrame({"b": [1, 2, 3, 4], "a": [1, 2, 1, 2]}, columns=["b", "a"])
+        if sort is True:
+            expected = expected[["a", "b"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_aligned_sort(self):
+        # GH-4588
+        df = DataFrame({"c": [1, 2], "b": [3, 4], "a": [5, 6]}, columns=["c", "b", "a"])
+        result = pd.concat([df, df], sort=True, ignore_index=True)
+        expected = DataFrame(
+            {"a": [5, 6, 5, 6], "b": [3, 4, 3, 4], "c": [1, 2, 1, 2]},
+            columns=["a", "b", "c"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = pd.concat(
+            [df, df[["c", "b"]]], join="inner", sort=True, ignore_index=True
+        )
+        expected = expected[["b", "c"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_aligned_sort_does_not_raise(self):
+        # GH-4588
+        # We catch TypeErrors from sorting internally and do not re-raise.
+        df = DataFrame({1: [1, 2], "a": [3, 4]}, columns=[1, "a"])
+        expected = DataFrame({1: [1, 2, 1, 2], "a": [3, 4, 3, 4]}, columns=[1, "a"])
+        result = pd.concat([df, df], ignore_index=True, sort=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_frame_with_sort_false(self):
+        # GH 43375
+        result = pd.concat(
+            [DataFrame({i: i}, index=[i]) for i in range(2, 0, -1)], sort=False
+        )
+        expected = DataFrame([[2, np.nan], [np.nan, 1]], index=[2, 1], columns=[2, 1])
+
+        tm.assert_frame_equal(result, expected)
+
+        # GH 37937
+        df1 = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=[1, 2, 3])
+        df2 = DataFrame({"c": [7, 8, 9], "d": [10, 11, 12]}, index=[3, 1, 6])
+        result = pd.concat([df2, df1], axis=1, sort=False)
+        expected = DataFrame(
+            [
+                [7.0, 10.0, 3.0, 6.0],
+                [8.0, 11.0, 1.0, 4.0],
+                [9.0, 12.0, np.nan, np.nan],
+                [np.nan, np.nan, 2.0, 5.0],
+            ],
+            index=[3, 1, 6, 2],
+            columns=["c", "d", "a", "b"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_concat_sort_none_raises(self):
+        # GH#41518
+        df = DataFrame({1: [1, 2], "a": [3, 4]})
+        msg = "The 'sort' keyword only accepts boolean values; None was passed."
+        with pytest.raises(ValueError, match=msg):
+            pd.concat([df, df], sort=None)

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_join.py

@@ -0,0 +1,994 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    Timestamp,
+    concat,
+    merge,
+)
+import pandas._testing as tm
+
+
+def get_test_data(ngroups=8, n=50):
+    unique_groups = list(range(ngroups))
+    arr = np.asarray(np.tile(unique_groups, n // ngroups))
+
+    if len(arr) < n:
+        arr = np.asarray(list(arr) + unique_groups[: n - len(arr)])
+
+    np.random.shuffle(arr)
+    return arr
+
+
+class TestJoin:
+    # aggregate multiple columns
+    @pytest.fixture
+    def df(self):
+        df = DataFrame(
+            {
+                "key1": get_test_data(),
+                "key2": get_test_data(),
+                "data1": np.random.randn(50),
+                "data2": np.random.randn(50),
+            }
+        )
+
+        # exclude a couple keys for fun
+        df = df[df["key2"] > 1]
+        return df
+
+    @pytest.fixture
+    def df2(self):
+        return DataFrame(
+            {
+                "key1": get_test_data(n=10),
+                "key2": get_test_data(ngroups=4, n=10),
+                "value": np.random.randn(10),
+            }
+        )
+
+    @pytest.fixture
+    def target_source(self):
+        index, data = tm.getMixedTypeDict()
+        target = DataFrame(data, index=index)
+
+        # Join on string value
+
+        source = DataFrame(
+            {"MergedA": data["A"], "MergedD": data["D"]}, index=data["C"]
+        )
+        return target, source
+
+    def test_left_outer_join(self, df, df2):
+        joined_key2 = merge(df, df2, on="key2")
+        _check_join(df, df2, joined_key2, ["key2"], how="left")
+
+        joined_both = merge(df, df2)
+        _check_join(df, df2, joined_both, ["key1", "key2"], how="left")
+
+    def test_right_outer_join(self, df, df2):
+        joined_key2 = merge(df, df2, on="key2", how="right")
+        _check_join(df, df2, joined_key2, ["key2"], how="right")
+
+        joined_both = merge(df, df2, how="right")
+        _check_join(df, df2, joined_both, ["key1", "key2"], how="right")
+
+    def test_full_outer_join(self, df, df2):
+        joined_key2 = merge(df, df2, on="key2", how="outer")
+        _check_join(df, df2, joined_key2, ["key2"], how="outer")
+
+        joined_both = merge(df, df2, how="outer")
+        _check_join(df, df2, joined_both, ["key1", "key2"], how="outer")
+
+    def test_inner_join(self, df, df2):
+        joined_key2 = merge(df, df2, on="key2", how="inner")
+        _check_join(df, df2, joined_key2, ["key2"], how="inner")
+
+        joined_both = merge(df, df2, how="inner")
+        _check_join(df, df2, joined_both, ["key1", "key2"], how="inner")
+
+    def test_handle_overlap(self, df, df2):
+        joined = merge(df, df2, on="key2", suffixes=(".foo", ".bar"))
+
+        assert "key1.foo" in joined
+        assert "key1.bar" in joined
+
+    def test_handle_overlap_arbitrary_key(self, df, df2):
+        joined = merge(
+            df,
+            df2,
+            left_on="key2",
+            right_on="key1",
+            suffixes=(".foo", ".bar"),
+        )
+        assert "key1.foo" in joined
+        assert "key2.bar" in joined
+
+    def test_join_on(self, target_source):
+        target, source = target_source
+
+        merged = target.join(source, on="C")
+        tm.assert_series_equal(merged["MergedA"], target["A"], check_names=False)
+        tm.assert_series_equal(merged["MergedD"], target["D"], check_names=False)
+
+        # join with duplicates (fix regression from DataFrame/Matrix merge)
+        df = DataFrame({"key": ["a", "a", "b", "b", "c"]})
+        df2 = DataFrame({"value": [0, 1, 2]}, index=["a", "b", "c"])
+        joined = df.join(df2, on="key")
+        expected = DataFrame(
+            {"key": ["a", "a", "b", "b", "c"], "value": [0, 0, 1, 1, 2]}
+        )
+        tm.assert_frame_equal(joined, expected)
+
+        # Test when some are missing
+        df_a = DataFrame([[1], [2], [3]], index=["a", "b", "c"], columns=["one"])
+        df_b = DataFrame([["foo"], ["bar"]], index=[1, 2], columns=["two"])
+        df_c = DataFrame([[1], [2]], index=[1, 2], columns=["three"])
+        joined = df_a.join(df_b, on="one")
+        joined = joined.join(df_c, on="one")
+        assert np.isnan(joined["two"]["c"])
+        assert np.isnan(joined["three"]["c"])
+
+        # merge column not p resent
+        with pytest.raises(KeyError, match="^'E'$"):
+            target.join(source, on="E")
+
+        # overlap
+        source_copy = source.copy()
+        source_copy["A"] = 0
+        msg = (
+            "You are trying to merge on float64 and object columns. If "
+            "you wish to proceed you should use pd.concat"
+        )
+        with pytest.raises(ValueError, match=msg):
+            target.join(source_copy, on="A")
+
+    def test_join_on_fails_with_different_right_index(self):
+        df = DataFrame(
+            {"a": np.random.choice(["m", "f"], size=3), "b": np.random.randn(3)}
+        )
+        df2 = DataFrame(
+            {"a": np.random.choice(["m", "f"], size=10), "b": np.random.randn(10)},
+            index=tm.makeCustomIndex(10, 2),
+        )
+        msg = r'len\(left_on\) must equal the number of levels in the index of "right"'
+        with pytest.raises(ValueError, match=msg):
+            merge(df, df2, left_on="a", right_index=True)
+
+    def test_join_on_fails_with_different_left_index(self):
+        df = DataFrame(
+            {"a": np.random.choice(["m", "f"], size=3), "b": np.random.randn(3)},
+            index=tm.makeCustomIndex(3, 2),
+        )
+        df2 = DataFrame(
+            {"a": np.random.choice(["m", "f"], size=10), "b": np.random.randn(10)}
+        )
+        msg = r'len\(right_on\) must equal the number of levels in the index of "left"'
+        with pytest.raises(ValueError, match=msg):
+            merge(df, df2, right_on="b", left_index=True)
+
+    def test_join_on_fails_with_different_column_counts(self):
+        df = DataFrame(
+            {"a": np.random.choice(["m", "f"], size=3), "b": np.random.randn(3)}
+        )
+        df2 = DataFrame(
+            {"a": np.random.choice(["m", "f"], size=10), "b": np.random.randn(10)},
+            index=tm.makeCustomIndex(10, 2),
+        )
+        msg = r"len\(right_on\) must equal len\(left_on\)"
+        with pytest.raises(ValueError, match=msg):
+            merge(df, df2, right_on="a", left_on=["a", "b"])
+
+    @pytest.mark.parametrize("wrong_type", [2, "str", None, np.array([0, 1])])
+    def test_join_on_fails_with_wrong_object_type(self, wrong_type):
+        # GH12081 - original issue
+
+        # GH21220 - merging of Series and DataFrame is now allowed
+        # Edited test to remove the Series object from test parameters
+
+        df = DataFrame({"a": [1, 1]})
+        msg = (
+            "Can only merge Series or DataFrame objects, "
+            f"a {type(wrong_type)} was passed"
+        )
+        with pytest.raises(TypeError, match=msg):
+            merge(wrong_type, df, left_on="a", right_on="a")
+        with pytest.raises(TypeError, match=msg):
+            merge(df, wrong_type, left_on="a", right_on="a")
+
+    def test_join_on_pass_vector(self, target_source):
+        target, source = target_source
+        expected = target.join(source, on="C")
+        del expected["C"]
+
+        join_col = target.pop("C")
+        result = target.join(source, on=join_col)
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_with_len0(self, target_source):
+        # nothing to merge
+        target, source = target_source
+        merged = target.join(source.reindex([]), on="C")
+        for col in source:
+            assert col in merged
+            assert merged[col].isna().all()
+
+        merged2 = target.join(source.reindex([]), on="C", how="inner")
+        tm.assert_index_equal(merged2.columns, merged.columns)
+        assert len(merged2) == 0
+
+    def test_join_on_inner(self):
+        df = DataFrame({"key": ["a", "a", "d", "b", "b", "c"]})
+        df2 = DataFrame({"value": [0, 1]}, index=["a", "b"])
+
+        joined = df.join(df2, on="key", how="inner")
+
+        expected = df.join(df2, on="key")
+        expected = expected[expected["value"].notna()]
+        tm.assert_series_equal(joined["key"], expected["key"])
+        tm.assert_series_equal(joined["value"], expected["value"], check_dtype=False)
+        tm.assert_index_equal(joined.index, expected.index)
+
+    def test_join_on_singlekey_list(self):
+        df = DataFrame({"key": ["a", "a", "b", "b", "c"]})
+        df2 = DataFrame({"value": [0, 1, 2]}, index=["a", "b", "c"])
+
+        # corner cases
+        joined = df.join(df2, on=["key"])
+        expected = df.join(df2, on="key")
+
+        tm.assert_frame_equal(joined, expected)
+
+    def test_join_on_series(self, target_source):
+        target, source = target_source
+        result = target.join(source["MergedA"], on="C")
+        expected = target.join(source[["MergedA"]], on="C")
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_on_series_buglet(self):
+        # GH #638
+        df = DataFrame({"a": [1, 1]})
+        ds = Series([2], index=[1], name="b")
+        result = df.join(ds, on="a")
+        expected = DataFrame({"a": [1, 1], "b": [2, 2]}, index=df.index)
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_index_mixed(self, join_type):
+        # no overlapping blocks
+        df1 = DataFrame(index=np.arange(10))
+        df1["bool"] = True
+        df1["string"] = "foo"
+
+        df2 = DataFrame(index=np.arange(5, 15))
+        df2["int"] = 1
+        df2["float"] = 1.0
+
+        joined = df1.join(df2, how=join_type)
+        expected = _join_by_hand(df1, df2, how=join_type)
+        tm.assert_frame_equal(joined, expected)
+
+        joined = df2.join(df1, how=join_type)
+        expected = _join_by_hand(df2, df1, how=join_type)
+        tm.assert_frame_equal(joined, expected)
+
+    def test_join_index_mixed_overlap(self):
+        df1 = DataFrame(
+            {"A": 1.0, "B": 2, "C": "foo", "D": True},
+            index=np.arange(10),
+            columns=["A", "B", "C", "D"],
+        )
+        assert df1["B"].dtype == np.int64
+        assert df1["D"].dtype == np.bool_
+
+        df2 = DataFrame(
+            {"A": 1.0, "B": 2, "C": "foo", "D": True},
+            index=np.arange(0, 10, 2),
+            columns=["A", "B", "C", "D"],
+        )
+
+        # overlap
+        joined = df1.join(df2, lsuffix="_one", rsuffix="_two")
+        expected_columns = [
+            "A_one",
+            "B_one",
+            "C_one",
+            "D_one",
+            "A_two",
+            "B_two",
+            "C_two",
+            "D_two",
+        ]
+        df1.columns = expected_columns[:4]
+        df2.columns = expected_columns[4:]
+        expected = _join_by_hand(df1, df2)
+        tm.assert_frame_equal(joined, expected)
+
+    def test_join_empty_bug(self):
+        # generated an exception in 0.4.3
+        x = DataFrame()
+        x.join(DataFrame([3], index=[0], columns=["A"]), how="outer")
+
+    def test_join_unconsolidated(self):
+        # GH #331
+        a = DataFrame(np.random.randn(30, 2), columns=["a", "b"])
+        c = Series(np.random.randn(30))
+        a["c"] = c
+        d = DataFrame(np.random.randn(30, 1), columns=["q"])
+
+        # it works!
+        a.join(d)
+        d.join(a)
+
+    def test_join_multiindex(self):
+        index1 = MultiIndex.from_arrays(
+            [["a", "a", "a", "b", "b", "b"], [1, 2, 3, 1, 2, 3]],
+            names=["first", "second"],
+        )
+
+        index2 = MultiIndex.from_arrays(
+            [["b", "b", "b", "c", "c", "c"], [1, 2, 3, 1, 2, 3]],
+            names=["first", "second"],
+        )
+
+        df1 = DataFrame(data=np.random.randn(6), index=index1, columns=["var X"])
+        df2 = DataFrame(data=np.random.randn(6), index=index2, columns=["var Y"])
+
+        df1 = df1.sort_index(level=0)
+        df2 = df2.sort_index(level=0)
+
+        joined = df1.join(df2, how="outer")
+        ex_index = Index(index1.values).union(Index(index2.values))
+        expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
+        expected.index.names = index1.names
+        tm.assert_frame_equal(joined, expected)
+        assert joined.index.names == index1.names
+
+        df1 = df1.sort_index(level=1)
+        df2 = df2.sort_index(level=1)
+
+        joined = df1.join(df2, how="outer").sort_index(level=0)
+        ex_index = Index(index1.values).union(Index(index2.values))
+        expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
+        expected.index.names = index1.names
+
+        tm.assert_frame_equal(joined, expected)
+        assert joined.index.names == index1.names
+
+    def test_join_inner_multiindex(self, lexsorted_two_level_string_multiindex):
+        key1 = ["bar", "bar", "bar", "foo", "foo", "baz", "baz", "qux", "qux", "snap"]
+        key2 = [
+            "two",
+            "one",
+            "three",
+            "one",
+            "two",
+            "one",
+            "two",
+            "two",
+            "three",
+            "one",
+        ]
+
+        data = np.random.randn(len(key1))
+        data = DataFrame({"key1": key1, "key2": key2, "data": data})
+
+        index = lexsorted_two_level_string_multiindex
+        to_join = DataFrame(
+            np.random.randn(10, 3), index=index, columns=["j_one", "j_two", "j_three"]
+        )
+
+        joined = data.join(to_join, on=["key1", "key2"], how="inner")
+        expected = merge(
+            data,
+            to_join.reset_index(),
+            left_on=["key1", "key2"],
+            right_on=["first", "second"],
+            how="inner",
+            sort=False,
+        )
+
+        expected2 = merge(
+            to_join,
+            data,
+            right_on=["key1", "key2"],
+            left_index=True,
+            how="inner",
+            sort=False,
+        )
+        tm.assert_frame_equal(joined, expected2.reindex_like(joined))
+
+        expected2 = merge(
+            to_join,
+            data,
+            right_on=["key1", "key2"],
+            left_index=True,
+            how="inner",
+            sort=False,
+        )
+
+        expected = expected.drop(["first", "second"], axis=1)
+        expected.index = joined.index
+
+        assert joined.index.is_monotonic_increasing
+        tm.assert_frame_equal(joined, expected)
+
+        # _assert_same_contents(expected, expected2.loc[:, expected.columns])
+
+    def test_join_hierarchical_mixed_raises(self):
+        # GH 2024
+        # GH 40993: For raising, enforced in 2.0
+        df = DataFrame([(1, 2, 3), (4, 5, 6)], columns=["a", "b", "c"])
+        new_df = df.groupby(["a"]).agg({"b": [np.mean, np.sum]})
+        other_df = DataFrame([(1, 2, 3), (7, 10, 6)], columns=["a", "b", "d"])
+        other_df.set_index("a", inplace=True)
+        # GH 9455, 12219
+        with pytest.raises(
+            pd.errors.MergeError, match="Not allowed to merge between different levels"
+        ):
+            merge(new_df, other_df, left_index=True, right_index=True)
+
+    def test_join_float64_float32(self):
+        a = DataFrame(np.random.randn(10, 2), columns=["a", "b"], dtype=np.float64)
+        b = DataFrame(np.random.randn(10, 1), columns=["c"], dtype=np.float32)
+        joined = a.join(b)
+        assert joined.dtypes["a"] == "float64"
+        assert joined.dtypes["b"] == "float64"
+        assert joined.dtypes["c"] == "float32"
+
+        a = np.random.randint(0, 5, 100).astype("int64")
+        b = np.random.random(100).astype("float64")
+        c = np.random.random(100).astype("float32")
+        df = DataFrame({"a": a, "b": b, "c": c})
+        xpdf = DataFrame({"a": a, "b": b, "c": c})
+        s = DataFrame(np.random.random(5).astype("float32"), columns=["md"])
+        rs = df.merge(s, left_on="a", right_index=True)
+        assert rs.dtypes["a"] == "int64"
+        assert rs.dtypes["b"] == "float64"
+        assert rs.dtypes["c"] == "float32"
+        assert rs.dtypes["md"] == "float32"
+
+        xp = xpdf.merge(s, left_on="a", right_index=True)
+        tm.assert_frame_equal(rs, xp)
+
+    def test_join_many_non_unique_index(self):
+        df1 = DataFrame({"a": [1, 1], "b": [1, 1], "c": [10, 20]})
+        df2 = DataFrame({"a": [1, 1], "b": [1, 2], "d": [100, 200]})
+        df3 = DataFrame({"a": [1, 1], "b": [1, 2], "e": [1000, 2000]})
+        idf1 = df1.set_index(["a", "b"])
+        idf2 = df2.set_index(["a", "b"])
+        idf3 = df3.set_index(["a", "b"])
+
+        result = idf1.join([idf2, idf3], how="outer")
+
+        df_partially_merged = merge(df1, df2, on=["a", "b"], how="outer")
+        expected = merge(df_partially_merged, df3, on=["a", "b"], how="outer")
+
+        result = result.reset_index()
+        expected = expected[result.columns]
+        expected["a"] = expected.a.astype("int64")
+        expected["b"] = expected.b.astype("int64")
+        tm.assert_frame_equal(result, expected)
+
+        df1 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 1], "c": [10, 20, 30]})
+        df2 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 2], "d": [100, 200, 300]})
+        df3 = DataFrame({"a": [1, 1, 1], "b": [1, 1, 2], "e": [1000, 2000, 3000]})
+        idf1 = df1.set_index(["a", "b"])
+        idf2 = df2.set_index(["a", "b"])
+        idf3 = df3.set_index(["a", "b"])
+        result = idf1.join([idf2, idf3], how="inner")
+
+        df_partially_merged = merge(df1, df2, on=["a", "b"], how="inner")
+        expected = merge(df_partially_merged, df3, on=["a", "b"], how="inner")
+
+        result = result.reset_index()
+
+        tm.assert_frame_equal(result, expected.loc[:, result.columns])
+
+        # GH 11519
+        df = DataFrame(
+            {
+                "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+                "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+                "C": np.random.randn(8),
+                "D": np.random.randn(8),
+            }
+        )
+        s = Series(
+            np.repeat(np.arange(8), 2), index=np.repeat(np.arange(8), 2), name="TEST"
+        )
+        inner = df.join(s, how="inner")
+        outer = df.join(s, how="outer")
+        left = df.join(s, how="left")
+        right = df.join(s, how="right")
+        tm.assert_frame_equal(inner, outer)
+        tm.assert_frame_equal(inner, left)
+        tm.assert_frame_equal(inner, right)
+
+    def test_join_sort(self):
+        left = DataFrame({"key": ["foo", "bar", "baz", "foo"], "value": [1, 2, 3, 4]})
+        right = DataFrame({"value2": ["a", "b", "c"]}, index=["bar", "baz", "foo"])
+
+        joined = left.join(right, on="key", sort=True)
+        expected = DataFrame(
+            {
+                "key": ["bar", "baz", "foo", "foo"],
+                "value": [2, 3, 1, 4],
+                "value2": ["a", "b", "c", "c"],
+            },
+            index=[1, 2, 0, 3],
+        )
+        tm.assert_frame_equal(joined, expected)
+
+        # smoke test
+        joined = left.join(right, on="key", sort=False)
+        tm.assert_index_equal(joined.index, Index(range(4)), exact=True)
+
+    def test_join_mixed_non_unique_index(self):
+        # GH 12814, unorderable types in py3 with a non-unique index
+        df1 = DataFrame({"a": [1, 2, 3, 4]}, index=[1, 2, 3, "a"])
+        df2 = DataFrame({"b": [5, 6, 7, 8]}, index=[1, 3, 3, 4])
+        result = df1.join(df2)
+        expected = DataFrame(
+            {"a": [1, 2, 3, 3, 4], "b": [5, np.nan, 6, 7, np.nan]},
+            index=[1, 2, 3, 3, "a"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        df3 = DataFrame({"a": [1, 2, 3, 4]}, index=[1, 2, 2, "a"])
+        df4 = DataFrame({"b": [5, 6, 7, 8]}, index=[1, 2, 3, 4])
+        result = df3.join(df4)
+        expected = DataFrame(
+            {"a": [1, 2, 3, 4], "b": [5, 6, 6, np.nan]}, index=[1, 2, 2, "a"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_non_unique_period_index(self):
+        # GH #16871
+        index = pd.period_range("2016-01-01", periods=16, freq="M")
+        df = DataFrame(list(range(len(index))), index=index, columns=["pnum"])
+        df2 = concat([df, df])
+        result = df.join(df2, how="inner", rsuffix="_df2")
+        expected = DataFrame(
+            np.tile(np.arange(16, dtype=np.int64).repeat(2).reshape(-1, 1), 2),
+            columns=["pnum", "pnum_df2"],
+            index=df2.sort_index().index,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_mixed_type_join_with_suffix(self):
+        # GH #916
+        df = DataFrame(np.random.randn(20, 6), columns=["a", "b", "c", "d", "e", "f"])
+        df.insert(0, "id", 0)
+        df.insert(5, "dt", "foo")
+
+        grouped = df.groupby("id")
+        with pytest.raises(TypeError, match="Could not convert"):
+            grouped.mean()
+        mn = grouped.mean(numeric_only=True)
+        cn = grouped.count()
+
+        # it works!
+        mn.join(cn, rsuffix="_right")
+
+    def test_join_many(self):
+        df = DataFrame(np.random.randn(10, 6), columns=list("abcdef"))
+        df_list = [df[["a", "b"]], df[["c", "d"]], df[["e", "f"]]]
+
+        joined = df_list[0].join(df_list[1:])
+        tm.assert_frame_equal(joined, df)
+
+        df_list = [df[["a", "b"]][:-2], df[["c", "d"]][2:], df[["e", "f"]][1:9]]
+
+        def _check_diff_index(df_list, result, exp_index):
+            reindexed = [x.reindex(exp_index) for x in df_list]
+            expected = reindexed[0].join(reindexed[1:])
+            tm.assert_frame_equal(result, expected)
+
+        # different join types
+        joined = df_list[0].join(df_list[1:], how="outer")
+        _check_diff_index(df_list, joined, df.index)
+
+        joined = df_list[0].join(df_list[1:])
+        _check_diff_index(df_list, joined, df_list[0].index)
+
+        joined = df_list[0].join(df_list[1:], how="inner")
+        _check_diff_index(df_list, joined, df.index[2:8])
+
+        msg = "Joining multiple DataFrames only supported for joining on index"
+        with pytest.raises(ValueError, match=msg):
+            df_list[0].join(df_list[1:], on="a")
+
+    def test_join_many_mixed(self):
+        df = DataFrame(np.random.randn(8, 4), columns=["A", "B", "C", "D"])
+        df["key"] = ["foo", "bar"] * 4
+        df1 = df.loc[:, ["A", "B"]]
+        df2 = df.loc[:, ["C", "D"]]
+        df3 = df.loc[:, ["key"]]
+
+        result = df1.join([df2, df3])
+        tm.assert_frame_equal(result, df)
+
+    def test_join_dups(self):
+        # joining dups
+        df = concat(
+            [
+                DataFrame(np.random.randn(10, 4), columns=["A", "A", "B", "B"]),
+                DataFrame(
+                    np.random.randint(0, 10, size=20).reshape(10, 2), columns=["A", "C"]
+                ),
+            ],
+            axis=1,
+        )
+
+        expected = concat([df, df], axis=1)
+        result = df.join(df, rsuffix="_2")
+        result.columns = expected.columns
+        tm.assert_frame_equal(result, expected)
+
+        # GH 4975, invalid join on dups
+        w = DataFrame(np.random.randn(4, 2), columns=["x", "y"])
+        x = DataFrame(np.random.randn(4, 2), columns=["x", "y"])
+        y = DataFrame(np.random.randn(4, 2), columns=["x", "y"])
+        z = DataFrame(np.random.randn(4, 2), columns=["x", "y"])
+
+        dta = x.merge(y, left_index=True, right_index=True).merge(
+            z, left_index=True, right_index=True, how="outer"
+        )
+        # GH 40991: As of 2.0 causes duplicate columns
+        with pytest.raises(
+            pd.errors.MergeError,
+            match="Passing 'suffixes' which cause duplicate columns",
+        ):
+            dta.merge(w, left_index=True, right_index=True)
+
+    def test_join_multi_to_multi(self, join_type):
+        # GH 20475
+        leftindex = MultiIndex.from_product(
+            [list("abc"), list("xy"), [1, 2]], names=["abc", "xy", "num"]
+        )
+        left = DataFrame({"v1": range(12)}, index=leftindex)
+
+        rightindex = MultiIndex.from_product(
+            [list("abc"), list("xy")], names=["abc", "xy"]
+        )
+        right = DataFrame({"v2": [100 * i for i in range(1, 7)]}, index=rightindex)
+
+        result = left.join(right, on=["abc", "xy"], how=join_type)
+        expected = (
+            left.reset_index()
+            .merge(right.reset_index(), on=["abc", "xy"], how=join_type)
+            .set_index(["abc", "xy", "num"])
+        )
+        tm.assert_frame_equal(expected, result)
+
+        msg = r'len\(left_on\) must equal the number of levels in the index of "right"'
+        with pytest.raises(ValueError, match=msg):
+            left.join(right, on="xy", how=join_type)
+
+        with pytest.raises(ValueError, match=msg):
+            right.join(left, on=["abc", "xy"], how=join_type)
+
+    def test_join_on_tz_aware_datetimeindex(self):
+        # GH 23931, 26335
+        df1 = DataFrame(
+            {
+                "date": pd.date_range(
+                    start="2018-01-01", periods=5, tz="America/Chicago"
+                ),
+                "vals": list("abcde"),
+            }
+        )
+
+        df2 = DataFrame(
+            {
+                "date": pd.date_range(
+                    start="2018-01-03", periods=5, tz="America/Chicago"
+                ),
+                "vals_2": list("tuvwx"),
+            }
+        )
+        result = df1.join(df2.set_index("date"), on="date")
+        expected = df1.copy()
+        expected["vals_2"] = Series([np.nan] * 2 + list("tuv"), dtype=object)
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_datetime_string(self):
+        # GH 5647
+        dfa = DataFrame(
+            [
+                ["2012-08-02", "L", 10],
+                ["2012-08-02", "J", 15],
+                ["2013-04-06", "L", 20],
+                ["2013-04-06", "J", 25],
+            ],
+            columns=["x", "y", "a"],
+        )
+        dfa["x"] = pd.to_datetime(dfa["x"])
+        dfb = DataFrame(
+            [["2012-08-02", "J", 1], ["2013-04-06", "L", 2]],
+            columns=["x", "y", "z"],
+            index=[2, 4],
+        )
+        dfb["x"] = pd.to_datetime(dfb["x"])
+        result = dfb.join(dfa.set_index(["x", "y"]), on=["x", "y"])
+        expected = DataFrame(
+            [
+                [Timestamp("2012-08-02 00:00:00"), "J", 1, 15],
+                [Timestamp("2013-04-06 00:00:00"), "L", 2, 20],
+            ],
+            index=[2, 4],
+            columns=["x", "y", "z", "a"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_with_categorical_index(self):
+        # GH47812
+        ix = ["a", "b"]
+        id1 = pd.CategoricalIndex(ix, categories=ix)
+        id2 = pd.CategoricalIndex(reversed(ix), categories=reversed(ix))
+
+        df1 = DataFrame({"c1": ix}, index=id1)
+        df2 = DataFrame({"c2": reversed(ix)}, index=id2)
+        result = df1.join(df2)
+        expected = DataFrame(
+            {"c1": ["a", "b"], "c2": ["a", "b"]},
+            index=pd.CategoricalIndex(["a", "b"], categories=["a", "b"]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+def _check_join(left, right, result, join_col, how="left", lsuffix="_x", rsuffix="_y"):
+    # some smoke tests
+    for c in join_col:
+        assert result[c].notna().all()
+
+    left_grouped = left.groupby(join_col)
+    right_grouped = right.groupby(join_col)
+
+    for group_key, group in result.groupby(
+        join_col if len(join_col) > 1 else join_col[0]
+    ):
+        l_joined = _restrict_to_columns(group, left.columns, lsuffix)
+        r_joined = _restrict_to_columns(group, right.columns, rsuffix)
+
+        try:
+            lgroup = left_grouped.get_group(group_key)
+        except KeyError as err:
+            if how in ("left", "inner"):
+                raise AssertionError(
+                    f"key {group_key} should not have been in the join"
+                ) from err
+
+            _assert_all_na(l_joined, left.columns, join_col)
+        else:
+            _assert_same_contents(l_joined, lgroup)
+
+        try:
+            rgroup = right_grouped.get_group(group_key)
+        except KeyError as err:
+            if how in ("right", "inner"):
+                raise AssertionError(
+                    f"key {group_key} should not have been in the join"
+                ) from err
+
+            _assert_all_na(r_joined, right.columns, join_col)
+        else:
+            _assert_same_contents(r_joined, rgroup)
+
+
+def _restrict_to_columns(group, columns, suffix):
+    found = [
+        c for c in group.columns if c in columns or c.replace(suffix, "") in columns
+    ]
+
+    # filter
+    group = group.loc[:, found]
+
+    # get rid of suffixes, if any
+    group = group.rename(columns=lambda x: x.replace(suffix, ""))
+
+    # put in the right order...
+    group = group.loc[:, columns]
+
+    return group
+
+
+def _assert_same_contents(join_chunk, source):
+    NA_SENTINEL = -1234567  # drop_duplicates not so NA-friendly...
+
+    jvalues = join_chunk.fillna(NA_SENTINEL).drop_duplicates().values
+    svalues = source.fillna(NA_SENTINEL).drop_duplicates().values
+
+    rows = {tuple(row) for row in jvalues}
+    assert len(rows) == len(source)
+    assert all(tuple(row) in rows for row in svalues)
+
+
+def _assert_all_na(join_chunk, source_columns, join_col):
+    for c in source_columns:
+        if c in join_col:
+            continue
+        assert join_chunk[c].isna().all()
+
+
+def _join_by_hand(a, b, how="left"):
+    join_index = a.index.join(b.index, how=how)
+
+    a_re = a.reindex(join_index)
+    b_re = b.reindex(join_index)
+
+    result_columns = a.columns.append(b.columns)
+
+    for col, s in b_re.items():
+        a_re[col] = s
+    return a_re.reindex(columns=result_columns)
+
+
+def test_join_inner_multiindex_deterministic_order():
+    # GH: 36910
+    left = DataFrame(
+        data={"e": 5},
+        index=MultiIndex.from_tuples([(1, 2, 4)], names=("a", "b", "d")),
+    )
+    right = DataFrame(
+        data={"f": 6}, index=MultiIndex.from_tuples([(2, 3)], names=("b", "c"))
+    )
+    result = left.join(right, how="inner")
+    expected = DataFrame(
+        {"e": [5], "f": [6]},
+        index=MultiIndex.from_tuples([(2, 1, 4, 3)], names=("b", "a", "d", "c")),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    ("input_col", "output_cols"), [("b", ["a", "b"]), ("a", ["a_x", "a_y"])]
+)
+def test_join_cross(input_col, output_cols):
+    # GH#5401
+    left = DataFrame({"a": [1, 3]})
+    right = DataFrame({input_col: [3, 4]})
+    result = left.join(right, how="cross", lsuffix="_x", rsuffix="_y")
+    expected = DataFrame({output_cols[0]: [1, 1, 3, 3], output_cols[1]: [3, 4, 3, 4]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_join_multiindex_one_level(join_type):
+    # GH#36909
+    left = DataFrame(
+        data={"c": 3}, index=MultiIndex.from_tuples([(1, 2)], names=("a", "b"))
+    )
+    right = DataFrame(data={"d": 4}, index=MultiIndex.from_tuples([(2,)], names=("b",)))
+    result = left.join(right, how=join_type)
+    expected = DataFrame(
+        {"c": [3], "d": [4]},
+        index=MultiIndex.from_tuples([(2, 1)], names=["b", "a"]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "categories, values",
+    [
+        (["Y", "X"], ["Y", "X", "X"]),
+        ([2, 1], [2, 1, 1]),
+        ([2.5, 1.5], [2.5, 1.5, 1.5]),
+        (
+            [Timestamp("2020-12-31"), Timestamp("2019-12-31")],
+            [Timestamp("2020-12-31"), Timestamp("2019-12-31"), Timestamp("2019-12-31")],
+        ),
+    ],
+)
+def test_join_multiindex_not_alphabetical_categorical(categories, values):
+    # GH#38502
+    left = DataFrame(
+        {
+            "first": ["A", "A"],
+            "second": Categorical(categories, categories=categories),
+            "value": [1, 2],
+        }
+    ).set_index(["first", "second"])
+    right = DataFrame(
+        {
+            "first": ["A", "A", "B"],
+            "second": Categorical(values, categories=categories),
+            "value": [3, 4, 5],
+        }
+    ).set_index(["first", "second"])
+    result = left.join(right, lsuffix="_left", rsuffix="_right")
+
+    expected = DataFrame(
+        {
+            "first": ["A", "A"],
+            "second": Categorical(categories, categories=categories),
+            "value_left": [1, 2],
+            "value_right": [3, 4],
+        }
+    ).set_index(["first", "second"])
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "left_empty, how, exp",
+    [
+        (False, "left", "left"),
+        (False, "right", "empty"),
+        (False, "inner", "empty"),
+        (False, "outer", "left"),
+        (False, "cross", "empty"),
+        (True, "left", "empty"),
+        (True, "right", "right"),
+        (True, "inner", "empty"),
+        (True, "outer", "right"),
+        (True, "cross", "empty"),
+    ],
+)
+def test_join_empty(left_empty, how, exp):
+    left = DataFrame({"A": [2, 1], "B": [3, 4]}, dtype="int64").set_index("A")
+    right = DataFrame({"A": [1], "C": [5]}, dtype="int64").set_index("A")
+
+    if left_empty:
+        left = left.head(0)
+    else:
+        right = right.head(0)
+
+    result = left.join(right, how=how)
+
+    if exp == "left":
+        expected = DataFrame({"A": [2, 1], "B": [3, 4], "C": [np.nan, np.nan]})
+        expected = expected.set_index("A")
+    elif exp == "right":
+        expected = DataFrame({"B": [np.nan], "A": [1], "C": [5]})
+        expected = expected.set_index("A")
+    elif exp == "empty":
+        expected = DataFrame(columns=["B", "C"], dtype="int64")
+        if how != "cross":
+            expected = expected.rename_axis("A")
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "how, values",
+    [
+        ("inner", [0, 1, 2]),
+        ("outer", [0, 1, 2]),
+        ("left", [0, 1, 2]),
+        ("right", [0, 2, 1]),
+    ],
+)
+def test_join_multiindex_categorical_output_index_dtype(how, values):
+    # GH#50906
+    df1 = DataFrame(
+        {
+            "a": Categorical([0, 1, 2]),
+            "b": Categorical([0, 1, 2]),
+            "c": [0, 1, 2],
+        }
+    ).set_index(["a", "b"])
+
+    df2 = DataFrame(
+        {
+            "a": Categorical([0, 2, 1]),
+            "b": Categorical([0, 2, 1]),
+            "d": [0, 2, 1],
+        }
+    ).set_index(["a", "b"])
+
+    expected = DataFrame(
+        {
+            "a": Categorical(values),
+            "b": Categorical(values),
+            "c": values,
+            "d": values,
+        }
+    ).set_index(["a", "b"])
+
+    result = df1.join(df2, how=how)
+    tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge.py

@@ -0,0 +1,2781 @@
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+)
+import re
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import (
+    is_categorical_dtype,
+    is_object_dtype,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    IntervalIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    Series,
+    TimedeltaIndex,
+)
+import pandas._testing as tm
+from pandas.api.types import CategoricalDtype as CDT
+from pandas.core.reshape.concat import concat
+from pandas.core.reshape.merge import (
+    MergeError,
+    merge,
+)
+
+
+def get_test_data(ngroups=8, n=50):
+    unique_groups = list(range(ngroups))
+    arr = np.asarray(np.tile(unique_groups, n // ngroups))
+
+    if len(arr) < n:
+        arr = np.asarray(list(arr) + unique_groups[: n - len(arr)])
+
+    np.random.shuffle(arr)
+    return arr
+
+
+def get_series():
+    return [
+        Series([1], dtype="int64"),
+        Series([1], dtype="Int64"),
+        Series([1.23]),
+        Series(["foo"]),
+        Series([True]),
+        Series([pd.Timestamp("2018-01-01")]),
+        Series([pd.Timestamp("2018-01-01", tz="US/Eastern")]),
+    ]
+
+
+def get_series_na():
+    return [
+        Series([np.nan], dtype="Int64"),
+        Series([np.nan], dtype="float"),
+        Series([np.nan], dtype="object"),
+        Series([pd.NaT]),
+    ]
+
+
+@pytest.fixture(params=get_series(), ids=lambda x: x.dtype.name)
+def series_of_dtype(request):
+    """
+    A parametrized fixture returning a variety of Series of different
+    dtypes
+    """
+    return request.param
+
+
+@pytest.fixture(params=get_series(), ids=lambda x: x.dtype.name)
+def series_of_dtype2(request):
+    """
+    A duplicate of the series_of_dtype fixture, so that it can be used
+    twice by a single function
+    """
+    return request.param
+
+
+@pytest.fixture(params=get_series_na(), ids=lambda x: x.dtype.name)
+def series_of_dtype_all_na(request):
+    """
+    A parametrized fixture returning a variety of Series with all NA
+    values
+    """
+    return request.param
+
+
+@pytest.fixture
+def dfs_for_indicator():
+    df1 = DataFrame({"col1": [0, 1], "col_conflict": [1, 2], "col_left": ["a", "b"]})
+    df2 = DataFrame(
+        {
+            "col1": [1, 2, 3, 4, 5],
+            "col_conflict": [1, 2, 3, 4, 5],
+            "col_right": [2, 2, 2, 2, 2],
+        }
+    )
+    return df1, df2
+
+
+class TestMerge:
+    @pytest.fixture
+    def df(self):
+        df = DataFrame(
+            {
+                "key1": get_test_data(),
+                "key2": get_test_data(),
+                "data1": np.random.randn(50),
+                "data2": np.random.randn(50),
+            }
+        )
+
+        # exclude a couple keys for fun
+        df = df[df["key2"] > 1]
+        return df
+
+    @pytest.fixture
+    def df2(self):
+        return DataFrame(
+            {
+                "key1": get_test_data(n=10),
+                "key2": get_test_data(ngroups=4, n=10),
+                "value": np.random.randn(10),
+            }
+        )
+
+    @pytest.fixture
+    def left(self):
+        return DataFrame(
+            {"key": ["a", "b", "c", "d", "e", "e", "a"], "v1": np.random.randn(7)}
+        )
+
+    @pytest.fixture
+    def right(self):
+        return DataFrame({"v2": np.random.randn(4)}, index=["d", "b", "c", "a"])
+
+    def test_merge_inner_join_empty(self):
+        # GH 15328
+        df_empty = DataFrame()
+        df_a = DataFrame({"a": [1, 2]}, index=[0, 1], dtype="int64")
+        result = merge(df_empty, df_a, left_index=True, right_index=True)
+        expected = DataFrame({"a": []}, dtype="int64")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_common(self, df, df2):
+        joined = merge(df, df2)
+        exp = merge(df, df2, on=["key1", "key2"])
+        tm.assert_frame_equal(joined, exp)
+
+    def test_merge_non_string_columns(self):
+        # https://github.com/pandas-dev/pandas/issues/17962
+        # Checks that method runs for non string column names
+        left = DataFrame(
+            {0: [1, 0, 1, 0], 1: [0, 1, 0, 0], 2: [0, 0, 2, 0], 3: [1, 0, 0, 3]}
+        )
+
+        right = left.astype(float)
+        expected = left
+        result = merge(left, right)
+        tm.assert_frame_equal(expected, result)
+
+    def test_merge_index_as_on_arg(self, df, df2):
+        # GH14355
+
+        left = df.set_index("key1")
+        right = df2.set_index("key1")
+        result = merge(left, right, on="key1")
+        expected = merge(df, df2, on="key1").set_index("key1")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_index_singlekey_right_vs_left(self):
+        left = DataFrame(
+            {"key": ["a", "b", "c", "d", "e", "e", "a"], "v1": np.random.randn(7)}
+        )
+        right = DataFrame({"v2": np.random.randn(4)}, index=["d", "b", "c", "a"])
+
+        merged1 = merge(
+            left, right, left_on="key", right_index=True, how="left", sort=False
+        )
+        merged2 = merge(
+            right, left, right_on="key", left_index=True, how="right", sort=False
+        )
+        tm.assert_frame_equal(merged1, merged2.loc[:, merged1.columns])
+
+        merged1 = merge(
+            left, right, left_on="key", right_index=True, how="left", sort=True
+        )
+        merged2 = merge(
+            right, left, right_on="key", left_index=True, how="right", sort=True
+        )
+        tm.assert_frame_equal(merged1, merged2.loc[:, merged1.columns])
+
+    def test_merge_index_singlekey_inner(self):
+        left = DataFrame(
+            {"key": ["a", "b", "c", "d", "e", "e", "a"], "v1": np.random.randn(7)}
+        )
+        right = DataFrame({"v2": np.random.randn(4)}, index=["d", "b", "c", "a"])
+
+        # inner join
+        result = merge(left, right, left_on="key", right_index=True, how="inner")
+        expected = left.join(right, on="key").loc[result.index]
+        tm.assert_frame_equal(result, expected)
+
+        result = merge(right, left, right_on="key", left_index=True, how="inner")
+        expected = left.join(right, on="key").loc[result.index]
+        tm.assert_frame_equal(result, expected.loc[:, result.columns])
+
+    def test_merge_misspecified(self, df, df2, left, right):
+        msg = "Must pass right_on or right_index=True"
+        with pytest.raises(pd.errors.MergeError, match=msg):
+            merge(left, right, left_index=True)
+        msg = "Must pass left_on or left_index=True"
+        with pytest.raises(pd.errors.MergeError, match=msg):
+            merge(left, right, right_index=True)
+
+        msg = (
+            'Can only pass argument "on" OR "left_on" and "right_on", not '
+            "a combination of both"
+        )
+        with pytest.raises(pd.errors.MergeError, match=msg):
+            merge(left, left, left_on="key", on="key")
+
+        msg = r"len\(right_on\) must equal len\(left_on\)"
+        with pytest.raises(ValueError, match=msg):
+            merge(df, df2, left_on=["key1"], right_on=["key1", "key2"])
+
+    def test_index_and_on_parameters_confusion(self, df, df2):
+        msg = "right_index parameter must be of type bool, not <class 'list'>"
+        with pytest.raises(ValueError, match=msg):
+            merge(
+                df,
+                df2,
+                how="left",
+                left_index=False,
+                right_index=["key1", "key2"],
+            )
+        msg = "left_index parameter must be of type bool, not <class 'list'>"
+        with pytest.raises(ValueError, match=msg):
+            merge(
+                df,
+                df2,
+                how="left",
+                left_index=["key1", "key2"],
+                right_index=False,
+            )
+        with pytest.raises(ValueError, match=msg):
+            merge(
+                df,
+                df2,
+                how="left",
+                left_index=["key1", "key2"],
+                right_index=["key1", "key2"],
+            )
+
+    def test_merge_overlap(self, left):
+        merged = merge(left, left, on="key")
+        exp_len = (left["key"].value_counts() ** 2).sum()
+        assert len(merged) == exp_len
+        assert "v1_x" in merged
+        assert "v1_y" in merged
+
+    def test_merge_different_column_key_names(self):
+        left = DataFrame({"lkey": ["foo", "bar", "baz", "foo"], "value": [1, 2, 3, 4]})
+        right = DataFrame({"rkey": ["foo", "bar", "qux", "foo"], "value": [5, 6, 7, 8]})
+
+        merged = left.merge(
+            right, left_on="lkey", right_on="rkey", how="outer", sort=True
+        )
+
+        exp = Series(["bar", "baz", "foo", "foo", "foo", "foo", np.nan], name="lkey")
+        tm.assert_series_equal(merged["lkey"], exp)
+
+        exp = Series(["bar", np.nan, "foo", "foo", "foo", "foo", "qux"], name="rkey")
+        tm.assert_series_equal(merged["rkey"], exp)
+
+        exp = Series([2, 3, 1, 1, 4, 4, np.nan], name="value_x")
+        tm.assert_series_equal(merged["value_x"], exp)
+
+        exp = Series([6, np.nan, 5, 8, 5, 8, 7], name="value_y")
+        tm.assert_series_equal(merged["value_y"], exp)
+
+    def test_merge_copy(self):
+        left = DataFrame({"a": 0, "b": 1}, index=range(10))
+        right = DataFrame({"c": "foo", "d": "bar"}, index=range(10))
+
+        merged = merge(left, right, left_index=True, right_index=True, copy=True)
+
+        merged["a"] = 6
+        assert (left["a"] == 0).all()
+
+        merged["d"] = "peekaboo"
+        assert (right["d"] == "bar").all()
+
+    def test_merge_nocopy(self, using_array_manager):
+        left = DataFrame({"a": 0, "b": 1}, index=range(10))
+        right = DataFrame({"c": "foo", "d": "bar"}, index=range(10))
+
+        merged = merge(left, right, left_index=True, right_index=True, copy=False)
+
+        assert np.shares_memory(merged["a"]._values, left["a"]._values)
+        assert np.shares_memory(merged["d"]._values, right["d"]._values)
+
+    def test_intelligently_handle_join_key(self):
+        # #733, be a bit more 1337 about not returning unconsolidated DataFrame
+
+        left = DataFrame(
+            {"key": [1, 1, 2, 2, 3], "value": list(range(5))}, columns=["value", "key"]
+        )
+        right = DataFrame({"key": [1, 1, 2, 3, 4, 5], "rvalue": list(range(6))})
+
+        joined = merge(left, right, on="key", how="outer")
+        expected = DataFrame(
+            {
+                "key": [1, 1, 1, 1, 2, 2, 3, 4, 5],
+                "value": np.array([0, 0, 1, 1, 2, 3, 4, np.nan, np.nan]),
+                "rvalue": [0, 1, 0, 1, 2, 2, 3, 4, 5],
+            },
+            columns=["value", "key", "rvalue"],
+        )
+        tm.assert_frame_equal(joined, expected)
+
+    def test_merge_join_key_dtype_cast(self):
+        # #8596
+
+        df1 = DataFrame({"key": [1], "v1": [10]})
+        df2 = DataFrame({"key": [2], "v1": [20]})
+        df = merge(df1, df2, how="outer")
+        assert df["key"].dtype == "int64"
+
+        df1 = DataFrame({"key": [True], "v1": [1]})
+        df2 = DataFrame({"key": [False], "v1": [0]})
+        df = merge(df1, df2, how="outer")
+
+        # GH13169
+        # GH#40073
+        assert df["key"].dtype == "bool"
+
+        df1 = DataFrame({"val": [1]})
+        df2 = DataFrame({"val": [2]})
+        lkey = np.array([1])
+        rkey = np.array([2])
+        df = merge(df1, df2, left_on=lkey, right_on=rkey, how="outer")
+        assert df["key_0"].dtype == np.int_
+
+    def test_handle_join_key_pass_array(self):
+        left = DataFrame(
+            {"key": [1, 1, 2, 2, 3], "value": np.arange(5)},
+            columns=["value", "key"],
+            dtype="int64",
+        )
+        right = DataFrame({"rvalue": np.arange(6)}, dtype="int64")
+        key = np.array([1, 1, 2, 3, 4, 5], dtype="int64")
+
+        merged = merge(left, right, left_on="key", right_on=key, how="outer")
+        merged2 = merge(right, left, left_on=key, right_on="key", how="outer")
+
+        tm.assert_series_equal(merged["key"], merged2["key"])
+        assert merged["key"].notna().all()
+        assert merged2["key"].notna().all()
+
+        left = DataFrame({"value": np.arange(5)}, columns=["value"])
+        right = DataFrame({"rvalue": np.arange(6)})
+        lkey = np.array([1, 1, 2, 2, 3])
+        rkey = np.array([1, 1, 2, 3, 4, 5])
+
+        merged = merge(left, right, left_on=lkey, right_on=rkey, how="outer")
+        expected = Series([1, 1, 1, 1, 2, 2, 3, 4, 5], dtype=np.int_, name="key_0")
+        tm.assert_series_equal(merged["key_0"], expected)
+
+        left = DataFrame({"value": np.arange(3)})
+        right = DataFrame({"rvalue": np.arange(6)})
+
+        key = np.array([0, 1, 1, 2, 2, 3], dtype=np.int64)
+        merged = merge(left, right, left_index=True, right_on=key, how="outer")
+        tm.assert_series_equal(merged["key_0"], Series(key, name="key_0"))
+
+    def test_no_overlap_more_informative_error(self):
+        dt = datetime.now()
+        df1 = DataFrame({"x": ["a"]}, index=[dt])
+
+        df2 = DataFrame({"y": ["b", "c"]}, index=[dt, dt])
+
+        msg = (
+            "No common columns to perform merge on. "
+            f"Merge options: left_on={None}, right_on={None}, "
+            f"left_index={False}, right_index={False}"
+        )
+
+        with pytest.raises(MergeError, match=msg):
+            merge(df1, df2)
+
+    def test_merge_non_unique_indexes(self):
+        dt = datetime(2012, 5, 1)
+        dt2 = datetime(2012, 5, 2)
+        dt3 = datetime(2012, 5, 3)
+        dt4 = datetime(2012, 5, 4)
+
+        df1 = DataFrame({"x": ["a"]}, index=[dt])
+        df2 = DataFrame({"y": ["b", "c"]}, index=[dt, dt])
+        _check_merge(df1, df2)
+
+        # Not monotonic
+        df1 = DataFrame({"x": ["a", "b", "q"]}, index=[dt2, dt, dt4])
+        df2 = DataFrame(
+            {"y": ["c", "d", "e", "f", "g", "h"]}, index=[dt3, dt3, dt2, dt2, dt, dt]
+        )
+        _check_merge(df1, df2)
+
+        df1 = DataFrame({"x": ["a", "b"]}, index=[dt, dt])
+        df2 = DataFrame({"y": ["c", "d"]}, index=[dt, dt])
+        _check_merge(df1, df2)
+
+    def test_merge_non_unique_index_many_to_many(self):
+        dt = datetime(2012, 5, 1)
+        dt2 = datetime(2012, 5, 2)
+        dt3 = datetime(2012, 5, 3)
+        df1 = DataFrame({"x": ["a", "b", "c", "d"]}, index=[dt2, dt2, dt, dt])
+        df2 = DataFrame(
+            {"y": ["e", "f", "g", " h", "i"]}, index=[dt2, dt2, dt3, dt, dt]
+        )
+        _check_merge(df1, df2)
+
+    def test_left_merge_empty_dataframe(self):
+        left = DataFrame({"key": [1], "value": [2]})
+        right = DataFrame({"key": []})
+
+        result = merge(left, right, on="key", how="left")
+        tm.assert_frame_equal(result, left)
+
+        result = merge(right, left, on="key", how="right")
+        tm.assert_frame_equal(result, left)
+
+    @pytest.mark.parametrize(
+        "kwarg",
+        [
+            {"left_index": True, "right_index": True},
+            {"left_index": True, "right_on": "x"},
+            {"left_on": "a", "right_index": True},
+            {"left_on": "a", "right_on": "x"},
+        ],
+    )
+    def test_merge_left_empty_right_empty(self, join_type, kwarg):
+        # GH 10824
+        left = DataFrame(columns=["a", "b", "c"])
+        right = DataFrame(columns=["x", "y", "z"])
+
+        exp_in = DataFrame(columns=["a", "b", "c", "x", "y", "z"], dtype=object)
+
+        result = merge(left, right, how=join_type, **kwarg)
+        tm.assert_frame_equal(result, exp_in)
+
+    def test_merge_left_empty_right_notempty(self):
+        # GH 10824
+        left = DataFrame(columns=["a", "b", "c"])
+        right = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["x", "y", "z"])
+
+        exp_out = DataFrame(
+            {
+                "a": np.array([np.nan] * 3, dtype=object),
+                "b": np.array([np.nan] * 3, dtype=object),
+                "c": np.array([np.nan] * 3, dtype=object),
+                "x": [1, 4, 7],
+                "y": [2, 5, 8],
+                "z": [3, 6, 9],
+            },
+            columns=["a", "b", "c", "x", "y", "z"],
+        )
+        exp_in = exp_out[0:0]  # make empty DataFrame keeping dtype
+
+        def check1(exp, kwarg):
+            result = merge(left, right, how="inner", **kwarg)
+            tm.assert_frame_equal(result, exp)
+            result = merge(left, right, how="left", **kwarg)
+            tm.assert_frame_equal(result, exp)
+
+        def check2(exp, kwarg):
+            result = merge(left, right, how="right", **kwarg)
+            tm.assert_frame_equal(result, exp)
+            result = merge(left, right, how="outer", **kwarg)
+            tm.assert_frame_equal(result, exp)
+
+        for kwarg in [
+            {"left_index": True, "right_index": True},
+            {"left_index": True, "right_on": "x"},
+        ]:
+            check1(exp_in, kwarg)
+            check2(exp_out, kwarg)
+
+        kwarg = {"left_on": "a", "right_index": True}
+        check1(exp_in, kwarg)
+        exp_out["a"] = [0, 1, 2]
+        check2(exp_out, kwarg)
+
+        kwarg = {"left_on": "a", "right_on": "x"}
+        check1(exp_in, kwarg)
+        exp_out["a"] = np.array([np.nan] * 3, dtype=object)
+        check2(exp_out, kwarg)
+
+    def test_merge_left_notempty_right_empty(self):
+        # GH 10824
+        left = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"])
+        right = DataFrame(columns=["x", "y", "z"])
+
+        exp_out = DataFrame(
+            {
+                "a": [1, 4, 7],
+                "b": [2, 5, 8],
+                "c": [3, 6, 9],
+                "x": np.array([np.nan] * 3, dtype=object),
+                "y": np.array([np.nan] * 3, dtype=object),
+                "z": np.array([np.nan] * 3, dtype=object),
+            },
+            columns=["a", "b", "c", "x", "y", "z"],
+        )
+        exp_in = exp_out[0:0]  # make empty DataFrame keeping dtype
+        # result will have object dtype
+        exp_in.index = exp_in.index.astype(object)
+
+        def check1(exp, kwarg):
+            result = merge(left, right, how="inner", **kwarg)
+            tm.assert_frame_equal(result, exp)
+            result = merge(left, right, how="right", **kwarg)
+            tm.assert_frame_equal(result, exp)
+
+        def check2(exp, kwarg):
+            result = merge(left, right, how="left", **kwarg)
+            tm.assert_frame_equal(result, exp)
+            result = merge(left, right, how="outer", **kwarg)
+            tm.assert_frame_equal(result, exp)
+
+            # TODO: should the next loop be un-indented? doing so breaks this test
+            for kwarg in [
+                {"left_index": True, "right_index": True},
+                {"left_index": True, "right_on": "x"},
+                {"left_on": "a", "right_index": True},
+                {"left_on": "a", "right_on": "x"},
+            ]:
+                check1(exp_in, kwarg)
+                check2(exp_out, kwarg)
+
+    def test_merge_empty_frame(self, series_of_dtype, series_of_dtype2):
+        # GH 25183
+        df = DataFrame(
+            {"key": series_of_dtype, "value": series_of_dtype2},
+            columns=["key", "value"],
+        )
+        df_empty = df[:0]
+        expected = DataFrame(
+            {
+                "value_x": Series(dtype=df.dtypes["value"]),
+                "key": Series(dtype=df.dtypes["key"]),
+                "value_y": Series(dtype=df.dtypes["value"]),
+            },
+            columns=["value_x", "key", "value_y"],
+        )
+        actual = df_empty.merge(df, on="key")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_merge_all_na_column(self, series_of_dtype, series_of_dtype_all_na):
+        # GH 25183
+        df_left = DataFrame(
+            {"key": series_of_dtype, "value": series_of_dtype_all_na},
+            columns=["key", "value"],
+        )
+        df_right = DataFrame(
+            {"key": series_of_dtype, "value": series_of_dtype_all_na},
+            columns=["key", "value"],
+        )
+        expected = DataFrame(
+            {
+                "key": series_of_dtype,
+                "value_x": series_of_dtype_all_na,
+                "value_y": series_of_dtype_all_na,
+            },
+            columns=["key", "value_x", "value_y"],
+        )
+        actual = df_left.merge(df_right, on="key")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_merge_nosort(self):
+        # GH#2098
+
+        d = {
+            "var1": np.random.randint(0, 10, size=10),
+            "var2": np.random.randint(0, 10, size=10),
+            "var3": [
+                datetime(2012, 1, 12),
+                datetime(2011, 2, 4),
+                datetime(2010, 2, 3),
+                datetime(2012, 1, 12),
+                datetime(2011, 2, 4),
+                datetime(2012, 4, 3),
+                datetime(2012, 3, 4),
+                datetime(2008, 5, 1),
+                datetime(2010, 2, 3),
+                datetime(2012, 2, 3),
+            ],
+        }
+        df = DataFrame.from_dict(d)
+        var3 = df.var3.unique()
+        var3 = np.sort(var3)
+        new = DataFrame.from_dict({"var3": var3, "var8": np.random.random(7)})
+
+        result = df.merge(new, on="var3", sort=False)
+        exp = merge(df, new, on="var3", sort=False)
+        tm.assert_frame_equal(result, exp)
+
+        assert (df.var3.unique() == result.var3.unique()).all()
+
+    @pytest.mark.parametrize(
+        ("sort", "values"), [(False, [1, 1, 0, 1, 1]), (True, [0, 1, 1, 1, 1])]
+    )
+    @pytest.mark.parametrize("how", ["left", "right"])
+    def test_merge_same_order_left_right(self, sort, values, how):
+        # GH#35382
+        df = DataFrame({"a": [1, 0, 1]})
+
+        result = df.merge(df, on="a", how=how, sort=sort)
+        expected = DataFrame(values, columns=["a"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_nan_right(self):
+        df1 = DataFrame({"i1": [0, 1], "i2": [0, 1]})
+        df2 = DataFrame({"i1": [0], "i3": [0]})
+        result = df1.join(df2, on="i1", rsuffix="_")
+        expected = (
+            DataFrame(
+                {
+                    "i1": {0: 0.0, 1: 1},
+                    "i2": {0: 0, 1: 1},
+                    "i1_": {0: 0, 1: np.nan},
+                    "i3": {0: 0.0, 1: np.nan},
+                    None: {0: 0, 1: 0},
+                }
+            )
+            .set_index(None)
+            .reset_index()[["i1", "i2", "i1_", "i3"]]
+        )
+        tm.assert_frame_equal(result, expected, check_dtype=False)
+
+    def test_merge_nan_right2(self):
+        df1 = DataFrame({"i1": [0, 1], "i2": [0.5, 1.5]})
+        df2 = DataFrame({"i1": [0], "i3": [0.7]})
+        result = df1.join(df2, rsuffix="_", on="i1")
+        expected = DataFrame(
+            {
+                "i1": {0: 0, 1: 1},
+                "i1_": {0: 0.0, 1: np.nan},
+                "i2": {0: 0.5, 1: 1.5},
+                "i3": {0: 0.69999999999999996, 1: np.nan},
+            }
+        )[["i1", "i2", "i1_", "i3"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_type(self, df, df2):
+        class NotADataFrame(DataFrame):
+            @property
+            def _constructor(self):
+                return NotADataFrame
+
+        nad = NotADataFrame(df)
+        result = nad.merge(df2, on="key1")
+
+        assert isinstance(result, NotADataFrame)
+
+    def test_join_append_timedeltas(self, using_array_manager):
+        # timedelta64 issues with join/merge
+        # GH 5695
+
+        d = DataFrame.from_dict(
+            {"d": [datetime(2013, 11, 5, 5, 56)], "t": [timedelta(0, 22500)]}
+        )
+        df = DataFrame(columns=list("dt"))
+        df = concat([df, d], ignore_index=True)
+        result = concat([df, d], ignore_index=True)
+        expected = DataFrame(
+            {
+                "d": [datetime(2013, 11, 5, 5, 56), datetime(2013, 11, 5, 5, 56)],
+                "t": [timedelta(0, 22500), timedelta(0, 22500)],
+            }
+        )
+        if using_array_manager:
+            # TODO(ArrayManager) decide on exact casting rules in concat
+            expected = expected.astype(object)
+        tm.assert_frame_equal(result, expected)
+
+    def test_join_append_timedeltas2(self):
+        # timedelta64 issues with join/merge
+        # GH 5695
+        td = np.timedelta64(300000000)
+        lhs = DataFrame(Series([td, td], index=["A", "B"]))
+        rhs = DataFrame(Series([td], index=["A"]))
+
+        result = lhs.join(rhs, rsuffix="r", how="left")
+        expected = DataFrame(
+            {
+                "0": Series([td, td], index=list("AB")),
+                "0r": Series([td, pd.NaT], index=list("AB")),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"])
+    def test_other_datetime_unit(self, unit):
+        # GH 13389
+        df1 = DataFrame({"entity_id": [101, 102]})
+        ser = Series([None, None], index=[101, 102], name="days")
+
+        dtype = f"datetime64[{unit}]"
+
+        if unit in ["D", "h", "m"]:
+            # not supported so we cast to the nearest supported unit, seconds
+            exp_dtype = "datetime64[s]"
+        else:
+            exp_dtype = dtype
+        df2 = ser.astype(exp_dtype).to_frame("days")
+        assert df2["days"].dtype == exp_dtype
+
+        result = df1.merge(df2, left_on="entity_id", right_index=True)
+
+        days = np.array(["nat", "nat"], dtype=exp_dtype)
+        days = pd.core.arrays.DatetimeArray._simple_new(days, dtype=days.dtype)
+        exp = DataFrame(
+            {
+                "entity_id": [101, 102],
+                "days": days,
+            },
+            columns=["entity_id", "days"],
+        )
+        assert exp["days"].dtype == exp_dtype
+        tm.assert_frame_equal(result, exp)
+
+    @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"])
+    def test_other_timedelta_unit(self, unit):
+        # GH 13389
+        df1 = DataFrame({"entity_id": [101, 102]})
+        ser = Series([None, None], index=[101, 102], name="days")
+
+        dtype = f"m8[{unit}]"
+        if unit in ["D", "h", "m"]:
+            # We cannot astype, instead do nearest supported unit, i.e. "s"
+            msg = "Supported resolutions are 's', 'ms', 'us', 'ns'"
+            with pytest.raises(ValueError, match=msg):
+                ser.astype(dtype)
+
+            df2 = ser.astype("m8[s]").to_frame("days")
+        else:
+            df2 = ser.astype(dtype).to_frame("days")
+            assert df2["days"].dtype == dtype
+
+        result = df1.merge(df2, left_on="entity_id", right_index=True)
+
+        exp = DataFrame(
+            {"entity_id": [101, 102], "days": np.array(["nat", "nat"], dtype=dtype)},
+            columns=["entity_id", "days"],
+        )
+        tm.assert_frame_equal(result, exp)
+
+    def test_overlapping_columns_error_message(self):
+        df = DataFrame({"key": [1, 2, 3], "v1": [4, 5, 6], "v2": [7, 8, 9]})
+        df2 = DataFrame({"key": [1, 2, 3], "v1": [4, 5, 6], "v2": [7, 8, 9]})
+
+        df.columns = ["key", "foo", "foo"]
+        df2.columns = ["key", "bar", "bar"]
+        expected = DataFrame(
+            {
+                "key": [1, 2, 3],
+                "v1": [4, 5, 6],
+                "v2": [7, 8, 9],
+                "v3": [4, 5, 6],
+                "v4": [7, 8, 9],
+            }
+        )
+        expected.columns = ["key", "foo", "foo", "bar", "bar"]
+        tm.assert_frame_equal(merge(df, df2), expected)
+
+        # #2649, #10639
+        df2.columns = ["key1", "foo", "foo"]
+        msg = r"Data columns not unique: Index\(\['foo'\], dtype='object'\)"
+        with pytest.raises(MergeError, match=msg):
+            merge(df, df2)
+
+    def test_merge_on_datetime64tz(self):
+        # GH11405
+        left = DataFrame(
+            {
+                "key": pd.date_range("20151010", periods=2, tz="US/Eastern"),
+                "value": [1, 2],
+            }
+        )
+        right = DataFrame(
+            {
+                "key": pd.date_range("20151011", periods=3, tz="US/Eastern"),
+                "value": [1, 2, 3],
+            }
+        )
+
+        expected = DataFrame(
+            {
+                "key": pd.date_range("20151010", periods=4, tz="US/Eastern"),
+                "value_x": [1, 2, np.nan, np.nan],
+                "value_y": [np.nan, 1, 2, 3],
+            }
+        )
+        result = merge(left, right, on="key", how="outer")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_datetime64tz_values(self):
+        left = DataFrame(
+            {
+                "key": [1, 2],
+                "value": pd.date_range("20151010", periods=2, tz="US/Eastern"),
+            }
+        )
+        right = DataFrame(
+            {
+                "key": [2, 3],
+                "value": pd.date_range("20151011", periods=2, tz="US/Eastern"),
+            }
+        )
+        expected = DataFrame(
+            {
+                "key": [1, 2, 3],
+                "value_x": list(pd.date_range("20151010", periods=2, tz="US/Eastern"))
+                + [pd.NaT],
+                "value_y": [pd.NaT]
+                + list(pd.date_range("20151011", periods=2, tz="US/Eastern")),
+            }
+        )
+        result = merge(left, right, on="key", how="outer")
+        tm.assert_frame_equal(result, expected)
+        assert result["value_x"].dtype == "datetime64[ns, US/Eastern]"
+        assert result["value_y"].dtype == "datetime64[ns, US/Eastern]"
+
+    def test_merge_on_datetime64tz_empty(self):
+        # https://github.com/pandas-dev/pandas/issues/25014
+        dtz = pd.DatetimeTZDtype(tz="UTC")
+        right = DataFrame(
+            {
+                "date": [pd.Timestamp("2018", tz=dtz.tz)],
+                "value": [4.0],
+                "date2": [pd.Timestamp("2019", tz=dtz.tz)],
+            },
+            columns=["date", "value", "date2"],
+        )
+        left = right[:0]
+        result = left.merge(right, on="date")
+        expected = DataFrame(
+            {
+                "value_x": Series(dtype=float),
+                "date2_x": Series(dtype=dtz),
+                "date": Series(dtype=dtz),
+                "value_y": Series(dtype=float),
+                "date2_y": Series(dtype=dtz),
+            },
+            columns=["value_x", "date2_x", "date", "value_y", "date2_y"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_datetime64tz_with_dst_transition(self):
+        # GH 18885
+        df1 = DataFrame(
+            pd.date_range("2017-10-29 01:00", periods=4, freq="H", tz="Europe/Madrid"),
+            columns=["date"],
+        )
+        df1["value"] = 1
+        df2 = DataFrame(
+            {
+                "date": pd.to_datetime(
+                    [
+                        "2017-10-29 03:00:00",
+                        "2017-10-29 04:00:00",
+                        "2017-10-29 05:00:00",
+                    ]
+                ),
+                "value": 2,
+            }
+        )
+        df2["date"] = df2["date"].dt.tz_localize("UTC").dt.tz_convert("Europe/Madrid")
+        result = merge(df1, df2, how="outer", on="date")
+        expected = DataFrame(
+            {
+                "date": pd.date_range(
+                    "2017-10-29 01:00", periods=7, freq="H", tz="Europe/Madrid"
+                ),
+                "value_x": [1] * 4 + [np.nan] * 3,
+                "value_y": [np.nan] * 4 + [2] * 3,
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_non_unique_period_index(self):
+        # GH #16871
+        index = pd.period_range("2016-01-01", periods=16, freq="M")
+        df = DataFrame(list(range(len(index))), index=index, columns=["pnum"])
+        df2 = concat([df, df])
+        result = df.merge(df2, left_index=True, right_index=True, how="inner")
+        expected = DataFrame(
+            np.tile(np.arange(16, dtype=np.int64).repeat(2).reshape(-1, 1), 2),
+            columns=["pnum_x", "pnum_y"],
+            index=df2.sort_index().index,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_on_periods(self):
+        left = DataFrame(
+            {"key": pd.period_range("20151010", periods=2, freq="D"), "value": [1, 2]}
+        )
+        right = DataFrame(
+            {
+                "key": pd.period_range("20151011", periods=3, freq="D"),
+                "value": [1, 2, 3],
+            }
+        )
+
+        expected = DataFrame(
+            {
+                "key": pd.period_range("20151010", periods=4, freq="D"),
+                "value_x": [1, 2, np.nan, np.nan],
+                "value_y": [np.nan, 1, 2, 3],
+            }
+        )
+        result = merge(left, right, on="key", how="outer")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_period_values(self):
+        left = DataFrame(
+            {"key": [1, 2], "value": pd.period_range("20151010", periods=2, freq="D")}
+        )
+        right = DataFrame(
+            {"key": [2, 3], "value": pd.period_range("20151011", periods=2, freq="D")}
+        )
+
+        exp_x = pd.period_range("20151010", periods=2, freq="D")
+        exp_y = pd.period_range("20151011", periods=2, freq="D")
+        expected = DataFrame(
+            {
+                "key": [1, 2, 3],
+                "value_x": list(exp_x) + [pd.NaT],
+                "value_y": [pd.NaT] + list(exp_y),
+            }
+        )
+        result = merge(left, right, on="key", how="outer")
+        tm.assert_frame_equal(result, expected)
+        assert result["value_x"].dtype == "Period[D]"
+        assert result["value_y"].dtype == "Period[D]"
+
+    def test_indicator(self, dfs_for_indicator):
+        # PR #10054. xref #7412 and closes #8790.
+        df1, df2 = dfs_for_indicator
+        df1_copy = df1.copy()
+
+        df2_copy = df2.copy()
+
+        df_result = DataFrame(
+            {
+                "col1": [0, 1, 2, 3, 4, 5],
+                "col_conflict_x": [1, 2, np.nan, np.nan, np.nan, np.nan],
+                "col_left": ["a", "b", np.nan, np.nan, np.nan, np.nan],
+                "col_conflict_y": [np.nan, 1, 2, 3, 4, 5],
+                "col_right": [np.nan, 2, 2, 2, 2, 2],
+            }
+        )
+        df_result["_merge"] = Categorical(
+            [
+                "left_only",
+                "both",
+                "right_only",
+                "right_only",
+                "right_only",
+                "right_only",
+            ],
+            categories=["left_only", "right_only", "both"],
+        )
+
+        df_result = df_result[
+            [
+                "col1",
+                "col_conflict_x",
+                "col_left",
+                "col_conflict_y",
+                "col_right",
+                "_merge",
+            ]
+        ]
+
+        test = merge(df1, df2, on="col1", how="outer", indicator=True)
+        tm.assert_frame_equal(test, df_result)
+        test = df1.merge(df2, on="col1", how="outer", indicator=True)
+        tm.assert_frame_equal(test, df_result)
+
+        # No side effects
+        tm.assert_frame_equal(df1, df1_copy)
+        tm.assert_frame_equal(df2, df2_copy)
+
+        # Check with custom name
+        df_result_custom_name = df_result
+        df_result_custom_name = df_result_custom_name.rename(
+            columns={"_merge": "custom_name"}
+        )
+
+        test_custom_name = merge(
+            df1, df2, on="col1", how="outer", indicator="custom_name"
+        )
+        tm.assert_frame_equal(test_custom_name, df_result_custom_name)
+        test_custom_name = df1.merge(
+            df2, on="col1", how="outer", indicator="custom_name"
+        )
+        tm.assert_frame_equal(test_custom_name, df_result_custom_name)
+
+    def test_merge_indicator_arg_validation(self, dfs_for_indicator):
+        # Check only accepts strings and booleans
+        df1, df2 = dfs_for_indicator
+
+        msg = "indicator option can only accept boolean or string arguments"
+        with pytest.raises(ValueError, match=msg):
+            merge(df1, df2, on="col1", how="outer", indicator=5)
+        with pytest.raises(ValueError, match=msg):
+            df1.merge(df2, on="col1", how="outer", indicator=5)
+
+    def test_merge_indicator_result_integrity(self, dfs_for_indicator):
+        # Check result integrity
+        df1, df2 = dfs_for_indicator
+
+        test2 = merge(df1, df2, on="col1", how="left", indicator=True)
+        assert (test2._merge != "right_only").all()
+        test2 = df1.merge(df2, on="col1", how="left", indicator=True)
+        assert (test2._merge != "right_only").all()
+
+        test3 = merge(df1, df2, on="col1", how="right", indicator=True)
+        assert (test3._merge != "left_only").all()
+        test3 = df1.merge(df2, on="col1", how="right", indicator=True)
+        assert (test3._merge != "left_only").all()
+
+        test4 = merge(df1, df2, on="col1", how="inner", indicator=True)
+        assert (test4._merge == "both").all()
+        test4 = df1.merge(df2, on="col1", how="inner", indicator=True)
+        assert (test4._merge == "both").all()
+
+    def test_merge_indicator_invalid(self, dfs_for_indicator):
+        # Check if working name in df
+        df1, _ = dfs_for_indicator
+
+        for i in ["_right_indicator", "_left_indicator", "_merge"]:
+            df_badcolumn = DataFrame({"col1": [1, 2], i: [2, 2]})
+
+            msg = (
+                "Cannot use `indicator=True` option when data contains a "
+                f"column named {i}|"
+                "Cannot use name of an existing column for indicator column"
+            )
+            with pytest.raises(ValueError, match=msg):
+                merge(df1, df_badcolumn, on="col1", how="outer", indicator=True)
+            with pytest.raises(ValueError, match=msg):
+                df1.merge(df_badcolumn, on="col1", how="outer", indicator=True)
+
+        # Check for name conflict with custom name
+        df_badcolumn = DataFrame({"col1": [1, 2], "custom_column_name": [2, 2]})
+
+        msg = "Cannot use name of an existing column for indicator column"
+        with pytest.raises(ValueError, match=msg):
+            merge(
+                df1,
+                df_badcolumn,
+                on="col1",
+                how="outer",
+                indicator="custom_column_name",
+            )
+        with pytest.raises(ValueError, match=msg):
+            df1.merge(
+                df_badcolumn, on="col1", how="outer", indicator="custom_column_name"
+            )
+
+    def test_merge_indicator_multiple_columns(self):
+        # Merge on multiple columns
+        df3 = DataFrame({"col1": [0, 1], "col2": ["a", "b"]})
+
+        df4 = DataFrame({"col1": [1, 1, 3], "col2": ["b", "x", "y"]})
+
+        hand_coded_result = DataFrame(
+            {"col1": [0, 1, 1, 3], "col2": ["a", "b", "x", "y"]}
+        )
+        hand_coded_result["_merge"] = Categorical(
+            ["left_only", "both", "right_only", "right_only"],
+            categories=["left_only", "right_only", "both"],
+        )
+
+        test5 = merge(df3, df4, on=["col1", "col2"], how="outer", indicator=True)
+        tm.assert_frame_equal(test5, hand_coded_result)
+        test5 = df3.merge(df4, on=["col1", "col2"], how="outer", indicator=True)
+        tm.assert_frame_equal(test5, hand_coded_result)
+
+    def test_validation(self):
+        left = DataFrame(
+            {"a": ["a", "b", "c", "d"], "b": ["cat", "dog", "weasel", "horse"]},
+            index=range(4),
+        )
+
+        right = DataFrame(
+            {
+                "a": ["a", "b", "c", "d", "e"],
+                "c": ["meow", "bark", "um... weasel noise?", "nay", "chirp"],
+            },
+            index=range(5),
+        )
+
+        # Make sure no side effects.
+        left_copy = left.copy()
+        right_copy = right.copy()
+
+        result = merge(left, right, left_index=True, right_index=True, validate="1:1")
+        tm.assert_frame_equal(left, left_copy)
+        tm.assert_frame_equal(right, right_copy)
+
+        # make sure merge still correct
+        expected = DataFrame(
+            {
+                "a_x": ["a", "b", "c", "d"],
+                "b": ["cat", "dog", "weasel", "horse"],
+                "a_y": ["a", "b", "c", "d"],
+                "c": ["meow", "bark", "um... weasel noise?", "nay"],
+            },
+            index=range(4),
+            columns=["a_x", "b", "a_y", "c"],
+        )
+
+        result = merge(
+            left, right, left_index=True, right_index=True, validate="one_to_one"
+        )
+        tm.assert_frame_equal(result, expected)
+
+        expected_2 = DataFrame(
+            {
+                "a": ["a", "b", "c", "d"],
+                "b": ["cat", "dog", "weasel", "horse"],
+                "c": ["meow", "bark", "um... weasel noise?", "nay"],
+            },
+            index=range(4),
+        )
+
+        result = merge(left, right, on="a", validate="1:1")
+        tm.assert_frame_equal(left, left_copy)
+        tm.assert_frame_equal(right, right_copy)
+        tm.assert_frame_equal(result, expected_2)
+
+        result = merge(left, right, on="a", validate="one_to_one")
+        tm.assert_frame_equal(result, expected_2)
+
+        # One index, one column
+        expected_3 = DataFrame(
+            {
+                "b": ["cat", "dog", "weasel", "horse"],
+                "a": ["a", "b", "c", "d"],
+                "c": ["meow", "bark", "um... weasel noise?", "nay"],
+            },
+            columns=["b", "a", "c"],
+            index=range(4),
+        )
+
+        left_index_reset = left.set_index("a")
+        result = merge(
+            left_index_reset,
+            right,
+            left_index=True,
+            right_on="a",
+            validate="one_to_one",
+        )
+        tm.assert_frame_equal(result, expected_3)
+
+        # Dups on right
+        right_w_dups = concat([right, DataFrame({"a": ["e"], "c": ["moo"]}, index=[4])])
+        merge(
+            left,
+            right_w_dups,
+            left_index=True,
+            right_index=True,
+            validate="one_to_many",
+        )
+
+        msg = "Merge keys are not unique in right dataset; not a one-to-one merge"
+        with pytest.raises(MergeError, match=msg):
+            merge(
+                left,
+                right_w_dups,
+                left_index=True,
+                right_index=True,
+                validate="one_to_one",
+            )
+
+        with pytest.raises(MergeError, match=msg):
+            merge(left, right_w_dups, on="a", validate="one_to_one")
+
+        # Dups on left
+        left_w_dups = concat(
+            [left, DataFrame({"a": ["a"], "c": ["cow"]}, index=[3])], sort=True
+        )
+        merge(
+            left_w_dups,
+            right,
+            left_index=True,
+            right_index=True,
+            validate="many_to_one",
+        )
+
+        msg = "Merge keys are not unique in left dataset; not a one-to-one merge"
+        with pytest.raises(MergeError, match=msg):
+            merge(
+                left_w_dups,
+                right,
+                left_index=True,
+                right_index=True,
+                validate="one_to_one",
+            )
+
+        with pytest.raises(MergeError, match=msg):
+            merge(left_w_dups, right, on="a", validate="one_to_one")
+
+        # Dups on both
+        merge(left_w_dups, right_w_dups, on="a", validate="many_to_many")
+
+        msg = "Merge keys are not unique in right dataset; not a many-to-one merge"
+        with pytest.raises(MergeError, match=msg):
+            merge(
+                left_w_dups,
+                right_w_dups,
+                left_index=True,
+                right_index=True,
+                validate="many_to_one",
+            )
+
+        msg = "Merge keys are not unique in left dataset; not a one-to-many merge"
+        with pytest.raises(MergeError, match=msg):
+            merge(left_w_dups, right_w_dups, on="a", validate="one_to_many")
+
+        # Check invalid arguments
+        msg = (
+            '"jibberish" 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):
+            merge(left, right, on="a", validate="jibberish")
+
+        # Two column merge, 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),
+        )
+
+        right = DataFrame(
+            {
+                "a": ["a", "a", "b"],
+                "b": [0, 1, 0],
+                "d": ["meow", "bark", "um... weasel noise?"],
+            },
+            index=range(3),
+        )
+
+        expected_multi = DataFrame(
+            {
+                "a": ["a", "a", "b"],
+                "b": [0, 1, 0],
+                "c": ["cat", "dog", "weasel"],
+                "d": ["meow", "bark", "um... weasel noise?"],
+            },
+            index=range(3),
+        )
+
+        msg = (
+            "Merge keys are not unique in either left or right dataset; "
+            "not a one-to-one merge"
+        )
+        with pytest.raises(MergeError, match=msg):
+            merge(left, right, on="a", validate="1:1")
+
+        result = merge(left, right, on=["a", "b"], validate="1:1")
+        tm.assert_frame_equal(result, expected_multi)
+
+    def test_merge_two_empty_df_no_division_error(self):
+        # GH17776, PR #17846
+        a = DataFrame({"a": [], "b": [], "c": []})
+        with np.errstate(divide="raise"):
+            merge(a, a, on=("a", "b"))
+
+    @pytest.mark.parametrize("how", ["right", "outer"])
+    @pytest.mark.parametrize(
+        "index,expected_index",
+        [
+            (
+                CategoricalIndex([1, 2, 4]),
+                CategoricalIndex([1, 2, 4, None, None, None]),
+            ),
+            (
+                DatetimeIndex(["2001-01-01", "2002-02-02", "2003-03-03"]),
+                DatetimeIndex(
+                    ["2001-01-01", "2002-02-02", "2003-03-03", pd.NaT, pd.NaT, pd.NaT]
+                ),
+            ),
+            *[
+                (
+                    Index([1, 2, 3], dtype=dtyp),
+                    Index([1, 2, 3, None, None, None], dtype=np.float64),
+                )
+                for dtyp in tm.ALL_REAL_NUMPY_DTYPES
+            ],
+            (
+                IntervalIndex.from_tuples([(1, 2), (2, 3), (3, 4)]),
+                IntervalIndex.from_tuples(
+                    [(1, 2), (2, 3), (3, 4), np.nan, np.nan, np.nan]
+                ),
+            ),
+            (
+                PeriodIndex(["2001-01-01", "2001-01-02", "2001-01-03"], freq="D"),
+                PeriodIndex(
+                    ["2001-01-01", "2001-01-02", "2001-01-03", pd.NaT, pd.NaT, pd.NaT],
+                    freq="D",
+                ),
+            ),
+            (
+                TimedeltaIndex(["1d", "2d", "3d"]),
+                TimedeltaIndex(["1d", "2d", "3d", pd.NaT, pd.NaT, pd.NaT]),
+            ),
+        ],
+    )
+    def test_merge_on_index_with_more_values(self, how, index, expected_index):
+        # GH 24212
+        # pd.merge gets [0, 1, 2, -1, -1, -1] as left_indexer, ensure that
+        # -1 is interpreted as a missing value instead of the last element
+        df1 = DataFrame({"a": [0, 1, 2], "key": [0, 1, 2]}, index=index)
+        df2 = DataFrame({"b": [0, 1, 2, 3, 4, 5]})
+        result = df1.merge(df2, left_on="key", right_index=True, how=how)
+        expected = DataFrame(
+            [
+                [0, 0, 0],
+                [1, 1, 1],
+                [2, 2, 2],
+                [np.nan, 3, 3],
+                [np.nan, 4, 4],
+                [np.nan, 5, 5],
+            ],
+            columns=["a", "key", "b"],
+        )
+        expected.set_index(expected_index, inplace=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_right_index_right(self):
+        # Note: the expected output here is probably incorrect.
+        # See https://github.com/pandas-dev/pandas/issues/17257 for more.
+        # We include this as a regression test for GH-24897.
+        left = DataFrame({"a": [1, 2, 3], "key": [0, 1, 1]})
+        right = DataFrame({"b": [1, 2, 3]})
+
+        expected = DataFrame(
+            {"a": [1, 2, 3, None], "key": [0, 1, 1, 2], "b": [1, 2, 2, 3]},
+            columns=["a", "key", "b"],
+            index=[0, 1, 2, np.nan],
+        )
+        result = left.merge(right, left_on="key", right_index=True, how="right")
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("how", ["left", "right"])
+    def test_merge_preserves_row_order(self, how):
+        # GH 27453
+        left_df = DataFrame({"animal": ["dog", "pig"], "max_speed": [40, 11]})
+        right_df = DataFrame({"animal": ["quetzal", "pig"], "max_speed": [80, 11]})
+        result = left_df.merge(right_df, on=["animal", "max_speed"], how=how)
+        if how == "right":
+            expected = DataFrame({"animal": ["quetzal", "pig"], "max_speed": [80, 11]})
+        else:
+            expected = DataFrame({"animal": ["dog", "pig"], "max_speed": [40, 11]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_take_missing_values_from_index_of_other_dtype(self):
+        # GH 24212
+        left = DataFrame(
+            {
+                "a": [1, 2, 3],
+                "key": Categorical(["a", "a", "b"], categories=list("abc")),
+            }
+        )
+        right = DataFrame({"b": [1, 2, 3]}, index=CategoricalIndex(["a", "b", "c"]))
+        result = left.merge(right, left_on="key", right_index=True, how="right")
+        expected = DataFrame(
+            {
+                "a": [1, 2, 3, None],
+                "key": Categorical(["a", "a", "b", "c"]),
+                "b": [1, 1, 2, 3],
+            },
+            index=[0, 1, 2, np.nan],
+        )
+        expected = expected.reindex(columns=["a", "key", "b"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_readonly(self):
+        # https://github.com/pandas-dev/pandas/issues/27943
+        data1 = DataFrame(
+            np.arange(20).reshape((4, 5)) + 1, columns=["a", "b", "c", "d", "e"]
+        )
+        data2 = DataFrame(
+            np.arange(20).reshape((5, 4)) + 1, columns=["a", "b", "x", "y"]
+        )
+
+        # make each underlying block array / column array read-only
+        for arr in data1._mgr.arrays:
+            arr.flags.writeable = False
+
+        data1.merge(data2)  # no error
+
+
+def _check_merge(x, y):
+    for how in ["inner", "left", "outer"]:
+        result = x.join(y, how=how)
+
+        expected = merge(x.reset_index(), y.reset_index(), how=how, sort=True)
+        expected = expected.set_index("index")
+
+        # TODO check_names on merge?
+        tm.assert_frame_equal(result, expected, check_names=False)
+
+
+class TestMergeDtypes:
+    @pytest.mark.parametrize(
+        "right_vals", [["foo", "bar"], Series(["foo", "bar"]).astype("category")]
+    )
+    def test_different(self, right_vals):
+        left = DataFrame(
+            {
+                "A": ["foo", "bar"],
+                "B": Series(["foo", "bar"]).astype("category"),
+                "C": [1, 2],
+                "D": [1.0, 2.0],
+                "E": Series([1, 2], dtype="uint64"),
+                "F": Series([1, 2], dtype="int32"),
+            }
+        )
+        right = DataFrame({"A": right_vals})
+
+        # GH 9780
+        # We allow merging on object and categorical cols and cast
+        # categorical cols to object
+        result = merge(left, right, on="A")
+        assert is_object_dtype(result.A.dtype)
+
+    @pytest.mark.parametrize(
+        "d1", [np.int64, np.int32, np.intc, np.int16, np.int8, np.uint8]
+    )
+    @pytest.mark.parametrize("d2", [np.int64, np.float64, np.float32, np.float16])
+    def test_join_multi_dtypes(self, d1, d2):
+        dtype1 = np.dtype(d1)
+        dtype2 = np.dtype(d2)
+
+        left = DataFrame(
+            {
+                "k1": np.array([0, 1, 2] * 8, dtype=dtype1),
+                "k2": ["foo", "bar"] * 12,
+                "v": np.array(np.arange(24), dtype=np.int64),
+            }
+        )
+
+        index = MultiIndex.from_tuples([(2, "bar"), (1, "foo")])
+        right = DataFrame({"v2": np.array([5, 7], dtype=dtype2)}, index=index)
+
+        result = left.join(right, on=["k1", "k2"])
+
+        expected = left.copy()
+
+        if dtype2.kind == "i":
+            dtype2 = np.dtype("float64")
+        expected["v2"] = np.array(np.nan, dtype=dtype2)
+        expected.loc[(expected.k1 == 2) & (expected.k2 == "bar"), "v2"] = 5
+        expected.loc[(expected.k1 == 1) & (expected.k2 == "foo"), "v2"] = 7
+
+        tm.assert_frame_equal(result, expected)
+
+        result = left.join(right, on=["k1", "k2"], sort=True)
+        expected.sort_values(["k1", "k2"], kind="mergesort", inplace=True)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "int_vals, float_vals, exp_vals",
+        [
+            ([1, 2, 3], [1.0, 2.0, 3.0], {"X": [1, 2, 3], "Y": [1.0, 2.0, 3.0]}),
+            ([1, 2, 3], [1.0, 3.0], {"X": [1, 3], "Y": [1.0, 3.0]}),
+            ([1, 2], [1.0, 2.0, 3.0], {"X": [1, 2], "Y": [1.0, 2.0]}),
+        ],
+    )
+    def test_merge_on_ints_floats(self, int_vals, float_vals, exp_vals):
+        # GH 16572
+        # Check that float column is not cast to object if
+        # merging on float and int columns
+        A = DataFrame({"X": int_vals})
+        B = DataFrame({"Y": float_vals})
+        expected = DataFrame(exp_vals)
+
+        result = A.merge(B, left_on="X", right_on="Y")
+        tm.assert_frame_equal(result, expected)
+
+        result = B.merge(A, left_on="Y", right_on="X")
+        tm.assert_frame_equal(result, expected[["Y", "X"]])
+
+    def test_merge_key_dtype_cast(self):
+        # GH 17044
+        df1 = DataFrame({"key": [1.0, 2.0], "v1": [10, 20]}, columns=["key", "v1"])
+        df2 = DataFrame({"key": [2], "v2": [200]}, columns=["key", "v2"])
+        result = df1.merge(df2, on="key", how="left")
+        expected = DataFrame(
+            {"key": [1.0, 2.0], "v1": [10, 20], "v2": [np.nan, 200.0]},
+            columns=["key", "v1", "v2"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_on_ints_floats_warning(self):
+        # GH 16572
+        # merge will produce a warning when merging on int and
+        # float columns where the float values are not exactly
+        # equal to their int representation
+        A = DataFrame({"X": [1, 2, 3]})
+        B = DataFrame({"Y": [1.1, 2.5, 3.0]})
+        expected = DataFrame({"X": [3], "Y": [3.0]})
+
+        with tm.assert_produces_warning(UserWarning):
+            result = A.merge(B, left_on="X", right_on="Y")
+            tm.assert_frame_equal(result, expected)
+
+        with tm.assert_produces_warning(UserWarning):
+            result = B.merge(A, left_on="Y", right_on="X")
+            tm.assert_frame_equal(result, expected[["Y", "X"]])
+
+        # test no warning if float has NaNs
+        B = DataFrame({"Y": [np.nan, np.nan, 3.0]})
+
+        with tm.assert_produces_warning(None):
+            result = B.merge(A, left_on="Y", right_on="X")
+            tm.assert_frame_equal(result, expected[["Y", "X"]])
+
+    def test_merge_incompat_infer_boolean_object(self):
+        # GH21119: bool + object bool merge OK
+        df1 = DataFrame({"key": Series([True, False], dtype=object)})
+        df2 = DataFrame({"key": [True, False]})
+
+        expected = DataFrame({"key": [True, False]}, dtype=object)
+        result = merge(df1, df2, on="key")
+        tm.assert_frame_equal(result, expected)
+        result = merge(df2, df1, on="key")
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_incompat_infer_boolean_object_with_missing(self):
+        # GH21119: bool + object bool merge OK
+        # with missing value
+        df1 = DataFrame({"key": Series([True, False, np.nan], dtype=object)})
+        df2 = DataFrame({"key": [True, False]})
+
+        expected = DataFrame({"key": [True, False]}, dtype=object)
+        result = merge(df1, df2, on="key")
+        tm.assert_frame_equal(result, expected)
+        result = merge(df2, df1, on="key")
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "df1_vals, df2_vals",
+        [
+            # merge on category coerces to object
+            ([0, 1, 2], Series(["a", "b", "a"]).astype("category")),
+            ([0.0, 1.0, 2.0], Series(["a", "b", "a"]).astype("category")),
+            # no not infer
+            ([0, 1], Series([False, True], dtype=object)),
+            ([0, 1], Series([False, True], dtype=bool)),
+        ],
+    )
+    def test_merge_incompat_dtypes_are_ok(self, df1_vals, df2_vals):
+        # these are explicitly allowed incompat merges, that pass thru
+        # the result type is dependent on if the values on the rhs are
+        # inferred, otherwise these will be coerced to object
+
+        df1 = DataFrame({"A": df1_vals})
+        df2 = DataFrame({"A": df2_vals})
+
+        result = merge(df1, df2, on=["A"])
+        assert is_object_dtype(result.A.dtype)
+        result = merge(df2, df1, on=["A"])
+        assert is_object_dtype(result.A.dtype)
+
+    @pytest.mark.parametrize(
+        "df1_vals, df2_vals",
+        [
+            # do not infer to numeric
+            (Series([1, 2], dtype="uint64"), ["a", "b", "c"]),
+            (Series([1, 2], dtype="int32"), ["a", "b", "c"]),
+            ([0, 1, 2], ["0", "1", "2"]),
+            ([0.0, 1.0, 2.0], ["0", "1", "2"]),
+            ([0, 1, 2], ["0", "1", "2"]),
+            (
+                pd.date_range("1/1/2011", periods=2, freq="D"),
+                ["2011-01-01", "2011-01-02"],
+            ),
+            (pd.date_range("1/1/2011", periods=2, freq="D"), [0, 1]),
+            (pd.date_range("1/1/2011", periods=2, freq="D"), [0.0, 1.0]),
+            (
+                pd.date_range("20130101", periods=3),
+                pd.date_range("20130101", periods=3, tz="US/Eastern"),
+            ),
+        ],
+    )
+    def test_merge_incompat_dtypes_error(self, df1_vals, df2_vals):
+        # GH 9780, GH 15800
+        # Raise a ValueError when a user tries to merge on
+        # dtypes that are incompatible (e.g., obj and int/float)
+
+        df1 = DataFrame({"A": df1_vals})
+        df2 = DataFrame({"A": df2_vals})
+
+        msg = (
+            f"You are trying to merge on {df1['A'].dtype} and "
+            f"{df2['A'].dtype} columns. If you wish to proceed "
+            "you should use pd.concat"
+        )
+        msg = re.escape(msg)
+        with pytest.raises(ValueError, match=msg):
+            merge(df1, df2, on=["A"])
+
+        # Check that error still raised when swapping order of dataframes
+        msg = (
+            f"You are trying to merge on {df2['A'].dtype} and "
+            f"{df1['A'].dtype} columns. If you wish to proceed "
+            "you should use pd.concat"
+        )
+        msg = re.escape(msg)
+        with pytest.raises(ValueError, match=msg):
+            merge(df2, df1, on=["A"])
+
+    @pytest.mark.parametrize(
+        "expected_data, how",
+        [
+            ([1, 2], "outer"),
+            ([], "inner"),
+            ([2], "right"),
+            ([1], "left"),
+        ],
+    )
+    def test_merge_EA_dtype(self, any_numeric_ea_dtype, how, expected_data):
+        # GH#40073
+        d1 = DataFrame([(1,)], columns=["id"], dtype=any_numeric_ea_dtype)
+        d2 = DataFrame([(2,)], columns=["id"], dtype=any_numeric_ea_dtype)
+        result = merge(d1, d2, how=how)
+        exp_index = RangeIndex(len(expected_data))
+        expected = DataFrame(
+            expected_data, index=exp_index, columns=["id"], dtype=any_numeric_ea_dtype
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "expected_data, how",
+        [
+            (["a", "b"], "outer"),
+            ([], "inner"),
+            (["b"], "right"),
+            (["a"], "left"),
+        ],
+    )
+    def test_merge_string_dtype(self, how, expected_data, any_string_dtype):
+        # GH#40073
+        d1 = DataFrame([("a",)], columns=["id"], dtype=any_string_dtype)
+        d2 = DataFrame([("b",)], columns=["id"], dtype=any_string_dtype)
+        result = merge(d1, d2, how=how)
+        exp_idx = RangeIndex(len(expected_data))
+        expected = DataFrame(
+            expected_data, index=exp_idx, columns=["id"], dtype=any_string_dtype
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "how, expected_data",
+        [
+            ("inner", [[True, 1, 4], [False, 5, 3]]),
+            ("outer", [[True, 1, 4], [False, 5, 3]]),
+            ("left", [[True, 1, 4], [False, 5, 3]]),
+            ("right", [[False, 5, 3], [True, 1, 4]]),
+        ],
+    )
+    def test_merge_bool_dtype(self, how, expected_data):
+        # GH#40073
+        df1 = DataFrame({"A": [True, False], "B": [1, 5]})
+        df2 = DataFrame({"A": [False, True], "C": [3, 4]})
+        result = merge(df1, df2, how=how)
+        expected = DataFrame(expected_data, columns=["A", "B", "C"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_ea_with_string(self, join_type, string_dtype):
+        # GH 43734 Avoid the use of `assign` with multi-index
+        df1 = DataFrame(
+            data={
+                ("lvl0", "lvl1-a"): ["1", "2", "3", "4", None],
+                ("lvl0", "lvl1-b"): ["4", "5", "6", "7", "8"],
+            },
+            dtype=pd.StringDtype(),
+        )
+        df1_copy = df1.copy()
+        df2 = DataFrame(
+            data={
+                ("lvl0", "lvl1-a"): ["1", "2", "3", pd.NA, "5"],
+                ("lvl0", "lvl1-c"): ["7", "8", "9", pd.NA, "11"],
+            },
+            dtype=string_dtype,
+        )
+        df2_copy = df2.copy()
+        merged = merge(left=df1, right=df2, on=[("lvl0", "lvl1-a")], how=join_type)
+
+        # No change in df1 and df2
+        tm.assert_frame_equal(df1, df1_copy)
+        tm.assert_frame_equal(df2, df2_copy)
+
+        # Check the expected types for the merged data frame
+        expected = Series(
+            [np.dtype("O"), pd.StringDtype(), np.dtype("O")],
+            index=MultiIndex.from_tuples(
+                [("lvl0", "lvl1-a"), ("lvl0", "lvl1-b"), ("lvl0", "lvl1-c")]
+            ),
+        )
+        tm.assert_series_equal(merged.dtypes, expected)
+
+    @pytest.mark.parametrize(
+        "left_empty, how, exp",
+        [
+            (False, "left", "left"),
+            (False, "right", "empty"),
+            (False, "inner", "empty"),
+            (False, "outer", "left"),
+            (False, "cross", "empty_cross"),
+            (True, "left", "empty"),
+            (True, "right", "right"),
+            (True, "inner", "empty"),
+            (True, "outer", "right"),
+            (True, "cross", "empty_cross"),
+        ],
+    )
+    def test_merge_empty(self, left_empty, how, exp):
+        left = DataFrame({"A": [2, 1], "B": [3, 4]})
+        right = DataFrame({"A": [1], "C": [5]}, dtype="int64")
+
+        if left_empty:
+            left = left.head(0)
+        else:
+            right = right.head(0)
+
+        result = left.merge(right, how=how)
+
+        if exp == "left":
+            expected = DataFrame({"A": [2, 1], "B": [3, 4], "C": [np.nan, np.nan]})
+        elif exp == "right":
+            expected = DataFrame({"B": [np.nan], "A": [1], "C": [5]})
+        elif exp == "empty":
+            expected = DataFrame(columns=["A", "B", "C"], dtype="int64")
+            if left_empty:
+                expected = expected[["B", "A", "C"]]
+        elif exp == "empty_cross":
+            expected = DataFrame(columns=["A_x", "B", "A_y", "C"], dtype="int64")
+
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.fixture
+def left():
+    np.random.seed(1234)
+    return DataFrame(
+        {
+            "X": Series(np.random.choice(["foo", "bar"], size=(10,))).astype(
+                CDT(["foo", "bar"])
+            ),
+            "Y": np.random.choice(["one", "two", "three"], size=(10,)),
+        }
+    )
+
+
+@pytest.fixture
+def right():
+    np.random.seed(1234)
+    return DataFrame(
+        {"X": Series(["foo", "bar"]).astype(CDT(["foo", "bar"])), "Z": [1, 2]}
+    )
+
+
+class TestMergeCategorical:
+    def test_identical(self, left):
+        # merging on the same, should preserve dtypes
+        merged = merge(left, left, on="X")
+        result = merged.dtypes.sort_index()
+        expected = Series(
+            [CategoricalDtype(categories=["foo", "bar"]), np.dtype("O"), np.dtype("O")],
+            index=["X", "Y_x", "Y_y"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_basic(self, left, right):
+        # we have matching Categorical dtypes in X
+        # so should preserve the merged column
+        merged = merge(left, right, on="X")
+        result = merged.dtypes.sort_index()
+        expected = Series(
+            [
+                CategoricalDtype(categories=["foo", "bar"]),
+                np.dtype("O"),
+                np.dtype("int64"),
+            ],
+            index=["X", "Y", "Z"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_merge_categorical(self):
+        # GH 9426
+
+        right = DataFrame(
+            {
+                "c": {0: "a", 1: "b", 2: "c", 3: "d", 4: "e"},
+                "d": {0: "null", 1: "null", 2: "null", 3: "null", 4: "null"},
+            }
+        )
+        left = DataFrame(
+            {
+                "a": {0: "f", 1: "f", 2: "f", 3: "f", 4: "f"},
+                "b": {0: "g", 1: "g", 2: "g", 3: "g", 4: "g"},
+            }
+        )
+        df = merge(left, right, how="left", left_on="b", right_on="c")
+
+        # object-object
+        expected = df.copy()
+
+        # object-cat
+        # note that we propagate the category
+        # because we don't have any matching rows
+        cright = right.copy()
+        cright["d"] = cright["d"].astype("category")
+        result = merge(left, cright, how="left", left_on="b", right_on="c")
+        expected["d"] = expected["d"].astype(CategoricalDtype(["null"]))
+        tm.assert_frame_equal(result, expected)
+
+        # cat-object
+        cleft = left.copy()
+        cleft["b"] = cleft["b"].astype("category")
+        result = merge(cleft, cright, how="left", left_on="b", right_on="c")
+        tm.assert_frame_equal(result, expected)
+
+        # cat-cat
+        cright = right.copy()
+        cright["d"] = cright["d"].astype("category")
+        cleft = left.copy()
+        cleft["b"] = cleft["b"].astype("category")
+        result = merge(cleft, cright, how="left", left_on="b", right_on="c")
+        tm.assert_frame_equal(result, expected)
+
+    def tests_merge_categorical_unordered_equal(self):
+        # GH-19551
+        df1 = DataFrame(
+            {
+                "Foo": Categorical(["A", "B", "C"], categories=["A", "B", "C"]),
+                "Left": ["A0", "B0", "C0"],
+            }
+        )
+
+        df2 = DataFrame(
+            {
+                "Foo": Categorical(["C", "B", "A"], categories=["C", "B", "A"]),
+                "Right": ["C1", "B1", "A1"],
+            }
+        )
+        result = merge(df1, df2, on=["Foo"])
+        expected = DataFrame(
+            {
+                "Foo": Categorical(["A", "B", "C"]),
+                "Left": ["A0", "B0", "C0"],
+                "Right": ["A1", "B1", "C1"],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("ordered", [True, False])
+    def test_multiindex_merge_with_unordered_categoricalindex(self, ordered):
+        # GH 36973
+        pcat = CategoricalDtype(categories=["P2", "P1"], ordered=ordered)
+        df1 = DataFrame(
+            {
+                "id": ["C", "C", "D"],
+                "p": Categorical(["P2", "P1", "P2"], dtype=pcat),
+                "a": [0, 1, 2],
+            }
+        ).set_index(["id", "p"])
+        df2 = DataFrame(
+            {
+                "id": ["A", "C", "C"],
+                "p": Categorical(["P2", "P2", "P1"], dtype=pcat),
+                "d1": [10, 11, 12],
+            }
+        ).set_index(["id", "p"])
+        result = merge(df1, df2, how="left", left_index=True, right_index=True)
+        expected = DataFrame(
+            {
+                "id": ["C", "C", "D"],
+                "p": Categorical(["P2", "P1", "P2"], dtype=pcat),
+                "a": [0, 1, 2],
+                "d1": [11.0, 12.0, np.nan],
+            }
+        ).set_index(["id", "p"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_other_columns(self, left, right):
+        # non-merge columns should preserve if possible
+        right = right.assign(Z=right.Z.astype("category"))
+
+        merged = merge(left, right, on="X")
+        result = merged.dtypes.sort_index()
+        expected = Series(
+            [
+                CategoricalDtype(categories=["foo", "bar"]),
+                np.dtype("O"),
+                CategoricalDtype(categories=[1, 2]),
+            ],
+            index=["X", "Y", "Z"],
+        )
+        tm.assert_series_equal(result, expected)
+
+        # categories are preserved
+        assert left.X.values._categories_match_up_to_permutation(merged.X.values)
+        assert right.Z.values._categories_match_up_to_permutation(merged.Z.values)
+
+    @pytest.mark.parametrize(
+        "change",
+        [
+            lambda x: x,
+            lambda x: x.astype(CDT(["foo", "bar", "bah"])),
+            lambda x: x.astype(CDT(ordered=True)),
+        ],
+    )
+    def test_dtype_on_merged_different(self, change, join_type, left, right):
+        # our merging columns, X now has 2 different dtypes
+        # so we must be object as a result
+
+        X = change(right.X.astype("object"))
+        right = right.assign(X=X)
+        assert is_categorical_dtype(left.X.values.dtype)
+        # assert not left.X.values._categories_match_up_to_permutation(right.X.values)
+
+        merged = merge(left, right, on="X", how=join_type)
+
+        result = merged.dtypes.sort_index()
+        expected = Series(
+            [np.dtype("O"), np.dtype("O"), np.dtype("int64")], index=["X", "Y", "Z"]
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_self_join_multiple_categories(self):
+        # GH 16767
+        # non-duplicates should work with multiple categories
+        m = 5
+        df = DataFrame(
+            {
+                "a": ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"] * m,
+                "b": ["t", "w", "x", "y", "z"] * 2 * m,
+                "c": [
+                    letter
+                    for each in ["m", "n", "u", "p", "o"]
+                    for letter in [each] * 2 * m
+                ],
+                "d": [
+                    letter
+                    for each in [
+                        "aa",
+                        "bb",
+                        "cc",
+                        "dd",
+                        "ee",
+                        "ff",
+                        "gg",
+                        "hh",
+                        "ii",
+                        "jj",
+                    ]
+                    for letter in [each] * m
+                ],
+            }
+        )
+
+        # change them all to categorical variables
+        df = df.apply(lambda x: x.astype("category"))
+
+        # self-join should equal ourselves
+        result = merge(df, df, on=list(df.columns))
+
+        tm.assert_frame_equal(result, df)
+
+    def test_dtype_on_categorical_dates(self):
+        # GH 16900
+        # dates should not be coerced to ints
+
+        df = DataFrame(
+            [[date(2001, 1, 1), 1.1], [date(2001, 1, 2), 1.3]], columns=["date", "num2"]
+        )
+        df["date"] = df["date"].astype("category")
+
+        df2 = DataFrame(
+            [[date(2001, 1, 1), 1.3], [date(2001, 1, 3), 1.4]], columns=["date", "num4"]
+        )
+        df2["date"] = df2["date"].astype("category")
+
+        expected_outer = DataFrame(
+            [
+                [pd.Timestamp("2001-01-01").date(), 1.1, 1.3],
+                [pd.Timestamp("2001-01-02").date(), 1.3, np.nan],
+                [pd.Timestamp("2001-01-03").date(), np.nan, 1.4],
+            ],
+            columns=["date", "num2", "num4"],
+        )
+        result_outer = merge(df, df2, how="outer", on=["date"])
+        tm.assert_frame_equal(result_outer, expected_outer)
+
+        expected_inner = DataFrame(
+            [[pd.Timestamp("2001-01-01").date(), 1.1, 1.3]],
+            columns=["date", "num2", "num4"],
+        )
+        result_inner = merge(df, df2, how="inner", on=["date"])
+        tm.assert_frame_equal(result_inner, expected_inner)
+
+    @pytest.mark.parametrize("ordered", [True, False])
+    @pytest.mark.parametrize(
+        "category_column,categories,expected_categories",
+        [
+            ([False, True, True, False], [True, False], [True, False]),
+            ([2, 1, 1, 2], [1, 2], [1, 2]),
+            (["False", "True", "True", "False"], ["True", "False"], ["True", "False"]),
+        ],
+    )
+    def test_merging_with_bool_or_int_cateorical_column(
+        self, category_column, categories, expected_categories, ordered
+    ):
+        # GH 17187
+        # merging with a boolean/int categorical column
+        df1 = DataFrame({"id": [1, 2, 3, 4], "cat": category_column})
+        df1["cat"] = df1["cat"].astype(CDT(categories, ordered=ordered))
+        df2 = DataFrame({"id": [2, 4], "num": [1, 9]})
+        result = df1.merge(df2)
+        expected = DataFrame({"id": [2, 4], "cat": expected_categories, "num": [1, 9]})
+        expected["cat"] = expected["cat"].astype(CDT(categories, ordered=ordered))
+        tm.assert_frame_equal(expected, result)
+
+    def test_merge_on_int_array(self):
+        # GH 23020
+        df = DataFrame({"A": Series([1, 2, np.nan], dtype="Int64"), "B": 1})
+        result = merge(df, df, on="A")
+        expected = DataFrame(
+            {"A": Series([1, 2, np.nan], dtype="Int64"), "B_x": 1, "B_y": 1}
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.fixture
+def left_df():
+    return DataFrame({"a": [20, 10, 0]}, index=[2, 1, 0])
+
+
+@pytest.fixture
+def right_df():
+    return DataFrame({"b": [300, 100, 200]}, index=[3, 1, 2])
+
+
+class TestMergeOnIndexes:
+    @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_merge_on_indexes(self, left_df, right_df, how, sort, expected):
+        result = merge(
+            left_df, right_df, left_index=True, right_index=True, how=how, sort=sort
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "index",
+    [Index([1, 2], dtype=dtyp, name="index_col") for dtyp in tm.ALL_REAL_NUMPY_DTYPES]
+    + [
+        CategoricalIndex(["A", "B"], categories=["A", "B"], name="index_col"),
+        RangeIndex(start=0, stop=2, name="index_col"),
+        DatetimeIndex(["2018-01-01", "2018-01-02"], name="index_col"),
+    ],
+    ids=lambda x: f"{type(x).__name__}[{x.dtype}]",
+)
+def test_merge_index_types(index):
+    # gh-20777
+    # assert key access is consistent across index types
+    left = DataFrame({"left_data": [1, 2]}, index=index)
+    right = DataFrame({"right_data": [1.0, 2.0]}, index=index)
+
+    result = left.merge(right, on=["index_col"])
+
+    expected = DataFrame({"left_data": [1, 2], "right_data": [1.0, 2.0]}, index=index)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "on,left_on,right_on,left_index,right_index,nm",
+    [
+        (["outer", "inner"], None, None, False, False, "B"),
+        (None, None, None, True, True, "B"),
+        (None, ["outer", "inner"], None, False, True, "B"),
+        (None, None, ["outer", "inner"], True, False, "B"),
+        (["outer", "inner"], None, None, False, False, None),
+        (None, None, None, True, True, None),
+        (None, ["outer", "inner"], None, False, True, None),
+        (None, None, ["outer", "inner"], True, False, None),
+    ],
+)
+def test_merge_series(on, left_on, right_on, left_index, right_index, nm):
+    # GH 21220
+    a = DataFrame(
+        {"A": [1, 2, 3, 4]},
+        index=MultiIndex.from_product([["a", "b"], [0, 1]], names=["outer", "inner"]),
+    )
+    b = Series(
+        [1, 2, 3, 4],
+        index=MultiIndex.from_product([["a", "b"], [1, 2]], names=["outer", "inner"]),
+        name=nm,
+    )
+    expected = DataFrame(
+        {"A": [2, 4], "B": [1, 3]},
+        index=MultiIndex.from_product([["a", "b"], [1]], names=["outer", "inner"]),
+    )
+    if nm is not None:
+        result = merge(
+            a,
+            b,
+            on=on,
+            left_on=left_on,
+            right_on=right_on,
+            left_index=left_index,
+            right_index=right_index,
+        )
+        tm.assert_frame_equal(result, expected)
+    else:
+        msg = "Cannot merge a Series without a name"
+        with pytest.raises(ValueError, match=msg):
+            result = merge(
+                a,
+                b,
+                on=on,
+                left_on=left_on,
+                right_on=right_on,
+                left_index=left_index,
+                right_index=right_index,
+            )
+
+
+def test_merge_series_multilevel():
+    # GH#47946
+    # GH 40993: For raising, enforced in 2.0
+    a = DataFrame(
+        {"A": [1, 2, 3, 4]},
+        index=MultiIndex.from_product([["a", "b"], [0, 1]], names=["outer", "inner"]),
+    )
+    b = Series(
+        [1, 2, 3, 4],
+        index=MultiIndex.from_product([["a", "b"], [1, 2]], names=["outer", "inner"]),
+        name=("B", "C"),
+    )
+    with pytest.raises(
+        MergeError, match="Not allowed to merge between different levels"
+    ):
+        merge(a, b, on=["outer", "inner"])
+
+
+@pytest.mark.parametrize(
+    "col1, col2, kwargs, expected_cols",
+    [
+        (0, 0, {"suffixes": ("", "_dup")}, ["0", "0_dup"]),
+        (0, 0, {"suffixes": (None, "_dup")}, [0, "0_dup"]),
+        (0, 0, {"suffixes": ("_x", "_y")}, ["0_x", "0_y"]),
+        (0, 0, {"suffixes": ["_x", "_y"]}, ["0_x", "0_y"]),
+        ("a", 0, {"suffixes": (None, "_y")}, ["a", 0]),
+        (0.0, 0.0, {"suffixes": ("_x", None)}, ["0.0_x", 0.0]),
+        ("b", "b", {"suffixes": (None, "_y")}, ["b", "b_y"]),
+        ("a", "a", {"suffixes": ("_x", None)}, ["a_x", "a"]),
+        ("a", "b", {"suffixes": ("_x", None)}, ["a", "b"]),
+        ("a", "a", {"suffixes": (None, "_x")}, ["a", "a_x"]),
+        (0, 0, {"suffixes": ("_a", None)}, ["0_a", 0]),
+        ("a", "a", {}, ["a_x", "a_y"]),
+        (0, 0, {}, ["0_x", "0_y"]),
+    ],
+)
+def test_merge_suffix(col1, col2, kwargs, expected_cols):
+    # issue: 24782
+    a = DataFrame({col1: [1, 2, 3]})
+    b = DataFrame({col2: [4, 5, 6]})
+
+    expected = DataFrame([[1, 4], [2, 5], [3, 6]], columns=expected_cols)
+
+    result = a.merge(b, left_index=True, right_index=True, **kwargs)
+    tm.assert_frame_equal(result, expected)
+
+    result = merge(a, b, left_index=True, right_index=True, **kwargs)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "how,expected",
+    [
+        (
+            "right",
+            DataFrame(
+                {"A": [100, 200, 300], "B1": [60, 70, np.nan], "B2": [600, 700, 800]}
+            ),
+        ),
+        (
+            "outer",
+            DataFrame(
+                {
+                    "A": [100, 200, 1, 300],
+                    "B1": [60, 70, 80, np.nan],
+                    "B2": [600, 700, np.nan, 800],
+                }
+            ),
+        ),
+    ],
+)
+def test_merge_duplicate_suffix(how, expected):
+    left_df = DataFrame({"A": [100, 200, 1], "B": [60, 70, 80]})
+    right_df = DataFrame({"A": [100, 200, 300], "B": [600, 700, 800]})
+    result = merge(left_df, right_df, on="A", how=how, suffixes=("_x", "_x"))
+    expected.columns = ["A", "B_x", "B_x"]
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "col1, col2, suffixes",
+    [("a", "a", (None, None)), ("a", "a", ("", None)), (0, 0, (None, ""))],
+)
+def test_merge_suffix_error(col1, col2, suffixes):
+    # issue: 24782
+    a = DataFrame({col1: [1, 2, 3]})
+    b = DataFrame({col2: [3, 4, 5]})
+
+    # TODO: might reconsider current raise behaviour, see issue 24782
+    msg = "columns overlap but no suffix specified"
+    with pytest.raises(ValueError, match=msg):
+        merge(a, b, left_index=True, right_index=True, suffixes=suffixes)
+
+
+@pytest.mark.parametrize("suffixes", [{"left", "right"}, {"left": 0, "right": 0}])
+def test_merge_suffix_raises(suffixes):
+    a = DataFrame({"a": [1, 2, 3]})
+    b = DataFrame({"b": [3, 4, 5]})
+
+    with pytest.raises(TypeError, match="Passing 'suffixes' as a"):
+        merge(a, b, left_index=True, right_index=True, suffixes=suffixes)
+
+
+@pytest.mark.parametrize(
+    "col1, col2, suffixes, msg",
+    [
+        ("a", "a", ("a", "b", "c"), r"too many values to unpack \(expected 2\)"),
+        ("a", "a", tuple("a"), r"not enough values to unpack \(expected 2, got 1\)"),
+    ],
+)
+def test_merge_suffix_length_error(col1, col2, suffixes, msg):
+    a = DataFrame({col1: [1, 2, 3]})
+    b = DataFrame({col2: [3, 4, 5]})
+
+    with pytest.raises(ValueError, match=msg):
+        merge(a, b, left_index=True, right_index=True, suffixes=suffixes)
+
+
+@pytest.mark.parametrize("cat_dtype", ["one", "two"])
+@pytest.mark.parametrize("reverse", [True, False])
+def test_merge_equal_cat_dtypes(cat_dtype, reverse):
+    # see gh-22501
+    cat_dtypes = {
+        "one": CategoricalDtype(categories=["a", "b", "c"], ordered=False),
+        "two": CategoricalDtype(categories=["a", "b", "c"], ordered=False),
+    }
+
+    df1 = DataFrame(
+        {"foo": Series(["a", "b", "c"]).astype(cat_dtypes["one"]), "left": [1, 2, 3]}
+    ).set_index("foo")
+
+    data_foo = ["a", "b", "c"]
+    data_right = [1, 2, 3]
+
+    if reverse:
+        data_foo.reverse()
+        data_right.reverse()
+
+    df2 = DataFrame(
+        {"foo": Series(data_foo).astype(cat_dtypes[cat_dtype]), "right": data_right}
+    ).set_index("foo")
+
+    result = df1.merge(df2, left_index=True, right_index=True)
+
+    expected = DataFrame(
+        {
+            "left": [1, 2, 3],
+            "right": [1, 2, 3],
+            "foo": Series(["a", "b", "c"]).astype(cat_dtypes["one"]),
+        }
+    ).set_index("foo")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_equal_cat_dtypes2():
+    # see gh-22501
+    cat_dtype = CategoricalDtype(categories=["a", "b", "c"], ordered=False)
+
+    # Test Data
+    df1 = DataFrame(
+        {"foo": Series(["a", "b"]).astype(cat_dtype), "left": [1, 2]}
+    ).set_index("foo")
+
+    df2 = DataFrame(
+        {"foo": Series(["a", "b", "c"]).astype(cat_dtype), "right": [3, 2, 1]}
+    ).set_index("foo")
+
+    result = df1.merge(df2, left_index=True, right_index=True)
+
+    expected = DataFrame(
+        {"left": [1, 2], "right": [3, 2], "foo": Series(["a", "b"]).astype(cat_dtype)}
+    ).set_index("foo")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_on_cat_and_ext_array():
+    # GH 28668
+    right = DataFrame(
+        {"a": Series([pd.Interval(0, 1), pd.Interval(1, 2)], dtype="interval")}
+    )
+    left = right.copy()
+    left["a"] = left["a"].astype("category")
+
+    result = merge(left, right, how="inner", on="a")
+    expected = right.copy()
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_multiindex_columns():
+    # Issue #28518
+    # Verify that merging two dataframes give the expected labels
+    # The original cause of this issue come from a bug lexsort_depth and is tested in
+    # test_lexsort_depth
+
+    letters = ["a", "b", "c", "d"]
+    numbers = ["1", "2", "3"]
+    index = MultiIndex.from_product((letters, numbers), names=["outer", "inner"])
+
+    frame_x = DataFrame(columns=index)
+    frame_x["id"] = ""
+    frame_y = DataFrame(columns=index)
+    frame_y["id"] = ""
+
+    l_suf = "_x"
+    r_suf = "_y"
+    result = frame_x.merge(frame_y, on="id", suffixes=((l_suf, r_suf)))
+
+    # Constructing the expected results
+    expected_labels = [letter + l_suf for letter in letters] + [
+        letter + r_suf for letter in letters
+    ]
+    expected_index = MultiIndex.from_product(
+        [expected_labels, numbers], names=["outer", "inner"]
+    )
+    expected = DataFrame(columns=expected_index)
+    expected["id"] = ""
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_datetime_upcast_dtype():
+    # https://github.com/pandas-dev/pandas/issues/31208
+    df1 = DataFrame({"x": ["a", "b", "c"], "y": ["1", "2", "4"]})
+    df2 = DataFrame(
+        {"y": ["1", "2", "3"], "z": pd.to_datetime(["2000", "2001", "2002"])}
+    )
+    result = merge(df1, df2, how="left", on="y")
+    expected = DataFrame(
+        {
+            "x": ["a", "b", "c"],
+            "y": ["1", "2", "4"],
+            "z": pd.to_datetime(["2000", "2001", "NaT"]),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("n_categories", [5, 128])
+def test_categorical_non_unique_monotonic(n_categories):
+    # GH 28189
+    # With n_categories as 5, we test the int8 case is hit in libjoin,
+    # with n_categories as 128 we test the int16 case.
+    left_index = CategoricalIndex([0] + list(range(n_categories)))
+    df1 = DataFrame(range(n_categories + 1), columns=["value"], index=left_index)
+    df2 = DataFrame(
+        [[6]],
+        columns=["value"],
+        index=CategoricalIndex([0], categories=list(range(n_categories))),
+    )
+
+    result = merge(df1, df2, how="left", left_index=True, right_index=True)
+    expected = DataFrame(
+        [[i, 6.0] if i < 2 else [i, np.nan] for i in range(n_categories + 1)],
+        columns=["value_x", "value_y"],
+        index=left_index,
+    )
+    tm.assert_frame_equal(expected, result)
+
+
+def test_merge_join_categorical_multiindex():
+    # From issue 16627
+    a = {
+        "Cat1": Categorical(["a", "b", "a", "c", "a", "b"], ["a", "b", "c"]),
+        "Int1": [0, 1, 0, 1, 0, 0],
+    }
+    a = DataFrame(a)
+
+    b = {
+        "Cat": Categorical(["a", "b", "c", "a", "b", "c"], ["a", "b", "c"]),
+        "Int": [0, 0, 0, 1, 1, 1],
+        "Factor": [1.1, 1.2, 1.3, 1.4, 1.5, 1.6],
+    }
+    b = DataFrame(b).set_index(["Cat", "Int"])["Factor"]
+
+    expected = merge(
+        a,
+        b.reset_index(),
+        left_on=["Cat1", "Int1"],
+        right_on=["Cat", "Int"],
+        how="left",
+    )
+    expected = expected.drop(["Cat", "Int"], axis=1)
+    result = a.join(b, on=["Cat1", "Int1"])
+    tm.assert_frame_equal(expected, result)
+
+    # Same test, but with ordered categorical
+    a = {
+        "Cat1": Categorical(
+            ["a", "b", "a", "c", "a", "b"], ["b", "a", "c"], ordered=True
+        ),
+        "Int1": [0, 1, 0, 1, 0, 0],
+    }
+    a = DataFrame(a)
+
+    b = {
+        "Cat": Categorical(
+            ["a", "b", "c", "a", "b", "c"], ["b", "a", "c"], ordered=True
+        ),
+        "Int": [0, 0, 0, 1, 1, 1],
+        "Factor": [1.1, 1.2, 1.3, 1.4, 1.5, 1.6],
+    }
+    b = DataFrame(b).set_index(["Cat", "Int"])["Factor"]
+
+    expected = merge(
+        a,
+        b.reset_index(),
+        left_on=["Cat1", "Int1"],
+        right_on=["Cat", "Int"],
+        how="left",
+    )
+    expected = expected.drop(["Cat", "Int"], axis=1)
+    result = a.join(b, on=["Cat1", "Int1"])
+    tm.assert_frame_equal(expected, result)
+
+
+@pytest.mark.parametrize("func", ["merge", "merge_asof"])
+@pytest.mark.parametrize(
+    ("kwargs", "err_msg"),
+    [
+        ({"left_on": "a", "left_index": True}, ["left_on", "left_index"]),
+        ({"right_on": "a", "right_index": True}, ["right_on", "right_index"]),
+    ],
+)
+def test_merge_join_cols_error_reporting_duplicates(func, kwargs, err_msg):
+    # GH: 16228
+    left = DataFrame({"a": [1, 2], "b": [3, 4]})
+    right = DataFrame({"a": [1, 1], "c": [5, 6]})
+    msg = rf'Can only pass argument "{err_msg[0]}" OR "{err_msg[1]}" not both\.'
+    with pytest.raises(MergeError, match=msg):
+        getattr(pd, func)(left, right, **kwargs)
+
+
+@pytest.mark.parametrize("func", ["merge", "merge_asof"])
+@pytest.mark.parametrize(
+    ("kwargs", "err_msg"),
+    [
+        ({"left_on": "a"}, ["right_on", "right_index"]),
+        ({"right_on": "a"}, ["left_on", "left_index"]),
+    ],
+)
+def test_merge_join_cols_error_reporting_missing(func, kwargs, err_msg):
+    # GH: 16228
+    left = DataFrame({"a": [1, 2], "b": [3, 4]})
+    right = DataFrame({"a": [1, 1], "c": [5, 6]})
+    msg = rf'Must pass "{err_msg[0]}" OR "{err_msg[1]}"\.'
+    with pytest.raises(MergeError, match=msg):
+        getattr(pd, func)(left, right, **kwargs)
+
+
+@pytest.mark.parametrize("func", ["merge", "merge_asof"])
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"right_index": True},
+        {"left_index": True},
+    ],
+)
+def test_merge_join_cols_error_reporting_on_and_index(func, kwargs):
+    # GH: 16228
+    left = DataFrame({"a": [1, 2], "b": [3, 4]})
+    right = DataFrame({"a": [1, 1], "c": [5, 6]})
+    msg = (
+        r'Can only pass argument "on" OR "left_index" '
+        r'and "right_index", not a combination of both\.'
+    )
+    with pytest.raises(MergeError, match=msg):
+        getattr(pd, func)(left, right, on="a", **kwargs)
+
+
+def test_merge_right_left_index():
+    # GH#38616
+    left = DataFrame({"x": [1, 1], "z": ["foo", "foo"]})
+    right = DataFrame({"x": [1, 1], "z": ["foo", "foo"]})
+    result = merge(left, right, how="right", left_index=True, right_on="x")
+    expected = DataFrame(
+        {
+            "x": [1, 1],
+            "x_x": [1, 1],
+            "z_x": ["foo", "foo"],
+            "x_y": [1, 1],
+            "z_y": ["foo", "foo"],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_result_empty_index_and_on():
+    # GH#33814
+    df1 = DataFrame({"a": [1], "b": [2]}).set_index(["a", "b"])
+    df2 = DataFrame({"b": [1]}).set_index(["b"])
+    expected = DataFrame({"a": [], "b": []}, dtype=np.int64).set_index(["a", "b"])
+    result = merge(df1, df2, left_on=["b"], right_index=True)
+    tm.assert_frame_equal(result, expected)
+
+    result = merge(df2, df1, left_index=True, right_on=["b"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_suffixes_produce_dup_columns_raises():
+    # GH#22818; Enforced in 2.0
+    left = DataFrame({"a": [1, 2, 3], "b": 1, "b_x": 2})
+    right = DataFrame({"a": [1, 2, 3], "b": 2})
+
+    with pytest.raises(MergeError, match="Passing 'suffixes' which cause duplicate"):
+        merge(left, right, on="a")
+
+    with pytest.raises(MergeError, match="Passing 'suffixes' which cause duplicate"):
+        merge(right, left, on="a", suffixes=("_y", "_x"))
+
+
+def test_merge_duplicate_columns_with_suffix_no_warning():
+    # GH#22818
+    # Do not raise warning when duplicates are caused by duplicates in origin
+    left = DataFrame([[1, 1, 1], [2, 2, 2]], columns=["a", "b", "b"])
+    right = DataFrame({"a": [1, 3], "b": 2})
+    result = merge(left, right, on="a")
+    expected = DataFrame([[1, 1, 1, 2]], columns=["a", "b_x", "b_x", "b_y"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_duplicate_columns_with_suffix_causing_another_duplicate_raises():
+    # GH#22818, Enforced in 2.0
+    # This should raise warning because suffixes cause another collision
+    left = DataFrame([[1, 1, 1, 1], [2, 2, 2, 2]], columns=["a", "b", "b", "b_x"])
+    right = DataFrame({"a": [1, 3], "b": 2})
+    with pytest.raises(MergeError, match="Passing 'suffixes' which cause duplicate"):
+        merge(left, right, on="a")
+
+
+def test_merge_string_float_column_result():
+    # GH 13353
+    df1 = DataFrame([[1, 2], [3, 4]], columns=Index(["a", 114.0]))
+    df2 = DataFrame([[9, 10], [11, 12]], columns=["x", "y"])
+    result = merge(df2, df1, how="inner", left_index=True, right_index=True)
+    expected = DataFrame(
+        [[9, 10, 1, 2], [11, 12, 3, 4]], columns=Index(["x", "y", "a", 114.0])
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_mergeerror_on_left_index_mismatched_dtypes():
+    # GH 22449
+    df_1 = DataFrame(data=["X"], columns=["C"], index=[22])
+    df_2 = DataFrame(data=["X"], columns=["C"], index=[999])
+    with pytest.raises(MergeError, match="Can only pass argument"):
+        merge(df_1, df_2, on=["C"], left_index=True)
+
+
+def test_merge_on_left_categoricalindex():
+    # GH#48464 don't raise when left_on is a CategoricalIndex
+    ci = CategoricalIndex(range(3))
+
+    right = DataFrame({"A": ci, "B": range(3)})
+    left = DataFrame({"C": range(3, 6)})
+
+    res = merge(left, right, left_on=ci, right_on="A")
+    expected = merge(left, right, left_on=ci._data, right_on="A")
+    tm.assert_frame_equal(res, expected)
+
+
+@pytest.mark.parametrize("dtype", [None, "Int64"])
+def test_merge_outer_with_NaN(dtype):
+    # GH#43550
+    left = DataFrame({"key": [1, 2], "col1": [1, 2]}, dtype=dtype)
+    right = DataFrame({"key": [np.nan, np.nan], "col2": [3, 4]}, dtype=dtype)
+    result = merge(left, right, on="key", how="outer")
+    expected = DataFrame(
+        {
+            "key": [1, 2, np.nan, np.nan],
+            "col1": [1, 2, np.nan, np.nan],
+            "col2": [np.nan, np.nan, 3, 4],
+        },
+        dtype=dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # switch left and right
+    result = merge(right, left, on="key", how="outer")
+    expected = DataFrame(
+        {
+            "key": [np.nan, np.nan, 1, 2],
+            "col2": [3, 4, np.nan, np.nan],
+            "col1": [np.nan, np.nan, 1, 2],
+        },
+        dtype=dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_different_index_names():
+    # GH#45094
+    left = DataFrame({"a": [1]}, index=Index([1], name="c"))
+    right = DataFrame({"a": [1]}, index=Index([1], name="d"))
+    result = merge(left, right, left_on="c", right_on="d")
+    expected = DataFrame({"a_x": [1], "a_y": 1})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_ea(any_numeric_ea_dtype, join_type):
+    # GH#44240
+    left = DataFrame({"a": [1, 2, 3], "b": 1}, dtype=any_numeric_ea_dtype)
+    right = DataFrame({"a": [1, 2, 3], "c": 2}, dtype=any_numeric_ea_dtype)
+    result = left.merge(right, how=join_type)
+    expected = DataFrame({"a": [1, 2, 3], "b": 1, "c": 2}, dtype=any_numeric_ea_dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_ea_and_non_ea(any_numeric_ea_dtype, join_type):
+    # GH#44240
+    left = DataFrame({"a": [1, 2, 3], "b": 1}, dtype=any_numeric_ea_dtype)
+    right = DataFrame({"a": [1, 2, 3], "c": 2}, dtype=any_numeric_ea_dtype.lower())
+    result = left.merge(right, how=join_type)
+    expected = DataFrame(
+        {
+            "a": Series([1, 2, 3], dtype=any_numeric_ea_dtype),
+            "b": Series([1, 1, 1], dtype=any_numeric_ea_dtype),
+            "c": Series([2, 2, 2], dtype=any_numeric_ea_dtype.lower()),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["int64", "int64[pyarrow]"])
+def test_merge_arrow_and_numpy_dtypes(dtype):
+    # GH#52406
+    pytest.importorskip("pyarrow")
+    df = DataFrame({"a": [1, 2]}, dtype=dtype)
+    df2 = DataFrame({"a": [1, 2]}, dtype="int64[pyarrow]")
+    result = df.merge(df2)
+    expected = df.copy()
+    tm.assert_frame_equal(result, expected)
+
+    result = df2.merge(df)
+    expected = df2.copy()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("how", ["inner", "left", "outer", "right"])
+@pytest.mark.parametrize("tz", [None, "America/Chicago"])
+def test_merge_datetime_different_resolution(tz, how):
+    # https://github.com/pandas-dev/pandas/issues/53200
+    vals = [
+        pd.Timestamp(2023, 5, 12, tz=tz),
+        pd.Timestamp(2023, 5, 13, tz=tz),
+        pd.Timestamp(2023, 5, 14, tz=tz),
+    ]
+    df1 = DataFrame({"t": vals[:2], "a": [1.0, 2.0]})
+    df1["t"] = df1["t"].dt.as_unit("ns")
+    df2 = DataFrame({"t": vals[1:], "b": [1.0, 2.0]})
+    df2["t"] = df2["t"].dt.as_unit("s")
+
+    expected = DataFrame({"t": vals, "a": [1.0, 2.0, np.nan], "b": [np.nan, 1.0, 2.0]})
+    expected["t"] = expected["t"].dt.as_unit("ns")
+    if how == "inner":
+        expected = expected.iloc[[1]].reset_index(drop=True)
+    elif how == "left":
+        expected = expected.iloc[[0, 1]]
+    elif how == "right":
+        expected = expected.iloc[[1, 2]].reset_index(drop=True)
+
+    result = df1.merge(df2, on="t", how=how)
+    tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_asof.py

@@ -0,0 +1,1591 @@
+import datetime
+
+import numpy as np
+import pytest
+import pytz
+
+import pandas as pd
+from pandas import (
+    Index,
+    Timedelta,
+    merge_asof,
+    read_csv,
+    to_datetime,
+)
+import pandas._testing as tm
+from pandas.core.reshape.merge import MergeError
+
+
+@pytest.fixture(params=["s", "ms", "us", "ns"])
+def unit(request):
+    """
+    Resolution for datetimelike dtypes.
+    """
+    return request.param
+
+
+class TestAsOfMerge:
+    def read_data(self, datapath, name, dedupe=False):
+        path = datapath("reshape", "merge", "data", name)
+        x = read_csv(path)
+        if dedupe:
+            x = x.drop_duplicates(["time", "ticker"], keep="last").reset_index(
+                drop=True
+            )
+        x.time = to_datetime(x.time)
+        return x
+
+    @pytest.fixture
+    def trades(self, datapath):
+        return self.read_data(datapath, "trades.csv")
+
+    @pytest.fixture
+    def quotes(self, datapath):
+        return self.read_data(datapath, "quotes.csv", dedupe=True)
+
+    @pytest.fixture
+    def asof(self, datapath):
+        return self.read_data(datapath, "asof.csv")
+
+    @pytest.fixture
+    def tolerance(self, datapath):
+        return self.read_data(datapath, "tolerance.csv")
+
+    @pytest.fixture
+    def allow_exact_matches(self, datapath):
+        return self.read_data(datapath, "allow_exact_matches.csv")
+
+    @pytest.fixture
+    def allow_exact_matches_and_tolerance(self, datapath):
+        return self.read_data(datapath, "allow_exact_matches_and_tolerance.csv")
+
+    def test_examples1(self):
+        """doc-string examples"""
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, 3, 7]}
+        )
+
+        result = merge_asof(left, right, on="a")
+        tm.assert_frame_equal(result, expected)
+
+    def test_examples2(self, unit):
+        """doc-string examples"""
+        if unit == "s":
+            pytest.skip(
+                "This test is invalid for unit='s' because that would "
+                "round the trades['time']]"
+            )
+        trades = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.038",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.048",
+                    ]
+                ).astype(f"M8[{unit}]"),
+                "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+                "quantity": [75, 155, 100, 100, 100],
+            },
+            columns=["time", "ticker", "price", "quantity"],
+        )
+
+        quotes = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.030",
+                        "20160525 13:30:00.041",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.049",
+                        "20160525 13:30:00.072",
+                        "20160525 13:30:00.075",
+                    ]
+                ).astype(f"M8[{unit}]"),
+                "ticker": [
+                    "GOOG",
+                    "MSFT",
+                    "MSFT",
+                    "MSFT",
+                    "GOOG",
+                    "AAPL",
+                    "GOOG",
+                    "MSFT",
+                ],
+                "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
+                "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03],
+            },
+            columns=["time", "ticker", "bid", "ask"],
+        )
+
+        merge_asof(trades, quotes, on="time", by="ticker")
+
+        merge_asof(trades, quotes, on="time", by="ticker", tolerance=Timedelta("2ms"))
+
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.038",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.048",
+                    ]
+                ).astype(f"M8[{unit}]"),
+                "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+                "quantity": [75, 155, 100, 100, 100],
+                "bid": [np.nan, 51.97, np.nan, np.nan, np.nan],
+                "ask": [np.nan, 51.98, np.nan, np.nan, np.nan],
+            },
+            columns=["time", "ticker", "price", "quantity", "bid", "ask"],
+        )
+
+        result = merge_asof(
+            trades,
+            quotes,
+            on="time",
+            by="ticker",
+            tolerance=Timedelta("10ms"),
+            allow_exact_matches=False,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_examples3(self):
+        """doc-string examples"""
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, 6, np.nan]}
+        )
+
+        result = merge_asof(left, right, on="a", direction="forward")
+        tm.assert_frame_equal(result, expected)
+
+    def test_examples4(self):
+        """doc-string examples"""
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, 6, 7]}
+        )
+
+        result = merge_asof(left, right, on="a", direction="nearest")
+        tm.assert_frame_equal(result, expected)
+
+    def test_basic(self, trades, asof, quotes):
+        expected = asof
+
+        result = merge_asof(trades, quotes, on="time", by="ticker")
+        tm.assert_frame_equal(result, expected)
+
+    def test_basic_categorical(self, trades, asof, quotes):
+        expected = asof
+        trades.ticker = trades.ticker.astype("category")
+        quotes.ticker = quotes.ticker.astype("category")
+        expected.ticker = expected.ticker.astype("category")
+
+        result = merge_asof(trades, quotes, on="time", by="ticker")
+        tm.assert_frame_equal(result, expected)
+
+    def test_basic_left_index(self, trades, asof, quotes):
+        # GH14253
+        expected = asof
+        trades = trades.set_index("time")
+
+        result = merge_asof(
+            trades, quotes, left_index=True, right_on="time", by="ticker"
+        )
+        # left-only index uses right"s index, oddly
+        expected.index = result.index
+        # time column appears after left"s columns
+        expected = expected[result.columns]
+        tm.assert_frame_equal(result, expected)
+
+    def test_basic_right_index(self, trades, asof, quotes):
+        expected = asof
+        quotes = quotes.set_index("time")
+
+        result = merge_asof(
+            trades, quotes, left_on="time", right_index=True, by="ticker"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_basic_left_index_right_index(self, trades, asof, quotes):
+        expected = asof.set_index("time")
+        trades = trades.set_index("time")
+        quotes = quotes.set_index("time")
+
+        result = merge_asof(
+            trades, quotes, left_index=True, right_index=True, by="ticker"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_multi_index_left(self, trades, quotes):
+        # MultiIndex is prohibited
+        trades = trades.set_index(["time", "price"])
+        quotes = quotes.set_index("time")
+        with pytest.raises(MergeError, match="left can only have one index"):
+            merge_asof(trades, quotes, left_index=True, right_index=True)
+
+    def test_multi_index_right(self, trades, quotes):
+        # MultiIndex is prohibited
+        trades = trades.set_index("time")
+        quotes = quotes.set_index(["time", "bid"])
+        with pytest.raises(MergeError, match="right can only have one index"):
+            merge_asof(trades, quotes, left_index=True, right_index=True)
+
+    def test_on_and_index_left_on(self, trades, quotes):
+        # "on" parameter and index together is prohibited
+        trades = trades.set_index("time")
+        quotes = quotes.set_index("time")
+        msg = 'Can only pass argument "left_on" OR "left_index" not both.'
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(
+                trades, quotes, left_on="price", left_index=True, right_index=True
+            )
+
+    def test_on_and_index_right_on(self, trades, quotes):
+        trades = trades.set_index("time")
+        quotes = quotes.set_index("time")
+        msg = 'Can only pass argument "right_on" OR "right_index" not both.'
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(
+                trades, quotes, right_on="bid", left_index=True, right_index=True
+            )
+
+    def test_basic_left_by_right_by(self, trades, asof, quotes):
+        # GH14253
+        expected = asof
+
+        result = merge_asof(
+            trades, quotes, on="time", left_by="ticker", right_by="ticker"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_missing_right_by(self, trades, asof, quotes):
+        expected = asof
+
+        q = quotes[quotes.ticker != "MSFT"]
+        result = merge_asof(trades, q, on="time", by="ticker")
+        expected.loc[expected.ticker == "MSFT", ["bid", "ask"]] = np.nan
+        tm.assert_frame_equal(result, expected)
+
+    def test_multiby(self):
+        # GH13936
+        trades = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.046",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.050",
+                    ]
+                ),
+                "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+                "exch": ["ARCA", "NSDQ", "NSDQ", "BATS", "NSDQ"],
+                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+                "quantity": [75, 155, 100, 100, 100],
+            },
+            columns=["time", "ticker", "exch", "price", "quantity"],
+        )
+
+        quotes = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.030",
+                        "20160525 13:30:00.041",
+                        "20160525 13:30:00.045",
+                        "20160525 13:30:00.049",
+                    ]
+                ),
+                "ticker": ["GOOG", "MSFT", "MSFT", "MSFT", "GOOG", "AAPL"],
+                "exch": ["BATS", "NSDQ", "ARCA", "ARCA", "NSDQ", "ARCA"],
+                "bid": [720.51, 51.95, 51.97, 51.99, 720.50, 97.99],
+                "ask": [720.92, 51.96, 51.98, 52.00, 720.93, 98.01],
+            },
+            columns=["time", "ticker", "exch", "bid", "ask"],
+        )
+
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.046",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.050",
+                    ]
+                ),
+                "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+                "exch": ["ARCA", "NSDQ", "NSDQ", "BATS", "NSDQ"],
+                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+                "quantity": [75, 155, 100, 100, 100],
+                "bid": [np.nan, 51.95, 720.50, 720.51, np.nan],
+                "ask": [np.nan, 51.96, 720.93, 720.92, np.nan],
+            },
+            columns=["time", "ticker", "exch", "price", "quantity", "bid", "ask"],
+        )
+
+        result = merge_asof(trades, quotes, on="time", by=["ticker", "exch"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_multiby_heterogeneous_types(self):
+        # GH13936
+        trades = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.046",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.050",
+                    ]
+                ),
+                "ticker": [0, 0, 1, 1, 2],
+                "exch": ["ARCA", "NSDQ", "NSDQ", "BATS", "NSDQ"],
+                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+                "quantity": [75, 155, 100, 100, 100],
+            },
+            columns=["time", "ticker", "exch", "price", "quantity"],
+        )
+
+        quotes = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.030",
+                        "20160525 13:30:00.041",
+                        "20160525 13:30:00.045",
+                        "20160525 13:30:00.049",
+                    ]
+                ),
+                "ticker": [1, 0, 0, 0, 1, 2],
+                "exch": ["BATS", "NSDQ", "ARCA", "ARCA", "NSDQ", "ARCA"],
+                "bid": [720.51, 51.95, 51.97, 51.99, 720.50, 97.99],
+                "ask": [720.92, 51.96, 51.98, 52.00, 720.93, 98.01],
+            },
+            columns=["time", "ticker", "exch", "bid", "ask"],
+        )
+
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.023",
+                        "20160525 13:30:00.046",
+                        "20160525 13:30:00.048",
+                        "20160525 13:30:00.050",
+                    ]
+                ),
+                "ticker": [0, 0, 1, 1, 2],
+                "exch": ["ARCA", "NSDQ", "NSDQ", "BATS", "NSDQ"],
+                "price": [51.95, 51.95, 720.77, 720.92, 98.00],
+                "quantity": [75, 155, 100, 100, 100],
+                "bid": [np.nan, 51.95, 720.50, 720.51, np.nan],
+                "ask": [np.nan, 51.96, 720.93, 720.92, np.nan],
+            },
+            columns=["time", "ticker", "exch", "price", "quantity", "bid", "ask"],
+        )
+
+        result = merge_asof(trades, quotes, on="time", by=["ticker", "exch"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_multiby_indexed(self):
+        # GH15676
+        left = pd.DataFrame(
+            [
+                [to_datetime("20160602"), 1, "a"],
+                [to_datetime("20160602"), 2, "a"],
+                [to_datetime("20160603"), 1, "b"],
+                [to_datetime("20160603"), 2, "b"],
+            ],
+            columns=["time", "k1", "k2"],
+        ).set_index("time")
+
+        right = pd.DataFrame(
+            [
+                [to_datetime("20160502"), 1, "a", 1.0],
+                [to_datetime("20160502"), 2, "a", 2.0],
+                [to_datetime("20160503"), 1, "b", 3.0],
+                [to_datetime("20160503"), 2, "b", 4.0],
+            ],
+            columns=["time", "k1", "k2", "value"],
+        ).set_index("time")
+
+        expected = pd.DataFrame(
+            [
+                [to_datetime("20160602"), 1, "a", 1.0],
+                [to_datetime("20160602"), 2, "a", 2.0],
+                [to_datetime("20160603"), 1, "b", 3.0],
+                [to_datetime("20160603"), 2, "b", 4.0],
+            ],
+            columns=["time", "k1", "k2", "value"],
+        ).set_index("time")
+
+        result = merge_asof(
+            left, right, left_index=True, right_index=True, by=["k1", "k2"]
+        )
+
+        tm.assert_frame_equal(expected, result)
+
+        with pytest.raises(
+            MergeError, match="left_by and right_by must be same length"
+        ):
+            merge_asof(
+                left,
+                right,
+                left_index=True,
+                right_index=True,
+                left_by=["k1", "k2"],
+                right_by=["k1"],
+            )
+
+    def test_basic2(self, datapath):
+        expected = self.read_data(datapath, "asof2.csv")
+        trades = self.read_data(datapath, "trades2.csv")
+        quotes = self.read_data(datapath, "quotes2.csv", dedupe=True)
+
+        result = merge_asof(trades, quotes, on="time", by="ticker")
+        tm.assert_frame_equal(result, expected)
+
+    def test_basic_no_by(self, trades, asof, quotes):
+        f = (
+            lambda x: x[x.ticker == "MSFT"]
+            .drop("ticker", axis=1)
+            .reset_index(drop=True)
+        )
+
+        # just use a single ticker
+        expected = f(asof)
+        trades = f(trades)
+        quotes = f(quotes)
+
+        result = merge_asof(trades, quotes, on="time")
+        tm.assert_frame_equal(result, expected)
+
+    def test_valid_join_keys(self, trades, quotes):
+        msg = r"incompatible merge keys \[1\] .* must be the same type"
+
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(trades, quotes, left_on="time", right_on="bid", by="ticker")
+
+        with pytest.raises(MergeError, match="can only asof on a key for left"):
+            merge_asof(trades, quotes, on=["time", "ticker"], by="ticker")
+
+        with pytest.raises(MergeError, match="can only asof on a key for left"):
+            merge_asof(trades, quotes, by="ticker")
+
+    def test_with_duplicates(self, datapath, trades, quotes):
+        q = (
+            pd.concat([quotes, quotes])
+            .sort_values(["time", "ticker"])
+            .reset_index(drop=True)
+        )
+        result = merge_asof(trades, q, on="time", by="ticker")
+        expected = self.read_data(datapath, "asof.csv")
+        tm.assert_frame_equal(result, expected)
+
+    def test_with_duplicates_no_on(self):
+        df1 = pd.DataFrame({"key": [1, 1, 3], "left_val": [1, 2, 3]})
+        df2 = pd.DataFrame({"key": [1, 2, 2], "right_val": [1, 2, 3]})
+        result = merge_asof(df1, df2, on="key")
+        expected = pd.DataFrame(
+            {"key": [1, 1, 3], "left_val": [1, 2, 3], "right_val": [1, 1, 3]}
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_valid_allow_exact_matches(self, trades, quotes):
+        msg = "allow_exact_matches must be boolean, passed foo"
+
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(
+                trades, quotes, on="time", by="ticker", allow_exact_matches="foo"
+            )
+
+    def test_valid_tolerance(self, trades, quotes):
+        # dti
+        merge_asof(trades, quotes, on="time", by="ticker", tolerance=Timedelta("1s"))
+
+        # integer
+        merge_asof(
+            trades.reset_index(),
+            quotes.reset_index(),
+            on="index",
+            by="ticker",
+            tolerance=1,
+        )
+
+        msg = r"incompatible tolerance .*, must be compat with type .*"
+
+        # incompat
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(trades, quotes, on="time", by="ticker", tolerance=1)
+
+        # invalid
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(
+                trades.reset_index(),
+                quotes.reset_index(),
+                on="index",
+                by="ticker",
+                tolerance=1.0,
+            )
+
+        msg = "tolerance must be positive"
+
+        # invalid negative
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(
+                trades, quotes, on="time", by="ticker", tolerance=-Timedelta("1s")
+            )
+
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(
+                trades.reset_index(),
+                quotes.reset_index(),
+                on="index",
+                by="ticker",
+                tolerance=-1,
+            )
+
+    def test_non_sorted(self, trades, quotes):
+        trades = trades.sort_values("time", ascending=False)
+        quotes = quotes.sort_values("time", ascending=False)
+
+        # we require that we are already sorted on time & quotes
+        assert not trades.time.is_monotonic_increasing
+        assert not quotes.time.is_monotonic_increasing
+        with pytest.raises(ValueError, match="left keys must be sorted"):
+            merge_asof(trades, quotes, on="time", by="ticker")
+
+        trades = trades.sort_values("time")
+        assert trades.time.is_monotonic_increasing
+        assert not quotes.time.is_monotonic_increasing
+        with pytest.raises(ValueError, match="right keys must be sorted"):
+            merge_asof(trades, quotes, on="time", by="ticker")
+
+        quotes = quotes.sort_values("time")
+        assert trades.time.is_monotonic_increasing
+        assert quotes.time.is_monotonic_increasing
+
+        # ok, though has dupes
+        merge_asof(trades, quotes, on="time", by="ticker")
+
+    @pytest.mark.parametrize(
+        "tolerance_ts",
+        [Timedelta("1day"), datetime.timedelta(days=1)],
+        ids=["Timedelta", "datetime.timedelta"],
+    )
+    def test_tolerance(self, tolerance_ts, trades, quotes, tolerance):
+        result = merge_asof(
+            trades, quotes, on="time", by="ticker", tolerance=tolerance_ts
+        )
+        expected = tolerance
+        tm.assert_frame_equal(result, expected)
+
+    def test_tolerance_forward(self):
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 7, 11], "right_val": [1, 2, 3, 7, 11]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, np.nan, 11]}
+        )
+
+        result = merge_asof(left, right, on="a", direction="forward", tolerance=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_tolerance_nearest(self):
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 7, 11], "right_val": [1, 2, 3, 7, 11]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [1, np.nan, 11]}
+        )
+
+        result = merge_asof(left, right, on="a", direction="nearest", tolerance=1)
+        tm.assert_frame_equal(result, expected)
+
+    def test_tolerance_tz(self, unit):
+        # GH 14844
+        left = pd.DataFrame(
+            {
+                "date": pd.date_range(
+                    start=to_datetime("2016-01-02"),
+                    freq="D",
+                    periods=5,
+                    tz=pytz.timezone("UTC"),
+                    unit=unit,
+                ),
+                "value1": np.arange(5),
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "date": pd.date_range(
+                    start=to_datetime("2016-01-01"),
+                    freq="D",
+                    periods=5,
+                    tz=pytz.timezone("UTC"),
+                    unit=unit,
+                ),
+                "value2": list("ABCDE"),
+            }
+        )
+        result = merge_asof(left, right, on="date", tolerance=Timedelta("1 day"))
+
+        expected = pd.DataFrame(
+            {
+                "date": pd.date_range(
+                    start=to_datetime("2016-01-02"),
+                    freq="D",
+                    periods=5,
+                    tz=pytz.timezone("UTC"),
+                    unit=unit,
+                ),
+                "value1": np.arange(5),
+                "value2": list("BCDEE"),
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_tolerance_float(self):
+        # GH22981
+        left = pd.DataFrame({"a": [1.1, 3.5, 10.9], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame(
+            {"a": [1.0, 2.5, 3.3, 7.5, 11.5], "right_val": [1.0, 2.5, 3.3, 7.5, 11.5]}
+        )
+
+        expected = pd.DataFrame(
+            {
+                "a": [1.1, 3.5, 10.9],
+                "left_val": ["a", "b", "c"],
+                "right_val": [1, 3.3, np.nan],
+            }
+        )
+
+        result = merge_asof(left, right, on="a", direction="nearest", tolerance=0.5)
+        tm.assert_frame_equal(result, expected)
+
+    def test_index_tolerance(self, trades, quotes, tolerance):
+        # GH 15135
+        expected = tolerance.set_index("time")
+        trades = trades.set_index("time")
+        quotes = quotes.set_index("time")
+
+        result = merge_asof(
+            trades,
+            quotes,
+            left_index=True,
+            right_index=True,
+            by="ticker",
+            tolerance=Timedelta("1day"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches(self, trades, quotes, allow_exact_matches):
+        result = merge_asof(
+            trades, quotes, on="time", by="ticker", allow_exact_matches=False
+        )
+        expected = allow_exact_matches
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_forward(self):
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 7, 11], "right_val": [1, 2, 3, 7, 11]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [2, 7, 11]}
+        )
+
+        result = merge_asof(
+            left, right, on="a", direction="forward", allow_exact_matches=False
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_nearest(self):
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 2, 3, 7, 11], "right_val": [1, 2, 3, 7, 11]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [2, 3, 11]}
+        )
+
+        result = merge_asof(
+            left, right, on="a", direction="nearest", allow_exact_matches=False
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_and_tolerance(
+        self, trades, quotes, allow_exact_matches_and_tolerance
+    ):
+        result = merge_asof(
+            trades,
+            quotes,
+            on="time",
+            by="ticker",
+            tolerance=Timedelta("100ms"),
+            allow_exact_matches=False,
+        )
+        expected = allow_exact_matches_and_tolerance
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_and_tolerance2(self):
+        # GH 13695
+        df1 = pd.DataFrame(
+            {"time": to_datetime(["2016-07-15 13:30:00.030"]), "username": ["bob"]}
+        )
+        df2 = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    ["2016-07-15 13:30:00.000", "2016-07-15 13:30:00.030"]
+                ),
+                "version": [1, 2],
+            }
+        )
+
+        result = merge_asof(df1, df2, on="time")
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(["2016-07-15 13:30:00.030"]),
+                "username": ["bob"],
+                "version": [2],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = merge_asof(df1, df2, on="time", allow_exact_matches=False)
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(["2016-07-15 13:30:00.030"]),
+                "username": ["bob"],
+                "version": [1],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = merge_asof(
+            df1,
+            df2,
+            on="time",
+            allow_exact_matches=False,
+            tolerance=Timedelta("10ms"),
+        )
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(["2016-07-15 13:30:00.030"]),
+                "username": ["bob"],
+                "version": [np.nan],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_and_tolerance3(self):
+        # GH 13709
+        df1 = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    ["2016-07-15 13:30:00.030", "2016-07-15 13:30:00.030"]
+                ),
+                "username": ["bob", "charlie"],
+            }
+        )
+        df2 = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    ["2016-07-15 13:30:00.000", "2016-07-15 13:30:00.030"]
+                ),
+                "version": [1, 2],
+            }
+        )
+
+        result = merge_asof(
+            df1,
+            df2,
+            on="time",
+            allow_exact_matches=False,
+            tolerance=Timedelta("10ms"),
+        )
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    ["2016-07-15 13:30:00.030", "2016-07-15 13:30:00.030"]
+                ),
+                "username": ["bob", "charlie"],
+                "version": [np.nan, np.nan],
+            }
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_and_tolerance_forward(self):
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 3, 4, 6, 11], "right_val": [1, 3, 4, 6, 11]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [np.nan, 6, 11]}
+        )
+
+        result = merge_asof(
+            left,
+            right,
+            on="a",
+            direction="forward",
+            allow_exact_matches=False,
+            tolerance=1,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_allow_exact_matches_and_tolerance_nearest(self):
+        # GH14887
+
+        left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
+        right = pd.DataFrame({"a": [1, 3, 4, 6, 11], "right_val": [1, 3, 4, 7, 11]})
+
+        expected = pd.DataFrame(
+            {"a": [1, 5, 10], "left_val": ["a", "b", "c"], "right_val": [np.nan, 4, 11]}
+        )
+
+        result = merge_asof(
+            left,
+            right,
+            on="a",
+            direction="nearest",
+            allow_exact_matches=False,
+            tolerance=1,
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_forward_by(self):
+        # GH14887
+
+        left = pd.DataFrame(
+            {
+                "a": [1, 5, 10, 12, 15],
+                "b": ["X", "X", "Y", "Z", "Y"],
+                "left_val": ["a", "b", "c", "d", "e"],
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "a": [1, 6, 11, 15, 16],
+                "b": ["X", "Z", "Y", "Z", "Y"],
+                "right_val": [1, 6, 11, 15, 16],
+            }
+        )
+
+        expected = pd.DataFrame(
+            {
+                "a": [1, 5, 10, 12, 15],
+                "b": ["X", "X", "Y", "Z", "Y"],
+                "left_val": ["a", "b", "c", "d", "e"],
+                "right_val": [1, np.nan, 11, 15, 16],
+            }
+        )
+
+        result = merge_asof(left, right, on="a", by="b", direction="forward")
+        tm.assert_frame_equal(result, expected)
+
+    def test_nearest_by(self):
+        # GH14887
+
+        left = pd.DataFrame(
+            {
+                "a": [1, 5, 10, 12, 15],
+                "b": ["X", "X", "Z", "Z", "Y"],
+                "left_val": ["a", "b", "c", "d", "e"],
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "a": [1, 6, 11, 15, 16],
+                "b": ["X", "Z", "Z", "Z", "Y"],
+                "right_val": [1, 6, 11, 15, 16],
+            }
+        )
+
+        expected = pd.DataFrame(
+            {
+                "a": [1, 5, 10, 12, 15],
+                "b": ["X", "X", "Z", "Z", "Y"],
+                "left_val": ["a", "b", "c", "d", "e"],
+                "right_val": [1, 1, 11, 11, 16],
+            }
+        )
+
+        result = merge_asof(left, right, on="a", by="b", direction="nearest")
+        tm.assert_frame_equal(result, expected)
+
+    def test_by_int(self):
+        # we specialize by type, so test that this is correct
+        df1 = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.020",
+                        "20160525 13:30:00.030",
+                        "20160525 13:30:00.040",
+                        "20160525 13:30:00.050",
+                        "20160525 13:30:00.060",
+                    ]
+                ),
+                "key": [1, 2, 1, 3, 2],
+                "value1": [1.1, 1.2, 1.3, 1.4, 1.5],
+            },
+            columns=["time", "key", "value1"],
+        )
+
+        df2 = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.015",
+                        "20160525 13:30:00.020",
+                        "20160525 13:30:00.025",
+                        "20160525 13:30:00.035",
+                        "20160525 13:30:00.040",
+                        "20160525 13:30:00.055",
+                        "20160525 13:30:00.060",
+                        "20160525 13:30:00.065",
+                    ]
+                ),
+                "key": [2, 1, 1, 3, 2, 1, 2, 3],
+                "value2": [2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8],
+            },
+            columns=["time", "key", "value2"],
+        )
+
+        result = merge_asof(df1, df2, on="time", by="key")
+
+        expected = pd.DataFrame(
+            {
+                "time": to_datetime(
+                    [
+                        "20160525 13:30:00.020",
+                        "20160525 13:30:00.030",
+                        "20160525 13:30:00.040",
+                        "20160525 13:30:00.050",
+                        "20160525 13:30:00.060",
+                    ]
+                ),
+                "key": [1, 2, 1, 3, 2],
+                "value1": [1.1, 1.2, 1.3, 1.4, 1.5],
+                "value2": [2.2, 2.1, 2.3, 2.4, 2.7],
+            },
+            columns=["time", "key", "value1", "value2"],
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_on_float(self):
+        # mimics how to determine the minimum-price variation
+        df1 = pd.DataFrame(
+            {
+                "price": [5.01, 0.0023, 25.13, 340.05, 30.78, 1040.90, 0.0078],
+                "symbol": list("ABCDEFG"),
+            },
+            columns=["symbol", "price"],
+        )
+
+        df2 = pd.DataFrame(
+            {"price": [0.0, 1.0, 100.0], "mpv": [0.0001, 0.01, 0.05]},
+            columns=["price", "mpv"],
+        )
+
+        df1 = df1.sort_values("price").reset_index(drop=True)
+
+        result = merge_asof(df1, df2, on="price")
+
+        expected = pd.DataFrame(
+            {
+                "symbol": list("BGACEDF"),
+                "price": [0.0023, 0.0078, 5.01, 25.13, 30.78, 340.05, 1040.90],
+                "mpv": [0.0001, 0.0001, 0.01, 0.01, 0.01, 0.05, 0.05],
+            },
+            columns=["symbol", "price", "mpv"],
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_on_specialized_type(self, any_real_numpy_dtype):
+        # see gh-13936
+        dtype = np.dtype(any_real_numpy_dtype).type
+
+        df1 = pd.DataFrame(
+            {"value": [5, 2, 25, 100, 78, 120, 79], "symbol": list("ABCDEFG")},
+            columns=["symbol", "value"],
+        )
+        df1.value = dtype(df1.value)
+
+        df2 = pd.DataFrame(
+            {"value": [0, 80, 120, 125], "result": list("xyzw")},
+            columns=["value", "result"],
+        )
+        df2.value = dtype(df2.value)
+
+        df1 = df1.sort_values("value").reset_index(drop=True)
+        result = merge_asof(df1, df2, on="value")
+
+        expected = pd.DataFrame(
+            {
+                "symbol": list("BACEGDF"),
+                "value": [2, 5, 25, 78, 79, 100, 120],
+                "result": list("xxxxxyz"),
+            },
+            columns=["symbol", "value", "result"],
+        )
+        expected.value = dtype(expected.value)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_on_specialized_type_by_int(self, any_real_numpy_dtype):
+        # see gh-13936
+        dtype = np.dtype(any_real_numpy_dtype).type
+
+        df1 = pd.DataFrame(
+            {
+                "value": [5, 2, 25, 100, 78, 120, 79],
+                "key": [1, 2, 3, 2, 3, 1, 2],
+                "symbol": list("ABCDEFG"),
+            },
+            columns=["symbol", "key", "value"],
+        )
+        df1.value = dtype(df1.value)
+
+        df2 = pd.DataFrame(
+            {"value": [0, 80, 120, 125], "key": [1, 2, 2, 3], "result": list("xyzw")},
+            columns=["value", "key", "result"],
+        )
+        df2.value = dtype(df2.value)
+
+        df1 = df1.sort_values("value").reset_index(drop=True)
+        result = merge_asof(df1, df2, on="value", by="key")
+
+        expected = pd.DataFrame(
+            {
+                "symbol": list("BACEGDF"),
+                "key": [2, 1, 3, 3, 2, 2, 1],
+                "value": [2, 5, 25, 78, 79, 100, 120],
+                "result": [np.nan, "x", np.nan, np.nan, np.nan, "y", "x"],
+            },
+            columns=["symbol", "key", "value", "result"],
+        )
+        expected.value = dtype(expected.value)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_on_float_by_int(self):
+        # type specialize both "by" and "on" parameters
+        df1 = pd.DataFrame(
+            {
+                "symbol": list("AAABBBCCC"),
+                "exch": [1, 2, 3, 1, 2, 3, 1, 2, 3],
+                "price": [
+                    3.26,
+                    3.2599,
+                    3.2598,
+                    12.58,
+                    12.59,
+                    12.5,
+                    378.15,
+                    378.2,
+                    378.25,
+                ],
+            },
+            columns=["symbol", "exch", "price"],
+        )
+
+        df2 = pd.DataFrame(
+            {
+                "exch": [1, 1, 1, 2, 2, 2, 3, 3, 3],
+                "price": [0.0, 1.0, 100.0, 0.0, 5.0, 100.0, 0.0, 5.0, 1000.0],
+                "mpv": [0.0001, 0.01, 0.05, 0.0001, 0.01, 0.1, 0.0001, 0.25, 1.0],
+            },
+            columns=["exch", "price", "mpv"],
+        )
+
+        df1 = df1.sort_values("price").reset_index(drop=True)
+        df2 = df2.sort_values("price").reset_index(drop=True)
+
+        result = merge_asof(df1, df2, on="price", by="exch")
+
+        expected = pd.DataFrame(
+            {
+                "symbol": list("AAABBBCCC"),
+                "exch": [3, 2, 1, 3, 1, 2, 1, 2, 3],
+                "price": [
+                    3.2598,
+                    3.2599,
+                    3.26,
+                    12.5,
+                    12.58,
+                    12.59,
+                    378.15,
+                    378.2,
+                    378.25,
+                ],
+                "mpv": [0.0001, 0.0001, 0.01, 0.25, 0.01, 0.01, 0.05, 0.1, 0.25],
+            },
+            columns=["symbol", "exch", "price", "mpv"],
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_datatype_error_raises(self):
+        msg = r"Incompatible merge dtype, .*, both sides must have numeric dtype"
+
+        left = pd.DataFrame({"left_val": [1, 5, 10], "a": ["a", "b", "c"]})
+        right = pd.DataFrame({"right_val": [1, 2, 3, 6, 7], "a": [1, 2, 3, 6, 7]})
+
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(left, right, on="a")
+
+    def test_merge_datatype_categorical_error_raises(self):
+        msg = (
+            r"incompatible merge keys \[0\] .* both sides category, "
+            "but not equal ones"
+        )
+
+        left = pd.DataFrame(
+            {"left_val": [1, 5, 10], "a": pd.Categorical(["a", "b", "c"])}
+        )
+        right = pd.DataFrame(
+            {
+                "right_val": [1, 2, 3, 6, 7],
+                "a": pd.Categorical(["a", "X", "c", "X", "b"]),
+            }
+        )
+
+        with pytest.raises(MergeError, match=msg):
+            merge_asof(left, right, on="a")
+
+    def test_merge_groupby_multiple_column_with_categorical_column(self):
+        # GH 16454
+        df = pd.DataFrame({"x": [0], "y": [0], "z": pd.Categorical([0])})
+        result = merge_asof(df, df, on="x", by=["y", "z"])
+        expected = pd.DataFrame({"x": [0], "y": [0], "z": pd.Categorical([0])})
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "func", [lambda x: x, lambda x: to_datetime(x)], ids=["numeric", "datetime"]
+    )
+    @pytest.mark.parametrize("side", ["left", "right"])
+    def test_merge_on_nans(self, func, side):
+        # GH 23189
+        msg = f"Merge keys contain null values on {side} side"
+        nulls = func([1.0, 5.0, np.nan])
+        non_nulls = func([1.0, 5.0, 10.0])
+        df_null = pd.DataFrame({"a": nulls, "left_val": ["a", "b", "c"]})
+        df = pd.DataFrame({"a": non_nulls, "right_val": [1, 6, 11]})
+
+        with pytest.raises(ValueError, match=msg):
+            if side == "left":
+                merge_asof(df_null, df, on="a")
+            else:
+                merge_asof(df, df_null, on="a")
+
+    def test_by_nullable(self, any_numeric_ea_dtype):
+        # Note: this test passes if instead of using pd.array we use
+        #  np.array([np.nan, 1]).  Other than that, I (@jbrockmendel)
+        #  have NO IDEA what the expected behavior is.
+        # TODO(GH#32306): may be relevant to the expected behavior here.
+
+        arr = pd.array([pd.NA, 0, 1], dtype=any_numeric_ea_dtype)
+        if arr.dtype.kind in ["i", "u"]:
+            max_val = np.iinfo(arr.dtype.numpy_dtype).max
+        else:
+            max_val = np.finfo(arr.dtype.numpy_dtype).max
+        # set value s.t. (at least for integer dtypes) arr._values_for_argsort
+        #  is not an injection
+        arr[2] = max_val
+
+        left = pd.DataFrame(
+            {
+                "by_col1": arr,
+                "by_col2": ["HELLO", "To", "You"],
+                "on_col": [2, 4, 6],
+                "value": ["a", "c", "e"],
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "by_col1": arr,
+                "by_col2": ["WORLD", "Wide", "Web"],
+                "on_col": [1, 2, 6],
+                "value": ["b", "d", "f"],
+            }
+        )
+
+        result = merge_asof(left, right, by=["by_col1", "by_col2"], on="on_col")
+        expected = pd.DataFrame(
+            {
+                "by_col1": arr,
+                "by_col2": ["HELLO", "To", "You"],
+                "on_col": [2, 4, 6],
+                "value_x": ["a", "c", "e"],
+            }
+        )
+        expected["value_y"] = np.array([np.nan, np.nan, np.nan], dtype=object)
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_by_col_tz_aware(self):
+        # GH 21184
+        left = pd.DataFrame(
+            {
+                "by_col": pd.DatetimeIndex(["2018-01-01"]).tz_localize("UTC"),
+                "on_col": [2],
+                "values": ["a"],
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "by_col": pd.DatetimeIndex(["2018-01-01"]).tz_localize("UTC"),
+                "on_col": [1],
+                "values": ["b"],
+            }
+        )
+        result = merge_asof(left, right, by="by_col", on="on_col")
+        expected = pd.DataFrame(
+            [[pd.Timestamp("2018-01-01", tz="UTC"), 2, "a", "b"]],
+            columns=["by_col", "on_col", "values_x", "values_y"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_by_mixed_tz_aware(self):
+        # GH 26649
+        left = pd.DataFrame(
+            {
+                "by_col1": pd.DatetimeIndex(["2018-01-01"]).tz_localize("UTC"),
+                "by_col2": ["HELLO"],
+                "on_col": [2],
+                "value": ["a"],
+            }
+        )
+        right = pd.DataFrame(
+            {
+                "by_col1": pd.DatetimeIndex(["2018-01-01"]).tz_localize("UTC"),
+                "by_col2": ["WORLD"],
+                "on_col": [1],
+                "value": ["b"],
+            }
+        )
+        result = merge_asof(left, right, by=["by_col1", "by_col2"], on="on_col")
+        expected = pd.DataFrame(
+            [[pd.Timestamp("2018-01-01", tz="UTC"), "HELLO", 2, "a"]],
+            columns=["by_col1", "by_col2", "on_col", "value_x"],
+        )
+        expected["value_y"] = np.array([np.nan], dtype=object)
+        tm.assert_frame_equal(result, expected)
+
+    def test_timedelta_tolerance_nearest(self, unit):
+        # GH 27642
+        if unit == "s":
+            pytest.skip(
+                "This test is invalid with unit='s' because that would "
+                "round left['time']"
+            )
+
+        left = pd.DataFrame(
+            list(zip([0, 5, 10, 15, 20, 25], [0, 1, 2, 3, 4, 5])),
+            columns=["time", "left"],
+        )
+
+        left["time"] = pd.to_timedelta(left["time"], "ms").astype(f"m8[{unit}]")
+
+        right = pd.DataFrame(
+            list(zip([0, 3, 9, 12, 15, 18], [0, 1, 2, 3, 4, 5])),
+            columns=["time", "right"],
+        )
+
+        right["time"] = pd.to_timedelta(right["time"], "ms").astype(f"m8[{unit}]")
+
+        expected = pd.DataFrame(
+            list(
+                zip(
+                    [0, 5, 10, 15, 20, 25],
+                    [0, 1, 2, 3, 4, 5],
+                    [0, np.nan, 2, 4, np.nan, np.nan],
+                )
+            ),
+            columns=["time", "left", "right"],
+        )
+
+        expected["time"] = pd.to_timedelta(expected["time"], "ms").astype(f"m8[{unit}]")
+
+        result = merge_asof(
+            left, right, on="time", tolerance=Timedelta("1ms"), direction="nearest"
+        )
+
+        tm.assert_frame_equal(result, expected)
+
+    # TODO: any_int_dtype; causes failures in _get_join_indexers
+    def test_int_type_tolerance(self, any_int_numpy_dtype):
+        # GH #28870
+
+        left = pd.DataFrame({"a": [0, 10, 20], "left_val": [1, 2, 3]})
+        right = pd.DataFrame({"a": [5, 15, 25], "right_val": [1, 2, 3]})
+        left["a"] = left["a"].astype(any_int_numpy_dtype)
+        right["a"] = right["a"].astype(any_int_numpy_dtype)
+
+        expected = pd.DataFrame(
+            {"a": [0, 10, 20], "left_val": [1, 2, 3], "right_val": [np.nan, 1.0, 2.0]}
+        )
+        expected["a"] = expected["a"].astype(any_int_numpy_dtype)
+
+        result = merge_asof(left, right, on="a", tolerance=10)
+        tm.assert_frame_equal(result, expected)
+
+    def test_merge_index_column_tz(self):
+        # GH 29864
+        index = pd.date_range("2019-10-01", freq="30min", periods=5, tz="UTC")
+        left = pd.DataFrame([0.9, 0.8, 0.7, 0.6], columns=["xyz"], index=index[1:])
+        right = pd.DataFrame({"from_date": index, "abc": [2.46] * 4 + [2.19]})
+        result = merge_asof(
+            left=left, right=right, left_index=True, right_on=["from_date"]
+        )
+        expected = pd.DataFrame(
+            {
+                "xyz": [0.9, 0.8, 0.7, 0.6],
+                "from_date": index[1:],
+                "abc": [2.46] * 3 + [2.19],
+            },
+            index=pd.date_range(
+                "2019-10-01 00:30:00", freq="30min", periods=4, tz="UTC"
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = merge_asof(
+            left=right, right=left, right_index=True, left_on=["from_date"]
+        )
+        expected = pd.DataFrame(
+            {
+                "from_date": index,
+                "abc": [2.46] * 4 + [2.19],
+                "xyz": [np.nan, 0.9, 0.8, 0.7, 0.6],
+            },
+            index=Index([0, 1, 2, 3, 4]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_left_index_right_index_tolerance(self, unit):
+        # https://github.com/pandas-dev/pandas/issues/35558
+        if unit == "s":
+            pytest.skip(
+                "This test is invalid with unit='s' because that would round dr1"
+            )
+
+        dr1 = pd.date_range(
+            start="1/1/2020", end="1/20/2020", freq="2D", unit=unit
+        ) + Timedelta(seconds=0.4).as_unit(unit)
+        dr2 = pd.date_range(start="1/1/2020", end="2/1/2020", unit=unit)
+
+        df1 = pd.DataFrame({"val1": "foo"}, index=pd.DatetimeIndex(dr1))
+        df2 = pd.DataFrame({"val2": "bar"}, index=pd.DatetimeIndex(dr2))
+
+        expected = pd.DataFrame(
+            {"val1": "foo", "val2": "bar"}, index=pd.DatetimeIndex(dr1)
+        )
+        result = merge_asof(
+            df1,
+            df2,
+            left_index=True,
+            right_index=True,
+            tolerance=Timedelta(seconds=0.5),
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "kwargs", [{"on": "x"}, {"left_index": True, "right_index": True}]
+)
+@pytest.mark.parametrize(
+    "data",
+    [["2019-06-01 00:09:12", "2019-06-01 00:10:29"], [1.0, "2019-06-01 00:10:29"]],
+)
+def test_merge_asof_non_numerical_dtype(kwargs, data):
+    # GH#29130
+    left = pd.DataFrame({"x": data}, index=data)
+    right = pd.DataFrame({"x": data}, index=data)
+    with pytest.raises(
+        MergeError,
+        match=r"Incompatible merge dtype, .*, both sides must have numeric dtype",
+    ):
+        merge_asof(left, right, **kwargs)
+
+
+def test_merge_asof_non_numerical_dtype_object():
+    # GH#29130
+    left = pd.DataFrame({"a": ["12", "13", "15"], "left_val1": ["a", "b", "c"]})
+    right = pd.DataFrame({"a": ["a", "b", "c"], "left_val": ["d", "e", "f"]})
+    with pytest.raises(
+        MergeError,
+        match=r"Incompatible merge dtype, .*, both sides must have numeric dtype",
+    ):
+        merge_asof(
+            left,
+            right,
+            left_on="left_val1",
+            right_on="a",
+            left_by="a",
+            right_by="left_val",
+        )
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"right_index": True, "left_index": True},
+        {"left_on": "left_time", "right_index": True},
+        {"left_index": True, "right_on": "right"},
+    ],
+)
+def test_merge_asof_index_behavior(kwargs):
+    # GH 33463
+    index = Index([1, 5, 10], name="test")
+    left = pd.DataFrame({"left": ["a", "b", "c"], "left_time": [1, 4, 10]}, index=index)
+    right = pd.DataFrame({"right": [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7])
+    result = merge_asof(left, right, **kwargs)
+
+    expected = pd.DataFrame(
+        {"left": ["a", "b", "c"], "left_time": [1, 4, 10], "right": [1, 3, 7]},
+        index=index,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_asof_numeri_column_in_index():
+    # GH#34488
+    left = pd.DataFrame({"b": [10, 11, 12]}, index=Index([1, 2, 3], name="a"))
+    right = pd.DataFrame({"c": [20, 21, 22]}, index=Index([0, 2, 3], name="a"))
+
+    result = merge_asof(left, right, left_on="a", right_on="a")
+    expected = pd.DataFrame({"a": [1, 2, 3], "b": [10, 11, 12], "c": [20, 21, 22]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_asof_numeri_column_in_multiindex():
+    # GH#34488
+    left = pd.DataFrame(
+        {"b": [10, 11, 12]},
+        index=pd.MultiIndex.from_arrays([[1, 2, 3], ["a", "b", "c"]], names=["a", "z"]),
+    )
+    right = pd.DataFrame(
+        {"c": [20, 21, 22]},
+        index=pd.MultiIndex.from_arrays([[1, 2, 3], ["x", "y", "z"]], names=["a", "y"]),
+    )
+
+    result = merge_asof(left, right, left_on="a", right_on="a")
+    expected = pd.DataFrame({"a": [1, 2, 3], "b": [10, 11, 12], "c": [20, 21, 22]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_asof_numeri_column_in_index_object_dtype():
+    # GH#34488
+    left = pd.DataFrame({"b": [10, 11, 12]}, index=Index(["1", "2", "3"], name="a"))
+    right = pd.DataFrame({"c": [20, 21, 22]}, index=Index(["m", "n", "o"], name="a"))
+
+    with pytest.raises(
+        MergeError,
+        match=r"Incompatible merge dtype, .*, both sides must have numeric dtype",
+    ):
+        merge_asof(left, right, left_on="a", right_on="a")
+
+    left = left.reset_index().set_index(["a", "b"])
+    right = right.reset_index().set_index(["a", "c"])
+
+    with pytest.raises(
+        MergeError,
+        match=r"Incompatible merge dtype, .*, both sides must have numeric dtype",
+    ):
+        merge_asof(left, right, left_on="a", right_on="a")
+
+
+def test_merge_asof_array_as_on():
+    # GH#42844
+    right = pd.DataFrame(
+        {
+            "a": [2, 6],
+            "ts": [pd.Timestamp("2021/01/01 00:37"), pd.Timestamp("2021/01/01 01:40")],
+        }
+    )
+    ts_merge = pd.date_range(
+        start=pd.Timestamp("2021/01/01 00:00"), periods=3, freq="1h"
+    )
+    left = pd.DataFrame({"b": [4, 8, 7]})
+    result = merge_asof(
+        left,
+        right,
+        left_on=ts_merge,
+        right_on="ts",
+        allow_exact_matches=False,
+        direction="backward",
+    )
+    expected = pd.DataFrame({"b": [4, 8, 7], "a": [np.nan, 2, 6], "ts": ts_merge})
+    tm.assert_frame_equal(result, expected)
+
+    result = merge_asof(
+        right,
+        left,
+        left_on="ts",
+        right_on=ts_merge,
+        allow_exact_matches=False,
+        direction="backward",
+    )
+    expected = pd.DataFrame(
+        {
+            "a": [2, 6],
+            "ts": [pd.Timestamp("2021/01/01 00:37"), pd.Timestamp("2021/01/01 01:40")],
+            "b": [4, 8],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_merge_asof_raise_for_duplicate_columns():
+    # GH#50102
+    left = pd.DataFrame([[1, 2, "a"]], columns=["a", "a", "left_val"])
+    right = pd.DataFrame([[1, 1, 1]], columns=["a", "a", "right_val"])
+
+    with pytest.raises(ValueError, match="column label 'a'"):
+        merge_asof(left, right, on="a")
+
+    with pytest.raises(ValueError, match="column label 'a'"):
+        merge_asof(left, right, left_on="a", right_on="right_val")
+
+    with pytest.raises(ValueError, match="column label 'a'"):
+        merge_asof(left, right, left_on="left_val", right_on="a")