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ckpts/llama-3b/global_step100/bf16_zero_pp_rank_79_mp_rank_02_optim_states.pt

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+oid sha256:7c621cdfe648640f959f7941fbd6731fa14500a48341eb294777ec47cb77b297
+size 41830394

ckpts/llama-3b/global_step100/bf16_zero_pp_rank_91_mp_rank_03_optim_states.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:992a571497b11e66e56a3bc164e5cae459140c44d281ec6fa6bec406d41e186a
+size 41830330

venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_odf.py

@@ -0,0 +1,77 @@
+import functools
+
+import numpy as np
+import pytest
+
+from pandas.compat import is_platform_windows
+
+import pandas as pd
+import pandas._testing as tm
+
+pytest.importorskip("odf")
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+@pytest.fixture(autouse=True)
+def cd_and_set_engine(monkeypatch, datapath):
+    func = functools.partial(pd.read_excel, engine="odf")
+    monkeypatch.setattr(pd, "read_excel", func)
+    monkeypatch.chdir(datapath("io", "data", "excel"))
+
+
+def test_read_invalid_types_raises():
+    # the invalid_value_type.ods required manually editing
+    # of the included content.xml file
+    with pytest.raises(ValueError, match="Unrecognized type awesome_new_type"):
+        pd.read_excel("invalid_value_type.ods")
+
+
+def test_read_writer_table():
+    # Also test reading tables from an text OpenDocument file
+    # (.odt)
+    index = pd.Index(["Row 1", "Row 2", "Row 3"], name="Header")
+    expected = pd.DataFrame(
+        [[1, np.nan, 7], [2, np.nan, 8], [3, np.nan, 9]],
+        index=index,
+        columns=["Column 1", "Unnamed: 2", "Column 3"],
+    )
+
+    result = pd.read_excel("writertable.odt", sheet_name="Table1", index_col=0)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_read_newlines_between_xml_elements_table():
+    # GH#45598
+    expected = pd.DataFrame(
+        [[1.0, 4.0, 7], [np.nan, np.nan, 8], [3.0, 6.0, 9]],
+        columns=["Column 1", "Column 2", "Column 3"],
+    )
+
+    result = pd.read_excel("test_newlines.ods")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_read_unempty_cells():
+    expected = pd.DataFrame(
+        [1, np.nan, 3, np.nan, 5],
+        columns=["Column 1"],
+    )
+
+    result = pd.read_excel("test_unempty_cells.ods")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_read_cell_annotation():
+    expected = pd.DataFrame(
+        ["test", np.nan, "test 3"],
+        columns=["Column 1"],
+    )
+
+    result = pd.read_excel("test_cell_annotation.ods")
+
+    tm.assert_frame_equal(result, expected)

venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_odswriter.py

@@ -0,0 +1,106 @@
+from datetime import (
+    date,
+    datetime,
+)
+import re
+
+import pytest
+
+from pandas.compat import is_platform_windows
+
+import pandas as pd
+import pandas._testing as tm
+
+from pandas.io.excel import ExcelWriter
+
+odf = pytest.importorskip("odf")
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+@pytest.fixture
+def ext():
+    return ".ods"
+
+
+def test_write_append_mode_raises(ext):
+    msg = "Append mode is not supported with odf!"
+
+    with tm.ensure_clean(ext) as f:
+        with pytest.raises(ValueError, match=msg):
+            ExcelWriter(f, engine="odf", mode="a")
+
+
+@pytest.mark.parametrize("engine_kwargs", [None, {"kwarg": 1}])
+def test_engine_kwargs(ext, engine_kwargs):
+    # GH 42286
+    # GH 43445
+    # test for error: OpenDocumentSpreadsheet does not accept any arguments
+    with tm.ensure_clean(ext) as f:
+        if engine_kwargs is not None:
+            error = re.escape(
+                "OpenDocumentSpreadsheet() got an unexpected keyword argument 'kwarg'"
+            )
+            with pytest.raises(
+                TypeError,
+                match=error,
+            ):
+                ExcelWriter(f, engine="odf", engine_kwargs=engine_kwargs)
+        else:
+            with ExcelWriter(f, engine="odf", engine_kwargs=engine_kwargs) as _:
+                pass
+
+
+def test_book_and_sheets_consistent(ext):
+    # GH#45687 - Ensure sheets is updated if user modifies book
+    with tm.ensure_clean(ext) as f:
+        with ExcelWriter(f) as writer:
+            assert writer.sheets == {}
+            table = odf.table.Table(name="test_name")
+            writer.book.spreadsheet.addElement(table)
+            assert writer.sheets == {"test_name": table}
+
+
+@pytest.mark.parametrize(
+    ["value", "cell_value_type", "cell_value_attribute", "cell_value"],
+    argvalues=[
+        (True, "boolean", "boolean-value", "true"),
+        ("test string", "string", "string-value", "test string"),
+        (1, "float", "value", "1"),
+        (1.5, "float", "value", "1.5"),
+        (
+            datetime(2010, 10, 10, 10, 10, 10),
+            "date",
+            "date-value",
+            "2010-10-10T10:10:10",
+        ),
+        (date(2010, 10, 10), "date", "date-value", "2010-10-10"),
+    ],
+)
+def test_cell_value_type(ext, value, cell_value_type, cell_value_attribute, cell_value):
+    # GH#54994 ODS: cell attributes should follow specification
+    # http://docs.oasis-open.org/office/v1.2/os/OpenDocument-v1.2-os-part1.html#refTable13
+    from odf.namespaces import OFFICENS
+    from odf.table import (
+        TableCell,
+        TableRow,
+    )
+
+    table_cell_name = TableCell().qname
+
+    with tm.ensure_clean(ext) as f:
+        pd.DataFrame([[value]]).to_excel(f, header=False, index=False)
+
+        with pd.ExcelFile(f) as wb:
+            sheet = wb._reader.get_sheet_by_index(0)
+            sheet_rows = sheet.getElementsByType(TableRow)
+            sheet_cells = [
+                x
+                for x in sheet_rows[0].childNodes
+                if hasattr(x, "qname") and x.qname == table_cell_name
+            ]
+
+            cell = sheet_cells[0]
+            assert cell.attributes.get((OFFICENS, "value-type")) == cell_value_type
+            assert cell.attributes.get((OFFICENS, cell_value_attribute)) == cell_value

venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_openpyxl.py

@@ -0,0 +1,432 @@
+import contextlib
+from pathlib import Path
+import re
+
+import numpy as np
+import pytest
+
+from pandas.compat import is_platform_windows
+
+import pandas as pd
+from pandas import DataFrame
+import pandas._testing as tm
+
+from pandas.io.excel import (
+    ExcelWriter,
+    _OpenpyxlWriter,
+)
+from pandas.io.excel._openpyxl import OpenpyxlReader
+
+openpyxl = pytest.importorskip("openpyxl")
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+@pytest.fixture
+def ext():
+    return ".xlsx"
+
+
+def test_to_excel_styleconverter():
+    from openpyxl import styles
+
+    hstyle = {
+        "font": {"color": "00FF0000", "bold": True},
+        "borders": {"top": "thin", "right": "thin", "bottom": "thin", "left": "thin"},
+        "alignment": {"horizontal": "center", "vertical": "top"},
+        "fill": {"patternType": "solid", "fgColor": {"rgb": "006666FF", "tint": 0.3}},
+        "number_format": {"format_code": "0.00"},
+        "protection": {"locked": True, "hidden": False},
+    }
+
+    font_color = styles.Color("00FF0000")
+    font = styles.Font(bold=True, color=font_color)
+    side = styles.Side(style=styles.borders.BORDER_THIN)
+    border = styles.Border(top=side, right=side, bottom=side, left=side)
+    alignment = styles.Alignment(horizontal="center", vertical="top")
+    fill_color = styles.Color(rgb="006666FF", tint=0.3)
+    fill = styles.PatternFill(patternType="solid", fgColor=fill_color)
+
+    number_format = "0.00"
+
+    protection = styles.Protection(locked=True, hidden=False)
+
+    kw = _OpenpyxlWriter._convert_to_style_kwargs(hstyle)
+    assert kw["font"] == font
+    assert kw["border"] == border
+    assert kw["alignment"] == alignment
+    assert kw["fill"] == fill
+    assert kw["number_format"] == number_format
+    assert kw["protection"] == protection
+
+
+def test_write_cells_merge_styled(ext):
+    from pandas.io.formats.excel import ExcelCell
+
+    sheet_name = "merge_styled"
+
+    sty_b1 = {"font": {"color": "00FF0000"}}
+    sty_a2 = {"font": {"color": "0000FF00"}}
+
+    initial_cells = [
+        ExcelCell(col=1, row=0, val=42, style=sty_b1),
+        ExcelCell(col=0, row=1, val=99, style=sty_a2),
+    ]
+
+    sty_merged = {"font": {"color": "000000FF", "bold": True}}
+    sty_kwargs = _OpenpyxlWriter._convert_to_style_kwargs(sty_merged)
+    openpyxl_sty_merged = sty_kwargs["font"]
+    merge_cells = [
+        ExcelCell(
+            col=0, row=0, val="pandas", mergestart=1, mergeend=1, style=sty_merged
+        )
+    ]
+
+    with tm.ensure_clean(ext) as path:
+        with _OpenpyxlWriter(path) as writer:
+            writer._write_cells(initial_cells, sheet_name=sheet_name)
+            writer._write_cells(merge_cells, sheet_name=sheet_name)
+
+            wks = writer.sheets[sheet_name]
+        xcell_b1 = wks["B1"]
+        xcell_a2 = wks["A2"]
+        assert xcell_b1.font == openpyxl_sty_merged
+        assert xcell_a2.font == openpyxl_sty_merged
+
+
+@pytest.mark.parametrize("iso_dates", [True, False])
+def test_engine_kwargs_write(ext, iso_dates):
+    # GH 42286 GH 43445
+    engine_kwargs = {"iso_dates": iso_dates}
+    with tm.ensure_clean(ext) as f:
+        with ExcelWriter(f, engine="openpyxl", engine_kwargs=engine_kwargs) as writer:
+            assert writer.book.iso_dates == iso_dates
+            # ExcelWriter won't allow us to close without writing something
+            DataFrame().to_excel(writer)
+
+
+def test_engine_kwargs_append_invalid(ext):
+    # GH 43445
+    # test whether an invalid engine kwargs actually raises
+    with tm.ensure_clean(ext) as f:
+        DataFrame(["hello", "world"]).to_excel(f)
+        with pytest.raises(
+            TypeError,
+            match=re.escape(
+                "load_workbook() got an unexpected keyword argument 'apple_banana'"
+            ),
+        ):
+            with ExcelWriter(
+                f, engine="openpyxl", mode="a", engine_kwargs={"apple_banana": "fruit"}
+            ) as writer:
+                # ExcelWriter needs us to write something to close properly
+                DataFrame(["good"]).to_excel(writer, sheet_name="Sheet2")
+
+
+@pytest.mark.parametrize("data_only, expected", [(True, 0), (False, "=1+1")])
+def test_engine_kwargs_append_data_only(ext, data_only, expected):
+    # GH 43445
+    # tests whether the data_only engine_kwarg actually works well for
+    # openpyxl's load_workbook
+    with tm.ensure_clean(ext) as f:
+        DataFrame(["=1+1"]).to_excel(f)
+        with ExcelWriter(
+            f, engine="openpyxl", mode="a", engine_kwargs={"data_only": data_only}
+        ) as writer:
+            assert writer.sheets["Sheet1"]["B2"].value == expected
+            # ExcelWriter needs us to writer something to close properly?
+            DataFrame().to_excel(writer, sheet_name="Sheet2")
+
+        # ensure that data_only also works for reading
+        #  and that formulas/values roundtrip
+        assert (
+            pd.read_excel(
+                f,
+                sheet_name="Sheet1",
+                engine="openpyxl",
+                engine_kwargs={"data_only": data_only},
+            ).iloc[0, 1]
+            == expected
+        )
+
+
+@pytest.mark.parametrize("kwarg_name", ["read_only", "data_only"])
+@pytest.mark.parametrize("kwarg_value", [True, False])
+def test_engine_kwargs_append_reader(datapath, ext, kwarg_name, kwarg_value):
+    # GH 55027
+    # test that `read_only` and `data_only` can be passed to
+    #  `openpyxl.reader.excel.load_workbook` via `engine_kwargs`
+    filename = datapath("io", "data", "excel", "test1" + ext)
+    with contextlib.closing(
+        OpenpyxlReader(filename, engine_kwargs={kwarg_name: kwarg_value})
+    ) as reader:
+        assert getattr(reader.book, kwarg_name) == kwarg_value
+
+
+@pytest.mark.parametrize(
+    "mode,expected", [("w", ["baz"]), ("a", ["foo", "bar", "baz"])]
+)
+def test_write_append_mode(ext, mode, expected):
+    df = DataFrame([1], columns=["baz"])
+
+    with tm.ensure_clean(ext) as f:
+        wb = openpyxl.Workbook()
+        wb.worksheets[0].title = "foo"
+        wb.worksheets[0]["A1"].value = "foo"
+        wb.create_sheet("bar")
+        wb.worksheets[1]["A1"].value = "bar"
+        wb.save(f)
+
+        with ExcelWriter(f, engine="openpyxl", mode=mode) as writer:
+            df.to_excel(writer, sheet_name="baz", index=False)
+
+        with contextlib.closing(openpyxl.load_workbook(f)) as wb2:
+            result = [sheet.title for sheet in wb2.worksheets]
+            assert result == expected
+
+            for index, cell_value in enumerate(expected):
+                assert wb2.worksheets[index]["A1"].value == cell_value
+
+
+@pytest.mark.parametrize(
+    "if_sheet_exists,num_sheets,expected",
+    [
+        ("new", 2, ["apple", "banana"]),
+        ("replace", 1, ["pear"]),
+        ("overlay", 1, ["pear", "banana"]),
+    ],
+)
+def test_if_sheet_exists_append_modes(ext, if_sheet_exists, num_sheets, expected):
+    # GH 40230
+    df1 = DataFrame({"fruit": ["apple", "banana"]})
+    df2 = DataFrame({"fruit": ["pear"]})
+
+    with tm.ensure_clean(ext) as f:
+        df1.to_excel(f, engine="openpyxl", sheet_name="foo", index=False)
+        with ExcelWriter(
+            f, engine="openpyxl", mode="a", if_sheet_exists=if_sheet_exists
+        ) as writer:
+            df2.to_excel(writer, sheet_name="foo", index=False)
+
+        with contextlib.closing(openpyxl.load_workbook(f)) as wb:
+            assert len(wb.sheetnames) == num_sheets
+            assert wb.sheetnames[0] == "foo"
+            result = pd.read_excel(wb, "foo", engine="openpyxl")
+            assert list(result["fruit"]) == expected
+            if len(wb.sheetnames) == 2:
+                result = pd.read_excel(wb, wb.sheetnames[1], engine="openpyxl")
+                tm.assert_frame_equal(result, df2)
+
+
+@pytest.mark.parametrize(
+    "startrow, startcol, greeting, goodbye",
+    [
+        (0, 0, ["poop", "world"], ["goodbye", "people"]),
+        (0, 1, ["hello", "world"], ["poop", "people"]),
+        (1, 0, ["hello", "poop"], ["goodbye", "people"]),
+        (1, 1, ["hello", "world"], ["goodbye", "poop"]),
+    ],
+)
+def test_append_overlay_startrow_startcol(ext, startrow, startcol, greeting, goodbye):
+    df1 = DataFrame({"greeting": ["hello", "world"], "goodbye": ["goodbye", "people"]})
+    df2 = DataFrame(["poop"])
+
+    with tm.ensure_clean(ext) as f:
+        df1.to_excel(f, engine="openpyxl", sheet_name="poo", index=False)
+        with ExcelWriter(
+            f, engine="openpyxl", mode="a", if_sheet_exists="overlay"
+        ) as writer:
+            # use startrow+1 because we don't have a header
+            df2.to_excel(
+                writer,
+                index=False,
+                header=False,
+                startrow=startrow + 1,
+                startcol=startcol,
+                sheet_name="poo",
+            )
+
+        result = pd.read_excel(f, sheet_name="poo", engine="openpyxl")
+        expected = DataFrame({"greeting": greeting, "goodbye": goodbye})
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "if_sheet_exists,msg",
+    [
+        (
+            "invalid",
+            "'invalid' is not valid for if_sheet_exists. Valid options "
+            "are 'error', 'new', 'replace' and 'overlay'.",
+        ),
+        (
+            "error",
+            "Sheet 'foo' already exists and if_sheet_exists is set to 'error'.",
+        ),
+        (
+            None,
+            "Sheet 'foo' already exists and if_sheet_exists is set to 'error'.",
+        ),
+    ],
+)
+def test_if_sheet_exists_raises(ext, if_sheet_exists, msg):
+    # GH 40230
+    df = DataFrame({"fruit": ["pear"]})
+    with tm.ensure_clean(ext) as f:
+        with pytest.raises(ValueError, match=re.escape(msg)):
+            df.to_excel(f, sheet_name="foo", engine="openpyxl")
+            with ExcelWriter(
+                f, engine="openpyxl", mode="a", if_sheet_exists=if_sheet_exists
+            ) as writer:
+                df.to_excel(writer, sheet_name="foo")
+
+
+def test_to_excel_with_openpyxl_engine(ext):
+    # GH 29854
+    with tm.ensure_clean(ext) as filename:
+        df1 = DataFrame({"A": np.linspace(1, 10, 10)})
+        df2 = DataFrame({"B": np.linspace(1, 20, 10)})
+        df = pd.concat([df1, df2], axis=1)
+        styled = df.style.map(
+            lambda val: f"color: {'red' if val < 0 else 'black'}"
+        ).highlight_max()
+
+        styled.to_excel(filename, engine="openpyxl")
+
+
+@pytest.mark.parametrize("read_only", [True, False])
+def test_read_workbook(datapath, ext, read_only):
+    # GH 39528
+    filename = datapath("io", "data", "excel", "test1" + ext)
+    with contextlib.closing(
+        openpyxl.load_workbook(filename, read_only=read_only)
+    ) as wb:
+        result = pd.read_excel(wb, engine="openpyxl")
+    expected = pd.read_excel(filename)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "header, expected_data",
+    [
+        (
+            0,
+            {
+                "Title": [np.nan, "A", 1, 2, 3],
+                "Unnamed: 1": [np.nan, "B", 4, 5, 6],
+                "Unnamed: 2": [np.nan, "C", 7, 8, 9],
+            },
+        ),
+        (2, {"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}),
+    ],
+)
+@pytest.mark.parametrize(
+    "filename", ["dimension_missing", "dimension_small", "dimension_large"]
+)
+# When read_only is None, use read_excel instead of a workbook
+@pytest.mark.parametrize("read_only", [True, False, None])
+def test_read_with_bad_dimension(
+    datapath, ext, header, expected_data, filename, read_only
+):
+    # GH 38956, 39001 - no/incorrect dimension information
+    path = datapath("io", "data", "excel", f"{filename}{ext}")
+    if read_only is None:
+        result = pd.read_excel(path, header=header)
+    else:
+        with contextlib.closing(
+            openpyxl.load_workbook(path, read_only=read_only)
+        ) as wb:
+            result = pd.read_excel(wb, engine="openpyxl", header=header)
+    expected = DataFrame(expected_data)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_append_mode_file(ext):
+    # GH 39576
+    df = DataFrame()
+
+    with tm.ensure_clean(ext) as f:
+        df.to_excel(f, engine="openpyxl")
+
+        with ExcelWriter(
+            f, mode="a", engine="openpyxl", if_sheet_exists="new"
+        ) as writer:
+            df.to_excel(writer)
+
+        # make sure that zip files are not concatenated by making sure that
+        # "docProps/app.xml" only occurs twice in the file
+        data = Path(f).read_bytes()
+        first = data.find(b"docProps/app.xml")
+        second = data.find(b"docProps/app.xml", first + 1)
+        third = data.find(b"docProps/app.xml", second + 1)
+        assert second != -1 and third == -1
+
+
+# When read_only is None, use read_excel instead of a workbook
+@pytest.mark.parametrize("read_only", [True, False, None])
+def test_read_with_empty_trailing_rows(datapath, ext, read_only):
+    # GH 39181
+    path = datapath("io", "data", "excel", f"empty_trailing_rows{ext}")
+    if read_only is None:
+        result = pd.read_excel(path)
+    else:
+        with contextlib.closing(
+            openpyxl.load_workbook(path, read_only=read_only)
+        ) as wb:
+            result = pd.read_excel(wb, engine="openpyxl")
+    expected = DataFrame(
+        {
+            "Title": [np.nan, "A", 1, 2, 3],
+            "Unnamed: 1": [np.nan, "B", 4, 5, 6],
+            "Unnamed: 2": [np.nan, "C", 7, 8, 9],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+# When read_only is None, use read_excel instead of a workbook
+@pytest.mark.parametrize("read_only", [True, False, None])
+def test_read_empty_with_blank_row(datapath, ext, read_only):
+    # GH 39547 - empty excel file with a row that has no data
+    path = datapath("io", "data", "excel", f"empty_with_blank_row{ext}")
+    if read_only is None:
+        result = pd.read_excel(path)
+    else:
+        with contextlib.closing(
+            openpyxl.load_workbook(path, read_only=read_only)
+        ) as wb:
+            result = pd.read_excel(wb, engine="openpyxl")
+    expected = DataFrame()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_book_and_sheets_consistent(ext):
+    # GH#45687 - Ensure sheets is updated if user modifies book
+    with tm.ensure_clean(ext) as f:
+        with ExcelWriter(f, engine="openpyxl") as writer:
+            assert writer.sheets == {}
+            sheet = writer.book.create_sheet("test_name", 0)
+            assert writer.sheets == {"test_name": sheet}
+
+
+def test_ints_spelled_with_decimals(datapath, ext):
+    # GH 46988 - openpyxl returns this sheet with floats
+    path = datapath("io", "data", "excel", f"ints_spelled_with_decimals{ext}")
+    result = pd.read_excel(path)
+    expected = DataFrame(range(2, 12), columns=[1])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_read_multiindex_header_no_index_names(datapath, ext):
+    # GH#47487
+    path = datapath("io", "data", "excel", f"multiindex_no_index_names{ext}")
+    result = pd.read_excel(path, index_col=[0, 1, 2], header=[0, 1, 2])
+    expected = DataFrame(
+        [[np.nan, "x", "x", "x"], ["x", np.nan, np.nan, np.nan]],
+        columns=pd.MultiIndex.from_tuples(
+            [("X", "Y", "A1"), ("X", "Y", "A2"), ("XX", "YY", "B1"), ("XX", "YY", "B2")]
+        ),
+        index=pd.MultiIndex.from_tuples([("A", "AA", "AAA"), ("A", "BB", "BBB")]),
+    )
+    tm.assert_frame_equal(result, expected)

venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_readers.py

@@ -0,0 +1,1751 @@
+from __future__ import annotations
+
+from datetime import (
+    datetime,
+    time,
+)
+from functools import partial
+from io import BytesIO
+import os
+from pathlib import Path
+import platform
+import re
+from urllib.error import URLError
+from zipfile import BadZipFile
+
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+from pandas.compat import is_platform_windows
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    read_csv,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    ArrowStringArray,
+    StringArray,
+)
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+read_ext_params = [".xls", ".xlsx", ".xlsm", ".xlsb", ".ods"]
+engine_params = [
+    # Add any engines to test here
+    # When defusedxml is installed it triggers deprecation warnings for
+    # xlrd and openpyxl, so catch those here
+    pytest.param(
+        "xlrd",
+        marks=[
+            td.skip_if_no("xlrd"),
+        ],
+    ),
+    pytest.param(
+        "openpyxl",
+        marks=[
+            td.skip_if_no("openpyxl"),
+        ],
+    ),
+    pytest.param(
+        None,
+        marks=[
+            td.skip_if_no("xlrd"),
+        ],
+    ),
+    pytest.param("pyxlsb", marks=td.skip_if_no("pyxlsb")),
+    pytest.param("odf", marks=td.skip_if_no("odf")),
+    pytest.param("calamine", marks=td.skip_if_no("python_calamine")),
+]
+
+
+def _is_valid_engine_ext_pair(engine, read_ext: str) -> bool:
+    """
+    Filter out invalid (engine, ext) pairs instead of skipping, as that
+    produces 500+ pytest.skips.
+    """
+    engine = engine.values[0]
+    if engine == "openpyxl" and read_ext == ".xls":
+        return False
+    if engine == "odf" and read_ext != ".ods":
+        return False
+    if read_ext == ".ods" and engine not in {"odf", "calamine"}:
+        return False
+    if engine == "pyxlsb" and read_ext != ".xlsb":
+        return False
+    if read_ext == ".xlsb" and engine not in {"pyxlsb", "calamine"}:
+        return False
+    if engine == "xlrd" and read_ext != ".xls":
+        return False
+    return True
+
+
+def _transfer_marks(engine, read_ext):
+    """
+    engine gives us a pytest.param object with some marks, read_ext is just
+    a string.  We need to generate a new pytest.param inheriting the marks.
+    """
+    values = engine.values + (read_ext,)
+    new_param = pytest.param(values, marks=engine.marks)
+    return new_param
+
+
+@pytest.fixture(
+    params=[
+        _transfer_marks(eng, ext)
+        for eng in engine_params
+        for ext in read_ext_params
+        if _is_valid_engine_ext_pair(eng, ext)
+    ],
+    ids=str,
+)
+def engine_and_read_ext(request):
+    """
+    Fixture for Excel reader engine and read_ext, only including valid pairs.
+    """
+    return request.param
+
+
+@pytest.fixture
+def engine(engine_and_read_ext):
+    engine, read_ext = engine_and_read_ext
+    return engine
+
+
+@pytest.fixture
+def read_ext(engine_and_read_ext):
+    engine, read_ext = engine_and_read_ext
+    return read_ext
+
+
+@pytest.fixture
+def df_ref(datapath):
+    """
+    Obtain the reference data from read_csv with the Python engine.
+    """
+    filepath = datapath("io", "data", "csv", "test1.csv")
+    df_ref = read_csv(filepath, index_col=0, parse_dates=True, engine="python")
+    return df_ref
+
+
+def get_exp_unit(read_ext: str, engine: str | None) -> str:
+    return "ns"
+
+
+def adjust_expected(expected: DataFrame, read_ext: str, engine: str) -> None:
+    expected.index.name = None
+    unit = get_exp_unit(read_ext, engine)
+    # error: "Index" has no attribute "as_unit"
+    expected.index = expected.index.as_unit(unit)  # type: ignore[attr-defined]
+
+
+def xfail_datetimes_with_pyxlsb(engine, request):
+    if engine == "pyxlsb":
+        request.applymarker(
+            pytest.mark.xfail(
+                reason="Sheets containing datetimes not supported by pyxlsb"
+            )
+        )
+
+
+class TestReaders:
+    @pytest.fixture(autouse=True)
+    def cd_and_set_engine(self, engine, datapath, monkeypatch):
+        """
+        Change directory and set engine for read_excel calls.
+        """
+        func = partial(pd.read_excel, engine=engine)
+        monkeypatch.chdir(datapath("io", "data", "excel"))
+        monkeypatch.setattr(pd, "read_excel", func)
+
+    def test_engine_used(self, read_ext, engine, monkeypatch):
+        # GH 38884
+        def parser(self, *args, **kwargs):
+            return self.engine
+
+        monkeypatch.setattr(pd.ExcelFile, "parse", parser)
+
+        expected_defaults = {
+            "xlsx": "openpyxl",
+            "xlsm": "openpyxl",
+            "xlsb": "pyxlsb",
+            "xls": "xlrd",
+            "ods": "odf",
+        }
+
+        with open("test1" + read_ext, "rb") as f:
+            result = pd.read_excel(f)
+
+        if engine is not None:
+            expected = engine
+        else:
+            expected = expected_defaults[read_ext[1:]]
+        assert result == expected
+
+    def test_engine_kwargs(self, read_ext, engine):
+        # GH#52214
+        expected_defaults = {
+            "xlsx": {"foo": "abcd"},
+            "xlsm": {"foo": 123},
+            "xlsb": {"foo": "True"},
+            "xls": {"foo": True},
+            "ods": {"foo": "abcd"},
+        }
+
+        if engine in {"xlrd", "pyxlsb"}:
+            msg = re.escape(r"open_workbook() got an unexpected keyword argument 'foo'")
+        elif engine == "odf":
+            msg = re.escape(r"load() got an unexpected keyword argument 'foo'")
+        else:
+            msg = re.escape(r"load_workbook() got an unexpected keyword argument 'foo'")
+
+        if engine is not None:
+            with pytest.raises(TypeError, match=msg):
+                pd.read_excel(
+                    "test1" + read_ext,
+                    sheet_name="Sheet1",
+                    index_col=0,
+                    engine_kwargs=expected_defaults[read_ext[1:]],
+                )
+
+    def test_usecols_int(self, read_ext):
+        # usecols as int
+        msg = "Passing an integer for `usecols`"
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel(
+                "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols=3
+            )
+
+        # usecols as int
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel(
+                "test1" + read_ext,
+                sheet_name="Sheet2",
+                skiprows=[1],
+                index_col=0,
+                usecols=3,
+            )
+
+    def test_usecols_list(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref[["B", "C"]]
+        adjust_expected(expected, read_ext, engine)
+
+        df1 = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols=[0, 2, 3]
+        )
+        df2 = pd.read_excel(
+            "test1" + read_ext,
+            sheet_name="Sheet2",
+            skiprows=[1],
+            index_col=0,
+            usecols=[0, 2, 3],
+        )
+
+        # TODO add index to xls file)
+        tm.assert_frame_equal(df1, expected)
+        tm.assert_frame_equal(df2, expected)
+
+    def test_usecols_str(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref[["A", "B", "C"]]
+        adjust_expected(expected, read_ext, engine)
+
+        df2 = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A:D"
+        )
+        df3 = pd.read_excel(
+            "test1" + read_ext,
+            sheet_name="Sheet2",
+            skiprows=[1],
+            index_col=0,
+            usecols="A:D",
+        )
+
+        # TODO add index to xls, read xls ignores index name ?
+        tm.assert_frame_equal(df2, expected)
+        tm.assert_frame_equal(df3, expected)
+
+        expected = df_ref[["B", "C"]]
+        adjust_expected(expected, read_ext, engine)
+
+        df2 = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A,C,D"
+        )
+        df3 = pd.read_excel(
+            "test1" + read_ext,
+            sheet_name="Sheet2",
+            skiprows=[1],
+            index_col=0,
+            usecols="A,C,D",
+        )
+        # TODO add index to xls file
+        tm.assert_frame_equal(df2, expected)
+        tm.assert_frame_equal(df3, expected)
+
+        df2 = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A,C:D"
+        )
+        df3 = pd.read_excel(
+            "test1" + read_ext,
+            sheet_name="Sheet2",
+            skiprows=[1],
+            index_col=0,
+            usecols="A,C:D",
+        )
+        tm.assert_frame_equal(df2, expected)
+        tm.assert_frame_equal(df3, expected)
+
+    @pytest.mark.parametrize(
+        "usecols", [[0, 1, 3], [0, 3, 1], [1, 0, 3], [1, 3, 0], [3, 0, 1], [3, 1, 0]]
+    )
+    def test_usecols_diff_positional_int_columns_order(
+        self, request, engine, read_ext, usecols, df_ref
+    ):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref[["A", "C"]]
+        adjust_expected(expected, read_ext, engine)
+
+        result = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols=usecols
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("usecols", [["B", "D"], ["D", "B"]])
+    def test_usecols_diff_positional_str_columns_order(self, read_ext, usecols, df_ref):
+        expected = df_ref[["B", "D"]]
+        expected.index = range(len(expected))
+
+        result = pd.read_excel("test1" + read_ext, sheet_name="Sheet1", usecols=usecols)
+        tm.assert_frame_equal(result, expected)
+
+    def test_read_excel_without_slicing(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref
+        adjust_expected(expected, read_ext, engine)
+
+        result = pd.read_excel("test1" + read_ext, sheet_name="Sheet1", index_col=0)
+        tm.assert_frame_equal(result, expected)
+
+    def test_usecols_excel_range_str(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref[["C", "D"]]
+        adjust_expected(expected, read_ext, engine)
+
+        result = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A,D:E"
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_usecols_excel_range_str_invalid(self, read_ext):
+        msg = "Invalid column name: E1"
+
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel("test1" + read_ext, sheet_name="Sheet1", usecols="D:E1")
+
+    def test_index_col_label_error(self, read_ext):
+        msg = "list indices must be integers.*, not str"
+
+        with pytest.raises(TypeError, match=msg):
+            pd.read_excel(
+                "test1" + read_ext,
+                sheet_name="Sheet1",
+                index_col=["A"],
+                usecols=["A", "C"],
+            )
+
+    def test_index_col_str(self, read_ext):
+        # see gh-52716
+        result = pd.read_excel("test1" + read_ext, sheet_name="Sheet3", index_col="A")
+        expected = DataFrame(
+            columns=["B", "C", "D", "E", "F"], index=Index([], name="A")
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_index_col_empty(self, read_ext):
+        # see gh-9208
+        result = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet3", index_col=["A", "B", "C"]
+        )
+        expected = DataFrame(
+            columns=["D", "E", "F"],
+            index=MultiIndex(levels=[[]] * 3, codes=[[]] * 3, names=["A", "B", "C"]),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("index_col", [None, 2])
+    def test_index_col_with_unnamed(self, read_ext, index_col):
+        # see gh-18792
+        result = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet4", index_col=index_col
+        )
+        expected = DataFrame(
+            [["i1", "a", "x"], ["i2", "b", "y"]], columns=["Unnamed: 0", "col1", "col2"]
+        )
+        if index_col:
+            expected = expected.set_index(expected.columns[index_col])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_usecols_pass_non_existent_column(self, read_ext):
+        msg = (
+            "Usecols do not match columns, "
+            "columns expected but not found: "
+            r"\['E'\]"
+        )
+
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel("test1" + read_ext, usecols=["E"])
+
+    def test_usecols_wrong_type(self, read_ext):
+        msg = (
+            "'usecols' must either be list-like of "
+            "all strings, all unicode, all integers or a callable."
+        )
+
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel("test1" + read_ext, usecols=["E1", 0])
+
+    def test_excel_stop_iterator(self, read_ext):
+        parsed = pd.read_excel("test2" + read_ext, sheet_name="Sheet1")
+        expected = DataFrame([["aaaa", "bbbbb"]], columns=["Test", "Test1"])
+        tm.assert_frame_equal(parsed, expected)
+
+    def test_excel_cell_error_na(self, request, engine, read_ext):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        # https://github.com/tafia/calamine/issues/355
+        if engine == "calamine" and read_ext == ".ods":
+            request.applymarker(
+                pytest.mark.xfail(reason="Calamine can't extract error from ods files")
+            )
+
+        parsed = pd.read_excel("test3" + read_ext, sheet_name="Sheet1")
+        expected = DataFrame([[np.nan]], columns=["Test"])
+        tm.assert_frame_equal(parsed, expected)
+
+    def test_excel_table(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref
+        adjust_expected(expected, read_ext, engine)
+
+        df1 = pd.read_excel("test1" + read_ext, sheet_name="Sheet1", index_col=0)
+        df2 = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet2", skiprows=[1], index_col=0
+        )
+        # TODO add index to file
+        tm.assert_frame_equal(df1, expected)
+        tm.assert_frame_equal(df2, expected)
+
+        df3 = pd.read_excel(
+            "test1" + read_ext, sheet_name="Sheet1", index_col=0, skipfooter=1
+        )
+        tm.assert_frame_equal(df3, df1.iloc[:-1])
+
+    def test_reader_special_dtypes(self, request, engine, read_ext):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        unit = get_exp_unit(read_ext, engine)
+        expected = DataFrame.from_dict(
+            {
+                "IntCol": [1, 2, -3, 4, 0],
+                "FloatCol": [1.25, 2.25, 1.83, 1.92, 0.0000000005],
+                "BoolCol": [True, False, True, True, False],
+                "StrCol": [1, 2, 3, 4, 5],
+                "Str2Col": ["a", 3, "c", "d", "e"],
+                "DateCol": Index(
+                    [
+                        datetime(2013, 10, 30),
+                        datetime(2013, 10, 31),
+                        datetime(1905, 1, 1),
+                        datetime(2013, 12, 14),
+                        datetime(2015, 3, 14),
+                    ],
+                    dtype=f"M8[{unit}]",
+                ),
+            },
+        )
+        basename = "test_types"
+
+        # should read in correctly and infer types
+        actual = pd.read_excel(basename + read_ext, sheet_name="Sheet1")
+        tm.assert_frame_equal(actual, expected)
+
+        # if not coercing number, then int comes in as float
+        float_expected = expected.copy()
+        float_expected.loc[float_expected.index[1], "Str2Col"] = 3.0
+        actual = pd.read_excel(basename + read_ext, sheet_name="Sheet1")
+        tm.assert_frame_equal(actual, float_expected)
+
+        # check setting Index (assuming xls and xlsx are the same here)
+        for icol, name in enumerate(expected.columns):
+            actual = pd.read_excel(
+                basename + read_ext, sheet_name="Sheet1", index_col=icol
+            )
+            exp = expected.set_index(name)
+            tm.assert_frame_equal(actual, exp)
+
+        expected["StrCol"] = expected["StrCol"].apply(str)
+        actual = pd.read_excel(
+            basename + read_ext, sheet_name="Sheet1", converters={"StrCol": str}
+        )
+        tm.assert_frame_equal(actual, expected)
+
+    # GH8212 - support for converters and missing values
+    def test_reader_converters(self, read_ext):
+        basename = "test_converters"
+
+        expected = DataFrame.from_dict(
+            {
+                "IntCol": [1, 2, -3, -1000, 0],
+                "FloatCol": [12.5, np.nan, 18.3, 19.2, 0.000000005],
+                "BoolCol": ["Found", "Found", "Found", "Not found", "Found"],
+                "StrCol": ["1", np.nan, "3", "4", "5"],
+            }
+        )
+
+        converters = {
+            "IntCol": lambda x: int(x) if x != "" else -1000,
+            "FloatCol": lambda x: 10 * x if x else np.nan,
+            2: lambda x: "Found" if x != "" else "Not found",
+            3: lambda x: str(x) if x else "",
+        }
+
+        # should read in correctly and set types of single cells (not array
+        # dtypes)
+        actual = pd.read_excel(
+            basename + read_ext, sheet_name="Sheet1", converters=converters
+        )
+        tm.assert_frame_equal(actual, expected)
+
+    def test_reader_dtype(self, read_ext):
+        # GH 8212
+        basename = "testdtype"
+        actual = pd.read_excel(basename + read_ext)
+
+        expected = DataFrame(
+            {
+                "a": [1, 2, 3, 4],
+                "b": [2.5, 3.5, 4.5, 5.5],
+                "c": [1, 2, 3, 4],
+                "d": [1.0, 2.0, np.nan, 4.0],
+            }
+        )
+
+        tm.assert_frame_equal(actual, expected)
+
+        actual = pd.read_excel(
+            basename + read_ext, dtype={"a": "float64", "b": "float32", "c": str}
+        )
+
+        expected["a"] = expected["a"].astype("float64")
+        expected["b"] = expected["b"].astype("float32")
+        expected["c"] = Series(["001", "002", "003", "004"], dtype=object)
+        tm.assert_frame_equal(actual, expected)
+
+        msg = "Unable to convert column d to type int64"
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel(basename + read_ext, dtype={"d": "int64"})
+
+    @pytest.mark.parametrize(
+        "dtype,expected",
+        [
+            (
+                None,
+                DataFrame(
+                    {
+                        "a": [1, 2, 3, 4],
+                        "b": [2.5, 3.5, 4.5, 5.5],
+                        "c": [1, 2, 3, 4],
+                        "d": [1.0, 2.0, np.nan, 4.0],
+                    }
+                ),
+            ),
+            (
+                {"a": "float64", "b": "float32", "c": str, "d": str},
+                DataFrame(
+                    {
+                        "a": Series([1, 2, 3, 4], dtype="float64"),
+                        "b": Series([2.5, 3.5, 4.5, 5.5], dtype="float32"),
+                        "c": Series(["001", "002", "003", "004"], dtype=object),
+                        "d": Series(["1", "2", np.nan, "4"], dtype=object),
+                    }
+                ),
+            ),
+        ],
+    )
+    def test_reader_dtype_str(self, read_ext, dtype, expected):
+        # see gh-20377
+        basename = "testdtype"
+
+        actual = pd.read_excel(basename + read_ext, dtype=dtype)
+        tm.assert_frame_equal(actual, expected)
+
+    def test_dtype_backend(self, read_ext, dtype_backend, engine):
+        # GH#36712
+        if read_ext in (".xlsb", ".xls"):
+            pytest.skip(f"No engine for filetype: '{read_ext}'")
+
+        df = DataFrame(
+            {
+                "a": Series([1, 3], dtype="Int64"),
+                "b": Series([2.5, 4.5], dtype="Float64"),
+                "c": Series([True, False], dtype="boolean"),
+                "d": Series(["a", "b"], dtype="string"),
+                "e": Series([pd.NA, 6], dtype="Int64"),
+                "f": Series([pd.NA, 7.5], dtype="Float64"),
+                "g": Series([pd.NA, True], dtype="boolean"),
+                "h": Series([pd.NA, "a"], dtype="string"),
+                "i": Series([pd.Timestamp("2019-12-31")] * 2),
+                "j": Series([pd.NA, pd.NA], dtype="Int64"),
+            }
+        )
+        with tm.ensure_clean(read_ext) as file_path:
+            df.to_excel(file_path, sheet_name="test", index=False)
+            result = pd.read_excel(
+                file_path, sheet_name="test", dtype_backend=dtype_backend
+            )
+        if dtype_backend == "pyarrow":
+            import pyarrow as pa
+
+            from pandas.arrays import ArrowExtensionArray
+
+            expected = DataFrame(
+                {
+                    col: ArrowExtensionArray(pa.array(df[col], from_pandas=True))
+                    for col in df.columns
+                }
+            )
+            # pyarrow by default infers timestamp resolution as us, not ns
+            expected["i"] = ArrowExtensionArray(
+                expected["i"].array._pa_array.cast(pa.timestamp(unit="us"))
+            )
+            # pyarrow supports a null type, so don't have to default to Int64
+            expected["j"] = ArrowExtensionArray(pa.array([None, None]))
+        else:
+            expected = df
+            unit = get_exp_unit(read_ext, engine)
+            expected["i"] = expected["i"].astype(f"M8[{unit}]")
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_dtype_backend_and_dtype(self, read_ext):
+        # GH#36712
+        if read_ext in (".xlsb", ".xls"):
+            pytest.skip(f"No engine for filetype: '{read_ext}'")
+
+        df = DataFrame({"a": [np.nan, 1.0], "b": [2.5, np.nan]})
+        with tm.ensure_clean(read_ext) as file_path:
+            df.to_excel(file_path, sheet_name="test", index=False)
+            result = pd.read_excel(
+                file_path,
+                sheet_name="test",
+                dtype_backend="numpy_nullable",
+                dtype="float64",
+            )
+        tm.assert_frame_equal(result, df)
+
+    @pytest.mark.xfail(
+        using_pyarrow_string_dtype(), reason="infer_string takes precedence"
+    )
+    def test_dtype_backend_string(self, read_ext, string_storage):
+        # GH#36712
+        if read_ext in (".xlsb", ".xls"):
+            pytest.skip(f"No engine for filetype: '{read_ext}'")
+
+        pa = pytest.importorskip("pyarrow")
+
+        with pd.option_context("mode.string_storage", string_storage):
+            df = DataFrame(
+                {
+                    "a": np.array(["a", "b"], dtype=np.object_),
+                    "b": np.array(["x", pd.NA], dtype=np.object_),
+                }
+            )
+            with tm.ensure_clean(read_ext) as file_path:
+                df.to_excel(file_path, sheet_name="test", index=False)
+                result = pd.read_excel(
+                    file_path, sheet_name="test", dtype_backend="numpy_nullable"
+                )
+
+            if string_storage == "python":
+                expected = DataFrame(
+                    {
+                        "a": StringArray(np.array(["a", "b"], dtype=np.object_)),
+                        "b": StringArray(np.array(["x", pd.NA], dtype=np.object_)),
+                    }
+                )
+            else:
+                expected = DataFrame(
+                    {
+                        "a": ArrowStringArray(pa.array(["a", "b"])),
+                        "b": ArrowStringArray(pa.array(["x", None])),
+                    }
+                )
+            tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtypes, exp_value", [({}, 1), ({"a.1": "int64"}, 1)])
+    def test_dtype_mangle_dup_cols(self, read_ext, dtypes, exp_value):
+        # GH#35211
+        basename = "df_mangle_dup_col_dtypes"
+        dtype_dict = {"a": object, **dtypes}
+        dtype_dict_copy = dtype_dict.copy()
+        # GH#42462
+        result = pd.read_excel(basename + read_ext, dtype=dtype_dict)
+        expected = DataFrame(
+            {
+                "a": Series([1], dtype=object),
+                "a.1": Series([exp_value], dtype=object if not dtypes else None),
+            }
+        )
+        assert dtype_dict == dtype_dict_copy, "dtype dict changed"
+        tm.assert_frame_equal(result, expected)
+
+    def test_reader_spaces(self, read_ext):
+        # see gh-32207
+        basename = "test_spaces"
+
+        actual = pd.read_excel(basename + read_ext)
+        expected = DataFrame(
+            {
+                "testcol": [
+                    "this is great",
+                    "4    spaces",
+                    "1 trailing ",
+                    " 1 leading",
+                    "2  spaces  multiple  times",
+                ]
+            }
+        )
+        tm.assert_frame_equal(actual, expected)
+
+    # gh-36122, gh-35802
+    @pytest.mark.parametrize(
+        "basename,expected",
+        [
+            ("gh-35802", DataFrame({"COLUMN": ["Test (1)"]})),
+            ("gh-36122", DataFrame(columns=["got 2nd sa"])),
+        ],
+    )
+    def test_read_excel_ods_nested_xml(self, engine, read_ext, basename, expected):
+        # see gh-35802
+        if engine != "odf":
+            pytest.skip(f"Skipped for engine: {engine}")
+
+        actual = pd.read_excel(basename + read_ext)
+        tm.assert_frame_equal(actual, expected)
+
+    def test_reading_all_sheets(self, read_ext):
+        # Test reading all sheet names by setting sheet_name to None,
+        # Ensure a dict is returned.
+        # See PR #9450
+        basename = "test_multisheet"
+        dfs = pd.read_excel(basename + read_ext, sheet_name=None)
+        # ensure this is not alphabetical to test order preservation
+        expected_keys = ["Charlie", "Alpha", "Beta"]
+        tm.assert_contains_all(expected_keys, dfs.keys())
+        # Issue 9930
+        # Ensure sheet order is preserved
+        assert expected_keys == list(dfs.keys())
+
+    def test_reading_multiple_specific_sheets(self, read_ext):
+        # Test reading specific sheet names by specifying a mixed list
+        # of integers and strings, and confirm that duplicated sheet
+        # references (positions/names) are removed properly.
+        # Ensure a dict is returned
+        # See PR #9450
+        basename = "test_multisheet"
+        # Explicitly request duplicates. Only the set should be returned.
+        expected_keys = [2, "Charlie", "Charlie"]
+        dfs = pd.read_excel(basename + read_ext, sheet_name=expected_keys)
+        expected_keys = list(set(expected_keys))
+        tm.assert_contains_all(expected_keys, dfs.keys())
+        assert len(expected_keys) == len(dfs.keys())
+
+    def test_reading_all_sheets_with_blank(self, read_ext):
+        # Test reading all sheet names by setting sheet_name to None,
+        # In the case where some sheets are blank.
+        # Issue #11711
+        basename = "blank_with_header"
+        dfs = pd.read_excel(basename + read_ext, sheet_name=None)
+        expected_keys = ["Sheet1", "Sheet2", "Sheet3"]
+        tm.assert_contains_all(expected_keys, dfs.keys())
+
+    # GH6403
+    def test_read_excel_blank(self, read_ext):
+        actual = pd.read_excel("blank" + read_ext, sheet_name="Sheet1")
+        tm.assert_frame_equal(actual, DataFrame())
+
+    def test_read_excel_blank_with_header(self, read_ext):
+        expected = DataFrame(columns=["col_1", "col_2"])
+        actual = pd.read_excel("blank_with_header" + read_ext, sheet_name="Sheet1")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_exception_message_includes_sheet_name(self, read_ext):
+        # GH 48706
+        with pytest.raises(ValueError, match=r" \(sheet: Sheet1\)$"):
+            pd.read_excel("blank_with_header" + read_ext, header=[1], sheet_name=None)
+        with pytest.raises(ZeroDivisionError, match=r" \(sheet: Sheet1\)$"):
+            pd.read_excel("test1" + read_ext, usecols=lambda x: 1 / 0, sheet_name=None)
+
+    @pytest.mark.filterwarnings("ignore:Cell A4 is marked:UserWarning:openpyxl")
+    def test_date_conversion_overflow(self, request, engine, read_ext):
+        # GH 10001 : pandas.ExcelFile ignore parse_dates=False
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = DataFrame(
+            [
+                [pd.Timestamp("2016-03-12"), "Marc Johnson"],
+                [pd.Timestamp("2016-03-16"), "Jack Black"],
+                [1e20, "Timothy Brown"],
+            ],
+            columns=["DateColWithBigInt", "StringCol"],
+        )
+
+        if engine == "openpyxl":
+            request.applymarker(
+                pytest.mark.xfail(reason="Maybe not supported by openpyxl")
+            )
+
+        if engine is None and read_ext in (".xlsx", ".xlsm"):
+            # GH 35029
+            request.applymarker(
+                pytest.mark.xfail(reason="Defaults to openpyxl, maybe not supported")
+            )
+
+        result = pd.read_excel("testdateoverflow" + read_ext)
+        tm.assert_frame_equal(result, expected)
+
+    def test_sheet_name(self, request, read_ext, engine, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        filename = "test1"
+        sheet_name = "Sheet1"
+
+        expected = df_ref
+        adjust_expected(expected, read_ext, engine)
+
+        df1 = pd.read_excel(
+            filename + read_ext, sheet_name=sheet_name, index_col=0
+        )  # doc
+        df2 = pd.read_excel(filename + read_ext, index_col=0, sheet_name=sheet_name)
+
+        tm.assert_frame_equal(df1, expected)
+        tm.assert_frame_equal(df2, expected)
+
+    def test_excel_read_buffer(self, read_ext):
+        pth = "test1" + read_ext
+        expected = pd.read_excel(pth, sheet_name="Sheet1", index_col=0)
+        with open(pth, "rb") as f:
+            actual = pd.read_excel(f, sheet_name="Sheet1", index_col=0)
+            tm.assert_frame_equal(expected, actual)
+
+    def test_bad_engine_raises(self):
+        bad_engine = "foo"
+        with pytest.raises(ValueError, match="Unknown engine: foo"):
+            pd.read_excel("", engine=bad_engine)
+
+    @pytest.mark.parametrize(
+        "sheet_name",
+        [3, [0, 3], [3, 0], "Sheet4", ["Sheet1", "Sheet4"], ["Sheet4", "Sheet1"]],
+    )
+    def test_bad_sheetname_raises(self, read_ext, sheet_name):
+        # GH 39250
+        msg = "Worksheet index 3 is invalid|Worksheet named 'Sheet4' not found"
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel("blank" + read_ext, sheet_name=sheet_name)
+
+    def test_missing_file_raises(self, read_ext):
+        bad_file = f"foo{read_ext}"
+        # CI tests with other languages, translates to "No such file or directory"
+        match = "|".join(
+            [
+                "(No such file or directory",
+                "没有那个文件或目录",
+                "File o directory non esistente)",
+            ]
+        )
+        with pytest.raises(FileNotFoundError, match=match):
+            pd.read_excel(bad_file)
+
+    def test_corrupt_bytes_raises(self, engine):
+        bad_stream = b"foo"
+        if engine is None:
+            error = ValueError
+            msg = (
+                "Excel file format cannot be determined, you must "
+                "specify an engine manually."
+            )
+        elif engine == "xlrd":
+            from xlrd import XLRDError
+
+            error = XLRDError
+            msg = (
+                "Unsupported format, or corrupt file: Expected BOF "
+                "record; found b'foo'"
+            )
+        elif engine == "calamine":
+            from python_calamine import CalamineError
+
+            error = CalamineError
+            msg = "Cannot detect file format"
+        else:
+            error = BadZipFile
+            msg = "File is not a zip file"
+        with pytest.raises(error, match=msg):
+            pd.read_excel(BytesIO(bad_stream))
+
+    @pytest.mark.network
+    @pytest.mark.single_cpu
+    def test_read_from_http_url(self, httpserver, read_ext):
+        with open("test1" + read_ext, "rb") as f:
+            httpserver.serve_content(content=f.read())
+        url_table = pd.read_excel(httpserver.url)
+        local_table = pd.read_excel("test1" + read_ext)
+        tm.assert_frame_equal(url_table, local_table)
+
+    @td.skip_if_not_us_locale
+    @pytest.mark.single_cpu
+    def test_read_from_s3_url(self, read_ext, s3_public_bucket, s3so):
+        # Bucket created in tests/io/conftest.py
+        with open("test1" + read_ext, "rb") as f:
+            s3_public_bucket.put_object(Key="test1" + read_ext, Body=f)
+
+        url = f"s3://{s3_public_bucket.name}/test1" + read_ext
+
+        url_table = pd.read_excel(url, storage_options=s3so)
+        local_table = pd.read_excel("test1" + read_ext)
+        tm.assert_frame_equal(url_table, local_table)
+
+    @pytest.mark.single_cpu
+    def test_read_from_s3_object(self, read_ext, s3_public_bucket, s3so):
+        # GH 38788
+        # Bucket created in tests/io/conftest.py
+        with open("test1" + read_ext, "rb") as f:
+            s3_public_bucket.put_object(Key="test1" + read_ext, Body=f)
+
+        import s3fs
+
+        s3 = s3fs.S3FileSystem(**s3so)
+
+        with s3.open(f"s3://{s3_public_bucket.name}/test1" + read_ext) as f:
+            url_table = pd.read_excel(f)
+
+        local_table = pd.read_excel("test1" + read_ext)
+        tm.assert_frame_equal(url_table, local_table)
+
+    @pytest.mark.slow
+    def test_read_from_file_url(self, read_ext, datapath):
+        # FILE
+        localtable = os.path.join(datapath("io", "data", "excel"), "test1" + read_ext)
+        local_table = pd.read_excel(localtable)
+
+        try:
+            url_table = pd.read_excel("file://localhost/" + localtable)
+        except URLError:
+            # fails on some systems
+            platform_info = " ".join(platform.uname()).strip()
+            pytest.skip(f"failing on {platform_info}")
+
+        tm.assert_frame_equal(url_table, local_table)
+
+    def test_read_from_pathlib_path(self, read_ext):
+        # GH12655
+        str_path = "test1" + read_ext
+        expected = pd.read_excel(str_path, sheet_name="Sheet1", index_col=0)
+
+        path_obj = Path("test1" + read_ext)
+        actual = pd.read_excel(path_obj, sheet_name="Sheet1", index_col=0)
+
+        tm.assert_frame_equal(expected, actual)
+
+    @td.skip_if_no("py.path")
+    def test_read_from_py_localpath(self, read_ext):
+        # GH12655
+        from py.path import local as LocalPath
+
+        str_path = os.path.join("test1" + read_ext)
+        expected = pd.read_excel(str_path, sheet_name="Sheet1", index_col=0)
+
+        path_obj = LocalPath().join("test1" + read_ext)
+        actual = pd.read_excel(path_obj, sheet_name="Sheet1", index_col=0)
+
+        tm.assert_frame_equal(expected, actual)
+
+    def test_close_from_py_localpath(self, read_ext):
+        # GH31467
+        str_path = os.path.join("test1" + read_ext)
+        with open(str_path, "rb") as f:
+            x = pd.read_excel(f, sheet_name="Sheet1", index_col=0)
+            del x
+            # should not throw an exception because the passed file was closed
+            f.read()
+
+    def test_reader_seconds(self, request, engine, read_ext):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        # GH 55045
+        if engine == "calamine" and read_ext == ".ods":
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="ODS file contains bad datetime (seconds as text)"
+                )
+            )
+
+        # Test reading times with and without milliseconds. GH5945.
+        expected = DataFrame.from_dict(
+            {
+                "Time": [
+                    time(1, 2, 3),
+                    time(2, 45, 56, 100000),
+                    time(4, 29, 49, 200000),
+                    time(6, 13, 42, 300000),
+                    time(7, 57, 35, 400000),
+                    time(9, 41, 28, 500000),
+                    time(11, 25, 21, 600000),
+                    time(13, 9, 14, 700000),
+                    time(14, 53, 7, 800000),
+                    time(16, 37, 0, 900000),
+                    time(18, 20, 54),
+                ]
+            }
+        )
+
+        actual = pd.read_excel("times_1900" + read_ext, sheet_name="Sheet1")
+        tm.assert_frame_equal(actual, expected)
+
+        actual = pd.read_excel("times_1904" + read_ext, sheet_name="Sheet1")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_read_excel_multiindex(self, request, engine, read_ext):
+        # see gh-4679
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        unit = get_exp_unit(read_ext, engine)
+
+        mi = MultiIndex.from_product([["foo", "bar"], ["a", "b"]])
+        mi_file = "testmultiindex" + read_ext
+
+        # "mi_column" sheet
+        expected = DataFrame(
+            [
+                [1, 2.5, pd.Timestamp("2015-01-01"), True],
+                [2, 3.5, pd.Timestamp("2015-01-02"), False],
+                [3, 4.5, pd.Timestamp("2015-01-03"), False],
+                [4, 5.5, pd.Timestamp("2015-01-04"), True],
+            ],
+            columns=mi,
+        )
+        expected[mi[2]] = expected[mi[2]].astype(f"M8[{unit}]")
+
+        actual = pd.read_excel(
+            mi_file, sheet_name="mi_column", header=[0, 1], index_col=0
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        # "mi_index" sheet
+        expected.index = mi
+        expected.columns = ["a", "b", "c", "d"]
+
+        actual = pd.read_excel(mi_file, sheet_name="mi_index", index_col=[0, 1])
+        tm.assert_frame_equal(actual, expected)
+
+        # "both" sheet
+        expected.columns = mi
+
+        actual = pd.read_excel(
+            mi_file, sheet_name="both", index_col=[0, 1], header=[0, 1]
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        # "mi_index_name" sheet
+        expected.columns = ["a", "b", "c", "d"]
+        expected.index = mi.set_names(["ilvl1", "ilvl2"])
+
+        actual = pd.read_excel(mi_file, sheet_name="mi_index_name", index_col=[0, 1])
+        tm.assert_frame_equal(actual, expected)
+
+        # "mi_column_name" sheet
+        expected.index = list(range(4))
+        expected.columns = mi.set_names(["c1", "c2"])
+        actual = pd.read_excel(
+            mi_file, sheet_name="mi_column_name", header=[0, 1], index_col=0
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        # see gh-11317
+        # "name_with_int" sheet
+        expected.columns = mi.set_levels([1, 2], level=1).set_names(["c1", "c2"])
+
+        actual = pd.read_excel(
+            mi_file, sheet_name="name_with_int", index_col=0, header=[0, 1]
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        # "both_name" sheet
+        expected.columns = mi.set_names(["c1", "c2"])
+        expected.index = mi.set_names(["ilvl1", "ilvl2"])
+
+        actual = pd.read_excel(
+            mi_file, sheet_name="both_name", index_col=[0, 1], header=[0, 1]
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        # "both_skiprows" sheet
+        actual = pd.read_excel(
+            mi_file,
+            sheet_name="both_name_skiprows",
+            index_col=[0, 1],
+            header=[0, 1],
+            skiprows=2,
+        )
+        tm.assert_frame_equal(actual, expected)
+
+    @pytest.mark.parametrize(
+        "sheet_name,idx_lvl2",
+        [
+            ("both_name_blank_after_mi_name", [np.nan, "b", "a", "b"]),
+            ("both_name_multiple_blanks", [np.nan] * 4),
+        ],
+    )
+    def test_read_excel_multiindex_blank_after_name(
+        self, request, engine, read_ext, sheet_name, idx_lvl2
+    ):
+        # GH34673
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        mi_file = "testmultiindex" + read_ext
+        mi = MultiIndex.from_product([["foo", "bar"], ["a", "b"]], names=["c1", "c2"])
+
+        unit = get_exp_unit(read_ext, engine)
+
+        expected = DataFrame(
+            [
+                [1, 2.5, pd.Timestamp("2015-01-01"), True],
+                [2, 3.5, pd.Timestamp("2015-01-02"), False],
+                [3, 4.5, pd.Timestamp("2015-01-03"), False],
+                [4, 5.5, pd.Timestamp("2015-01-04"), True],
+            ],
+            columns=mi,
+            index=MultiIndex.from_arrays(
+                (["foo", "foo", "bar", "bar"], idx_lvl2),
+                names=["ilvl1", "ilvl2"],
+            ),
+        )
+        expected[mi[2]] = expected[mi[2]].astype(f"M8[{unit}]")
+        result = pd.read_excel(
+            mi_file,
+            sheet_name=sheet_name,
+            index_col=[0, 1],
+            header=[0, 1],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_read_excel_multiindex_header_only(self, read_ext):
+        # see gh-11733.
+        #
+        # Don't try to parse a header name if there isn't one.
+        mi_file = "testmultiindex" + read_ext
+        result = pd.read_excel(mi_file, sheet_name="index_col_none", header=[0, 1])
+
+        exp_columns = MultiIndex.from_product([("A", "B"), ("key", "val")])
+        expected = DataFrame([[1, 2, 3, 4]] * 2, columns=exp_columns)
+        tm.assert_frame_equal(result, expected)
+
+    def test_excel_old_index_format(self, read_ext):
+        # see gh-4679
+        filename = "test_index_name_pre17" + read_ext
+
+        # We detect headers to determine if index names exist, so
+        # that "index" name in the "names" version of the data will
+        # now be interpreted as rows that include null data.
+        data = np.array(
+            [
+                [np.nan, np.nan, np.nan, np.nan, np.nan],
+                ["R0C0", "R0C1", "R0C2", "R0C3", "R0C4"],
+                ["R1C0", "R1C1", "R1C2", "R1C3", "R1C4"],
+                ["R2C0", "R2C1", "R2C2", "R2C3", "R2C4"],
+                ["R3C0", "R3C1", "R3C2", "R3C3", "R3C4"],
+                ["R4C0", "R4C1", "R4C2", "R4C3", "R4C4"],
+            ],
+            dtype=object,
+        )
+        columns = ["C_l0_g0", "C_l0_g1", "C_l0_g2", "C_l0_g3", "C_l0_g4"]
+        mi = MultiIndex(
+            levels=[
+                ["R0", "R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"],
+                ["R1", "R_l1_g0", "R_l1_g1", "R_l1_g2", "R_l1_g3", "R_l1_g4"],
+            ],
+            codes=[[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]],
+            names=[None, None],
+        )
+        si = Index(
+            ["R0", "R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"], name=None
+        )
+
+        expected = DataFrame(data, index=si, columns=columns)
+
+        actual = pd.read_excel(filename, sheet_name="single_names", index_col=0)
+        tm.assert_frame_equal(actual, expected)
+
+        expected.index = mi
+
+        actual = pd.read_excel(filename, sheet_name="multi_names", index_col=[0, 1])
+        tm.assert_frame_equal(actual, expected)
+
+        # The analogous versions of the "names" version data
+        # where there are explicitly no names for the indices.
+        data = np.array(
+            [
+                ["R0C0", "R0C1", "R0C2", "R0C3", "R0C4"],
+                ["R1C0", "R1C1", "R1C2", "R1C3", "R1C4"],
+                ["R2C0", "R2C1", "R2C2", "R2C3", "R2C4"],
+                ["R3C0", "R3C1", "R3C2", "R3C3", "R3C4"],
+                ["R4C0", "R4C1", "R4C2", "R4C3", "R4C4"],
+            ]
+        )
+        columns = ["C_l0_g0", "C_l0_g1", "C_l0_g2", "C_l0_g3", "C_l0_g4"]
+        mi = MultiIndex(
+            levels=[
+                ["R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"],
+                ["R_l1_g0", "R_l1_g1", "R_l1_g2", "R_l1_g3", "R_l1_g4"],
+            ],
+            codes=[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4]],
+            names=[None, None],
+        )
+        si = Index(["R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"], name=None)
+
+        expected = DataFrame(data, index=si, columns=columns)
+
+        actual = pd.read_excel(filename, sheet_name="single_no_names", index_col=0)
+        tm.assert_frame_equal(actual, expected)
+
+        expected.index = mi
+
+        actual = pd.read_excel(filename, sheet_name="multi_no_names", index_col=[0, 1])
+        tm.assert_frame_equal(actual, expected)
+
+    def test_read_excel_bool_header_arg(self, read_ext):
+        # GH 6114
+        msg = "Passing a bool to header is invalid"
+        for arg in [True, False]:
+            with pytest.raises(TypeError, match=msg):
+                pd.read_excel("test1" + read_ext, header=arg)
+
+    def test_read_excel_skiprows(self, request, engine, read_ext):
+        # GH 4903
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        unit = get_exp_unit(read_ext, engine)
+
+        actual = pd.read_excel(
+            "testskiprows" + read_ext, sheet_name="skiprows_list", skiprows=[0, 2]
+        )
+        expected = DataFrame(
+            [
+                [1, 2.5, pd.Timestamp("2015-01-01"), True],
+                [2, 3.5, pd.Timestamp("2015-01-02"), False],
+                [3, 4.5, pd.Timestamp("2015-01-03"), False],
+                [4, 5.5, pd.Timestamp("2015-01-04"), True],
+            ],
+            columns=["a", "b", "c", "d"],
+        )
+        expected["c"] = expected["c"].astype(f"M8[{unit}]")
+        tm.assert_frame_equal(actual, expected)
+
+        actual = pd.read_excel(
+            "testskiprows" + read_ext,
+            sheet_name="skiprows_list",
+            skiprows=np.array([0, 2]),
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        # GH36435
+        actual = pd.read_excel(
+            "testskiprows" + read_ext,
+            sheet_name="skiprows_list",
+            skiprows=lambda x: x in [0, 2],
+        )
+        tm.assert_frame_equal(actual, expected)
+
+        actual = pd.read_excel(
+            "testskiprows" + read_ext,
+            sheet_name="skiprows_list",
+            skiprows=3,
+            names=["a", "b", "c", "d"],
+        )
+        expected = DataFrame(
+            [
+                # [1, 2.5, pd.Timestamp("2015-01-01"), True],
+                [2, 3.5, pd.Timestamp("2015-01-02"), False],
+                [3, 4.5, pd.Timestamp("2015-01-03"), False],
+                [4, 5.5, pd.Timestamp("2015-01-04"), True],
+            ],
+            columns=["a", "b", "c", "d"],
+        )
+        expected["c"] = expected["c"].astype(f"M8[{unit}]")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_read_excel_skiprows_callable_not_in(self, request, engine, read_ext):
+        # GH 4903
+        xfail_datetimes_with_pyxlsb(engine, request)
+        unit = get_exp_unit(read_ext, engine)
+
+        actual = pd.read_excel(
+            "testskiprows" + read_ext,
+            sheet_name="skiprows_list",
+            skiprows=lambda x: x not in [1, 3, 5],
+        )
+        expected = DataFrame(
+            [
+                [1, 2.5, pd.Timestamp("2015-01-01"), True],
+                # [2, 3.5, pd.Timestamp("2015-01-02"), False],
+                [3, 4.5, pd.Timestamp("2015-01-03"), False],
+                # [4, 5.5, pd.Timestamp("2015-01-04"), True],
+            ],
+            columns=["a", "b", "c", "d"],
+        )
+        expected["c"] = expected["c"].astype(f"M8[{unit}]")
+        tm.assert_frame_equal(actual, expected)
+
+    def test_read_excel_nrows(self, read_ext):
+        # GH 16645
+        num_rows_to_pull = 5
+        actual = pd.read_excel("test1" + read_ext, nrows=num_rows_to_pull)
+        expected = pd.read_excel("test1" + read_ext)
+        expected = expected[:num_rows_to_pull]
+        tm.assert_frame_equal(actual, expected)
+
+    def test_read_excel_nrows_greater_than_nrows_in_file(self, read_ext):
+        # GH 16645
+        expected = pd.read_excel("test1" + read_ext)
+        num_records_in_file = len(expected)
+        num_rows_to_pull = num_records_in_file + 10
+        actual = pd.read_excel("test1" + read_ext, nrows=num_rows_to_pull)
+        tm.assert_frame_equal(actual, expected)
+
+    def test_read_excel_nrows_non_integer_parameter(self, read_ext):
+        # GH 16645
+        msg = "'nrows' must be an integer >=0"
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel("test1" + read_ext, nrows="5")
+
+    @pytest.mark.parametrize(
+        "filename,sheet_name,header,index_col,skiprows",
+        [
+            ("testmultiindex", "mi_column", [0, 1], 0, None),
+            ("testmultiindex", "mi_index", None, [0, 1], None),
+            ("testmultiindex", "both", [0, 1], [0, 1], None),
+            ("testmultiindex", "mi_column_name", [0, 1], 0, None),
+            ("testskiprows", "skiprows_list", None, None, [0, 2]),
+            ("testskiprows", "skiprows_list", None, None, lambda x: x in (0, 2)),
+        ],
+    )
+    def test_read_excel_nrows_params(
+        self, read_ext, filename, sheet_name, header, index_col, skiprows
+    ):
+        """
+        For various parameters, we should get the same result whether we
+        limit the rows during load (nrows=3) or after (df.iloc[:3]).
+        """
+        # GH 46894
+        expected = pd.read_excel(
+            filename + read_ext,
+            sheet_name=sheet_name,
+            header=header,
+            index_col=index_col,
+            skiprows=skiprows,
+        ).iloc[:3]
+        actual = pd.read_excel(
+            filename + read_ext,
+            sheet_name=sheet_name,
+            header=header,
+            index_col=index_col,
+            skiprows=skiprows,
+            nrows=3,
+        )
+        tm.assert_frame_equal(actual, expected)
+
+    def test_deprecated_kwargs(self, read_ext):
+        with pytest.raises(TypeError, match="but 3 positional arguments"):
+            pd.read_excel("test1" + read_ext, "Sheet1", 0)
+
+    def test_no_header_with_list_index_col(self, read_ext):
+        # GH 31783
+        file_name = "testmultiindex" + read_ext
+        data = [("B", "B"), ("key", "val"), (3, 4), (3, 4)]
+        idx = MultiIndex.from_tuples(
+            [("A", "A"), ("key", "val"), (1, 2), (1, 2)], names=(0, 1)
+        )
+        expected = DataFrame(data, index=idx, columns=(2, 3))
+        result = pd.read_excel(
+            file_name, sheet_name="index_col_none", index_col=[0, 1], header=None
+        )
+        tm.assert_frame_equal(expected, result)
+
+    def test_one_col_noskip_blank_line(self, read_ext):
+        # GH 39808
+        file_name = "one_col_blank_line" + read_ext
+        data = [0.5, np.nan, 1, 2]
+        expected = DataFrame(data, columns=["numbers"])
+        result = pd.read_excel(file_name)
+        tm.assert_frame_equal(result, expected)
+
+    def test_multiheader_two_blank_lines(self, read_ext):
+        # GH 40442
+        file_name = "testmultiindex" + read_ext
+        columns = MultiIndex.from_tuples([("a", "A"), ("b", "B")])
+        data = [[np.nan, np.nan], [np.nan, np.nan], [1, 3], [2, 4]]
+        expected = DataFrame(data, columns=columns)
+        result = pd.read_excel(
+            file_name, sheet_name="mi_column_empty_rows", header=[0, 1]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_trailing_blanks(self, read_ext):
+        """
+        Sheets can contain blank cells with no data. Some of our readers
+        were including those cells, creating many empty rows and columns
+        """
+        file_name = "trailing_blanks" + read_ext
+        result = pd.read_excel(file_name)
+        assert result.shape == (3, 3)
+
+    def test_ignore_chartsheets_by_str(self, request, engine, read_ext):
+        # GH 41448
+        if read_ext == ".ods":
+            pytest.skip("chartsheets do not exist in the ODF format")
+        if engine == "pyxlsb":
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="pyxlsb can't distinguish chartsheets from worksheets"
+                )
+            )
+        with pytest.raises(ValueError, match="Worksheet named 'Chart1' not found"):
+            pd.read_excel("chartsheet" + read_ext, sheet_name="Chart1")
+
+    def test_ignore_chartsheets_by_int(self, request, engine, read_ext):
+        # GH 41448
+        if read_ext == ".ods":
+            pytest.skip("chartsheets do not exist in the ODF format")
+        if engine == "pyxlsb":
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="pyxlsb can't distinguish chartsheets from worksheets"
+                )
+            )
+        with pytest.raises(
+            ValueError, match="Worksheet index 1 is invalid, 1 worksheets found"
+        ):
+            pd.read_excel("chartsheet" + read_ext, sheet_name=1)
+
+    def test_euro_decimal_format(self, read_ext):
+        # copied from read_csv
+        result = pd.read_excel("test_decimal" + read_ext, decimal=",", skiprows=1)
+        expected = DataFrame(
+            [
+                [1, 1521.1541, 187101.9543, "ABC", "poi", 4.738797819],
+                [2, 121.12, 14897.76, "DEF", "uyt", 0.377320872],
+                [3, 878.158, 108013.434, "GHI", "rez", 2.735694704],
+            ],
+            columns=["Id", "Number1", "Number2", "Text1", "Text2", "Number3"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+class TestExcelFileRead:
+    def test_deprecate_bytes_input(self, engine, read_ext):
+        # GH 53830
+        msg = (
+            "Passing bytes to 'read_excel' is deprecated and "
+            "will be removed in a future version. To read from a "
+            "byte string, wrap it in a `BytesIO` object."
+        )
+
+        with tm.assert_produces_warning(
+            FutureWarning, match=msg, raise_on_extra_warnings=False
+        ):
+            with open("test1" + read_ext, "rb") as f:
+                pd.read_excel(f.read(), engine=engine)
+
+    @pytest.fixture(autouse=True)
+    def cd_and_set_engine(self, engine, datapath, monkeypatch):
+        """
+        Change directory and set engine for ExcelFile objects.
+        """
+        func = partial(pd.ExcelFile, engine=engine)
+        monkeypatch.chdir(datapath("io", "data", "excel"))
+        monkeypatch.setattr(pd, "ExcelFile", func)
+
+    def test_engine_used(self, read_ext, engine):
+        expected_defaults = {
+            "xlsx": "openpyxl",
+            "xlsm": "openpyxl",
+            "xlsb": "pyxlsb",
+            "xls": "xlrd",
+            "ods": "odf",
+        }
+
+        with pd.ExcelFile("test1" + read_ext) as excel:
+            result = excel.engine
+
+        if engine is not None:
+            expected = engine
+        else:
+            expected = expected_defaults[read_ext[1:]]
+        assert result == expected
+
+    def test_excel_passes_na(self, read_ext):
+        with pd.ExcelFile("test4" + read_ext) as excel:
+            parsed = pd.read_excel(
+                excel, sheet_name="Sheet1", keep_default_na=False, na_values=["apple"]
+            )
+        expected = DataFrame(
+            [["NA"], [1], ["NA"], [np.nan], ["rabbit"]], columns=["Test"]
+        )
+        tm.assert_frame_equal(parsed, expected)
+
+        with pd.ExcelFile("test4" + read_ext) as excel:
+            parsed = pd.read_excel(
+                excel, sheet_name="Sheet1", keep_default_na=True, na_values=["apple"]
+            )
+        expected = DataFrame(
+            [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]], columns=["Test"]
+        )
+        tm.assert_frame_equal(parsed, expected)
+
+        # 13967
+        with pd.ExcelFile("test5" + read_ext) as excel:
+            parsed = pd.read_excel(
+                excel, sheet_name="Sheet1", keep_default_na=False, na_values=["apple"]
+            )
+        expected = DataFrame(
+            [["1.#QNAN"], [1], ["nan"], [np.nan], ["rabbit"]], columns=["Test"]
+        )
+        tm.assert_frame_equal(parsed, expected)
+
+        with pd.ExcelFile("test5" + read_ext) as excel:
+            parsed = pd.read_excel(
+                excel, sheet_name="Sheet1", keep_default_na=True, na_values=["apple"]
+            )
+        expected = DataFrame(
+            [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]], columns=["Test"]
+        )
+        tm.assert_frame_equal(parsed, expected)
+
+    @pytest.mark.parametrize("na_filter", [None, True, False])
+    def test_excel_passes_na_filter(self, read_ext, na_filter):
+        # gh-25453
+        kwargs = {}
+
+        if na_filter is not None:
+            kwargs["na_filter"] = na_filter
+
+        with pd.ExcelFile("test5" + read_ext) as excel:
+            parsed = pd.read_excel(
+                excel,
+                sheet_name="Sheet1",
+                keep_default_na=True,
+                na_values=["apple"],
+                **kwargs,
+            )
+
+        if na_filter is False:
+            expected = [["1.#QNAN"], [1], ["nan"], ["apple"], ["rabbit"]]
+        else:
+            expected = [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]]
+
+        expected = DataFrame(expected, columns=["Test"])
+        tm.assert_frame_equal(parsed, expected)
+
+    def test_excel_table_sheet_by_index(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref
+        adjust_expected(expected, read_ext, engine)
+
+        with pd.ExcelFile("test1" + read_ext) as excel:
+            df1 = pd.read_excel(excel, sheet_name=0, index_col=0)
+            df2 = pd.read_excel(excel, sheet_name=1, skiprows=[1], index_col=0)
+        tm.assert_frame_equal(df1, expected)
+        tm.assert_frame_equal(df2, expected)
+
+        with pd.ExcelFile("test1" + read_ext) as excel:
+            df1 = excel.parse(0, index_col=0)
+            df2 = excel.parse(1, skiprows=[1], index_col=0)
+        tm.assert_frame_equal(df1, expected)
+        tm.assert_frame_equal(df2, expected)
+
+        with pd.ExcelFile("test1" + read_ext) as excel:
+            df3 = pd.read_excel(excel, sheet_name=0, index_col=0, skipfooter=1)
+        tm.assert_frame_equal(df3, df1.iloc[:-1])
+
+        with pd.ExcelFile("test1" + read_ext) as excel:
+            df3 = excel.parse(0, index_col=0, skipfooter=1)
+
+        tm.assert_frame_equal(df3, df1.iloc[:-1])
+
+    def test_sheet_name(self, request, engine, read_ext, df_ref):
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        expected = df_ref
+        adjust_expected(expected, read_ext, engine)
+
+        filename = "test1"
+        sheet_name = "Sheet1"
+
+        with pd.ExcelFile(filename + read_ext) as excel:
+            df1_parse = excel.parse(sheet_name=sheet_name, index_col=0)  # doc
+
+        with pd.ExcelFile(filename + read_ext) as excel:
+            df2_parse = excel.parse(index_col=0, sheet_name=sheet_name)
+
+        tm.assert_frame_equal(df1_parse, expected)
+        tm.assert_frame_equal(df2_parse, expected)
+
+    @pytest.mark.parametrize(
+        "sheet_name",
+        [3, [0, 3], [3, 0], "Sheet4", ["Sheet1", "Sheet4"], ["Sheet4", "Sheet1"]],
+    )
+    def test_bad_sheetname_raises(self, read_ext, sheet_name):
+        # GH 39250
+        msg = "Worksheet index 3 is invalid|Worksheet named 'Sheet4' not found"
+        with pytest.raises(ValueError, match=msg):
+            with pd.ExcelFile("blank" + read_ext) as excel:
+                excel.parse(sheet_name=sheet_name)
+
+    def test_excel_read_buffer(self, engine, read_ext):
+        pth = "test1" + read_ext
+        expected = pd.read_excel(pth, sheet_name="Sheet1", index_col=0, engine=engine)
+
+        with open(pth, "rb") as f:
+            with pd.ExcelFile(f) as xls:
+                actual = pd.read_excel(xls, sheet_name="Sheet1", index_col=0)
+
+        tm.assert_frame_equal(expected, actual)
+
+    def test_reader_closes_file(self, engine, read_ext):
+        with open("test1" + read_ext, "rb") as f:
+            with pd.ExcelFile(f) as xlsx:
+                # parses okay
+                pd.read_excel(xlsx, sheet_name="Sheet1", index_col=0, engine=engine)
+
+        assert f.closed
+
+    def test_conflicting_excel_engines(self, read_ext):
+        # GH 26566
+        msg = "Engine should not be specified when passing an ExcelFile"
+
+        with pd.ExcelFile("test1" + read_ext) as xl:
+            with pytest.raises(ValueError, match=msg):
+                pd.read_excel(xl, engine="foo")
+
+    def test_excel_read_binary(self, engine, read_ext):
+        # GH 15914
+        expected = pd.read_excel("test1" + read_ext, engine=engine)
+
+        with open("test1" + read_ext, "rb") as f:
+            data = f.read()
+
+        actual = pd.read_excel(BytesIO(data), engine=engine)
+        tm.assert_frame_equal(expected, actual)
+
+    def test_excel_read_binary_via_read_excel(self, read_ext, engine):
+        # GH 38424
+        with open("test1" + read_ext, "rb") as f:
+            result = pd.read_excel(f, engine=engine)
+        expected = pd.read_excel("test1" + read_ext, engine=engine)
+        tm.assert_frame_equal(result, expected)
+
+    def test_read_excel_header_index_out_of_range(self, engine):
+        # GH#43143
+        with open("df_header_oob.xlsx", "rb") as f:
+            with pytest.raises(ValueError, match="exceeds maximum"):
+                pd.read_excel(f, header=[0, 1])
+
+    @pytest.mark.parametrize("filename", ["df_empty.xlsx", "df_equals.xlsx"])
+    def test_header_with_index_col(self, filename):
+        # GH 33476
+        idx = Index(["Z"], name="I2")
+        cols = MultiIndex.from_tuples([("A", "B"), ("A", "B.1")], names=["I11", "I12"])
+        expected = DataFrame([[1, 3]], index=idx, columns=cols, dtype="int64")
+        result = pd.read_excel(
+            filename, sheet_name="Sheet1", index_col=0, header=[0, 1]
+        )
+        tm.assert_frame_equal(expected, result)
+
+    def test_read_datetime_multiindex(self, request, engine, read_ext):
+        # GH 34748
+        xfail_datetimes_with_pyxlsb(engine, request)
+
+        f = "test_datetime_mi" + read_ext
+        with pd.ExcelFile(f) as excel:
+            actual = pd.read_excel(excel, header=[0, 1], index_col=0, engine=engine)
+
+        unit = get_exp_unit(read_ext, engine)
+        dti = pd.DatetimeIndex(["2020-02-29", "2020-03-01"], dtype=f"M8[{unit}]")
+        expected_column_index = MultiIndex.from_arrays(
+            [dti[:1], dti[1:]],
+            names=[
+                dti[0].to_pydatetime(),
+                dti[1].to_pydatetime(),
+            ],
+        )
+        expected = DataFrame([], index=[], columns=expected_column_index)
+
+        tm.assert_frame_equal(expected, actual)
+
+    def test_engine_invalid_option(self, read_ext):
+        # read_ext includes the '.' hence the weird formatting
+        with pytest.raises(ValueError, match="Value must be one of *"):
+            with pd.option_context(f"io.excel{read_ext}.reader", "abc"):
+                pass
+
+    def test_ignore_chartsheets(self, request, engine, read_ext):
+        # GH 41448
+        if read_ext == ".ods":
+            pytest.skip("chartsheets do not exist in the ODF format")
+        if engine == "pyxlsb":
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="pyxlsb can't distinguish chartsheets from worksheets"
+                )
+            )
+        with pd.ExcelFile("chartsheet" + read_ext) as excel:
+            assert excel.sheet_names == ["Sheet1"]
+
+    def test_corrupt_files_closed(self, engine, read_ext):
+        # GH41778
+        errors = (BadZipFile,)
+        if engine is None:
+            pytest.skip(f"Invalid test for engine={engine}")
+        elif engine == "xlrd":
+            import xlrd
+
+            errors = (BadZipFile, xlrd.biffh.XLRDError)
+        elif engine == "calamine":
+            from python_calamine import CalamineError
+
+            errors = (CalamineError,)
+
+        with tm.ensure_clean(f"corrupt{read_ext}") as file:
+            Path(file).write_text("corrupt", encoding="utf-8")
+            with tm.assert_produces_warning(False):
+                try:
+                    pd.ExcelFile(file, engine=engine)
+                except errors:
+                    pass

venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_style.py

@@ -0,0 +1,298 @@
+import contextlib
+import time
+
+import numpy as np
+import pytest
+
+from pandas.compat import is_platform_windows
+import pandas.util._test_decorators as td
+
+from pandas import (
+    DataFrame,
+    read_excel,
+)
+import pandas._testing as tm
+
+from pandas.io.excel import ExcelWriter
+from pandas.io.formats.excel import ExcelFormatter
+
+pytest.importorskip("jinja2")
+# jinja2 is currently required for Styler.__init__(). Technically Styler.to_excel
+# could compute styles and render to excel without jinja2, since there is no
+# 'template' file, but this needs the import error to delayed until render time.
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+def assert_equal_cell_styles(cell1, cell2):
+    # TODO: should find a better way to check equality
+    assert cell1.alignment.__dict__ == cell2.alignment.__dict__
+    assert cell1.border.__dict__ == cell2.border.__dict__
+    assert cell1.fill.__dict__ == cell2.fill.__dict__
+    assert cell1.font.__dict__ == cell2.font.__dict__
+    assert cell1.number_format == cell2.number_format
+    assert cell1.protection.__dict__ == cell2.protection.__dict__
+
+
+@pytest.mark.parametrize(
+    "engine",
+    ["xlsxwriter", "openpyxl"],
+)
+def test_styler_to_excel_unstyled(engine):
+    # compare DataFrame.to_excel and Styler.to_excel when no styles applied
+    pytest.importorskip(engine)
+    df = DataFrame(np.random.default_rng(2).standard_normal((2, 2)))
+    with tm.ensure_clean(".xlsx") as path:
+        with ExcelWriter(path, engine=engine) as writer:
+            df.to_excel(writer, sheet_name="dataframe")
+            df.style.to_excel(writer, sheet_name="unstyled")
+
+        openpyxl = pytest.importorskip("openpyxl")  # test loading only with openpyxl
+        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
+            for col1, col2 in zip(wb["dataframe"].columns, wb["unstyled"].columns):
+                assert len(col1) == len(col2)
+                for cell1, cell2 in zip(col1, col2):
+                    assert cell1.value == cell2.value
+                    assert_equal_cell_styles(cell1, cell2)
+
+
+shared_style_params = [
+    (
+        "background-color: #111222",
+        ["fill", "fgColor", "rgb"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    (
+        "color: #111222",
+        ["font", "color", "value"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    ("font-family: Arial;", ["font", "name"], "arial"),
+    ("font-weight: bold;", ["font", "b"], True),
+    ("font-style: italic;", ["font", "i"], True),
+    ("text-decoration: underline;", ["font", "u"], "single"),
+    ("number-format: $??,???.00;", ["number_format"], "$??,???.00"),
+    ("text-align: left;", ["alignment", "horizontal"], "left"),
+    (
+        "vertical-align: bottom;",
+        ["alignment", "vertical"],
+        {"xlsxwriter": None, "openpyxl": "bottom"},  # xlsxwriter Fails
+    ),
+    ("vertical-align: middle;", ["alignment", "vertical"], "center"),
+    # Border widths
+    ("border-left: 2pt solid red", ["border", "left", "style"], "medium"),
+    ("border-left: 1pt dotted red", ["border", "left", "style"], "dotted"),
+    ("border-left: 2pt dotted red", ["border", "left", "style"], "mediumDashDotDot"),
+    ("border-left: 1pt dashed red", ["border", "left", "style"], "dashed"),
+    ("border-left: 2pt dashed red", ["border", "left", "style"], "mediumDashed"),
+    ("border-left: 1pt solid red", ["border", "left", "style"], "thin"),
+    ("border-left: 3pt solid red", ["border", "left", "style"], "thick"),
+    # Border expansion
+    (
+        "border-left: 2pt solid #111222",
+        ["border", "left", "color", "rgb"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    ("border: 1pt solid red", ["border", "top", "style"], "thin"),
+    (
+        "border: 1pt solid #111222",
+        ["border", "top", "color", "rgb"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    ("border: 1pt solid red", ["border", "right", "style"], "thin"),
+    (
+        "border: 1pt solid #111222",
+        ["border", "right", "color", "rgb"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    ("border: 1pt solid red", ["border", "bottom", "style"], "thin"),
+    (
+        "border: 1pt solid #111222",
+        ["border", "bottom", "color", "rgb"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    ("border: 1pt solid red", ["border", "left", "style"], "thin"),
+    (
+        "border: 1pt solid #111222",
+        ["border", "left", "color", "rgb"],
+        {"xlsxwriter": "FF111222", "openpyxl": "00111222"},
+    ),
+    # Border styles
+    (
+        "border-left-style: hair; border-left-color: black",
+        ["border", "left", "style"],
+        "hair",
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    "engine",
+    ["xlsxwriter", "openpyxl"],
+)
+@pytest.mark.parametrize("css, attrs, expected", shared_style_params)
+def test_styler_to_excel_basic(engine, css, attrs, expected):
+    pytest.importorskip(engine)
+    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
+    styler = df.style.map(lambda x: css)
+
+    with tm.ensure_clean(".xlsx") as path:
+        with ExcelWriter(path, engine=engine) as writer:
+            df.to_excel(writer, sheet_name="dataframe")
+            styler.to_excel(writer, sheet_name="styled")
+
+        openpyxl = pytest.importorskip("openpyxl")  # test loading only with openpyxl
+        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
+            # test unstyled data cell does not have expected styles
+            # test styled cell has expected styles
+            u_cell, s_cell = wb["dataframe"].cell(2, 2), wb["styled"].cell(2, 2)
+        for attr in attrs:
+            u_cell, s_cell = getattr(u_cell, attr, None), getattr(s_cell, attr)
+
+        if isinstance(expected, dict):
+            assert u_cell is None or u_cell != expected[engine]
+            assert s_cell == expected[engine]
+        else:
+            assert u_cell is None or u_cell != expected
+            assert s_cell == expected
+
+
+@pytest.mark.parametrize(
+    "engine",
+    ["xlsxwriter", "openpyxl"],
+)
+@pytest.mark.parametrize("css, attrs, expected", shared_style_params)
+def test_styler_to_excel_basic_indexes(engine, css, attrs, expected):
+    pytest.importorskip(engine)
+    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
+
+    styler = df.style
+    styler.map_index(lambda x: css, axis=0)
+    styler.map_index(lambda x: css, axis=1)
+
+    null_styler = df.style
+    null_styler.map(lambda x: "null: css;")
+    null_styler.map_index(lambda x: "null: css;", axis=0)
+    null_styler.map_index(lambda x: "null: css;", axis=1)
+
+    with tm.ensure_clean(".xlsx") as path:
+        with ExcelWriter(path, engine=engine) as writer:
+            null_styler.to_excel(writer, sheet_name="null_styled")
+            styler.to_excel(writer, sheet_name="styled")
+
+        openpyxl = pytest.importorskip("openpyxl")  # test loading only with openpyxl
+        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
+            # test null styled index cells does not have expected styles
+            # test styled cell has expected styles
+            ui_cell, si_cell = wb["null_styled"].cell(2, 1), wb["styled"].cell(2, 1)
+            uc_cell, sc_cell = wb["null_styled"].cell(1, 2), wb["styled"].cell(1, 2)
+        for attr in attrs:
+            ui_cell, si_cell = getattr(ui_cell, attr, None), getattr(si_cell, attr)
+            uc_cell, sc_cell = getattr(uc_cell, attr, None), getattr(sc_cell, attr)
+
+        if isinstance(expected, dict):
+            assert ui_cell is None or ui_cell != expected[engine]
+            assert si_cell == expected[engine]
+            assert uc_cell is None or uc_cell != expected[engine]
+            assert sc_cell == expected[engine]
+        else:
+            assert ui_cell is None or ui_cell != expected
+            assert si_cell == expected
+            assert uc_cell is None or uc_cell != expected
+            assert sc_cell == expected
+
+
+# From https://openpyxl.readthedocs.io/en/stable/api/openpyxl.styles.borders.html
+# Note: Leaving behavior of "width"-type styles undefined; user should use border-width
+# instead
+excel_border_styles = [
+    # "thin",
+    "dashed",
+    "mediumDashDot",
+    "dashDotDot",
+    "hair",
+    "dotted",
+    "mediumDashDotDot",
+    # "medium",
+    "double",
+    "dashDot",
+    "slantDashDot",
+    # "thick",
+    "mediumDashed",
+]
+
+
+@pytest.mark.parametrize(
+    "engine",
+    ["xlsxwriter", "openpyxl"],
+)
+@pytest.mark.parametrize("border_style", excel_border_styles)
+def test_styler_to_excel_border_style(engine, border_style):
+    css = f"border-left: {border_style} black thin"
+    attrs = ["border", "left", "style"]
+    expected = border_style
+
+    pytest.importorskip(engine)
+    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
+    styler = df.style.map(lambda x: css)
+
+    with tm.ensure_clean(".xlsx") as path:
+        with ExcelWriter(path, engine=engine) as writer:
+            df.to_excel(writer, sheet_name="dataframe")
+            styler.to_excel(writer, sheet_name="styled")
+
+        openpyxl = pytest.importorskip("openpyxl")  # test loading only with openpyxl
+        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
+            # test unstyled data cell does not have expected styles
+            # test styled cell has expected styles
+            u_cell, s_cell = wb["dataframe"].cell(2, 2), wb["styled"].cell(2, 2)
+        for attr in attrs:
+            u_cell, s_cell = getattr(u_cell, attr, None), getattr(s_cell, attr)
+
+        if isinstance(expected, dict):
+            assert u_cell is None or u_cell != expected[engine]
+            assert s_cell == expected[engine]
+        else:
+            assert u_cell is None or u_cell != expected
+            assert s_cell == expected
+
+
+def test_styler_custom_converter():
+    openpyxl = pytest.importorskip("openpyxl")
+
+    def custom_converter(css):
+        return {"font": {"color": {"rgb": "111222"}}}
+
+    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
+    styler = df.style.map(lambda x: "color: #888999")
+    with tm.ensure_clean(".xlsx") as path:
+        with ExcelWriter(path, engine="openpyxl") as writer:
+            ExcelFormatter(styler, style_converter=custom_converter).write(
+                writer, sheet_name="custom"
+            )
+
+        with contextlib.closing(openpyxl.load_workbook(path)) as wb:
+            assert wb["custom"].cell(2, 2).font.color.value == "00111222"
+
+
+@pytest.mark.single_cpu
+@td.skip_if_not_us_locale
+def test_styler_to_s3(s3_public_bucket, s3so):
+    # GH#46381
+
+    mock_bucket_name, target_file = s3_public_bucket.name, "test.xlsx"
+    df = DataFrame({"x": [1, 2, 3], "y": [2, 4, 6]})
+    styler = df.style.set_sticky(axis="index")
+    styler.to_excel(f"s3://{mock_bucket_name}/{target_file}", storage_options=s3so)
+    timeout = 5
+    while True:
+        if target_file in (obj.key for obj in s3_public_bucket.objects.all()):
+            break
+        time.sleep(0.1)
+        timeout -= 0.1
+        assert timeout > 0, "Timed out waiting for file to appear on moto"
+        result = read_excel(
+            f"s3://{mock_bucket_name}/{target_file}", index_col=0, storage_options=s3so
+        )
+        tm.assert_frame_equal(result, df)

venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_writers.py

@@ -0,0 +1,1511 @@
+from datetime import (
+    date,
+    datetime,
+    timedelta,
+)
+from functools import partial
+from io import BytesIO
+import os
+import re
+
+import numpy as np
+import pytest
+
+from pandas.compat import is_platform_windows
+from pandas.compat._constants import PY310
+from pandas.compat._optional import import_optional_dependency
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    date_range,
+    option_context,
+)
+import pandas._testing as tm
+
+from pandas.io.excel import (
+    ExcelFile,
+    ExcelWriter,
+    _OpenpyxlWriter,
+    _XlsxWriter,
+    register_writer,
+)
+from pandas.io.excel._util import _writers
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+def get_exp_unit(path: str) -> str:
+    return "ns"
+
+
+@pytest.fixture
+def frame(float_frame):
+    """
+    Returns the first ten items in fixture "float_frame".
+    """
+    return float_frame[:10]
+
+
+@pytest.fixture(params=[True, False])
+def merge_cells(request):
+    return request.param
+
+
+@pytest.fixture
+def path(ext):
+    """
+    Fixture to open file for use in each test case.
+    """
+    with tm.ensure_clean(ext) as file_path:
+        yield file_path
+
+
+@pytest.fixture
+def set_engine(engine, ext):
+    """
+    Fixture to set engine for use in each test case.
+
+    Rather than requiring `engine=...` to be provided explicitly as an
+    argument in each test, this fixture sets a global option to dictate
+    which engine should be used to write Excel files. After executing
+    the test it rolls back said change to the global option.
+    """
+    option_name = f"io.excel.{ext.strip('.')}.writer"
+    with option_context(option_name, engine):
+        yield
+
+
+@pytest.mark.parametrize(
+    "ext",
+    [
+        pytest.param(".xlsx", marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")]),
+        pytest.param(".xlsm", marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")]),
+        pytest.param(
+            ".xlsx", marks=[td.skip_if_no("xlsxwriter"), td.skip_if_no("xlrd")]
+        ),
+        pytest.param(".ods", marks=td.skip_if_no("odf")),
+    ],
+)
+class TestRoundTrip:
+    @pytest.mark.parametrize(
+        "header,expected",
+        [(None, DataFrame([np.nan] * 4)), (0, DataFrame({"Unnamed: 0": [np.nan] * 3}))],
+    )
+    def test_read_one_empty_col_no_header(self, ext, header, expected):
+        # xref gh-12292
+        filename = "no_header"
+        df = DataFrame([["", 1, 100], ["", 2, 200], ["", 3, 300], ["", 4, 400]])
+
+        with tm.ensure_clean(ext) as path:
+            df.to_excel(path, sheet_name=filename, index=False, header=False)
+            result = pd.read_excel(
+                path, sheet_name=filename, usecols=[0], header=header
+            )
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "header,expected",
+        [(None, DataFrame([0] + [np.nan] * 4)), (0, DataFrame([np.nan] * 4))],
+    )
+    def test_read_one_empty_col_with_header(self, ext, header, expected):
+        filename = "with_header"
+        df = DataFrame([["", 1, 100], ["", 2, 200], ["", 3, 300], ["", 4, 400]])
+
+        with tm.ensure_clean(ext) as path:
+            df.to_excel(path, sheet_name="with_header", index=False, header=True)
+            result = pd.read_excel(
+                path, sheet_name=filename, usecols=[0], header=header
+            )
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_set_column_names_in_parameter(self, ext):
+        # GH 12870 : pass down column names associated with
+        # keyword argument names
+        refdf = DataFrame([[1, "foo"], [2, "bar"], [3, "baz"]], columns=["a", "b"])
+
+        with tm.ensure_clean(ext) as pth:
+            with ExcelWriter(pth) as writer:
+                refdf.to_excel(
+                    writer, sheet_name="Data_no_head", header=False, index=False
+                )
+                refdf.to_excel(writer, sheet_name="Data_with_head", index=False)
+
+            refdf.columns = ["A", "B"]
+
+            with ExcelFile(pth) as reader:
+                xlsdf_no_head = pd.read_excel(
+                    reader, sheet_name="Data_no_head", header=None, names=["A", "B"]
+                )
+                xlsdf_with_head = pd.read_excel(
+                    reader,
+                    sheet_name="Data_with_head",
+                    index_col=None,
+                    names=["A", "B"],
+                )
+
+            tm.assert_frame_equal(xlsdf_no_head, refdf)
+            tm.assert_frame_equal(xlsdf_with_head, refdf)
+
+    def test_creating_and_reading_multiple_sheets(self, ext):
+        # see gh-9450
+        #
+        # Test reading multiple sheets, from a runtime
+        # created Excel file with multiple sheets.
+        def tdf(col_sheet_name):
+            d, i = [11, 22, 33], [1, 2, 3]
+            return DataFrame(d, i, columns=[col_sheet_name])
+
+        sheets = ["AAA", "BBB", "CCC"]
+
+        dfs = [tdf(s) for s in sheets]
+        dfs = dict(zip(sheets, dfs))
+
+        with tm.ensure_clean(ext) as pth:
+            with ExcelWriter(pth) as ew:
+                for sheetname, df in dfs.items():
+                    df.to_excel(ew, sheet_name=sheetname)
+
+            dfs_returned = pd.read_excel(pth, sheet_name=sheets, index_col=0)
+
+            for s in sheets:
+                tm.assert_frame_equal(dfs[s], dfs_returned[s])
+
+    def test_read_excel_multiindex_empty_level(self, ext):
+        # see gh-12453
+        with tm.ensure_clean(ext) as path:
+            df = DataFrame(
+                {
+                    ("One", "x"): {0: 1},
+                    ("Two", "X"): {0: 3},
+                    ("Two", "Y"): {0: 7},
+                    ("Zero", ""): {0: 0},
+                }
+            )
+
+            expected = DataFrame(
+                {
+                    ("One", "x"): {0: 1},
+                    ("Two", "X"): {0: 3},
+                    ("Two", "Y"): {0: 7},
+                    ("Zero", "Unnamed: 4_level_1"): {0: 0},
+                }
+            )
+
+            df.to_excel(path)
+            actual = pd.read_excel(path, header=[0, 1], index_col=0)
+            tm.assert_frame_equal(actual, expected)
+
+            df = DataFrame(
+                {
+                    ("Beg", ""): {0: 0},
+                    ("Middle", "x"): {0: 1},
+                    ("Tail", "X"): {0: 3},
+                    ("Tail", "Y"): {0: 7},
+                }
+            )
+
+            expected = DataFrame(
+                {
+                    ("Beg", "Unnamed: 1_level_1"): {0: 0},
+                    ("Middle", "x"): {0: 1},
+                    ("Tail", "X"): {0: 3},
+                    ("Tail", "Y"): {0: 7},
+                }
+            )
+
+            df.to_excel(path)
+            actual = pd.read_excel(path, header=[0, 1], index_col=0)
+            tm.assert_frame_equal(actual, expected)
+
+    @pytest.mark.parametrize("c_idx_names", ["a", None])
+    @pytest.mark.parametrize("r_idx_names", ["b", None])
+    @pytest.mark.parametrize("c_idx_levels", [1, 3])
+    @pytest.mark.parametrize("r_idx_levels", [1, 3])
+    def test_excel_multindex_roundtrip(
+        self, ext, c_idx_names, r_idx_names, c_idx_levels, r_idx_levels, request
+    ):
+        # see gh-4679
+        with tm.ensure_clean(ext) as pth:
+            # Empty name case current read in as
+            # unnamed levels, not Nones.
+            check_names = bool(r_idx_names) or r_idx_levels <= 1
+
+            if c_idx_levels == 1:
+                columns = Index(list("abcde"))
+            else:
+                columns = MultiIndex.from_arrays(
+                    [range(5) for _ in range(c_idx_levels)],
+                    names=[f"{c_idx_names}-{i}" for i in range(c_idx_levels)],
+                )
+            if r_idx_levels == 1:
+                index = Index(list("ghijk"))
+            else:
+                index = MultiIndex.from_arrays(
+                    [range(5) for _ in range(r_idx_levels)],
+                    names=[f"{r_idx_names}-{i}" for i in range(r_idx_levels)],
+                )
+            df = DataFrame(
+                1.1 * np.ones((5, 5)),
+                columns=columns,
+                index=index,
+            )
+            df.to_excel(pth)
+
+            act = pd.read_excel(
+                pth,
+                index_col=list(range(r_idx_levels)),
+                header=list(range(c_idx_levels)),
+            )
+            tm.assert_frame_equal(df, act, check_names=check_names)
+
+            df.iloc[0, :] = np.nan
+            df.to_excel(pth)
+
+            act = pd.read_excel(
+                pth,
+                index_col=list(range(r_idx_levels)),
+                header=list(range(c_idx_levels)),
+            )
+            tm.assert_frame_equal(df, act, check_names=check_names)
+
+            df.iloc[-1, :] = np.nan
+            df.to_excel(pth)
+            act = pd.read_excel(
+                pth,
+                index_col=list(range(r_idx_levels)),
+                header=list(range(c_idx_levels)),
+            )
+            tm.assert_frame_equal(df, act, check_names=check_names)
+
+    def test_read_excel_parse_dates(self, ext):
+        # see gh-11544, gh-12051
+        df = DataFrame(
+            {"col": [1, 2, 3], "date_strings": date_range("2012-01-01", periods=3)}
+        )
+        df2 = df.copy()
+        df2["date_strings"] = df2["date_strings"].dt.strftime("%m/%d/%Y")
+
+        with tm.ensure_clean(ext) as pth:
+            df2.to_excel(pth)
+
+            res = pd.read_excel(pth, index_col=0)
+            tm.assert_frame_equal(df2, res)
+
+            res = pd.read_excel(pth, parse_dates=["date_strings"], index_col=0)
+            tm.assert_frame_equal(df, res)
+
+            date_parser = lambda x: datetime.strptime(x, "%m/%d/%Y")
+            with tm.assert_produces_warning(
+                FutureWarning,
+                match="use 'date_format' instead",
+                raise_on_extra_warnings=False,
+            ):
+                res = pd.read_excel(
+                    pth,
+                    parse_dates=["date_strings"],
+                    date_parser=date_parser,
+                    index_col=0,
+                )
+            tm.assert_frame_equal(df, res)
+            res = pd.read_excel(
+                pth, parse_dates=["date_strings"], date_format="%m/%d/%Y", index_col=0
+            )
+            tm.assert_frame_equal(df, res)
+
+    def test_multiindex_interval_datetimes(self, ext):
+        # GH 30986
+        midx = MultiIndex.from_arrays(
+            [
+                range(4),
+                pd.interval_range(
+                    start=pd.Timestamp("2020-01-01"), periods=4, freq="6ME"
+                ),
+            ]
+        )
+        df = DataFrame(range(4), index=midx)
+        with tm.ensure_clean(ext) as pth:
+            df.to_excel(pth)
+            result = pd.read_excel(pth, index_col=[0, 1])
+        expected = DataFrame(
+            range(4),
+            MultiIndex.from_arrays(
+                [
+                    range(4),
+                    [
+                        "(2020-01-31 00:00:00, 2020-07-31 00:00:00]",
+                        "(2020-07-31 00:00:00, 2021-01-31 00:00:00]",
+                        "(2021-01-31 00:00:00, 2021-07-31 00:00:00]",
+                        "(2021-07-31 00:00:00, 2022-01-31 00:00:00]",
+                    ],
+                ]
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "engine,ext",
+    [
+        pytest.param(
+            "openpyxl",
+            ".xlsx",
+            marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")],
+        ),
+        pytest.param(
+            "openpyxl",
+            ".xlsm",
+            marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")],
+        ),
+        pytest.param(
+            "xlsxwriter",
+            ".xlsx",
+            marks=[td.skip_if_no("xlsxwriter"), td.skip_if_no("xlrd")],
+        ),
+        pytest.param("odf", ".ods", marks=td.skip_if_no("odf")),
+    ],
+)
+@pytest.mark.usefixtures("set_engine")
+class TestExcelWriter:
+    def test_excel_sheet_size(self, path):
+        # GH 26080
+        breaking_row_count = 2**20 + 1
+        breaking_col_count = 2**14 + 1
+        # purposely using two arrays to prevent memory issues while testing
+        row_arr = np.zeros(shape=(breaking_row_count, 1))
+        col_arr = np.zeros(shape=(1, breaking_col_count))
+        row_df = DataFrame(row_arr)
+        col_df = DataFrame(col_arr)
+
+        msg = "sheet is too large"
+        with pytest.raises(ValueError, match=msg):
+            row_df.to_excel(path)
+
+        with pytest.raises(ValueError, match=msg):
+            col_df.to_excel(path)
+
+    def test_excel_sheet_by_name_raise(self, path):
+        gt = DataFrame(np.random.default_rng(2).standard_normal((10, 2)))
+        gt.to_excel(path)
+
+        with ExcelFile(path) as xl:
+            df = pd.read_excel(xl, sheet_name=0, index_col=0)
+
+        tm.assert_frame_equal(gt, df)
+
+        msg = "Worksheet named '0' not found"
+        with pytest.raises(ValueError, match=msg):
+            pd.read_excel(xl, "0")
+
+    def test_excel_writer_context_manager(self, frame, path):
+        with ExcelWriter(path) as writer:
+            frame.to_excel(writer, sheet_name="Data1")
+            frame2 = frame.copy()
+            frame2.columns = frame.columns[::-1]
+            frame2.to_excel(writer, sheet_name="Data2")
+
+        with ExcelFile(path) as reader:
+            found_df = pd.read_excel(reader, sheet_name="Data1", index_col=0)
+            found_df2 = pd.read_excel(reader, sheet_name="Data2", index_col=0)
+
+            tm.assert_frame_equal(found_df, frame)
+            tm.assert_frame_equal(found_df2, frame2)
+
+    def test_roundtrip(self, frame, path):
+        frame = frame.copy()
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
+
+        frame.to_excel(path, sheet_name="test1")
+        frame.to_excel(path, sheet_name="test1", columns=["A", "B"])
+        frame.to_excel(path, sheet_name="test1", header=False)
+        frame.to_excel(path, sheet_name="test1", index=False)
+
+        # test roundtrip
+        frame.to_excel(path, sheet_name="test1")
+        recons = pd.read_excel(path, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(frame, recons)
+
+        frame.to_excel(path, sheet_name="test1", index=False)
+        recons = pd.read_excel(path, sheet_name="test1", index_col=None)
+        recons.index = frame.index
+        tm.assert_frame_equal(frame, recons)
+
+        frame.to_excel(path, sheet_name="test1", na_rep="NA")
+        recons = pd.read_excel(path, sheet_name="test1", index_col=0, na_values=["NA"])
+        tm.assert_frame_equal(frame, recons)
+
+        # GH 3611
+        frame.to_excel(path, sheet_name="test1", na_rep="88")
+        recons = pd.read_excel(path, sheet_name="test1", index_col=0, na_values=["88"])
+        tm.assert_frame_equal(frame, recons)
+
+        frame.to_excel(path, sheet_name="test1", na_rep="88")
+        recons = pd.read_excel(
+            path, sheet_name="test1", index_col=0, na_values=[88, 88.0]
+        )
+        tm.assert_frame_equal(frame, recons)
+
+        # GH 6573
+        frame.to_excel(path, sheet_name="Sheet1")
+        recons = pd.read_excel(path, index_col=0)
+        tm.assert_frame_equal(frame, recons)
+
+        frame.to_excel(path, sheet_name="0")
+        recons = pd.read_excel(path, index_col=0)
+        tm.assert_frame_equal(frame, recons)
+
+        # GH 8825 Pandas Series should provide to_excel method
+        s = frame["A"]
+        s.to_excel(path)
+        recons = pd.read_excel(path, index_col=0)
+        tm.assert_frame_equal(s.to_frame(), recons)
+
+    def test_mixed(self, frame, path):
+        mixed_frame = frame.copy()
+        mixed_frame["foo"] = "bar"
+
+        mixed_frame.to_excel(path, sheet_name="test1")
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(mixed_frame, recons)
+
+    def test_ts_frame(self, path):
+        unit = get_exp_unit(path)
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)),
+            columns=Index(list("ABCD")),
+            index=date_range("2000-01-01", periods=5, freq="B"),
+        )
+
+        # freq doesn't round-trip
+        index = pd.DatetimeIndex(np.asarray(df.index), freq=None)
+        df.index = index
+
+        expected = df[:]
+        expected.index = expected.index.as_unit(unit)
+
+        df.to_excel(path, sheet_name="test1")
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(expected, recons)
+
+    def test_basics_with_nan(self, frame, path):
+        frame = frame.copy()
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
+        frame.to_excel(path, sheet_name="test1")
+        frame.to_excel(path, sheet_name="test1", columns=["A", "B"])
+        frame.to_excel(path, sheet_name="test1", header=False)
+        frame.to_excel(path, sheet_name="test1", index=False)
+
+    @pytest.mark.parametrize("np_type", [np.int8, np.int16, np.int32, np.int64])
+    def test_int_types(self, np_type, path):
+        # Test np.int values read come back as int
+        # (rather than float which is Excel's format).
+        df = DataFrame(
+            np.random.default_rng(2).integers(-10, 10, size=(10, 2)), dtype=np_type
+        )
+        df.to_excel(path, sheet_name="test1")
+
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+
+        int_frame = df.astype(np.int64)
+        tm.assert_frame_equal(int_frame, recons)
+
+        recons2 = pd.read_excel(path, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(int_frame, recons2)
+
+    @pytest.mark.parametrize("np_type", [np.float16, np.float32, np.float64])
+    def test_float_types(self, np_type, path):
+        # Test np.float values read come back as float.
+        df = DataFrame(np.random.default_rng(2).random(10), dtype=np_type)
+        df.to_excel(path, sheet_name="test1")
+
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype(
+                np_type
+            )
+
+        tm.assert_frame_equal(df, recons)
+
+    def test_bool_types(self, path):
+        # Test np.bool_ values read come back as float.
+        df = DataFrame([1, 0, True, False], dtype=np.bool_)
+        df.to_excel(path, sheet_name="test1")
+
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype(
+                np.bool_
+            )
+
+        tm.assert_frame_equal(df, recons)
+
+    def test_inf_roundtrip(self, path):
+        df = DataFrame([(1, np.inf), (2, 3), (5, -np.inf)])
+        df.to_excel(path, sheet_name="test1")
+
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+
+        tm.assert_frame_equal(df, recons)
+
+    def test_sheets(self, frame, path):
+        # freq doesn't round-trip
+        unit = get_exp_unit(path)
+        tsframe = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)),
+            columns=Index(list("ABCD")),
+            index=date_range("2000-01-01", periods=5, freq="B"),
+        )
+        index = pd.DatetimeIndex(np.asarray(tsframe.index), freq=None)
+        tsframe.index = index
+
+        expected = tsframe[:]
+        expected.index = expected.index.as_unit(unit)
+
+        frame = frame.copy()
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
+
+        frame.to_excel(path, sheet_name="test1")
+        frame.to_excel(path, sheet_name="test1", columns=["A", "B"])
+        frame.to_excel(path, sheet_name="test1", header=False)
+        frame.to_excel(path, sheet_name="test1", index=False)
+
+        # Test writing to separate sheets
+        with ExcelWriter(path) as writer:
+            frame.to_excel(writer, sheet_name="test1")
+            tsframe.to_excel(writer, sheet_name="test2")
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+            tm.assert_frame_equal(frame, recons)
+            recons = pd.read_excel(reader, sheet_name="test2", index_col=0)
+        tm.assert_frame_equal(expected, recons)
+        assert 2 == len(reader.sheet_names)
+        assert "test1" == reader.sheet_names[0]
+        assert "test2" == reader.sheet_names[1]
+
+    def test_colaliases(self, frame, path):
+        frame = frame.copy()
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
+
+        frame.to_excel(path, sheet_name="test1")
+        frame.to_excel(path, sheet_name="test1", columns=["A", "B"])
+        frame.to_excel(path, sheet_name="test1", header=False)
+        frame.to_excel(path, sheet_name="test1", index=False)
+
+        # column aliases
+        col_aliases = Index(["AA", "X", "Y", "Z"])
+        frame.to_excel(path, sheet_name="test1", header=col_aliases)
+        with ExcelFile(path) as reader:
+            rs = pd.read_excel(reader, sheet_name="test1", index_col=0)
+        xp = frame.copy()
+        xp.columns = col_aliases
+        tm.assert_frame_equal(xp, rs)
+
+    def test_roundtrip_indexlabels(self, merge_cells, frame, path):
+        frame = frame.copy()
+        frame.iloc[:5, frame.columns.get_loc("A")] = np.nan
+
+        frame.to_excel(path, sheet_name="test1")
+        frame.to_excel(path, sheet_name="test1", columns=["A", "B"])
+        frame.to_excel(path, sheet_name="test1", header=False)
+        frame.to_excel(path, sheet_name="test1", index=False)
+
+        # test index_label
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) >= 0
+        df.to_excel(
+            path, sheet_name="test1", index_label=["test"], merge_cells=merge_cells
+        )
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype(
+                np.int64
+            )
+        df.index.names = ["test"]
+        assert df.index.names == recons.index.names
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) >= 0
+        df.to_excel(
+            path,
+            sheet_name="test1",
+            index_label=["test", "dummy", "dummy2"],
+            merge_cells=merge_cells,
+        )
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype(
+                np.int64
+            )
+        df.index.names = ["test"]
+        assert df.index.names == recons.index.names
+
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) >= 0
+        df.to_excel(
+            path, sheet_name="test1", index_label="test", merge_cells=merge_cells
+        )
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype(
+                np.int64
+            )
+        df.index.names = ["test"]
+        tm.assert_frame_equal(df, recons.astype(bool))
+
+        frame.to_excel(
+            path,
+            sheet_name="test1",
+            columns=["A", "B", "C", "D"],
+            index=False,
+            merge_cells=merge_cells,
+        )
+        # take 'A' and 'B' as indexes (same row as cols 'C', 'D')
+        df = frame.copy()
+        df = df.set_index(["A", "B"])
+
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=[0, 1])
+        tm.assert_frame_equal(df, recons)
+
+    def test_excel_roundtrip_indexname(self, merge_cells, path):
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
+        df.index.name = "foo"
+
+        df.to_excel(path, merge_cells=merge_cells)
+
+        with ExcelFile(path) as xf:
+            result = pd.read_excel(xf, sheet_name=xf.sheet_names[0], index_col=0)
+
+        tm.assert_frame_equal(result, df)
+        assert result.index.name == "foo"
+
+    def test_excel_roundtrip_datetime(self, merge_cells, path):
+        # datetime.date, not sure what to test here exactly
+        unit = get_exp_unit(path)
+
+        # freq does not round-trip
+        tsframe = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)),
+            columns=Index(list("ABCD")),
+            index=date_range("2000-01-01", periods=5, freq="B"),
+        )
+        index = pd.DatetimeIndex(np.asarray(tsframe.index), freq=None)
+        tsframe.index = index
+
+        tsf = tsframe.copy()
+
+        tsf.index = [x.date() for x in tsframe.index]
+        tsf.to_excel(path, sheet_name="test1", merge_cells=merge_cells)
+
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+
+        expected = tsframe[:]
+        expected.index = expected.index.as_unit(unit)
+        tm.assert_frame_equal(expected, recons)
+
+    def test_excel_date_datetime_format(self, ext, path):
+        # see gh-4133
+        #
+        # Excel output format strings
+        unit = get_exp_unit(path)
+
+        df = DataFrame(
+            [
+                [date(2014, 1, 31), date(1999, 9, 24)],
+                [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)],
+            ],
+            index=["DATE", "DATETIME"],
+            columns=["X", "Y"],
+        )
+        df_expected = DataFrame(
+            [
+                [datetime(2014, 1, 31), datetime(1999, 9, 24)],
+                [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)],
+            ],
+            index=["DATE", "DATETIME"],
+            columns=["X", "Y"],
+        )
+        df_expected = df_expected.astype(f"M8[{unit}]")
+
+        with tm.ensure_clean(ext) as filename2:
+            with ExcelWriter(path) as writer1:
+                df.to_excel(writer1, sheet_name="test1")
+
+            with ExcelWriter(
+                filename2,
+                date_format="DD.MM.YYYY",
+                datetime_format="DD.MM.YYYY HH-MM-SS",
+            ) as writer2:
+                df.to_excel(writer2, sheet_name="test1")
+
+            with ExcelFile(path) as reader1:
+                rs1 = pd.read_excel(reader1, sheet_name="test1", index_col=0)
+
+            with ExcelFile(filename2) as reader2:
+                rs2 = pd.read_excel(reader2, sheet_name="test1", index_col=0)
+
+        tm.assert_frame_equal(rs1, rs2)
+
+        # Since the reader returns a datetime object for dates,
+        # we need to use df_expected to check the result.
+        tm.assert_frame_equal(rs2, df_expected)
+
+    def test_to_excel_interval_no_labels(self, path, using_infer_string):
+        # see gh-19242
+        #
+        # Test writing Interval without labels.
+        df = DataFrame(
+            np.random.default_rng(2).integers(-10, 10, size=(20, 1)), dtype=np.int64
+        )
+        expected = df.copy()
+
+        df["new"] = pd.cut(df[0], 10)
+        expected["new"] = pd.cut(expected[0], 10).astype(
+            str if not using_infer_string else "string[pyarrow_numpy]"
+        )
+
+        df.to_excel(path, sheet_name="test1")
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(expected, recons)
+
+    def test_to_excel_interval_labels(self, path):
+        # see gh-19242
+        #
+        # Test writing Interval with labels.
+        df = DataFrame(
+            np.random.default_rng(2).integers(-10, 10, size=(20, 1)), dtype=np.int64
+        )
+        expected = df.copy()
+        intervals = pd.cut(
+            df[0], 10, labels=["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"]
+        )
+        df["new"] = intervals
+        expected["new"] = pd.Series(list(intervals))
+
+        df.to_excel(path, sheet_name="test1")
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(expected, recons)
+
+    def test_to_excel_timedelta(self, path):
+        # see gh-19242, gh-9155
+        #
+        # Test writing timedelta to xls.
+        df = DataFrame(
+            np.random.default_rng(2).integers(-10, 10, size=(20, 1)),
+            columns=["A"],
+            dtype=np.int64,
+        )
+        expected = df.copy()
+
+        df["new"] = df["A"].apply(lambda x: timedelta(seconds=x))
+        expected["new"] = expected["A"].apply(
+            lambda x: timedelta(seconds=x).total_seconds() / 86400
+        )
+
+        df.to_excel(path, sheet_name="test1")
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(expected, recons)
+
+    def test_to_excel_periodindex(self, path):
+        # xp has a PeriodIndex
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)),
+            columns=Index(list("ABCD")),
+            index=date_range("2000-01-01", periods=5, freq="B"),
+        )
+        xp = df.resample("ME").mean().to_period("M")
+
+        xp.to_excel(path, sheet_name="sht1")
+
+        with ExcelFile(path) as reader:
+            rs = pd.read_excel(reader, sheet_name="sht1", index_col=0)
+        tm.assert_frame_equal(xp, rs.to_period("M"))
+
+    def test_to_excel_multiindex(self, merge_cells, frame, path):
+        arrays = np.arange(len(frame.index) * 2, dtype=np.int64).reshape(2, -1)
+        new_index = MultiIndex.from_arrays(arrays, names=["first", "second"])
+        frame.index = new_index
+
+        frame.to_excel(path, sheet_name="test1", header=False)
+        frame.to_excel(path, sheet_name="test1", columns=["A", "B"])
+
+        # round trip
+        frame.to_excel(path, sheet_name="test1", merge_cells=merge_cells)
+        with ExcelFile(path) as reader:
+            df = pd.read_excel(reader, sheet_name="test1", index_col=[0, 1])
+        tm.assert_frame_equal(frame, df)
+
+    # GH13511
+    def test_to_excel_multiindex_nan_label(self, merge_cells, path):
+        df = DataFrame(
+            {
+                "A": [None, 2, 3],
+                "B": [10, 20, 30],
+                "C": np.random.default_rng(2).random(3),
+            }
+        )
+        df = df.set_index(["A", "B"])
+
+        df.to_excel(path, merge_cells=merge_cells)
+        df1 = pd.read_excel(path, index_col=[0, 1])
+        tm.assert_frame_equal(df, df1)
+
+    # Test for Issue 11328. If column indices are integers, make
+    # sure they are handled correctly for either setting of
+    # merge_cells
+    def test_to_excel_multiindex_cols(self, merge_cells, frame, path):
+        arrays = np.arange(len(frame.index) * 2, dtype=np.int64).reshape(2, -1)
+        new_index = MultiIndex.from_arrays(arrays, names=["first", "second"])
+        frame.index = new_index
+
+        new_cols_index = MultiIndex.from_tuples([(40, 1), (40, 2), (50, 1), (50, 2)])
+        frame.columns = new_cols_index
+        header = [0, 1]
+        if not merge_cells:
+            header = 0
+
+        # round trip
+        frame.to_excel(path, sheet_name="test1", merge_cells=merge_cells)
+        with ExcelFile(path) as reader:
+            df = pd.read_excel(
+                reader, sheet_name="test1", header=header, index_col=[0, 1]
+            )
+        if not merge_cells:
+            fm = frame.columns._format_multi(sparsify=False, include_names=False)
+            frame.columns = [".".join(map(str, q)) for q in zip(*fm)]
+        tm.assert_frame_equal(frame, df)
+
+    def test_to_excel_multiindex_dates(self, merge_cells, path):
+        # try multiindex with dates
+        unit = get_exp_unit(path)
+        tsframe = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 4)),
+            columns=Index(list("ABCD")),
+            index=date_range("2000-01-01", periods=5, freq="B"),
+        )
+        tsframe.index = MultiIndex.from_arrays(
+            [
+                tsframe.index.as_unit(unit),
+                np.arange(len(tsframe.index), dtype=np.int64),
+            ],
+            names=["time", "foo"],
+        )
+
+        tsframe.to_excel(path, sheet_name="test1", merge_cells=merge_cells)
+        with ExcelFile(path) as reader:
+            recons = pd.read_excel(reader, sheet_name="test1", index_col=[0, 1])
+
+        tm.assert_frame_equal(tsframe, recons)
+        assert recons.index.names == ("time", "foo")
+
+    def test_to_excel_multiindex_no_write_index(self, path):
+        # Test writing and re-reading a MI without the index. GH 5616.
+
+        # Initial non-MI frame.
+        frame1 = DataFrame({"a": [10, 20], "b": [30, 40], "c": [50, 60]})
+
+        # Add a MI.
+        frame2 = frame1.copy()
+        multi_index = MultiIndex.from_tuples([(70, 80), (90, 100)])
+        frame2.index = multi_index
+
+        # Write out to Excel without the index.
+        frame2.to_excel(path, sheet_name="test1", index=False)
+
+        # Read it back in.
+        with ExcelFile(path) as reader:
+            frame3 = pd.read_excel(reader, sheet_name="test1")
+
+        # Test that it is the same as the initial frame.
+        tm.assert_frame_equal(frame1, frame3)
+
+    def test_to_excel_empty_multiindex(self, path):
+        # GH 19543.
+        expected = DataFrame([], columns=[0, 1, 2])
+
+        df = DataFrame([], index=MultiIndex.from_tuples([], names=[0, 1]), columns=[2])
+        df.to_excel(path, sheet_name="test1")
+
+        with ExcelFile(path) as reader:
+            result = pd.read_excel(reader, sheet_name="test1")
+        tm.assert_frame_equal(
+            result, expected, check_index_type=False, check_dtype=False
+        )
+
+    def test_to_excel_float_format(self, path):
+        df = DataFrame(
+            [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
+            index=["A", "B"],
+            columns=["X", "Y", "Z"],
+        )
+        df.to_excel(path, sheet_name="test1", float_format="%.2f")
+
+        with ExcelFile(path) as reader:
+            result = pd.read_excel(reader, sheet_name="test1", index_col=0)
+
+        expected = DataFrame(
+            [[0.12, 0.23, 0.57], [12.32, 123123.20, 321321.20]],
+            index=["A", "B"],
+            columns=["X", "Y", "Z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_to_excel_output_encoding(self, ext):
+        # Avoid mixed inferred_type.
+        df = DataFrame(
+            [["\u0192", "\u0193", "\u0194"], ["\u0195", "\u0196", "\u0197"]],
+            index=["A\u0192", "B"],
+            columns=["X\u0193", "Y", "Z"],
+        )
+
+        with tm.ensure_clean("__tmp_to_excel_float_format__." + ext) as filename:
+            df.to_excel(filename, sheet_name="TestSheet")
+            result = pd.read_excel(filename, sheet_name="TestSheet", index_col=0)
+            tm.assert_frame_equal(result, df)
+
+    def test_to_excel_unicode_filename(self, ext):
+        with tm.ensure_clean("\u0192u." + ext) as filename:
+            try:
+                with open(filename, "wb"):
+                    pass
+            except UnicodeEncodeError:
+                pytest.skip("No unicode file names on this system")
+
+            df = DataFrame(
+                [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
+                index=["A", "B"],
+                columns=["X", "Y", "Z"],
+            )
+            df.to_excel(filename, sheet_name="test1", float_format="%.2f")
+
+            with ExcelFile(filename) as reader:
+                result = pd.read_excel(reader, sheet_name="test1", index_col=0)
+
+        expected = DataFrame(
+            [[0.12, 0.23, 0.57], [12.32, 123123.20, 321321.20]],
+            index=["A", "B"],
+            columns=["X", "Y", "Z"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("use_headers", [True, False])
+    @pytest.mark.parametrize("r_idx_nlevels", [1, 2, 3])
+    @pytest.mark.parametrize("c_idx_nlevels", [1, 2, 3])
+    def test_excel_010_hemstring(
+        self, merge_cells, c_idx_nlevels, r_idx_nlevels, use_headers, path
+    ):
+        def roundtrip(data, header=True, parser_hdr=0, index=True):
+            data.to_excel(path, header=header, merge_cells=merge_cells, index=index)
+
+            with ExcelFile(path) as xf:
+                return pd.read_excel(
+                    xf, sheet_name=xf.sheet_names[0], header=parser_hdr
+                )
+
+        # Basic test.
+        parser_header = 0 if use_headers else None
+        res = roundtrip(DataFrame([0]), use_headers, parser_header)
+
+        assert res.shape == (1, 2)
+        assert res.iloc[0, 0] is not np.nan
+
+        # More complex tests with multi-index.
+        nrows = 5
+        ncols = 3
+
+        # ensure limited functionality in 0.10
+        # override of gh-2370 until sorted out in 0.11
+
+        if c_idx_nlevels == 1:
+            columns = Index([f"a-{i}" for i in range(ncols)], dtype=object)
+        else:
+            columns = MultiIndex.from_arrays(
+                [range(ncols) for _ in range(c_idx_nlevels)],
+                names=[f"i-{i}" for i in range(c_idx_nlevels)],
+            )
+        if r_idx_nlevels == 1:
+            index = Index([f"b-{i}" for i in range(nrows)], dtype=object)
+        else:
+            index = MultiIndex.from_arrays(
+                [range(nrows) for _ in range(r_idx_nlevels)],
+                names=[f"j-{i}" for i in range(r_idx_nlevels)],
+            )
+
+        df = DataFrame(
+            np.ones((nrows, ncols)),
+            columns=columns,
+            index=index,
+        )
+
+        # This if will be removed once multi-column Excel writing
+        # is implemented. For now fixing gh-9794.
+        if c_idx_nlevels > 1:
+            msg = (
+                "Writing to Excel with MultiIndex columns and no index "
+                "\\('index'=False\\) is not yet implemented."
+            )
+            with pytest.raises(NotImplementedError, match=msg):
+                roundtrip(df, use_headers, index=False)
+        else:
+            res = roundtrip(df, use_headers)
+
+            if use_headers:
+                assert res.shape == (nrows, ncols + r_idx_nlevels)
+            else:
+                # First row taken as columns.
+                assert res.shape == (nrows - 1, ncols + r_idx_nlevels)
+
+            # No NaNs.
+            for r in range(len(res.index)):
+                for c in range(len(res.columns)):
+                    assert res.iloc[r, c] is not np.nan
+
+    def test_duplicated_columns(self, path):
+        # see gh-5235
+        df = DataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3]], columns=["A", "B", "B"])
+        df.to_excel(path, sheet_name="test1")
+        expected = DataFrame(
+            [[1, 2, 3], [1, 2, 3], [1, 2, 3]], columns=["A", "B", "B.1"]
+        )
+
+        # By default, we mangle.
+        result = pd.read_excel(path, sheet_name="test1", index_col=0)
+        tm.assert_frame_equal(result, expected)
+
+        # see gh-11007, gh-10970
+        df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], columns=["A", "B", "A", "B"])
+        df.to_excel(path, sheet_name="test1")
+
+        result = pd.read_excel(path, sheet_name="test1", index_col=0)
+        expected = DataFrame(
+            [[1, 2, 3, 4], [5, 6, 7, 8]], columns=["A", "B", "A.1", "B.1"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # see gh-10982
+        df.to_excel(path, sheet_name="test1", index=False, header=False)
+        result = pd.read_excel(path, sheet_name="test1", header=None)
+
+        expected = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]])
+        tm.assert_frame_equal(result, expected)
+
+    def test_swapped_columns(self, path):
+        # Test for issue #5427.
+        write_frame = DataFrame({"A": [1, 1, 1], "B": [2, 2, 2]})
+        write_frame.to_excel(path, sheet_name="test1", columns=["B", "A"])
+
+        read_frame = pd.read_excel(path, sheet_name="test1", header=0)
+
+        tm.assert_series_equal(write_frame["A"], read_frame["A"])
+        tm.assert_series_equal(write_frame["B"], read_frame["B"])
+
+    def test_invalid_columns(self, path):
+        # see gh-10982
+        write_frame = DataFrame({"A": [1, 1, 1], "B": [2, 2, 2]})
+
+        with pytest.raises(KeyError, match="Not all names specified"):
+            write_frame.to_excel(path, sheet_name="test1", columns=["B", "C"])
+
+        with pytest.raises(
+            KeyError, match="'passes columns are not ALL present dataframe'"
+        ):
+            write_frame.to_excel(path, sheet_name="test1", columns=["C", "D"])
+
+    @pytest.mark.parametrize(
+        "to_excel_index,read_excel_index_col",
+        [
+            (True, 0),  # Include index in write to file
+            (False, None),  # Dont include index in write to file
+        ],
+    )
+    def test_write_subset_columns(self, path, to_excel_index, read_excel_index_col):
+        # GH 31677
+        write_frame = DataFrame({"A": [1, 1, 1], "B": [2, 2, 2], "C": [3, 3, 3]})
+        write_frame.to_excel(
+            path, sheet_name="col_subset_bug", columns=["A", "B"], index=to_excel_index
+        )
+
+        expected = write_frame[["A", "B"]]
+        read_frame = pd.read_excel(
+            path, sheet_name="col_subset_bug", index_col=read_excel_index_col
+        )
+
+        tm.assert_frame_equal(expected, read_frame)
+
+    def test_comment_arg(self, path):
+        # see gh-18735
+        #
+        # Test the comment argument functionality to pd.read_excel.
+
+        # Create file to read in.
+        df = DataFrame({"A": ["one", "#one", "one"], "B": ["two", "two", "#two"]})
+        df.to_excel(path, sheet_name="test_c")
+
+        # Read file without comment arg.
+        result1 = pd.read_excel(path, sheet_name="test_c", index_col=0)
+
+        result1.iloc[1, 0] = None
+        result1.iloc[1, 1] = None
+        result1.iloc[2, 1] = None
+
+        result2 = pd.read_excel(path, sheet_name="test_c", comment="#", index_col=0)
+        tm.assert_frame_equal(result1, result2)
+
+    def test_comment_default(self, path):
+        # Re issue #18735
+        # Test the comment argument default to pd.read_excel
+
+        # Create file to read in
+        df = DataFrame({"A": ["one", "#one", "one"], "B": ["two", "two", "#two"]})
+        df.to_excel(path, sheet_name="test_c")
+
+        # Read file with default and explicit comment=None
+        result1 = pd.read_excel(path, sheet_name="test_c")
+        result2 = pd.read_excel(path, sheet_name="test_c", comment=None)
+        tm.assert_frame_equal(result1, result2)
+
+    def test_comment_used(self, path):
+        # see gh-18735
+        #
+        # Test the comment argument is working as expected when used.
+
+        # Create file to read in.
+        df = DataFrame({"A": ["one", "#one", "one"], "B": ["two", "two", "#two"]})
+        df.to_excel(path, sheet_name="test_c")
+
+        # Test read_frame_comment against manually produced expected output.
+        expected = DataFrame({"A": ["one", None, "one"], "B": ["two", None, None]})
+        result = pd.read_excel(path, sheet_name="test_c", comment="#", index_col=0)
+        tm.assert_frame_equal(result, expected)
+
+    def test_comment_empty_line(self, path):
+        # Re issue #18735
+        # Test that pd.read_excel ignores commented lines at the end of file
+
+        df = DataFrame({"a": ["1", "#2"], "b": ["2", "3"]})
+        df.to_excel(path, index=False)
+
+        # Test that all-comment lines at EoF are ignored
+        expected = DataFrame({"a": [1], "b": [2]})
+        result = pd.read_excel(path, comment="#")
+        tm.assert_frame_equal(result, expected)
+
+    def test_datetimes(self, path):
+        # Test writing and reading datetimes. For issue #9139. (xref #9185)
+        unit = get_exp_unit(path)
+        datetimes = [
+            datetime(2013, 1, 13, 1, 2, 3),
+            datetime(2013, 1, 13, 2, 45, 56),
+            datetime(2013, 1, 13, 4, 29, 49),
+            datetime(2013, 1, 13, 6, 13, 42),
+            datetime(2013, 1, 13, 7, 57, 35),
+            datetime(2013, 1, 13, 9, 41, 28),
+            datetime(2013, 1, 13, 11, 25, 21),
+            datetime(2013, 1, 13, 13, 9, 14),
+            datetime(2013, 1, 13, 14, 53, 7),
+            datetime(2013, 1, 13, 16, 37, 0),
+            datetime(2013, 1, 13, 18, 20, 52),
+        ]
+
+        write_frame = DataFrame({"A": datetimes})
+        write_frame.to_excel(path, sheet_name="Sheet1")
+        read_frame = pd.read_excel(path, sheet_name="Sheet1", header=0)
+
+        expected = write_frame.astype(f"M8[{unit}]")
+        tm.assert_series_equal(expected["A"], read_frame["A"])
+
+    def test_bytes_io(self, engine):
+        # see gh-7074
+        with BytesIO() as bio:
+            df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)))
+
+            # Pass engine explicitly, as there is no file path to infer from.
+            with ExcelWriter(bio, engine=engine) as writer:
+                df.to_excel(writer)
+
+            bio.seek(0)
+            reread_df = pd.read_excel(bio, index_col=0)
+            tm.assert_frame_equal(df, reread_df)
+
+    def test_engine_kwargs(self, engine, path):
+        # GH#52368
+        df = DataFrame([{"A": 1, "B": 2}, {"A": 3, "B": 4}])
+
+        msgs = {
+            "odf": r"OpenDocumentSpreadsheet() got an unexpected keyword "
+            r"argument 'foo'",
+            "openpyxl": r"__init__() got an unexpected keyword argument 'foo'",
+            "xlsxwriter": r"__init__() got an unexpected keyword argument 'foo'",
+        }
+
+        if PY310:
+            msgs[
+                "openpyxl"
+            ] = "Workbook.__init__() got an unexpected keyword argument 'foo'"
+            msgs[
+                "xlsxwriter"
+            ] = "Workbook.__init__() got an unexpected keyword argument 'foo'"
+
+        # Handle change in error message for openpyxl (write and append mode)
+        if engine == "openpyxl" and not os.path.exists(path):
+            msgs[
+                "openpyxl"
+            ] = r"load_workbook() got an unexpected keyword argument 'foo'"
+
+        with pytest.raises(TypeError, match=re.escape(msgs[engine])):
+            df.to_excel(
+                path,
+                engine=engine,
+                engine_kwargs={"foo": "bar"},
+            )
+
+    def test_write_lists_dict(self, path):
+        # see gh-8188.
+        df = DataFrame(
+            {
+                "mixed": ["a", ["b", "c"], {"d": "e", "f": 2}],
+                "numeric": [1, 2, 3.0],
+                "str": ["apple", "banana", "cherry"],
+            }
+        )
+        df.to_excel(path, sheet_name="Sheet1")
+        read = pd.read_excel(path, sheet_name="Sheet1", header=0, index_col=0)
+
+        expected = df.copy()
+        expected.mixed = expected.mixed.apply(str)
+        expected.numeric = expected.numeric.astype("int64")
+
+        tm.assert_frame_equal(read, expected)
+
+    def test_render_as_column_name(self, path):
+        # see gh-34331
+        df = DataFrame({"render": [1, 2], "data": [3, 4]})
+        df.to_excel(path, sheet_name="Sheet1")
+        read = pd.read_excel(path, "Sheet1", index_col=0)
+        expected = df
+        tm.assert_frame_equal(read, expected)
+
+    def test_true_and_false_value_options(self, path):
+        # see gh-13347
+        df = DataFrame([["foo", "bar"]], columns=["col1", "col2"], dtype=object)
+        with option_context("future.no_silent_downcasting", True):
+            expected = df.replace({"foo": True, "bar": False}).astype("bool")
+
+        df.to_excel(path)
+        read_frame = pd.read_excel(
+            path, true_values=["foo"], false_values=["bar"], index_col=0
+        )
+        tm.assert_frame_equal(read_frame, expected)
+
+    def test_freeze_panes(self, path):
+        # see gh-15160
+        expected = DataFrame([[1, 2], [3, 4]], columns=["col1", "col2"])
+        expected.to_excel(path, sheet_name="Sheet1", freeze_panes=(1, 1))
+
+        result = pd.read_excel(path, index_col=0)
+        tm.assert_frame_equal(result, expected)
+
+    def test_path_path_lib(self, engine, ext):
+        df = DataFrame(
+            1.1 * np.arange(120).reshape((30, 4)),
+            columns=Index(list("ABCD")),
+            index=Index([f"i-{i}" for i in range(30)], dtype=object),
+        )
+        writer = partial(df.to_excel, engine=engine)
+
+        reader = partial(pd.read_excel, index_col=0)
+        result = tm.round_trip_pathlib(writer, reader, path=f"foo{ext}")
+        tm.assert_frame_equal(result, df)
+
+    def test_path_local_path(self, engine, ext):
+        df = DataFrame(
+            1.1 * np.arange(120).reshape((30, 4)),
+            columns=Index(list("ABCD")),
+            index=Index([f"i-{i}" for i in range(30)]),
+        )
+        writer = partial(df.to_excel, engine=engine)
+
+        reader = partial(pd.read_excel, index_col=0)
+        result = tm.round_trip_localpath(writer, reader, path=f"foo{ext}")
+        tm.assert_frame_equal(result, df)
+
+    def test_merged_cell_custom_objects(self, path):
+        # see GH-27006
+        mi = MultiIndex.from_tuples(
+            [
+                (pd.Period("2018"), pd.Period("2018Q1")),
+                (pd.Period("2018"), pd.Period("2018Q2")),
+            ]
+        )
+        expected = DataFrame(np.ones((2, 2), dtype="int64"), columns=mi)
+        expected.to_excel(path)
+        result = pd.read_excel(path, header=[0, 1], index_col=0)
+        # need to convert PeriodIndexes to standard Indexes for assert equal
+        expected.columns = expected.columns.set_levels(
+            [[str(i) for i in mi.levels[0]], [str(i) for i in mi.levels[1]]],
+            level=[0, 1],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [None, object])
+    def test_raise_when_saving_timezones(self, dtype, tz_aware_fixture, path):
+        # GH 27008, GH 7056
+        tz = tz_aware_fixture
+        data = pd.Timestamp("2019", tz=tz)
+        df = DataFrame([data], dtype=dtype)
+        with pytest.raises(ValueError, match="Excel does not support"):
+            df.to_excel(path)
+
+        data = data.to_pydatetime()
+        df = DataFrame([data], dtype=dtype)
+        with pytest.raises(ValueError, match="Excel does not support"):
+            df.to_excel(path)
+
+    def test_excel_duplicate_columns_with_names(self, path):
+        # GH#39695
+        df = DataFrame({"A": [0, 1], "B": [10, 11]})
+        df.to_excel(path, columns=["A", "B", "A"], index=False)
+
+        result = pd.read_excel(path)
+        expected = DataFrame([[0, 10, 0], [1, 11, 1]], columns=["A", "B", "A.1"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_if_sheet_exists_raises(self, ext):
+        # GH 40230
+        msg = "if_sheet_exists is only valid in append mode (mode='a')"
+
+        with tm.ensure_clean(ext) as f:
+            with pytest.raises(ValueError, match=re.escape(msg)):
+                ExcelWriter(f, if_sheet_exists="replace")
+
+    def test_excel_writer_empty_frame(self, engine, ext):
+        # GH#45793
+        with tm.ensure_clean(ext) as path:
+            with ExcelWriter(path, engine=engine) as writer:
+                DataFrame().to_excel(writer)
+            result = pd.read_excel(path)
+            expected = DataFrame()
+            tm.assert_frame_equal(result, expected)
+
+    def test_to_excel_empty_frame(self, engine, ext):
+        # GH#45793
+        with tm.ensure_clean(ext) as path:
+            DataFrame().to_excel(path, engine=engine)
+            result = pd.read_excel(path)
+            expected = DataFrame()
+            tm.assert_frame_equal(result, expected)
+
+
+class TestExcelWriterEngineTests:
+    @pytest.mark.parametrize(
+        "klass,ext",
+        [
+            pytest.param(_XlsxWriter, ".xlsx", marks=td.skip_if_no("xlsxwriter")),
+            pytest.param(_OpenpyxlWriter, ".xlsx", marks=td.skip_if_no("openpyxl")),
+        ],
+    )
+    def test_ExcelWriter_dispatch(self, klass, ext):
+        with tm.ensure_clean(ext) as path:
+            with ExcelWriter(path) as writer:
+                if ext == ".xlsx" and bool(
+                    import_optional_dependency("xlsxwriter", errors="ignore")
+                ):
+                    # xlsxwriter has preference over openpyxl if both installed
+                    assert isinstance(writer, _XlsxWriter)
+                else:
+                    assert isinstance(writer, klass)
+
+    def test_ExcelWriter_dispatch_raises(self):
+        with pytest.raises(ValueError, match="No engine"):
+            ExcelWriter("nothing")
+
+    def test_register_writer(self):
+        class DummyClass(ExcelWriter):
+            called_save = False
+            called_write_cells = False
+            called_sheets = False
+            _supported_extensions = ("xlsx", "xls")
+            _engine = "dummy"
+
+            def book(self):
+                pass
+
+            def _save(self):
+                type(self).called_save = True
+
+            def _write_cells(self, *args, **kwargs):
+                type(self).called_write_cells = True
+
+            @property
+            def sheets(self):
+                type(self).called_sheets = True
+
+            @classmethod
+            def assert_called_and_reset(cls):
+                assert cls.called_save
+                assert cls.called_write_cells
+                assert not cls.called_sheets
+                cls.called_save = False
+                cls.called_write_cells = False
+
+        register_writer(DummyClass)
+
+        with option_context("io.excel.xlsx.writer", "dummy"):
+            path = "something.xlsx"
+            with tm.ensure_clean(path) as filepath:
+                with ExcelWriter(filepath) as writer:
+                    assert isinstance(writer, DummyClass)
+                df = DataFrame(
+                    ["a"],
+                    columns=Index(["b"], name="foo"),
+                    index=Index(["c"], name="bar"),
+                )
+                df.to_excel(filepath)
+            DummyClass.assert_called_and_reset()
+
+        with tm.ensure_clean("something.xls") as filepath:
+            df.to_excel(filepath, engine="dummy")
+        DummyClass.assert_called_and_reset()
+
+
+@td.skip_if_no("xlrd")
+@td.skip_if_no("openpyxl")
+class TestFSPath:
+    def test_excelfile_fspath(self):
+        with tm.ensure_clean("foo.xlsx") as path:
+            df = DataFrame({"A": [1, 2]})
+            df.to_excel(path)
+            with ExcelFile(path) as xl:
+                result = os.fspath(xl)
+            assert result == path
+
+    def test_excelwriter_fspath(self):
+        with tm.ensure_clean("foo.xlsx") as path:
+            with ExcelWriter(path) as writer:
+                assert os.fspath(writer) == str(path)
+
+    def test_to_excel_pos_args_deprecation(self):
+        # GH-54229
+        df = DataFrame({"a": [1, 2, 3]})
+        msg = (
+            r"Starting with pandas version 3.0 all arguments of to_excel except "
+            r"for the argument 'excel_writer' will be keyword-only."
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            buf = BytesIO()
+            writer = ExcelWriter(buf)
+            df.to_excel(writer, "Sheet_name_1")
+
+
+@pytest.mark.parametrize("klass", _writers.values())
+def test_subclass_attr(klass):
+    # testing that subclasses of ExcelWriter don't have public attributes (issue 49602)
+    attrs_base = {name for name in dir(ExcelWriter) if not name.startswith("_")}
+    attrs_klass = {name for name in dir(klass) if not name.startswith("_")}
+    assert not attrs_base.symmetric_difference(attrs_klass)

venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_xlrd.py

@@ -0,0 +1,76 @@
+import io
+
+import numpy as np
+import pytest
+
+from pandas.compat import is_platform_windows
+
+import pandas as pd
+import pandas._testing as tm
+
+from pandas.io.excel import ExcelFile
+from pandas.io.excel._base import inspect_excel_format
+
+xlrd = pytest.importorskip("xlrd")
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+@pytest.fixture(params=[".xls"])
+def read_ext_xlrd(request):
+    """
+    Valid extensions for reading Excel files with xlrd.
+
+    Similar to read_ext, but excludes .ods, .xlsb, and for xlrd>2 .xlsx, .xlsm
+    """
+    return request.param
+
+
+def test_read_xlrd_book(read_ext_xlrd, datapath):
+    engine = "xlrd"
+    sheet_name = "Sheet1"
+    pth = datapath("io", "data", "excel", "test1.xls")
+    with xlrd.open_workbook(pth) as book:
+        with ExcelFile(book, engine=engine) as xl:
+            result = pd.read_excel(xl, sheet_name=sheet_name, index_col=0)
+
+        expected = pd.read_excel(
+            book, sheet_name=sheet_name, engine=engine, index_col=0
+        )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_read_xlsx_fails(datapath):
+    # GH 29375
+    from xlrd.biffh import XLRDError
+
+    path = datapath("io", "data", "excel", "test1.xlsx")
+    with pytest.raises(XLRDError, match="Excel xlsx file; not supported"):
+        pd.read_excel(path, engine="xlrd")
+
+
+def test_nan_in_xls(datapath):
+    # GH 54564
+    path = datapath("io", "data", "excel", "test6.xls")
+
+    expected = pd.DataFrame({0: np.r_[0, 2].astype("int64"), 1: np.r_[1, np.nan]})
+
+    result = pd.read_excel(path, header=None)
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "file_header",
+    [
+        b"\x09\x00\x04\x00\x07\x00\x10\x00",
+        b"\x09\x02\x06\x00\x00\x00\x10\x00",
+        b"\x09\x04\x06\x00\x00\x00\x10\x00",
+        b"\xd0\xcf\x11\xe0\xa1\xb1\x1a\xe1",
+    ],
+)
+def test_read_old_xls_files(file_header):
+    # GH 41226
+    f = io.BytesIO(file_header)
+    assert inspect_excel_format(f) == "xls"

venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_xlsxwriter.py

@@ -0,0 +1,86 @@
+import contextlib
+
+import pytest
+
+from pandas.compat import is_platform_windows
+
+from pandas import DataFrame
+import pandas._testing as tm
+
+from pandas.io.excel import ExcelWriter
+
+xlsxwriter = pytest.importorskip("xlsxwriter")
+
+if is_platform_windows():
+    pytestmark = pytest.mark.single_cpu
+
+
+@pytest.fixture
+def ext():
+    return ".xlsx"
+
+
+def test_column_format(ext):
+    # Test that column formats are applied to cells. Test for issue #9167.
+    # Applicable to xlsxwriter only.
+    openpyxl = pytest.importorskip("openpyxl")
+
+    with tm.ensure_clean(ext) as path:
+        frame = DataFrame({"A": [123456, 123456], "B": [123456, 123456]})
+
+        with ExcelWriter(path) as writer:
+            frame.to_excel(writer)
+
+            # Add a number format to col B and ensure it is applied to cells.
+            num_format = "#,##0"
+            write_workbook = writer.book
+            write_worksheet = write_workbook.worksheets()[0]
+            col_format = write_workbook.add_format({"num_format": num_format})
+            write_worksheet.set_column("B:B", None, col_format)
+
+        with contextlib.closing(openpyxl.load_workbook(path)) as read_workbook:
+            try:
+                read_worksheet = read_workbook["Sheet1"]
+            except TypeError:
+                # compat
+                read_worksheet = read_workbook.get_sheet_by_name(name="Sheet1")
+
+        # Get the number format from the cell.
+        try:
+            cell = read_worksheet["B2"]
+        except TypeError:
+            # compat
+            cell = read_worksheet.cell("B2")
+
+        try:
+            read_num_format = cell.number_format
+        except AttributeError:
+            read_num_format = cell.style.number_format._format_code
+
+        assert read_num_format == num_format
+
+
+def test_write_append_mode_raises(ext):
+    msg = "Append mode is not supported with xlsxwriter!"
+
+    with tm.ensure_clean(ext) as f:
+        with pytest.raises(ValueError, match=msg):
+            ExcelWriter(f, engine="xlsxwriter", mode="a")
+
+
+@pytest.mark.parametrize("nan_inf_to_errors", [True, False])
+def test_engine_kwargs(ext, nan_inf_to_errors):
+    # GH 42286
+    engine_kwargs = {"options": {"nan_inf_to_errors": nan_inf_to_errors}}
+    with tm.ensure_clean(ext) as f:
+        with ExcelWriter(f, engine="xlsxwriter", engine_kwargs=engine_kwargs) as writer:
+            assert writer.book.nan_inf_to_errors == nan_inf_to_errors
+
+
+def test_book_and_sheets_consistent(ext):
+    # GH#45687 - Ensure sheets is updated if user modifies book
+    with tm.ensure_clean(ext) as f:
+        with ExcelWriter(f, engine="xlsxwriter") as writer:
+            assert writer.sheets == {}
+            sheet = writer.book.add_worksheet("test_name")
+            assert writer.sheets == {"test_name": sheet}

venv/lib/python3.10/site-packages/pandas/tests/io/json/conftest.py

@@ -0,0 +1,9 @@
+import pytest
+
+
+@pytest.fixture(params=["split", "records", "index", "columns", "values"])
+def orient(request):
+    """
+    Fixture for orients excluding the table format.
+    """
+    return request.param

venv/lib/python3.10/site-packages/pandas/tests/io/json/test_json_table_schema_ext_dtype.py

@@ -0,0 +1,317 @@
+"""Tests for ExtensionDtype Table Schema integration."""
+
+from collections import OrderedDict
+import datetime as dt
+import decimal
+from io import StringIO
+import json
+
+import pytest
+
+from pandas import (
+    NA,
+    DataFrame,
+    Index,
+    array,
+    read_json,
+)
+import pandas._testing as tm
+from pandas.core.arrays.integer import Int64Dtype
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.series import Series
+from pandas.tests.extension.date import (
+    DateArray,
+    DateDtype,
+)
+from pandas.tests.extension.decimal.array import (
+    DecimalArray,
+    DecimalDtype,
+)
+
+from pandas.io.json._table_schema import (
+    as_json_table_type,
+    build_table_schema,
+)
+
+
+class TestBuildSchema:
+    def test_build_table_schema(self):
+        df = DataFrame(
+            {
+                "A": DateArray([dt.date(2021, 10, 10)]),
+                "B": DecimalArray([decimal.Decimal(10)]),
+                "C": array(["pandas"], dtype="string"),
+                "D": array([10], dtype="Int64"),
+            }
+        )
+        result = build_table_schema(df, version=False)
+        expected = {
+            "fields": [
+                {"name": "index", "type": "integer"},
+                {"name": "A", "type": "any", "extDtype": "DateDtype"},
+                {"name": "B", "type": "number", "extDtype": "decimal"},
+                {"name": "C", "type": "any", "extDtype": "string"},
+                {"name": "D", "type": "integer", "extDtype": "Int64"},
+            ],
+            "primaryKey": ["index"],
+        }
+        assert result == expected
+        result = build_table_schema(df)
+        assert "pandas_version" in result
+
+
+class TestTableSchemaType:
+    @pytest.mark.parametrize(
+        "date_data",
+        [
+            DateArray([dt.date(2021, 10, 10)]),
+            DateArray(dt.date(2021, 10, 10)),
+            Series(DateArray(dt.date(2021, 10, 10))),
+        ],
+    )
+    def test_as_json_table_type_ext_date_array_dtype(self, date_data):
+        assert as_json_table_type(date_data.dtype) == "any"
+
+    def test_as_json_table_type_ext_date_dtype(self):
+        assert as_json_table_type(DateDtype()) == "any"
+
+    @pytest.mark.parametrize(
+        "decimal_data",
+        [
+            DecimalArray([decimal.Decimal(10)]),
+            Series(DecimalArray([decimal.Decimal(10)])),
+        ],
+    )
+    def test_as_json_table_type_ext_decimal_array_dtype(self, decimal_data):
+        assert as_json_table_type(decimal_data.dtype) == "number"
+
+    def test_as_json_table_type_ext_decimal_dtype(self):
+        assert as_json_table_type(DecimalDtype()) == "number"
+
+    @pytest.mark.parametrize(
+        "string_data",
+        [
+            array(["pandas"], dtype="string"),
+            Series(array(["pandas"], dtype="string")),
+        ],
+    )
+    def test_as_json_table_type_ext_string_array_dtype(self, string_data):
+        assert as_json_table_type(string_data.dtype) == "any"
+
+    def test_as_json_table_type_ext_string_dtype(self):
+        assert as_json_table_type(StringDtype()) == "any"
+
+    @pytest.mark.parametrize(
+        "integer_data",
+        [
+            array([10], dtype="Int64"),
+            Series(array([10], dtype="Int64")),
+        ],
+    )
+    def test_as_json_table_type_ext_integer_array_dtype(self, integer_data):
+        assert as_json_table_type(integer_data.dtype) == "integer"
+
+    def test_as_json_table_type_ext_integer_dtype(self):
+        assert as_json_table_type(Int64Dtype()) == "integer"
+
+
+class TestTableOrient:
+    @pytest.fixture
+    def da(self):
+        return DateArray([dt.date(2021, 10, 10)])
+
+    @pytest.fixture
+    def dc(self):
+        return DecimalArray([decimal.Decimal(10)])
+
+    @pytest.fixture
+    def sa(self):
+        return array(["pandas"], dtype="string")
+
+    @pytest.fixture
+    def ia(self):
+        return array([10], dtype="Int64")
+
+    @pytest.fixture
+    def df(self, da, dc, sa, ia):
+        return DataFrame(
+            {
+                "A": da,
+                "B": dc,
+                "C": sa,
+                "D": ia,
+            }
+        )
+
+    def test_build_date_series(self, da):
+        s = Series(da, name="a")
+        s.index.name = "id"
+        result = s.to_json(orient="table", date_format="iso")
+        result = json.loads(result, object_pairs_hook=OrderedDict)
+
+        assert "pandas_version" in result["schema"]
+        result["schema"].pop("pandas_version")
+
+        fields = [
+            {"name": "id", "type": "integer"},
+            {"name": "a", "type": "any", "extDtype": "DateDtype"},
+        ]
+
+        schema = {"fields": fields, "primaryKey": ["id"]}
+
+        expected = OrderedDict(
+            [
+                ("schema", schema),
+                ("data", [OrderedDict([("id", 0), ("a", "2021-10-10T00:00:00.000")])]),
+            ]
+        )
+
+        assert result == expected
+
+    def test_build_decimal_series(self, dc):
+        s = Series(dc, name="a")
+        s.index.name = "id"
+        result = s.to_json(orient="table", date_format="iso")
+        result = json.loads(result, object_pairs_hook=OrderedDict)
+
+        assert "pandas_version" in result["schema"]
+        result["schema"].pop("pandas_version")
+
+        fields = [
+            {"name": "id", "type": "integer"},
+            {"name": "a", "type": "number", "extDtype": "decimal"},
+        ]
+
+        schema = {"fields": fields, "primaryKey": ["id"]}
+
+        expected = OrderedDict(
+            [
+                ("schema", schema),
+                ("data", [OrderedDict([("id", 0), ("a", 10.0)])]),
+            ]
+        )
+
+        assert result == expected
+
+    def test_build_string_series(self, sa):
+        s = Series(sa, name="a")
+        s.index.name = "id"
+        result = s.to_json(orient="table", date_format="iso")
+        result = json.loads(result, object_pairs_hook=OrderedDict)
+
+        assert "pandas_version" in result["schema"]
+        result["schema"].pop("pandas_version")
+
+        fields = [
+            {"name": "id", "type": "integer"},
+            {"name": "a", "type": "any", "extDtype": "string"},
+        ]
+
+        schema = {"fields": fields, "primaryKey": ["id"]}
+
+        expected = OrderedDict(
+            [
+                ("schema", schema),
+                ("data", [OrderedDict([("id", 0), ("a", "pandas")])]),
+            ]
+        )
+
+        assert result == expected
+
+    def test_build_int64_series(self, ia):
+        s = Series(ia, name="a")
+        s.index.name = "id"
+        result = s.to_json(orient="table", date_format="iso")
+        result = json.loads(result, object_pairs_hook=OrderedDict)
+
+        assert "pandas_version" in result["schema"]
+        result["schema"].pop("pandas_version")
+
+        fields = [
+            {"name": "id", "type": "integer"},
+            {"name": "a", "type": "integer", "extDtype": "Int64"},
+        ]
+
+        schema = {"fields": fields, "primaryKey": ["id"]}
+
+        expected = OrderedDict(
+            [
+                ("schema", schema),
+                ("data", [OrderedDict([("id", 0), ("a", 10)])]),
+            ]
+        )
+
+        assert result == expected
+
+    def test_to_json(self, df):
+        df = df.copy()
+        df.index.name = "idx"
+        result = df.to_json(orient="table", date_format="iso")
+        result = json.loads(result, object_pairs_hook=OrderedDict)
+
+        assert "pandas_version" in result["schema"]
+        result["schema"].pop("pandas_version")
+
+        fields = [
+            OrderedDict({"name": "idx", "type": "integer"}),
+            OrderedDict({"name": "A", "type": "any", "extDtype": "DateDtype"}),
+            OrderedDict({"name": "B", "type": "number", "extDtype": "decimal"}),
+            OrderedDict({"name": "C", "type": "any", "extDtype": "string"}),
+            OrderedDict({"name": "D", "type": "integer", "extDtype": "Int64"}),
+        ]
+
+        schema = OrderedDict({"fields": fields, "primaryKey": ["idx"]})
+        data = [
+            OrderedDict(
+                [
+                    ("idx", 0),
+                    ("A", "2021-10-10T00:00:00.000"),
+                    ("B", 10.0),
+                    ("C", "pandas"),
+                    ("D", 10),
+                ]
+            )
+        ]
+        expected = OrderedDict([("schema", schema), ("data", data)])
+
+        assert result == expected
+
+    def test_json_ext_dtype_reading_roundtrip(self):
+        # GH#40255
+        df = DataFrame(
+            {
+                "a": Series([2, NA], dtype="Int64"),
+                "b": Series([1.5, NA], dtype="Float64"),
+                "c": Series([True, NA], dtype="boolean"),
+            },
+            index=Index([1, NA], dtype="Int64"),
+        )
+        expected = df.copy()
+        data_json = df.to_json(orient="table", indent=4)
+        result = read_json(StringIO(data_json), orient="table")
+        tm.assert_frame_equal(result, expected)
+
+    def test_json_ext_dtype_reading(self):
+        # GH#40255
+        data_json = """{
+            "schema":{
+                "fields":[
+                    {
+                        "name":"a",
+                        "type":"integer",
+                        "extDtype":"Int64"
+                    }
+                ],
+            },
+            "data":[
+                {
+                    "a":2
+                },
+                {
+                    "a":null
+                }
+            ]
+        }"""
+        result = read_json(StringIO(data_json), orient="table")
+        expected = DataFrame({"a": Series([2, NA], dtype="Int64")})
+        tm.assert_frame_equal(result, expected)

venv/lib/python3.10/site-packages/pandas/tests/io/json/test_normalize.py

@@ -0,0 +1,907 @@
+import json
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    json_normalize,
+)
+import pandas._testing as tm
+
+from pandas.io.json._normalize import nested_to_record
+
+
+@pytest.fixture
+def deep_nested():
+    # deeply nested data
+    return [
+        {
+            "country": "USA",
+            "states": [
+                {
+                    "name": "California",
+                    "cities": [
+                        {"name": "San Francisco", "pop": 12345},
+                        {"name": "Los Angeles", "pop": 12346},
+                    ],
+                },
+                {
+                    "name": "Ohio",
+                    "cities": [
+                        {"name": "Columbus", "pop": 1234},
+                        {"name": "Cleveland", "pop": 1236},
+                    ],
+                },
+            ],
+        },
+        {
+            "country": "Germany",
+            "states": [
+                {"name": "Bayern", "cities": [{"name": "Munich", "pop": 12347}]},
+                {
+                    "name": "Nordrhein-Westfalen",
+                    "cities": [
+                        {"name": "Duesseldorf", "pop": 1238},
+                        {"name": "Koeln", "pop": 1239},
+                    ],
+                },
+            ],
+        },
+    ]
+
+
+@pytest.fixture
+def state_data():
+    return [
+        {
+            "counties": [
+                {"name": "Dade", "population": 12345},
+                {"name": "Broward", "population": 40000},
+                {"name": "Palm Beach", "population": 60000},
+            ],
+            "info": {"governor": "Rick Scott"},
+            "shortname": "FL",
+            "state": "Florida",
+        },
+        {
+            "counties": [
+                {"name": "Summit", "population": 1234},
+                {"name": "Cuyahoga", "population": 1337},
+            ],
+            "info": {"governor": "John Kasich"},
+            "shortname": "OH",
+            "state": "Ohio",
+        },
+    ]
+
+
+@pytest.fixture
+def author_missing_data():
+    return [
+        {"info": None},
+        {
+            "info": {"created_at": "11/08/1993", "last_updated": "26/05/2012"},
+            "author_name": {"first": "Jane", "last_name": "Doe"},
+        },
+    ]
+
+
+@pytest.fixture
+def missing_metadata():
+    return [
+        {
+            "name": "Alice",
+            "addresses": [
+                {
+                    "number": 9562,
+                    "street": "Morris St.",
+                    "city": "Massillon",
+                    "state": "OH",
+                    "zip": 44646,
+                }
+            ],
+            "previous_residences": {"cities": [{"city_name": "Foo York City"}]},
+        },
+        {
+            "addresses": [
+                {
+                    "number": 8449,
+                    "street": "Spring St.",
+                    "city": "Elizabethton",
+                    "state": "TN",
+                    "zip": 37643,
+                }
+            ],
+            "previous_residences": {"cities": [{"city_name": "Barmingham"}]},
+        },
+    ]
+
+
+@pytest.fixture
+def max_level_test_input_data():
+    """
+    input data to test json_normalize with max_level param
+    """
+    return [
+        {
+            "CreatedBy": {"Name": "User001"},
+            "Lookup": {
+                "TextField": "Some text",
+                "UserField": {"Id": "ID001", "Name": "Name001"},
+            },
+            "Image": {"a": "b"},
+        }
+    ]
+
+
+class TestJSONNormalize:
+    def test_simple_records(self):
+        recs = [
+            {"a": 1, "b": 2, "c": 3},
+            {"a": 4, "b": 5, "c": 6},
+            {"a": 7, "b": 8, "c": 9},
+            {"a": 10, "b": 11, "c": 12},
+        ]
+
+        result = json_normalize(recs)
+        expected = DataFrame(recs)
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_simple_normalize(self, state_data):
+        result = json_normalize(state_data[0], "counties")
+        expected = DataFrame(state_data[0]["counties"])
+        tm.assert_frame_equal(result, expected)
+
+        result = json_normalize(state_data, "counties")
+
+        expected = []
+        for rec in state_data:
+            expected.extend(rec["counties"])
+        expected = DataFrame(expected)
+
+        tm.assert_frame_equal(result, expected)
+
+        result = json_normalize(state_data, "counties", meta="state")
+        expected["state"] = np.array(["Florida", "Ohio"]).repeat([3, 2])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_fields_list_type_normalize(self):
+        parse_metadata_fields_list_type = [
+            {"values": [1, 2, 3], "metadata": {"listdata": [1, 2]}}
+        ]
+        result = json_normalize(
+            parse_metadata_fields_list_type,
+            record_path=["values"],
+            meta=[["metadata", "listdata"]],
+        )
+        expected = DataFrame(
+            {0: [1, 2, 3], "metadata.listdata": [[1, 2], [1, 2], [1, 2]]}
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_empty_array(self):
+        result = json_normalize([])
+        expected = DataFrame()
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data, record_path, exception_type",
+        [
+            ([{"a": 0}, {"a": 1}], None, None),
+            ({"a": [{"a": 0}, {"a": 1}]}, "a", None),
+            ('{"a": [{"a": 0}, {"a": 1}]}', None, NotImplementedError),
+            (None, None, NotImplementedError),
+        ],
+    )
+    def test_accepted_input(self, data, record_path, exception_type):
+        if exception_type is not None:
+            with pytest.raises(exception_type, match=""):
+                json_normalize(data, record_path=record_path)
+        else:
+            result = json_normalize(data, record_path=record_path)
+            expected = DataFrame([0, 1], columns=["a"])
+            tm.assert_frame_equal(result, expected)
+
+    def test_simple_normalize_with_separator(self, deep_nested):
+        # GH 14883
+        result = json_normalize({"A": {"A": 1, "B": 2}})
+        expected = DataFrame([[1, 2]], columns=["A.A", "A.B"])
+        tm.assert_frame_equal(result.reindex_like(expected), expected)
+
+        result = json_normalize({"A": {"A": 1, "B": 2}}, sep="_")
+        expected = DataFrame([[1, 2]], columns=["A_A", "A_B"])
+        tm.assert_frame_equal(result.reindex_like(expected), expected)
+
+        result = json_normalize({"A": {"A": 1, "B": 2}}, sep="\u03c3")
+        expected = DataFrame([[1, 2]], columns=["A\u03c3A", "A\u03c3B"])
+        tm.assert_frame_equal(result.reindex_like(expected), expected)
+
+        result = json_normalize(
+            deep_nested,
+            ["states", "cities"],
+            meta=["country", ["states", "name"]],
+            sep="_",
+        )
+        expected = Index(["name", "pop", "country", "states_name"]).sort_values()
+        assert result.columns.sort_values().equals(expected)
+
+    def test_normalize_with_multichar_separator(self):
+        # GH #43831
+        data = {"a": [1, 2], "b": {"b_1": 2, "b_2": (3, 4)}}
+        result = json_normalize(data, sep="__")
+        expected = DataFrame([[[1, 2], 2, (3, 4)]], columns=["a", "b__b_1", "b__b_2"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_value_array_record_prefix(self):
+        # GH 21536
+        result = json_normalize({"A": [1, 2]}, "A", record_prefix="Prefix.")
+        expected = DataFrame([[1], [2]], columns=["Prefix.0"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_nested_object_record_path(self):
+        # GH 22706
+        data = {
+            "state": "Florida",
+            "info": {
+                "governor": "Rick Scott",
+                "counties": [
+                    {"name": "Dade", "population": 12345},
+                    {"name": "Broward", "population": 40000},
+                    {"name": "Palm Beach", "population": 60000},
+                ],
+            },
+        }
+        result = json_normalize(data, record_path=["info", "counties"])
+        expected = DataFrame(
+            [["Dade", 12345], ["Broward", 40000], ["Palm Beach", 60000]],
+            columns=["name", "population"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_more_deeply_nested(self, deep_nested):
+        result = json_normalize(
+            deep_nested, ["states", "cities"], meta=["country", ["states", "name"]]
+        )
+        ex_data = {
+            "country": ["USA"] * 4 + ["Germany"] * 3,
+            "states.name": [
+                "California",
+                "California",
+                "Ohio",
+                "Ohio",
+                "Bayern",
+                "Nordrhein-Westfalen",
+                "Nordrhein-Westfalen",
+            ],
+            "name": [
+                "San Francisco",
+                "Los Angeles",
+                "Columbus",
+                "Cleveland",
+                "Munich",
+                "Duesseldorf",
+                "Koeln",
+            ],
+            "pop": [12345, 12346, 1234, 1236, 12347, 1238, 1239],
+        }
+
+        expected = DataFrame(ex_data, columns=result.columns)
+        tm.assert_frame_equal(result, expected)
+
+    def test_shallow_nested(self):
+        data = [
+            {
+                "state": "Florida",
+                "shortname": "FL",
+                "info": {"governor": "Rick Scott"},
+                "counties": [
+                    {"name": "Dade", "population": 12345},
+                    {"name": "Broward", "population": 40000},
+                    {"name": "Palm Beach", "population": 60000},
+                ],
+            },
+            {
+                "state": "Ohio",
+                "shortname": "OH",
+                "info": {"governor": "John Kasich"},
+                "counties": [
+                    {"name": "Summit", "population": 1234},
+                    {"name": "Cuyahoga", "population": 1337},
+                ],
+            },
+        ]
+
+        result = json_normalize(
+            data, "counties", ["state", "shortname", ["info", "governor"]]
+        )
+        ex_data = {
+            "name": ["Dade", "Broward", "Palm Beach", "Summit", "Cuyahoga"],
+            "state": ["Florida"] * 3 + ["Ohio"] * 2,
+            "shortname": ["FL", "FL", "FL", "OH", "OH"],
+            "info.governor": ["Rick Scott"] * 3 + ["John Kasich"] * 2,
+            "population": [12345, 40000, 60000, 1234, 1337],
+        }
+        expected = DataFrame(ex_data, columns=result.columns)
+        tm.assert_frame_equal(result, expected)
+
+    def test_nested_meta_path_with_nested_record_path(self, state_data):
+        # GH 27220
+        result = json_normalize(
+            data=state_data,
+            record_path=["counties"],
+            meta=["state", "shortname", ["info", "governor"]],
+            errors="ignore",
+        )
+
+        ex_data = {
+            "name": ["Dade", "Broward", "Palm Beach", "Summit", "Cuyahoga"],
+            "population": [12345, 40000, 60000, 1234, 1337],
+            "state": ["Florida"] * 3 + ["Ohio"] * 2,
+            "shortname": ["FL"] * 3 + ["OH"] * 2,
+            "info.governor": ["Rick Scott"] * 3 + ["John Kasich"] * 2,
+        }
+
+        expected = DataFrame(ex_data)
+        tm.assert_frame_equal(result, expected)
+
+    def test_meta_name_conflict(self):
+        data = [
+            {
+                "foo": "hello",
+                "bar": "there",
+                "data": [
+                    {"foo": "something", "bar": "else"},
+                    {"foo": "something2", "bar": "else2"},
+                ],
+            }
+        ]
+
+        msg = r"Conflicting metadata name (foo|bar), need distinguishing prefix"
+        with pytest.raises(ValueError, match=msg):
+            json_normalize(data, "data", meta=["foo", "bar"])
+
+        result = json_normalize(data, "data", meta=["foo", "bar"], meta_prefix="meta")
+
+        for val in ["metafoo", "metabar", "foo", "bar"]:
+            assert val in result
+
+    def test_meta_parameter_not_modified(self):
+        # GH 18610
+        data = [
+            {
+                "foo": "hello",
+                "bar": "there",
+                "data": [
+                    {"foo": "something", "bar": "else"},
+                    {"foo": "something2", "bar": "else2"},
+                ],
+            }
+        ]
+
+        COLUMNS = ["foo", "bar"]
+        result = json_normalize(data, "data", meta=COLUMNS, meta_prefix="meta")
+
+        assert COLUMNS == ["foo", "bar"]
+        for val in ["metafoo", "metabar", "foo", "bar"]:
+            assert val in result
+
+    def test_record_prefix(self, state_data):
+        result = json_normalize(state_data[0], "counties")
+        expected = DataFrame(state_data[0]["counties"])
+        tm.assert_frame_equal(result, expected)
+
+        result = json_normalize(
+            state_data, "counties", meta="state", record_prefix="county_"
+        )
+
+        expected = []
+        for rec in state_data:
+            expected.extend(rec["counties"])
+        expected = DataFrame(expected)
+        expected = expected.rename(columns=lambda x: "county_" + x)
+        expected["state"] = np.array(["Florida", "Ohio"]).repeat([3, 2])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_non_ascii_key(self):
+        testjson = (
+            b'[{"\xc3\x9cnic\xc3\xb8de":0,"sub":{"A":1, "B":2}},'
+            b'{"\xc3\x9cnic\xc3\xb8de":1,"sub":{"A":3, "B":4}}]'
+        ).decode("utf8")
+
+        testdata = {
+            b"\xc3\x9cnic\xc3\xb8de".decode("utf8"): [0, 1],
+            "sub.A": [1, 3],
+            "sub.B": [2, 4],
+        }
+        expected = DataFrame(testdata)
+
+        result = json_normalize(json.loads(testjson))
+        tm.assert_frame_equal(result, expected)
+
+    def test_missing_field(self, author_missing_data):
+        # GH20030:
+        result = json_normalize(author_missing_data)
+        ex_data = [
+            {
+                "info": np.nan,
+                "info.created_at": np.nan,
+                "info.last_updated": np.nan,
+                "author_name.first": np.nan,
+                "author_name.last_name": np.nan,
+            },
+            {
+                "info": None,
+                "info.created_at": "11/08/1993",
+                "info.last_updated": "26/05/2012",
+                "author_name.first": "Jane",
+                "author_name.last_name": "Doe",
+            },
+        ]
+        expected = DataFrame(ex_data)
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "max_level,expected",
+        [
+            (
+                0,
+                [
+                    {
+                        "TextField": "Some text",
+                        "UserField": {"Id": "ID001", "Name": "Name001"},
+                        "CreatedBy": {"Name": "User001"},
+                        "Image": {"a": "b"},
+                    },
+                    {
+                        "TextField": "Some text",
+                        "UserField": {"Id": "ID001", "Name": "Name001"},
+                        "CreatedBy": {"Name": "User001"},
+                        "Image": {"a": "b"},
+                    },
+                ],
+            ),
+            (
+                1,
+                [
+                    {
+                        "TextField": "Some text",
+                        "UserField.Id": "ID001",
+                        "UserField.Name": "Name001",
+                        "CreatedBy": {"Name": "User001"},
+                        "Image": {"a": "b"},
+                    },
+                    {
+                        "TextField": "Some text",
+                        "UserField.Id": "ID001",
+                        "UserField.Name": "Name001",
+                        "CreatedBy": {"Name": "User001"},
+                        "Image": {"a": "b"},
+                    },
+                ],
+            ),
+        ],
+    )
+    def test_max_level_with_records_path(self, max_level, expected):
+        # GH23843: Enhanced JSON normalize
+        test_input = [
+            {
+                "CreatedBy": {"Name": "User001"},
+                "Lookup": [
+                    {
+                        "TextField": "Some text",
+                        "UserField": {"Id": "ID001", "Name": "Name001"},
+                    },
+                    {
+                        "TextField": "Some text",
+                        "UserField": {"Id": "ID001", "Name": "Name001"},
+                    },
+                ],
+                "Image": {"a": "b"},
+                "tags": [
+                    {"foo": "something", "bar": "else"},
+                    {"foo": "something2", "bar": "else2"},
+                ],
+            }
+        ]
+
+        result = json_normalize(
+            test_input,
+            record_path=["Lookup"],
+            meta=[["CreatedBy"], ["Image"]],
+            max_level=max_level,
+        )
+        expected_df = DataFrame(data=expected, columns=result.columns.values)
+        tm.assert_equal(expected_df, result)
+
+    def test_nested_flattening_consistent(self):
+        # see gh-21537
+        df1 = json_normalize([{"A": {"B": 1}}])
+        df2 = json_normalize({"dummy": [{"A": {"B": 1}}]}, "dummy")
+
+        # They should be the same.
+        tm.assert_frame_equal(df1, df2)
+
+    def test_nonetype_record_path(self, nulls_fixture):
+        # see gh-30148
+        # should not raise TypeError
+        result = json_normalize(
+            [
+                {"state": "Texas", "info": nulls_fixture},
+                {"state": "Florida", "info": [{"i": 2}]},
+            ],
+            record_path=["info"],
+        )
+        expected = DataFrame({"i": 2}, index=[0])
+        tm.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("value", ["false", "true", "{}", "1", '"text"'])
+    def test_non_list_record_path_errors(self, value):
+        # see gh-30148, GH 26284
+        parsed_value = json.loads(value)
+        test_input = {"state": "Texas", "info": parsed_value}
+        test_path = "info"
+        msg = (
+            f"{test_input} has non list value {parsed_value} for path {test_path}. "
+            "Must be list or null."
+        )
+        with pytest.raises(TypeError, match=msg):
+            json_normalize([test_input], record_path=[test_path])
+
+    def test_meta_non_iterable(self):
+        # GH 31507
+        data = """[{"id": 99, "data": [{"one": 1, "two": 2}]}]"""
+
+        result = json_normalize(json.loads(data), record_path=["data"], meta=["id"])
+        expected = DataFrame(
+            {"one": [1], "two": [2], "id": np.array([99], dtype=object)}
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_generator(self, state_data):
+        # GH35923 Fix pd.json_normalize to not skip the first element of a
+        # generator input
+        def generator_data():
+            yield from state_data[0]["counties"]
+
+        result = json_normalize(generator_data())
+        expected = DataFrame(state_data[0]["counties"])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_top_column_with_leading_underscore(self):
+        # 49861
+        data = {"_id": {"a1": 10, "l2": {"l3": 0}}, "gg": 4}
+        result = json_normalize(data, sep="_")
+        expected = DataFrame([[4, 10, 0]], columns=["gg", "_id_a1", "_id_l2_l3"])
+
+        tm.assert_frame_equal(result, expected)
+
+
+class TestNestedToRecord:
+    def test_flat_stays_flat(self):
+        recs = [{"flat1": 1, "flat2": 2}, {"flat3": 3, "flat2": 4}]
+        result = nested_to_record(recs)
+        expected = recs
+        assert result == expected
+
+    def test_one_level_deep_flattens(self):
+        data = {"flat1": 1, "dict1": {"c": 1, "d": 2}}
+
+        result = nested_to_record(data)
+        expected = {"dict1.c": 1, "dict1.d": 2, "flat1": 1}
+
+        assert result == expected
+
+    def test_nested_flattens(self):
+        data = {
+            "flat1": 1,
+            "dict1": {"c": 1, "d": 2},
+            "nested": {"e": {"c": 1, "d": 2}, "d": 2},
+        }
+
+        result = nested_to_record(data)
+        expected = {
+            "dict1.c": 1,
+            "dict1.d": 2,
+            "flat1": 1,
+            "nested.d": 2,
+            "nested.e.c": 1,
+            "nested.e.d": 2,
+        }
+
+        assert result == expected
+
+    def test_json_normalize_errors(self, missing_metadata):
+        # GH14583:
+        # If meta keys are not always present a new option to set
+        # errors='ignore' has been implemented
+
+        msg = (
+            "Key 'name' not found. To replace missing values of "
+            "'name' with np.nan, pass in errors='ignore'"
+        )
+        with pytest.raises(KeyError, match=msg):
+            json_normalize(
+                data=missing_metadata,
+                record_path="addresses",
+                meta="name",
+                errors="raise",
+            )
+
+    def test_missing_meta(self, missing_metadata):
+        # GH25468
+        # If metadata is nullable with errors set to ignore, the null values
+        # should be numpy.nan values
+        result = json_normalize(
+            data=missing_metadata, record_path="addresses", meta="name", errors="ignore"
+        )
+        ex_data = [
+            [9562, "Morris St.", "Massillon", "OH", 44646, "Alice"],
+            [8449, "Spring St.", "Elizabethton", "TN", 37643, np.nan],
+        ]
+        columns = ["number", "street", "city", "state", "zip", "name"]
+        expected = DataFrame(ex_data, columns=columns)
+        tm.assert_frame_equal(result, expected)
+
+    def test_missing_nested_meta(self):
+        # GH44312
+        # If errors="ignore" and nested metadata is null, we should return nan
+        data = {"meta": "foo", "nested_meta": None, "value": [{"rec": 1}, {"rec": 2}]}
+        result = json_normalize(
+            data,
+            record_path="value",
+            meta=["meta", ["nested_meta", "leaf"]],
+            errors="ignore",
+        )
+        ex_data = [[1, "foo", np.nan], [2, "foo", np.nan]]
+        columns = ["rec", "meta", "nested_meta.leaf"]
+        expected = DataFrame(ex_data, columns=columns).astype(
+            {"nested_meta.leaf": object}
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # If errors="raise" and nested metadata is null, we should raise with the
+        # key of the first missing level
+        with pytest.raises(KeyError, match="'leaf' not found"):
+            json_normalize(
+                data,
+                record_path="value",
+                meta=["meta", ["nested_meta", "leaf"]],
+                errors="raise",
+            )
+
+    def test_missing_meta_multilevel_record_path_errors_raise(self, missing_metadata):
+        # GH41876
+        # Ensure errors='raise' works as intended even when a record_path of length
+        # greater than one is passed in
+        msg = (
+            "Key 'name' not found. To replace missing values of "
+            "'name' with np.nan, pass in errors='ignore'"
+        )
+        with pytest.raises(KeyError, match=msg):
+            json_normalize(
+                data=missing_metadata,
+                record_path=["previous_residences", "cities"],
+                meta="name",
+                errors="raise",
+            )
+
+    def test_missing_meta_multilevel_record_path_errors_ignore(self, missing_metadata):
+        # GH41876
+        # Ensure errors='ignore' works as intended even when a record_path of length
+        # greater than one is passed in
+        result = json_normalize(
+            data=missing_metadata,
+            record_path=["previous_residences", "cities"],
+            meta="name",
+            errors="ignore",
+        )
+        ex_data = [
+            ["Foo York City", "Alice"],
+            ["Barmingham", np.nan],
+        ]
+        columns = ["city_name", "name"]
+        expected = DataFrame(ex_data, columns=columns)
+        tm.assert_frame_equal(result, expected)
+
+    def test_donot_drop_nonevalues(self):
+        # GH21356
+        data = [
+            {"info": None, "author_name": {"first": "Smith", "last_name": "Appleseed"}},
+            {
+                "info": {"created_at": "11/08/1993", "last_updated": "26/05/2012"},
+                "author_name": {"first": "Jane", "last_name": "Doe"},
+            },
+        ]
+        result = nested_to_record(data)
+        expected = [
+            {
+                "info": None,
+                "author_name.first": "Smith",
+                "author_name.last_name": "Appleseed",
+            },
+            {
+                "author_name.first": "Jane",
+                "author_name.last_name": "Doe",
+                "info.created_at": "11/08/1993",
+                "info.last_updated": "26/05/2012",
+            },
+        ]
+
+        assert result == expected
+
+    def test_nonetype_top_level_bottom_level(self):
+        # GH21158: If inner level json has a key with a null value
+        # make sure it does not do a new_d.pop twice and except
+        data = {
+            "id": None,
+            "location": {
+                "country": {
+                    "state": {
+                        "id": None,
+                        "town.info": {
+                            "id": None,
+                            "region": None,
+                            "x": 49.151580810546875,
+                            "y": -33.148521423339844,
+                            "z": 27.572303771972656,
+                        },
+                    }
+                }
+            },
+        }
+        result = nested_to_record(data)
+        expected = {
+            "id": None,
+            "location.country.state.id": None,
+            "location.country.state.town.info.id": None,
+            "location.country.state.town.info.region": None,
+            "location.country.state.town.info.x": 49.151580810546875,
+            "location.country.state.town.info.y": -33.148521423339844,
+            "location.country.state.town.info.z": 27.572303771972656,
+        }
+        assert result == expected
+
+    def test_nonetype_multiple_levels(self):
+        # GH21158: If inner level json has a key with a null value
+        # make sure it does not do a new_d.pop twice and except
+        data = {
+            "id": None,
+            "location": {
+                "id": None,
+                "country": {
+                    "id": None,
+                    "state": {
+                        "id": None,
+                        "town.info": {
+                            "region": None,
+                            "x": 49.151580810546875,
+                            "y": -33.148521423339844,
+                            "z": 27.572303771972656,
+                        },
+                    },
+                },
+            },
+        }
+        result = nested_to_record(data)
+        expected = {
+            "id": None,
+            "location.id": None,
+            "location.country.id": None,
+            "location.country.state.id": None,
+            "location.country.state.town.info.region": None,
+            "location.country.state.town.info.x": 49.151580810546875,
+            "location.country.state.town.info.y": -33.148521423339844,
+            "location.country.state.town.info.z": 27.572303771972656,
+        }
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "max_level, expected",
+        [
+            (
+                None,
+                [
+                    {
+                        "CreatedBy.Name": "User001",
+                        "Lookup.TextField": "Some text",
+                        "Lookup.UserField.Id": "ID001",
+                        "Lookup.UserField.Name": "Name001",
+                        "Image.a": "b",
+                    }
+                ],
+            ),
+            (
+                0,
+                [
+                    {
+                        "CreatedBy": {"Name": "User001"},
+                        "Lookup": {
+                            "TextField": "Some text",
+                            "UserField": {"Id": "ID001", "Name": "Name001"},
+                        },
+                        "Image": {"a": "b"},
+                    }
+                ],
+            ),
+            (
+                1,
+                [
+                    {
+                        "CreatedBy.Name": "User001",
+                        "Lookup.TextField": "Some text",
+                        "Lookup.UserField": {"Id": "ID001", "Name": "Name001"},
+                        "Image.a": "b",
+                    }
+                ],
+            ),
+        ],
+    )
+    def test_with_max_level(self, max_level, expected, max_level_test_input_data):
+        # GH23843: Enhanced JSON normalize
+        output = nested_to_record(max_level_test_input_data, max_level=max_level)
+        assert output == expected
+
+    def test_with_large_max_level(self):
+        # GH23843: Enhanced JSON normalize
+        max_level = 100
+        input_data = [
+            {
+                "CreatedBy": {
+                    "user": {
+                        "name": {"firstname": "Leo", "LastName": "Thomson"},
+                        "family_tree": {
+                            "father": {
+                                "name": "Father001",
+                                "father": {
+                                    "Name": "Father002",
+                                    "father": {
+                                        "name": "Father003",
+                                        "father": {"Name": "Father004"},
+                                    },
+                                },
+                            }
+                        },
+                    }
+                }
+            }
+        ]
+        expected = [
+            {
+                "CreatedBy.user.name.firstname": "Leo",
+                "CreatedBy.user.name.LastName": "Thomson",
+                "CreatedBy.user.family_tree.father.name": "Father001",
+                "CreatedBy.user.family_tree.father.father.Name": "Father002",
+                "CreatedBy.user.family_tree.father.father.father.name": "Father003",
+                "CreatedBy.user.family_tree.father.father.father.father.Name": "Father004",  # noqa: E501
+            }
+        ]
+        output = nested_to_record(input_data, max_level=max_level)
+        assert output == expected
+
+    def test_series_non_zero_index(self):
+        # GH 19020
+        data = {
+            0: {"id": 1, "name": "Foo", "elements": {"a": 1}},
+            1: {"id": 2, "name": "Bar", "elements": {"b": 2}},
+            2: {"id": 3, "name": "Baz", "elements": {"c": 3}},
+        }
+        s = Series(data)
+        s.index = [1, 2, 3]
+        result = json_normalize(s)
+        expected = DataFrame(
+            {
+                "id": [1, 2, 3],
+                "name": ["Foo", "Bar", "Baz"],
+                "elements.a": [1.0, np.nan, np.nan],
+                "elements.b": [np.nan, 2.0, np.nan],
+                "elements.c": [np.nan, np.nan, 3.0],
+            }
+        )
+        tm.assert_frame_equal(result, expected)

venv/lib/python3.10/site-packages/pandas/tests/io/json/test_pandas.py

@@ -0,0 +1,2202 @@
+import datetime
+from datetime import timedelta
+from decimal import Decimal
+from io import (
+    BytesIO,
+    StringIO,
+)
+import json
+import os
+import sys
+import time
+
+import numpy as np
+import pytest
+
+from pandas._config import using_pyarrow_string_dtype
+
+from pandas.compat import IS64
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import (
+    NA,
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    RangeIndex,
+    Series,
+    Timestamp,
+    date_range,
+    read_json,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    ArrowStringArray,
+    StringArray,
+)
+from pandas.core.arrays.string_arrow import ArrowStringArrayNumpySemantics
+
+from pandas.io.json import ujson_dumps
+
+
+def test_literal_json_deprecation():
+    # PR 53409
+    expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
+
+    jsonl = """{"a": 1, "b": 2}
+        {"a": 3, "b": 4}
+        {"a": 5, "b": 6}
+        {"a": 7, "b": 8}"""
+
+    msg = (
+        "Passing literal json to 'read_json' is deprecated and "
+        "will be removed in a future version. To read from a "
+        "literal string, wrap it in a 'StringIO' object."
+    )
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        try:
+            read_json(jsonl, lines=False)
+        except ValueError:
+            pass
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        read_json(expected.to_json(), lines=False)
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = read_json('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n', lines=True)
+        tm.assert_frame_equal(result, expected)
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        try:
+            result = read_json(
+                '{"a\\\\":"foo\\\\","b":"bar"}\n{"a\\\\":"foo\\"","b":"bar"}\n',
+                lines=False,
+            )
+        except ValueError:
+            pass
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        try:
+            result = read_json('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n', lines=False)
+        except ValueError:
+            pass
+        tm.assert_frame_equal(result, expected)
+
+
+def assert_json_roundtrip_equal(result, expected, orient):
+    if orient in ("records", "values"):
+        expected = expected.reset_index(drop=True)
+    if orient == "values":
+        expected.columns = range(len(expected.columns))
+    tm.assert_frame_equal(result, expected)
+
+
+class TestPandasContainer:
+    @pytest.fixture
+    def categorical_frame(self):
+        data = {
+            c: np.random.default_rng(i).standard_normal(30)
+            for i, c in enumerate(list("ABCD"))
+        }
+        cat = ["bah"] * 5 + ["bar"] * 5 + ["baz"] * 5 + ["foo"] * 15
+        data["E"] = list(reversed(cat))
+        data["sort"] = np.arange(30, dtype="int64")
+        return DataFrame(data, index=pd.CategoricalIndex(cat, name="E"))
+
+    @pytest.fixture
+    def datetime_series(self):
+        # Same as usual datetime_series, but with index freq set to None,
+        #  since that doesn't round-trip, see GH#33711
+        ser = Series(
+            1.1 * np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
+        ser.index = ser.index._with_freq(None)
+        return ser
+
+    @pytest.fixture
+    def datetime_frame(self):
+        # Same as usual datetime_frame, but with index freq set to None,
+        #  since that doesn't round-trip, see GH#33711
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((30, 4)),
+            columns=Index(list("ABCD"), dtype=object),
+            index=date_range("2000-01-01", periods=30, freq="B"),
+        )
+        df.index = df.index._with_freq(None)
+        return df
+
+    def test_frame_double_encoded_labels(self, orient):
+        df = DataFrame(
+            [["a", "b"], ["c", "d"]],
+            index=['index " 1', "index / 2"],
+            columns=["a \\ b", "y / z"],
+        )
+
+        data = StringIO(df.to_json(orient=orient))
+        result = read_json(data, orient=orient)
+        expected = df.copy()
+        assert_json_roundtrip_equal(result, expected, orient)
+
+    @pytest.mark.parametrize("orient", ["split", "records", "values"])
+    def test_frame_non_unique_index(self, orient):
+        df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 1], columns=["x", "y"])
+        data = StringIO(df.to_json(orient=orient))
+        result = read_json(data, orient=orient)
+        expected = df.copy()
+
+        assert_json_roundtrip_equal(result, expected, orient)
+
+    @pytest.mark.parametrize("orient", ["index", "columns"])
+    def test_frame_non_unique_index_raises(self, orient):
+        df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 1], columns=["x", "y"])
+        msg = f"DataFrame index must be unique for orient='{orient}'"
+        with pytest.raises(ValueError, match=msg):
+            df.to_json(orient=orient)
+
+    @pytest.mark.parametrize("orient", ["split", "values"])
+    @pytest.mark.parametrize(
+        "data",
+        [
+            [["a", "b"], ["c", "d"]],
+            [[1.5, 2.5], [3.5, 4.5]],
+            [[1, 2.5], [3, 4.5]],
+            [[Timestamp("20130101"), 3.5], [Timestamp("20130102"), 4.5]],
+        ],
+    )
+    def test_frame_non_unique_columns(self, orient, data):
+        df = DataFrame(data, index=[1, 2], columns=["x", "x"])
+
+        result = read_json(
+            StringIO(df.to_json(orient=orient)), orient=orient, convert_dates=["x"]
+        )
+        if orient == "values":
+            expected = DataFrame(data)
+            if expected.iloc[:, 0].dtype == "datetime64[ns]":
+                # orient == "values" by default will write Timestamp objects out
+                # in milliseconds; these are internally stored in nanosecond,
+                # so divide to get where we need
+                # TODO: a to_epoch method would also solve; see GH 14772
+                expected.isetitem(0, expected.iloc[:, 0].astype(np.int64) // 1000000)
+        elif orient == "split":
+            expected = df
+            expected.columns = ["x", "x.1"]
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("orient", ["index", "columns", "records"])
+    def test_frame_non_unique_columns_raises(self, orient):
+        df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 2], columns=["x", "x"])
+
+        msg = f"DataFrame columns must be unique for orient='{orient}'"
+        with pytest.raises(ValueError, match=msg):
+            df.to_json(orient=orient)
+
+    def test_frame_default_orient(self, float_frame):
+        assert float_frame.to_json() == float_frame.to_json(orient="columns")
+
+    @pytest.mark.parametrize("dtype", [False, float])
+    @pytest.mark.parametrize("convert_axes", [True, False])
+    def test_roundtrip_simple(self, orient, convert_axes, dtype, float_frame):
+        data = StringIO(float_frame.to_json(orient=orient))
+        result = read_json(data, orient=orient, convert_axes=convert_axes, dtype=dtype)
+
+        expected = float_frame
+
+        assert_json_roundtrip_equal(result, expected, orient)
+
+    @pytest.mark.parametrize("dtype", [False, np.int64])
+    @pytest.mark.parametrize("convert_axes", [True, False])
+    def test_roundtrip_intframe(self, orient, convert_axes, dtype, int_frame):
+        data = StringIO(int_frame.to_json(orient=orient))
+        result = read_json(data, orient=orient, convert_axes=convert_axes, dtype=dtype)
+        expected = int_frame
+        assert_json_roundtrip_equal(result, expected, orient)
+
+    @pytest.mark.parametrize("dtype", [None, np.float64, int, "U3"])
+    @pytest.mark.parametrize("convert_axes", [True, False])
+    def test_roundtrip_str_axes(self, orient, convert_axes, dtype):
+        df = DataFrame(
+            np.zeros((200, 4)),
+            columns=[str(i) for i in range(4)],
+            index=[str(i) for i in range(200)],
+            dtype=dtype,
+        )
+
+        data = StringIO(df.to_json(orient=orient))
+        result = read_json(data, orient=orient, convert_axes=convert_axes, dtype=dtype)
+
+        expected = df.copy()
+        if not dtype:
+            expected = expected.astype(np.int64)
+
+        # index columns, and records orients cannot fully preserve the string
+        # dtype for axes as the index and column labels are used as keys in
+        # JSON objects. JSON keys are by definition strings, so there's no way
+        # to disambiguate whether those keys actually were strings or numeric
+        # beforehand and numeric wins out.
+        if convert_axes and (orient in ("index", "columns")):
+            expected.columns = expected.columns.astype(np.int64)
+            expected.index = expected.index.astype(np.int64)
+        elif orient == "records" and convert_axes:
+            expected.columns = expected.columns.astype(np.int64)
+        elif convert_axes and orient == "split":
+            expected.columns = expected.columns.astype(np.int64)
+
+        assert_json_roundtrip_equal(result, expected, orient)
+
+    @pytest.mark.parametrize("convert_axes", [True, False])
+    def test_roundtrip_categorical(
+        self, request, orient, categorical_frame, convert_axes, using_infer_string
+    ):
+        # TODO: create a better frame to test with and improve coverage
+        if orient in ("index", "columns"):
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason=f"Can't have duplicate index values for orient '{orient}')"
+                )
+            )
+
+        data = StringIO(categorical_frame.to_json(orient=orient))
+        result = read_json(data, orient=orient, convert_axes=convert_axes)
+
+        expected = categorical_frame.copy()
+        expected.index = expected.index.astype(
+            str if not using_infer_string else "string[pyarrow_numpy]"
+        )  # Categorical not preserved
+        expected.index.name = None  # index names aren't preserved in JSON
+        assert_json_roundtrip_equal(result, expected, orient)
+
+    @pytest.mark.parametrize("convert_axes", [True, False])
+    def test_roundtrip_empty(self, orient, convert_axes):
+        empty_frame = DataFrame()
+        data = StringIO(empty_frame.to_json(orient=orient))
+        result = read_json(data, orient=orient, convert_axes=convert_axes)
+        if orient == "split":
+            idx = Index([], dtype=(float if convert_axes else object))
+            expected = DataFrame(index=idx, columns=idx)
+        elif orient in ["index", "columns"]:
+            expected = DataFrame()
+        else:
+            expected = empty_frame.copy()
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("convert_axes", [True, False])
+    def test_roundtrip_timestamp(self, orient, convert_axes, datetime_frame):
+        # TODO: improve coverage with date_format parameter
+        data = StringIO(datetime_frame.to_json(orient=orient))
+        result = read_json(data, orient=orient, convert_axes=convert_axes)
+        expected = datetime_frame.copy()
+
+        if not convert_axes:  # one off for ts handling
+            # DTI gets converted to epoch values
+            idx = expected.index.view(np.int64) // 1000000
+            if orient != "split":  # TODO: handle consistently across orients
+                idx = idx.astype(str)
+
+            expected.index = idx
+
+        assert_json_roundtrip_equal(result, expected, orient)
+
+    @pytest.mark.parametrize("convert_axes", [True, False])
+    def test_roundtrip_mixed(self, orient, convert_axes):
+        index = Index(["a", "b", "c", "d", "e"])
+        values = {
+            "A": [0.0, 1.0, 2.0, 3.0, 4.0],
+            "B": [0.0, 1.0, 0.0, 1.0, 0.0],
+            "C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
+            "D": [True, False, True, False, True],
+        }
+
+        df = DataFrame(data=values, index=index)
+
+        data = StringIO(df.to_json(orient=orient))
+        result = read_json(data, orient=orient, convert_axes=convert_axes)
+
+        expected = df.copy()
+        expected = expected.assign(**expected.select_dtypes("number").astype(np.int64))
+
+        assert_json_roundtrip_equal(result, expected, orient)
+
+    @pytest.mark.xfail(
+        reason="#50456 Column multiindex is stored and loaded differently",
+        raises=AssertionError,
+    )
+    @pytest.mark.parametrize(
+        "columns",
+        [
+            [["2022", "2022"], ["JAN", "FEB"]],
+            [["2022", "2023"], ["JAN", "JAN"]],
+            [["2022", "2022"], ["JAN", "JAN"]],
+        ],
+    )
+    def test_roundtrip_multiindex(self, columns):
+        df = DataFrame(
+            [[1, 2], [3, 4]],
+            columns=pd.MultiIndex.from_arrays(columns),
+        )
+        data = StringIO(df.to_json(orient="split"))
+        result = read_json(data, orient="split")
+        tm.assert_frame_equal(result, df)
+
+    @pytest.mark.parametrize(
+        "data,msg,orient",
+        [
+            ('{"key":b:a:d}', "Expected object or value", "columns"),
+            # too few indices
+            (
+                '{"columns":["A","B"],'
+                '"index":["2","3"],'
+                '"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}',
+                "|".join(
+                    [
+                        r"Length of values \(3\) does not match length of index \(2\)",
+                    ]
+                ),
+                "split",
+            ),
+            # too many columns
+            (
+                '{"columns":["A","B","C"],'
+                '"index":["1","2","3"],'
+                '"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}',
+                "3 columns passed, passed data had 2 columns",
+                "split",
+            ),
+            # bad key
+            (
+                '{"badkey":["A","B"],'
+                '"index":["2","3"],'
+                '"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}',
+                r"unexpected key\(s\): badkey",
+                "split",
+            ),
+        ],
+    )
+    def test_frame_from_json_bad_data_raises(self, data, msg, orient):
+        with pytest.raises(ValueError, match=msg):
+            read_json(StringIO(data), orient=orient)
+
+    @pytest.mark.parametrize("dtype", [True, False])
+    @pytest.mark.parametrize("convert_axes", [True, False])
+    def test_frame_from_json_missing_data(self, orient, convert_axes, dtype):
+        num_df = DataFrame([[1, 2], [4, 5, 6]])
+
+        result = read_json(
+            StringIO(num_df.to_json(orient=orient)),
+            orient=orient,
+            convert_axes=convert_axes,
+            dtype=dtype,
+        )
+        assert np.isnan(result.iloc[0, 2])
+
+        obj_df = DataFrame([["1", "2"], ["4", "5", "6"]])
+        result = read_json(
+            StringIO(obj_df.to_json(orient=orient)),
+            orient=orient,
+            convert_axes=convert_axes,
+            dtype=dtype,
+        )
+        assert np.isnan(result.iloc[0, 2])
+
+    @pytest.mark.parametrize("dtype", [True, False])
+    def test_frame_read_json_dtype_missing_value(self, dtype):
+        # GH28501 Parse missing values using read_json with dtype=False
+        # to NaN instead of None
+        result = read_json(StringIO("[null]"), dtype=dtype)
+        expected = DataFrame([np.nan])
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("inf", [np.inf, -np.inf])
+    @pytest.mark.parametrize("dtype", [True, False])
+    def test_frame_infinity(self, inf, dtype):
+        # infinities get mapped to nulls which get mapped to NaNs during
+        # deserialisation
+        df = DataFrame([[1, 2], [4, 5, 6]])
+        df.loc[0, 2] = inf
+
+        data = StringIO(df.to_json())
+        result = read_json(data, dtype=dtype)
+        assert np.isnan(result.iloc[0, 2])
+
+    @pytest.mark.skipif(not IS64, reason="not compliant on 32-bit, xref #15865")
+    @pytest.mark.parametrize(
+        "value,precision,expected_val",
+        [
+            (0.95, 1, 1.0),
+            (1.95, 1, 2.0),
+            (-1.95, 1, -2.0),
+            (0.995, 2, 1.0),
+            (0.9995, 3, 1.0),
+            (0.99999999999999944, 15, 1.0),
+        ],
+    )
+    def test_frame_to_json_float_precision(self, value, precision, expected_val):
+        df = DataFrame([{"a_float": value}])
+        encoded = df.to_json(double_precision=precision)
+        assert encoded == f'{{"a_float":{{"0":{expected_val}}}}}'
+
+    def test_frame_to_json_except(self):
+        df = DataFrame([1, 2, 3])
+        msg = "Invalid value 'garbage' for option 'orient'"
+        with pytest.raises(ValueError, match=msg):
+            df.to_json(orient="garbage")
+
+    def test_frame_empty(self):
+        df = DataFrame(columns=["jim", "joe"])
+        assert not df._is_mixed_type
+
+        data = StringIO(df.to_json())
+        result = read_json(data, dtype=dict(df.dtypes))
+        tm.assert_frame_equal(result, df, check_index_type=False)
+
+    def test_frame_empty_to_json(self):
+        # GH 7445
+        df = DataFrame({"test": []}, index=[])
+        result = df.to_json(orient="columns")
+        expected = '{"test":{}}'
+        assert result == expected
+
+    def test_frame_empty_mixedtype(self):
+        # mixed type
+        df = DataFrame(columns=["jim", "joe"])
+        df["joe"] = df["joe"].astype("i8")
+        assert df._is_mixed_type
+        data = df.to_json()
+        tm.assert_frame_equal(
+            read_json(StringIO(data), dtype=dict(df.dtypes)),
+            df,
+            check_index_type=False,
+        )
+
+    def test_frame_mixedtype_orient(self):  # GH10289
+        vals = [
+            [10, 1, "foo", 0.1, 0.01],
+            [20, 2, "bar", 0.2, 0.02],
+            [30, 3, "baz", 0.3, 0.03],
+            [40, 4, "qux", 0.4, 0.04],
+        ]
+
+        df = DataFrame(
+            vals, index=list("abcd"), columns=["1st", "2nd", "3rd", "4th", "5th"]
+        )
+
+        assert df._is_mixed_type
+        right = df.copy()
+
+        for orient in ["split", "index", "columns"]:
+            inp = StringIO(df.to_json(orient=orient))
+            left = read_json(inp, orient=orient, convert_axes=False)
+            tm.assert_frame_equal(left, right)
+
+        right.index = RangeIndex(len(df))
+        inp = StringIO(df.to_json(orient="records"))
+        left = read_json(inp, orient="records", convert_axes=False)
+        tm.assert_frame_equal(left, right)
+
+        right.columns = RangeIndex(df.shape[1])
+        inp = StringIO(df.to_json(orient="values"))
+        left = read_json(inp, orient="values", convert_axes=False)
+        tm.assert_frame_equal(left, right)
+
+    def test_v12_compat(self, datapath):
+        dti = date_range("2000-01-03", "2000-01-07")
+        # freq doesn't roundtrip
+        dti = DatetimeIndex(np.asarray(dti), freq=None)
+        df = DataFrame(
+            [
+                [1.56808523, 0.65727391, 1.81021139, -0.17251653],
+                [-0.2550111, -0.08072427, -0.03202878, -0.17581665],
+                [1.51493992, 0.11805825, 1.629455, -1.31506612],
+                [-0.02765498, 0.44679743, 0.33192641, -0.27885413],
+                [0.05951614, -2.69652057, 1.28163262, 0.34703478],
+            ],
+            columns=["A", "B", "C", "D"],
+            index=dti,
+        )
+        df["date"] = Timestamp("19920106 18:21:32.12").as_unit("ns")
+        df.iloc[3, df.columns.get_loc("date")] = Timestamp("20130101")
+        df["modified"] = df["date"]
+        df.iloc[1, df.columns.get_loc("modified")] = pd.NaT
+
+        dirpath = datapath("io", "json", "data")
+        v12_json = os.path.join(dirpath, "tsframe_v012.json")
+        df_unser = read_json(v12_json)
+        tm.assert_frame_equal(df, df_unser)
+
+        df_iso = df.drop(["modified"], axis=1)
+        v12_iso_json = os.path.join(dirpath, "tsframe_iso_v012.json")
+        df_unser_iso = read_json(v12_iso_json)
+        tm.assert_frame_equal(df_iso, df_unser_iso, check_column_type=False)
+
+    def test_blocks_compat_GH9037(self, using_infer_string):
+        index = date_range("20000101", periods=10, freq="h")
+        # freq doesn't round-trip
+        index = DatetimeIndex(list(index), freq=None)
+
+        df_mixed = DataFrame(
+            {
+                "float_1": [
+                    -0.92077639,
+                    0.77434435,
+                    1.25234727,
+                    0.61485564,
+                    -0.60316077,
+                    0.24653374,
+                    0.28668979,
+                    -2.51969012,
+                    0.95748401,
+                    -1.02970536,
+                ],
+                "int_1": [
+                    19680418,
+                    75337055,
+                    99973684,
+                    65103179,
+                    79373900,
+                    40314334,
+                    21290235,
+                    4991321,
+                    41903419,
+                    16008365,
+                ],
+                "str_1": [
+                    "78c608f1",
+                    "64a99743",
+                    "13d2ff52",
+                    "ca7f4af2",
+                    "97236474",
+                    "bde7e214",
+                    "1a6bde47",
+                    "b1190be5",
+                    "7a669144",
+                    "8d64d068",
+                ],
+                "float_2": [
+                    -0.0428278,
+                    -1.80872357,
+                    3.36042349,
+                    -0.7573685,
+                    -0.48217572,
+                    0.86229683,
+                    1.08935819,
+                    0.93898739,
+                    -0.03030452,
+                    1.43366348,
+                ],
+                "str_2": [
+                    "14f04af9",
+                    "d085da90",
+                    "4bcfac83",
+                    "81504caf",
+                    "2ffef4a9",
+                    "08e2f5c4",
+                    "07e1af03",
+                    "addbd4a7",
+                    "1f6a09ba",
+                    "4bfc4d87",
+                ],
+                "int_2": [
+                    86967717,
+                    98098830,
+                    51927505,
+                    20372254,
+                    12601730,
+                    20884027,
+                    34193846,
+                    10561746,
+                    24867120,
+                    76131025,
+                ],
+            },
+            index=index,
+        )
+
+        # JSON deserialisation always creates unicode strings
+        df_mixed.columns = df_mixed.columns.astype(
+            np.str_ if not using_infer_string else "string[pyarrow_numpy]"
+        )
+        data = StringIO(df_mixed.to_json(orient="split"))
+        df_roundtrip = read_json(data, orient="split")
+        tm.assert_frame_equal(
+            df_mixed,
+            df_roundtrip,
+            check_index_type=True,
+            check_column_type=True,
+            by_blocks=True,
+            check_exact=True,
+        )
+
+    def test_frame_nonprintable_bytes(self):
+        # GH14256: failing column caused segfaults, if it is not the last one
+
+        class BinaryThing:
+            def __init__(self, hexed) -> None:
+                self.hexed = hexed
+                self.binary = bytes.fromhex(hexed)
+
+            def __str__(self) -> str:
+                return self.hexed
+
+        hexed = "574b4454ba8c5eb4f98a8f45"
+        binthing = BinaryThing(hexed)
+
+        # verify the proper conversion of printable content
+        df_printable = DataFrame({"A": [binthing.hexed]})
+        assert df_printable.to_json() == f'{{"A":{{"0":"{hexed}"}}}}'
+
+        # check if non-printable content throws appropriate Exception
+        df_nonprintable = DataFrame({"A": [binthing]})
+        msg = "Unsupported UTF-8 sequence length when encoding string"
+        with pytest.raises(OverflowError, match=msg):
+            df_nonprintable.to_json()
+
+        # the same with multiple columns threw segfaults
+        df_mixed = DataFrame({"A": [binthing], "B": [1]}, columns=["A", "B"])
+        with pytest.raises(OverflowError, match=msg):
+            df_mixed.to_json()
+
+        # default_handler should resolve exceptions for non-string types
+        result = df_nonprintable.to_json(default_handler=str)
+        expected = f'{{"A":{{"0":"{hexed}"}}}}'
+        assert result == expected
+        assert (
+            df_mixed.to_json(default_handler=str)
+            == f'{{"A":{{"0":"{hexed}"}},"B":{{"0":1}}}}'
+        )
+
+    def test_label_overflow(self):
+        # GH14256: buffer length not checked when writing label
+        result = DataFrame({"bar" * 100000: [1], "foo": [1337]}).to_json()
+        expected = f'{{"{"bar" * 100000}":{{"0":1}},"foo":{{"0":1337}}}}'
+        assert result == expected
+
+    def test_series_non_unique_index(self):
+        s = Series(["a", "b"], index=[1, 1])
+
+        msg = "Series index must be unique for orient='index'"
+        with pytest.raises(ValueError, match=msg):
+            s.to_json(orient="index")
+
+        tm.assert_series_equal(
+            s,
+            read_json(
+                StringIO(s.to_json(orient="split")), orient="split", typ="series"
+            ),
+        )
+        unserialized = read_json(
+            StringIO(s.to_json(orient="records")), orient="records", typ="series"
+        )
+        tm.assert_equal(s.values, unserialized.values)
+
+    def test_series_default_orient(self, string_series):
+        assert string_series.to_json() == string_series.to_json(orient="index")
+
+    def test_series_roundtrip_simple(self, orient, string_series, using_infer_string):
+        data = StringIO(string_series.to_json(orient=orient))
+        result = read_json(data, typ="series", orient=orient)
+
+        expected = string_series
+        if using_infer_string and orient in ("split", "index", "columns"):
+            # These schemas don't contain dtypes, so we infer string
+            expected.index = expected.index.astype("string[pyarrow_numpy]")
+        if orient in ("values", "records"):
+            expected = expected.reset_index(drop=True)
+        if orient != "split":
+            expected.name = None
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [False, None])
+    def test_series_roundtrip_object(self, orient, dtype, object_series):
+        data = StringIO(object_series.to_json(orient=orient))
+        result = read_json(data, typ="series", orient=orient, dtype=dtype)
+
+        expected = object_series
+        if orient in ("values", "records"):
+            expected = expected.reset_index(drop=True)
+        if orient != "split":
+            expected.name = None
+
+        tm.assert_series_equal(result, expected)
+
+    def test_series_roundtrip_empty(self, orient):
+        empty_series = Series([], index=[], dtype=np.float64)
+        data = StringIO(empty_series.to_json(orient=orient))
+        result = read_json(data, typ="series", orient=orient)
+
+        expected = empty_series.reset_index(drop=True)
+        if orient in ("split"):
+            expected.index = expected.index.astype(np.float64)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_series_roundtrip_timeseries(self, orient, datetime_series):
+        data = StringIO(datetime_series.to_json(orient=orient))
+        result = read_json(data, typ="series", orient=orient)
+
+        expected = datetime_series
+        if orient in ("values", "records"):
+            expected = expected.reset_index(drop=True)
+        if orient != "split":
+            expected.name = None
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [np.float64, int])
+    def test_series_roundtrip_numeric(self, orient, dtype):
+        s = Series(range(6), index=["a", "b", "c", "d", "e", "f"])
+        data = StringIO(s.to_json(orient=orient))
+        result = read_json(data, typ="series", orient=orient)
+
+        expected = s.copy()
+        if orient in ("values", "records"):
+            expected = expected.reset_index(drop=True)
+
+        tm.assert_series_equal(result, expected)
+
+    def test_series_to_json_except(self):
+        s = Series([1, 2, 3])
+        msg = "Invalid value 'garbage' for option 'orient'"
+        with pytest.raises(ValueError, match=msg):
+            s.to_json(orient="garbage")
+
+    def test_series_from_json_precise_float(self):
+        s = Series([4.56, 4.56, 4.56])
+        result = read_json(StringIO(s.to_json()), typ="series", precise_float=True)
+        tm.assert_series_equal(result, s, check_index_type=False)
+
+    def test_series_with_dtype(self):
+        # GH 21986
+        s = Series([4.56, 4.56, 4.56])
+        result = read_json(StringIO(s.to_json()), typ="series", dtype=np.int64)
+        expected = Series([4] * 3)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dtype,expected",
+        [
+            (True, Series(["2000-01-01"], dtype="datetime64[ns]")),
+            (False, Series([946684800000])),
+        ],
+    )
+    def test_series_with_dtype_datetime(self, dtype, expected):
+        s = Series(["2000-01-01"], dtype="datetime64[ns]")
+        data = StringIO(s.to_json())
+        result = read_json(data, typ="series", dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_frame_from_json_precise_float(self):
+        df = DataFrame([[4.56, 4.56, 4.56], [4.56, 4.56, 4.56]])
+        result = read_json(StringIO(df.to_json()), precise_float=True)
+        tm.assert_frame_equal(result, df)
+
+    def test_typ(self):
+        s = Series(range(6), index=["a", "b", "c", "d", "e", "f"], dtype="int64")
+        result = read_json(StringIO(s.to_json()), typ=None)
+        tm.assert_series_equal(result, s)
+
+    def test_reconstruction_index(self):
+        df = DataFrame([[1, 2, 3], [4, 5, 6]])
+        result = read_json(StringIO(df.to_json()))
+        tm.assert_frame_equal(result, df)
+
+        df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=["A", "B", "C"])
+        result = read_json(StringIO(df.to_json()))
+        tm.assert_frame_equal(result, df)
+
+    def test_path(self, float_frame, int_frame, datetime_frame):
+        with tm.ensure_clean("test.json") as path:
+            for df in [float_frame, int_frame, datetime_frame]:
+                df.to_json(path)
+                read_json(path)
+
+    def test_axis_dates(self, datetime_series, datetime_frame):
+        # frame
+        json = StringIO(datetime_frame.to_json())
+        result = read_json(json)
+        tm.assert_frame_equal(result, datetime_frame)
+
+        # series
+        json = StringIO(datetime_series.to_json())
+        result = read_json(json, typ="series")
+        tm.assert_series_equal(result, datetime_series, check_names=False)
+        assert result.name is None
+
+    def test_convert_dates(self, datetime_series, datetime_frame):
+        # frame
+        df = datetime_frame
+        df["date"] = Timestamp("20130101").as_unit("ns")
+
+        json = StringIO(df.to_json())
+        result = read_json(json)
+        tm.assert_frame_equal(result, df)
+
+        df["foo"] = 1.0
+        json = StringIO(df.to_json(date_unit="ns"))
+
+        result = read_json(json, convert_dates=False)
+        expected = df.copy()
+        expected["date"] = expected["date"].values.view("i8")
+        expected["foo"] = expected["foo"].astype("int64")
+        tm.assert_frame_equal(result, expected)
+
+        # series
+        ts = Series(Timestamp("20130101").as_unit("ns"), index=datetime_series.index)
+        json = StringIO(ts.to_json())
+        result = read_json(json, typ="series")
+        tm.assert_series_equal(result, ts)
+
+    @pytest.mark.parametrize("date_format", ["epoch", "iso"])
+    @pytest.mark.parametrize("as_object", [True, False])
+    @pytest.mark.parametrize("date_typ", [datetime.date, datetime.datetime, Timestamp])
+    def test_date_index_and_values(self, date_format, as_object, date_typ):
+        data = [date_typ(year=2020, month=1, day=1), pd.NaT]
+        if as_object:
+            data.append("a")
+
+        ser = Series(data, index=data)
+        result = ser.to_json(date_format=date_format)
+
+        if date_format == "epoch":
+            expected = '{"1577836800000":1577836800000,"null":null}'
+        else:
+            expected = (
+                '{"2020-01-01T00:00:00.000":"2020-01-01T00:00:00.000","null":null}'
+            )
+
+        if as_object:
+            expected = expected.replace("}", ',"a":"a"}')
+
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "infer_word",
+        [
+            "trade_time",
+            "date",
+            "datetime",
+            "sold_at",
+            "modified",
+            "timestamp",
+            "timestamps",
+        ],
+    )
+    def test_convert_dates_infer(self, infer_word):
+        # GH10747
+
+        data = [{"id": 1, infer_word: 1036713600000}, {"id": 2}]
+        expected = DataFrame(
+            [[1, Timestamp("2002-11-08")], [2, pd.NaT]], columns=["id", infer_word]
+        )
+
+        result = read_json(StringIO(ujson_dumps(data)))[["id", infer_word]]
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "date,date_unit",
+        [
+            ("20130101 20:43:42.123", None),
+            ("20130101 20:43:42", "s"),
+            ("20130101 20:43:42.123", "ms"),
+            ("20130101 20:43:42.123456", "us"),
+            ("20130101 20:43:42.123456789", "ns"),
+        ],
+    )
+    def test_date_format_frame(self, date, date_unit, datetime_frame):
+        df = datetime_frame
+
+        df["date"] = Timestamp(date).as_unit("ns")
+        df.iloc[1, df.columns.get_loc("date")] = pd.NaT
+        df.iloc[5, df.columns.get_loc("date")] = pd.NaT
+        if date_unit:
+            json = df.to_json(date_format="iso", date_unit=date_unit)
+        else:
+            json = df.to_json(date_format="iso")
+
+        result = read_json(StringIO(json))
+        expected = df.copy()
+        tm.assert_frame_equal(result, expected)
+
+    def test_date_format_frame_raises(self, datetime_frame):
+        df = datetime_frame
+        msg = "Invalid value 'foo' for option 'date_unit'"
+        with pytest.raises(ValueError, match=msg):
+            df.to_json(date_format="iso", date_unit="foo")
+
+    @pytest.mark.parametrize(
+        "date,date_unit",
+        [
+            ("20130101 20:43:42.123", None),
+            ("20130101 20:43:42", "s"),
+            ("20130101 20:43:42.123", "ms"),
+            ("20130101 20:43:42.123456", "us"),
+            ("20130101 20:43:42.123456789", "ns"),
+        ],
+    )
+    def test_date_format_series(self, date, date_unit, datetime_series):
+        ts = Series(Timestamp(date).as_unit("ns"), index=datetime_series.index)
+        ts.iloc[1] = pd.NaT
+        ts.iloc[5] = pd.NaT
+        if date_unit:
+            json = ts.to_json(date_format="iso", date_unit=date_unit)
+        else:
+            json = ts.to_json(date_format="iso")
+
+        result = read_json(StringIO(json), typ="series")
+        expected = ts.copy()
+        tm.assert_series_equal(result, expected)
+
+    def test_date_format_series_raises(self, datetime_series):
+        ts = Series(Timestamp("20130101 20:43:42.123"), index=datetime_series.index)
+        msg = "Invalid value 'foo' for option 'date_unit'"
+        with pytest.raises(ValueError, match=msg):
+            ts.to_json(date_format="iso", date_unit="foo")
+
+    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+    def test_date_unit(self, unit, datetime_frame):
+        df = datetime_frame
+        df["date"] = Timestamp("20130101 20:43:42").as_unit("ns")
+        dl = df.columns.get_loc("date")
+        df.iloc[1, dl] = Timestamp("19710101 20:43:42")
+        df.iloc[2, dl] = Timestamp("21460101 20:43:42")
+        df.iloc[4, dl] = pd.NaT
+
+        json = df.to_json(date_format="epoch", date_unit=unit)
+
+        # force date unit
+        result = read_json(StringIO(json), date_unit=unit)
+        tm.assert_frame_equal(result, df)
+
+        # detect date unit
+        result = read_json(StringIO(json), date_unit=None)
+        tm.assert_frame_equal(result, df)
+
+    @pytest.mark.parametrize("unit", ["s", "ms", "us"])
+    def test_iso_non_nano_datetimes(self, unit):
+        # Test that numpy datetimes
+        # in an Index or a column with non-nano resolution can be serialized
+        # correctly
+        # GH53686
+        index = DatetimeIndex(
+            [np.datetime64("2023-01-01T11:22:33.123456", unit)],
+            dtype=f"datetime64[{unit}]",
+        )
+        df = DataFrame(
+            {
+                "date": Series(
+                    [np.datetime64("2022-01-01T11:22:33.123456", unit)],
+                    dtype=f"datetime64[{unit}]",
+                    index=index,
+                ),
+                "date_obj": Series(
+                    [np.datetime64("2023-01-01T11:22:33.123456", unit)],
+                    dtype=object,
+                    index=index,
+                ),
+            },
+        )
+
+        buf = StringIO()
+        df.to_json(buf, date_format="iso", date_unit=unit)
+        buf.seek(0)
+
+        # read_json always reads datetimes in nanosecond resolution
+        # TODO: check_dtype/check_index_type should be removable
+        # once read_json gets non-nano support
+        tm.assert_frame_equal(
+            read_json(buf, convert_dates=["date", "date_obj"]),
+            df,
+            check_index_type=False,
+            check_dtype=False,
+        )
+
+    def test_weird_nested_json(self):
+        # this used to core dump the parser
+        s = r"""{
+        "status": "success",
+        "data": {
+        "posts": [
+            {
+            "id": 1,
+            "title": "A blog post",
+            "body": "Some useful content"
+            },
+            {
+            "id": 2,
+            "title": "Another blog post",
+            "body": "More content"
+            }
+           ]
+          }
+        }"""
+        read_json(StringIO(s))
+
+    def test_doc_example(self):
+        dfj2 = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 2)), columns=list("AB")
+        )
+        dfj2["date"] = Timestamp("20130101")
+        dfj2["ints"] = range(5)
+        dfj2["bools"] = True
+        dfj2.index = date_range("20130101", periods=5)
+
+        json = StringIO(dfj2.to_json())
+        result = read_json(json, dtype={"ints": np.int64, "bools": np.bool_})
+        tm.assert_frame_equal(result, result)
+
+    def test_round_trip_exception(self, datapath):
+        # GH 3867
+        path = datapath("io", "json", "data", "teams.csv")
+        df = pd.read_csv(path)
+        s = df.to_json()
+
+        result = read_json(StringIO(s))
+        res = result.reindex(index=df.index, columns=df.columns)
+        msg = "The 'downcast' keyword in fillna is deprecated"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            res = res.fillna(np.nan, downcast=False)
+        tm.assert_frame_equal(res, df)
+
+    @pytest.mark.network
+    @pytest.mark.single_cpu
+    @pytest.mark.parametrize(
+        "field,dtype",
+        [
+            ["created_at", pd.DatetimeTZDtype(tz="UTC")],
+            ["closed_at", "datetime64[ns]"],
+            ["updated_at", pd.DatetimeTZDtype(tz="UTC")],
+        ],
+    )
+    def test_url(self, field, dtype, httpserver):
+        data = '{"created_at": ["2023-06-23T18:21:36Z"], "closed_at": ["2023-06-23T18:21:36"], "updated_at": ["2023-06-23T18:21:36Z"]}\n'  # noqa: E501
+        httpserver.serve_content(content=data)
+        result = read_json(httpserver.url, convert_dates=True)
+        assert result[field].dtype == dtype
+
+    def test_timedelta(self):
+        converter = lambda x: pd.to_timedelta(x, unit="ms")
+
+        ser = Series([timedelta(23), timedelta(seconds=5)])
+        assert ser.dtype == "timedelta64[ns]"
+
+        result = read_json(StringIO(ser.to_json()), typ="series").apply(converter)
+        tm.assert_series_equal(result, ser)
+
+        ser = Series([timedelta(23), timedelta(seconds=5)], index=Index([0, 1]))
+        assert ser.dtype == "timedelta64[ns]"
+        result = read_json(StringIO(ser.to_json()), typ="series").apply(converter)
+        tm.assert_series_equal(result, ser)
+
+        frame = DataFrame([timedelta(23), timedelta(seconds=5)])
+        assert frame[0].dtype == "timedelta64[ns]"
+        tm.assert_frame_equal(
+            frame, read_json(StringIO(frame.to_json())).apply(converter)
+        )
+
+    def test_timedelta2(self):
+        frame = DataFrame(
+            {
+                "a": [timedelta(days=23), timedelta(seconds=5)],
+                "b": [1, 2],
+                "c": date_range(start="20130101", periods=2),
+            }
+        )
+        data = StringIO(frame.to_json(date_unit="ns"))
+        result = read_json(data)
+        result["a"] = pd.to_timedelta(result.a, unit="ns")
+        result["c"] = pd.to_datetime(result.c)
+        tm.assert_frame_equal(frame, result)
+
+    def test_mixed_timedelta_datetime(self):
+        td = timedelta(23)
+        ts = Timestamp("20130101")
+        frame = DataFrame({"a": [td, ts]}, dtype=object)
+
+        expected = DataFrame(
+            {"a": [pd.Timedelta(td).as_unit("ns")._value, ts.as_unit("ns")._value]}
+        )
+        data = StringIO(frame.to_json(date_unit="ns"))
+        result = read_json(data, dtype={"a": "int64"})
+        tm.assert_frame_equal(result, expected, check_index_type=False)
+
+    @pytest.mark.parametrize("as_object", [True, False])
+    @pytest.mark.parametrize("date_format", ["iso", "epoch"])
+    @pytest.mark.parametrize("timedelta_typ", [pd.Timedelta, timedelta])
+    def test_timedelta_to_json(self, as_object, date_format, timedelta_typ):
+        # GH28156: to_json not correctly formatting Timedelta
+        data = [timedelta_typ(days=1), timedelta_typ(days=2), pd.NaT]
+        if as_object:
+            data.append("a")
+
+        ser = Series(data, index=data)
+        if date_format == "iso":
+            expected = (
+                '{"P1DT0H0M0S":"P1DT0H0M0S","P2DT0H0M0S":"P2DT0H0M0S","null":null}'
+            )
+        else:
+            expected = '{"86400000":86400000,"172800000":172800000,"null":null}'
+
+        if as_object:
+            expected = expected.replace("}", ',"a":"a"}')
+
+        result = ser.to_json(date_format=date_format)
+        assert result == expected
+
+    @pytest.mark.parametrize("as_object", [True, False])
+    @pytest.mark.parametrize("timedelta_typ", [pd.Timedelta, timedelta])
+    def test_timedelta_to_json_fractional_precision(self, as_object, timedelta_typ):
+        data = [timedelta_typ(milliseconds=42)]
+        ser = Series(data, index=data)
+        if as_object:
+            ser = ser.astype(object)
+
+        result = ser.to_json()
+        expected = '{"42":42}'
+        assert result == expected
+
+    def test_default_handler(self):
+        value = object()
+        frame = DataFrame({"a": [7, value]})
+        expected = DataFrame({"a": [7, str(value)]})
+        result = read_json(StringIO(frame.to_json(default_handler=str)))
+        tm.assert_frame_equal(expected, result, check_index_type=False)
+
+    def test_default_handler_indirect(self):
+        def default(obj):
+            if isinstance(obj, complex):
+                return [("mathjs", "Complex"), ("re", obj.real), ("im", obj.imag)]
+            return str(obj)
+
+        df_list = [
+            9,
+            DataFrame(
+                {"a": [1, "STR", complex(4, -5)], "b": [float("nan"), None, "N/A"]},
+                columns=["a", "b"],
+            ),
+        ]
+        expected = (
+            '[9,[[1,null],["STR",null],[[["mathjs","Complex"],'
+            '["re",4.0],["im",-5.0]],"N\\/A"]]]'
+        )
+        assert (
+            ujson_dumps(df_list, default_handler=default, orient="values") == expected
+        )
+
+    def test_default_handler_numpy_unsupported_dtype(self):
+        # GH12554 to_json raises 'Unhandled numpy dtype 15'
+        df = DataFrame(
+            {"a": [1, 2.3, complex(4, -5)], "b": [float("nan"), None, complex(1.2, 0)]},
+            columns=["a", "b"],
+        )
+        expected = (
+            '[["(1+0j)","(nan+0j)"],'
+            '["(2.3+0j)","(nan+0j)"],'
+            '["(4-5j)","(1.2+0j)"]]'
+        )
+        assert df.to_json(default_handler=str, orient="values") == expected
+
+    def test_default_handler_raises(self):
+        msg = "raisin"
+
+        def my_handler_raises(obj):
+            raise TypeError(msg)
+
+        with pytest.raises(TypeError, match=msg):
+            DataFrame({"a": [1, 2, object()]}).to_json(
+                default_handler=my_handler_raises
+            )
+        with pytest.raises(TypeError, match=msg):
+            DataFrame({"a": [1, 2, complex(4, -5)]}).to_json(
+                default_handler=my_handler_raises
+            )
+
+    def test_categorical(self):
+        # GH4377 df.to_json segfaults with non-ndarray blocks
+        df = DataFrame({"A": ["a", "b", "c", "a", "b", "b", "a"]})
+        df["B"] = df["A"]
+        expected = df.to_json()
+
+        df["B"] = df["A"].astype("category")
+        assert expected == df.to_json()
+
+        s = df["A"]
+        sc = df["B"]
+        assert s.to_json() == sc.to_json()
+
+    def test_datetime_tz(self):
+        # GH4377 df.to_json segfaults with non-ndarray blocks
+        tz_range = date_range("20130101", periods=3, tz="US/Eastern")
+        tz_naive = tz_range.tz_convert("utc").tz_localize(None)
+
+        df = DataFrame({"A": tz_range, "B": date_range("20130101", periods=3)})
+
+        df_naive = df.copy()
+        df_naive["A"] = tz_naive
+        expected = df_naive.to_json()
+        assert expected == df.to_json()
+
+        stz = Series(tz_range)
+        s_naive = Series(tz_naive)
+        assert stz.to_json() == s_naive.to_json()
+
+    def test_sparse(self):
+        # GH4377 df.to_json segfaults with non-ndarray blocks
+        df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)))
+        df.loc[:8] = np.nan
+
+        sdf = df.astype("Sparse")
+        expected = df.to_json()
+        assert expected == sdf.to_json()
+
+        s = Series(np.random.default_rng(2).standard_normal(10))
+        s.loc[:8] = np.nan
+        ss = s.astype("Sparse")
+
+        expected = s.to_json()
+        assert expected == ss.to_json()
+
+    @pytest.mark.parametrize(
+        "ts",
+        [
+            Timestamp("2013-01-10 05:00:00Z"),
+            Timestamp("2013-01-10 00:00:00", tz="US/Eastern"),
+            Timestamp("2013-01-10 00:00:00-0500"),
+        ],
+    )
+    def test_tz_is_utc(self, ts):
+        exp = '"2013-01-10T05:00:00.000Z"'
+
+        assert ujson_dumps(ts, iso_dates=True) == exp
+        dt = ts.to_pydatetime()
+        assert ujson_dumps(dt, iso_dates=True) == exp
+
+    def test_tz_is_naive(self):
+        ts = Timestamp("2013-01-10 05:00:00")
+        exp = '"2013-01-10T05:00:00.000"'
+
+        assert ujson_dumps(ts, iso_dates=True) == exp
+        dt = ts.to_pydatetime()
+        assert ujson_dumps(dt, iso_dates=True) == exp
+
+    @pytest.mark.parametrize(
+        "tz_range",
+        [
+            date_range("2013-01-01 05:00:00Z", periods=2),
+            date_range("2013-01-01 00:00:00", periods=2, tz="US/Eastern"),
+            date_range("2013-01-01 00:00:00-0500", periods=2),
+        ],
+    )
+    def test_tz_range_is_utc(self, tz_range):
+        exp = '["2013-01-01T05:00:00.000Z","2013-01-02T05:00:00.000Z"]'
+        dfexp = (
+            '{"DT":{'
+            '"0":"2013-01-01T05:00:00.000Z",'
+            '"1":"2013-01-02T05:00:00.000Z"}}'
+        )
+
+        assert ujson_dumps(tz_range, iso_dates=True) == exp
+        dti = DatetimeIndex(tz_range)
+        # Ensure datetimes in object array are serialized correctly
+        # in addition to the normal DTI case
+        assert ujson_dumps(dti, iso_dates=True) == exp
+        assert ujson_dumps(dti.astype(object), iso_dates=True) == exp
+        df = DataFrame({"DT": dti})
+        result = ujson_dumps(df, iso_dates=True)
+        assert result == dfexp
+        assert ujson_dumps(df.astype({"DT": object}), iso_dates=True)
+
+    def test_tz_range_is_naive(self):
+        dti = date_range("2013-01-01 05:00:00", periods=2)
+
+        exp = '["2013-01-01T05:00:00.000","2013-01-02T05:00:00.000"]'
+        dfexp = '{"DT":{"0":"2013-01-01T05:00:00.000","1":"2013-01-02T05:00:00.000"}}'
+
+        # Ensure datetimes in object array are serialized correctly
+        # in addition to the normal DTI case
+        assert ujson_dumps(dti, iso_dates=True) == exp
+        assert ujson_dumps(dti.astype(object), iso_dates=True) == exp
+        df = DataFrame({"DT": dti})
+        result = ujson_dumps(df, iso_dates=True)
+        assert result == dfexp
+        assert ujson_dumps(df.astype({"DT": object}), iso_dates=True)
+
+    def test_read_inline_jsonl(self):
+        # GH9180
+
+        result = read_json(StringIO('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n'), lines=True)
+        expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.single_cpu
+    @td.skip_if_not_us_locale
+    def test_read_s3_jsonl(self, s3_public_bucket_with_data, s3so):
+        # GH17200
+
+        result = read_json(
+            f"s3n://{s3_public_bucket_with_data.name}/items.jsonl",
+            lines=True,
+            storage_options=s3so,
+        )
+        expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
+        tm.assert_frame_equal(result, expected)
+
+    def test_read_local_jsonl(self):
+        # GH17200
+        with tm.ensure_clean("tmp_items.json") as path:
+            with open(path, "w", encoding="utf-8") as infile:
+                infile.write('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n')
+            result = read_json(path, lines=True)
+            expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
+            tm.assert_frame_equal(result, expected)
+
+    def test_read_jsonl_unicode_chars(self):
+        # GH15132: non-ascii unicode characters
+        # \u201d == RIGHT DOUBLE QUOTATION MARK
+
+        # simulate file handle
+        json = '{"a": "foo”", "b": "bar"}\n{"a": "foo", "b": "bar"}\n'
+        json = StringIO(json)
+        result = read_json(json, lines=True)
+        expected = DataFrame([["foo\u201d", "bar"], ["foo", "bar"]], columns=["a", "b"])
+        tm.assert_frame_equal(result, expected)
+
+        # simulate string
+        json = StringIO('{"a": "foo”", "b": "bar"}\n{"a": "foo", "b": "bar"}\n')
+        result = read_json(json, lines=True)
+        expected = DataFrame([["foo\u201d", "bar"], ["foo", "bar"]], columns=["a", "b"])
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("bigNum", [sys.maxsize + 1, -(sys.maxsize + 2)])
+    def test_to_json_large_numbers(self, bigNum):
+        # GH34473
+        series = Series(bigNum, dtype=object, index=["articleId"])
+        json = series.to_json()
+        expected = '{"articleId":' + str(bigNum) + "}"
+        assert json == expected
+
+        df = DataFrame(bigNum, dtype=object, index=["articleId"], columns=[0])
+        json = df.to_json()
+        expected = '{"0":{"articleId":' + str(bigNum) + "}}"
+        assert json == expected
+
+    @pytest.mark.parametrize("bigNum", [-(2**63) - 1, 2**64])
+    def test_read_json_large_numbers(self, bigNum):
+        # GH20599, 26068
+        json = StringIO('{"articleId":' + str(bigNum) + "}")
+        msg = r"Value is too small|Value is too big"
+        with pytest.raises(ValueError, match=msg):
+            read_json(json)
+
+        json = StringIO('{"0":{"articleId":' + str(bigNum) + "}}")
+        with pytest.raises(ValueError, match=msg):
+            read_json(json)
+
+    def test_read_json_large_numbers2(self):
+        # GH18842
+        json = '{"articleId": "1404366058080022500245"}'
+        json = StringIO(json)
+        result = read_json(json, typ="series")
+        expected = Series(1.404366e21, index=["articleId"])
+        tm.assert_series_equal(result, expected)
+
+        json = '{"0": {"articleId": "1404366058080022500245"}}'
+        json = StringIO(json)
+        result = read_json(json)
+        expected = DataFrame(1.404366e21, index=["articleId"], columns=[0])
+        tm.assert_frame_equal(result, expected)
+
+    def test_to_jsonl(self):
+        # GH9180
+        df = DataFrame([[1, 2], [1, 2]], columns=["a", "b"])
+        result = df.to_json(orient="records", lines=True)
+        expected = '{"a":1,"b":2}\n{"a":1,"b":2}\n'
+        assert result == expected
+
+        df = DataFrame([["foo}", "bar"], ['foo"', "bar"]], columns=["a", "b"])
+        result = df.to_json(orient="records", lines=True)
+        expected = '{"a":"foo}","b":"bar"}\n{"a":"foo\\"","b":"bar"}\n'
+        assert result == expected
+        tm.assert_frame_equal(read_json(StringIO(result), lines=True), df)
+
+        # GH15096: escaped characters in columns and data
+        df = DataFrame([["foo\\", "bar"], ['foo"', "bar"]], columns=["a\\", "b"])
+        result = df.to_json(orient="records", lines=True)
+        expected = '{"a\\\\":"foo\\\\","b":"bar"}\n{"a\\\\":"foo\\"","b":"bar"}\n'
+        assert result == expected
+
+        tm.assert_frame_equal(read_json(StringIO(result), lines=True), df)
+
+    # TODO: there is a near-identical test for pytables; can we share?
+    @pytest.mark.xfail(reason="GH#13774 encoding kwarg not supported", raises=TypeError)
+    @pytest.mark.parametrize(
+        "val",
+        [
+            [b"E\xc9, 17", b"", b"a", b"b", b"c"],
+            [b"E\xc9, 17", b"a", b"b", b"c"],
+            [b"EE, 17", b"", b"a", b"b", b"c"],
+            [b"E\xc9, 17", b"\xf8\xfc", b"a", b"b", b"c"],
+            [b"", b"a", b"b", b"c"],
+            [b"\xf8\xfc", b"a", b"b", b"c"],
+            [b"A\xf8\xfc", b"", b"a", b"b", b"c"],
+            [np.nan, b"", b"b", b"c"],
+            [b"A\xf8\xfc", np.nan, b"", b"b", b"c"],
+        ],
+    )
+    @pytest.mark.parametrize("dtype", ["category", object])
+    def test_latin_encoding(self, dtype, val):
+        # GH 13774
+        ser = Series(
+            [x.decode("latin-1") if isinstance(x, bytes) else x for x in val],
+            dtype=dtype,
+        )
+        encoding = "latin-1"
+        with tm.ensure_clean("test.json") as path:
+            ser.to_json(path, encoding=encoding)
+            retr = read_json(StringIO(path), encoding=encoding)
+            tm.assert_series_equal(ser, retr, check_categorical=False)
+
+    def test_data_frame_size_after_to_json(self):
+        # GH15344
+        df = DataFrame({"a": [str(1)]})
+
+        size_before = df.memory_usage(index=True, deep=True).sum()
+        df.to_json()
+        size_after = df.memory_usage(index=True, deep=True).sum()
+
+        assert size_before == size_after
+
+    @pytest.mark.parametrize(
+        "index", [None, [1, 2], [1.0, 2.0], ["a", "b"], ["1", "2"], ["1.", "2."]]
+    )
+    @pytest.mark.parametrize("columns", [["a", "b"], ["1", "2"], ["1.", "2."]])
+    def test_from_json_to_json_table_index_and_columns(self, index, columns):
+        # GH25433 GH25435
+        expected = DataFrame([[1, 2], [3, 4]], index=index, columns=columns)
+        dfjson = expected.to_json(orient="table")
+
+        result = read_json(StringIO(dfjson), orient="table")
+        tm.assert_frame_equal(result, expected)
+
+    def test_from_json_to_json_table_dtypes(self):
+        # GH21345
+        expected = DataFrame({"a": [1, 2], "b": [3.0, 4.0], "c": ["5", "6"]})
+        dfjson = expected.to_json(orient="table")
+        result = read_json(StringIO(dfjson), orient="table")
+        tm.assert_frame_equal(result, expected)
+
+    # TODO: We are casting to string which coerces None to NaN before casting back
+    # to object, ending up with incorrect na values
+    @pytest.mark.xfail(using_pyarrow_string_dtype(), reason="incorrect na conversion")
+    @pytest.mark.parametrize("orient", ["split", "records", "index", "columns"])
+    def test_to_json_from_json_columns_dtypes(self, orient):
+        # GH21892 GH33205
+        expected = DataFrame.from_dict(
+            {
+                "Integer": Series([1, 2, 3], dtype="int64"),
+                "Float": Series([None, 2.0, 3.0], dtype="float64"),
+                "Object": Series([None, "", "c"], dtype="object"),
+                "Bool": Series([True, False, True], dtype="bool"),
+                "Category": Series(["a", "b", None], dtype="category"),
+                "Datetime": Series(
+                    ["2020-01-01", None, "2020-01-03"], dtype="datetime64[ns]"
+                ),
+            }
+        )
+        dfjson = expected.to_json(orient=orient)
+
+        result = read_json(
+            StringIO(dfjson),
+            orient=orient,
+            dtype={
+                "Integer": "int64",
+                "Float": "float64",
+                "Object": "object",
+                "Bool": "bool",
+                "Category": "category",
+                "Datetime": "datetime64[ns]",
+            },
+        )
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", [True, {"b": int, "c": int}])
+    def test_read_json_table_dtype_raises(self, dtype):
+        # GH21345
+        df = DataFrame({"a": [1, 2], "b": [3.0, 4.0], "c": ["5", "6"]})
+        dfjson = df.to_json(orient="table")
+        msg = "cannot pass both dtype and orient='table'"
+        with pytest.raises(ValueError, match=msg):
+            read_json(dfjson, orient="table", dtype=dtype)
+
+    @pytest.mark.parametrize("orient", ["index", "columns", "records", "values"])
+    def test_read_json_table_empty_axes_dtype(self, orient):
+        # GH28558
+
+        expected = DataFrame()
+        result = read_json(StringIO("{}"), orient=orient, convert_axes=True)
+        tm.assert_index_equal(result.index, expected.index)
+        tm.assert_index_equal(result.columns, expected.columns)
+
+    def test_read_json_table_convert_axes_raises(self):
+        # GH25433 GH25435
+        df = DataFrame([[1, 2], [3, 4]], index=[1.0, 2.0], columns=["1.", "2."])
+        dfjson = df.to_json(orient="table")
+        msg = "cannot pass both convert_axes and orient='table'"
+        with pytest.raises(ValueError, match=msg):
+            read_json(dfjson, orient="table", convert_axes=True)
+
+    @pytest.mark.parametrize(
+        "data, expected",
+        [
+            (
+                DataFrame([[1, 2], [4, 5]], columns=["a", "b"]),
+                {"columns": ["a", "b"], "data": [[1, 2], [4, 5]]},
+            ),
+            (
+                DataFrame([[1, 2], [4, 5]], columns=["a", "b"]).rename_axis("foo"),
+                {"columns": ["a", "b"], "data": [[1, 2], [4, 5]]},
+            ),
+            (
+                DataFrame(
+                    [[1, 2], [4, 5]], columns=["a", "b"], index=[["a", "b"], ["c", "d"]]
+                ),
+                {"columns": ["a", "b"], "data": [[1, 2], [4, 5]]},
+            ),
+            (Series([1, 2, 3], name="A"), {"name": "A", "data": [1, 2, 3]}),
+            (
+                Series([1, 2, 3], name="A").rename_axis("foo"),
+                {"name": "A", "data": [1, 2, 3]},
+            ),
+            (
+                Series([1, 2], name="A", index=[["a", "b"], ["c", "d"]]),
+                {"name": "A", "data": [1, 2]},
+            ),
+        ],
+    )
+    def test_index_false_to_json_split(self, data, expected):
+        # GH 17394
+        # Testing index=False in to_json with orient='split'
+
+        result = data.to_json(orient="split", index=False)
+        result = json.loads(result)
+
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "data",
+        [
+            (DataFrame([[1, 2], [4, 5]], columns=["a", "b"])),
+            (DataFrame([[1, 2], [4, 5]], columns=["a", "b"]).rename_axis("foo")),
+            (
+                DataFrame(
+                    [[1, 2], [4, 5]], columns=["a", "b"], index=[["a", "b"], ["c", "d"]]
+                )
+            ),
+            (Series([1, 2, 3], name="A")),
+            (Series([1, 2, 3], name="A").rename_axis("foo")),
+            (Series([1, 2], name="A", index=[["a", "b"], ["c", "d"]])),
+        ],
+    )
+    def test_index_false_to_json_table(self, data):
+        # GH 17394
+        # Testing index=False in to_json with orient='table'
+
+        result = data.to_json(orient="table", index=False)
+        result = json.loads(result)
+
+        expected = {
+            "schema": pd.io.json.build_table_schema(data, index=False),
+            "data": DataFrame(data).to_dict(orient="records"),
+        }
+
+        assert result == expected
+
+    @pytest.mark.parametrize("orient", ["index", "columns"])
+    def test_index_false_error_to_json(self, orient):
+        # GH 17394, 25513
+        # Testing error message from to_json with index=False
+
+        df = DataFrame([[1, 2], [4, 5]], columns=["a", "b"])
+
+        msg = (
+            "'index=False' is only valid when 'orient' is 'split', "
+            "'table', 'records', or 'values'"
+        )
+        with pytest.raises(ValueError, match=msg):
+            df.to_json(orient=orient, index=False)
+
+    @pytest.mark.parametrize("orient", ["records", "values"])
+    def test_index_true_error_to_json(self, orient):
+        # GH 25513
+        # Testing error message from to_json with index=True
+
+        df = DataFrame([[1, 2], [4, 5]], columns=["a", "b"])
+
+        msg = (
+            "'index=True' is only valid when 'orient' is 'split', "
+            "'table', 'index', or 'columns'"
+        )
+        with pytest.raises(ValueError, match=msg):
+            df.to_json(orient=orient, index=True)
+
+    @pytest.mark.parametrize("orient", ["split", "table"])
+    @pytest.mark.parametrize("index", [True, False])
+    def test_index_false_from_json_to_json(self, orient, index):
+        # GH25170
+        # Test index=False in from_json to_json
+        expected = DataFrame({"a": [1, 2], "b": [3, 4]})
+        dfjson = expected.to_json(orient=orient, index=index)
+        result = read_json(StringIO(dfjson), orient=orient)
+        tm.assert_frame_equal(result, expected)
+
+    def test_read_timezone_information(self):
+        # GH 25546
+        result = read_json(
+            StringIO('{"2019-01-01T11:00:00.000Z":88}'), typ="series", orient="index"
+        )
+        exp_dti = DatetimeIndex(["2019-01-01 11:00:00"], dtype="M8[ns, UTC]")
+        expected = Series([88], index=exp_dti)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "url",
+        [
+            "s3://example-fsspec/",
+            "gcs://another-fsspec/file.json",
+            "https://example-site.com/data",
+            "some-protocol://data.txt",
+        ],
+    )
+    def test_read_json_with_url_value(self, url):
+        # GH 36271
+        result = read_json(StringIO(f'{{"url":{{"0":"{url}"}}}}'))
+        expected = DataFrame({"url": [url]})
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "compression",
+        ["", ".gz", ".bz2", ".tar"],
+    )
+    def test_read_json_with_very_long_file_path(self, compression):
+        # GH 46718
+        long_json_path = f'{"a" * 1000}.json{compression}'
+        with pytest.raises(
+            FileNotFoundError, match=f"File {long_json_path} does not exist"
+        ):
+            # path too long for Windows is handled in file_exists() but raises in
+            # _get_data_from_filepath()
+            read_json(long_json_path)
+
+    @pytest.mark.parametrize(
+        "date_format,key", [("epoch", 86400000), ("iso", "P1DT0H0M0S")]
+    )
+    def test_timedelta_as_label(self, date_format, key):
+        df = DataFrame([[1]], columns=[pd.Timedelta("1D")])
+        expected = f'{{"{key}":{{"0":1}}}}'
+        result = df.to_json(date_format=date_format)
+
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "orient,expected",
+        [
+            ("index", "{\"('a', 'b')\":{\"('c', 'd')\":1}}"),
+            ("columns", "{\"('c', 'd')\":{\"('a', 'b')\":1}}"),
+            # TODO: the below have separate encoding procedures
+            pytest.param(
+                "split",
+                "",
+                marks=pytest.mark.xfail(
+                    reason="Produces JSON but not in a consistent manner"
+                ),
+            ),
+            pytest.param(
+                "table",
+                "",
+                marks=pytest.mark.xfail(
+                    reason="Produces JSON but not in a consistent manner"
+                ),
+            ),
+        ],
+    )
+    def test_tuple_labels(self, orient, expected):
+        # GH 20500
+        df = DataFrame([[1]], index=[("a", "b")], columns=[("c", "d")])
+        result = df.to_json(orient=orient)
+        assert result == expected
+
+    @pytest.mark.parametrize("indent", [1, 2, 4])
+    def test_to_json_indent(self, indent):
+        # GH 12004
+        df = DataFrame([["foo", "bar"], ["baz", "qux"]], columns=["a", "b"])
+
+        result = df.to_json(indent=indent)
+        spaces = " " * indent
+        expected = f"""{{
+{spaces}"a":{{
+{spaces}{spaces}"0":"foo",
+{spaces}{spaces}"1":"baz"
+{spaces}}},
+{spaces}"b":{{
+{spaces}{spaces}"0":"bar",
+{spaces}{spaces}"1":"qux"
+{spaces}}}
+}}"""
+
+        assert result == expected
+
+    @pytest.mark.skipif(
+        using_pyarrow_string_dtype(),
+        reason="Adjust expected when infer_string is default, no bug here, "
+        "just a complicated parametrization",
+    )
+    @pytest.mark.parametrize(
+        "orient,expected",
+        [
+            (
+                "split",
+                """{
+    "columns":[
+        "a",
+        "b"
+    ],
+    "index":[
+        0,
+        1
+    ],
+    "data":[
+        [
+            "foo",
+            "bar"
+        ],
+        [
+            "baz",
+            "qux"
+        ]
+    ]
+}""",
+            ),
+            (
+                "records",
+                """[
+    {
+        "a":"foo",
+        "b":"bar"
+    },
+    {
+        "a":"baz",
+        "b":"qux"
+    }
+]""",
+            ),
+            (
+                "index",
+                """{
+    "0":{
+        "a":"foo",
+        "b":"bar"
+    },
+    "1":{
+        "a":"baz",
+        "b":"qux"
+    }
+}""",
+            ),
+            (
+                "columns",
+                """{
+    "a":{
+        "0":"foo",
+        "1":"baz"
+    },
+    "b":{
+        "0":"bar",
+        "1":"qux"
+    }
+}""",
+            ),
+            (
+                "values",
+                """[
+    [
+        "foo",
+        "bar"
+    ],
+    [
+        "baz",
+        "qux"
+    ]
+]""",
+            ),
+            (
+                "table",
+                """{
+    "schema":{
+        "fields":[
+            {
+                "name":"index",
+                "type":"integer"
+            },
+            {
+                "name":"a",
+                "type":"string"
+            },
+            {
+                "name":"b",
+                "type":"string"
+            }
+        ],
+        "primaryKey":[
+            "index"
+        ],
+        "pandas_version":"1.4.0"
+    },
+    "data":[
+        {
+            "index":0,
+            "a":"foo",
+            "b":"bar"
+        },
+        {
+            "index":1,
+            "a":"baz",
+            "b":"qux"
+        }
+    ]
+}""",
+            ),
+        ],
+    )
+    def test_json_indent_all_orients(self, orient, expected):
+        # GH 12004
+        df = DataFrame([["foo", "bar"], ["baz", "qux"]], columns=["a", "b"])
+        result = df.to_json(orient=orient, indent=4)
+        assert result == expected
+
+    def test_json_negative_indent_raises(self):
+        with pytest.raises(ValueError, match="must be a nonnegative integer"):
+            DataFrame().to_json(indent=-1)
+
+    def test_emca_262_nan_inf_support(self):
+        # GH 12213
+        data = StringIO(
+            '["a", NaN, "NaN", Infinity, "Infinity", -Infinity, "-Infinity"]'
+        )
+        result = read_json(data)
+        expected = DataFrame(
+            ["a", None, "NaN", np.inf, "Infinity", -np.inf, "-Infinity"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_frame_int_overflow(self):
+        # GH 30320
+        encoded_json = json.dumps([{"col": "31900441201190696999"}, {"col": "Text"}])
+        expected = DataFrame({"col": ["31900441201190696999", "Text"]})
+        result = read_json(StringIO(encoded_json))
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "dataframe,expected",
+        [
+            (
+                DataFrame({"x": [1, 2, 3], "y": ["a", "b", "c"]}),
+                '{"(0, \'x\')":1,"(0, \'y\')":"a","(1, \'x\')":2,'
+                '"(1, \'y\')":"b","(2, \'x\')":3,"(2, \'y\')":"c"}',
+            )
+        ],
+    )
+    def test_json_multiindex(self, dataframe, expected):
+        series = dataframe.stack(future_stack=True)
+        result = series.to_json(orient="index")
+        assert result == expected
+
+    @pytest.mark.single_cpu
+    def test_to_s3(self, s3_public_bucket, s3so):
+        # GH 28375
+        mock_bucket_name, target_file = s3_public_bucket.name, "test.json"
+        df = DataFrame({"x": [1, 2, 3], "y": [2, 4, 6]})
+        df.to_json(f"s3://{mock_bucket_name}/{target_file}", storage_options=s3so)
+        timeout = 5
+        while True:
+            if target_file in (obj.key for obj in s3_public_bucket.objects.all()):
+                break
+            time.sleep(0.1)
+            timeout -= 0.1
+            assert timeout > 0, "Timed out waiting for file to appear on moto"
+
+    def test_json_pandas_nulls(self, nulls_fixture, request):
+        # GH 31615
+        if isinstance(nulls_fixture, Decimal):
+            mark = pytest.mark.xfail(reason="not implemented")
+            request.applymarker(mark)
+
+        result = DataFrame([[nulls_fixture]]).to_json()
+        assert result == '{"0":{"0":null}}'
+
+    def test_readjson_bool_series(self):
+        # GH31464
+        result = read_json(StringIO("[true, true, false]"), typ="series")
+        expected = Series([True, True, False])
+        tm.assert_series_equal(result, expected)
+
+    def test_to_json_multiindex_escape(self):
+        # GH 15273
+        df = DataFrame(
+            True,
+            index=date_range("2017-01-20", "2017-01-23"),
+            columns=["foo", "bar"],
+        ).stack(future_stack=True)
+        result = df.to_json()
+        expected = (
+            "{\"(Timestamp('2017-01-20 00:00:00'), 'foo')\":true,"
+            "\"(Timestamp('2017-01-20 00:00:00'), 'bar')\":true,"
+            "\"(Timestamp('2017-01-21 00:00:00'), 'foo')\":true,"
+            "\"(Timestamp('2017-01-21 00:00:00'), 'bar')\":true,"
+            "\"(Timestamp('2017-01-22 00:00:00'), 'foo')\":true,"
+            "\"(Timestamp('2017-01-22 00:00:00'), 'bar')\":true,"
+            "\"(Timestamp('2017-01-23 00:00:00'), 'foo')\":true,"
+            "\"(Timestamp('2017-01-23 00:00:00'), 'bar')\":true}"
+        )
+        assert result == expected
+
+    def test_to_json_series_of_objects(self):
+        class _TestObject:
+            def __init__(self, a, b, _c, d) -> None:
+                self.a = a
+                self.b = b
+                self._c = _c
+                self.d = d
+
+            def e(self):
+                return 5
+
+        # JSON keys should be all non-callable non-underscore attributes, see GH-42768
+        series = Series([_TestObject(a=1, b=2, _c=3, d=4)])
+        assert json.loads(series.to_json()) == {"0": {"a": 1, "b": 2, "d": 4}}
+
+    @pytest.mark.parametrize(
+        "data,expected",
+        [
+            (
+                Series({0: -6 + 8j, 1: 0 + 1j, 2: 9 - 5j}),
+                '{"0":{"imag":8.0,"real":-6.0},'
+                '"1":{"imag":1.0,"real":0.0},'
+                '"2":{"imag":-5.0,"real":9.0}}',
+            ),
+            (
+                Series({0: -9.39 + 0.66j, 1: 3.95 + 9.32j, 2: 4.03 - 0.17j}),
+                '{"0":{"imag":0.66,"real":-9.39},'
+                '"1":{"imag":9.32,"real":3.95},'
+                '"2":{"imag":-0.17,"real":4.03}}',
+            ),
+            (
+                DataFrame([[-2 + 3j, -1 - 0j], [4 - 3j, -0 - 10j]]),
+                '{"0":{"0":{"imag":3.0,"real":-2.0},'
+                '"1":{"imag":-3.0,"real":4.0}},'
+                '"1":{"0":{"imag":0.0,"real":-1.0},'
+                '"1":{"imag":-10.0,"real":0.0}}}',
+            ),
+            (
+                DataFrame(
+                    [[-0.28 + 0.34j, -1.08 - 0.39j], [0.41 - 0.34j, -0.78 - 1.35j]]
+                ),
+                '{"0":{"0":{"imag":0.34,"real":-0.28},'
+                '"1":{"imag":-0.34,"real":0.41}},'
+                '"1":{"0":{"imag":-0.39,"real":-1.08},'
+                '"1":{"imag":-1.35,"real":-0.78}}}',
+            ),
+        ],
+    )
+    def test_complex_data_tojson(self, data, expected):
+        # GH41174
+        result = data.to_json()
+        assert result == expected
+
+    def test_json_uint64(self):
+        # GH21073
+        expected = (
+            '{"columns":["col1"],"index":[0,1],'
+            '"data":[[13342205958987758245],[12388075603347835679]]}'
+        )
+        df = DataFrame(data={"col1": [13342205958987758245, 12388075603347835679]})
+        result = df.to_json(orient="split")
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "orient", ["split", "records", "values", "index", "columns"]
+    )
+    def test_read_json_dtype_backend(
+        self, string_storage, dtype_backend, orient, using_infer_string
+    ):
+        # GH#50750
+        pa = pytest.importorskip("pyarrow")
+        df = DataFrame(
+            {
+                "a": Series([1, np.nan, 3], dtype="Int64"),
+                "b": Series([1, 2, 3], dtype="Int64"),
+                "c": Series([1.5, np.nan, 2.5], dtype="Float64"),
+                "d": Series([1.5, 2.0, 2.5], dtype="Float64"),
+                "e": [True, False, None],
+                "f": [True, False, True],
+                "g": ["a", "b", "c"],
+                "h": ["a", "b", None],
+            }
+        )
+
+        if using_infer_string:
+            string_array = ArrowStringArrayNumpySemantics(pa.array(["a", "b", "c"]))
+            string_array_na = ArrowStringArrayNumpySemantics(pa.array(["a", "b", None]))
+        elif string_storage == "python":
+            string_array = StringArray(np.array(["a", "b", "c"], dtype=np.object_))
+            string_array_na = StringArray(np.array(["a", "b", NA], dtype=np.object_))
+
+        elif dtype_backend == "pyarrow":
+            pa = pytest.importorskip("pyarrow")
+            from pandas.arrays import ArrowExtensionArray
+
+            string_array = ArrowExtensionArray(pa.array(["a", "b", "c"]))
+            string_array_na = ArrowExtensionArray(pa.array(["a", "b", None]))
+
+        else:
+            string_array = ArrowStringArray(pa.array(["a", "b", "c"]))
+            string_array_na = ArrowStringArray(pa.array(["a", "b", None]))
+
+        out = df.to_json(orient=orient)
+        with pd.option_context("mode.string_storage", string_storage):
+            result = read_json(
+                StringIO(out), dtype_backend=dtype_backend, orient=orient
+            )
+
+        expected = DataFrame(
+            {
+                "a": Series([1, np.nan, 3], dtype="Int64"),
+                "b": Series([1, 2, 3], dtype="Int64"),
+                "c": Series([1.5, np.nan, 2.5], dtype="Float64"),
+                "d": Series([1.5, 2.0, 2.5], dtype="Float64"),
+                "e": Series([True, False, NA], dtype="boolean"),
+                "f": Series([True, False, True], dtype="boolean"),
+                "g": string_array,
+                "h": string_array_na,
+            }
+        )
+
+        if dtype_backend == "pyarrow":
+            from pandas.arrays import ArrowExtensionArray
+
+            expected = DataFrame(
+                {
+                    col: ArrowExtensionArray(pa.array(expected[col], from_pandas=True))
+                    for col in expected.columns
+                }
+            )
+
+        if orient == "values":
+            expected.columns = list(range(8))
+
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("orient", ["split", "records", "index"])
+    def test_read_json_nullable_series(self, string_storage, dtype_backend, orient):
+        # GH#50750
+        pa = pytest.importorskip("pyarrow")
+        ser = Series([1, np.nan, 3], dtype="Int64")
+
+        out = ser.to_json(orient=orient)
+        with pd.option_context("mode.string_storage", string_storage):
+            result = read_json(
+                StringIO(out), dtype_backend=dtype_backend, orient=orient, typ="series"
+            )
+
+        expected = Series([1, np.nan, 3], dtype="Int64")
+
+        if dtype_backend == "pyarrow":
+            from pandas.arrays import ArrowExtensionArray
+
+            expected = Series(ArrowExtensionArray(pa.array(expected, from_pandas=True)))
+
+        tm.assert_series_equal(result, expected)
+
+    def test_invalid_dtype_backend(self):
+        msg = (
+            "dtype_backend numpy is invalid, only 'numpy_nullable' and "
+            "'pyarrow' are allowed."
+        )
+        with pytest.raises(ValueError, match=msg):
+            read_json("test", dtype_backend="numpy")
+
+
+def test_invalid_engine():
+    # GH 48893
+    ser = Series(range(1))
+    out = ser.to_json()
+    with pytest.raises(ValueError, match="The engine type foo"):
+        read_json(out, engine="foo")
+
+
+def test_pyarrow_engine_lines_false():
+    # GH 48893
+    ser = Series(range(1))
+    out = ser.to_json()
+    with pytest.raises(ValueError, match="currently pyarrow engine only supports"):
+        read_json(out, engine="pyarrow", lines=False)
+
+
+def test_json_roundtrip_string_inference(orient):
+    pytest.importorskip("pyarrow")
+    df = DataFrame(
+        [["a", "b"], ["c", "d"]], index=["row 1", "row 2"], columns=["col 1", "col 2"]
+    )
+    out = df.to_json()
+    with pd.option_context("future.infer_string", True):
+        result = read_json(StringIO(out))
+    expected = DataFrame(
+        [["a", "b"], ["c", "d"]],
+        dtype="string[pyarrow_numpy]",
+        index=Index(["row 1", "row 2"], dtype="string[pyarrow_numpy]"),
+        columns=Index(["col 1", "col 2"], dtype="string[pyarrow_numpy]"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_json_pos_args_deprecation():
+    # GH-54229
+    df = DataFrame({"a": [1, 2, 3]})
+    msg = (
+        r"Starting with pandas version 3.0 all arguments of to_json except for the "
+        r"argument 'path_or_buf' will be keyword-only."
+    )
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        buf = BytesIO()
+        df.to_json(buf, "split")
+
+
+@td.skip_if_no("pyarrow")
+def test_to_json_ea_null():
+    # GH#57224
+    df = DataFrame(
+        {
+            "a": Series([1, NA], dtype="int64[pyarrow]"),
+            "b": Series([2, NA], dtype="Int64"),
+        }
+    )
+    result = df.to_json(orient="records", lines=True)
+    expected = """{"a":1,"b":2}
+{"a":null,"b":null}
+"""
+    assert result == expected
+
+
+def test_read_json_lines_rangeindex():
+    # GH 57429
+    data = """
+{"a": 1, "b": 2}
+{"a": 3, "b": 4}
+"""
+    result = read_json(StringIO(data), lines=True).index
+    expected = RangeIndex(2)
+    tm.assert_index_equal(result, expected, exact=True)

venv/lib/python3.10/site-packages/pandas/tests/io/parser/conftest.py

@@ -0,0 +1,319 @@
+from __future__ import annotations
+
+import os
+
+import pytest
+
+from pandas.compat._optional import VERSIONS
+
+from pandas import (
+    read_csv,
+    read_table,
+)
+import pandas._testing as tm
+
+
+class BaseParser:
+    engine: str | None = None
+    low_memory = True
+    float_precision_choices: list[str | None] = []
+
+    def update_kwargs(self, kwargs):
+        kwargs = kwargs.copy()
+        kwargs.update({"engine": self.engine, "low_memory": self.low_memory})
+
+        return kwargs
+
+    def read_csv(self, *args, **kwargs):
+        kwargs = self.update_kwargs(kwargs)
+        return read_csv(*args, **kwargs)
+
+    def read_csv_check_warnings(
+        self,
+        warn_type: type[Warning],
+        warn_msg: str,
+        *args,
+        raise_on_extra_warnings=True,
+        check_stacklevel: bool = True,
+        **kwargs,
+    ):
+        # We need to check the stacklevel here instead of in the tests
+        # since this is where read_csv is called and where the warning
+        # should point to.
+        kwargs = self.update_kwargs(kwargs)
+        with tm.assert_produces_warning(
+            warn_type,
+            match=warn_msg,
+            raise_on_extra_warnings=raise_on_extra_warnings,
+            check_stacklevel=check_stacklevel,
+        ):
+            return read_csv(*args, **kwargs)
+
+    def read_table(self, *args, **kwargs):
+        kwargs = self.update_kwargs(kwargs)
+        return read_table(*args, **kwargs)
+
+    def read_table_check_warnings(
+        self,
+        warn_type: type[Warning],
+        warn_msg: str,
+        *args,
+        raise_on_extra_warnings=True,
+        **kwargs,
+    ):
+        # We need to check the stacklevel here instead of in the tests
+        # since this is where read_table is called and where the warning
+        # should point to.
+        kwargs = self.update_kwargs(kwargs)
+        with tm.assert_produces_warning(
+            warn_type, match=warn_msg, raise_on_extra_warnings=raise_on_extra_warnings
+        ):
+            return read_table(*args, **kwargs)
+
+
+class CParser(BaseParser):
+    engine = "c"
+    float_precision_choices = [None, "high", "round_trip"]
+
+
+class CParserHighMemory(CParser):
+    low_memory = False
+
+
+class CParserLowMemory(CParser):
+    low_memory = True
+
+
+class PythonParser(BaseParser):
+    engine = "python"
+    float_precision_choices = [None]
+
+
+class PyArrowParser(BaseParser):
+    engine = "pyarrow"
+    float_precision_choices = [None]
+
+
+@pytest.fixture
+def csv_dir_path(datapath):
+    """
+    The directory path to the data files needed for parser tests.
+    """
+    return datapath("io", "parser", "data")
+
+
+@pytest.fixture
+def csv1(datapath):
+    """
+    The path to the data file "test1.csv" needed for parser tests.
+    """
+    return os.path.join(datapath("io", "data", "csv"), "test1.csv")
+
+
+_cParserHighMemory = CParserHighMemory
+_cParserLowMemory = CParserLowMemory
+_pythonParser = PythonParser
+_pyarrowParser = PyArrowParser
+
+_py_parsers_only = [_pythonParser]
+_c_parsers_only = [_cParserHighMemory, _cParserLowMemory]
+_pyarrow_parsers_only = [pytest.param(_pyarrowParser, marks=pytest.mark.single_cpu)]
+
+_all_parsers = [*_c_parsers_only, *_py_parsers_only, *_pyarrow_parsers_only]
+
+_py_parser_ids = ["python"]
+_c_parser_ids = ["c_high", "c_low"]
+_pyarrow_parsers_ids = ["pyarrow"]
+
+_all_parser_ids = [*_c_parser_ids, *_py_parser_ids, *_pyarrow_parsers_ids]
+
+
+@pytest.fixture(params=_all_parsers, ids=_all_parser_ids)
+def all_parsers(request):
+    """
+    Fixture all of the CSV parsers.
+    """
+    parser = request.param()
+    if parser.engine == "pyarrow":
+        pytest.importorskip("pyarrow", VERSIONS["pyarrow"])
+        # Try finding a way to disable threads all together
+        # for more stable CI runs
+        import pyarrow
+
+        pyarrow.set_cpu_count(1)
+    return parser
+
+
+@pytest.fixture(params=_c_parsers_only, ids=_c_parser_ids)
+def c_parser_only(request):
+    """
+    Fixture all of the CSV parsers using the C engine.
+    """
+    return request.param()
+
+
+@pytest.fixture(params=_py_parsers_only, ids=_py_parser_ids)
+def python_parser_only(request):
+    """
+    Fixture all of the CSV parsers using the Python engine.
+    """
+    return request.param()
+
+
+@pytest.fixture(params=_pyarrow_parsers_only, ids=_pyarrow_parsers_ids)
+def pyarrow_parser_only(request):
+    """
+    Fixture all of the CSV parsers using the Pyarrow engine.
+    """
+    return request.param()
+
+
+def _get_all_parser_float_precision_combinations():
+    """
+    Return all allowable parser and float precision
+    combinations and corresponding ids.
+    """
+    params = []
+    ids = []
+    for parser, parser_id in zip(_all_parsers, _all_parser_ids):
+        if hasattr(parser, "values"):
+            # Wrapped in pytest.param, get the actual parser back
+            parser = parser.values[0]
+        for precision in parser.float_precision_choices:
+            # Re-wrap in pytest.param for pyarrow
+            mark = pytest.mark.single_cpu if parser.engine == "pyarrow" else ()
+            param = pytest.param((parser(), precision), marks=mark)
+            params.append(param)
+            ids.append(f"{parser_id}-{precision}")
+
+    return {"params": params, "ids": ids}
+
+
+@pytest.fixture(
+    params=_get_all_parser_float_precision_combinations()["params"],
+    ids=_get_all_parser_float_precision_combinations()["ids"],
+)
+def all_parsers_all_precisions(request):
+    """
+    Fixture for all allowable combinations of parser
+    and float precision
+    """
+    return request.param
+
+
+_utf_values = [8, 16, 32]
+
+_encoding_seps = ["", "-", "_"]
+_encoding_prefixes = ["utf", "UTF"]
+
+_encoding_fmts = [
+    f"{prefix}{sep}{{0}}" for sep in _encoding_seps for prefix in _encoding_prefixes
+]
+
+
+@pytest.fixture(params=_utf_values)
+def utf_value(request):
+    """
+    Fixture for all possible integer values for a UTF encoding.
+    """
+    return request.param
+
+
+@pytest.fixture(params=_encoding_fmts)
+def encoding_fmt(request):
+    """
+    Fixture for all possible string formats of a UTF encoding.
+    """
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        ("-1,0", -1.0),
+        ("-1,2e0", -1.2),
+        ("-1e0", -1.0),
+        ("+1e0", 1.0),
+        ("+1e+0", 1.0),
+        ("+1e-1", 0.1),
+        ("+,1e1", 1.0),
+        ("+1,e0", 1.0),
+        ("-,1e1", -1.0),
+        ("-1,e0", -1.0),
+        ("0,1", 0.1),
+        ("1,", 1.0),
+        (",1", 0.1),
+        ("-,1", -0.1),
+        ("1_,", 1.0),
+        ("1_234,56", 1234.56),
+        ("1_234,56e0", 1234.56),
+        # negative cases; must not parse as float
+        ("_", "_"),
+        ("-_", "-_"),
+        ("-_1", "-_1"),
+        ("-_1e0", "-_1e0"),
+        ("_1", "_1"),
+        ("_1,", "_1,"),
+        ("_1,_", "_1,_"),
+        ("_1e0", "_1e0"),
+        ("1,2e_1", "1,2e_1"),
+        ("1,2e1_0", "1,2e1_0"),
+        ("1,_2", "1,_2"),
+        (",1__2", ",1__2"),
+        (",1e", ",1e"),
+        ("-,1e", "-,1e"),
+        ("1_000,000_000", "1_000,000_000"),
+        ("1,e1_2", "1,e1_2"),
+        ("e11,2", "e11,2"),
+        ("1e11,2", "1e11,2"),
+        ("1,2,2", "1,2,2"),
+        ("1,2_1", "1,2_1"),
+        ("1,2e-10e1", "1,2e-10e1"),
+        ("--1,2", "--1,2"),
+        ("1a_2,1", "1a_2,1"),
+        ("1,2E-1", 0.12),
+        ("1,2E1", 12.0),
+    ]
+)
+def numeric_decimal(request):
+    """
+    Fixture for all numeric formats which should get recognized. The first entry
+    represents the value to read while the second represents the expected result.
+    """
+    return request.param
+
+
+@pytest.fixture
+def pyarrow_xfail(request):
+    """
+    Fixture that xfails a test if the engine is pyarrow.
+
+    Use if failure is do to unsupported keywords or inconsistent results.
+    """
+    if "all_parsers" in request.fixturenames:
+        parser = request.getfixturevalue("all_parsers")
+    elif "all_parsers_all_precisions" in request.fixturenames:
+        # Return value is tuple of (engine, precision)
+        parser = request.getfixturevalue("all_parsers_all_precisions")[0]
+    else:
+        return
+    if parser.engine == "pyarrow":
+        mark = pytest.mark.xfail(reason="pyarrow doesn't support this.")
+        request.applymarker(mark)
+
+
+@pytest.fixture
+def pyarrow_skip(request):
+    """
+    Fixture that skips a test if the engine is pyarrow.
+
+    Use if failure is do a parsing failure from pyarrow.csv.read_csv
+    """
+    if "all_parsers" in request.fixturenames:
+        parser = request.getfixturevalue("all_parsers")
+    elif "all_parsers_all_precisions" in request.fixturenames:
+        # Return value is tuple of (engine, precision)
+        parser = request.getfixturevalue("all_parsers_all_precisions")[0]
+    else:
+        return
+    if parser.engine == "pyarrow":
+        pytest.skip(reason="https://github.com/apache/arrow/issues/38676")

venv/lib/python3.10/site-packages/pandas/tests/io/parser/dtypes/test_categorical.py

@@ -0,0 +1,334 @@
+"""
+Tests dtype specification during parsing
+for all of the parsers defined in parsers.py
+"""
+from io import StringIO
+import os
+
+import numpy as np
+import pytest
+
+from pandas._libs import parsers as libparsers
+
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Timestamp,
+)
+import pandas._testing as tm
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
+)
+
+xfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
+
+
+@xfail_pyarrow  # AssertionError: Attributes of DataFrame.iloc[:, 0] are different
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        "category",
+        CategoricalDtype(),
+        {"a": "category", "b": "category", "c": CategoricalDtype()},
+    ],
+)
+def test_categorical_dtype(all_parsers, dtype):
+    # see gh-10153
+    parser = all_parsers
+    data = """a,b,c
+1,a,3.4
+1,a,3.4
+2,b,4.5"""
+    expected = DataFrame(
+        {
+            "a": Categorical(["1", "1", "2"]),
+            "b": Categorical(["a", "a", "b"]),
+            "c": Categorical(["3.4", "3.4", "4.5"]),
+        }
+    )
+    actual = parser.read_csv(StringIO(data), dtype=dtype)
+    tm.assert_frame_equal(actual, expected)
+
+
+@pytest.mark.parametrize("dtype", [{"b": "category"}, {1: "category"}])
+def test_categorical_dtype_single(all_parsers, dtype, request):
+    # see gh-10153
+    parser = all_parsers
+    data = """a,b,c
+1,a,3.4
+1,a,3.4
+2,b,4.5"""
+    expected = DataFrame(
+        {"a": [1, 1, 2], "b": Categorical(["a", "a", "b"]), "c": [3.4, 3.4, 4.5]}
+    )
+    if parser.engine == "pyarrow":
+        mark = pytest.mark.xfail(
+            strict=False,
+            reason="Flaky test sometimes gives object dtype instead of Categorical",
+        )
+        request.applymarker(mark)
+
+    actual = parser.read_csv(StringIO(data), dtype=dtype)
+    tm.assert_frame_equal(actual, expected)
+
+
+@xfail_pyarrow  # AssertionError: Attributes of DataFrame.iloc[:, 0] are different
+def test_categorical_dtype_unsorted(all_parsers):
+    # see gh-10153
+    parser = all_parsers
+    data = """a,b,c
+1,b,3.4
+1,b,3.4
+2,a,4.5"""
+    expected = DataFrame(
+        {
+            "a": Categorical(["1", "1", "2"]),
+            "b": Categorical(["b", "b", "a"]),
+            "c": Categorical(["3.4", "3.4", "4.5"]),
+        }
+    )
+    actual = parser.read_csv(StringIO(data), dtype="category")
+    tm.assert_frame_equal(actual, expected)
+
+
+@xfail_pyarrow  # AssertionError: Attributes of DataFrame.iloc[:, 0] are different
+def test_categorical_dtype_missing(all_parsers):
+    # see gh-10153
+    parser = all_parsers
+    data = """a,b,c
+1,b,3.4
+1,nan,3.4
+2,a,4.5"""
+    expected = DataFrame(
+        {
+            "a": Categorical(["1", "1", "2"]),
+            "b": Categorical(["b", np.nan, "a"]),
+            "c": Categorical(["3.4", "3.4", "4.5"]),
+        }
+    )
+    actual = parser.read_csv(StringIO(data), dtype="category")
+    tm.assert_frame_equal(actual, expected)
+
+
+@xfail_pyarrow  # AssertionError: Attributes of DataFrame.iloc[:, 0] are different
+@pytest.mark.slow
+def test_categorical_dtype_high_cardinality_numeric(all_parsers, monkeypatch):
+    # see gh-18186
+    # was an issue with C parser, due to DEFAULT_BUFFER_HEURISTIC
+    parser = all_parsers
+    heuristic = 2**5
+    data = np.sort([str(i) for i in range(heuristic + 1)])
+    expected = DataFrame({"a": Categorical(data, ordered=True)})
+    with monkeypatch.context() as m:
+        m.setattr(libparsers, "DEFAULT_BUFFER_HEURISTIC", heuristic)
+        actual = parser.read_csv(StringIO("a\n" + "\n".join(data)), dtype="category")
+    actual["a"] = actual["a"].cat.reorder_categories(
+        np.sort(actual.a.cat.categories), ordered=True
+    )
+    tm.assert_frame_equal(actual, expected)
+
+
+def test_categorical_dtype_utf16(all_parsers, csv_dir_path):
+    # see gh-10153
+    pth = os.path.join(csv_dir_path, "utf16_ex.txt")
+    parser = all_parsers
+    encoding = "utf-16"
+    sep = "\t"
+
+    expected = parser.read_csv(pth, sep=sep, encoding=encoding)
+    expected = expected.apply(Categorical)
+
+    actual = parser.read_csv(pth, sep=sep, encoding=encoding, dtype="category")
+    tm.assert_frame_equal(actual, expected)
+
+
+def test_categorical_dtype_chunksize_infer_categories(all_parsers):
+    # see gh-10153
+    parser = all_parsers
+    data = """a,b
+1,a
+1,b
+1,b
+2,c"""
+    expecteds = [
+        DataFrame({"a": [1, 1], "b": Categorical(["a", "b"])}),
+        DataFrame({"a": [1, 2], "b": Categorical(["b", "c"])}, index=[2, 3]),
+    ]
+
+    if parser.engine == "pyarrow":
+        msg = "The 'chunksize' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), dtype={"b": "category"}, chunksize=2)
+        return
+
+    with parser.read_csv(
+        StringIO(data), dtype={"b": "category"}, chunksize=2
+    ) as actuals:
+        for actual, expected in zip(actuals, expecteds):
+            tm.assert_frame_equal(actual, expected)
+
+
+def test_categorical_dtype_chunksize_explicit_categories(all_parsers):
+    # see gh-10153
+    parser = all_parsers
+    data = """a,b
+1,a
+1,b
+1,b
+2,c"""
+    cats = ["a", "b", "c"]
+    expecteds = [
+        DataFrame({"a": [1, 1], "b": Categorical(["a", "b"], categories=cats)}),
+        DataFrame(
+            {"a": [1, 2], "b": Categorical(["b", "c"], categories=cats)},
+            index=[2, 3],
+        ),
+    ]
+    dtype = CategoricalDtype(cats)
+
+    if parser.engine == "pyarrow":
+        msg = "The 'chunksize' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), dtype={"b": dtype}, chunksize=2)
+        return
+
+    with parser.read_csv(StringIO(data), dtype={"b": dtype}, chunksize=2) as actuals:
+        for actual, expected in zip(actuals, expecteds):
+            tm.assert_frame_equal(actual, expected)
+
+
+def test_categorical_dtype_latin1(all_parsers, csv_dir_path):
+    # see gh-10153
+    pth = os.path.join(csv_dir_path, "unicode_series.csv")
+    parser = all_parsers
+    encoding = "latin-1"
+
+    expected = parser.read_csv(pth, header=None, encoding=encoding)
+    expected[1] = Categorical(expected[1])
+
+    actual = parser.read_csv(pth, header=None, encoding=encoding, dtype={1: "category"})
+    tm.assert_frame_equal(actual, expected)
+
+
+@pytest.mark.parametrize("ordered", [False, True])
+@pytest.mark.parametrize(
+    "categories",
+    [["a", "b", "c"], ["a", "c", "b"], ["a", "b", "c", "d"], ["c", "b", "a"]],
+)
+def test_categorical_category_dtype(all_parsers, categories, ordered):
+    parser = all_parsers
+    data = """a,b
+1,a
+1,b
+1,b
+2,c"""
+    expected = DataFrame(
+        {
+            "a": [1, 1, 1, 2],
+            "b": Categorical(
+                ["a", "b", "b", "c"], categories=categories, ordered=ordered
+            ),
+        }
+    )
+
+    dtype = {"b": CategoricalDtype(categories=categories, ordered=ordered)}
+    result = parser.read_csv(StringIO(data), dtype=dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_categorical_category_dtype_unsorted(all_parsers):
+    parser = all_parsers
+    data = """a,b
+1,a
+1,b
+1,b
+2,c"""
+    dtype = CategoricalDtype(["c", "b", "a"])
+    expected = DataFrame(
+        {
+            "a": [1, 1, 1, 2],
+            "b": Categorical(["a", "b", "b", "c"], categories=["c", "b", "a"]),
+        }
+    )
+
+    result = parser.read_csv(StringIO(data), dtype={"b": dtype})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_categorical_coerces_numeric(all_parsers):
+    parser = all_parsers
+    dtype = {"b": CategoricalDtype([1, 2, 3])}
+
+    data = "b\n1\n1\n2\n3"
+    expected = DataFrame({"b": Categorical([1, 1, 2, 3])})
+
+    result = parser.read_csv(StringIO(data), dtype=dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_categorical_coerces_datetime(all_parsers):
+    parser = all_parsers
+    dti = pd.DatetimeIndex(["2017-01-01", "2018-01-01", "2019-01-01"], freq=None)
+    dtype = {"b": CategoricalDtype(dti)}
+
+    data = "b\n2017-01-01\n2018-01-01\n2019-01-01"
+    expected = DataFrame({"b": Categorical(dtype["b"].categories)})
+
+    result = parser.read_csv(StringIO(data), dtype=dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_categorical_coerces_timestamp(all_parsers):
+    parser = all_parsers
+    dtype = {"b": CategoricalDtype([Timestamp("2014")])}
+
+    data = "b\n2014-01-01\n2014-01-01"
+    expected = DataFrame({"b": Categorical([Timestamp("2014")] * 2)})
+
+    result = parser.read_csv(StringIO(data), dtype=dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_categorical_coerces_timedelta(all_parsers):
+    parser = all_parsers
+    dtype = {"b": CategoricalDtype(pd.to_timedelta(["1h", "2h", "3h"]))}
+
+    data = "b\n1h\n2h\n3h"
+    expected = DataFrame({"b": Categorical(dtype["b"].categories)})
+
+    result = parser.read_csv(StringIO(data), dtype=dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        "b\nTrue\nFalse\nNA\nFalse",
+        "b\ntrue\nfalse\nNA\nfalse",
+        "b\nTRUE\nFALSE\nNA\nFALSE",
+        "b\nTrue\nFalse\nNA\nFALSE",
+    ],
+)
+def test_categorical_dtype_coerces_boolean(all_parsers, data):
+    # see gh-20498
+    parser = all_parsers
+    dtype = {"b": CategoricalDtype([False, True])}
+    expected = DataFrame({"b": Categorical([True, False, None, False])})
+
+    result = parser.read_csv(StringIO(data), dtype=dtype)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_categorical_unexpected_categories(all_parsers):
+    parser = all_parsers
+    dtype = {"b": CategoricalDtype(["a", "b", "d", "e"])}
+
+    data = "b\nd\na\nc\nd"  # Unexpected c
+    expected = DataFrame({"b": Categorical(list("dacd"), dtype=dtype["b"])})
+
+    result = parser.read_csv(StringIO(data), dtype=dtype)
+    tm.assert_frame_equal(result, expected)

venv/lib/python3.10/site-packages/pandas/tests/io/parser/dtypes/test_dtypes_basic.py

@@ -0,0 +1,643 @@
+"""
+Tests dtype specification during parsing
+for all of the parsers defined in parsers.py
+"""
+from collections import defaultdict
+from io import StringIO
+
+import numpy as np
+import pytest
+
+from pandas.errors import ParserWarning
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Timestamp,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    ArrowStringArray,
+    IntegerArray,
+    StringArray,
+)
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
+)
+
+
+@pytest.mark.parametrize("dtype", [str, object])
+@pytest.mark.parametrize("check_orig", [True, False])
+@pytest.mark.usefixtures("pyarrow_xfail")
+def test_dtype_all_columns(all_parsers, dtype, check_orig):
+    # see gh-3795, gh-6607
+    parser = all_parsers
+
+    df = DataFrame(
+        np.random.default_rng(2).random((5, 2)).round(4),
+        columns=list("AB"),
+        index=["1A", "1B", "1C", "1D", "1E"],
+    )
+
+    with tm.ensure_clean("__passing_str_as_dtype__.csv") as path:
+        df.to_csv(path)
+
+        result = parser.read_csv(path, dtype=dtype, index_col=0)
+
+        if check_orig:
+            expected = df.copy()
+            result = result.astype(float)
+        else:
+            expected = df.astype(str)
+
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.usefixtures("pyarrow_xfail")
+def test_dtype_per_column(all_parsers):
+    parser = all_parsers
+    data = """\
+one,two
+1,2.5
+2,3.5
+3,4.5
+4,5.5"""
+    expected = DataFrame(
+        [[1, "2.5"], [2, "3.5"], [3, "4.5"], [4, "5.5"]], columns=["one", "two"]
+    )
+    expected["one"] = expected["one"].astype(np.float64)
+    expected["two"] = expected["two"].astype(object)
+
+    result = parser.read_csv(StringIO(data), dtype={"one": np.float64, 1: str})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_invalid_dtype_per_column(all_parsers):
+    parser = all_parsers
+    data = """\
+one,two
+1,2.5
+2,3.5
+3,4.5
+4,5.5"""
+
+    with pytest.raises(TypeError, match="data type [\"']foo[\"'] not understood"):
+        parser.read_csv(StringIO(data), dtype={"one": "foo", 1: "int"})
+
+
+def test_raise_on_passed_int_dtype_with_nas(all_parsers):
+    # see gh-2631
+    parser = all_parsers
+    data = """YEAR, DOY, a
+2001,106380451,10
+2001,,11
+2001,106380451,67"""
+
+    if parser.engine == "c":
+        msg = "Integer column has NA values"
+    elif parser.engine == "pyarrow":
+        msg = "The 'skipinitialspace' option is not supported with the 'pyarrow' engine"
+    else:
+        msg = "Unable to convert column DOY"
+
+    with pytest.raises(ValueError, match=msg):
+        parser.read_csv(StringIO(data), dtype={"DOY": np.int64}, skipinitialspace=True)
+
+
+def test_dtype_with_converters(all_parsers):
+    parser = all_parsers
+    data = """a,b
+1.1,2.2
+1.2,2.3"""
+
+    if parser.engine == "pyarrow":
+        msg = "The 'converters' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(
+                StringIO(data), dtype={"a": "i8"}, converters={"a": lambda x: str(x)}
+            )
+        return
+
+    # Dtype spec ignored if converted specified.
+    result = parser.read_csv_check_warnings(
+        ParserWarning,
+        "Both a converter and dtype were specified for column a "
+        "- only the converter will be used.",
+        StringIO(data),
+        dtype={"a": "i8"},
+        converters={"a": lambda x: str(x)},
+    )
+    expected = DataFrame({"a": ["1.1", "1.2"], "b": [2.2, 2.3]})
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype", list(np.typecodes["AllInteger"] + np.typecodes["Float"])
+)
+def test_numeric_dtype(all_parsers, dtype):
+    data = "0\n1"
+    parser = all_parsers
+    expected = DataFrame([0, 1], dtype=dtype)
+
+    result = parser.read_csv(StringIO(data), header=None, dtype=dtype)
+    tm.assert_frame_equal(expected, result)
+
+
+@pytest.mark.usefixtures("pyarrow_xfail")
+def test_boolean_dtype(all_parsers):
+    parser = all_parsers
+    data = "\n".join(
+        [
+            "a",
+            "True",
+            "TRUE",
+            "true",
+            "1",
+            "1.0",
+            "False",
+            "FALSE",
+            "false",
+            "0",
+            "0.0",
+            "NaN",
+            "nan",
+            "NA",
+            "null",
+            "NULL",
+        ]
+    )
+
+    result = parser.read_csv(StringIO(data), dtype="boolean")
+    expected = DataFrame(
+        {
+            "a": pd.array(
+                [
+                    True,
+                    True,
+                    True,
+                    True,
+                    True,
+                    False,
+                    False,
+                    False,
+                    False,
+                    False,
+                    None,
+                    None,
+                    None,
+                    None,
+                    None,
+                ],
+                dtype="boolean",
+            )
+        }
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.usefixtures("pyarrow_xfail")
+def test_delimiter_with_usecols_and_parse_dates(all_parsers):
+    # GH#35873
+    result = all_parsers.read_csv(
+        StringIO('"dump","-9,1","-9,1",20101010'),
+        engine="python",
+        names=["col", "col1", "col2", "col3"],
+        usecols=["col1", "col2", "col3"],
+        parse_dates=["col3"],
+        decimal=",",
+    )
+    expected = DataFrame(
+        {"col1": [-9.1], "col2": [-9.1], "col3": [Timestamp("2010-10-10")]}
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("thousands", ["_", None])
+def test_decimal_and_exponential(
+    request, python_parser_only, numeric_decimal, thousands
+):
+    # GH#31920
+    decimal_number_check(request, python_parser_only, numeric_decimal, thousands, None)
+
+
+@pytest.mark.parametrize("thousands", ["_", None])
+@pytest.mark.parametrize("float_precision", [None, "legacy", "high", "round_trip"])
+def test_1000_sep_decimal_float_precision(
+    request, c_parser_only, numeric_decimal, float_precision, thousands
+):
+    # test decimal and thousand sep handling in across 'float_precision'
+    # parsers
+    decimal_number_check(
+        request, c_parser_only, numeric_decimal, thousands, float_precision
+    )
+    text, value = numeric_decimal
+    text = " " + text + " "
+    if isinstance(value, str):  # the negative cases (parse as text)
+        value = " " + value + " "
+    decimal_number_check(
+        request, c_parser_only, (text, value), thousands, float_precision
+    )
+
+
+def decimal_number_check(request, parser, numeric_decimal, thousands, float_precision):
+    # GH#31920
+    value = numeric_decimal[0]
+    if thousands is None and value in ("1_,", "1_234,56", "1_234,56e0"):
+        request.applymarker(
+            pytest.mark.xfail(reason=f"thousands={thousands} and sep is in {value}")
+        )
+    df = parser.read_csv(
+        StringIO(value),
+        float_precision=float_precision,
+        sep="|",
+        thousands=thousands,
+        decimal=",",
+        header=None,
+    )
+    val = df.iloc[0, 0]
+    assert val == numeric_decimal[1]
+
+
+@pytest.mark.parametrize("float_precision", [None, "legacy", "high", "round_trip"])
+def test_skip_whitespace(c_parser_only, float_precision):
+    DATA = """id\tnum\t
+1\t1.2 \t
+1\t 2.1\t
+2\t 1\t
+2\t 1.2 \t
+"""
+    df = c_parser_only.read_csv(
+        StringIO(DATA),
+        float_precision=float_precision,
+        sep="\t",
+        header=0,
+        dtype={1: np.float64},
+    )
+    tm.assert_series_equal(df.iloc[:, 1], pd.Series([1.2, 2.1, 1.0, 1.2], name="num"))
+
+
+@pytest.mark.usefixtures("pyarrow_xfail")
+def test_true_values_cast_to_bool(all_parsers):
+    # GH#34655
+    text = """a,b
+yes,xxx
+no,yyy
+1,zzz
+0,aaa
+    """
+    parser = all_parsers
+    result = parser.read_csv(
+        StringIO(text),
+        true_values=["yes"],
+        false_values=["no"],
+        dtype={"a": "boolean"},
+    )
+    expected = DataFrame(
+        {"a": [True, False, True, False], "b": ["xxx", "yyy", "zzz", "aaa"]}
+    )
+    expected["a"] = expected["a"].astype("boolean")
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.usefixtures("pyarrow_xfail")
+@pytest.mark.parametrize("dtypes, exp_value", [({}, "1"), ({"a.1": "int64"}, 1)])
+def test_dtype_mangle_dup_cols(all_parsers, dtypes, exp_value):
+    # GH#35211
+    parser = all_parsers
+    data = """a,a\n1,1"""
+    dtype_dict = {"a": str, **dtypes}
+    # GH#42462
+    dtype_dict_copy = dtype_dict.copy()
+    result = parser.read_csv(StringIO(data), dtype=dtype_dict)
+    expected = DataFrame({"a": ["1"], "a.1": [exp_value]})
+    assert dtype_dict == dtype_dict_copy, "dtype dict changed"
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.usefixtures("pyarrow_xfail")
+def test_dtype_mangle_dup_cols_single_dtype(all_parsers):
+    # GH#42022
+    parser = all_parsers
+    data = """a,a\n1,1"""
+    result = parser.read_csv(StringIO(data), dtype=str)
+    expected = DataFrame({"a": ["1"], "a.1": ["1"]})
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.usefixtures("pyarrow_xfail")
+def test_dtype_multi_index(all_parsers):
+    # GH 42446
+    parser = all_parsers
+    data = "A,B,B\nX,Y,Z\n1,2,3"
+
+    result = parser.read_csv(
+        StringIO(data),
+        header=list(range(2)),
+        dtype={
+            ("A", "X"): np.int32,
+            ("B", "Y"): np.int32,
+            ("B", "Z"): np.float32,
+        },
+    )
+
+    expected = DataFrame(
+        {
+            ("A", "X"): np.int32([1]),
+            ("B", "Y"): np.int32([2]),
+            ("B", "Z"): np.float32([3]),
+        }
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_nullable_int_dtype(all_parsers, any_int_ea_dtype):
+    # GH 25472
+    parser = all_parsers
+    dtype = any_int_ea_dtype
+
+    data = """a,b,c
+,3,5
+1,,6
+2,4,"""
+    expected = DataFrame(
+        {
+            "a": pd.array([pd.NA, 1, 2], dtype=dtype),
+            "b": pd.array([3, pd.NA, 4], dtype=dtype),
+            "c": pd.array([5, 6, pd.NA], dtype=dtype),
+        }
+    )
+    actual = parser.read_csv(StringIO(data), dtype=dtype)
+    tm.assert_frame_equal(actual, expected)
+
+
+@pytest.mark.usefixtures("pyarrow_xfail")
+@pytest.mark.parametrize("default", ["float", "float64"])
+def test_dtypes_defaultdict(all_parsers, default):
+    # GH#41574
+    data = """a,b
+1,2
+"""
+    dtype = defaultdict(lambda: default, a="int64")
+    parser = all_parsers
+    result = parser.read_csv(StringIO(data), dtype=dtype)
+    expected = DataFrame({"a": [1], "b": 2.0})
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.usefixtures("pyarrow_xfail")
+def test_dtypes_defaultdict_mangle_dup_cols(all_parsers):
+    # GH#41574
+    data = """a,b,a,b,b.1
+1,2,3,4,5
+"""
+    dtype = defaultdict(lambda: "float64", a="int64")
+    dtype["b.1"] = "int64"
+    parser = all_parsers
+    result = parser.read_csv(StringIO(data), dtype=dtype)
+    expected = DataFrame({"a": [1], "b": [2.0], "a.1": [3], "b.2": [4.0], "b.1": [5]})
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.usefixtures("pyarrow_xfail")
+def test_dtypes_defaultdict_invalid(all_parsers):
+    # GH#41574
+    data = """a,b
+1,2
+"""
+    dtype = defaultdict(lambda: "invalid_dtype", a="int64")
+    parser = all_parsers
+    with pytest.raises(TypeError, match="not understood"):
+        parser.read_csv(StringIO(data), dtype=dtype)
+
+
+def test_dtype_backend(all_parsers):
+    # GH#36712
+
+    parser = all_parsers
+
+    data = """a,b,c,d,e,f,g,h,i,j
+1,2.5,True,a,,,,,12-31-2019,
+3,4.5,False,b,6,7.5,True,a,12-31-2019,
+"""
+    result = parser.read_csv(
+        StringIO(data), dtype_backend="numpy_nullable", parse_dates=["i"]
+    )
+    expected = DataFrame(
+        {
+            "a": pd.Series([1, 3], dtype="Int64"),
+            "b": pd.Series([2.5, 4.5], dtype="Float64"),
+            "c": pd.Series([True, False], dtype="boolean"),
+            "d": pd.Series(["a", "b"], dtype="string"),
+            "e": pd.Series([pd.NA, 6], dtype="Int64"),
+            "f": pd.Series([pd.NA, 7.5], dtype="Float64"),
+            "g": pd.Series([pd.NA, True], dtype="boolean"),
+            "h": pd.Series([pd.NA, "a"], dtype="string"),
+            "i": pd.Series([Timestamp("2019-12-31")] * 2),
+            "j": pd.Series([pd.NA, pd.NA], dtype="Int64"),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_dtype_backend_and_dtype(all_parsers):
+    # GH#36712
+
+    parser = all_parsers
+
+    data = """a,b
+1,2.5
+,
+"""
+    result = parser.read_csv(
+        StringIO(data), dtype_backend="numpy_nullable", dtype="float64"
+    )
+    expected = DataFrame({"a": [1.0, np.nan], "b": [2.5, np.nan]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_dtype_backend_string(all_parsers, string_storage):
+    # GH#36712
+    pa = pytest.importorskip("pyarrow")
+
+    with pd.option_context("mode.string_storage", string_storage):
+        parser = all_parsers
+
+        data = """a,b
+a,x
+b,
+"""
+        result = parser.read_csv(StringIO(data), dtype_backend="numpy_nullable")
+
+        if string_storage == "python":
+            expected = DataFrame(
+                {
+                    "a": StringArray(np.array(["a", "b"], dtype=np.object_)),
+                    "b": StringArray(np.array(["x", pd.NA], dtype=np.object_)),
+                }
+            )
+        else:
+            expected = DataFrame(
+                {
+                    "a": ArrowStringArray(pa.array(["a", "b"])),
+                    "b": ArrowStringArray(pa.array(["x", None])),
+                }
+            )
+        tm.assert_frame_equal(result, expected)
+
+
+def test_dtype_backend_ea_dtype_specified(all_parsers):
+    # GH#491496
+    data = """a,b
+1,2
+"""
+    parser = all_parsers
+    result = parser.read_csv(
+        StringIO(data), dtype="Int64", dtype_backend="numpy_nullable"
+    )
+    expected = DataFrame({"a": [1], "b": 2}, dtype="Int64")
+    tm.assert_frame_equal(result, expected)
+
+
+def test_dtype_backend_pyarrow(all_parsers, request):
+    # GH#36712
+    pa = pytest.importorskip("pyarrow")
+    parser = all_parsers
+
+    data = """a,b,c,d,e,f,g,h,i,j
+1,2.5,True,a,,,,,12-31-2019,
+3,4.5,False,b,6,7.5,True,a,12-31-2019,
+"""
+    result = parser.read_csv(StringIO(data), dtype_backend="pyarrow", parse_dates=["i"])
+    expected = DataFrame(
+        {
+            "a": pd.Series([1, 3], dtype="int64[pyarrow]"),
+            "b": pd.Series([2.5, 4.5], dtype="float64[pyarrow]"),
+            "c": pd.Series([True, False], dtype="bool[pyarrow]"),
+            "d": pd.Series(["a", "b"], dtype=pd.ArrowDtype(pa.string())),
+            "e": pd.Series([pd.NA, 6], dtype="int64[pyarrow]"),
+            "f": pd.Series([pd.NA, 7.5], dtype="float64[pyarrow]"),
+            "g": pd.Series([pd.NA, True], dtype="bool[pyarrow]"),
+            "h": pd.Series(
+                [pd.NA, "a"],
+                dtype=pd.ArrowDtype(pa.string()),
+            ),
+            "i": pd.Series([Timestamp("2019-12-31")] * 2),
+            "j": pd.Series([pd.NA, pd.NA], dtype="null[pyarrow]"),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+# pyarrow engine failing:
+# https://github.com/pandas-dev/pandas/issues/56136
+@pytest.mark.usefixtures("pyarrow_xfail")
+def test_ea_int_avoid_overflow(all_parsers):
+    # GH#32134
+    parser = all_parsers
+    data = """a,b
+1,1
+,1
+1582218195625938945,1
+"""
+    result = parser.read_csv(StringIO(data), dtype={"a": "Int64"})
+    expected = DataFrame(
+        {
+            "a": IntegerArray(
+                np.array([1, 1, 1582218195625938945]), np.array([False, True, False])
+            ),
+            "b": 1,
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_string_inference(all_parsers):
+    # GH#54430
+    pytest.importorskip("pyarrow")
+    dtype = "string[pyarrow_numpy]"
+
+    data = """a,b
+x,1
+y,2
+,3"""
+    parser = all_parsers
+    with pd.option_context("future.infer_string", True):
+        result = parser.read_csv(StringIO(data))
+
+    expected = DataFrame(
+        {"a": pd.Series(["x", "y", None], dtype=dtype), "b": [1, 2, 3]},
+        columns=pd.Index(["a", "b"], dtype=dtype),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["O", object, "object", np.object_, str, np.str_])
+def test_string_inference_object_dtype(all_parsers, dtype):
+    # GH#56047
+    pytest.importorskip("pyarrow")
+
+    data = """a,b
+x,a
+y,a
+z,a"""
+    parser = all_parsers
+    with pd.option_context("future.infer_string", True):
+        result = parser.read_csv(StringIO(data), dtype=dtype)
+
+    expected = DataFrame(
+        {
+            "a": pd.Series(["x", "y", "z"], dtype=object),
+            "b": pd.Series(["a", "a", "a"], dtype=object),
+        },
+        columns=pd.Index(["a", "b"], dtype="string[pyarrow_numpy]"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+    with pd.option_context("future.infer_string", True):
+        result = parser.read_csv(StringIO(data), dtype={"a": dtype})
+
+    expected = DataFrame(
+        {
+            "a": pd.Series(["x", "y", "z"], dtype=object),
+            "b": pd.Series(["a", "a", "a"], dtype="string[pyarrow_numpy]"),
+        },
+        columns=pd.Index(["a", "b"], dtype="string[pyarrow_numpy]"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_accurate_parsing_of_large_integers(all_parsers):
+    # GH#52505
+    data = """SYMBOL,MOMENT,ID,ID_DEAL
+AAPL,20230301181139587,1925036343869802844,
+AAPL,20230301181139587,2023552585717889863,2023552585717263358
+NVDA,20230301181139587,2023552585717889863,2023552585717263359
+AMC,20230301181139587,2023552585717889863,2023552585717263360
+AMZN,20230301181139587,2023552585717889759,2023552585717263360
+MSFT,20230301181139587,2023552585717889863,2023552585717263361
+NVDA,20230301181139587,2023552585717889827,2023552585717263361"""
+    orders = pd.read_csv(StringIO(data), dtype={"ID_DEAL": pd.Int64Dtype()})
+    assert len(orders.loc[orders["ID_DEAL"] == 2023552585717263358, "ID_DEAL"]) == 1
+    assert len(orders.loc[orders["ID_DEAL"] == 2023552585717263359, "ID_DEAL"]) == 1
+    assert len(orders.loc[orders["ID_DEAL"] == 2023552585717263360, "ID_DEAL"]) == 2
+    assert len(orders.loc[orders["ID_DEAL"] == 2023552585717263361, "ID_DEAL"]) == 2
+
+
+def test_dtypes_with_usecols(all_parsers):
+    # GH#54868
+
+    parser = all_parsers
+    data = """a,b,c
+1,2,3
+4,5,6"""
+
+    result = parser.read_csv(StringIO(data), usecols=["a", "c"], dtype={"a": object})
+    if parser.engine == "pyarrow":
+        values = [1, 4]
+    else:
+        values = ["1", "4"]
+    expected = DataFrame({"a": pd.Series(values, dtype=object), "c": [3, 6]})
+    tm.assert_frame_equal(result, expected)

venv/lib/python3.10/site-packages/pandas/tests/io/parser/dtypes/test_empty.py

@@ -0,0 +1,181 @@
+"""
+Tests dtype specification during parsing
+for all of the parsers defined in parsers.py
+"""
+from io import StringIO
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    concat,
+)
+import pandas._testing as tm
+
+skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
+
+
+@skip_pyarrow  # CSV parse error: Empty CSV file or block
+def test_dtype_all_columns_empty(all_parsers):
+    # see gh-12048
+    parser = all_parsers
+    result = parser.read_csv(StringIO("A,B"), dtype=str)
+
+    expected = DataFrame({"A": [], "B": []}, dtype=str)
+    tm.assert_frame_equal(result, expected)
+
+
+@skip_pyarrow  # CSV parse error: Empty CSV file or block
+def test_empty_pass_dtype(all_parsers):
+    parser = all_parsers
+
+    data = "one,two"
+    result = parser.read_csv(StringIO(data), dtype={"one": "u1"})
+
+    expected = DataFrame(
+        {"one": np.empty(0, dtype="u1"), "two": np.empty(0, dtype=object)},
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@skip_pyarrow  # CSV parse error: Empty CSV file or block
+def test_empty_with_index_pass_dtype(all_parsers):
+    parser = all_parsers
+
+    data = "one,two"
+    result = parser.read_csv(
+        StringIO(data), index_col=["one"], dtype={"one": "u1", 1: "f"}
+    )
+
+    expected = DataFrame(
+        {"two": np.empty(0, dtype="f")}, index=Index([], dtype="u1", name="one")
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@skip_pyarrow  # CSV parse error: Empty CSV file or block
+def test_empty_with_multi_index_pass_dtype(all_parsers):
+    parser = all_parsers
+
+    data = "one,two,three"
+    result = parser.read_csv(
+        StringIO(data), index_col=["one", "two"], dtype={"one": "u1", 1: "f8"}
+    )
+
+    exp_idx = MultiIndex.from_arrays(
+        [np.empty(0, dtype="u1"), np.empty(0, dtype=np.float64)],
+        names=["one", "two"],
+    )
+    expected = DataFrame({"three": np.empty(0, dtype=object)}, index=exp_idx)
+    tm.assert_frame_equal(result, expected)
+
+
+@skip_pyarrow  # CSV parse error: Empty CSV file or block
+def test_empty_with_mangled_column_pass_dtype_by_names(all_parsers):
+    parser = all_parsers
+
+    data = "one,one"
+    result = parser.read_csv(StringIO(data), dtype={"one": "u1", "one.1": "f"})
+
+    expected = DataFrame(
+        {"one": np.empty(0, dtype="u1"), "one.1": np.empty(0, dtype="f")},
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@skip_pyarrow  # CSV parse error: Empty CSV file or block
+def test_empty_with_mangled_column_pass_dtype_by_indexes(all_parsers):
+    parser = all_parsers
+
+    data = "one,one"
+    result = parser.read_csv(StringIO(data), dtype={0: "u1", 1: "f"})
+
+    expected = DataFrame(
+        {"one": np.empty(0, dtype="u1"), "one.1": np.empty(0, dtype="f")},
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@skip_pyarrow  # CSV parse error: Empty CSV file or block
+def test_empty_with_dup_column_pass_dtype_by_indexes(all_parsers):
+    # see gh-9424
+    parser = all_parsers
+    expected = concat(
+        [Series([], name="one", dtype="u1"), Series([], name="one.1", dtype="f")],
+        axis=1,
+    )
+
+    data = "one,one"
+    result = parser.read_csv(StringIO(data), dtype={0: "u1", 1: "f"})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_empty_with_dup_column_pass_dtype_by_indexes_raises(all_parsers):
+    # see gh-9424
+    parser = all_parsers
+    expected = concat(
+        [Series([], name="one", dtype="u1"), Series([], name="one.1", dtype="f")],
+        axis=1,
+    )
+    expected.index = expected.index.astype(object)
+
+    with pytest.raises(ValueError, match="Duplicate names"):
+        data = ""
+        parser.read_csv(StringIO(data), names=["one", "one"], dtype={0: "u1", 1: "f"})
+
+
+@pytest.mark.parametrize(
+    "dtype,expected",
+    [
+        (np.float64, DataFrame(columns=["a", "b"], dtype=np.float64)),
+        (
+            "category",
+            DataFrame({"a": Categorical([]), "b": Categorical([])}),
+        ),
+        (
+            {"a": "category", "b": "category"},
+            DataFrame({"a": Categorical([]), "b": Categorical([])}),
+        ),
+        ("datetime64[ns]", DataFrame(columns=["a", "b"], dtype="datetime64[ns]")),
+        (
+            "timedelta64[ns]",
+            DataFrame(
+                {
+                    "a": Series([], dtype="timedelta64[ns]"),
+                    "b": Series([], dtype="timedelta64[ns]"),
+                },
+            ),
+        ),
+        (
+            {"a": np.int64, "b": np.int32},
+            DataFrame(
+                {"a": Series([], dtype=np.int64), "b": Series([], dtype=np.int32)},
+            ),
+        ),
+        (
+            {0: np.int64, 1: np.int32},
+            DataFrame(
+                {"a": Series([], dtype=np.int64), "b": Series([], dtype=np.int32)},
+            ),
+        ),
+        (
+            {"a": np.int64, 1: np.int32},
+            DataFrame(
+                {"a": Series([], dtype=np.int64), "b": Series([], dtype=np.int32)},
+            ),
+        ),
+    ],
+)
+@skip_pyarrow  # CSV parse error: Empty CSV file or block
+def test_empty_dtype(all_parsers, dtype, expected):
+    # see gh-14712
+    parser = all_parsers
+    data = "a,b"
+
+    result = parser.read_csv(StringIO(data), header=0, dtype=dtype)
+    tm.assert_frame_equal(result, expected)

venv/lib/python3.10/site-packages/pandas/tests/io/parser/test_c_parser_only.py

@@ -0,0 +1,643 @@
+"""
+Tests that apply specifically to the CParser. Unless specifically stated
+as a CParser-specific issue, the goal is to eventually move as many of
+these tests out of this module as soon as the Python parser can accept
+further arguments when parsing.
+"""
+from decimal import Decimal
+from io import (
+    BytesIO,
+    StringIO,
+    TextIOWrapper,
+)
+import mmap
+import os
+import tarfile
+
+import numpy as np
+import pytest
+
+from pandas.compat.numpy import np_version_gte1p24
+from pandas.errors import (
+    ParserError,
+    ParserWarning,
+)
+import pandas.util._test_decorators as td
+
+from pandas import (
+    DataFrame,
+    concat,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "malformed",
+    ["1\r1\r1\r 1\r 1\r", "1\r1\r1\r 1\r 1\r11\r", "1\r1\r1\r 1\r 1\r11\r1\r"],
+    ids=["words pointer", "stream pointer", "lines pointer"],
+)
+def test_buffer_overflow(c_parser_only, malformed):
+    # see gh-9205: test certain malformed input files that cause
+    # buffer overflows in tokenizer.c
+    msg = "Buffer overflow caught - possible malformed input file."
+    parser = c_parser_only
+
+    with pytest.raises(ParserError, match=msg):
+        parser.read_csv(StringIO(malformed))
+
+
+def test_delim_whitespace_custom_terminator(c_parser_only):
+    # See gh-12912
+    data = "a b c~1 2 3~4 5 6~7 8 9"
+    parser = c_parser_only
+
+    depr_msg = "The 'delim_whitespace' keyword in pd.read_csv is deprecated"
+    with tm.assert_produces_warning(
+        FutureWarning, match=depr_msg, check_stacklevel=False
+    ):
+        df = parser.read_csv(StringIO(data), lineterminator="~", delim_whitespace=True)
+    expected = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"])
+    tm.assert_frame_equal(df, expected)
+
+
+def test_dtype_and_names_error(c_parser_only):
+    # see gh-8833: passing both dtype and names
+    # resulting in an error reporting issue
+    parser = c_parser_only
+    data = """
+1.0 1
+2.0 2
+3.0 3
+"""
+    # base cases
+    result = parser.read_csv(StringIO(data), sep=r"\s+", header=None)
+    expected = DataFrame([[1.0, 1], [2.0, 2], [3.0, 3]])
+    tm.assert_frame_equal(result, expected)
+
+    result = parser.read_csv(StringIO(data), sep=r"\s+", header=None, names=["a", "b"])
+    expected = DataFrame([[1.0, 1], [2.0, 2], [3.0, 3]], columns=["a", "b"])
+    tm.assert_frame_equal(result, expected)
+
+    # fallback casting
+    result = parser.read_csv(
+        StringIO(data), sep=r"\s+", header=None, names=["a", "b"], dtype={"a": np.int32}
+    )
+    expected = DataFrame([[1, 1], [2, 2], [3, 3]], columns=["a", "b"])
+    expected["a"] = expected["a"].astype(np.int32)
+    tm.assert_frame_equal(result, expected)
+
+    data = """
+1.0 1
+nan 2
+3.0 3
+"""
+    # fallback casting, but not castable
+    warning = RuntimeWarning if np_version_gte1p24 else None
+    with pytest.raises(ValueError, match="cannot safely convert"):
+        with tm.assert_produces_warning(warning, check_stacklevel=False):
+            parser.read_csv(
+                StringIO(data),
+                sep=r"\s+",
+                header=None,
+                names=["a", "b"],
+                dtype={"a": np.int32},
+            )
+
+
+@pytest.mark.parametrize(
+    "match,kwargs",
+    [
+        # For each of these cases, all of the dtypes are valid, just unsupported.
+        (
+            (
+                "the dtype datetime64 is not supported for parsing, "
+                "pass this column using parse_dates instead"
+            ),
+            {"dtype": {"A": "datetime64", "B": "float64"}},
+        ),
+        (
+            (
+                "the dtype datetime64 is not supported for parsing, "
+                "pass this column using parse_dates instead"
+            ),
+            {"dtype": {"A": "datetime64", "B": "float64"}, "parse_dates": ["B"]},
+        ),
+        (
+            "the dtype timedelta64 is not supported for parsing",
+            {"dtype": {"A": "timedelta64", "B": "float64"}},
+        ),
+        (
+            f"the dtype {tm.ENDIAN}U8 is not supported for parsing",
+            {"dtype": {"A": "U8"}},
+        ),
+    ],
+    ids=["dt64-0", "dt64-1", "td64", f"{tm.ENDIAN}U8"],
+)
+def test_unsupported_dtype(c_parser_only, match, kwargs):
+    parser = c_parser_only
+    df = DataFrame(
+        np.random.default_rng(2).random((5, 2)),
+        columns=list("AB"),
+        index=["1A", "1B", "1C", "1D", "1E"],
+    )
+
+    with tm.ensure_clean("__unsupported_dtype__.csv") as path:
+        df.to_csv(path)
+
+        with pytest.raises(TypeError, match=match):
+            parser.read_csv(path, index_col=0, **kwargs)
+
+
+@td.skip_if_32bit
+@pytest.mark.slow
+# test numbers between 1 and 2
+@pytest.mark.parametrize("num", np.linspace(1.0, 2.0, num=21))
+def test_precise_conversion(c_parser_only, num):
+    parser = c_parser_only
+
+    normal_errors = []
+    precise_errors = []
+
+    def error(val: float, actual_val: Decimal) -> Decimal:
+        return abs(Decimal(f"{val:.100}") - actual_val)
+
+    # 25 decimal digits of precision
+    text = f"a\n{num:.25}"
+
+    normal_val = float(
+        parser.read_csv(StringIO(text), float_precision="legacy")["a"][0]
+    )
+    precise_val = float(parser.read_csv(StringIO(text), float_precision="high")["a"][0])
+    roundtrip_val = float(
+        parser.read_csv(StringIO(text), float_precision="round_trip")["a"][0]
+    )
+    actual_val = Decimal(text[2:])
+
+    normal_errors.append(error(normal_val, actual_val))
+    precise_errors.append(error(precise_val, actual_val))
+
+    # round-trip should match float()
+    assert roundtrip_val == float(text[2:])
+
+    assert sum(precise_errors) <= sum(normal_errors)
+    assert max(precise_errors) <= max(normal_errors)
+
+
+def test_usecols_dtypes(c_parser_only):
+    parser = c_parser_only
+    data = """\
+1,2,3
+4,5,6
+7,8,9
+10,11,12"""
+
+    result = parser.read_csv(
+        StringIO(data),
+        usecols=(0, 1, 2),
+        names=("a", "b", "c"),
+        header=None,
+        converters={"a": str},
+        dtype={"b": int, "c": float},
+    )
+    result2 = parser.read_csv(
+        StringIO(data),
+        usecols=(0, 2),
+        names=("a", "b", "c"),
+        header=None,
+        converters={"a": str},
+        dtype={"b": int, "c": float},
+    )
+
+    assert (result.dtypes == [object, int, float]).all()
+    assert (result2.dtypes == [object, float]).all()
+
+
+def test_disable_bool_parsing(c_parser_only):
+    # see gh-2090
+
+    parser = c_parser_only
+    data = """A,B,C
+Yes,No,Yes
+No,Yes,Yes
+Yes,,Yes
+No,No,No"""
+
+    result = parser.read_csv(StringIO(data), dtype=object)
+    assert (result.dtypes == object).all()
+
+    result = parser.read_csv(StringIO(data), dtype=object, na_filter=False)
+    assert result["B"][2] == ""
+
+
+def test_custom_lineterminator(c_parser_only):
+    parser = c_parser_only
+    data = "a,b,c~1,2,3~4,5,6"
+
+    result = parser.read_csv(StringIO(data), lineterminator="~")
+    expected = parser.read_csv(StringIO(data.replace("~", "\n")))
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_parse_ragged_csv(c_parser_only):
+    parser = c_parser_only
+    data = """1,2,3
+1,2,3,4
+1,2,3,4,5
+1,2
+1,2,3,4"""
+
+    nice_data = """1,2,3,,
+1,2,3,4,
+1,2,3,4,5
+1,2,,,
+1,2,3,4,"""
+    result = parser.read_csv(
+        StringIO(data), header=None, names=["a", "b", "c", "d", "e"]
+    )
+
+    expected = parser.read_csv(
+        StringIO(nice_data), header=None, names=["a", "b", "c", "d", "e"]
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+    # too many columns, cause segfault if not careful
+    data = "1,2\n3,4,5"
+
+    result = parser.read_csv(StringIO(data), header=None, names=range(50))
+    expected = parser.read_csv(StringIO(data), header=None, names=range(3)).reindex(
+        columns=range(50)
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_tokenize_CR_with_quoting(c_parser_only):
+    # see gh-3453
+    parser = c_parser_only
+    data = ' a,b,c\r"a,b","e,d","f,f"'
+
+    result = parser.read_csv(StringIO(data), header=None)
+    expected = parser.read_csv(StringIO(data.replace("\r", "\n")), header=None)
+    tm.assert_frame_equal(result, expected)
+
+    result = parser.read_csv(StringIO(data))
+    expected = parser.read_csv(StringIO(data.replace("\r", "\n")))
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("count", [3 * 2**n for n in range(6)])
+def test_grow_boundary_at_cap(c_parser_only, count):
+    # See gh-12494
+    #
+    # Cause of error was that the C parser
+    # was not increasing the buffer size when
+    # the desired space would fill the buffer
+    # to capacity, which would later cause a
+    # buffer overflow error when checking the
+    # EOF terminator of the CSV stream.
+    # 3 * 2^n commas was observed to break the parser
+    parser = c_parser_only
+
+    with StringIO("," * count) as s:
+        expected = DataFrame(columns=[f"Unnamed: {i}" for i in range(count + 1)])
+        df = parser.read_csv(s)
+    tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("encoding", [None, "utf-8"])
+def test_parse_trim_buffers(c_parser_only, encoding):
+    # This test is part of a bugfix for gh-13703. It attempts to
+    # to stress the system memory allocator, to cause it to move the
+    # stream buffer and either let the OS reclaim the region, or let
+    # other memory requests of parser otherwise modify the contents
+    # of memory space, where it was formally located.
+    # This test is designed to cause a `segfault` with unpatched
+    # `tokenizer.c`. Sometimes the test fails on `segfault`, other
+    # times it fails due to memory corruption, which causes the
+    # loaded DataFrame to differ from the expected one.
+
+    # Also force 'utf-8' encoding, so that `_string_convert` would take
+    # a different execution branch.
+
+    parser = c_parser_only
+
+    # Generate a large mixed-type CSV file on-the-fly (one record is
+    # approx 1.5KiB).
+    record_ = (
+        """9999-9,99:99,,,,ZZ,ZZ,,,ZZZ-ZZZZ,.Z-ZZZZ,-9.99,,,9.99,Z"""
+        """ZZZZ,,-99,9,ZZZ-ZZZZ,ZZ-ZZZZ,,9.99,ZZZ-ZZZZZ,ZZZ-ZZZZZ,"""
+        """ZZZ-ZZZZ,ZZZ-ZZZZ,ZZZ-ZZZZ,ZZZ-ZZZZ,ZZZ-ZZZZ,ZZZ-ZZZZ,9"""
+        """99,ZZZ-ZZZZ,,ZZ-ZZZZ,,,,,ZZZZ,ZZZ-ZZZZZ,ZZZ-ZZZZ,,,9,9,"""
+        """9,9,99,99,999,999,ZZZZZ,ZZZ-ZZZZZ,ZZZ-ZZZZ,9,ZZ-ZZZZ,9."""
+        """99,ZZ-ZZZZ,ZZ-ZZZZ,,,,ZZZZ,,,ZZ,ZZ,,,,,,,,,,,,,9,,,999."""
+        """99,999.99,,,ZZZZZ,,,Z9,,,,,,,ZZZ,ZZZ,,,,,,,,,,,ZZZZZ,ZZ"""
+        """ZZZ,ZZZ-ZZZZZZ,ZZZ-ZZZZZZ,ZZ-ZZZZ,ZZ-ZZZZ,ZZ-ZZZZ,ZZ-ZZ"""
+        """ZZ,,,999999,999999,ZZZ,ZZZ,,,ZZZ,ZZZ,999.99,999.99,,,,Z"""
+        """ZZ-ZZZ,ZZZ-ZZZ,-9.99,-9.99,9,9,,99,,9.99,9.99,9,9,9.99,"""
+        """9.99,,,,9.99,9.99,,99,,99,9.99,9.99,,,ZZZ,ZZZ,,999.99,,"""
+        """999.99,ZZZ,ZZZ-ZZZZ,ZZZ-ZZZZ,,,ZZZZZ,ZZZZZ,ZZZ,ZZZ,9,9,"""
+        """,,,,,ZZZ-ZZZZ,ZZZ999Z,,,999.99,,999.99,ZZZ-ZZZZ,,,9.999"""
+        """,9.999,9.999,9.999,-9.999,-9.999,-9.999,-9.999,9.999,9."""
+        """999,9.999,9.999,9.999,9.999,9.999,9.999,99999,ZZZ-ZZZZ,"""
+        """,9.99,ZZZ,,,,,,,,ZZZ,,,,,9,,,,9,,,,,,,,,,ZZZ-ZZZZ,ZZZ-Z"""
+        """ZZZ,,ZZZZZ,ZZZZZ,ZZZZZ,ZZZZZ,,,9.99,,ZZ-ZZZZ,ZZ-ZZZZ,ZZ"""
+        """,999,,,,ZZ-ZZZZ,ZZZ,ZZZ,ZZZ-ZZZZ,ZZZ-ZZZZ,,,99.99,99.99"""
+        """,,,9.99,9.99,9.99,9.99,ZZZ-ZZZZ,,,ZZZ-ZZZZZ,,,,,-9.99,-"""
+        """9.99,-9.99,-9.99,,,,,,,,,ZZZ-ZZZZ,,9,9.99,9.99,99ZZ,,-9"""
+        """.99,-9.99,ZZZ-ZZZZ,,,,,,,ZZZ-ZZZZ,9.99,9.99,9999,,,,,,,"""
+        """,,,-9.9,Z/Z-ZZZZ,999.99,9.99,,999.99,ZZ-ZZZZ,ZZ-ZZZZ,9."""
+        """99,9.99,9.99,9.99,9.99,9.99,,ZZZ-ZZZZZ,ZZZ-ZZZZZ,ZZZ-ZZ"""
+        """ZZZ,ZZZ-ZZZZZ,ZZZ-ZZZZZ,ZZZ,ZZZ,ZZZ,ZZZ,9.99,,,-9.99,ZZ"""
+        """-ZZZZ,-999.99,,-9999,,999.99,,,,999.99,99.99,,,ZZ-ZZZZZ"""
+        """ZZZ,ZZ-ZZZZ-ZZZZZZZ,,,,ZZ-ZZ-ZZZZZZZZ,ZZZZZZZZ,ZZZ-ZZZZ"""
+        """,9999,999.99,ZZZ-ZZZZ,-9.99,-9.99,ZZZ-ZZZZ,99:99:99,,99"""
+        """,99,,9.99,,-99.99,,,,,,9.99,ZZZ-ZZZZ,-9.99,-9.99,9.99,9"""
+        """.99,,ZZZ,,,,,,,ZZZ,ZZZ,,,,,"""
+    )
+
+    # Set the number of lines so that a call to `parser_trim_buffers`
+    # is triggered: after a couple of full chunks are consumed a
+    # relatively small 'residual' chunk would cause reallocation
+    # within the parser.
+    chunksize, n_lines = 128, 2 * 128 + 15
+    csv_data = "\n".join([record_] * n_lines) + "\n"
+
+    # We will use StringIO to load the CSV from this text buffer.
+    # pd.read_csv() will iterate over the file in chunks and will
+    # finally read a residual chunk of really small size.
+
+    # Generate the expected output: manually create the dataframe
+    # by splitting by comma and repeating the `n_lines` times.
+    row = tuple(val_ if val_ else np.nan for val_ in record_.split(","))
+    expected = DataFrame(
+        [row for _ in range(n_lines)], dtype=object, columns=None, index=None
+    )
+
+    # Iterate over the CSV file in chunks of `chunksize` lines
+    with parser.read_csv(
+        StringIO(csv_data),
+        header=None,
+        dtype=object,
+        chunksize=chunksize,
+        encoding=encoding,
+    ) as chunks_:
+        result = concat(chunks_, axis=0, ignore_index=True)
+
+    # Check for data corruption if there was no segfault
+    tm.assert_frame_equal(result, expected)
+
+
+def test_internal_null_byte(c_parser_only):
+    # see gh-14012
+    #
+    # The null byte ('\x00') should not be used as a
+    # true line terminator, escape character, or comment
+    # character, only as a placeholder to indicate that
+    # none was specified.
+    #
+    # This test should be moved to test_common.py ONLY when
+    # Python's csv class supports parsing '\x00'.
+    parser = c_parser_only
+
+    names = ["a", "b", "c"]
+    data = "1,2,3\n4,\x00,6\n7,8,9"
+    expected = DataFrame([[1, 2.0, 3], [4, np.nan, 6], [7, 8, 9]], columns=names)
+
+    result = parser.read_csv(StringIO(data), names=names)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_read_nrows_large(c_parser_only):
+    # gh-7626 - Read only nrows of data in for large inputs (>262144b)
+    parser = c_parser_only
+    header_narrow = "\t".join(["COL_HEADER_" + str(i) for i in range(10)]) + "\n"
+    data_narrow = "\t".join(["somedatasomedatasomedata1" for _ in range(10)]) + "\n"
+    header_wide = "\t".join(["COL_HEADER_" + str(i) for i in range(15)]) + "\n"
+    data_wide = "\t".join(["somedatasomedatasomedata2" for _ in range(15)]) + "\n"
+    test_input = header_narrow + data_narrow * 1050 + header_wide + data_wide * 2
+
+    df = parser.read_csv(StringIO(test_input), sep="\t", nrows=1010)
+
+    assert df.size == 1010 * 10
+
+
+def test_float_precision_round_trip_with_text(c_parser_only):
+    # see gh-15140
+    parser = c_parser_only
+    df = parser.read_csv(StringIO("a"), header=None, float_precision="round_trip")
+    tm.assert_frame_equal(df, DataFrame({0: ["a"]}))
+
+
+def test_large_difference_in_columns(c_parser_only):
+    # see gh-14125
+    parser = c_parser_only
+
+    count = 10000
+    large_row = ("X," * count)[:-1] + "\n"
+    normal_row = "XXXXXX XXXXXX,111111111111111\n"
+    test_input = (large_row + normal_row * 6)[:-1]
+
+    result = parser.read_csv(StringIO(test_input), header=None, usecols=[0])
+    rows = test_input.split("\n")
+
+    expected = DataFrame([row.split(",")[0] for row in rows])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_data_after_quote(c_parser_only):
+    # see gh-15910
+    parser = c_parser_only
+
+    data = 'a\n1\n"b"a'
+    result = parser.read_csv(StringIO(data))
+
+    expected = DataFrame({"a": ["1", "ba"]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_comment_whitespace_delimited(c_parser_only):
+    parser = c_parser_only
+    test_input = """\
+1 2
+2 2 3
+3 2 3 # 3 fields
+4 2 3# 3 fields
+5 2 # 2 fields
+6 2# 2 fields
+7 # 1 field, NaN
+8# 1 field, NaN
+9 2 3 # skipped line
+# comment"""
+    with tm.assert_produces_warning(
+        ParserWarning, match="Skipping line", check_stacklevel=False
+    ):
+        df = parser.read_csv(
+            StringIO(test_input),
+            comment="#",
+            header=None,
+            delimiter="\\s+",
+            skiprows=0,
+            on_bad_lines="warn",
+        )
+    expected = DataFrame([[1, 2], [5, 2], [6, 2], [7, np.nan], [8, np.nan]])
+    tm.assert_frame_equal(df, expected)
+
+
+def test_file_like_no_next(c_parser_only):
+    # gh-16530: the file-like need not have a "next" or "__next__"
+    # attribute despite having an "__iter__" attribute.
+    #
+    # NOTE: This is only true for the C engine, not Python engine.
+    class NoNextBuffer(StringIO):
+        def __next__(self):
+            raise AttributeError("No next method")
+
+        next = __next__
+
+    parser = c_parser_only
+    data = "a\n1"
+
+    expected = DataFrame({"a": [1]})
+    result = parser.read_csv(NoNextBuffer(data))
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_buffer_rd_bytes_bad_unicode(c_parser_only):
+    # see gh-22748
+    t = BytesIO(b"\xB0")
+    t = TextIOWrapper(t, encoding="ascii", errors="surrogateescape")
+    msg = "'utf-8' codec can't encode character"
+    with pytest.raises(UnicodeError, match=msg):
+        c_parser_only.read_csv(t, encoding="UTF-8")
+
+
+@pytest.mark.parametrize("tar_suffix", [".tar", ".tar.gz"])
+def test_read_tarfile(c_parser_only, csv_dir_path, tar_suffix):
+    # see gh-16530
+    #
+    # Unfortunately, Python's CSV library can't handle
+    # tarfile objects (expects string, not bytes when
+    # iterating through a file-like).
+    parser = c_parser_only
+    tar_path = os.path.join(csv_dir_path, "tar_csv" + tar_suffix)
+
+    with tarfile.open(tar_path, "r") as tar:
+        data_file = tar.extractfile("tar_data.csv")
+
+        out = parser.read_csv(data_file)
+        expected = DataFrame({"a": [1]})
+        tm.assert_frame_equal(out, expected)
+
+
+def test_chunk_whitespace_on_boundary(c_parser_only):
+    # see gh-9735: this issue is C parser-specific (bug when
+    # parsing whitespace and characters at chunk boundary)
+    #
+    # This test case has a field too large for the Python parser / CSV library.
+    parser = c_parser_only
+
+    chunk1 = "a" * (1024 * 256 - 2) + "\na"
+    chunk2 = "\n a"
+    result = parser.read_csv(StringIO(chunk1 + chunk2), header=None)
+
+    expected = DataFrame(["a" * (1024 * 256 - 2), "a", " a"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_file_handles_mmap(c_parser_only, csv1):
+    # gh-14418
+    #
+    # Don't close user provided file handles.
+    parser = c_parser_only
+
+    with open(csv1, encoding="utf-8") as f:
+        with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as m:
+            parser.read_csv(m)
+            assert not m.closed
+
+
+def test_file_binary_mode(c_parser_only):
+    # see gh-23779
+    parser = c_parser_only
+    expected = DataFrame([[1, 2, 3], [4, 5, 6]])
+
+    with tm.ensure_clean() as path:
+        with open(path, "w", encoding="utf-8") as f:
+            f.write("1,2,3\n4,5,6")
+
+        with open(path, "rb") as f:
+            result = parser.read_csv(f, header=None)
+            tm.assert_frame_equal(result, expected)
+
+
+def test_unix_style_breaks(c_parser_only):
+    # GH 11020
+    parser = c_parser_only
+    with tm.ensure_clean() as path:
+        with open(path, "w", newline="\n", encoding="utf-8") as f:
+            f.write("blah\n\ncol_1,col_2,col_3\n\n")
+        result = parser.read_csv(path, skiprows=2, encoding="utf-8", engine="c")
+    expected = DataFrame(columns=["col_1", "col_2", "col_3"])
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("float_precision", [None, "legacy", "high", "round_trip"])
+@pytest.mark.parametrize(
+    "data,thousands,decimal",
+    [
+        (
+            """A|B|C
+1|2,334.01|5
+10|13|10.
+""",
+            ",",
+            ".",
+        ),
+        (
+            """A|B|C
+1|2.334,01|5
+10|13|10,
+""",
+            ".",
+            ",",
+        ),
+    ],
+)
+def test_1000_sep_with_decimal(
+    c_parser_only, data, thousands, decimal, float_precision
+):
+    parser = c_parser_only
+    expected = DataFrame({"A": [1, 10], "B": [2334.01, 13], "C": [5, 10.0]})
+
+    result = parser.read_csv(
+        StringIO(data),
+        sep="|",
+        thousands=thousands,
+        decimal=decimal,
+        float_precision=float_precision,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_float_precision_options(c_parser_only):
+    # GH 17154, 36228
+    parser = c_parser_only
+    s = "foo\n243.164\n"
+    df = parser.read_csv(StringIO(s))
+    df2 = parser.read_csv(StringIO(s), float_precision="high")
+
+    tm.assert_frame_equal(df, df2)
+
+    df3 = parser.read_csv(StringIO(s), float_precision="legacy")
+
+    assert not df.iloc[0, 0] == df3.iloc[0, 0]
+
+    msg = "Unrecognized float_precision option: junk"
+
+    with pytest.raises(ValueError, match=msg):
+        parser.read_csv(StringIO(s), float_precision="junk")

venv/lib/python3.10/site-packages/pandas/tests/io/parser/test_comment.py

@@ -0,0 +1,227 @@
+"""
+Tests that comments are properly handled during parsing
+for all of the parsers defined in parsers.py
+"""
+from io import StringIO
+
+import numpy as np
+import pytest
+
+from pandas import DataFrame
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("na_values", [None, ["NaN"]])
+def test_comment(all_parsers, na_values):
+    parser = all_parsers
+    data = """A,B,C
+1,2.,4.#hello world
+5.,NaN,10.0
+"""
+    expected = DataFrame(
+        [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"]
+    )
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), comment="#", na_values=na_values)
+        return
+    result = parser.read_csv(StringIO(data), comment="#", na_values=na_values)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "read_kwargs", [{}, {"lineterminator": "*"}, {"delim_whitespace": True}]
+)
+def test_line_comment(all_parsers, read_kwargs, request):
+    parser = all_parsers
+    data = """# empty
+A,B,C
+1,2.,4.#hello world
+#ignore this line
+5.,NaN,10.0
+"""
+    warn = None
+    depr_msg = "The 'delim_whitespace' keyword in pd.read_csv is deprecated"
+
+    if read_kwargs.get("delim_whitespace"):
+        data = data.replace(",", " ")
+        warn = FutureWarning
+    elif read_kwargs.get("lineterminator"):
+        data = data.replace("\n", read_kwargs.get("lineterminator"))
+
+    read_kwargs["comment"] = "#"
+    if parser.engine == "pyarrow":
+        if "lineterminator" in read_kwargs:
+            msg = (
+                "The 'lineterminator' option is not supported with the 'pyarrow' engine"
+            )
+        else:
+            msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(
+                warn, match=depr_msg, check_stacklevel=False
+            ):
+                parser.read_csv(StringIO(data), **read_kwargs)
+        return
+    elif parser.engine == "python" and read_kwargs.get("lineterminator"):
+        msg = r"Custom line terminators not supported in python parser \(yet\)"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(
+                warn, match=depr_msg, check_stacklevel=False
+            ):
+                parser.read_csv(StringIO(data), **read_kwargs)
+        return
+
+    with tm.assert_produces_warning(warn, match=depr_msg, check_stacklevel=False):
+        result = parser.read_csv(StringIO(data), **read_kwargs)
+
+    expected = DataFrame(
+        [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"]
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_comment_skiprows(all_parsers):
+    parser = all_parsers
+    data = """# empty
+random line
+# second empty line
+1,2,3
+A,B,C
+1,2.,4.
+5.,NaN,10.0
+"""
+    # This should ignore the first four lines (including comments).
+    expected = DataFrame(
+        [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"]
+    )
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), comment="#", skiprows=4)
+        return
+
+    result = parser.read_csv(StringIO(data), comment="#", skiprows=4)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_comment_header(all_parsers):
+    parser = all_parsers
+    data = """# empty
+# second empty line
+1,2,3
+A,B,C
+1,2.,4.
+5.,NaN,10.0
+"""
+    # Header should begin at the second non-comment line.
+    expected = DataFrame(
+        [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"]
+    )
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), comment="#", header=1)
+        return
+    result = parser.read_csv(StringIO(data), comment="#", header=1)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_comment_skiprows_header(all_parsers):
+    parser = all_parsers
+    data = """# empty
+# second empty line
+# third empty line
+X,Y,Z
+1,2,3
+A,B,C
+1,2.,4.
+5.,NaN,10.0
+"""
+    # Skiprows should skip the first 4 lines (including comments),
+    # while header should start from the second non-commented line,
+    # starting with line 5.
+    expected = DataFrame(
+        [[1.0, 2.0, 4.0], [5.0, np.nan, 10.0]], columns=["A", "B", "C"]
+    )
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), comment="#", skiprows=4, header=1)
+        return
+
+    result = parser.read_csv(StringIO(data), comment="#", skiprows=4, header=1)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("comment_char", ["#", "~", "&", "^", "*", "@"])
+def test_custom_comment_char(all_parsers, comment_char):
+    parser = all_parsers
+    data = "a,b,c\n1,2,3#ignore this!\n4,5,6#ignorethistoo"
+
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(
+                StringIO(data.replace("#", comment_char)), comment=comment_char
+            )
+        return
+    result = parser.read_csv(
+        StringIO(data.replace("#", comment_char)), comment=comment_char
+    )
+
+    expected = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "b", "c"])
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("header", ["infer", None])
+def test_comment_first_line(all_parsers, header):
+    # see gh-4623
+    parser = all_parsers
+    data = "# notes\na,b,c\n# more notes\n1,2,3"
+
+    if header is None:
+        expected = DataFrame({0: ["a", "1"], 1: ["b", "2"], 2: ["c", "3"]})
+    else:
+        expected = DataFrame([[1, 2, 3]], columns=["a", "b", "c"])
+
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), comment="#", header=header)
+        return
+    result = parser.read_csv(StringIO(data), comment="#", header=header)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_comment_char_in_default_value(all_parsers, request):
+    # GH#34002
+    if all_parsers.engine == "c":
+        reason = "see gh-34002: works on the python engine but not the c engine"
+        # NA value containing comment char is interpreted as comment
+        request.applymarker(pytest.mark.xfail(reason=reason, raises=AssertionError))
+    parser = all_parsers
+
+    data = (
+        "# this is a comment\n"
+        "col1,col2,col3,col4\n"
+        "1,2,3,4#inline comment\n"
+        "4,5#,6,10\n"
+        "7,8,#N/A,11\n"
+    )
+    if parser.engine == "pyarrow":
+        msg = "The 'comment' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), comment="#", na_values="#N/A")
+        return
+    result = parser.read_csv(StringIO(data), comment="#", na_values="#N/A")
+    expected = DataFrame(
+        {
+            "col1": [1, 4, 7],
+            "col2": [2, 5, 8],
+            "col3": [3.0, np.nan, np.nan],
+            "col4": [4.0, np.nan, 11.0],
+        }
+    )
+    tm.assert_frame_equal(result, expected)