Initial commit: 首次建仓，建立目录结构

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/conftest.py

@@ -0,0 +1,197 @@
+import uuid
+
+import pytest
+
+from pandas.compat import (
+    is_ci_environment,
+    is_platform_arm,
+    is_platform_mac,
+    is_platform_windows,
+)
+import pandas.util._test_decorators as td
+
+import pandas.io.common as icom
+from pandas.io.parsers import read_csv
+
+
+@pytest.fixture
+def compression_to_extension():
+    return {value: key for key, value in icom.extension_to_compression.items()}
+
+
+@pytest.fixture
+def tips_file(datapath):
+    """Path to the tips dataset"""
+    return datapath("io", "data", "csv", "tips.csv")
+
+
+@pytest.fixture
+def jsonl_file(datapath):
+    """Path to a JSONL dataset"""
+    return datapath("io", "parser", "data", "items.jsonl")
+
+
+@pytest.fixture
+def salaries_table(datapath):
+    """DataFrame with the salaries dataset"""
+    return read_csv(datapath("io", "parser", "data", "salaries.csv"), sep="\t")
+
+
+@pytest.fixture
+def feather_file(datapath):
+    return datapath("io", "data", "feather", "feather-0_3_1.feather")
+
+
+@pytest.fixture
+def xml_file(datapath):
+    return datapath("io", "data", "xml", "books.xml")
+
+
+@pytest.fixture(scope="session")
+def aws_credentials(monkeysession):
+    """Mocked AWS Credentials for moto."""
+    monkeysession.setenv("AWS_ACCESS_KEY_ID", "testing")
+    monkeysession.setenv("AWS_SECRET_ACCESS_KEY", "testing")
+    monkeysession.setenv("AWS_SECURITY_TOKEN", "testing")
+    monkeysession.setenv("AWS_SESSION_AWS_SESSION_TOKEN", "testing")
+    monkeysession.setenv("AWS_DEFAULT_REGION", "us-east-1")
+
+
+@pytest.fixture(scope="session")
+def moto_server(aws_credentials):
+    # use service container for Linux on GitHub Actions
+    if is_ci_environment() and not (
+        is_platform_mac() or is_platform_arm() or is_platform_windows()
+    ):
+        yield "http://localhost:5000"
+    else:
+        moto_server = pytest.importorskip("moto.server")
+        server = moto_server.ThreadedMotoServer(port=0)
+        server.start()
+        host, port = server.get_host_and_port()
+        yield f"http://{host}:{port}"
+        server.stop()
+
+
+@pytest.fixture
+def moto_s3_resource(moto_server):
+    boto3 = pytest.importorskip("boto3")
+    s3 = boto3.resource("s3", endpoint_url=moto_server)
+    return s3
+
+
+@pytest.fixture(scope="session")
+def s3so(moto_server):
+    return {
+        "client_kwargs": {
+            "endpoint_url": moto_server,
+        }
+    }
+
+
+@pytest.fixture
+def s3_bucket_public(moto_s3_resource):
+    """
+    Create a public S3 bucket using moto.
+    """
+    bucket_name = f"pandas-test-{uuid.uuid4()}"
+    bucket = moto_s3_resource.Bucket(bucket_name)
+    bucket.create(ACL="public-read")
+    yield bucket
+    bucket.objects.delete()
+    bucket.delete()
+
+
+@pytest.fixture
+def s3_bucket_private(moto_s3_resource):
+    """
+    Create a private S3 bucket using moto.
+    """
+    bucket_name = f"cant_get_it-{uuid.uuid4()}"
+    bucket = moto_s3_resource.Bucket(bucket_name)
+    bucket.create(ACL="private")
+    yield bucket
+    bucket.objects.delete()
+    bucket.delete()
+
+
+@pytest.fixture
+def s3_bucket_public_with_data(
+    s3_bucket_public, tips_file, jsonl_file, feather_file, xml_file
+):
+    """
+    The following datasets
+    are loaded.
+
+    - tips.csv
+    - tips.csv.gz
+    - tips.csv.bz2
+    - items.jsonl
+    """
+    test_s3_files = [
+        ("tips#1.csv", tips_file),
+        ("tips.csv", tips_file),
+        ("tips.csv.gz", tips_file + ".gz"),
+        ("tips.csv.bz2", tips_file + ".bz2"),
+        ("items.jsonl", jsonl_file),
+        ("simple_dataset.feather", feather_file),
+        ("books.xml", xml_file),
+    ]
+    for s3_key, file_name in test_s3_files:
+        with open(file_name, "rb") as f:
+            s3_bucket_public.put_object(Key=s3_key, Body=f)
+    return s3_bucket_public
+
+
+@pytest.fixture
+def s3_bucket_private_with_data(
+    s3_bucket_private, tips_file, jsonl_file, feather_file, xml_file
+):
+    """
+    The following datasets
+    are loaded.
+
+    - tips.csv
+    - tips.csv.gz
+    - tips.csv.bz2
+    - items.jsonl
+    """
+    test_s3_files = [
+        ("tips#1.csv", tips_file),
+        ("tips.csv", tips_file),
+        ("tips.csv.gz", tips_file + ".gz"),
+        ("tips.csv.bz2", tips_file + ".bz2"),
+        ("items.jsonl", jsonl_file),
+        ("simple_dataset.feather", feather_file),
+        ("books.xml", xml_file),
+    ]
+    for s3_key, file_name in test_s3_files:
+        with open(file_name, "rb") as f:
+            s3_bucket_private.put_object(Key=s3_key, Body=f)
+    return s3_bucket_private
+
+
+_compression_formats_params = [
+    (".no_compress", None),
+    ("", None),
+    (".gz", "gzip"),
+    (".GZ", "gzip"),
+    (".bz2", "bz2"),
+    (".BZ2", "bz2"),
+    (".zip", "zip"),
+    (".ZIP", "zip"),
+    (".xz", "xz"),
+    (".XZ", "xz"),
+    pytest.param((".zst", "zstd"), marks=td.skip_if_no("zstandard")),
+    pytest.param((".ZST", "zstd"), marks=td.skip_if_no("zstandard")),
+]
+
+
+@pytest.fixture(params=_compression_formats_params[1:])
+def compression_format(request):
+    return request.param
+
+
+@pytest.fixture(params=_compression_formats_params)
+def compression_ext(request):
+    return request.param[0]

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/excel/test_odf.py

@@ -0,0 +1,72 @@
+import functools
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+pytest.importorskip("odf")
+
+
+@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 a 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)

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/excel/test_odswriter.py

@@ -0,0 +1,106 @@
+from datetime import (
+    date,
+    datetime,
+)
+import re
+import uuid
+
+import pytest
+
+import pandas as pd
+
+from pandas.io.excel import ExcelWriter
+
+odf = pytest.importorskip("odf")
+
+
+@pytest.fixture
+def ext():
+    return ".ods"
+
+
+@pytest.fixture
+def tmp_excel(ext, tmp_path):
+    tmp = tmp_path / f"{uuid.uuid4()}{ext}"
+    tmp.touch()
+    return str(tmp)
+
+
+def test_write_append_mode_raises(tmp_excel):
+    msg = "Append mode is not supported with odf!"
+
+    with pytest.raises(ValueError, match=msg):
+        ExcelWriter(tmp_excel, engine="odf", mode="a")
+
+
+@pytest.mark.parametrize("engine_kwargs", [None, {"kwarg": 1}])
+def test_engine_kwargs(tmp_excel, engine_kwargs):
+    # GH 42286
+    # GH 43445
+    # test for error: OpenDocumentSpreadsheet does not accept any arguments
+    if engine_kwargs is not None:
+        error = re.escape(
+            "OpenDocumentSpreadsheet() got an unexpected keyword argument 'kwarg'"
+        )
+        with pytest.raises(
+            TypeError,
+            match=error,
+        ):
+            ExcelWriter(tmp_excel, engine="odf", engine_kwargs=engine_kwargs)
+    else:
+        with ExcelWriter(tmp_excel, engine="odf", engine_kwargs=engine_kwargs) as _:
+            pass
+
+
+def test_book_and_sheets_consistent(tmp_excel):
+    # GH#45687 - Ensure sheets is updated if user modifies book
+    with ExcelWriter(tmp_excel) 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(
+    tmp_excel, 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
+
+    pd.DataFrame([[value]]).to_excel(tmp_excel, header=False, index=False)
+
+    with pd.ExcelFile(tmp_excel) 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

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/excel/test_openpyxl.py

@@ -0,0 +1,431 @@
+import contextlib
+from pathlib import Path
+import re
+import uuid
+
+import numpy as np
+import pytest
+
+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")
+
+
+@pytest.fixture
+def ext():
+    return ".xlsx"
+
+
+@pytest.fixture
+def tmp_excel(ext, tmp_path):
+    tmp = tmp_path / f"{uuid.uuid4()}{ext}"
+    tmp.touch()
+    return str(tmp)
+
+
+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(tmp_excel):
+    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 _OpenpyxlWriter(tmp_excel) 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(tmp_excel, iso_dates):
+    # GH 42286 GH 43445
+    engine_kwargs = {"iso_dates": iso_dates}
+    with ExcelWriter(
+        tmp_excel, 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(tmp_excel):
+    # GH 43445
+    # test whether an invalid engine kwargs actually raises
+    DataFrame(["hello", "world"]).to_excel(tmp_excel)
+    with pytest.raises(
+        TypeError,
+        match=re.escape(
+            "load_workbook() got an unexpected keyword argument 'apple_banana'"
+        ),
+    ):
+        with ExcelWriter(
+            tmp_excel,
+            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(tmp_excel, data_only, expected):
+    # GH 43445
+    # tests whether the data_only engine_kwarg actually works well for
+    # openpyxl's load_workbook
+    DataFrame(["=1+1"]).to_excel(tmp_excel)
+    with ExcelWriter(
+        tmp_excel, 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(
+            tmp_excel,
+            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(tmp_excel, mode, expected):
+    df = DataFrame([1], columns=["baz"])
+
+    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(tmp_excel)
+
+    with ExcelWriter(tmp_excel, engine="openpyxl", mode=mode) as writer:
+        df.to_excel(writer, sheet_name="baz", index=False)
+
+    with contextlib.closing(openpyxl.load_workbook(tmp_excel)) 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(tmp_excel, if_sheet_exists, num_sheets, expected):
+    # GH 40230
+    df1 = DataFrame({"fruit": ["apple", "banana"]})
+    df2 = DataFrame({"fruit": ["pear"]})
+
+    df1.to_excel(tmp_excel, engine="openpyxl", sheet_name="foo", index=False)
+    with ExcelWriter(
+        tmp_excel, 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(tmp_excel)) 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(
+    tmp_excel, startrow, startcol, greeting, goodbye
+):
+    df1 = DataFrame({"greeting": ["hello", "world"], "goodbye": ["goodbye", "people"]})
+    df2 = DataFrame(["poop"])
+
+    df1.to_excel(tmp_excel, engine="openpyxl", sheet_name="poo", index=False)
+    with ExcelWriter(
+        tmp_excel, 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(tmp_excel, 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(tmp_excel, if_sheet_exists, msg):
+    # GH 40230
+    df = DataFrame({"fruit": ["pear"]})
+    df.to_excel(tmp_excel, sheet_name="foo", engine="openpyxl")
+    with pytest.raises(ValueError, match=re.escape(msg)):
+        with ExcelWriter(
+            tmp_excel, 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(tmp_excel):
+    # GH 29854
+    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(tmp_excel, 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(tmp_excel):
+    # GH 39576
+    df = DataFrame()
+
+    df.to_excel(tmp_excel, engine="openpyxl")
+
+    with ExcelWriter(
+        tmp_excel, 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(tmp_excel).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(tmp_excel):
+    # GH#45687 - Ensure sheets is updated if user modifies book
+    with ExcelWriter(tmp_excel, 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)

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/excel/test_style.py

@@ -0,0 +1,356 @@
+import contextlib
+import uuid
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    DataFrame,
+    MultiIndex,
+    Timestamp,
+    period_range,
+    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.
+
+
+@pytest.fixture
+def tmp_excel(tmp_path):
+    tmp = tmp_path / f"{uuid.uuid4()}.xlsx"
+    tmp.touch()
+    return str(tmp)
+
+
+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__
+
+
+def test_styler_default_values(tmp_excel):
+    # GH 54154
+    openpyxl = pytest.importorskip("openpyxl")
+    df = DataFrame([{"A": 1, "B": 2, "C": 3}, {"A": 1, "B": 2, "C": 3}])
+
+    with ExcelWriter(tmp_excel, engine="openpyxl") as writer:
+        df.to_excel(writer, sheet_name="custom")
+
+    with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb:
+        # Check font, spacing, indentation
+        assert wb["custom"].cell(1, 1).font.bold is False
+        assert wb["custom"].cell(1, 1).alignment.horizontal is None
+        assert wb["custom"].cell(1, 1).alignment.vertical is None
+
+        # Check border
+        assert wb["custom"].cell(1, 1).border.bottom.color is None
+        assert wb["custom"].cell(1, 1).border.top.color is None
+        assert wb["custom"].cell(1, 1).border.left.color is None
+        assert wb["custom"].cell(1, 1).border.right.color is None
+
+
+@pytest.mark.parametrize("engine", ["xlsxwriter", "openpyxl"])
+def test_styler_to_excel_unstyled(engine, tmp_excel):
+    # 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 ExcelWriter(tmp_excel, 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(tmp_excel)) as wb:
+        for col1, col2 in zip(
+            wb["dataframe"].columns,
+            wb["unstyled"].columns,
+            strict=True,
+        ):
+            assert len(col1) == len(col2)
+            for cell1, cell2 in zip(col1, col2, strict=True):
+                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",
+    ),
+]
+
+
+def test_styler_custom_style(tmp_excel):
+    # GH 54154
+    css_style = "background-color: #111222"
+    openpyxl = pytest.importorskip("openpyxl")
+    df = DataFrame([{"A": 1, "B": 2}, {"A": 1, "B": 2}])
+
+    with ExcelWriter(tmp_excel, engine="openpyxl") as writer:
+        styler = df.style.map(lambda x: css_style)
+        styler.to_excel(writer, sheet_name="custom", index=False)
+
+    with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb:
+        # Check font, spacing, indentation
+        assert wb["custom"].cell(1, 1).font.bold is False
+        assert wb["custom"].cell(1, 1).alignment.horizontal is None
+        assert wb["custom"].cell(1, 1).alignment.vertical is None
+
+        # Check border
+        assert wb["custom"].cell(1, 1).border.bottom.color is None
+        assert wb["custom"].cell(1, 1).border.top.color is None
+        assert wb["custom"].cell(1, 1).border.left.color is None
+        assert wb["custom"].cell(1, 1).border.right.color is None
+
+        # Check background color
+        assert wb["custom"].cell(2, 1).fill.fgColor.index == "00111222"
+        assert wb["custom"].cell(3, 1).fill.fgColor.index == "00111222"
+        assert wb["custom"].cell(2, 2).fill.fgColor.index == "00111222"
+        assert wb["custom"].cell(3, 2).fill.fgColor.index == "00111222"
+
+
+@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, tmp_excel):
+    pytest.importorskip(engine)
+    df = DataFrame(np.random.default_rng(2).standard_normal((1, 1)))
+    styler = df.style.map(lambda x: css)
+
+    with ExcelWriter(tmp_excel, 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(tmp_excel)) 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, tmp_excel):
+    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 ExcelWriter(tmp_excel, 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(tmp_excel)) 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, tmp_excel):
+    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 ExcelWriter(tmp_excel, 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(tmp_excel)) 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(tmp_excel):
+    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 ExcelWriter(tmp_excel, engine="openpyxl") as writer:
+        ExcelFormatter(styler, style_converter=custom_converter).write(
+            writer, sheet_name="custom"
+        )
+
+    with contextlib.closing(openpyxl.load_workbook(tmp_excel)) 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_bucket_public, s3so):
+    # GH#46381
+    mock_bucket_name = s3_bucket_public.name
+    target_file = f"{uuid.uuid4()}.xlsx"
+    df = DataFrame({"x": [1, 2, 3], "y": [2, 4, 6]})
+    styler = df.style.set_sticky(axis="index")
+    uri = f"s3://{mock_bucket_name}/{target_file}"
+    styler.to_excel(uri, storage_options=s3so)
+    result = read_excel(uri, index_col=0, storage_options=s3so)
+    tm.assert_frame_equal(result, df)
+
+
+@pytest.mark.parametrize("merge_cells", [True, False, "columns"])
+def test_format_hierarchical_rows_periodindex(merge_cells):
+    # GH#60099
+    df = DataFrame(
+        {"A": [1, 2]},
+        index=MultiIndex.from_arrays(
+            [
+                period_range(start="2006-10-06", end="2006-10-07", freq="D"),
+                ["X", "Y"],
+            ],
+            names=["date", "category"],
+        ),
+    )
+    formatter = ExcelFormatter(df, merge_cells=merge_cells)
+    formatted_cells = formatter._format_hierarchical_rows()
+
+    for cell in formatted_cells:
+        if cell.row != 0 and cell.col == 0:
+            assert isinstance(cell.val, Timestamp), (
+                "Period should be converted to Timestamp"
+            )

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/excel/test_xlrd.py

@@ -0,0 +1,71 @@
+import io
+
+import numpy as np
+import pytest
+
+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")
+
+
+@pytest.fixture
+def read_ext_xlrd():
+    """
+    Valid extensions for reading Excel files with xlrd.
+
+    Similar to read_ext, but excludes .ods, .xlsb, and for xlrd>2 .xlsx, .xlsm
+    """
+    return ".xls"
+
+
+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"

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/excel/test_xlsxwriter.py

@@ -0,0 +1,86 @@
+import contextlib
+import uuid
+
+import pytest
+
+from pandas import DataFrame
+
+from pandas.io.excel import ExcelWriter
+
+xlsxwriter = pytest.importorskip("xlsxwriter")
+
+
+@pytest.fixture
+def ext():
+    return ".xlsx"
+
+
+@pytest.fixture
+def tmp_excel(ext, tmp_path):
+    tmp = tmp_path / f"{uuid.uuid4()}{ext}"
+    tmp.touch()
+    return str(tmp)
+
+
+def test_column_format(tmp_excel):
+    # Test that column formats are applied to cells. Test for issue #9167.
+    # Applicable to xlsxwriter only.
+    openpyxl = pytest.importorskip("openpyxl")
+
+    frame = DataFrame({"A": [123456, 123456], "B": [123456, 123456]})
+
+    with ExcelWriter(tmp_excel) 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(tmp_excel)) 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(tmp_excel):
+    msg = "Append mode is not supported with xlsxwriter!"
+
+    with pytest.raises(ValueError, match=msg):
+        ExcelWriter(tmp_excel, engine="xlsxwriter", mode="a")
+
+
+@pytest.mark.parametrize("nan_inf_to_errors", [True, False])
+def test_engine_kwargs(tmp_excel, nan_inf_to_errors):
+    # GH 42286
+    engine_kwargs = {"options": {"nan_inf_to_errors": nan_inf_to_errors}}
+    with ExcelWriter(
+        tmp_excel, 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(tmp_excel):
+    # GH#45687 - Ensure sheets is updated if user modifies book
+    with ExcelWriter(tmp_excel, engine="xlsxwriter") as writer:
+        assert writer.sheets == {}
+        sheet = writer.book.add_worksheet("test_name")
+        assert writer.sheets == {"test_name": sheet}

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/style/test_bar.py

@@ -0,0 +1,360 @@
+import io
+
+import numpy as np
+import pytest
+
+from pandas import (
+    NA,
+    DataFrame,
+    read_csv,
+)
+
+pytest.importorskip("jinja2")
+
+
+def bar_grad(a=None, b=None, c=None, d=None):
+    """Used in multiple tests to simplify formatting of expected result"""
+    ret = [("width", "10em")]
+    if all(x is None for x in [a, b, c, d]):
+        return ret
+    return [
+        *ret,
+        (
+            "background",
+            f"linear-gradient(90deg,{','.join([x for x in [a, b, c, d] if x])})",
+        ),
+    ]
+
+
+def no_bar():
+    return bar_grad()
+
+
+def bar_to(x, color="#d65f5f"):
+    return bar_grad(f" {color} {x:.1f}%", f" transparent {x:.1f}%")
+
+
+def bar_from_to(x, y, color="#d65f5f"):
+    return bar_grad(
+        f" transparent {x:.1f}%",
+        f" {color} {x:.1f}%",
+        f" {color} {y:.1f}%",
+        f" transparent {y:.1f}%",
+    )
+
+
+@pytest.fixture
+def df_pos():
+    return DataFrame([[1], [2], [3]])
+
+
+@pytest.fixture
+def df_neg():
+    return DataFrame([[-1], [-2], [-3]])
+
+
+@pytest.fixture
+def df_mix():
+    return DataFrame([[-3], [1], [2]])
+
+
+@pytest.mark.parametrize(
+    "align, exp",
+    [
+        ("left", [no_bar(), bar_to(50), bar_to(100)]),
+        ("right", [bar_to(100), bar_from_to(50, 100), no_bar()]),
+        ("mid", [bar_to(33.33), bar_to(66.66), bar_to(100)]),
+        ("zero", [bar_from_to(50, 66.7), bar_from_to(50, 83.3), bar_from_to(50, 100)]),
+        ("mean", [bar_to(50), no_bar(), bar_from_to(50, 100)]),
+        (2.0, [bar_to(50), no_bar(), bar_from_to(50, 100)]),
+        (np.median, [bar_to(50), no_bar(), bar_from_to(50, 100)]),
+    ],
+)
+def test_align_positive_cases(df_pos, align, exp):
+    # test different align cases for all positive values
+    result = df_pos.style.bar(align=align)._compute().ctx
+    expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]}
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "align, exp",
+    [
+        ("left", [bar_to(100), bar_to(50), no_bar()]),
+        ("right", [no_bar(), bar_from_to(50, 100), bar_to(100)]),
+        ("mid", [bar_from_to(66.66, 100), bar_from_to(33.33, 100), bar_to(100)]),
+        ("zero", [bar_from_to(33.33, 50), bar_from_to(16.66, 50), bar_to(50)]),
+        ("mean", [bar_from_to(50, 100), no_bar(), bar_to(50)]),
+        (-2.0, [bar_from_to(50, 100), no_bar(), bar_to(50)]),
+        (np.median, [bar_from_to(50, 100), no_bar(), bar_to(50)]),
+    ],
+)
+def test_align_negative_cases(df_neg, align, exp):
+    # test different align cases for all negative values
+    result = df_neg.style.bar(align=align)._compute().ctx
+    expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]}
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "align, exp",
+    [
+        ("left", [no_bar(), bar_to(80), bar_to(100)]),
+        ("right", [bar_to(100), bar_from_to(80, 100), no_bar()]),
+        ("mid", [bar_to(60), bar_from_to(60, 80), bar_from_to(60, 100)]),
+        ("zero", [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]),
+        ("mean", [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]),
+        (-0.0, [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]),
+        (np.nanmedian, [bar_to(50), no_bar(), bar_from_to(50, 62.5)]),
+    ],
+)
+@pytest.mark.parametrize("nans", [True, False])
+def test_align_mixed_cases(df_mix, align, exp, nans):
+    # test different align cases for mixed positive and negative values
+    # also test no impact of NaNs and no_bar
+    expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]}
+    if nans:
+        df_mix.loc[3, :] = np.nan
+        expected.update({(3, 0): no_bar()})
+    result = df_mix.style.bar(align=align)._compute().ctx
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "align, exp",
+    [
+        (
+            "left",
+            {
+                "index": [[no_bar(), no_bar()], [bar_to(100), bar_to(100)]],
+                "columns": [[no_bar(), bar_to(100)], [no_bar(), bar_to(100)]],
+                "none": [[no_bar(), bar_to(33.33)], [bar_to(66.66), bar_to(100)]],
+            },
+        ),
+        (
+            "mid",
+            {
+                "index": [[bar_to(33.33), bar_to(50)], [bar_to(100), bar_to(100)]],
+                "columns": [[bar_to(50), bar_to(100)], [bar_to(75), bar_to(100)]],
+                "none": [[bar_to(25), bar_to(50)], [bar_to(75), bar_to(100)]],
+            },
+        ),
+        (
+            "zero",
+            {
+                "index": [
+                    [bar_from_to(50, 66.66), bar_from_to(50, 75)],
+                    [bar_from_to(50, 100), bar_from_to(50, 100)],
+                ],
+                "columns": [
+                    [bar_from_to(50, 75), bar_from_to(50, 100)],
+                    [bar_from_to(50, 87.5), bar_from_to(50, 100)],
+                ],
+                "none": [
+                    [bar_from_to(50, 62.5), bar_from_to(50, 75)],
+                    [bar_from_to(50, 87.5), bar_from_to(50, 100)],
+                ],
+            },
+        ),
+        (
+            2,
+            {
+                "index": [
+                    [bar_to(50), no_bar()],
+                    [bar_from_to(50, 100), bar_from_to(50, 100)],
+                ],
+                "columns": [
+                    [bar_to(50), no_bar()],
+                    [bar_from_to(50, 75), bar_from_to(50, 100)],
+                ],
+                "none": [
+                    [bar_from_to(25, 50), no_bar()],
+                    [bar_from_to(50, 75), bar_from_to(50, 100)],
+                ],
+            },
+        ),
+    ],
+)
+@pytest.mark.parametrize("axis", ["index", "columns", "none"])
+def test_align_axis(align, exp, axis):
+    # test all axis combinations with positive values and different aligns
+    data = DataFrame([[1, 2], [3, 4]])
+    result = (
+        data.style.bar(align=align, axis=None if axis == "none" else axis)
+        ._compute()
+        .ctx
+    )
+    expected = {
+        (0, 0): exp[axis][0][0],
+        (0, 1): exp[axis][0][1],
+        (1, 0): exp[axis][1][0],
+        (1, 1): exp[axis][1][1],
+    }
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "values, vmin, vmax",
+    [
+        ("positive", 1.5, 2.5),
+        ("negative", -2.5, -1.5),
+        ("mixed", -2.5, 1.5),
+    ],
+)
+@pytest.mark.parametrize("nullify", [None, "vmin", "vmax"])  # test min/max separately
+@pytest.mark.parametrize("align", ["left", "right", "zero", "mid"])
+def test_vmin_vmax_clipping(df_pos, df_neg, df_mix, values, vmin, vmax, nullify, align):
+    # test that clipping occurs if any vmin > data_values or vmax < data_values
+    if align == "mid":  # mid acts as left or right in each case
+        if values == "positive":
+            align = "left"
+        elif values == "negative":
+            align = "right"
+    df = {"positive": df_pos, "negative": df_neg, "mixed": df_mix}[values]
+    vmin = None if nullify == "vmin" else vmin
+    vmax = None if nullify == "vmax" else vmax
+
+    clip_df = df.where(df <= (vmax if vmax else 999), other=vmax)
+    clip_df = clip_df.where(clip_df >= (vmin if vmin else -999), other=vmin)
+
+    result = (
+        df.style.bar(align=align, vmin=vmin, vmax=vmax, color=["red", "green"])
+        ._compute()
+        .ctx
+    )
+    expected = clip_df.style.bar(align=align, color=["red", "green"])._compute().ctx
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "values, vmin, vmax",
+    [
+        ("positive", 0.5, 4.5),
+        ("negative", -4.5, -0.5),
+        ("mixed", -4.5, 4.5),
+    ],
+)
+@pytest.mark.parametrize("nullify", [None, "vmin", "vmax"])  # test min/max separately
+@pytest.mark.parametrize("align", ["left", "right", "zero", "mid"])
+def test_vmin_vmax_widening(df_pos, df_neg, df_mix, values, vmin, vmax, nullify, align):
+    # test that widening occurs if any vmax > data_values or vmin < data_values
+    if align == "mid":  # mid acts as left or right in each case
+        if values == "positive":
+            align = "left"
+        elif values == "negative":
+            align = "right"
+    df = {"positive": df_pos, "negative": df_neg, "mixed": df_mix}[values]
+    vmin = None if nullify == "vmin" else vmin
+    vmax = None if nullify == "vmax" else vmax
+
+    expand_df = df.copy()
+    expand_df.loc[3, :], expand_df.loc[4, :] = vmin, vmax
+
+    result = (
+        df.style.bar(align=align, vmin=vmin, vmax=vmax, color=["red", "green"])
+        ._compute()
+        .ctx
+    )
+    expected = expand_df.style.bar(align=align, color=["red", "green"])._compute().ctx
+    assert result.items() <= expected.items()
+
+
+def test_numerics():
+    # test data is pre-selected for numeric values
+    data = DataFrame([[1, "a"], [2, "b"]])
+    result = data.style.bar()._compute().ctx
+    assert (0, 1) not in result
+    assert (1, 1) not in result
+
+
+@pytest.mark.parametrize(
+    "align, exp",
+    [
+        ("left", [no_bar(), bar_to(100, "green")]),
+        ("right", [bar_to(100, "red"), no_bar()]),
+        ("mid", [bar_to(25, "red"), bar_from_to(25, 100, "green")]),
+        ("zero", [bar_from_to(33.33, 50, "red"), bar_from_to(50, 100, "green")]),
+    ],
+)
+def test_colors_mixed(align, exp):
+    data = DataFrame([[-1], [3]])
+    result = data.style.bar(align=align, color=["red", "green"])._compute().ctx
+    assert result == {(0, 0): exp[0], (1, 0): exp[1]}
+
+
+def test_bar_align_height():
+    # test when keyword height is used 'no-repeat center' and 'background-size' present
+    data = DataFrame([[1], [2]])
+    result = data.style.bar(align="left", height=50)._compute().ctx
+    bg_s = "linear-gradient(90deg, #d65f5f 100.0%, transparent 100.0%) no-repeat center"
+    expected = {
+        (0, 0): [("width", "10em")],
+        (1, 0): [
+            ("width", "10em"),
+            ("background", bg_s),
+            ("background-size", "100% 50.0%"),
+        ],
+    }
+    assert result == expected
+
+
+def test_bar_value_error_raises():
+    df = DataFrame({"A": [-100, -60, -30, -20]})
+
+    msg = "`align` should be in {'left', 'right', 'mid', 'mean', 'zero'} or"
+    with pytest.raises(ValueError, match=msg):
+        df.style.bar(align="poorly", color=["#d65f5f", "#5fba7d"]).to_html()
+
+    msg = r"`width` must be a value in \[0, 100\]"
+    with pytest.raises(ValueError, match=msg):
+        df.style.bar(width=200).to_html()
+
+    msg = r"`height` must be a value in \[0, 100\]"
+    with pytest.raises(ValueError, match=msg):
+        df.style.bar(height=200).to_html()
+
+
+def test_bar_color_and_cmap_error_raises():
+    df = DataFrame({"A": [1, 2, 3, 4]})
+    msg = "`color` and `cmap` cannot both be given"
+    # Test that providing both color and cmap raises a ValueError
+    with pytest.raises(ValueError, match=msg):
+        df.style.bar(color="#d65f5f", cmap="viridis").to_html()
+
+
+def test_bar_invalid_color_type_error_raises():
+    df = DataFrame({"A": [1, 2, 3, 4]})
+    msg = (
+        r"`color` must be string or list or tuple of 2 strings,"
+        r"\(eg: color=\['#d65f5f', '#5fba7d'\]\)"
+    )
+    # Test that providing an invalid color type raises a ValueError
+    with pytest.raises(ValueError, match=msg):
+        df.style.bar(color=123).to_html()
+
+    # Test that providing a color list with more than two elements raises a ValueError
+    with pytest.raises(ValueError, match=msg):
+        df.style.bar(color=["#d65f5f", "#5fba7d", "#abcdef"]).to_html()
+
+
+def test_styler_bar_with_NA_values():
+    df1 = DataFrame({"A": [1, 2, NA, 4]})
+    df2 = DataFrame([[NA, NA], [NA, NA]])
+    expected_substring = "style type="
+    html_output1 = df1.style.bar(subset="A").to_html()
+    html_output2 = df2.style.bar(align="left", axis=None).to_html()
+    assert expected_substring in html_output1
+    assert expected_substring in html_output2
+
+
+def test_style_bar_with_pyarrow_NA_values():
+    pytest.importorskip("pyarrow")
+    data = """name,age,test1,test2,teacher
+        Adam,15,95.0,80,Ashby
+        Bob,16,81.0,82,Ashby
+        Dave,16,89.0,84,Jones
+        Fred,15,,88,Jones"""
+    df = read_csv(io.StringIO(data), dtype_backend="pyarrow")
+    expected_substring = "style type="
+    html_output = df.style.bar(subset="test1").to_html()
+    assert expected_substring in html_output

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/style/test_exceptions.py

@@ -0,0 +1,44 @@
+import pytest
+
+jinja2 = pytest.importorskip("jinja2")
+
+from pandas import (
+    DataFrame,
+    MultiIndex,
+)
+
+from pandas.io.formats.style import Styler
+
+
+@pytest.fixture
+def df():
+    return DataFrame(
+        data=[[0, -0.609], [1, -1.228]],
+        columns=["A", "B"],
+        index=["x", "y"],
+    )
+
+
+@pytest.fixture
+def styler(df):
+    return Styler(df, uuid_len=0)
+
+
+def test_concat_bad_columns(styler):
+    msg = "`other.data` must have same columns as `Styler.data"
+    with pytest.raises(ValueError, match=msg):
+        styler.concat(DataFrame([[1, 2]]).style)
+
+
+def test_concat_bad_type(styler):
+    msg = "`other` must be of type `Styler`"
+    with pytest.raises(TypeError, match=msg):
+        styler.concat(DataFrame([[1, 2]]))
+
+
+def test_concat_bad_index_levels(styler, df):
+    df = df.copy()
+    df.index = MultiIndex.from_tuples([(0, 0), (1, 1)])
+    msg = "number of index levels must be same in `other`"
+    with pytest.raises(ValueError, match=msg):
+        styler.concat(df.style)

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/style/test_format.py

@@ -0,0 +1,661 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    NA,
+    DataFrame,
+    IndexSlice,
+    MultiIndex,
+    NaT,
+    Timestamp,
+    option_context,
+)
+
+pytest.importorskip("jinja2")
+from pandas.io.formats.style import Styler
+from pandas.io.formats.style_render import _str_escape
+
+
+@pytest.fixture
+def df():
+    return DataFrame(
+        data=[[0, -0.609], [1, -1.228]],
+        columns=["A", "B"],
+        index=["x", "y"],
+    )
+
+
+@pytest.fixture
+def styler(df):
+    return Styler(df, uuid_len=0)
+
+
+@pytest.fixture
+def df_multi():
+    return (
+        DataFrame(
+            data=np.arange(16).reshape(4, 4),
+            columns=MultiIndex.from_product([["A", "B"], ["a", "b"]]),
+            index=MultiIndex.from_product([["X", "Y"], ["x", "y"]]),
+        )
+        .rename_axis(["0_0", "0_1"], axis=0)
+        .rename_axis(["1_0", "1_1"], axis=1)
+    )
+
+
+@pytest.fixture
+def styler_multi(df_multi):
+    return Styler(df_multi, uuid_len=0)
+
+
+def test_display_format(styler):
+    ctx = styler.format("{:0.1f}")._translate(True, True)
+    assert all(["display_value" in c for c in row] for row in ctx["body"])
+    assert all([len(c["display_value"]) <= 3 for c in row[1:]] for row in ctx["body"])
+    assert len(ctx["body"][0][1]["display_value"].lstrip("-")) <= 3
+
+
+@pytest.mark.parametrize("index", [True, False])
+@pytest.mark.parametrize("columns", [True, False])
+def test_display_format_index(styler, index, columns):
+    exp_index = ["x", "y"]
+    if index:
+        styler.format_index(lambda v: v.upper(), axis=0)  # test callable
+        exp_index = ["X", "Y"]
+
+    exp_columns = ["A", "B"]
+    if columns:
+        styler.format_index("*{}*", axis=1)  # test string
+        exp_columns = ["*A*", "*B*"]
+
+    ctx = styler._translate(True, True)
+
+    for r, row in enumerate(ctx["body"]):
+        assert row[0]["display_value"] == exp_index[r]
+
+    for c, col in enumerate(ctx["head"][1:]):
+        assert col["display_value"] == exp_columns[c]
+
+
+def test_format_dict(styler):
+    ctx = styler.format({"A": "{:0.1f}", "B": "{0:.2%}"})._translate(True, True)
+    assert ctx["body"][0][1]["display_value"] == "0.0"
+    assert ctx["body"][0][2]["display_value"] == "-60.90%"
+
+
+def test_format_index_dict(styler):
+    ctx = styler.format_index({0: lambda v: v.upper()})._translate(True, True)
+    for i, val in enumerate(["X", "Y"]):
+        assert ctx["body"][i][0]["display_value"] == val
+
+
+def test_format_string(styler):
+    ctx = styler.format("{:.2f}")._translate(True, True)
+    assert ctx["body"][0][1]["display_value"] == "0.00"
+    assert ctx["body"][0][2]["display_value"] == "-0.61"
+    assert ctx["body"][1][1]["display_value"] == "1.00"
+    assert ctx["body"][1][2]["display_value"] == "-1.23"
+
+
+def test_format_callable(styler):
+    ctx = styler.format(lambda v: "neg" if v < 0 else "pos")._translate(True, True)
+    assert ctx["body"][0][1]["display_value"] == "pos"
+    assert ctx["body"][0][2]["display_value"] == "neg"
+    assert ctx["body"][1][1]["display_value"] == "pos"
+    assert ctx["body"][1][2]["display_value"] == "neg"
+
+
+def test_format_with_na_rep():
+    # GH 21527 28358
+    df = DataFrame([[None, None], [1.1, 1.2]], columns=["A", "B"])
+
+    ctx = df.style.format(None, na_rep="-")._translate(True, True)
+    assert ctx["body"][0][1]["display_value"] == "-"
+    assert ctx["body"][0][2]["display_value"] == "-"
+
+    ctx = df.style.format("{:.2%}", na_rep="-")._translate(True, True)
+    assert ctx["body"][0][1]["display_value"] == "-"
+    assert ctx["body"][0][2]["display_value"] == "-"
+    assert ctx["body"][1][1]["display_value"] == "110.00%"
+    assert ctx["body"][1][2]["display_value"] == "120.00%"
+
+    ctx = df.style.format("{:.2%}", na_rep="-", subset=["B"])._translate(True, True)
+    assert ctx["body"][0][2]["display_value"] == "-"
+    assert ctx["body"][1][2]["display_value"] == "120.00%"
+
+
+def test_format_index_with_na_rep():
+    df = DataFrame([[1, 2, 3, 4, 5]], columns=["A", None, np.nan, NaT, NA])
+    ctx = df.style.format_index(None, na_rep="--", axis=1)._translate(True, True)
+    assert ctx["head"][0][1]["display_value"] == "A"
+    for i in [2, 3, 4, 5]:
+        assert ctx["head"][0][i]["display_value"] == "--"
+
+
+def test_format_non_numeric_na():
+    # GH 21527 28358
+    df = DataFrame(
+        {
+            "object": [None, np.nan, "foo"],
+            "datetime": [None, NaT, Timestamp("20120101")],
+        }
+    )
+    ctx = df.style.format(None, na_rep="-")._translate(True, True)
+    assert ctx["body"][0][1]["display_value"] == "-"
+    assert ctx["body"][0][2]["display_value"] == "-"
+    assert ctx["body"][1][1]["display_value"] == "-"
+    assert ctx["body"][1][2]["display_value"] == "-"
+
+
+@pytest.mark.parametrize(
+    "func, attr, kwargs",
+    [
+        ("format", "_display_funcs", {}),
+        ("format_index", "_display_funcs_index", {"axis": 0}),
+        ("format_index", "_display_funcs_columns", {"axis": 1}),
+    ],
+)
+def test_format_clear(styler, func, attr, kwargs):
+    assert (0, 0) not in getattr(styler, attr)  # using default
+    getattr(styler, func)("{:.2f}", **kwargs)
+    assert (0, 0) in getattr(styler, attr)  # formatter is specified
+    getattr(styler, func)(**kwargs)
+    assert (0, 0) not in getattr(styler, attr)  # formatter cleared to default
+
+
+@pytest.mark.parametrize(
+    "escape, exp",
+    [
+        ("html", "&lt;&gt;&amp;&#34;%$#_{}~^\\~ ^ \\ "),
+        (
+            "latex",
+            '<>\\&"\\%\\$\\#\\_\\{\\}\\textasciitilde \\textasciicircum '
+            "\\textbackslash \\textasciitilde \\space \\textasciicircum \\space "
+            "\\textbackslash \\space ",
+        ),
+    ],
+)
+def test_format_escape_html(escape, exp):
+    chars = '<>&"%$#_{}~^\\~ ^ \\ '
+    df = DataFrame([[chars]])
+
+    s = Styler(df, uuid_len=0).format("&{0}&", escape=None)
+    expected = f'<td id="T__row0_col0" class="data row0 col0" >&{chars}&</td>'
+    assert expected in s.to_html()
+
+    # only the value should be escaped before passing to the formatter
+    s = Styler(df, uuid_len=0).format("&{0}&", escape=escape)
+    expected = f'<td id="T__row0_col0" class="data row0 col0" >&{exp}&</td>'
+    assert expected in s.to_html()
+
+    # also test format_index()
+    styler = Styler(DataFrame(columns=[chars]), uuid_len=0)
+    styler.format_index("&{0}&", escape=None, axis=1)
+    assert styler._translate(True, True)["head"][0][1]["display_value"] == f"&{chars}&"
+    styler.format_index("&{0}&", escape=escape, axis=1)
+    assert styler._translate(True, True)["head"][0][1]["display_value"] == f"&{exp}&"
+
+
+@pytest.mark.parametrize(
+    "chars, expected",
+    [
+        (
+            r"$ \$&%#_{}~^\ $ &%#_{}~^\ $",
+            "".join(
+                [
+                    r"$ \$&%#_{}~^\ $ ",
+                    r"\&\%\#\_\{\}\textasciitilde \textasciicircum ",
+                    r"\textbackslash \space \$",
+                ]
+            ),
+        ),
+        (
+            r"\( &%#_{}~^\ \) &%#_{}~^\ \(",
+            "".join(
+                [
+                    r"\( &%#_{}~^\ \) ",
+                    r"\&\%\#\_\{\}\textasciitilde \textasciicircum ",
+                    r"\textbackslash \space \textbackslash (",
+                ]
+            ),
+        ),
+        (
+            r"$\&%#_{}^\$",
+            r"\$\textbackslash \&\%\#\_\{\}\textasciicircum \textbackslash \$",
+        ),
+        (
+            r"$ \frac{1}{2} $ \( \frac{1}{2} \)",
+            "".join(
+                [
+                    r"$ \frac{1}{2} $",
+                    r" \textbackslash ( \textbackslash frac\{1\}\{2\} \textbackslash )",
+                ]
+            ),
+        ),
+    ],
+)
+def test_format_escape_latex_math(chars, expected):
+    # GH 51903
+    # latex-math escape works for each DataFrame cell separately. If we have
+    # a combination of dollar signs and brackets, the dollar sign would apply.
+    df = DataFrame([[chars]])
+    s = df.style.format("{0}", escape="latex-math")
+    assert s._translate(True, True)["body"][0][1]["display_value"] == expected
+
+
+def test_format_escape_na_rep():
+    # tests the na_rep is not escaped
+    df = DataFrame([['<>&"', None]])
+    s = Styler(df, uuid_len=0).format("X&{0}>X", escape="html", na_rep="&")
+    ex = '<td id="T__row0_col0" class="data row0 col0" >X&&lt;&gt;&amp;&#34;>X</td>'
+    expected2 = '<td id="T__row0_col1" class="data row0 col1" >&</td>'
+    assert ex in s.to_html()
+    assert expected2 in s.to_html()
+
+    # also test for format_index()
+    df = DataFrame(columns=['<>&"', None])
+    styler = Styler(df, uuid_len=0)
+    styler.format_index("X&{0}>X", escape="html", na_rep="&", axis=1)
+    ctx = styler._translate(True, True)
+    assert ctx["head"][0][1]["display_value"] == "X&&lt;&gt;&amp;&#34;>X"
+    assert ctx["head"][0][2]["display_value"] == "&"
+
+
+def test_format_escape_floats(styler):
+    # test given formatter for number format is not impacted by escape
+    s = styler.format("{:.1f}", escape="html")
+    for expected in [">0.0<", ">1.0<", ">-1.2<", ">-0.6<"]:
+        assert expected in s.to_html()
+    # tests precision of floats is not impacted by escape
+    s = styler.format(precision=1, escape="html")
+    for expected in [">0<", ">1<", ">-1.2<", ">-0.6<"]:
+        assert expected in s.to_html()
+
+
+@pytest.mark.parametrize("formatter", [5, True, [2.0]])
+@pytest.mark.parametrize("func", ["format", "format_index"])
+def test_format_raises(styler, formatter, func):
+    with pytest.raises(TypeError, match="expected str or callable"):
+        getattr(styler, func)(formatter)
+
+
+@pytest.mark.parametrize(
+    "precision, expected",
+    [
+        (1, ["1.0", "2.0", "3.2", "4.6"]),
+        (2, ["1.00", "2.01", "3.21", "4.57"]),
+        (3, ["1.000", "2.009", "3.212", "4.566"]),
+    ],
+)
+def test_format_with_precision(precision, expected):
+    # Issue #13257
+    df = DataFrame([[1.0, 2.0090, 3.2121, 4.566]], columns=[1.0, 2.0090, 3.2121, 4.566])
+    styler = Styler(df)
+    styler.format(precision=precision)
+    styler.format_index(precision=precision, axis=1)
+
+    ctx = styler._translate(True, True)
+    for col, exp in enumerate(expected):
+        assert ctx["body"][0][col + 1]["display_value"] == exp  # format test
+        assert ctx["head"][0][col + 1]["display_value"] == exp  # format_index test
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+@pytest.mark.parametrize(
+    "level, expected",
+    [
+        (0, ["X", "X", "_", "_"]),  # level int
+        ("zero", ["X", "X", "_", "_"]),  # level name
+        (1, ["_", "_", "X", "X"]),  # other level int
+        ("one", ["_", "_", "X", "X"]),  # other level name
+        ([0, 1], ["X", "X", "X", "X"]),  # both levels
+        ([0, "zero"], ["X", "X", "_", "_"]),  # level int and name simultaneous
+        ([0, "one"], ["X", "X", "X", "X"]),  # both levels as int and name
+        (["one", "zero"], ["X", "X", "X", "X"]),  # both level names, reversed
+    ],
+)
+def test_format_index_level(axis, level, expected):
+    midx = MultiIndex.from_arrays([["_", "_"], ["_", "_"]], names=["zero", "one"])
+    df = DataFrame([[1, 2], [3, 4]])
+    if axis == 0:
+        df.index = midx
+    else:
+        df.columns = midx
+
+    styler = df.style.format_index(lambda v: "X", level=level, axis=axis)
+    ctx = styler._translate(True, True)
+
+    if axis == 0:  # compare index
+        result = [ctx["body"][s][0]["display_value"] for s in range(2)]
+        result += [ctx["body"][s][1]["display_value"] for s in range(2)]
+    else:  # compare columns
+        result = [ctx["head"][0][s + 1]["display_value"] for s in range(2)]
+        result += [ctx["head"][1][s + 1]["display_value"] for s in range(2)]
+
+    assert expected == result
+
+
+def test_format_subset():
+    df = DataFrame([[0.1234, 0.1234], [1.1234, 1.1234]], columns=["a", "b"])
+    ctx = df.style.format(
+        {"a": "{:0.1f}", "b": "{0:.2%}"}, subset=IndexSlice[0, :]
+    )._translate(True, True)
+    expected = "0.1"
+    raw_11 = "1.123400"
+    assert ctx["body"][0][1]["display_value"] == expected
+    assert ctx["body"][1][1]["display_value"] == raw_11
+    assert ctx["body"][0][2]["display_value"] == "12.34%"
+
+    ctx = df.style.format("{:0.1f}", subset=IndexSlice[0, :])._translate(True, True)
+    assert ctx["body"][0][1]["display_value"] == expected
+    assert ctx["body"][1][1]["display_value"] == raw_11
+
+    ctx = df.style.format("{:0.1f}", subset=IndexSlice["a"])._translate(True, True)
+    assert ctx["body"][0][1]["display_value"] == expected
+    assert ctx["body"][0][2]["display_value"] == "0.123400"
+
+    ctx = df.style.format("{:0.1f}", subset=IndexSlice[0, "a"])._translate(True, True)
+    assert ctx["body"][0][1]["display_value"] == expected
+    assert ctx["body"][1][1]["display_value"] == raw_11
+
+    ctx = df.style.format("{:0.1f}", subset=IndexSlice[[0, 1], ["a"]])._translate(
+        True, True
+    )
+    assert ctx["body"][0][1]["display_value"] == expected
+    assert ctx["body"][1][1]["display_value"] == "1.1"
+    assert ctx["body"][0][2]["display_value"] == "0.123400"
+    assert ctx["body"][1][2]["display_value"] == raw_11
+
+
+@pytest.mark.parametrize("formatter", [None, "{:,.1f}"])
+@pytest.mark.parametrize("decimal", [".", "*"])
+@pytest.mark.parametrize("precision", [None, 2])
+@pytest.mark.parametrize("func, col", [("format", 1), ("format_index", 0)])
+def test_format_thousands(formatter, decimal, precision, func, col):
+    styler = DataFrame([[1000000.123456789]], index=[1000000.123456789]).style
+    result = getattr(styler, func)(  # testing float
+        thousands="_", formatter=formatter, decimal=decimal, precision=precision
+    )._translate(True, True)
+    assert "1_000_000" in result["body"][0][col]["display_value"]
+
+    styler = DataFrame([[1000000]], index=[1000000]).style
+    result = getattr(styler, func)(  # testing int
+        thousands="_", formatter=formatter, decimal=decimal, precision=precision
+    )._translate(True, True)
+    assert "1_000_000" in result["body"][0][col]["display_value"]
+
+    styler = DataFrame([[1 + 1000000.123456789j]], index=[1 + 1000000.123456789j]).style
+    result = getattr(styler, func)(  # testing complex
+        thousands="_", formatter=formatter, decimal=decimal, precision=precision
+    )._translate(True, True)
+    assert "1_000_000" in result["body"][0][col]["display_value"]
+
+
+@pytest.mark.parametrize("formatter", [None, "{:,.4f}"])
+@pytest.mark.parametrize("thousands", [None, ",", "*"])
+@pytest.mark.parametrize("precision", [None, 4])
+@pytest.mark.parametrize("func, col", [("format", 1), ("format_index", 0)])
+def test_format_decimal(formatter, thousands, precision, func, col):
+    styler = DataFrame([[1000000.123456789]], index=[1000000.123456789]).style
+    result = getattr(styler, func)(  # testing float
+        decimal="_", formatter=formatter, thousands=thousands, precision=precision
+    )._translate(True, True)
+    assert "000_123" in result["body"][0][col]["display_value"]
+
+    styler = DataFrame([[1 + 1000000.123456789j]], index=[1 + 1000000.123456789j]).style
+    result = getattr(styler, func)(  # testing complex
+        decimal="_", formatter=formatter, thousands=thousands, precision=precision
+    )._translate(True, True)
+    assert "000_123" in result["body"][0][col]["display_value"]
+
+
+def test_str_escape_error():
+    msg = "`escape` only permitted in {'html', 'latex', 'latex-math'}, got "
+    with pytest.raises(ValueError, match=msg):
+        _str_escape("text", "bad_escape")
+
+    with pytest.raises(ValueError, match=msg):
+        _str_escape("text", [])
+
+    _str_escape(2.00, "bad_escape")  # OK since dtype is float
+
+
+def test_long_int_formatting():
+    df = DataFrame(data=[[1234567890123456789]], columns=["test"])
+    styler = df.style
+    ctx = styler._translate(True, True)
+    assert ctx["body"][0][1]["display_value"] == "1234567890123456789"
+
+    styler = df.style.format(thousands="_")
+    ctx = styler._translate(True, True)
+    assert ctx["body"][0][1]["display_value"] == "1_234_567_890_123_456_789"
+
+
+def test_format_options():
+    df = DataFrame({"int": [2000, 1], "float": [1.009, None], "str": ["&<", "&~"]})
+    ctx = df.style._translate(True, True)
+
+    # test option: na_rep
+    assert ctx["body"][1][2]["display_value"] == "nan"
+    with option_context("styler.format.na_rep", "MISSING"):
+        ctx_with_op = df.style._translate(True, True)
+        assert ctx_with_op["body"][1][2]["display_value"] == "MISSING"
+
+    # test option: decimal and precision
+    assert ctx["body"][0][2]["display_value"] == "1.009000"
+    with option_context("styler.format.decimal", "_"):
+        ctx_with_op = df.style._translate(True, True)
+        assert ctx_with_op["body"][0][2]["display_value"] == "1_009000"
+    with option_context("styler.format.precision", 2):
+        ctx_with_op = df.style._translate(True, True)
+        assert ctx_with_op["body"][0][2]["display_value"] == "1.01"
+
+    # test option: thousands
+    assert ctx["body"][0][1]["display_value"] == "2000"
+    with option_context("styler.format.thousands", "_"):
+        ctx_with_op = df.style._translate(True, True)
+        assert ctx_with_op["body"][0][1]["display_value"] == "2_000"
+
+    # test option: escape
+    assert ctx["body"][0][3]["display_value"] == "&<"
+    assert ctx["body"][1][3]["display_value"] == "&~"
+    with option_context("styler.format.escape", "html"):
+        ctx_with_op = df.style._translate(True, True)
+        assert ctx_with_op["body"][0][3]["display_value"] == "&amp;&lt;"
+    with option_context("styler.format.escape", "latex"):
+        ctx_with_op = df.style._translate(True, True)
+        assert ctx_with_op["body"][1][3]["display_value"] == "\\&\\textasciitilde "
+    with option_context("styler.format.escape", "latex-math"):
+        ctx_with_op = df.style._translate(True, True)
+        assert ctx_with_op["body"][1][3]["display_value"] == "\\&\\textasciitilde "
+
+    # test option: formatter
+    with option_context("styler.format.formatter", {"int": "{:,.2f}"}):
+        ctx_with_op = df.style._translate(True, True)
+        assert ctx_with_op["body"][0][1]["display_value"] == "2,000.00"
+
+
+def test_precision_zero(df):
+    styler = Styler(df, precision=0)
+    ctx = styler._translate(True, True)
+    assert ctx["body"][0][2]["display_value"] == "-1"
+    assert ctx["body"][1][2]["display_value"] == "-1"
+
+
+@pytest.mark.parametrize(
+    "formatter, exp",
+    [
+        (lambda x: f"{x:.3f}", "9.000"),
+        ("{:.2f}", "9.00"),
+        ({0: "{:.1f}"}, "9.0"),
+        (None, "9"),
+    ],
+)
+def test_formatter_options_validator(formatter, exp):
+    df = DataFrame([[9]])
+    with option_context("styler.format.formatter", formatter):
+        assert f" {exp} " in df.style.to_latex()
+
+
+def test_formatter_options_raises():
+    msg = "Value must be an instance of"
+    with pytest.raises(ValueError, match=msg):
+        with option_context("styler.format.formatter", ["bad", "type"]):
+            DataFrame().style.to_latex()
+
+
+def test_1level_multiindex():
+    # GH 43383
+    midx = MultiIndex.from_product([[1, 2]], names=[""])
+    df = DataFrame(-1, index=midx, columns=[0, 1])
+    ctx = df.style._translate(True, True)
+    assert ctx["body"][0][0]["display_value"] == "1"
+    assert ctx["body"][0][0]["is_visible"] is True
+    assert ctx["body"][1][0]["display_value"] == "2"
+    assert ctx["body"][1][0]["is_visible"] is True
+
+
+def test_boolean_format():
+    # gh 46384: booleans do not collapse to integer representation on display
+    df = DataFrame([[True, False]])
+    ctx = df.style._translate(True, True)
+    assert ctx["body"][0][1]["display_value"] is True
+    assert ctx["body"][0][2]["display_value"] is False
+
+
+@pytest.mark.parametrize(
+    "hide, labels",
+    [
+        (False, [1, 2]),
+        (True, [1, 2, 3, 4]),
+    ],
+)
+def test_relabel_raise_length(styler_multi, hide, labels):
+    if hide:
+        styler_multi.hide(axis=0, subset=[("X", "x"), ("Y", "y")])
+    with pytest.raises(ValueError, match="``labels`` must be of length equal"):
+        styler_multi.relabel_index(labels=labels)
+
+
+def test_relabel_index(styler_multi):
+    labels = [(1, 2), (3, 4)]
+    styler_multi.hide(axis=0, subset=[("X", "x"), ("Y", "y")])
+    styler_multi.relabel_index(labels=labels)
+    ctx = styler_multi._translate(True, True)
+    assert {"value": "X", "display_value": 1}.items() <= ctx["body"][0][0].items()
+    assert {"value": "y", "display_value": 2}.items() <= ctx["body"][0][1].items()
+    assert {"value": "Y", "display_value": 3}.items() <= ctx["body"][1][0].items()
+    assert {"value": "x", "display_value": 4}.items() <= ctx["body"][1][1].items()
+
+
+def test_relabel_columns(styler_multi):
+    labels = [(1, 2), (3, 4)]
+    styler_multi.hide(axis=1, subset=[("A", "a"), ("B", "b")])
+    styler_multi.relabel_index(axis=1, labels=labels)
+    ctx = styler_multi._translate(True, True)
+    assert {"value": "A", "display_value": 1}.items() <= ctx["head"][0][3].items()
+    assert {"value": "B", "display_value": 3}.items() <= ctx["head"][0][4].items()
+    assert {"value": "b", "display_value": 2}.items() <= ctx["head"][1][3].items()
+    assert {"value": "a", "display_value": 4}.items() <= ctx["head"][1][4].items()
+
+
+def test_relabel_roundtrip(styler):
+    styler.relabel_index(["{}", "{}"])
+    ctx = styler._translate(True, True)
+    assert {"value": "x", "display_value": "x"}.items() <= ctx["body"][0][0].items()
+    assert {"value": "y", "display_value": "y"}.items() <= ctx["body"][1][0].items()
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+@pytest.mark.parametrize(
+    "level, expected",
+    [
+        (0, ["X", "one"]),  # level int
+        ("zero", ["X", "one"]),  # level name
+        (1, ["zero", "X"]),  # other level int
+        ("one", ["zero", "X"]),  # other level name
+        ([0, 1], ["X", "X"]),  # both levels
+        ([0, "zero"], ["X", "one"]),  # level int and name simultaneous
+        ([0, "one"], ["X", "X"]),  # both levels as int and name
+        (["one", "zero"], ["X", "X"]),  # both level names, reversed
+    ],
+)
+def test_format_index_names_level(axis, level, expected):
+    midx = MultiIndex.from_arrays([["_", "_"], ["_", "_"]], names=["zero", "one"])
+    df = DataFrame([[1, 2], [3, 4]])
+    if axis == 0:
+        df.index = midx
+    else:
+        df.columns = midx
+
+    styler = df.style.format_index_names(lambda v: "X", level=level, axis=axis)
+    ctx = styler._translate(True, True)
+
+    if axis == 0:  # compare index
+        result = [ctx["head"][1][s]["display_value"] for s in range(2)]
+    else:  # compare columns
+        result = [ctx["head"][s][0]["display_value"] for s in range(2)]
+    assert expected == result
+
+
+@pytest.mark.parametrize(
+    "attr, kwargs",
+    [
+        ("_display_funcs_index_names", {"axis": 0}),
+        ("_display_funcs_column_names", {"axis": 1}),
+    ],
+)
+def test_format_index_names_clear(styler, attr, kwargs):
+    assert 0 not in getattr(styler, attr)  # using default
+    styler.format_index_names("{:.2f}", **kwargs)
+    assert 0 in getattr(styler, attr)  # formatter is specified
+    styler.format_index_names(**kwargs)
+    assert 0 not in getattr(styler, attr)  # formatter cleared to default
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+def test_format_index_names_callable(styler_multi, axis):
+    ctx = styler_multi.format_index_names(
+        lambda v: v.replace("_", "A"), axis=axis
+    )._translate(True, True)
+    result = [
+        ctx["head"][2][0]["display_value"],
+        ctx["head"][2][1]["display_value"],
+        ctx["head"][0][1]["display_value"],
+        ctx["head"][1][1]["display_value"],
+    ]
+    if axis == 0:
+        expected = ["0A0", "0A1", "1_0", "1_1"]
+    else:
+        expected = ["0_0", "0_1", "1A0", "1A1"]
+    assert result == expected
+
+
+def test_format_index_names_dict(styler_multi):
+    ctx = (
+        styler_multi.format_index_names({"0_0": "{:<<5}"})
+        .format_index_names({"1_1": "{:>>4}"}, axis=1)
+        ._translate(True, True)
+    )
+    assert ctx["head"][2][0]["display_value"] == "0_0<<"
+    assert ctx["head"][1][1]["display_value"] == ">1_1"
+
+
+def test_format_index_names_with_hidden_levels(styler_multi):
+    ctx = styler_multi._translate(True, True)
+    full_head_height = len(ctx["head"])
+    full_head_width = len(ctx["head"][0])
+    assert full_head_height == 3
+    assert full_head_width == 6
+
+    ctx = (
+        styler_multi.hide(axis=0, level=1)
+        .hide(axis=1, level=1)
+        .format_index_names("{:>>4}", axis=1)
+        .format_index_names("{:!<5}")
+        ._translate(True, True)
+    )
+    assert len(ctx["head"]) == full_head_height - 1
+    assert len(ctx["head"][0]) == full_head_width - 1
+    assert ctx["head"][0][0]["display_value"] == ">1_0"
+    assert ctx["head"][1][0]["display_value"] == "0_0!!"

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/style/test_highlight.py

@@ -0,0 +1,221 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    NA,
+    DataFrame,
+    IndexSlice,
+)
+
+pytest.importorskip("jinja2")
+
+from pandas.io.formats.style import Styler
+
+
+@pytest.fixture(params=[(None, "float64"), (NA, "Int64")])
+def df(request):
+    # GH 45804
+    dtype = request.param[1]
+    item = np.nan if dtype == "float64" else NA
+    return DataFrame(
+        {"A": [0, item, 10], "B": [1, request.param[0], 2]}, dtype=request.param[1]
+    )
+
+
+@pytest.fixture
+def styler(df):
+    return Styler(df, uuid_len=0)
+
+
+def test_highlight_null(styler):
+    result = styler.highlight_null()._compute().ctx
+    expected = {
+        (1, 0): [("background-color", "red")],
+        (1, 1): [("background-color", "red")],
+    }
+    assert result == expected
+
+
+def test_highlight_null_subset(styler):
+    # GH 31345
+    result = (
+        styler.highlight_null(color="red", subset=["A"])
+        .highlight_null(color="green", subset=["B"])
+        ._compute()
+        .ctx
+    )
+    expected = {
+        (1, 0): [("background-color", "red")],
+        (1, 1): [("background-color", "green")],
+    }
+    assert result == expected
+
+
+@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"])
+def test_highlight_minmax_basic(df, f):
+    expected = {
+        (0, 1): [("background-color", "red")],
+        # ignores NaN row,
+        (2, 0): [("background-color", "red")],
+    }
+    if f == "highlight_min":
+        df = -df
+    result = getattr(df.style, f)(axis=1, color="red")._compute().ctx
+    assert result == expected
+
+
+@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"])
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"axis": None, "color": "red"},  # test axis
+        {"axis": 0, "subset": ["A"], "color": "red"},  # test subset and ignores NaN
+        {"axis": None, "props": "background-color: red"},  # test props
+    ],
+)
+def test_highlight_minmax_ext(df, f, kwargs):
+    expected = {(2, 0): [("background-color", "red")]}
+    if f == "highlight_min":
+        df = -df
+    result = getattr(df.style, f)(**kwargs)._compute().ctx
+    assert result == expected
+
+
+@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"])
+@pytest.mark.parametrize("axis", [None, 0, 1])
+def test_highlight_minmax_nulls(f, axis):
+    # GH 42750
+    expected = {
+        (1, 0): [("background-color", "yellow")],
+        (1, 1): [("background-color", "yellow")],
+    }
+    if axis == 1:
+        expected.update({(2, 1): [("background-color", "yellow")]})
+
+    if f == "highlight_max":
+        df = DataFrame({"a": [NA, 1, None], "b": [np.nan, 1, -1]})
+    else:
+        df = DataFrame({"a": [NA, -1, None], "b": [np.nan, -1, 1]})
+
+    result = getattr(df.style, f)(axis=axis)._compute().ctx
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"left": 0, "right": 1},  # test basic range
+        {"left": 0, "right": 1, "props": "background-color: yellow"},  # test props
+        {"left": -100, "right": 100, "subset": IndexSlice[[0, 1], :]},  # test subset
+        {"left": 0, "subset": IndexSlice[[0, 1], :]},  # test no right
+        {"right": 1},  # test no left
+        {"left": [0, 0, 11], "axis": 0},  # test left as sequence
+        {"left": DataFrame({"A": [0, 0, 11], "B": [1, 1, 11]}), "axis": None},  # axis
+        {"left": 0, "right": [0, 1], "axis": 1},  # test sequence right
+    ],
+)
+def test_highlight_between(styler, kwargs):
+    expected = {
+        (0, 0): [("background-color", "yellow")],
+        (0, 1): [("background-color", "yellow")],
+    }
+    result = styler.highlight_between(**kwargs)._compute().ctx
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "arg, map, axis",
+    [
+        ("left", [1, 2], 0),  # 0 axis has 3 elements not 2
+        ("left", [1, 2, 3], 1),  # 1 axis has 2 elements not 3
+        ("left", np.array([[1, 2], [1, 2]]), None),  # df is (2,3) not (2,2)
+        ("right", [1, 2], 0),  # same tests as above for 'right' not 'left'
+        ("right", [1, 2, 3], 1),  # ..
+        ("right", np.array([[1, 2], [1, 2]]), None),  # ..
+    ],
+)
+def test_highlight_between_raises(arg, styler, map, axis):
+    msg = f"supplied '{arg}' is not correct shape"
+    with pytest.raises(ValueError, match=msg):
+        styler.highlight_between(**{arg: map, "axis": axis})._compute()
+
+
+def test_highlight_between_raises2(styler):
+    msg = "values can be 'both', 'left', 'right', or 'neither'"
+    with pytest.raises(ValueError, match=msg):
+        styler.highlight_between(inclusive="badstring")._compute()
+
+    with pytest.raises(ValueError, match=msg):
+        styler.highlight_between(inclusive=1)._compute()
+
+
+@pytest.mark.parametrize(
+    "inclusive, expected",
+    [
+        (
+            "both",
+            {
+                (0, 0): [("background-color", "yellow")],
+                (0, 1): [("background-color", "yellow")],
+            },
+        ),
+        ("neither", {}),
+        ("left", {(0, 0): [("background-color", "yellow")]}),
+        ("right", {(0, 1): [("background-color", "yellow")]}),
+    ],
+)
+def test_highlight_between_inclusive(styler, inclusive, expected):
+    kwargs = {"left": 0, "right": 1, "subset": IndexSlice[[0, 1], :]}
+    result = styler.highlight_between(**kwargs, inclusive=inclusive)._compute()
+    assert result.ctx == expected
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"q_left": 0.5, "q_right": 1, "axis": 0},  # base case
+        {"q_left": 0.5, "q_right": 1, "axis": None},  # test axis
+        {"q_left": 0, "q_right": 1, "subset": IndexSlice[2, :]},  # test subset
+        {"q_left": 0.5, "axis": 0},  # test no high
+        {"q_right": 1, "subset": IndexSlice[2, :], "axis": 1},  # test no low
+        {"q_left": 0.5, "axis": 0, "props": "background-color: yellow"},  # tst prop
+    ],
+)
+def test_highlight_quantile(styler, kwargs):
+    expected = {
+        (2, 0): [("background-color", "yellow")],
+        (2, 1): [("background-color", "yellow")],
+    }
+    result = styler.highlight_quantile(**kwargs)._compute().ctx
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "f,kwargs",
+    [
+        ("highlight_min", {"axis": 1, "subset": IndexSlice[1, :]}),
+        ("highlight_max", {"axis": 0, "subset": [0]}),
+        ("highlight_quantile", {"axis": None, "q_left": 0.6, "q_right": 0.8}),
+        ("highlight_between", {"subset": [0]}),
+    ],
+)
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        int,
+        float,
+        "datetime64[ns]",
+        str,
+        "timedelta64[ns]",
+    ],
+)
+def test_all_highlight_dtypes(f, kwargs, dtype):
+    df = DataFrame([[0, 10], [20, 30]], dtype=dtype)
+    if f == "highlight_quantile" and isinstance(df.iloc[0, 0], (str)):
+        return None  # quantile incompatible with str
+    if f == "highlight_between":
+        kwargs["left"] = df.iloc[1, 0]  # set the range low for testing
+
+    expected = {(1, 0): [("background-color", "yellow")]}
+    result = getattr(df.style, f)(**kwargs)._compute().ctx
+    assert result == expected

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/style/test_matplotlib.py

@@ -0,0 +1,305 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    IndexSlice,
+    Series,
+)
+
+mpl = pytest.importorskip("matplotlib")
+pytest.importorskip("jinja2")
+
+from pandas.io.formats.style import Styler
+
+pytestmark = pytest.mark.usefixtures("mpl_cleanup")
+
+
+@pytest.fixture
+def df():
+    return DataFrame([[1, 2], [2, 4]], columns=["A", "B"])
+
+
+@pytest.fixture
+def styler(df):
+    return Styler(df, uuid_len=0)
+
+
+@pytest.fixture
+def df_blank():
+    return DataFrame([[0, 0], [0, 0]], columns=["A", "B"], index=["X", "Y"])
+
+
+@pytest.fixture
+def styler_blank(df_blank):
+    return Styler(df_blank, uuid_len=0)
+
+
+@pytest.mark.parametrize("f", ["background_gradient", "text_gradient"])
+def test_function_gradient(styler, f):
+    for c_map in [None, "YlOrRd"]:
+        result = getattr(styler, f)(cmap=c_map)._compute().ctx
+        assert all("#" in x[0][1] for x in result.values())
+        assert result[(0, 0)] == result[(0, 1)]
+        assert result[(1, 0)] == result[(1, 1)]
+
+
+@pytest.mark.parametrize("f", ["background_gradient", "text_gradient"])
+def test_background_gradient_color(styler, f):
+    result = getattr(styler, f)(subset=IndexSlice[1, "A"])._compute().ctx
+    if f == "background_gradient":
+        assert result[(1, 0)] == [("background-color", "#fff7fb"), ("color", "#000000")]
+    elif f == "text_gradient":
+        assert result[(1, 0)] == [("color", "#fff7fb")]
+
+
+@pytest.mark.parametrize(
+    "axis, expected",
+    [
+        (0, ["low", "low", "high", "high"]),
+        (1, ["low", "high", "low", "high"]),
+        (None, ["low", "mid", "mid", "high"]),
+    ],
+)
+@pytest.mark.parametrize("f", ["background_gradient", "text_gradient"])
+def test_background_gradient_axis(styler, axis, expected, f):
+    if f == "background_gradient":
+        colors = {
+            "low": [("background-color", "#f7fbff"), ("color", "#000000")],
+            "mid": [("background-color", "#abd0e6"), ("color", "#000000")],
+            "high": [("background-color", "#08306b"), ("color", "#f1f1f1")],
+        }
+    elif f == "text_gradient":
+        colors = {
+            "low": [("color", "#f7fbff")],
+            "mid": [("color", "#abd0e6")],
+            "high": [("color", "#08306b")],
+        }
+    result = getattr(styler, f)(cmap="Blues", axis=axis)._compute().ctx
+    for i, cell in enumerate([(0, 0), (0, 1), (1, 0), (1, 1)]):
+        assert result[cell] == colors[expected[i]]
+
+
+@pytest.mark.parametrize(
+    "cmap, expected",
+    [
+        (
+            "PuBu",
+            {
+                (4, 5): [("background-color", "#86b0d3"), ("color", "#000000")],
+                (4, 6): [("background-color", "#83afd3"), ("color", "#f1f1f1")],
+            },
+        ),
+        (
+            "YlOrRd",
+            {
+                (4, 8): [("background-color", "#fd913e"), ("color", "#000000")],
+                (4, 9): [("background-color", "#fd8f3d"), ("color", "#f1f1f1")],
+            },
+        ),
+        (
+            None,
+            {
+                (7, 0): [("background-color", "#48c16e"), ("color", "#f1f1f1")],
+                (7, 1): [("background-color", "#4cc26c"), ("color", "#000000")],
+            },
+        ),
+    ],
+)
+def test_text_color_threshold(cmap, expected):
+    # GH 39888
+    df = DataFrame(np.arange(100).reshape(10, 10))
+    result = df.style.background_gradient(cmap=cmap, axis=None)._compute().ctx
+    for k in expected.keys():
+        assert result[k] == expected[k]
+
+
+def test_background_gradient_vmin_vmax():
+    # GH 12145
+    df = DataFrame(range(5))
+    ctx = df.style.background_gradient(vmin=1, vmax=3)._compute().ctx
+    assert ctx[(0, 0)] == ctx[(1, 0)]
+    assert ctx[(4, 0)] == ctx[(3, 0)]
+
+
+def test_background_gradient_int64():
+    # GH 28869
+    df1 = Series(range(3)).to_frame()
+    df2 = Series(range(3), dtype="Int64").to_frame()
+    ctx1 = df1.style.background_gradient()._compute().ctx
+    ctx2 = df2.style.background_gradient()._compute().ctx
+    assert ctx2[(0, 0)] == ctx1[(0, 0)]
+    assert ctx2[(1, 0)] == ctx1[(1, 0)]
+    assert ctx2[(2, 0)] == ctx1[(2, 0)]
+
+
+@pytest.mark.parametrize(
+    "axis, gmap, expected",
+    [
+        (
+            0,
+            [1, 2],
+            {
+                (0, 0): [("background-color", "#fff7fb"), ("color", "#000000")],
+                (1, 0): [("background-color", "#023858"), ("color", "#f1f1f1")],
+                (0, 1): [("background-color", "#fff7fb"), ("color", "#000000")],
+                (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")],
+            },
+        ),
+        (
+            1,
+            [1, 2],
+            {
+                (0, 0): [("background-color", "#fff7fb"), ("color", "#000000")],
+                (1, 0): [("background-color", "#fff7fb"), ("color", "#000000")],
+                (0, 1): [("background-color", "#023858"), ("color", "#f1f1f1")],
+                (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")],
+            },
+        ),
+        (
+            None,
+            np.array([[2, 1], [1, 2]]),
+            {
+                (0, 0): [("background-color", "#023858"), ("color", "#f1f1f1")],
+                (1, 0): [("background-color", "#fff7fb"), ("color", "#000000")],
+                (0, 1): [("background-color", "#fff7fb"), ("color", "#000000")],
+                (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")],
+            },
+        ),
+    ],
+)
+def test_background_gradient_gmap_array(styler_blank, axis, gmap, expected):
+    # tests when gmap is given as a sequence and converted to ndarray
+    result = styler_blank.background_gradient(axis=axis, gmap=gmap)._compute().ctx
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "gmap, axis", [([1, 2, 3], 0), ([1, 2], 1), (np.array([[1, 2], [1, 2]]), None)]
+)
+def test_background_gradient_gmap_array_raises(gmap, axis):
+    # test when gmap as converted ndarray is bad shape
+    df = DataFrame([[0, 0, 0], [0, 0, 0]])
+    msg = "supplied 'gmap' is not correct shape"
+    with pytest.raises(ValueError, match=msg):
+        df.style.background_gradient(gmap=gmap, axis=axis)._compute()
+
+
+@pytest.mark.parametrize(
+    "gmap",
+    [
+        DataFrame(  # reverse the columns
+            [[2, 1], [1, 2]], columns=["B", "A"], index=["X", "Y"]
+        ),
+        DataFrame(  # reverse the index
+            [[2, 1], [1, 2]], columns=["A", "B"], index=["Y", "X"]
+        ),
+        DataFrame(  # reverse the index and columns
+            [[1, 2], [2, 1]], columns=["B", "A"], index=["Y", "X"]
+        ),
+        DataFrame(  # add unnecessary columns
+            [[1, 2, 3], [2, 1, 3]], columns=["A", "B", "C"], index=["X", "Y"]
+        ),
+        DataFrame(  # add unnecessary index
+            [[1, 2], [2, 1], [3, 3]], columns=["A", "B"], index=["X", "Y", "Z"]
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "subset, exp_gmap",  # exp_gmap is underlying map DataFrame should conform to
+    [
+        (None, [[1, 2], [2, 1]]),
+        (["A"], [[1], [2]]),  # slice only column "A" in data and gmap
+        (["B", "A"], [[2, 1], [1, 2]]),  # reverse the columns in data
+        (IndexSlice["X", :], [[1, 2]]),  # slice only index "X" in data and gmap
+        (IndexSlice[["Y", "X"], :], [[2, 1], [1, 2]]),  # reverse the index in data
+    ],
+)
+def test_background_gradient_gmap_dataframe_align(styler_blank, gmap, subset, exp_gmap):
+    # test gmap given as DataFrame that it aligns to the data including subset
+    expected = styler_blank.background_gradient(axis=None, gmap=exp_gmap, subset=subset)
+    result = styler_blank.background_gradient(axis=None, gmap=gmap, subset=subset)
+    assert expected._compute().ctx == result._compute().ctx
+
+
+@pytest.mark.parametrize(
+    "gmap, axis, exp_gmap",
+    [
+        (Series([2, 1], index=["Y", "X"]), 0, [[1, 1], [2, 2]]),  # reverse the index
+        (Series([2, 1], index=["B", "A"]), 1, [[1, 2], [1, 2]]),  # reverse the cols
+        (Series([1, 2, 3], index=["X", "Y", "Z"]), 0, [[1, 1], [2, 2]]),  # add idx
+        (Series([1, 2, 3], index=["A", "B", "C"]), 1, [[1, 2], [1, 2]]),  # add col
+    ],
+)
+def test_background_gradient_gmap_series_align(styler_blank, gmap, axis, exp_gmap):
+    # test gmap given as Series that it aligns to the data including subset
+    expected = styler_blank.background_gradient(axis=None, gmap=exp_gmap)._compute()
+    result = styler_blank.background_gradient(axis=axis, gmap=gmap)._compute()
+    assert expected.ctx == result.ctx
+
+
+@pytest.mark.parametrize("axis", [1, 0])
+def test_background_gradient_gmap_wrong_dataframe(styler_blank, axis):
+    # test giving a gmap in DataFrame but with wrong axis
+    gmap = DataFrame([[1, 2], [2, 1]], columns=["A", "B"], index=["X", "Y"])
+    msg = "'gmap' is a DataFrame but underlying data for operations is a Series"
+    with pytest.raises(ValueError, match=msg):
+        styler_blank.background_gradient(gmap=gmap, axis=axis)._compute()
+
+
+def test_background_gradient_gmap_wrong_series(styler_blank):
+    # test giving a gmap in Series form but with wrong axis
+    msg = "'gmap' is a Series but underlying data for operations is a DataFrame"
+    gmap = Series([1, 2], index=["X", "Y"])
+    with pytest.raises(ValueError, match=msg):
+        styler_blank.background_gradient(gmap=gmap, axis=None)._compute()
+
+
+def test_background_gradient_nullable_dtypes():
+    # GH 50712
+    df1 = DataFrame([[1], [0], [np.nan]], dtype=float)
+    df2 = DataFrame([[1], [0], [None]], dtype="Int64")
+
+    ctx1 = df1.style.background_gradient()._compute().ctx
+    ctx2 = df2.style.background_gradient()._compute().ctx
+    assert ctx1 == ctx2
+
+
+@pytest.mark.parametrize(
+    "cmap",
+    ["PuBu", mpl.colormaps["PuBu"]],
+)
+def test_bar_colormap(cmap):
+    data = DataFrame([[1, 2], [3, 4]])
+    ctx = data.style.bar(cmap=cmap, axis=None)._compute().ctx
+    pubu_colors = {
+        (0, 0): "#d0d1e6",
+        (1, 0): "#056faf",
+        (0, 1): "#73a9cf",
+        (1, 1): "#023858",
+    }
+    for k, v in pubu_colors.items():
+        assert v in ctx[k][1][1]
+
+
+def test_bar_color_raises(df):
+    msg = "`color` must be string or list or tuple of 2 strings"
+    with pytest.raises(ValueError, match=msg):
+        df.style.bar(color={"a", "b"}).to_html()
+    with pytest.raises(ValueError, match=msg):
+        df.style.bar(color=["a", "b", "c"]).to_html()
+
+    msg = "`color` and `cmap` cannot both be given"
+    with pytest.raises(ValueError, match=msg):
+        df.style.bar(color="something", cmap="something else").to_html()
+
+
+@pytest.mark.parametrize("plot_method", ["scatter", "hexbin"])
+def test_pass_colormap_instance(df, plot_method):
+    # https://github.com/pandas-dev/pandas/issues/49374
+    cmap = mpl.colors.ListedColormap([[1, 1, 1], [0, 0, 0]])
+    df["c"] = df.A + df.B
+    kwargs = {"x": "A", "y": "B", "c": "c", "colormap": cmap}
+    if plot_method == "hexbin":
+        kwargs["C"] = kwargs.pop("c")
+    getattr(df.plot, plot_method)(**kwargs)

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/style/test_non_unique.py

@@ -0,0 +1,140 @@
+from textwrap import dedent
+
+import pytest
+
+from pandas import (
+    DataFrame,
+    IndexSlice,
+)
+
+pytest.importorskip("jinja2")
+
+from pandas.io.formats.style import Styler
+
+
+@pytest.fixture
+def df():
+    return DataFrame(
+        [[1, 2, 3], [4, 5, 6], [7, 8, 9]],
+        index=["i", "j", "j"],
+        columns=["c", "d", "d"],
+        dtype=float,
+    )
+
+
+@pytest.fixture
+def styler(df):
+    return Styler(df, uuid_len=0)
+
+
+def test_format_non_unique(df):
+    # GH 41269
+
+    # test dict
+    html = df.style.format({"d": "{:.1f}"}).to_html()
+    for val in ["1.000000<", "4.000000<", "7.000000<"]:
+        assert val in html
+    for val in ["2.0<", "3.0<", "5.0<", "6.0<", "8.0<", "9.0<"]:
+        assert val in html
+
+    # test subset
+    html = df.style.format(precision=1, subset=IndexSlice["j", "d"]).to_html()
+    for val in ["1.000000<", "4.000000<", "7.000000<", "2.000000<", "3.000000<"]:
+        assert val in html
+    for val in ["5.0<", "6.0<", "8.0<", "9.0<"]:
+        assert val in html
+
+
+@pytest.mark.parametrize("func", ["apply", "map"])
+def test_apply_map_non_unique_raises(df, func):
+    # GH 41269
+    if func == "apply":
+        op = lambda s: ["color: red;"] * len(s)
+    else:
+        op = lambda v: "color: red;"
+
+    with pytest.raises(KeyError, match="`Styler.apply` and `.map` are not"):
+        getattr(df.style, func)(op)._compute()
+
+
+def test_table_styles_dict_non_unique_index(styler):
+    styles = styler.set_table_styles(
+        {"j": [{"selector": "td", "props": "a: v;"}]}, axis=1
+    ).table_styles
+    assert styles == [
+        {"selector": "td.row1", "props": [("a", "v")]},
+        {"selector": "td.row2", "props": [("a", "v")]},
+    ]
+
+
+def test_table_styles_dict_non_unique_columns(styler):
+    styles = styler.set_table_styles(
+        {"d": [{"selector": "td", "props": "a: v;"}]}, axis=0
+    ).table_styles
+    assert styles == [
+        {"selector": "td.col1", "props": [("a", "v")]},
+        {"selector": "td.col2", "props": [("a", "v")]},
+    ]
+
+
+def test_tooltips_non_unique_raises(styler):
+    # ttips has unique keys
+    ttips = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "d"], index=["a", "b"])
+    styler.set_tooltips(ttips=ttips)  # OK
+
+    # ttips has non-unique columns
+    ttips = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "c"], index=["a", "b"])
+    with pytest.raises(KeyError, match="Tooltips render only if `ttips` has unique"):
+        styler.set_tooltips(ttips=ttips)
+
+    # ttips has non-unique index
+    ttips = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "d"], index=["a", "a"])
+    with pytest.raises(KeyError, match="Tooltips render only if `ttips` has unique"):
+        styler.set_tooltips(ttips=ttips)
+
+
+def test_set_td_classes_non_unique_raises(styler):
+    # classes has unique keys
+    classes = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "d"], index=["a", "b"])
+    styler.set_td_classes(classes=classes)  # OK
+
+    # classes has non-unique columns
+    classes = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "c"], index=["a", "b"])
+    with pytest.raises(KeyError, match="Classes render only if `classes` has unique"):
+        styler.set_td_classes(classes=classes)
+
+    # classes has non-unique index
+    classes = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "d"], index=["a", "a"])
+    with pytest.raises(KeyError, match="Classes render only if `classes` has unique"):
+        styler.set_td_classes(classes=classes)
+
+
+def test_hide_columns_non_unique(styler):
+    ctx = styler.hide(["d"], axis="columns")._translate(True, True)
+
+    assert ctx["head"][0][1]["display_value"] == "c"
+    assert ctx["head"][0][1]["is_visible"] is True
+
+    assert ctx["head"][0][2]["display_value"] == "d"
+    assert ctx["head"][0][2]["is_visible"] is False
+
+    assert ctx["head"][0][3]["display_value"] == "d"
+    assert ctx["head"][0][3]["is_visible"] is False
+
+    assert ctx["body"][0][1]["is_visible"] is True
+    assert ctx["body"][0][2]["is_visible"] is False
+    assert ctx["body"][0][3]["is_visible"] is False
+
+
+def test_latex_non_unique(styler):
+    result = styler.to_latex()
+    assert result == dedent(
+        """\
+        \\begin{tabular}{lrrr}
+         & c & d & d \\\\
+        i & 1.000000 & 2.000000 & 3.000000 \\\\
+        j & 4.000000 & 5.000000 & 6.000000 \\\\
+        j & 7.000000 & 8.000000 & 9.000000 \\\\
+        \\end{tabular}
+    """
+    )

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/style/test_to_string.py

@@ -0,0 +1,96 @@
+from textwrap import dedent
+
+import pytest
+
+from pandas import (
+    DataFrame,
+    Series,
+)
+
+pytest.importorskip("jinja2")
+from pandas.io.formats.style import Styler
+
+
+@pytest.fixture
+def df():
+    return DataFrame(
+        {"A": [0, 1], "B": [-0.61, -1.22], "C": Series(["ab", "cd"], dtype=object)}
+    )
+
+
+@pytest.fixture
+def styler(df):
+    return Styler(df, uuid_len=0, precision=2)
+
+
+def test_basic_string(styler):
+    result = styler.to_string()
+    expected = dedent(
+        """\
+     A B C
+    0 0 -0.61 ab
+    1 1 -1.22 cd
+    """
+    )
+    assert result == expected
+
+
+def test_string_delimiter(styler):
+    result = styler.to_string(delimiter=";")
+    expected = dedent(
+        """\
+    ;A;B;C
+    0;0;-0.61;ab
+    1;1;-1.22;cd
+    """
+    )
+    assert result == expected
+
+
+def test_concat(styler):
+    result = styler.concat(styler.data.agg(["sum"]).style).to_string()
+    expected = dedent(
+        """\
+     A B C
+    0 0 -0.61 ab
+    1 1 -1.22 cd
+    sum 1 -1.830000 abcd
+    """
+    )
+    assert result == expected
+
+
+def test_concat_recursion(styler):
+    df = styler.data
+    styler1 = styler
+    styler2 = Styler(df.agg(["sum"]), uuid_len=0, precision=3)
+    styler3 = Styler(df.agg(["sum"]), uuid_len=0, precision=4)
+    result = styler1.concat(styler2.concat(styler3)).to_string()
+    expected = dedent(
+        """\
+     A B C
+    0 0 -0.61 ab
+    1 1 -1.22 cd
+    sum 1 -1.830 abcd
+    sum 1 -1.8300 abcd
+    """
+    )
+    assert result == expected
+
+
+def test_concat_chain(styler):
+    df = styler.data
+    styler1 = styler
+    styler2 = Styler(df.agg(["sum"]), uuid_len=0, precision=3)
+    styler3 = Styler(df.agg(["sum"]), uuid_len=0, precision=4)
+    result = styler1.concat(styler2).concat(styler3).to_string()
+    expected = dedent(
+        """\
+     A B C
+    0 0 -0.61 ab
+    1 1 -1.22 cd
+    sum 1 -1.830 abcd
+    sum 1 -1.8300 abcd
+    """
+    )
+    assert result == expected

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/style/test_to_typst.py

@@ -0,0 +1,96 @@
+from textwrap import dedent
+
+import pytest
+
+from pandas import (
+    DataFrame,
+    Series,
+)
+
+pytest.importorskip("jinja2")
+from pandas.io.formats.style import Styler
+
+
+@pytest.fixture
+def df():
+    return DataFrame(
+        {"A": [0, 1], "B": [-0.61, -1.22], "C": Series(["ab", "cd"], dtype=object)}
+    )
+
+
+@pytest.fixture
+def styler(df):
+    return Styler(df, uuid_len=0, precision=2)
+
+
+def test_basic_table(styler):
+    result = styler.to_typst()
+    expected = dedent(
+        """\
+    #table(
+      columns: 4,
+      [], [A], [B], [C],
+
+      [0], [0], [-0.61], [ab],
+      [1], [1], [-1.22], [cd],
+    )"""
+    )
+    assert result == expected
+
+
+def test_concat(styler):
+    result = styler.concat(styler.data.agg(["sum"]).style).to_typst()
+    expected = dedent(
+        """\
+    #table(
+      columns: 4,
+      [], [A], [B], [C],
+
+      [0], [0], [-0.61], [ab],
+      [1], [1], [-1.22], [cd],
+      [sum], [1], [-1.830000], [abcd],
+    )"""
+    )
+    assert result == expected
+
+
+def test_concat_recursion(styler):
+    df = styler.data
+    styler1 = styler
+    styler2 = Styler(df.agg(["sum"]), uuid_len=0, precision=3)
+    styler3 = Styler(df.agg(["sum"]), uuid_len=0, precision=4)
+    result = styler1.concat(styler2.concat(styler3)).to_typst()
+    expected = dedent(
+        """\
+    #table(
+      columns: 4,
+      [], [A], [B], [C],
+
+      [0], [0], [-0.61], [ab],
+      [1], [1], [-1.22], [cd],
+      [sum], [1], [-1.830], [abcd],
+      [sum], [1], [-1.8300], [abcd],
+    )"""
+    )
+    assert result == expected
+
+
+def test_concat_chain(styler):
+    df = styler.data
+    styler1 = styler
+    styler2 = Styler(df.agg(["sum"]), uuid_len=0, precision=3)
+    styler3 = Styler(df.agg(["sum"]), uuid_len=0, precision=4)
+    result = styler1.concat(styler2).concat(styler3).to_typst()
+    expected = dedent(
+        """\
+    #table(
+      columns: 4,
+      [], [A], [B], [C],
+
+      [0], [0], [-0.61], [ab],
+      [1], [1], [-1.22], [cd],
+      [sum], [1], [-1.830], [abcd],
+      [sum], [1], [-1.8300], [abcd],
+    )"""
+    )
+    assert result == expected

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/style/test_tooltip.py

@@ -0,0 +1,179 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    MultiIndex,
+)
+
+pytest.importorskip("jinja2")
+from pandas.io.formats.style import Styler
+
+
+@pytest.fixture
+def df():
+    return DataFrame(
+        data=[[0, 1, 2], [3, 4, 5], [6, 7, 8]],
+        columns=["A", "B", "C"],
+        index=["x", "y", "z"],
+    )
+
+
+@pytest.fixture
+def styler(df):
+    return Styler(df, uuid_len=0)
+
+
+@pytest.mark.parametrize(
+    "data, columns, index",
+    [
+        # Test basic reindex and ignoring blank
+        ([["Min", "Max"], [np.nan, ""]], ["A", "C"], ["x", "y"]),
+        # Test non-referenced columns, reversed col names, short index
+        ([["Max", "Min", "Bad-Col"]], ["C", "A", "D"], ["x"]),
+    ],
+)
+def test_tooltip_render(data, columns, index, styler):
+    ttips = DataFrame(data=data, columns=columns, index=index)
+
+    # GH 21266
+    result = styler.set_tooltips(ttips).to_html()
+
+    # test tooltip table level class
+    assert "#T_ .pd-t {\n  visibility: hidden;\n" in result
+
+    # test 'Min' tooltip added
+    assert "#T_ #T__row0_col0:hover .pd-t {\n  visibility: visible;\n}" in result
+    assert '#T_ #T__row0_col0 .pd-t::after {\n  content: "Min";\n}' in result
+    assert 'class="data row0 col0" >0<span class="pd-t"></span></td>' in result
+
+    # test 'Max' tooltip added
+    assert "#T_ #T__row0_col2:hover .pd-t {\n  visibility: visible;\n}" in result
+    assert '#T_ #T__row0_col2 .pd-t::after {\n  content: "Max";\n}' in result
+    assert 'class="data row0 col2" >2<span class="pd-t"></span></td>' in result
+
+    # test Nan, empty string and bad column ignored
+    assert "#T_ #T__row1_col0:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert "#T_ #T__row1_col1:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert "#T_ #T__row0_col1:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert "#T_ #T__row1_col2:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert "Bad-Col" not in result
+
+
+def test_tooltip_ignored(styler):
+    # GH 21266
+    result = styler.to_html()  # no set_tooltips() creates no <span>
+    assert '<style type="text/css">\n</style>' in result
+    assert '<span class="pd-t"></span>' not in result
+    assert 'title="' not in result
+
+
+def test_tooltip_css_class(styler):
+    # GH 21266
+    result = styler.set_tooltips(
+        DataFrame([["tooltip"]], index=["x"], columns=["A"]),
+        css_class="other-class",
+        props=[("color", "green")],
+    ).to_html()
+    assert "#T_ .other-class {\n  color: green;\n" in result
+    assert '#T_ #T__row0_col0 .other-class::after {\n  content: "tooltip";\n' in result
+
+    # GH 39563
+    result = styler.set_tooltips(  # set_tooltips overwrites previous
+        DataFrame([["tooltip"]], index=["x"], columns=["A"]),
+        css_class="another-class",
+        props="color:green;color:red;",
+    ).to_html()
+    assert "#T_ .another-class {\n  color: green;\n  color: red;\n}" in result
+
+
+@pytest.mark.parametrize(
+    "data, columns, index",
+    [
+        # Test basic reindex and ignoring blank
+        ([["Min", "Max"], [np.nan, ""]], ["A", "C"], ["x", "y"]),
+        # Test non-referenced columns, reversed col names, short index
+        ([["Max", "Min", "Bad-Col"]], ["C", "A", "D"], ["x"]),
+    ],
+)
+def test_tooltip_render_as_title(data, columns, index, styler):
+    ttips = DataFrame(data=data, columns=columns, index=index)
+    # GH 56605
+    result = styler.set_tooltips(ttips, as_title_attribute=True).to_html()
+
+    # test css not added
+    assert "#T_ .pd-t {\n  visibility: hidden;\n" not in result
+
+    # test 'Min' tooltip added as title attribute and css does not exist
+    assert "#T_ #T__row0_col0:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert '#T_ #T__row0_col0 .pd-t::after {\n  content: "Min";\n}' not in result
+    assert 'class="data row0 col0"  title="Min">0</td>' in result
+
+    # test 'Max' tooltip added as title attribute and css does not exist
+    assert "#T_ #T__row0_col2:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert '#T_ #T__row0_col2 .pd-t::after {\n  content: "Max";\n}' not in result
+    assert 'class="data row0 col2"  title="Max">2</td>' in result
+
+    # test Nan, empty string and bad column ignored
+    assert "#T_ #T__row1_col0:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert "#T_ #T__row1_col1:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert "#T_ #T__row0_col1:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert "#T_ #T__row1_col2:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert "Bad-Col" not in result
+    assert 'class="data row0 col1" >1</td>' in result
+    assert 'class="data row1 col0" >3</td>' in result
+    assert 'class="data row1 col1" >4</td>' in result
+    assert 'class="data row1 col2" >5</td>' in result
+    assert 'class="data row2 col0" >6</td>' in result
+    assert 'class="data row2 col1" >7</td>' in result
+    assert 'class="data row2 col2" >8</td>' in result
+
+
+def test_tooltip_render_as_title_with_hidden_index_level():
+    df = DataFrame(
+        data=[[0, 1, 2], [3, 4, 5], [6, 7, 8]],
+        columns=["A", "B", "C"],
+        index=MultiIndex.from_arrays(
+            [["x", "y", "z"], [1, 2, 3], ["aa", "bb", "cc"]],
+            names=["alpha", "num", "char"],
+        ),
+    )
+    ttips = DataFrame(
+        # Test basic reindex and ignoring blank, and hide level 2 (num) from index
+        data=[["Min", "Max"], [np.nan, ""]],
+        columns=["A", "C"],
+        index=MultiIndex.from_arrays(
+            [["x", "y"], [1, 2], ["aa", "bb"]], names=["alpha", "num", "char"]
+        ),
+    )
+    styler = Styler(df, uuid_len=0)
+    styler = styler.hide(axis=0, level=-1, names=True)
+    # GH 56605
+    result = styler.set_tooltips(ttips, as_title_attribute=True).to_html()
+
+    # test css not added
+    assert "#T_ .pd-t {\n  visibility: hidden;\n" not in result
+
+    # test 'Min' tooltip added as title attribute and css does not exist
+    assert "#T_ #T__row0_col0:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert '#T_ #T__row0_col0 .pd-t::after {\n  content: "Min";\n}' not in result
+    assert 'class="data row0 col0"  title="Min">0</td>' in result
+
+    # test 'Max' tooltip added as title attribute and css does not exist
+    assert "#T_ #T__row0_col2:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert '#T_ #T__row0_col2 .pd-t::after {\n  content: "Max";\n}' not in result
+    assert 'class="data row0 col2"  title="Max">2</td>' in result
+
+    # test Nan, empty string and bad column ignored
+    assert "#T_ #T__row1_col0:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert "#T_ #T__row1_col1:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert "#T_ #T__row0_col1:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert "#T_ #T__row1_col2:hover .pd-t {\n  visibility: visible;\n}" not in result
+    assert "Bad-Col" not in result
+    assert 'class="data row0 col1" >1</td>' in result
+    assert 'class="data row1 col0" >3</td>' in result
+    assert 'class="data row1 col1" >4</td>' in result
+    assert 'class="data row1 col2" >5</td>' in result
+    assert 'class="data row2 col0" >6</td>' in result
+    assert 'class="data row2 col1" >7</td>' in result
+    assert 'class="data row2 col2" >8</td>' in result

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/test_console.py

@@ -0,0 +1,72 @@
+import locale
+
+import pytest
+
+from pandas._config import detect_console_encoding
+
+
+class MockEncoding:
+    """
+    Used to add a side effect when accessing the 'encoding' property. If the
+    side effect is a str in nature, the value will be returned. Otherwise, the
+    side effect should be an exception that will be raised.
+    """
+
+    def __init__(self, encoding) -> None:
+        super().__init__()
+        self.val = encoding
+
+    @property
+    def encoding(self):
+        return self.raise_or_return(self.val)
+
+    @staticmethod
+    def raise_or_return(val):
+        if isinstance(val, str):
+            return val
+        else:
+            raise val
+
+
+@pytest.mark.parametrize("empty,filled", [["stdin", "stdout"], ["stdout", "stdin"]])
+def test_detect_console_encoding_from_stdout_stdin(monkeypatch, empty, filled):
+    # Ensures that when sys.stdout.encoding or sys.stdin.encoding is used when
+    # they have values filled.
+    # GH 21552
+    with monkeypatch.context() as context:
+        context.setattr(f"sys.{empty}", MockEncoding(""))
+        context.setattr(f"sys.{filled}", MockEncoding(filled))
+        assert detect_console_encoding() == filled
+
+
+@pytest.mark.parametrize("encoding", [AttributeError, OSError, "ascii"])
+def test_detect_console_encoding_fallback_to_locale(monkeypatch, encoding):
+    # GH 21552
+    with monkeypatch.context() as context:
+        context.setattr("locale.getpreferredencoding", lambda: "foo")
+        context.setattr("sys.stdout", MockEncoding(encoding))
+        assert detect_console_encoding() == "foo"
+
+
+@pytest.mark.parametrize(
+    "std,locale",
+    [
+        ["ascii", "ascii"],
+        ["ascii", locale.Error],
+        [AttributeError, "ascii"],
+        [AttributeError, locale.Error],
+        [OSError, "ascii"],
+        [OSError, locale.Error],
+    ],
+)
+def test_detect_console_encoding_fallback_to_default(monkeypatch, std, locale):
+    # When both the stdout/stdin encoding and locale preferred encoding checks
+    # fail (or return 'ascii', we should default to the sys default encoding.
+    # GH 21552
+    with monkeypatch.context() as context:
+        context.setattr(
+            "locale.getpreferredencoding", lambda: MockEncoding.raise_or_return(locale)
+        )
+        context.setattr("sys.stdout", MockEncoding(std))
+        context.setattr("sys.getdefaultencoding", lambda: "sysDefaultEncoding")
+        assert detect_console_encoding() == "sysDefaultEncoding"

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/test_css.py

@@ -0,0 +1,288 @@
+import pytest
+
+from pandas.errors import CSSWarning
+
+import pandas._testing as tm
+
+from pandas.io.formats.css import CSSResolver
+
+
+def assert_resolves(css, props, inherited=None):
+    resolve = CSSResolver()
+    actual = resolve(css, inherited=inherited)
+    assert props == actual
+
+
+def assert_same_resolution(css1, css2, inherited=None):
+    resolve = CSSResolver()
+    resolved1 = resolve(css1, inherited=inherited)
+    resolved2 = resolve(css2, inherited=inherited)
+    assert resolved1 == resolved2
+
+
+@pytest.mark.parametrize(
+    "name,norm,abnorm",
+    [
+        (
+            "whitespace",
+            "hello: world; foo: bar",
+            " \t hello \t :\n  world \n  ;  \n foo: \tbar\n\n",
+        ),
+        ("case", "hello: world; foo: bar", "Hello: WORLD; foO: bar"),
+        ("empty-decl", "hello: world; foo: bar", "; hello: world;; foo: bar;\n; ;"),
+        ("empty-list", "", ";"),
+    ],
+)
+def test_css_parse_normalisation(name, norm, abnorm):
+    assert_same_resolution(norm, abnorm)
+
+
+@pytest.mark.parametrize(
+    "invalid_css,remainder,msg",
+    [
+        # No colon
+        ("hello-world", "", "expected a colon"),
+        ("border-style: solid; hello-world", "border-style: solid", "expected a colon"),
+        (
+            "border-style: solid; hello-world; font-weight: bold",
+            "border-style: solid; font-weight: bold",
+            "expected a colon",
+        ),
+        # Unclosed string fail
+        # Invalid size
+        ("font-size: blah", "font-size: 1em", "Unhandled size"),
+        ("font-size: 1a2b", "font-size: 1em", "Unhandled size"),
+        ("font-size: 1e5pt", "font-size: 1em", "Unhandled size"),
+        ("font-size: 1+6pt", "font-size: 1em", "Unhandled size"),
+        ("font-size: 1unknownunit", "font-size: 1em", "Unhandled size"),
+        ("font-size: 10", "font-size: 1em", "Unhandled size"),
+        ("font-size: 10 pt", "font-size: 1em", "Unhandled size"),
+        # Too many args
+        ("border-top: 1pt solid red green", "border-top: 1pt solid green", "Too many"),
+    ],
+)
+def test_css_parse_invalid(invalid_css, remainder, msg):
+    with tm.assert_produces_warning(CSSWarning, match=msg):
+        assert_same_resolution(invalid_css, remainder)
+
+
+@pytest.mark.parametrize(
+    "shorthand,expansions",
+    [
+        ("margin", ["margin-top", "margin-right", "margin-bottom", "margin-left"]),
+        ("padding", ["padding-top", "padding-right", "padding-bottom", "padding-left"]),
+        (
+            "border-width",
+            [
+                "border-top-width",
+                "border-right-width",
+                "border-bottom-width",
+                "border-left-width",
+            ],
+        ),
+        (
+            "border-color",
+            [
+                "border-top-color",
+                "border-right-color",
+                "border-bottom-color",
+                "border-left-color",
+            ],
+        ),
+        (
+            "border-style",
+            [
+                "border-top-style",
+                "border-right-style",
+                "border-bottom-style",
+                "border-left-style",
+            ],
+        ),
+    ],
+)
+def test_css_side_shorthands(shorthand, expansions):
+    top, right, bottom, left = expansions
+
+    assert_resolves(
+        f"{shorthand}: 1pt", {top: "1pt", right: "1pt", bottom: "1pt", left: "1pt"}
+    )
+
+    assert_resolves(
+        f"{shorthand}: 1pt 4pt", {top: "1pt", right: "4pt", bottom: "1pt", left: "4pt"}
+    )
+
+    assert_resolves(
+        f"{shorthand}: 1pt 4pt 2pt",
+        {top: "1pt", right: "4pt", bottom: "2pt", left: "4pt"},
+    )
+
+    assert_resolves(
+        f"{shorthand}: 1pt 4pt 2pt 0pt",
+        {top: "1pt", right: "4pt", bottom: "2pt", left: "0pt"},
+    )
+
+    with tm.assert_produces_warning(CSSWarning, match="Could not expand"):
+        assert_resolves(f"{shorthand}: 1pt 1pt 1pt 1pt 1pt", {})
+
+
+@pytest.mark.parametrize(
+    "shorthand,sides",
+    [
+        ("border-top", ["top"]),
+        ("border-right", ["right"]),
+        ("border-bottom", ["bottom"]),
+        ("border-left", ["left"]),
+        ("border", ["top", "right", "bottom", "left"]),
+    ],
+)
+def test_css_border_shorthand_sides(shorthand, sides):
+    def create_border_dict(sides, color=None, style=None, width=None):
+        resolved = {}
+        for side in sides:
+            if color:
+                resolved[f"border-{side}-color"] = color
+            if style:
+                resolved[f"border-{side}-style"] = style
+            if width:
+                resolved[f"border-{side}-width"] = width
+        return resolved
+
+    assert_resolves(
+        f"{shorthand}: 1pt red solid", create_border_dict(sides, "red", "solid", "1pt")
+    )
+
+
+@pytest.mark.parametrize(
+    "prop, expected",
+    [
+        ("1pt red solid", ("red", "solid", "1pt")),
+        ("red 1pt solid", ("red", "solid", "1pt")),
+        ("red solid 1pt", ("red", "solid", "1pt")),
+        ("solid 1pt red", ("red", "solid", "1pt")),
+        ("red solid", ("red", "solid", "1.500000pt")),
+        # Note: color=black is not CSS conforming
+        # (See https://drafts.csswg.org/css-backgrounds/#border-shorthands)
+        ("1pt solid", ("black", "solid", "1pt")),
+        ("1pt red", ("red", "none", "1pt")),
+        ("red", ("red", "none", "1.500000pt")),
+        ("1pt", ("black", "none", "1pt")),
+        ("solid", ("black", "solid", "1.500000pt")),
+        # Sizes
+        ("1em", ("black", "none", "12pt")),
+    ],
+)
+def test_css_border_shorthands(prop, expected):
+    color, style, width = expected
+
+    assert_resolves(
+        f"border-left: {prop}",
+        {
+            "border-left-color": color,
+            "border-left-style": style,
+            "border-left-width": width,
+        },
+    )
+
+
+@pytest.mark.parametrize(
+    "style,inherited,equiv",
+    [
+        ("margin: 1px; margin: 2px", "", "margin: 2px"),
+        ("margin: 1px", "margin: 2px", "margin: 1px"),
+        ("margin: 1px; margin: inherit", "margin: 2px", "margin: 2px"),
+        (
+            "margin: 1px; margin-top: 2px",
+            "",
+            "margin-left: 1px; margin-right: 1px; margin-bottom: 1px; margin-top: 2px",
+        ),
+        ("margin-top: 2px", "margin: 1px", "margin: 1px; margin-top: 2px"),
+        ("margin: 1px", "margin-top: 2px", "margin: 1px"),
+        (
+            "margin: 1px; margin-top: inherit",
+            "margin: 2px",
+            "margin: 1px; margin-top: 2px",
+        ),
+    ],
+)
+def test_css_precedence(style, inherited, equiv):
+    resolve = CSSResolver()
+    inherited_props = resolve(inherited)
+    style_props = resolve(style, inherited=inherited_props)
+    equiv_props = resolve(equiv)
+    assert style_props == equiv_props
+
+
+@pytest.mark.parametrize(
+    "style,equiv",
+    [
+        (
+            "margin: 1px; margin-top: inherit",
+            "margin-bottom: 1px; margin-right: 1px; margin-left: 1px",
+        ),
+        ("margin-top: inherit", ""),
+        ("margin-top: initial", ""),
+    ],
+)
+def test_css_none_absent(style, equiv):
+    assert_same_resolution(style, equiv)
+
+
+@pytest.mark.parametrize(
+    "size,resolved",
+    [
+        ("xx-small", "6pt"),
+        ("x-small", f"{7.5:f}pt"),
+        ("small", f"{9.6:f}pt"),
+        ("medium", "12pt"),
+        ("large", f"{13.5:f}pt"),
+        ("x-large", "18pt"),
+        ("xx-large", "24pt"),
+        ("8px", "6pt"),
+        ("1.25pc", "15pt"),
+        (".25in", "18pt"),
+        ("02.54cm", "72pt"),
+        ("25.4mm", "72pt"),
+        ("101.6q", "72pt"),
+    ],
+)
+@pytest.mark.parametrize("relative_to", [None, "16pt"])  # invariant to inherited size
+def test_css_absolute_font_size(size, relative_to, resolved):
+    if relative_to is None:
+        inherited = None
+    else:
+        inherited = {"font-size": relative_to}
+    assert_resolves(f"font-size: {size}", {"font-size": resolved}, inherited=inherited)
+
+
+@pytest.mark.parametrize(
+    "size,relative_to,resolved",
+    [
+        ("1em", None, "12pt"),
+        ("1.0em", None, "12pt"),
+        ("1.25em", None, "15pt"),
+        ("1em", "16pt", "16pt"),
+        ("1.0em", "16pt", "16pt"),
+        ("1.25em", "16pt", "20pt"),
+        ("1rem", "16pt", "12pt"),
+        ("1.0rem", "16pt", "12pt"),
+        ("1.25rem", "16pt", "15pt"),
+        ("100%", None, "12pt"),
+        ("125%", None, "15pt"),
+        ("100%", "16pt", "16pt"),
+        ("125%", "16pt", "20pt"),
+        ("2ex", None, "12pt"),
+        ("2.0ex", None, "12pt"),
+        ("2.50ex", None, "15pt"),
+        ("inherit", "16pt", "16pt"),
+        ("smaller", None, "10pt"),
+        ("smaller", "18pt", "15pt"),
+        ("larger", None, f"{14.4:f}pt"),
+        ("larger", "15pt", "18pt"),
+    ],
+)
+def test_css_relative_font_size(size, relative_to, resolved):
+    if relative_to is None:
+        inherited = None
+    else:
+        inherited = {"font-size": relative_to}
+    assert_resolves(f"font-size: {size}", {"font-size": resolved}, inherited=inherited)

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/test_eng_formatting.py

@@ -0,0 +1,254 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    reset_option,
+    set_eng_float_format,
+)
+
+from pandas.io.formats.format import EngFormatter
+
+
+@pytest.fixture(autouse=True)
+def reset_float_format():
+    yield
+    reset_option("display.float_format")
+
+
+class TestEngFormatter:
+    def test_eng_float_formatter2(self, float_frame):
+        df = float_frame
+        df.loc[5] = 0
+
+        set_eng_float_format()
+        repr(df)
+
+        set_eng_float_format(use_eng_prefix=True)
+        repr(df)
+
+        set_eng_float_format(accuracy=0)
+        repr(df)
+
+    def test_eng_float_formatter(self):
+        df = DataFrame({"A": [1.41, 141.0, 14100, 1410000.0]})
+
+        set_eng_float_format()
+        result = df.to_string()
+        expected = (
+            "             A\n"
+            "0    1.410E+00\n"
+            "1  141.000E+00\n"
+            "2   14.100E+03\n"
+            "3    1.410E+06"
+        )
+        assert result == expected
+
+        set_eng_float_format(use_eng_prefix=True)
+        result = df.to_string()
+        expected = "         A\n0    1.410\n1  141.000\n2  14.100k\n3   1.410M"
+        assert result == expected
+
+        set_eng_float_format(accuracy=0)
+        result = df.to_string()
+        expected = "         A\n0    1E+00\n1  141E+00\n2   14E+03\n3    1E+06"
+        assert result == expected
+
+    def compare(self, formatter, input, output):
+        formatted_input = formatter(input)
+        assert formatted_input == output
+
+    def compare_all(self, formatter, in_out):
+        """
+        Parameters:
+        -----------
+        formatter: EngFormatter under test
+        in_out: list of tuples. Each tuple = (number, expected_formatting)
+
+        It is tested if 'formatter(number) == expected_formatting'.
+        *number* should be >= 0 because formatter(-number) == fmt is also
+        tested. *fmt* is derived from *expected_formatting*
+        """
+        for input, output in in_out:
+            self.compare(formatter, input, output)
+            self.compare(formatter, -input, "-" + output[1:])
+
+    def test_exponents_with_eng_prefix(self):
+        formatter = EngFormatter(accuracy=3, use_eng_prefix=True)
+        f = np.sqrt(2)
+        in_out = [
+            (f * 10**-24, " 1.414y"),
+            (f * 10**-23, " 14.142y"),
+            (f * 10**-22, " 141.421y"),
+            (f * 10**-21, " 1.414z"),
+            (f * 10**-20, " 14.142z"),
+            (f * 10**-19, " 141.421z"),
+            (f * 10**-18, " 1.414a"),
+            (f * 10**-17, " 14.142a"),
+            (f * 10**-16, " 141.421a"),
+            (f * 10**-15, " 1.414f"),
+            (f * 10**-14, " 14.142f"),
+            (f * 10**-13, " 141.421f"),
+            (f * 10**-12, " 1.414p"),
+            (f * 10**-11, " 14.142p"),
+            (f * 10**-10, " 141.421p"),
+            (f * 10**-9, " 1.414n"),
+            (f * 10**-8, " 14.142n"),
+            (f * 10**-7, " 141.421n"),
+            (f * 10**-6, " 1.414u"),
+            (f * 10**-5, " 14.142u"),
+            (f * 10**-4, " 141.421u"),
+            (f * 10**-3, " 1.414m"),
+            (f * 10**-2, " 14.142m"),
+            (f * 10**-1, " 141.421m"),
+            (f * 10**0, " 1.414"),
+            (f * 10**1, " 14.142"),
+            (f * 10**2, " 141.421"),
+            (f * 10**3, " 1.414k"),
+            (f * 10**4, " 14.142k"),
+            (f * 10**5, " 141.421k"),
+            (f * 10**6, " 1.414M"),
+            (f * 10**7, " 14.142M"),
+            (f * 10**8, " 141.421M"),
+            (f * 10**9, " 1.414G"),
+            (f * 10**10, " 14.142G"),
+            (f * 10**11, " 141.421G"),
+            (f * 10**12, " 1.414T"),
+            (f * 10**13, " 14.142T"),
+            (f * 10**14, " 141.421T"),
+            (f * 10**15, " 1.414P"),
+            (f * 10**16, " 14.142P"),
+            (f * 10**17, " 141.421P"),
+            (f * 10**18, " 1.414E"),
+            (f * 10**19, " 14.142E"),
+            (f * 10**20, " 141.421E"),
+            (f * 10**21, " 1.414Z"),
+            (f * 10**22, " 14.142Z"),
+            (f * 10**23, " 141.421Z"),
+            (f * 10**24, " 1.414Y"),
+            (f * 10**25, " 14.142Y"),
+            (f * 10**26, " 141.421Y"),
+        ]
+        self.compare_all(formatter, in_out)
+
+    def test_exponents_without_eng_prefix(self):
+        formatter = EngFormatter(accuracy=4, use_eng_prefix=False)
+        f = np.pi
+        in_out = [
+            (f * 10**-24, " 3.1416E-24"),
+            (f * 10**-23, " 31.4159E-24"),
+            (f * 10**-22, " 314.1593E-24"),
+            (f * 10**-21, " 3.1416E-21"),
+            (f * 10**-20, " 31.4159E-21"),
+            (f * 10**-19, " 314.1593E-21"),
+            (f * 10**-18, " 3.1416E-18"),
+            (f * 10**-17, " 31.4159E-18"),
+            (f * 10**-16, " 314.1593E-18"),
+            (f * 10**-15, " 3.1416E-15"),
+            (f * 10**-14, " 31.4159E-15"),
+            (f * 10**-13, " 314.1593E-15"),
+            (f * 10**-12, " 3.1416E-12"),
+            (f * 10**-11, " 31.4159E-12"),
+            (f * 10**-10, " 314.1593E-12"),
+            (f * 10**-9, " 3.1416E-09"),
+            (f * 10**-8, " 31.4159E-09"),
+            (f * 10**-7, " 314.1593E-09"),
+            (f * 10**-6, " 3.1416E-06"),
+            (f * 10**-5, " 31.4159E-06"),
+            (f * 10**-4, " 314.1593E-06"),
+            (f * 10**-3, " 3.1416E-03"),
+            (f * 10**-2, " 31.4159E-03"),
+            (f * 10**-1, " 314.1593E-03"),
+            (f * 10**0, " 3.1416E+00"),
+            (f * 10**1, " 31.4159E+00"),
+            (f * 10**2, " 314.1593E+00"),
+            (f * 10**3, " 3.1416E+03"),
+            (f * 10**4, " 31.4159E+03"),
+            (f * 10**5, " 314.1593E+03"),
+            (f * 10**6, " 3.1416E+06"),
+            (f * 10**7, " 31.4159E+06"),
+            (f * 10**8, " 314.1593E+06"),
+            (f * 10**9, " 3.1416E+09"),
+            (f * 10**10, " 31.4159E+09"),
+            (f * 10**11, " 314.1593E+09"),
+            (f * 10**12, " 3.1416E+12"),
+            (f * 10**13, " 31.4159E+12"),
+            (f * 10**14, " 314.1593E+12"),
+            (f * 10**15, " 3.1416E+15"),
+            (f * 10**16, " 31.4159E+15"),
+            (f * 10**17, " 314.1593E+15"),
+            (f * 10**18, " 3.1416E+18"),
+            (f * 10**19, " 31.4159E+18"),
+            (f * 10**20, " 314.1593E+18"),
+            (f * 10**21, " 3.1416E+21"),
+            (f * 10**22, " 31.4159E+21"),
+            (f * 10**23, " 314.1593E+21"),
+            (f * 10**24, " 3.1416E+24"),
+            (f * 10**25, " 31.4159E+24"),
+            (f * 10**26, " 314.1593E+24"),
+        ]
+        self.compare_all(formatter, in_out)
+
+    def test_rounding(self):
+        formatter = EngFormatter(accuracy=3, use_eng_prefix=True)
+        in_out = [
+            (5.55555, " 5.556"),
+            (55.5555, " 55.556"),
+            (555.555, " 555.555"),
+            (5555.55, " 5.556k"),
+            (55555.5, " 55.556k"),
+            (555555, " 555.555k"),
+        ]
+        self.compare_all(formatter, in_out)
+
+        formatter = EngFormatter(accuracy=1, use_eng_prefix=True)
+        in_out = [
+            (5.55555, " 5.6"),
+            (55.5555, " 55.6"),
+            (555.555, " 555.6"),
+            (5555.55, " 5.6k"),
+            (55555.5, " 55.6k"),
+            (555555, " 555.6k"),
+        ]
+        self.compare_all(formatter, in_out)
+
+        formatter = EngFormatter(accuracy=0, use_eng_prefix=True)
+        in_out = [
+            (5.55555, " 6"),
+            (55.5555, " 56"),
+            (555.555, " 556"),
+            (5555.55, " 6k"),
+            (55555.5, " 56k"),
+            (555555, " 556k"),
+        ]
+        self.compare_all(formatter, in_out)
+
+        formatter = EngFormatter(accuracy=3, use_eng_prefix=True)
+        result = formatter(0)
+        assert result == " 0.000"
+
+    def test_nan(self):
+        # Issue #11981
+
+        formatter = EngFormatter(accuracy=1, use_eng_prefix=True)
+        result = formatter(np.nan)
+        assert result == "NaN"
+
+        df = DataFrame(
+            {
+                "a": [1.5, 10.3, 20.5],
+                "b": [50.3, 60.67, 70.12],
+                "c": [100.2, 101.33, 120.33],
+            }
+        )
+        pt = df.pivot_table(values="a", index="b", columns="c")
+        set_eng_float_format(accuracy=1)
+        result = pt.to_string()
+        assert "NaN" in result
+
+    def test_inf(self):
+        # Issue #11981
+
+        formatter = EngFormatter(accuracy=1, use_eng_prefix=True)
+        result = formatter(np.inf)
+        assert result == "inf"

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/test_ipython_compat.py

@@ -0,0 +1,91 @@
+import numpy as np
+
+import pandas._config.config as cf
+
+from pandas import (
+    DataFrame,
+    MultiIndex,
+)
+
+
+class TestTableSchemaRepr:
+    def test_publishes(self, ip):
+        ipython = ip.instance(config=ip.config)
+        df = DataFrame({"A": [1, 2]})
+        objects = [df["A"], df]  # dataframe / series
+        expected_keys = [
+            {"text/plain", "application/vnd.dataresource+json"},
+            {"text/plain", "text/html", "application/vnd.dataresource+json"},
+        ]
+
+        opt = cf.option_context("display.html.table_schema", True)
+        last_obj = None
+        for obj, expected in zip(objects, expected_keys):
+            last_obj = obj
+            with cf.option_context("display.html.table_schema", True):
+                # Can't reuse opt on all systems GH#58055
+                formatted = ipython.display_formatter.format(obj)
+            assert set(formatted[0].keys()) == expected
+
+        with_latex = cf.option_context("styler.render.repr", "latex")
+
+        with opt, with_latex:
+            formatted = ipython.display_formatter.format(last_obj)
+
+        expected = {
+            "text/plain",
+            "text/html",
+            "text/latex",
+            "application/vnd.dataresource+json",
+        }
+        assert set(formatted[0].keys()) == expected
+
+    def test_publishes_not_implemented(self, ip):
+        # column MultiIndex
+        # GH#15996
+        midx = MultiIndex.from_product([["A", "B"], ["a", "b", "c"]])
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, len(midx))), columns=midx
+        )
+
+        opt = cf.option_context("display.html.table_schema", True)
+
+        with opt:
+            formatted = ip.instance(config=ip.config).display_formatter.format(df)
+
+        expected = {"text/plain", "text/html"}
+        assert set(formatted[0].keys()) == expected
+
+    def test_config_on(self):
+        df = DataFrame({"A": [1, 2]})
+        with cf.option_context("display.html.table_schema", True):
+            result = df._repr_data_resource_()
+
+        assert result is not None
+
+    def test_config_default_off(self):
+        df = DataFrame({"A": [1, 2]})
+        with cf.option_context("display.html.table_schema", False):
+            result = df._repr_data_resource_()
+
+        assert result is None
+
+    def test_enable_data_resource_formatter(self, ip):
+        # GH#10491
+        formatters = ip.instance(config=ip.config).display_formatter.formatters
+        mimetype = "application/vnd.dataresource+json"
+
+        with cf.option_context("display.html.table_schema", True):
+            assert "application/vnd.dataresource+json" in formatters
+            assert formatters[mimetype].enabled
+
+        # still there, just disabled
+        assert "application/vnd.dataresource+json" in formatters
+        assert not formatters[mimetype].enabled
+
+        # able to re-set
+        with cf.option_context("display.html.table_schema", True):
+            assert "application/vnd.dataresource+json" in formatters
+            assert formatters[mimetype].enabled
+            # smoke test that it works
+            ip.instance(config=ip.config).display_formatter.format(cf)

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/test_printing.py

@@ -0,0 +1,172 @@
+# Note! This file is aimed specifically at pandas.io.formats.printing utility
+# functions, not the general printing of pandas objects.
+from collections.abc import Mapping
+import string
+
+import pytest
+
+import pandas._config.config as cf
+
+import pandas as pd
+
+from pandas.io.formats import printing
+
+
+@pytest.mark.parametrize(
+    "input_names, expected_names",
+    [
+        (["'a b"], "['\\'a b']"),  # Escape leading quote
+        (["test's b"], "['test\\'s b']"),  # Escape apostrophe
+        (["'test' b"], "['\\'test\\' b']"),  # Escape surrounding quotes
+        (["test b'"], "['test b\\'']"),  # Escape single quote
+        (["test\n' b"], "['test\\n\\' b']"),  # Escape quotes, preserve newline
+    ],
+)
+def test_formatted_index_names(input_names, expected_names):
+    # GH#60190
+    df = pd.DataFrame({name: [1, 2, 3] for name in input_names}).set_index(input_names)
+    formatted_names = str(df.index.names)
+
+    assert formatted_names == expected_names
+
+
+def test_adjoin():
+    data = [["a", "b", "c"], ["dd", "ee", "ff"], ["ggg", "hhh", "iii"]]
+    expected = "a  dd  ggg\nb  ee  hhh\nc  ff  iii"
+
+    adjoined = printing.adjoin(2, *data)
+
+    assert adjoined == expected
+
+
+class MyMapping(Mapping):
+    def __getitem__(self, key):
+        return 4
+
+    def __iter__(self):
+        return iter(["a", "b"])
+
+    def __len__(self):
+        return 2
+
+
+class TestPPrintThing:
+    def test_repr_binary_type(self):
+        letters = string.ascii_letters
+        try:
+            raw = bytes(letters, encoding=cf.get_option("display.encoding"))
+        except TypeError:
+            raw = bytes(letters)
+        b = str(raw.decode("utf-8"))
+        res = printing.pprint_thing(b, quote_strings=True)
+        assert res == repr(b)
+        res = printing.pprint_thing(b, quote_strings=False)
+        assert res == b
+
+    def test_repr_obeys_max_seq_limit(self):
+        with cf.option_context("display.max_seq_items", 2000):
+            assert len(printing.pprint_thing(list(range(1000)))) > 1000
+
+        with cf.option_context("display.max_seq_items", 5):
+            assert len(printing.pprint_thing(list(range(1000)))) < 100
+
+        with cf.option_context("display.max_seq_items", 1):
+            assert len(printing.pprint_thing(list(range(1000)))) < 9
+
+    def test_repr_set(self):
+        assert printing.pprint_thing({1}) == "{1}"
+
+    def test_repr_dict(self):
+        assert printing.pprint_thing({"a": 4, "b": 4}) == "{'a': 4, 'b': 4}"
+
+    def test_repr_mapping(self):
+        assert printing.pprint_thing(MyMapping()) == "{'a': 4, 'b': 4}"
+
+    def test_repr_frozenset(self):
+        assert printing.pprint_thing(frozenset([1, 2])) == "frozenset({1, 2})"
+
+
+class TestFormatBase:
+    def test_adjoin(self):
+        data = [["a", "b", "c"], ["dd", "ee", "ff"], ["ggg", "hhh", "iii"]]
+        expected = "a  dd  ggg\nb  ee  hhh\nc  ff  iii"
+
+        adjoined = printing.adjoin(2, *data)
+
+        assert adjoined == expected
+
+    def test_adjoin_unicode(self):
+        data = [["あ", "b", "c"], ["dd", "ええ", "ff"], ["ggg", "hhh", "いいい"]]
+        expected = "あ  dd  ggg\nb  ええ  hhh\nc  ff  いいい"
+        adjoined = printing.adjoin(2, *data)
+        assert adjoined == expected
+
+        adj = printing._EastAsianTextAdjustment()
+
+        expected = """あ  dd    ggg
+b   ええ  hhh
+c   ff    いいい"""
+
+        adjoined = adj.adjoin(2, *data)
+        assert adjoined == expected
+        cols = adjoined.split("\n")
+        assert adj.len(cols[0]) == 13
+        assert adj.len(cols[1]) == 13
+        assert adj.len(cols[2]) == 16
+
+        expected = """あ       dd         ggg
+b        ええ       hhh
+c        ff         いいい"""
+
+        adjoined = adj.adjoin(7, *data)
+        assert adjoined == expected
+        cols = adjoined.split("\n")
+        assert adj.len(cols[0]) == 23
+        assert adj.len(cols[1]) == 23
+        assert adj.len(cols[2]) == 26
+
+    def test_justify(self):
+        adj = printing._EastAsianTextAdjustment()
+
+        def just(x, *args, **kwargs):
+            # wrapper to test single str
+            return adj.justify([x], *args, **kwargs)[0]
+
+        assert just("abc", 5, mode="left") == "abc  "
+        assert just("abc", 5, mode="center") == " abc "
+        assert just("abc", 5, mode="right") == "  abc"
+        assert just("abc", 5, mode="left") == "abc  "
+        assert just("abc", 5, mode="center") == " abc "
+        assert just("abc", 5, mode="right") == "  abc"
+
+        assert just("パンダ", 5, mode="left") == "パンダ"
+        assert just("パンダ", 5, mode="center") == "パンダ"
+        assert just("パンダ", 5, mode="right") == "パンダ"
+
+        assert just("パンダ", 10, mode="left") == "パンダ    "
+        assert just("パンダ", 10, mode="center") == "  パンダ  "
+        assert just("パンダ", 10, mode="right") == "    パンダ"
+
+    def test_east_asian_len(self):
+        adj = printing._EastAsianTextAdjustment()
+
+        assert adj.len("abc") == 3
+        assert adj.len("abc") == 3
+
+        assert adj.len("パンダ") == 6
+        assert adj.len("ﾊﾟﾝﾀﾞ") == 5
+        assert adj.len("パンダpanda") == 11
+        assert adj.len("ﾊﾟﾝﾀﾞpanda") == 10
+
+    def test_ambiguous_width(self):
+        adj = printing._EastAsianTextAdjustment()
+        assert adj.len("¡¡ab") == 4
+
+        with cf.option_context("display.unicode.ambiguous_as_wide", True):
+            adj = printing._EastAsianTextAdjustment()
+            assert adj.len("¡¡ab") == 6
+
+        data = [["あ", "b", "c"], ["dd", "ええ", "ff"], ["ggg", "¡¡ab", "いいい"]]
+        expected = "あ  dd    ggg \nb   ええ  ¡¡ab\nc   ff    いいい"
+        adjoined = adj.adjoin(2, *data)
+        assert adjoined == expected

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/test_to_csv.py

@@ -0,0 +1,886 @@
+import io
+import os
+import sys
+from zipfile import ZipFile
+
+from _csv import Error
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    compat,
+)
+import pandas._testing as tm
+
+
+class TestToCSV:
+    def test_to_csv_with_single_column(self, temp_file):
+        # see gh-18676, https://bugs.python.org/issue32255
+        #
+        # Python's CSV library adds an extraneous '""'
+        # before the newline when the NaN-value is in
+        # the first row. Otherwise, only the newline
+        # character is added. This behavior is inconsistent
+        # and was patched in https://bugs.python.org/pull_request4672.
+        df1 = DataFrame([None, 1])
+        expected1 = """\
+""
+1.0
+"""
+        df1.to_csv(temp_file, header=None, index=None)
+        with open(temp_file, encoding="utf-8") as f:
+            assert f.read() == expected1
+
+        df2 = DataFrame([1, None])
+        expected2 = """\
+1.0
+""
+"""
+        df2.to_csv(temp_file, header=None, index=None)
+        with open(temp_file, encoding="utf-8") as f:
+            assert f.read() == expected2
+
+    def test_to_csv_default_encoding(self, temp_file):
+        # GH17097
+        df = DataFrame({"col": ["AAAAA", "ÄÄÄÄÄ", "ßßßßß", "聞聞聞聞聞"]})
+
+        # the default to_csv encoding is uft-8.
+        df.to_csv(temp_file)
+        tm.assert_frame_equal(pd.read_csv(temp_file, index_col=0), df)
+
+    def test_to_csv_quotechar(self, temp_file):
+        df = DataFrame({"col": [1, 2]})
+        expected = """\
+"","col"
+"0","1"
+"1","2"
+"""
+
+        df.to_csv(temp_file, quoting=1)  # 1=QUOTE_ALL
+        with open(temp_file, encoding="utf-8") as f:
+            assert f.read() == expected
+
+        expected = """\
+$$,$col$
+$0$,$1$
+$1$,$2$
+"""
+
+        df.to_csv(temp_file, quoting=1, quotechar="$")
+        with open(temp_file, encoding="utf-8") as f:
+            assert f.read() == expected
+
+        with pytest.raises(TypeError, match="quotechar"):
+            df.to_csv(temp_file, quoting=1, quotechar=None)
+
+    def test_to_csv_doublequote(self, temp_file):
+        df = DataFrame({"col": ['a"a', '"bb"']})
+        expected = '''\
+"","col"
+"0","a""a"
+"1","""bb"""
+'''
+
+        df.to_csv(temp_file, quoting=1, doublequote=True)  # QUOTE_ALL
+        with open(temp_file, encoding="utf-8") as f:
+            assert f.read() == expected
+
+        with pytest.raises(Error, match="escapechar"):
+            df.to_csv(temp_file, doublequote=False)  # no escapechar set
+
+    def test_to_csv_escapechar(self, temp_file):
+        df = DataFrame({"col": ['a"a', '"bb"']})
+        expected = """\
+"","col"
+"0","a\\"a"
+"1","\\"bb\\""
+"""
+
+        df.to_csv(temp_file, quoting=1, doublequote=False, escapechar="\\")
+        with open(temp_file, encoding="utf-8") as f:
+            assert f.read() == expected
+
+        df = DataFrame({"col": ["a,a", ",bb,"]})
+        expected = """\
+,col
+0,a\\,a
+1,\\,bb\\,
+"""
+
+        df.to_csv(temp_file, quoting=3, escapechar="\\")  # QUOTE_NONE
+        with open(temp_file, encoding="utf-8") as f:
+            assert f.read() == expected
+
+    def test_csv_to_string(self):
+        df = DataFrame({"col": [1, 2]})
+        expected_rows = [",col", "0,1", "1,2"]
+        expected = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert df.to_csv() == expected
+
+    def test_to_csv_decimal(self):
+        # see gh-781
+        df = DataFrame({"col1": [1], "col2": ["a"], "col3": [10.1]})
+
+        expected_rows = [",col1,col2,col3", "0,1,a,10.1"]
+        expected_default = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert df.to_csv() == expected_default
+
+        expected_rows = [";col1;col2;col3", "0;1;a;10,1"]
+        expected_european_excel = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert df.to_csv(decimal=",", sep=";") == expected_european_excel
+
+        expected_rows = [",col1,col2,col3", "0,1,a,10.10"]
+        expected_float_format_default = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert df.to_csv(float_format="%.2f") == expected_float_format_default
+
+        expected_rows = [";col1;col2;col3", "0;1;a;10,10"]
+        expected_float_format = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert (
+            df.to_csv(decimal=",", sep=";", float_format="%.2f")
+            == expected_float_format
+        )
+
+        # see gh-11553: testing if decimal is taken into account for '0.0'
+        df = DataFrame({"a": [0, 1.1], "b": [2.2, 3.3], "c": 1})
+
+        expected_rows = ["a,b,c", "0^0,2^2,1", "1^1,3^3,1"]
+        expected = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert df.to_csv(index=False, decimal="^") == expected
+
+        # same but for an index
+        assert df.set_index("a").to_csv(decimal="^") == expected
+
+        # same for a multi-index
+        assert df.set_index(["a", "b"]).to_csv(decimal="^") == expected
+
+    def test_to_csv_float_format(self):
+        # testing if float_format is taken into account for the index
+        # GH 11553
+        df = DataFrame({"a": [0, 1], "b": [2.2, 3.3], "c": 1})
+
+        expected_rows = ["a,b,c", "0,2.20,1", "1,3.30,1"]
+        expected = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert df.set_index("a").to_csv(float_format="%.2f") == expected
+
+        # same for a multi-index
+        assert df.set_index(["a", "b"]).to_csv(float_format="%.2f") == expected
+
+    def test_to_csv_na_rep(self):
+        # see gh-11553
+        #
+        # Testing if NaN values are correctly represented in the index.
+        df = DataFrame({"a": [0, np.nan], "b": [0, 1], "c": [2, 3]})
+        expected_rows = ["a,b,c", "0.0,0,2", "_,1,3"]
+        expected = tm.convert_rows_list_to_csv_str(expected_rows)
+
+        assert df.set_index("a").to_csv(na_rep="_") == expected
+        assert df.set_index(["a", "b"]).to_csv(na_rep="_") == expected
+
+        # now with an index containing only NaNs
+        df = DataFrame({"a": np.nan, "b": [0, 1], "c": [2, 3]})
+        expected_rows = ["a,b,c", "_,0,2", "_,1,3"]
+        expected = tm.convert_rows_list_to_csv_str(expected_rows)
+
+        assert df.set_index("a").to_csv(na_rep="_") == expected
+        assert df.set_index(["a", "b"]).to_csv(na_rep="_") == expected
+
+        # check if na_rep parameter does not break anything when no NaN
+        df = DataFrame({"a": 0, "b": [0, 1], "c": [2, 3]})
+        expected_rows = ["a,b,c", "0,0,2", "0,1,3"]
+        expected = tm.convert_rows_list_to_csv_str(expected_rows)
+
+        assert df.set_index("a").to_csv(na_rep="_") == expected
+        assert df.set_index(["a", "b"]).to_csv(na_rep="_") == expected
+
+        csv = pd.Series(["a", pd.NA, "c"]).to_csv(na_rep="ZZZZZ")
+        expected = tm.convert_rows_list_to_csv_str([",0", "0,a", "1,ZZZZZ", "2,c"])
+        assert expected == csv
+
+    def test_to_csv_na_rep_nullable_string(self, nullable_string_dtype):
+        # GH 29975
+        # Make sure full na_rep shows up when a dtype is provided
+        expected = tm.convert_rows_list_to_csv_str([",0", "0,a", "1,ZZZZZ", "2,c"])
+        csv = pd.Series(["a", pd.NA, "c"], dtype=nullable_string_dtype).to_csv(
+            na_rep="ZZZZZ"
+        )
+        assert expected == csv
+
+    def test_to_csv_date_format(self):
+        # GH 10209
+        df_sec = DataFrame({"A": pd.date_range("20130101", periods=5, freq="s")})
+        df_day = DataFrame({"A": pd.date_range("20130101", periods=5, freq="D")})
+
+        expected_rows = [
+            ",A",
+            "0,2013-01-01 00:00:00",
+            "1,2013-01-01 00:00:01",
+            "2,2013-01-01 00:00:02",
+            "3,2013-01-01 00:00:03",
+            "4,2013-01-01 00:00:04",
+        ]
+        expected_default_sec = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert df_sec.to_csv() == expected_default_sec
+
+        expected_rows = [
+            ",A",
+            "0,2013-01-01 00:00:00",
+            "1,2013-01-02 00:00:00",
+            "2,2013-01-03 00:00:00",
+            "3,2013-01-04 00:00:00",
+            "4,2013-01-05 00:00:00",
+        ]
+        expected_ymdhms_day = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert df_day.to_csv(date_format="%Y-%m-%d %H:%M:%S") == expected_ymdhms_day
+
+        expected_rows = [
+            ",A",
+            "0,2013-01-01",
+            "1,2013-01-01",
+            "2,2013-01-01",
+            "3,2013-01-01",
+            "4,2013-01-01",
+        ]
+        expected_ymd_sec = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert df_sec.to_csv(date_format="%Y-%m-%d") == expected_ymd_sec
+
+        expected_rows = [
+            ",A",
+            "0,2013-01-01",
+            "1,2013-01-02",
+            "2,2013-01-03",
+            "3,2013-01-04",
+            "4,2013-01-05",
+        ]
+        expected_default_day = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert df_day.to_csv() == expected_default_day
+        assert df_day.to_csv(date_format="%Y-%m-%d") == expected_default_day
+
+        # see gh-7791
+        #
+        # Testing if date_format parameter is taken into account
+        # for multi-indexed DataFrames.
+        df_sec["B"] = 0
+        df_sec["C"] = 1
+
+        expected_rows = ["A,B,C", "2013-01-01,0,1.0"]
+        expected_ymd_sec = tm.convert_rows_list_to_csv_str(expected_rows)
+
+        df_sec_grouped = df_sec.groupby([pd.Grouper(key="A", freq="1h"), "B"])
+        assert df_sec_grouped.mean().to_csv(date_format="%Y-%m-%d") == expected_ymd_sec
+
+    def test_to_csv_different_datetime_formats(self):
+        # GH#21734
+        df = DataFrame(
+            {
+                "date": pd.to_datetime("1970-01-01"),
+                "datetime": pd.date_range("1970-01-01", periods=2, freq="h"),
+            }
+        )
+        expected_rows = [
+            "date,datetime",
+            "1970-01-01,1970-01-01 00:00:00",
+            "1970-01-01,1970-01-01 01:00:00",
+        ]
+        expected = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert df.to_csv(index=False) == expected
+
+    def test_to_csv_date_format_in_categorical(self):
+        # GH#40754
+        ser = pd.Series(pd.to_datetime(["2021-03-27", pd.NaT], format="%Y-%m-%d"))
+        ser = ser.astype("category")
+        expected = tm.convert_rows_list_to_csv_str(["0", "2021-03-27", '""'])
+        assert ser.to_csv(index=False) == expected
+
+        ser = pd.Series(
+            pd.date_range(
+                start="2021-03-27", freq="D", periods=1, tz="Europe/Berlin"
+            ).append(pd.DatetimeIndex([pd.NaT]))
+        )
+        ser = ser.astype("category")
+        assert ser.to_csv(index=False, date_format="%Y-%m-%d") == expected
+
+    def test_to_csv_float_ea_float_format(self):
+        # GH#45991
+        df = DataFrame({"a": [1.1, 2.02, pd.NA, 6.000006], "b": "c"})
+        df["a"] = df["a"].astype("Float64")
+        result = df.to_csv(index=False, float_format="%.5f")
+        expected = tm.convert_rows_list_to_csv_str(
+            ["a,b", "1.10000,c", "2.02000,c", ",c", "6.00001,c"]
+        )
+        assert result == expected
+
+    def test_to_csv_float_ea_no_float_format(self):
+        # GH#45991
+        df = DataFrame({"a": [1.1, 2.02, pd.NA, 6.000006], "b": "c"})
+        df["a"] = df["a"].astype("Float64")
+        result = df.to_csv(index=False)
+        expected = tm.convert_rows_list_to_csv_str(
+            ["a,b", "1.1,c", "2.02,c", ",c", "6.000006,c"]
+        )
+        assert result == expected
+
+    def test_to_csv_float_ea_nan_distinguish(self, using_nan_is_na):
+        # GH#61617, GH#65227 - to_csv should not crash when FloatingArray
+        # contains unmasked NaN (with distinguish_nan_and_na=True)
+        df = DataFrame({"a": pd.array([np.nan, pd.NA, 3.0], dtype="Float64"), "b": "c"})
+        result = df.to_csv(index=False)
+        if using_nan_is_na:
+            expected = tm.convert_rows_list_to_csv_str(["a,b", ",c", ",c", "3.0,c"])
+        else:
+            expected = tm.convert_rows_list_to_csv_str(["a,b", "nan,c", ",c", "3.0,c"])
+        assert result == expected
+
+    def test_to_csv_float_ea_nan_distinguish_series(self, using_nan_is_na):
+        # GH#65227 - Series.to_csv with FloatingArray containing both NaN and NA
+        ser = pd.Series((1, pd.NA, 0), index=["a", "b", "c"], dtype="Float64", name="x")
+        ser = ser / ser
+        result = ser.to_csv()
+        if using_nan_is_na:
+            expected = tm.convert_rows_list_to_csv_str([",x", "a,1.0", "b,", "c,"])
+        else:
+            expected = tm.convert_rows_list_to_csv_str([",x", "a,1.0", "b,", "c,nan"])
+        assert result == expected
+
+    def test_to_csv_multi_index(self):
+        # see gh-6618
+        df = DataFrame([1], columns=pd.MultiIndex.from_arrays([[1], [2]]))
+
+        exp_rows = [",1", ",2", "0,1"]
+        exp = tm.convert_rows_list_to_csv_str(exp_rows)
+        assert df.to_csv() == exp
+
+        exp_rows = ["1", "2", "1"]
+        exp = tm.convert_rows_list_to_csv_str(exp_rows)
+        assert df.to_csv(index=False) == exp
+
+        df = DataFrame(
+            [1],
+            columns=pd.MultiIndex.from_arrays([[1], [2]]),
+            index=pd.MultiIndex.from_arrays([[1], [2]]),
+        )
+
+        exp_rows = [",,1", ",,2", "1,2,1"]
+        exp = tm.convert_rows_list_to_csv_str(exp_rows)
+        assert df.to_csv() == exp
+
+        exp_rows = ["1", "2", "1"]
+        exp = tm.convert_rows_list_to_csv_str(exp_rows)
+        assert df.to_csv(index=False) == exp
+
+        df = DataFrame([1], columns=pd.MultiIndex.from_arrays([["foo"], ["bar"]]))
+
+        exp_rows = [",foo", ",bar", "0,1"]
+        exp = tm.convert_rows_list_to_csv_str(exp_rows)
+        assert df.to_csv() == exp
+
+        exp_rows = ["foo", "bar", "1"]
+        exp = tm.convert_rows_list_to_csv_str(exp_rows)
+        assert df.to_csv(index=False) == exp
+
+    @pytest.mark.parametrize(
+        "ind,expected",
+        [
+            (
+                pd.MultiIndex(levels=[[1.0]], codes=[[0]], names=["x"]),
+                "x,data\n1.0,1\n",
+            ),
+            (
+                pd.MultiIndex(
+                    levels=[[1.0], [2.0]], codes=[[0], [0]], names=["x", "y"]
+                ),
+                "x,y,data\n1.0,2.0,1\n",
+            ),
+        ],
+    )
+    def test_to_csv_single_level_multi_index(self, ind, expected, frame_or_series):
+        # see gh-19589
+        obj = frame_or_series(pd.Series([1], ind, name="data"))
+
+        result = obj.to_csv(lineterminator="\n", header=True)
+        assert result == expected
+
+    def test_to_csv_string_array_ascii(self, temp_file):
+        # GH 10813
+        str_array = [{"names": ["foo", "bar"]}, {"names": ["baz", "qux"]}]
+        df = DataFrame(str_array)
+        expected_ascii = """\
+,names
+0,"['foo', 'bar']"
+1,"['baz', 'qux']"
+"""
+        df.to_csv(temp_file, encoding="ascii")
+        with open(temp_file, encoding="utf-8") as f:
+            assert f.read() == expected_ascii
+
+    def test_to_csv_string_array_utf8(self, temp_file):
+        # GH 10813
+        str_array = [{"names": ["foo", "bar"]}, {"names": ["baz", "qux"]}]
+        df = DataFrame(str_array)
+        expected_utf8 = """\
+,names
+0,"['foo', 'bar']"
+1,"['baz', 'qux']"
+"""
+        df.to_csv(temp_file, encoding="utf-8")
+        with open(temp_file, encoding="utf-8") as f:
+            assert f.read() == expected_utf8
+
+    def test_to_csv_string_with_lf(self, temp_file):
+        # GH 20353
+        data = {"int": [1, 2, 3], "str_lf": ["abc", "d\nef", "g\nh\n\ni"]}
+        df = DataFrame(data)
+
+        # case 1: The default line terminator(=os.linesep)(PR 21406)
+        os_linesep = os.linesep.encode("utf-8")
+        expected_noarg = (
+            b"int,str_lf"
+            + os_linesep
+            + b"1,abc"
+            + os_linesep
+            + b'2,"d\nef"'
+            + os_linesep
+            + b'3,"g\nh\n\ni"'
+            + os_linesep
+        )
+        df.to_csv(temp_file, index=False)
+        with open(temp_file, "rb") as f:
+            assert f.read() == expected_noarg
+
+        # case 2: LF as line terminator
+        expected_lf = b'int,str_lf\n1,abc\n2,"d\nef"\n3,"g\nh\n\ni"\n'
+        df.to_csv(temp_file, lineterminator="\n", index=False)
+        with open(temp_file, "rb") as f:
+            assert f.read() == expected_lf
+
+        # case 3: CRLF as line terminator
+        # 'lineterminator' should not change inner element
+        expected_crlf = b'int,str_lf\r\n1,abc\r\n2,"d\nef"\r\n3,"g\nh\n\ni"\r\n'
+        df.to_csv(temp_file, lineterminator="\r\n", index=False)
+        with open(temp_file, "rb") as f:
+            assert f.read() == expected_crlf
+
+    def test_to_csv_string_with_crlf(self, temp_file):
+        # GH 20353
+        data = {"int": [1, 2, 3], "str_crlf": ["abc", "d\r\nef", "g\r\nh\r\n\r\ni"]}
+        df = DataFrame(data)
+        # case 1: The default line terminator(=os.linesep)(PR 21406)
+        os_linesep = os.linesep.encode("utf-8")
+        expected_noarg = (
+            b"int,str_crlf"
+            + os_linesep
+            + b"1,abc"
+            + os_linesep
+            + b'2,"d\r\nef"'
+            + os_linesep
+            + b'3,"g\r\nh\r\n\r\ni"'
+            + os_linesep
+        )
+        df.to_csv(temp_file, index=False)
+        with open(temp_file, "rb") as f:
+            assert f.read() == expected_noarg
+
+        # case 2: LF as line terminator
+        expected_lf = b'int,str_crlf\n1,abc\n2,"d\r\nef"\n3,"g\r\nh\r\n\r\ni"\n'
+        df.to_csv(temp_file, lineterminator="\n", index=False)
+        with open(temp_file, "rb") as f:
+            assert f.read() == expected_lf
+
+        # case 3: CRLF as line terminator
+        # 'lineterminator' should not change inner element
+        expected_crlf = (
+            b'int,str_crlf\r\n1,abc\r\n2,"d\r\nef"\r\n3,"g\r\nh\r\n\r\ni"\r\n'
+        )
+        df.to_csv(temp_file, lineterminator="\r\n", index=False)
+        with open(temp_file, "rb") as f:
+            assert f.read() == expected_crlf
+
+    def test_to_csv_stdout_file(self, capsys):
+        # GH 21561
+        df = DataFrame([["foo", "bar"], ["baz", "qux"]], columns=["name_1", "name_2"])
+        expected_rows = [",name_1,name_2", "0,foo,bar", "1,baz,qux"]
+        expected_ascii = tm.convert_rows_list_to_csv_str(expected_rows)
+
+        df.to_csv(sys.stdout, encoding="ascii")
+        captured = capsys.readouterr()
+
+        assert captured.out == expected_ascii
+        assert not sys.stdout.closed
+
+    @pytest.mark.xfail(
+        compat.is_platform_windows(),
+        reason=(
+            "Especially in Windows, file stream should not be passed"
+            "to csv writer without newline='' option."
+            "(https://docs.python.org/3/library/csv.html#csv.writer)"
+        ),
+    )
+    def test_to_csv_write_to_open_file(self, temp_file):
+        # GH 21696
+        df = DataFrame({"a": ["x", "y", "z"]})
+        expected = """\
+manual header
+x
+y
+z
+"""
+        with open(temp_file, "w", encoding="utf-8") as f:
+            f.write("manual header\n")
+            df.to_csv(f, header=None, index=None)
+        with open(temp_file, encoding="utf-8") as f:
+            assert f.read() == expected
+
+    def test_to_csv_write_to_open_file_with_newline_py3(self, temp_file):
+        # see gh-21696
+        # see gh-20353
+        df = DataFrame({"a": ["x", "y", "z"]})
+        expected_rows = ["x", "y", "z"]
+        expected = "manual header\n" + tm.convert_rows_list_to_csv_str(expected_rows)
+
+        with open(temp_file, "w", newline="", encoding="utf-8") as f:
+            f.write("manual header\n")
+            df.to_csv(f, header=None, index=None)
+
+        with open(temp_file, "rb") as f:
+            assert f.read() == bytes(expected, "utf-8")
+
+    @pytest.mark.parametrize("to_infer", [True, False])
+    @pytest.mark.parametrize("read_infer", [True, False])
+    def test_to_csv_compression(
+        self,
+        compression_only,
+        read_infer,
+        to_infer,
+        compression_to_extension,
+        temp_file,
+    ):
+        # see gh-15008
+        compression = compression_only
+
+        df = DataFrame({"A": [1]})
+
+        to_compression = "infer" if to_infer else compression
+        read_compression = "infer" if read_infer else compression
+
+        path_ext = str(temp_file) + "." + compression_to_extension[compression]
+        df.to_csv(path_ext, compression=to_compression)
+        result = pd.read_csv(path_ext, index_col=0, compression=read_compression)
+        tm.assert_frame_equal(result, df)
+
+    def test_to_csv_compression_dict(self, compression_only, temp_file):
+        # GH 26023
+        method = compression_only
+        df = DataFrame({"ABC": [1]})
+        extension = {
+            "gzip": "gz",
+            "zstd": "zst",
+        }.get(method, method)
+
+        path = str(temp_file) + "." + extension
+        df.to_csv(path, compression={"method": method})
+        read_df = pd.read_csv(path, index_col=0)
+        tm.assert_frame_equal(read_df, df)
+
+    def test_to_csv_compression_dict_no_method_raises(self, temp_file):
+        # GH 26023
+        df = DataFrame({"ABC": [1]})
+        compression = {"some_option": True}
+        msg = "must have key 'method'"
+
+        with pytest.raises(ValueError, match=msg):
+            df.to_csv(temp_file, compression=compression)
+
+    @pytest.mark.parametrize("compression", ["zip", "infer"])
+    @pytest.mark.parametrize("archive_name", ["test_to_csv.csv", "test_to_csv.zip"])
+    def test_to_csv_zip_arguments(self, compression, archive_name, temp_file):
+        # GH 26023
+        df = DataFrame({"ABC": [1]})
+
+        path = str(temp_file) + ".zip"
+        df.to_csv(
+            path, compression={"method": compression, "archive_name": archive_name}
+        )
+        with ZipFile(path) as zp:
+            assert len(zp.filelist) == 1
+            archived_file = zp.filelist[0].filename
+            assert archived_file == archive_name
+
+    @pytest.mark.parametrize(
+        "filename,expected_arcname",
+        [
+            ("archive.csv", "archive.csv"),
+            ("archive.tsv", "archive.tsv"),
+            ("archive.csv.zip", "archive.csv"),
+            ("archive.tsv.zip", "archive.tsv"),
+            ("archive.zip", "archive"),
+        ],
+    )
+    def test_to_csv_zip_infer_name(self, tmp_path, filename, expected_arcname):
+        # GH 39465
+        df = DataFrame({"ABC": [1]})
+        path = tmp_path / filename
+        df.to_csv(path, compression="zip")
+        with ZipFile(path) as zp:
+            assert len(zp.filelist) == 1
+            archived_file = zp.filelist[0].filename
+            assert archived_file == expected_arcname
+
+    @pytest.mark.parametrize("df_new_type", ["Int64"])
+    def test_to_csv_na_rep_long_string(self, df_new_type):
+        # see gh-25099
+        df = DataFrame({"c": [pd.NA] * 3})
+        df = df.astype(df_new_type)
+        expected_rows = ["c", "mynull", "mynull", "mynull"]
+        expected = tm.convert_rows_list_to_csv_str(expected_rows)
+
+        result = df.to_csv(index=False, na_rep="mynull", encoding="ascii")
+
+        assert expected == result
+
+    def test_to_csv_timedelta_precision(self):
+        # GH 6783
+        s = pd.Series([1, 1]).astype("timedelta64[ns]")
+        buf = io.StringIO()
+        s.to_csv(buf)
+        result = buf.getvalue()
+        expected_rows = [
+            ",0",
+            "0,0 days 00:00:00.000000001",
+            "1,0 days 00:00:00.000000001",
+        ]
+        expected = tm.convert_rows_list_to_csv_str(expected_rows)
+        assert result == expected
+
+    def test_na_rep_truncated(self):
+        # https://github.com/pandas-dev/pandas/issues/31447
+        result = pd.Series(range(8, 12)).to_csv(na_rep="-")
+        expected = tm.convert_rows_list_to_csv_str([",0", "0,8", "1,9", "2,10", "3,11"])
+        assert result == expected
+
+        result = pd.Series([True, False]).to_csv(na_rep="nan")
+        expected = tm.convert_rows_list_to_csv_str([",0", "0,True", "1,False"])
+        assert result == expected
+
+        result = pd.Series([1.1, 2.2]).to_csv(na_rep=".")
+        expected = tm.convert_rows_list_to_csv_str([",0", "0,1.1", "1,2.2"])
+        assert result == expected
+
+    @pytest.mark.parametrize("errors", ["surrogatepass", "ignore", "replace"])
+    def test_to_csv_errors(self, errors, temp_file):
+        # GH 22610
+        data = ["\ud800foo"]
+        ser = pd.Series(data, index=Index(data, dtype=object), dtype=object)
+
+        ser.to_csv(temp_file, errors=errors)
+        # No use in reading back the data as it is not the same anymore
+        # due to the error handling
+
+    @pytest.mark.parametrize("mode", ["wb", "w"])
+    def test_to_csv_binary_handle(self, mode, temp_file):
+        """
+        Binary file objects should work (if 'mode' contains a 'b') or even without
+        it in most cases.
+
+        GH 35058 and GH 19827
+        """
+        df = DataFrame(
+            1.1 * np.arange(120).reshape((30, 4)),
+            columns=Index(list("ABCD")),
+            index=Index([f"i-{i}" for i in range(30)]),
+        )
+
+        with open(temp_file, mode="w+b") as handle:
+            df.to_csv(handle, mode=mode)
+        tm.assert_frame_equal(df, pd.read_csv(temp_file, index_col=0))
+
+    @pytest.mark.parametrize("mode", ["wb", "w"])
+    def test_to_csv_encoding_binary_handle(self, mode, temp_file):
+        """
+        Binary file objects should honor a specified encoding.
+
+        GH 23854 and GH 13068 with binary handles
+        """
+        # example from GH 23854
+        content = "a, b, 🐟".encode("utf-8-sig")
+        buffer = io.BytesIO(content)
+        df = pd.read_csv(buffer, encoding="utf-8-sig")
+
+        buffer = io.BytesIO()
+        df.to_csv(buffer, mode=mode, encoding="utf-8-sig", index=False)
+        buffer.seek(0)  # tests whether file handle wasn't closed
+        assert buffer.getvalue().startswith(content)
+
+        # example from GH 13068
+        with open(temp_file, "w+b") as handle:
+            DataFrame().to_csv(handle, mode=mode, encoding="utf-8-sig")
+
+            handle.seek(0)
+            assert handle.read().startswith(b'\xef\xbb\xbf""')
+
+
+def test_to_csv_iterative_compression_name(compression, temp_file):
+    # GH 38714
+    df = DataFrame(
+        1.1 * np.arange(120).reshape((30, 4)),
+        columns=Index(list("ABCD")),
+        index=Index([f"i-{i}" for i in range(30)]),
+    )
+    df.to_csv(temp_file, compression=compression, chunksize=1)
+    tm.assert_frame_equal(
+        pd.read_csv(temp_file, compression=compression, index_col=0), df
+    )
+
+
+def test_to_csv_iterative_compression_buffer(compression):
+    # GH 38714
+    df = DataFrame(
+        1.1 * np.arange(120).reshape((30, 4)),
+        columns=Index(list("ABCD")),
+        index=Index([f"i-{i}" for i in range(30)]),
+    )
+    with io.BytesIO() as buffer:
+        df.to_csv(buffer, compression=compression, chunksize=1)
+        buffer.seek(0)
+        tm.assert_frame_equal(
+            pd.read_csv(buffer, compression=compression, index_col=0), df
+        )
+        assert not buffer.closed
+
+
+def test_new_style_float_format_basic():
+    df = DataFrame({"A": [1234.56789, 9876.54321]})
+    result = df.to_csv(float_format="{:.2f}", lineterminator="\n")
+    expected = ",A\n0,1234.57\n1,9876.54\n"
+    assert result == expected
+
+
+def test_new_style_float_format_thousands():
+    df = DataFrame({"A": [1234.56789, 9876.54321]})
+    result = df.to_csv(float_format="{:,.2f}", lineterminator="\n")
+    expected = ',A\n0,"1,234.57"\n1,"9,876.54"\n'
+    assert result == expected
+
+
+def test_new_style_scientific_format():
+    df = DataFrame({"A": [0.000123, 0.000456]})
+    result = df.to_csv(float_format="{:.2e}", lineterminator="\n")
+    expected = ",A\n0,1.23e-04\n1,4.56e-04\n"
+    assert result == expected
+
+
+def test_new_style_with_nan():
+    df = DataFrame({"A": [1.23, np.nan, 4.56]})
+    result = df.to_csv(float_format="{:.2f}", na_rep="NA", lineterminator="\n")
+    expected = ",A\n0,1.23\n1,NA\n2,4.56\n"
+    assert result == expected
+
+
+def test_new_style_with_mixed_types():
+    df = DataFrame({"A": [1.23, 4.56], "B": ["x", "y"]})
+    result = df.to_csv(float_format="{:.2f}", lineterminator="\n")
+    expected = ",A,B\n0,1.23,x\n1,4.56,y\n"
+    assert result == expected
+
+
+def test_new_style_with_mixed_types_in_column():
+    df = DataFrame({"A": [1.23, "text", 4.56]})
+    result = df.to_csv(float_format="{:.2f}", lineterminator="\n")
+    expected = ",A\n0,1.23\n1,text\n2,4.56\n"
+    assert result == expected
+
+
+def test_invalid_new_style_format_missing_brace():
+    df = DataFrame({"A": [1.23]})
+    with pytest.raises(ValueError, match="Invalid new-style format string '{:.2f"):
+        df.to_csv(float_format="{:.2f")
+
+
+def test_invalid_new_style_format_specifier():
+    df = DataFrame({"A": [1.23]})
+    with pytest.raises(ValueError, match="Invalid new-style format string '{:.2z}'"):
+        df.to_csv(float_format="{:.2z}")
+
+
+def test_old_style_format_compatibility():
+    df = DataFrame({"A": [1234.56789, 9876.54321]})
+    result = df.to_csv(float_format="%.2f", lineterminator="\n")
+    expected = ",A\n0,1234.57\n1,9876.54\n"
+    assert result == expected
+
+
+def test_callable_float_format_compatibility():
+    df = DataFrame({"A": [1234.56789, 9876.54321]})
+    result = df.to_csv(float_format=lambda x: f"{x:,.2f}", lineterminator="\n")
+    expected = ',A\n0,"1,234.57"\n1,"9,876.54"\n'
+    assert result == expected
+
+
+def test_no_float_format():
+    df = DataFrame({"A": [1.23, 4.56]})
+    result = df.to_csv(float_format=None, lineterminator="\n")
+    expected = ",A\n0,1.23\n1,4.56\n"
+    assert result == expected
+
+
+def test_large_numbers():
+    df = DataFrame({"A": [1e308, 2e308]})
+    result = df.to_csv(float_format="{:.2e}", lineterminator="\n")
+    expected = ",A\n0,1.00e+308\n1,inf\n"
+    assert result == expected
+
+
+def test_zero_and_negative():
+    df = DataFrame({"A": [0.0, -1.23456]})
+    result = df.to_csv(float_format="{:+.2f}", lineterminator="\n")
+    expected = ",A\n0,+0.00\n1,-1.23\n"
+    assert result == expected
+
+
+def test_unicode_format():
+    df = DataFrame({"A": [1.23, 4.56]})
+    result = df.to_csv(float_format="{:.2f}€", encoding="utf-8", lineterminator="\n")
+    expected = ",A\n0,1.23€\n1,4.56€\n"
+    assert result == expected
+
+
+def test_empty_dataframe():
+    df = DataFrame({"A": []})
+    result = df.to_csv(float_format="{:.2f}", lineterminator="\n")
+    expected = ",A\n"
+    assert result == expected
+
+
+def test_multi_column_float():
+    df = DataFrame({"A": [1.23, 4.56], "B": [7.89, 0.12]})
+    result = df.to_csv(float_format="{:.2f}", lineterminator="\n")
+    expected = ",A,B\n0,1.23,7.89\n1,4.56,0.12\n"
+    assert result == expected
+
+
+def test_invalid_float_format_type():
+    df = DataFrame({"A": [1.23]})
+    with pytest.raises(ValueError, match="float_format must be a string or callable"):
+        df.to_csv(float_format=123)
+
+
+def test_new_style_with_inf():
+    df = DataFrame({"A": [1.23, np.inf, -np.inf]})
+    result = df.to_csv(float_format="{:.2f}", na_rep="NA", lineterminator="\n")
+    expected = ",A\n0,1.23\n1,inf\n2,-inf\n"
+    assert result == expected
+
+
+def test_new_style_with_precision_edge():
+    df = DataFrame({"A": [1.23456789]})
+    result = df.to_csv(float_format="{:.10f}", lineterminator="\n")
+    expected = ",A\n0,1.2345678900\n"
+    assert result == expected
+
+
+def test_new_style_with_template():
+    df = DataFrame({"A": [1234.56789]})
+    result = df.to_csv(float_format="Value: {:,.2f}", lineterminator="\n")
+    expected = ',A\n0,"Value: 1,234.57"\n'
+    assert result == expected

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/test_to_excel.py

@@ -0,0 +1,473 @@
+"""Tests formatting as writer-agnostic ExcelCells
+
+ExcelFormatter is tested implicitly in pandas/tests/io/excel
+"""
+
+import string
+
+import pytest
+
+from pandas.errors import CSSWarning
+
+import pandas._testing as tm
+
+from pandas.io.formats.excel import (
+    CssExcelCell,
+    CSSToExcelConverter,
+)
+
+
+@pytest.mark.parametrize(
+    "css,expected",
+    [
+        # FONT
+        # - name
+        ("font-family: foo,bar", {"font": {"name": "foo"}}),
+        ('font-family: "foo bar",baz', {"font": {"name": "foo bar"}}),
+        ("font-family: foo,\nbar", {"font": {"name": "foo"}}),
+        ("font-family: foo, bar,    baz", {"font": {"name": "foo"}}),
+        ("font-family: bar, foo", {"font": {"name": "bar"}}),
+        ("font-family: 'foo bar', baz", {"font": {"name": "foo bar"}}),
+        ("font-family: 'foo \\'bar', baz", {"font": {"name": "foo 'bar"}}),
+        ('font-family: "foo \\"bar", baz', {"font": {"name": 'foo "bar'}}),
+        ('font-family: "foo ,bar", baz', {"font": {"name": "foo ,bar"}}),
+        # - family
+        ("font-family: serif", {"font": {"name": "serif", "family": 1}}),
+        ("font-family: Serif", {"font": {"name": "serif", "family": 1}}),
+        ("font-family: roman, serif", {"font": {"name": "roman", "family": 1}}),
+        ("font-family: roman, sans-serif", {"font": {"name": "roman", "family": 2}}),
+        ("font-family: roman, sans serif", {"font": {"name": "roman"}}),
+        ("font-family: roman, sansserif", {"font": {"name": "roman"}}),
+        ("font-family: roman, cursive", {"font": {"name": "roman", "family": 4}}),
+        ("font-family: roman, fantasy", {"font": {"name": "roman", "family": 5}}),
+        # - size
+        ("font-size: 1em", {"font": {"size": 12}}),
+        ("font-size: xx-small", {"font": {"size": 6}}),
+        ("font-size: x-small", {"font": {"size": 7.5}}),
+        ("font-size: small", {"font": {"size": 9.6}}),
+        ("font-size: medium", {"font": {"size": 12}}),
+        ("font-size: large", {"font": {"size": 13.5}}),
+        ("font-size: x-large", {"font": {"size": 18}}),
+        ("font-size: xx-large", {"font": {"size": 24}}),
+        ("font-size: 50%", {"font": {"size": 6}}),
+        # - bold
+        ("font-weight: 100", {"font": {"bold": False}}),
+        ("font-weight: 200", {"font": {"bold": False}}),
+        ("font-weight: 300", {"font": {"bold": False}}),
+        ("font-weight: 400", {"font": {"bold": False}}),
+        ("font-weight: normal", {"font": {"bold": False}}),
+        ("font-weight: lighter", {"font": {"bold": False}}),
+        ("font-weight: bold", {"font": {"bold": True}}),
+        ("font-weight: bolder", {"font": {"bold": True}}),
+        ("font-weight: 700", {"font": {"bold": True}}),
+        ("font-weight: 800", {"font": {"bold": True}}),
+        ("font-weight: 900", {"font": {"bold": True}}),
+        # - italic
+        ("font-style: italic", {"font": {"italic": True}}),
+        ("font-style: oblique", {"font": {"italic": True}}),
+        # - underline
+        ("text-decoration: underline", {"font": {"underline": "single"}}),
+        ("text-decoration: overline", {}),
+        ("text-decoration: none", {}),
+        # - strike
+        ("text-decoration: line-through", {"font": {"strike": True}}),
+        (
+            "text-decoration: underline line-through",
+            {"font": {"strike": True, "underline": "single"}},
+        ),
+        (
+            "text-decoration: underline; text-decoration: line-through",
+            {"font": {"strike": True}},
+        ),
+        # - color
+        ("color: red", {"font": {"color": "FF0000"}}),
+        ("color: #ff0000", {"font": {"color": "FF0000"}}),
+        ("color: #f0a", {"font": {"color": "FF00AA"}}),
+        # - shadow
+        ("text-shadow: none", {"font": {"shadow": False}}),
+        ("text-shadow: 0px -0em 0px #CCC", {"font": {"shadow": False}}),
+        ("text-shadow: 0px -0em 0px #999", {"font": {"shadow": False}}),
+        ("text-shadow: 0px -0em 0px", {"font": {"shadow": False}}),
+        ("text-shadow: 2px -0em 0px #CCC", {"font": {"shadow": True}}),
+        ("text-shadow: 0px -2em 0px #CCC", {"font": {"shadow": True}}),
+        ("text-shadow: 0px -0em 2px #CCC", {"font": {"shadow": True}}),
+        ("text-shadow: 0px -0em 2px", {"font": {"shadow": True}}),
+        ("text-shadow: 0px -2em", {"font": {"shadow": True}}),
+        # FILL
+        # - color, fillType
+        (
+            "background-color: red",
+            {"fill": {"fgColor": "FF0000", "patternType": "solid"}},
+        ),
+        (
+            "background-color: #ff0000",
+            {"fill": {"fgColor": "FF0000", "patternType": "solid"}},
+        ),
+        (
+            "background-color: #f0a",
+            {"fill": {"fgColor": "FF00AA", "patternType": "solid"}},
+        ),
+        # BORDER
+        # - style
+        (
+            "border-style: solid",
+            {
+                "border": {
+                    "top": {"style": "medium"},
+                    "bottom": {"style": "medium"},
+                    "left": {"style": "medium"},
+                    "right": {"style": "medium"},
+                }
+            },
+        ),
+        (
+            "border-style: solid; border-width: thin",
+            {
+                "border": {
+                    "top": {"style": "thin"},
+                    "bottom": {"style": "thin"},
+                    "left": {"style": "thin"},
+                    "right": {"style": "thin"},
+                }
+            },
+        ),
+        (
+            "border-top-style: solid; border-top-width: thin",
+            {"border": {"top": {"style": "thin"}}},
+        ),
+        (
+            "border-top-style: solid; border-top-width: 1pt",
+            {"border": {"top": {"style": "thin"}}},
+        ),
+        ("border-top-style: solid", {"border": {"top": {"style": "medium"}}}),
+        (
+            "border-top-style: solid; border-top-width: medium",
+            {"border": {"top": {"style": "medium"}}},
+        ),
+        (
+            "border-top-style: solid; border-top-width: 2pt",
+            {"border": {"top": {"style": "medium"}}},
+        ),
+        (
+            "border-top-style: solid; border-top-width: thick",
+            {"border": {"top": {"style": "thick"}}},
+        ),
+        (
+            "border-top-style: solid; border-top-width: 4pt",
+            {"border": {"top": {"style": "thick"}}},
+        ),
+        (
+            "border-top-style: solid; border-top-width: none",
+            {"border": {"top": {"style": "none"}}},
+        ),
+        (
+            "border-top-style: solid; border-top-width: 0.000001pt",
+            {"border": {"top": {"style": "none"}}},
+        ),
+        (
+            "border-top-style: dotted",
+            {"border": {"top": {"style": "mediumDashDotDot"}}},
+        ),
+        (
+            "border-top-style: dotted; border-top-width: thin",
+            {"border": {"top": {"style": "dotted"}}},
+        ),
+        ("border-top-style: dashed", {"border": {"top": {"style": "mediumDashed"}}}),
+        (
+            "border-top-style: dashed; border-top-width: thin",
+            {"border": {"top": {"style": "dashed"}}},
+        ),
+        ("border-top-style: double", {"border": {"top": {"style": "double"}}}),
+        # - color
+        (
+            "border-style: solid; border-color: #0000ff",
+            {
+                "border": {
+                    "top": {"style": "medium", "color": "0000FF"},
+                    "right": {"style": "medium", "color": "0000FF"},
+                    "bottom": {"style": "medium", "color": "0000FF"},
+                    "left": {"style": "medium", "color": "0000FF"},
+                }
+            },
+        ),
+        (
+            "border-top-style: double; border-top-color: blue",
+            {"border": {"top": {"style": "double", "color": "0000FF"}}},
+        ),
+        (
+            "border-top-style: solid; border-top-color: #06c",
+            {"border": {"top": {"style": "medium", "color": "0066CC"}}},
+        ),
+        (
+            "border-top-color: blue",
+            {"border": {"top": {"color": "0000FF", "style": "none"}}},
+        ),
+        (
+            "border-top-style: slantDashDot; border-top-color: blue",
+            {"border": {"top": {"style": "slantDashDot", "color": "0000FF"}}},
+        ),
+        # ALIGNMENT
+        # - horizontal
+        ("text-align: center", {"alignment": {"horizontal": "center"}}),
+        ("text-align: left", {"alignment": {"horizontal": "left"}}),
+        ("text-align: right", {"alignment": {"horizontal": "right"}}),
+        ("text-align: justify", {"alignment": {"horizontal": "justify"}}),
+        # - vertical
+        ("vertical-align: top", {"alignment": {"vertical": "top"}}),
+        ("vertical-align: text-top", {"alignment": {"vertical": "top"}}),
+        ("vertical-align: middle", {"alignment": {"vertical": "center"}}),
+        ("vertical-align: bottom", {"alignment": {"vertical": "bottom"}}),
+        ("vertical-align: text-bottom", {"alignment": {"vertical": "bottom"}}),
+        # - wrap_text
+        ("white-space: nowrap", {"alignment": {"wrap_text": False}}),
+        ("white-space: pre", {"alignment": {"wrap_text": False}}),
+        ("white-space: pre-line", {"alignment": {"wrap_text": False}}),
+        ("white-space: normal", {"alignment": {"wrap_text": True}}),
+        # NUMBER FORMAT
+        ("number-format: 0%", {"number_format": {"format_code": "0%"}}),
+        (
+            "number-format: 0§[Red](0)§-§@;",
+            {"number_format": {"format_code": "0;[red](0);-;@"}},  # GH 46152
+        ),
+    ],
+)
+def test_css_to_excel(css, expected):
+    convert = CSSToExcelConverter()
+    assert expected == convert(css)
+
+
+def test_css_to_excel_multiple():
+    convert = CSSToExcelConverter()
+    actual = convert(
+        """
+        font-weight: bold;
+        text-decoration: underline;
+        color: red;
+        border-width: thin;
+        text-align: center;
+        vertical-align: top;
+        unused: something;
+    """
+    )
+    assert {
+        "font": {"bold": True, "underline": "single", "color": "FF0000"},
+        "border": {
+            "top": {"style": "thin"},
+            "right": {"style": "thin"},
+            "bottom": {"style": "thin"},
+            "left": {"style": "thin"},
+        },
+        "alignment": {"horizontal": "center", "vertical": "top"},
+    } == actual
+
+
+@pytest.mark.parametrize(
+    "css",
+    [
+        "border-top-style: unhandled-border-style",
+        "border-style: another-unhandled-style",
+    ],
+)
+def test_css_to_excel_unhandled_border_style_warns(css):
+    """Test that unhandled border styles raise a CSSWarning."""
+    convert = CSSToExcelConverter()
+    with tm.assert_produces_warning(CSSWarning, match="Unhandled border style format"):
+        convert(css)
+
+
+@pytest.mark.parametrize(
+    "css,inherited,expected",
+    [
+        ("font-weight: bold", "", {"font": {"bold": True}}),
+        ("", "font-weight: bold", {"font": {"bold": True}}),
+        (
+            "font-weight: bold",
+            "font-style: italic",
+            {"font": {"bold": True, "italic": True}},
+        ),
+        ("font-style: normal", "font-style: italic", {"font": {"italic": False}}),
+        ("font-style: inherit", "", {}),
+        (
+            "font-style: normal; font-style: inherit",
+            "font-style: italic",
+            {"font": {"italic": True}},
+        ),
+    ],
+)
+def test_css_to_excel_inherited(css, inherited, expected):
+    convert = CSSToExcelConverter(inherited)
+    assert expected == convert(css)
+
+
+@pytest.mark.parametrize(
+    "input_color,output_color",
+    (
+        list(CSSToExcelConverter.NAMED_COLORS.items())
+        + [("#" + rgb, rgb) for rgb in CSSToExcelConverter.NAMED_COLORS.values()]
+        + [("#F0F", "FF00FF"), ("#ABC", "AABBCC")]
+    ),
+)
+def test_css_to_excel_good_colors(input_color, output_color):
+    # see gh-18392
+    css = (
+        f"border-top-color: {input_color}; "
+        f"border-right-color: {input_color}; "
+        f"border-bottom-color: {input_color}; "
+        f"border-left-color: {input_color}; "
+        f"background-color: {input_color}; "
+        f"color: {input_color}"
+    )
+
+    expected = {}
+
+    expected["fill"] = {"patternType": "solid", "fgColor": output_color}
+
+    expected["font"] = {"color": output_color}
+
+    expected["border"] = {
+        k: {"color": output_color, "style": "none"}
+        for k in ("top", "right", "bottom", "left")
+    }
+
+    with tm.assert_produces_warning(None):
+        convert = CSSToExcelConverter()
+        assert expected == convert(css)
+
+
+@pytest.mark.parametrize("input_color", [None, "not-a-color"])
+def test_css_to_excel_bad_colors(input_color):
+    # see gh-18392
+    css = (
+        f"border-top-color: {input_color}; "
+        f"border-right-color: {input_color}; "
+        f"border-bottom-color: {input_color}; "
+        f"border-left-color: {input_color}; "
+        f"background-color: {input_color}; "
+        f"color: {input_color}"
+    )
+
+    expected = {}
+
+    if input_color is not None:
+        expected["fill"] = {"patternType": "solid"}
+
+    with tm.assert_produces_warning(CSSWarning, match="Unhandled color format"):
+        convert = CSSToExcelConverter()
+        assert expected == convert(css)
+
+
+@pytest.mark.parametrize("input_color", ["#", "#1234567"])
+def test_css_to_excel_invalid_color_raises(input_color):
+    """Test that invalid colors raise a ValueError."""
+    css = (
+        f"border-top-color: {input_color}; "
+        f"border-right-color: {input_color}; "
+        f"border-bottom-color: {input_color}; "
+        f"border-left-color: {input_color}; "
+        f"background-color: {input_color}; "
+        f"color: {input_color}"
+    )
+
+    convert = CSSToExcelConverter()
+    with pytest.raises(ValueError, match=f"Unexpected color {input_color}"):
+        convert(css)
+
+
+def tests_css_named_colors_valid():
+    upper_hexs = set(map(str.upper, string.hexdigits))
+    for color in CSSToExcelConverter.NAMED_COLORS.values():
+        assert len(color) == 6 and all(c in upper_hexs for c in color)
+
+
+def test_css_named_colors_from_mpl_present():
+    mpl_colors = pytest.importorskip("matplotlib.colors")
+
+    pd_colors = CSSToExcelConverter.NAMED_COLORS
+    for name, color in mpl_colors.CSS4_COLORS.items():
+        assert name in pd_colors and pd_colors[name] == color[1:]
+
+
+@pytest.mark.parametrize(
+    "styles,expected",
+    [
+        ([("color", "green"), ("color", "red")], "color: red;"),
+        ([("font-weight", "bold"), ("font-weight", "normal")], "font-weight: normal;"),
+        ([("text-align", "center"), ("TEXT-ALIGN", "right")], "text-align: right;"),
+    ],
+)
+def test_css_excel_cell_precedence(styles, expected):
+    """It applies favors latter declarations over former declarations"""
+    # See GH 47371
+    converter = CSSToExcelConverter()
+    converter._call_cached.cache_clear()
+    css_styles = {(0, 0): styles}
+    cell = CssExcelCell(
+        row=0,
+        col=0,
+        val="",
+        style=None,
+        css_styles=css_styles,
+        css_row=0,
+        css_col=0,
+        css_converter=converter,
+    )
+    converter._call_cached.cache_clear()
+
+    assert cell.style == converter(expected)
+
+
+@pytest.mark.parametrize(
+    "styles,cache_hits,cache_misses",
+    [
+        ([[("color", "green"), ("color", "red"), ("color", "green")]], 0, 1),
+        (
+            [
+                [("font-weight", "bold")],
+                [("font-weight", "normal"), ("font-weight", "bold")],
+            ],
+            1,
+            1,
+        ),
+        ([[("text-align", "center")], [("TEXT-ALIGN", "center")]], 1, 1),
+        (
+            [
+                [("font-weight", "bold"), ("text-align", "center")],
+                [("font-weight", "bold"), ("text-align", "left")],
+            ],
+            0,
+            2,
+        ),
+        (
+            [
+                [("font-weight", "bold"), ("text-align", "center")],
+                [("font-weight", "bold"), ("text-align", "left")],
+                [("font-weight", "bold"), ("text-align", "center")],
+            ],
+            1,
+            2,
+        ),
+    ],
+)
+def test_css_excel_cell_cache(styles, cache_hits, cache_misses):
+    """It caches unique cell styles"""
+    # See GH 47371
+    converter = CSSToExcelConverter()
+    converter._call_cached.cache_clear()
+
+    css_styles = {(0, i): _style for i, _style in enumerate(styles)}
+    for css_row, css_col in css_styles:
+        CssExcelCell(
+            row=0,
+            col=0,
+            val="",
+            style=None,
+            css_styles=css_styles,
+            css_row=css_row,
+            css_col=css_col,
+            css_converter=converter,
+        )
+    cache_info = converter._call_cached.cache_info()
+    converter._call_cached.cache_clear()
+
+    assert cache_info.hits == cache_hits
+    assert cache_info.misses == cache_misses

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/formats/test_to_markdown.py

@@ -0,0 +1,102 @@
+from io import StringIO
+
+import pytest
+
+from pandas.errors import Pandas4Warning
+
+import pandas as pd
+import pandas._testing as tm
+
+pytest.importorskip("tabulate")
+
+
+def test_keyword_deprecation():
+    # GH 57280
+    msg = (
+        "Starting with pandas version 4.0 all arguments of to_markdown "
+        "except for the argument 'buf' will be keyword-only."
+    )
+    s = pd.Series()
+    with tm.assert_produces_warning(Pandas4Warning, match=msg):
+        s.to_markdown(None, "wt")
+
+
+def test_simple():
+    buf = StringIO()
+    df = pd.DataFrame([1, 2, 3])
+    df.to_markdown(buf=buf)
+    result = buf.getvalue()
+    assert (
+        result == "|    |   0 |\n|---:|----:|\n|  0 |   1 |\n|  1 |   2 |\n|  2 |   3 |"
+    )
+
+
+def test_empty_frame():
+    buf = StringIO()
+    df = pd.DataFrame({"id": [], "first_name": [], "last_name": []}).set_index("id")
+    df.to_markdown(buf=buf)
+    result = buf.getvalue()
+    assert result == (
+        "| id   | first_name   | last_name   |\n|------|--------------|-------------|"
+    )
+
+
+def test_other_tablefmt():
+    buf = StringIO()
+    df = pd.DataFrame([1, 2, 3])
+    df.to_markdown(buf=buf, tablefmt="jira")
+    result = buf.getvalue()
+    assert result == "||    ||   0 ||\n|  0 |   1 |\n|  1 |   2 |\n|  2 |   3 |"
+
+
+def test_other_headers():
+    buf = StringIO()
+    df = pd.DataFrame([1, 2, 3])
+    df.to_markdown(buf=buf, headers=["foo", "bar"])
+    result = buf.getvalue()
+    assert result == (
+        "|   foo |   bar |\n|------:|------:|\n|     0 "
+        "|     1 |\n|     1 |     2 |\n|     2 |     3 |"
+    )
+
+
+def test_series():
+    buf = StringIO()
+    s = pd.Series([1, 2, 3], name="foo")
+    s.to_markdown(buf=buf)
+    result = buf.getvalue()
+    assert result == (
+        "|    |   foo |\n|---:|------:|\n|  0 |     1 |\n|  1 |     2 |\n|  2 |     3 |"
+    )
+
+
+def test_no_buf():
+    df = pd.DataFrame([1, 2, 3])
+    result = df.to_markdown()
+    assert (
+        result == "|    |   0 |\n|---:|----:|\n|  0 |   1 |\n|  1 |   2 |\n|  2 |   3 |"
+    )
+
+
+@pytest.mark.parametrize("index", [True, False])
+def test_index(index):
+    # GH 32667
+
+    df = pd.DataFrame([1, 2, 3])
+
+    result = df.to_markdown(index=index)
+
+    if index:
+        expected = (
+            "|    |   0 |\n|---:|----:|\n|  0 |   1 |\n|  1 |   2 |\n|  2 |   3 |"
+        )
+    else:
+        expected = "|   0 |\n|----:|\n|   1 |\n|   2 |\n|   3 |"
+    assert result == expected
+
+
+def test_showindex_disallowed_in_kwargs():
+    # GH 32667; disallowing showindex in kwargs enforced in 2.0
+    df = pd.DataFrame([1, 2, 3])
+    with pytest.raises(ValueError, match="Pass 'index' instead of 'showindex"):
+        df.to_markdown(index=True, showindex=True)

dashboard/venv/lib/python3.12/site-packages/pandas/tests/io/generate_legacy_storage_files.py

@@ -0,0 +1,421 @@
+"""
+self-contained to write legacy storage pickle files
+
+To use this script. Create an environment where you want
+generate pickles, say its for 0.20.3, with your pandas clone
+in ~/pandas
+
+. activate pandas_0.20.3
+cd ~/pandas/pandas
+
+$ python -m tests.io.generate_legacy_storage_files \
+    tests/io/data/legacy_pickle/0.20.3/ pickle
+
+This script generates a storage file for the current arch, system,
+and python version
+  pandas version: 0.20.3
+  output dir    : pandas/pandas/tests/io/data/legacy_pickle/0.20.3/
+  storage format: pickle
+created pickle file: 0.20.3_x86_64_darwin_3.5.2.pickle
+
+The idea here is you are using the *current* version of the
+generate_legacy_storage_files with an *older* version of pandas to
+generate a pickle file. We will then check this file into a current
+branch, and test using test_pickle.py. This will load the *older*
+pickles and test versus the current data that is generated
+(with main). These are then compared.
+
+If we have cases where we changed the signature (e.g. we renamed
+offset -> freq in Timestamp). Then we have to conditionally execute
+in the generate_legacy_storage_files.py to make it
+run under the older AND the newer version.
+
+"""
+
+from datetime import timedelta
+import os
+import pickle
+import platform as pl
+import sys
+
+# Remove script directory from path, otherwise Python will try to
+# import the JSON test directory as the json module
+sys.path.pop(0)
+
+import numpy as np
+
+import pandas
+from pandas import (
+    Categorical,
+    DataFrame,
+    Index,
+    MultiIndex,
+    NaT,
+    Period,
+    RangeIndex,
+    Series,
+    Timestamp,
+    bdate_range,
+    date_range,
+    interval_range,
+    period_range,
+    timedelta_range,
+)
+from pandas.arrays import SparseArray
+
+from pandas.tseries.offsets import (
+    FY5253,
+    BusinessDay,
+    BusinessHour,
+    CustomBusinessDay,
+    DateOffset,
+    Day,
+    Easter,
+    Hour,
+    LastWeekOfMonth,
+    Minute,
+    MonthBegin,
+    MonthEnd,
+    QuarterBegin,
+    QuarterEnd,
+    SemiMonthBegin,
+    SemiMonthEnd,
+    Week,
+    WeekOfMonth,
+    YearBegin,
+    YearEnd,
+)
+
+
+def _create_sp_series():
+    nan = np.nan
+
+    # nan-based
+    arr = np.arange(15, dtype=np.float64)
+    arr[7:12] = nan
+    arr[-1:] = nan
+
+    bseries = Series(SparseArray(arr, kind="block"))
+    bseries.name = "bseries"
+    return bseries
+
+
+def _create_sp_tsseries():
+    nan = np.nan
+
+    # nan-based
+    arr = np.arange(15, dtype=np.float64)
+    arr[7:12] = nan
+    arr[-1:] = nan
+
+    date_index = bdate_range("1/1/2011", periods=len(arr))
+    bseries = Series(SparseArray(arr, kind="block"), index=date_index)
+    bseries.name = "btsseries"
+    return bseries
+
+
+def _create_sp_frame():
+    nan = np.nan
+
+    data = {
+        "A": [nan, nan, nan, 0, 1, 2, 3, 4, 5, 6],
+        "B": [0, 1, 2, nan, nan, nan, 3, 4, 5, 6],
+        "C": np.arange(10).astype(np.int64),
+        "D": [0, 1, 2, 3, 4, 5, nan, nan, nan, nan],
+    }
+
+    dates = bdate_range("1/1/2011", periods=10)
+    return DataFrame(data, index=dates).apply(SparseArray)
+
+
+def create_pickle_data(test: bool = True):
+    """create the pickle data"""
+    data = {
+        "A": [0.0, 1.0, 2.0, 3.0, np.nan],
+        "B": [0, 1, 0, 1, 0],
+        "C": ["foo1", "foo2", "foo3", "foo4", "foo5"],
+        "D": date_range("1/1/2009", periods=5),
+        "E": [0.0, 1, Timestamp("20100101"), "foo", 2.0],
+    }
+
+    scalars = {"timestamp": Timestamp("20130101"), "period": Period("2012", "M")}
+
+    index = {
+        "int": Index(np.arange(10)),
+        "date": date_range("20130101", periods=10),
+        "period": period_range("2013-01-01", freq="M", periods=10),
+        "float": Index(np.arange(10, dtype=np.float64)),
+        "uint": Index(np.arange(10, dtype=np.uint64)),
+        "timedelta": timedelta_range("00:00:00", freq="30min", periods=10),
+        "string": Index(["foo", "bar", "baz", "qux", "quux"], dtype="string"),
+    }
+
+    index["range"] = RangeIndex(10)
+
+    index["interval"] = interval_range(0, periods=10)
+
+    mi = {
+        "reg2": MultiIndex.from_tuples(
+            tuple(
+                zip(
+                    *[
+                        ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
+                        ["one", "two", "one", "two", "one", "two", "one", "two"],
+                    ]
+                )
+            ),
+            names=["first", "second"],
+        )
+    }
+
+    series = {
+        "float": Series(data["A"]),
+        "int": Series(data["B"]),
+        "mixed": Series(data["E"]),
+        "ts": Series(
+            np.arange(10).astype(np.int64), index=date_range("20130101", periods=10)
+        ),
+        "mi": Series(
+            np.arange(5).astype(np.float64),
+            index=MultiIndex.from_tuples(
+                tuple(zip(*[[1, 1, 2, 2, 2], [3, 4, 3, 4, 5]])), names=["one", "two"]
+            ),
+        ),
+        "dup": Series(np.arange(5).astype(np.float64), index=["A", "B", "C", "D", "A"]),
+        "cat": Series(Categorical(["foo", "bar", "baz"])),
+        "dt": Series(date_range("20130101", periods=5)),
+        "dt_tz": Series(date_range("20130101", periods=5, tz="US/Eastern")),
+        "period": Series([Period("2000Q1")] * 5),
+        "string": Series(["foo", "bar", "baz", "qux", "quux"], dtype="string"),
+    }
+
+    mixed_dup_df = DataFrame(data)
+    mixed_dup_df.columns = list("ABCDA")
+    frame = {
+        "float": DataFrame({"A": series["float"], "B": series["float"] + 1}),
+        "int": DataFrame({"A": series["int"], "B": series["int"] + 1}),
+        "mixed": DataFrame({k: data[k] for k in ["A", "B", "C", "D"]}),
+        "mi": DataFrame(
+            {"A": np.arange(5).astype(np.float64), "B": np.arange(5).astype(np.int64)},
+            index=MultiIndex.from_tuples(
+                tuple(
+                    zip(
+                        *[
+                            ["bar", "bar", "baz", "baz", "baz"],
+                            ["one", "two", "one", "two", "three"],
+                        ]
+                    )
+                ),
+                names=["first", "second"],
+            ),
+        ),
+        "dup": DataFrame(
+            np.arange(15).reshape(5, 3).astype(np.float64), columns=["A", "B", "A"]
+        ),
+        "cat_onecol": DataFrame({"A": Categorical(["foo", "bar"])}),
+        "cat_and_float": DataFrame(
+            {
+                "A": Categorical(["foo", "bar", "baz"]),
+                "B": np.arange(3).astype(np.int64),
+            }
+        ),
+        "mixed_dup": mixed_dup_df,
+        "dt_mixed_tzs": DataFrame(
+            {
+                "A": Timestamp("20130102", tz="US/Eastern"),
+                "B": Timestamp("20130603", tz="CET"),
+            },
+            index=range(5),
+        ),
+        "dt_mixed2_tzs": DataFrame(
+            {
+                "A": Timestamp("20130102", tz="US/Eastern"),
+                "B": Timestamp("20130603", tz="CET"),
+                "C": Timestamp("20130603", tz="UTC"),
+            },
+            index=range(5),
+        ),
+        "string": DataFrame(
+            {
+                "A": Series(["foo", "bar", "baz", "qux", "quux"], dtype="string"),
+                "B": Series(["one", "two", "one", "two", "three"], dtype="string"),
+            }
+        ),
+    }
+
+    cat = {
+        "int8": Categorical(list("abcdefg")),
+        "int16": Categorical(np.arange(1000)),
+        "int32": Categorical(np.arange(10000)),
+    }
+
+    timestamp = {
+        "normal": Timestamp("2011-01-01"),
+        "nat": NaT,
+        "tz": Timestamp("2011-01-01", tz="US/Eastern"),
+    }
+    if test:
+        # kept because those are present in the legacy pickles (<= 1.4)
+        timestamp["freq"] = Timestamp("2011-01-01")
+        timestamp["both"] = Timestamp("2011-01-01", tz="Asia/Tokyo")
+
+    off = {
+        "DateOffset": DateOffset(years=1),
+        "DateOffset_h_ns": DateOffset(hour=6, nanoseconds=5824),
+        "BusinessDay": BusinessDay(offset=timedelta(seconds=9)),
+        "BusinessHour": BusinessHour(normalize=True, n=6, end="15:14"),
+        "CustomBusinessDay": CustomBusinessDay(weekmask="Mon Fri"),
+        "SemiMonthBegin": SemiMonthBegin(day_of_month=9),
+        "SemiMonthEnd": SemiMonthEnd(day_of_month=24),
+        "MonthBegin": MonthBegin(1),
+        "MonthEnd": MonthEnd(1),
+        "QuarterBegin": QuarterBegin(1),
+        "QuarterEnd": QuarterEnd(1),
+        "Day": Day(1),
+        "YearBegin": YearBegin(1),
+        "YearEnd": YearEnd(1),
+        "Week": Week(1),
+        "Week_Tues": Week(2, normalize=False, weekday=1),
+        "WeekOfMonth": WeekOfMonth(week=3, weekday=4),
+        "LastWeekOfMonth": LastWeekOfMonth(n=1, weekday=3),
+        "FY5253": FY5253(n=2, weekday=6, startingMonth=7, variation="last"),
+        "Easter": Easter(),
+        "Hour": Hour(1),
+        "Minute": Minute(1),
+    }
+
+    return {
+        "series": series,
+        "frame": frame,
+        "index": index,
+        "scalars": scalars,
+        "mi": mi,
+        "sp_series": {"float": _create_sp_series(), "ts": _create_sp_tsseries()},
+        "sp_frame": {"float": _create_sp_frame()},
+        "cat": cat,
+        "timestamp": timestamp,
+        "offsets": off,
+    }
+
+
+def create_dataframe_all_types():
+    timestamps = Series(
+        [
+            Timestamp("2013-01-01"),
+            NaT,
+            Timestamp("2013-01-03"),
+            Timestamp("2013-01-04"),
+            Timestamp("2013-01-05"),
+        ]
+    )
+    timedeltas = timestamps - timestamps[0]
+
+    data = {
+        # "string": Series(
+        #     ["a", "b", "c", None, "e"], dtype=StringDtype(na_value=np.nan)
+        # ),
+        # "object": Series(["a", "b", "c", None, "e"], dtype=object),
+        # "object_nan": Series(["a", "b", "c", np.nan, "e"], dtype=object),
+        "int": list(range(1, 6)),
+        "uint64": np.arange(3, 8).astype("uint64"),
+        "float": [0.1, 0.2, 0.3, 0.4, np.nan],
+        "float32": Series([0.1, 0.2, 0.3, 0.4, np.nan], dtype="float32"),
+        "bool": [True, False, True, False, True],
+        "datetime_ns": timestamps.dt.as_unit("ns"),
+        "datetime_us": timestamps.dt.as_unit("us"),
+        "datetime_ms": timestamps.dt.as_unit("ms"),
+        "datetime_s": timestamps.dt.as_unit("s"),
+        "datetimetz_ns": timestamps.dt.tz_localize("US/Eastern").dt.as_unit("ns"),
+        "datetimetz_us": timestamps.dt.tz_localize("US/Eastern").dt.as_unit("us"),
+        "timedelta_ns": timedeltas.dt.as_unit("ns"),
+        "timedelta_us": timedeltas.dt.as_unit("us"),
+        "timedelta_ms": timedeltas.dt.as_unit("ms"),
+        "timedelta_s": timedeltas.dt.as_unit("s"),
+        # "categorical": Categorical(
+        #     Series(
+        #         ["foo", "bar", "baz",np.nan,"foo"],dtype=StringDtype(na_value=np.nan)
+        #     )
+        # ),
+        # "categorical_object": Categorical(
+        #     Series(["foo", "bar", "baz", np.nan, "foo"], dtype=object)
+        # ),
+        "categorical_int": Categorical([1, 2, 3, np.nan, 1]),
+    }
+    return DataFrame(data)
+
+
+def platform_name():
+    return "_".join(
+        [
+            str(pandas.__version__),
+            str(pl.machine()),
+            str(pl.system().lower()),
+            str(pl.python_version()),
+        ]
+    )
+
+
+def write_legacy_pickles(output_dir):
+    pth = f"{platform_name()}.pickle"
+
+    with open(os.path.join(output_dir, pth), "wb") as fh:
+        pickle.dump(create_pickle_data(test=False), fh, pickle.DEFAULT_PROTOCOL)
+
+    print(f"created pickle file: {pth}")
+
+
+def write_legacy_hdf(output_dir, format):
+    import tables
+
+    pth = f"{platform_name()}_pytables-{tables.__version__}_{format}.h5"
+
+    df = create_dataframe_all_types()
+    if format == "fixed":
+        # df = df.drop(columns=["categorical", "categorical_object", "categorical_int"])
+        df = df.drop(columns=["categorical_int"])
+    complevel = 9 if format == "table" else None
+    df.to_hdf(
+        os.path.join(output_dir, pth),
+        key="df_alltypes",
+        format=format,
+        complevel=complevel,
+    )
+
+    print(f"created hdf file: {pth}")
+
+
+def write_legacy_file():
+    # force our cwd to be the first searched
+    sys.path.insert(0, "")
+
+    if not 3 <= len(sys.argv) <= 4:
+        sys.exit(
+            "Specify output directory and storage type: generate_legacy_"
+            "storage_files.py <output_dir> <storage_type> "
+        )
+
+    output_dir = str(sys.argv[1])
+    storage_type = str(sys.argv[2])
+
+    print(
+        "This script generates a storage file for the current arch, system, "
+        "and python version"
+    )
+    print(f"  pandas version: {pandas.__version__}")
+    print(f"  output dir    : {output_dir}")
+    print(f"  storage format: {storage_type}")
+
+    if not os.path.exists(output_dir):
+        os.mkdir(output_dir)
+
+    if storage_type == "pickle":
+        write_legacy_pickles(output_dir=output_dir)
+    elif storage_type == "hdf":
+        write_legacy_hdf(output_dir=output_dir, format="fixed")
+        write_legacy_hdf(output_dir=output_dir, format="table")
+    else:
+        sys.exit("storage_type must be one of {'pickle', 'hdf'}")
+
+
+if __name__ == "__main__":
+    write_legacy_file()