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ckpts/llama-3b/global_step100/bf16_zero_pp_rank_117_mp_rank_02_optim_states.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8634498c78ef5764c676db79ff9d360b4afd7e3a4e6c68c11730145ccfab4d01
+size 41830340

venv/lib/python3.10/site-packages/pandas/compat/__init__.py

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+"""
+compat
+======
+
+Cross-compatible functions for different versions of Python.
+
+Other items:
+* platform checker
+"""
+from __future__ import annotations
+
+import os
+import platform
+import sys
+from typing import TYPE_CHECKING
+
+from pandas.compat._constants import (
+    IS64,
+    ISMUSL,
+    PY310,
+    PY311,
+    PY312,
+    PYPY,
+)
+import pandas.compat.compressors
+from pandas.compat.numpy import is_numpy_dev
+from pandas.compat.pyarrow import (
+    pa_version_under10p1,
+    pa_version_under11p0,
+    pa_version_under13p0,
+    pa_version_under14p0,
+    pa_version_under14p1,
+    pa_version_under16p0,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import F
+
+
+def set_function_name(f: F, name: str, cls: type) -> F:
+    """
+    Bind the name/qualname attributes of the function.
+    """
+    f.__name__ = name
+    f.__qualname__ = f"{cls.__name__}.{name}"
+    f.__module__ = cls.__module__
+    return f
+
+
+def is_platform_little_endian() -> bool:
+    """
+    Checking if the running platform is little endian.
+
+    Returns
+    -------
+    bool
+        True if the running platform is little endian.
+    """
+    return sys.byteorder == "little"
+
+
+def is_platform_windows() -> bool:
+    """
+    Checking if the running platform is windows.
+
+    Returns
+    -------
+    bool
+        True if the running platform is windows.
+    """
+    return sys.platform in ["win32", "cygwin"]
+
+
+def is_platform_linux() -> bool:
+    """
+    Checking if the running platform is linux.
+
+    Returns
+    -------
+    bool
+        True if the running platform is linux.
+    """
+    return sys.platform == "linux"
+
+
+def is_platform_mac() -> bool:
+    """
+    Checking if the running platform is mac.
+
+    Returns
+    -------
+    bool
+        True if the running platform is mac.
+    """
+    return sys.platform == "darwin"
+
+
+def is_platform_arm() -> bool:
+    """
+    Checking if the running platform use ARM architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses ARM architecture.
+    """
+    return platform.machine() in ("arm64", "aarch64") or platform.machine().startswith(
+        "armv"
+    )
+
+
+def is_platform_power() -> bool:
+    """
+    Checking if the running platform use Power architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses ARM architecture.
+    """
+    return platform.machine() in ("ppc64", "ppc64le")
+
+
+def is_ci_environment() -> bool:
+    """
+    Checking if running in a continuous integration environment by checking
+    the PANDAS_CI environment variable.
+
+    Returns
+    -------
+    bool
+        True if the running in a continuous integration environment.
+    """
+    return os.environ.get("PANDAS_CI", "0") == "1"
+
+
+def get_lzma_file() -> type[pandas.compat.compressors.LZMAFile]:
+    """
+    Importing the `LZMAFile` class from the `lzma` module.
+
+    Returns
+    -------
+    class
+        The `LZMAFile` class from the `lzma` module.
+
+    Raises
+    ------
+    RuntimeError
+        If the `lzma` module was not imported correctly, or didn't exist.
+    """
+    if not pandas.compat.compressors.has_lzma:
+        raise RuntimeError(
+            "lzma module not available. "
+            "A Python re-install with the proper dependencies, "
+            "might be required to solve this issue."
+        )
+    return pandas.compat.compressors.LZMAFile
+
+
+def get_bz2_file() -> type[pandas.compat.compressors.BZ2File]:
+    """
+    Importing the `BZ2File` class from the `bz2` module.
+
+    Returns
+    -------
+    class
+        The `BZ2File` class from the `bz2` module.
+
+    Raises
+    ------
+    RuntimeError
+        If the `bz2` module was not imported correctly, or didn't exist.
+    """
+    if not pandas.compat.compressors.has_bz2:
+        raise RuntimeError(
+            "bz2 module not available. "
+            "A Python re-install with the proper dependencies, "
+            "might be required to solve this issue."
+        )
+    return pandas.compat.compressors.BZ2File
+
+
+__all__ = [
+    "is_numpy_dev",
+    "pa_version_under10p1",
+    "pa_version_under11p0",
+    "pa_version_under13p0",
+    "pa_version_under14p0",
+    "pa_version_under14p1",
+    "pa_version_under16p0",
+    "IS64",
+    "ISMUSL",
+    "PY310",
+    "PY311",
+    "PY312",
+    "PYPY",
+]

venv/lib/python3.10/site-packages/pandas/compat/_constants.py

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+"""
+_constants
+======
+
+Constants relevant for the Python implementation.
+"""
+
+from __future__ import annotations
+
+import platform
+import sys
+import sysconfig
+
+IS64 = sys.maxsize > 2**32
+
+PY310 = sys.version_info >= (3, 10)
+PY311 = sys.version_info >= (3, 11)
+PY312 = sys.version_info >= (3, 12)
+PYPY = platform.python_implementation() == "PyPy"
+ISMUSL = "musl" in (sysconfig.get_config_var("HOST_GNU_TYPE") or "")
+REF_COUNT = 2 if PY311 else 3
+
+__all__ = [
+    "IS64",
+    "ISMUSL",
+    "PY310",
+    "PY311",
+    "PY312",
+    "PYPY",
+]

venv/lib/python3.10/site-packages/pandas/compat/_optional.py

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+from __future__ import annotations
+
+import importlib
+import sys
+from typing import TYPE_CHECKING
+import warnings
+
+from pandas.util._exceptions import find_stack_level
+
+from pandas.util.version import Version
+
+if TYPE_CHECKING:
+    import types
+
+# Update install.rst & setup.cfg when updating versions!
+
+VERSIONS = {
+    "adbc-driver-postgresql": "0.8.0",
+    "adbc-driver-sqlite": "0.8.0",
+    "bs4": "4.11.2",
+    "blosc": "1.21.3",
+    "bottleneck": "1.3.6",
+    "dataframe-api-compat": "0.1.7",
+    "fastparquet": "2022.12.0",
+    "fsspec": "2022.11.0",
+    "html5lib": "1.1",
+    "hypothesis": "6.46.1",
+    "gcsfs": "2022.11.0",
+    "jinja2": "3.1.2",
+    "lxml.etree": "4.9.2",
+    "matplotlib": "3.6.3",
+    "numba": "0.56.4",
+    "numexpr": "2.8.4",
+    "odfpy": "1.4.1",
+    "openpyxl": "3.1.0",
+    "pandas_gbq": "0.19.0",
+    "psycopg2": "2.9.6",  # (dt dec pq3 ext lo64)
+    "pymysql": "1.0.2",
+    "pyarrow": "10.0.1",
+    "pyreadstat": "1.2.0",
+    "pytest": "7.3.2",
+    "python-calamine": "0.1.7",
+    "pyxlsb": "1.0.10",
+    "s3fs": "2022.11.0",
+    "scipy": "1.10.0",
+    "sqlalchemy": "2.0.0",
+    "tables": "3.8.0",
+    "tabulate": "0.9.0",
+    "xarray": "2022.12.0",
+    "xlrd": "2.0.1",
+    "xlsxwriter": "3.0.5",
+    "zstandard": "0.19.0",
+    "tzdata": "2022.7",
+    "qtpy": "2.3.0",
+    "pyqt5": "5.15.9",
+}
+
+# A mapping from import name to package name (on PyPI) for packages where
+# these two names are different.
+
+INSTALL_MAPPING = {
+    "bs4": "beautifulsoup4",
+    "bottleneck": "Bottleneck",
+    "jinja2": "Jinja2",
+    "lxml.etree": "lxml",
+    "odf": "odfpy",
+    "pandas_gbq": "pandas-gbq",
+    "python_calamine": "python-calamine",
+    "sqlalchemy": "SQLAlchemy",
+    "tables": "pytables",
+}
+
+
+def get_version(module: types.ModuleType) -> str:
+    version = getattr(module, "__version__", None)
+
+    if version is None:
+        raise ImportError(f"Can't determine version for {module.__name__}")
+    if module.__name__ == "psycopg2":
+        # psycopg2 appends " (dt dec pq3 ext lo64)" to it's version
+        version = version.split()[0]
+    return version
+
+
+def import_optional_dependency(
+    name: str,
+    extra: str = "",
+    errors: str = "raise",
+    min_version: str | None = None,
+):
+    """
+    Import an optional dependency.
+
+    By default, if a dependency is missing an ImportError with a nice
+    message will be raised. If a dependency is present, but too old,
+    we raise.
+
+    Parameters
+    ----------
+    name : str
+        The module name.
+    extra : str
+        Additional text to include in the ImportError message.
+    errors : str {'raise', 'warn', 'ignore'}
+        What to do when a dependency is not found or its version is too old.
+
+        * raise : Raise an ImportError
+        * warn : Only applicable when a module's version is to old.
+          Warns that the version is too old and returns None
+        * ignore: If the module is not installed, return None, otherwise,
+          return the module, even if the version is too old.
+          It's expected that users validate the version locally when
+          using ``errors="ignore"`` (see. ``io/html.py``)
+    min_version : str, default None
+        Specify a minimum version that is different from the global pandas
+        minimum version required.
+    Returns
+    -------
+    maybe_module : Optional[ModuleType]
+        The imported module, when found and the version is correct.
+        None is returned when the package is not found and `errors`
+        is False, or when the package's version is too old and `errors`
+        is ``'warn'`` or ``'ignore'``.
+    """
+    assert errors in {"warn", "raise", "ignore"}
+
+    package_name = INSTALL_MAPPING.get(name)
+    install_name = package_name if package_name is not None else name
+
+    msg = (
+        f"Missing optional dependency '{install_name}'. {extra} "
+        f"Use pip or conda to install {install_name}."
+    )
+    try:
+        module = importlib.import_module(name)
+    except ImportError:
+        if errors == "raise":
+            raise ImportError(msg)
+        return None
+
+    # Handle submodules: if we have submodule, grab parent module from sys.modules
+    parent = name.split(".")[0]
+    if parent != name:
+        install_name = parent
+        module_to_get = sys.modules[install_name]
+    else:
+        module_to_get = module
+    minimum_version = min_version if min_version is not None else VERSIONS.get(parent)
+    if minimum_version:
+        version = get_version(module_to_get)
+        if version and Version(version) < Version(minimum_version):
+            msg = (
+                f"Pandas requires version '{minimum_version}' or newer of '{parent}' "
+                f"(version '{version}' currently installed)."
+            )
+            if errors == "warn":
+                warnings.warn(
+                    msg,
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
+                return None
+            elif errors == "raise":
+                raise ImportError(msg)
+            else:
+                return None
+
+    return module

venv/lib/python3.10/site-packages/pandas/compat/compressors.py

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+"""
+Patched ``BZ2File`` and ``LZMAFile`` to handle pickle protocol 5.
+"""
+
+from __future__ import annotations
+
+from pickle import PickleBuffer
+
+from pandas.compat._constants import PY310
+
+try:
+    import bz2
+
+    has_bz2 = True
+except ImportError:
+    has_bz2 = False
+
+try:
+    import lzma
+
+    has_lzma = True
+except ImportError:
+    has_lzma = False
+
+
+def flatten_buffer(
+    b: bytes | bytearray | memoryview | PickleBuffer,
+) -> bytes | bytearray | memoryview:
+    """
+    Return some 1-D `uint8` typed buffer.
+
+    Coerces anything that does not match that description to one that does
+    without copying if possible (otherwise will copy).
+    """
+
+    if isinstance(b, (bytes, bytearray)):
+        return b
+
+    if not isinstance(b, PickleBuffer):
+        b = PickleBuffer(b)
+
+    try:
+        # coerce to 1-D `uint8` C-contiguous `memoryview` zero-copy
+        return b.raw()
+    except BufferError:
+        # perform in-memory copy if buffer is not contiguous
+        return memoryview(b).tobytes("A")
+
+
+if has_bz2:
+
+    class BZ2File(bz2.BZ2File):
+        if not PY310:
+
+            def write(self, b) -> int:
+                # Workaround issue where `bz2.BZ2File` expects `len`
+                # to return the number of bytes in `b` by converting
+                # `b` into something that meets that constraint with
+                # minimal copying.
+                #
+                # Note: This is fixed in Python 3.10.
+                return super().write(flatten_buffer(b))
+
+
+if has_lzma:
+
+    class LZMAFile(lzma.LZMAFile):
+        if not PY310:
+
+            def write(self, b) -> int:
+                # Workaround issue where `lzma.LZMAFile` expects `len`
+                # to return the number of bytes in `b` by converting
+                # `b` into something that meets that constraint with
+                # minimal copying.
+                #
+                # Note: This is fixed in Python 3.10.
+                return super().write(flatten_buffer(b))

venv/lib/python3.10/site-packages/pandas/compat/numpy/__init__.py

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+""" support numpy compatibility across versions """
+import warnings
+
+import numpy as np
+
+from pandas.util.version import Version
+
+# numpy versioning
+_np_version = np.__version__
+_nlv = Version(_np_version)
+np_version_lt1p23 = _nlv < Version("1.23")
+np_version_gte1p24 = _nlv >= Version("1.24")
+np_version_gte1p24p3 = _nlv >= Version("1.24.3")
+np_version_gte1p25 = _nlv >= Version("1.25")
+np_version_gt2 = _nlv >= Version("2.0.0.dev0")
+is_numpy_dev = _nlv.dev is not None
+_min_numpy_ver = "1.22.4"
+
+
+if _nlv < Version(_min_numpy_ver):
+    raise ImportError(
+        f"this version of pandas is incompatible with numpy < {_min_numpy_ver}\n"
+        f"your numpy version is {_np_version}.\n"
+        f"Please upgrade numpy to >= {_min_numpy_ver} to use this pandas version"
+    )
+
+
+np_long: type
+np_ulong: type
+
+if np_version_gt2:
+    try:
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                r".*In the future `np\.long` will be defined as.*",
+                FutureWarning,
+            )
+            np_long = np.long  # type: ignore[attr-defined]
+            np_ulong = np.ulong  # type: ignore[attr-defined]
+    except AttributeError:
+        np_long = np.int_
+        np_ulong = np.uint
+else:
+    np_long = np.int_
+    np_ulong = np.uint
+
+
+__all__ = [
+    "np",
+    "_np_version",
+    "is_numpy_dev",
+]

venv/lib/python3.10/site-packages/pandas/compat/numpy/function.py

@@ -0,0 +1,418 @@
+"""
+For compatibility with numpy libraries, pandas functions or methods have to
+accept '*args' and '**kwargs' parameters to accommodate numpy arguments that
+are not actually used or respected in the pandas implementation.
+
+To ensure that users do not abuse these parameters, validation is performed in
+'validators.py' to make sure that any extra parameters passed correspond ONLY
+to those in the numpy signature. Part of that validation includes whether or
+not the user attempted to pass in non-default values for these extraneous
+parameters. As we want to discourage users from relying on these parameters
+when calling the pandas implementation, we want them only to pass in the
+default values for these parameters.
+
+This module provides a set of commonly used default arguments for functions and
+methods that are spread throughout the codebase. This module will make it
+easier to adjust to future upstream changes in the analogous numpy signatures.
+"""
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    TypeVar,
+    cast,
+    overload,
+)
+
+import numpy as np
+from numpy import ndarray
+
+from pandas._libs.lib import (
+    is_bool,
+    is_integer,
+)
+from pandas.errors import UnsupportedFunctionCall
+from pandas.util._validators import (
+    validate_args,
+    validate_args_and_kwargs,
+    validate_kwargs,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        AxisInt,
+    )
+
+    AxisNoneT = TypeVar("AxisNoneT", Axis, None)
+
+
+class CompatValidator:
+    def __init__(
+        self,
+        defaults,
+        fname=None,
+        method: str | None = None,
+        max_fname_arg_count=None,
+    ) -> None:
+        self.fname = fname
+        self.method = method
+        self.defaults = defaults
+        self.max_fname_arg_count = max_fname_arg_count
+
+    def __call__(
+        self,
+        args,
+        kwargs,
+        fname=None,
+        max_fname_arg_count=None,
+        method: str | None = None,
+    ) -> None:
+        if not args and not kwargs:
+            return None
+
+        fname = self.fname if fname is None else fname
+        max_fname_arg_count = (
+            self.max_fname_arg_count
+            if max_fname_arg_count is None
+            else max_fname_arg_count
+        )
+        method = self.method if method is None else method
+
+        if method == "args":
+            validate_args(fname, args, max_fname_arg_count, self.defaults)
+        elif method == "kwargs":
+            validate_kwargs(fname, kwargs, self.defaults)
+        elif method == "both":
+            validate_args_and_kwargs(
+                fname, args, kwargs, max_fname_arg_count, self.defaults
+            )
+        else:
+            raise ValueError(f"invalid validation method '{method}'")
+
+
+ARGMINMAX_DEFAULTS = {"out": None}
+validate_argmin = CompatValidator(
+    ARGMINMAX_DEFAULTS, fname="argmin", method="both", max_fname_arg_count=1
+)
+validate_argmax = CompatValidator(
+    ARGMINMAX_DEFAULTS, fname="argmax", method="both", max_fname_arg_count=1
+)
+
+
+def process_skipna(skipna: bool | ndarray | None, args) -> tuple[bool, Any]:
+    if isinstance(skipna, ndarray) or skipna is None:
+        args = (skipna,) + args
+        skipna = True
+
+    return skipna, args
+
+
+def validate_argmin_with_skipna(skipna: bool | ndarray | None, args, kwargs) -> bool:
+    """
+    If 'Series.argmin' is called via the 'numpy' library, the third parameter
+    in its signature is 'out', which takes either an ndarray or 'None', so
+    check if the 'skipna' parameter is either an instance of ndarray or is
+    None, since 'skipna' itself should be a boolean
+    """
+    skipna, args = process_skipna(skipna, args)
+    validate_argmin(args, kwargs)
+    return skipna
+
+
+def validate_argmax_with_skipna(skipna: bool | ndarray | None, args, kwargs) -> bool:
+    """
+    If 'Series.argmax' is called via the 'numpy' library, the third parameter
+    in its signature is 'out', which takes either an ndarray or 'None', so
+    check if the 'skipna' parameter is either an instance of ndarray or is
+    None, since 'skipna' itself should be a boolean
+    """
+    skipna, args = process_skipna(skipna, args)
+    validate_argmax(args, kwargs)
+    return skipna
+
+
+ARGSORT_DEFAULTS: dict[str, int | str | None] = {}
+ARGSORT_DEFAULTS["axis"] = -1
+ARGSORT_DEFAULTS["kind"] = "quicksort"
+ARGSORT_DEFAULTS["order"] = None
+ARGSORT_DEFAULTS["kind"] = None
+ARGSORT_DEFAULTS["stable"] = None
+
+
+validate_argsort = CompatValidator(
+    ARGSORT_DEFAULTS, fname="argsort", max_fname_arg_count=0, method="both"
+)
+
+# two different signatures of argsort, this second validation for when the
+# `kind` param is supported
+ARGSORT_DEFAULTS_KIND: dict[str, int | None] = {}
+ARGSORT_DEFAULTS_KIND["axis"] = -1
+ARGSORT_DEFAULTS_KIND["order"] = None
+ARGSORT_DEFAULTS_KIND["stable"] = None
+validate_argsort_kind = CompatValidator(
+    ARGSORT_DEFAULTS_KIND, fname="argsort", max_fname_arg_count=0, method="both"
+)
+
+
+def validate_argsort_with_ascending(ascending: bool | int | None, args, kwargs) -> bool:
+    """
+    If 'Categorical.argsort' is called via the 'numpy' library, the first
+    parameter in its signature is 'axis', which takes either an integer or
+    'None', so check if the 'ascending' parameter has either integer type or is
+    None, since 'ascending' itself should be a boolean
+    """
+    if is_integer(ascending) or ascending is None:
+        args = (ascending,) + args
+        ascending = True
+
+    validate_argsort_kind(args, kwargs, max_fname_arg_count=3)
+    ascending = cast(bool, ascending)
+    return ascending
+
+
+CLIP_DEFAULTS: dict[str, Any] = {"out": None}
+validate_clip = CompatValidator(
+    CLIP_DEFAULTS, fname="clip", method="both", max_fname_arg_count=3
+)
+
+
+@overload
+def validate_clip_with_axis(axis: ndarray, args, kwargs) -> None:
+    ...
+
+
+@overload
+def validate_clip_with_axis(axis: AxisNoneT, args, kwargs) -> AxisNoneT:
+    ...
+
+
+def validate_clip_with_axis(
+    axis: ndarray | AxisNoneT, args, kwargs
+) -> AxisNoneT | None:
+    """
+    If 'NDFrame.clip' is called via the numpy library, the third parameter in
+    its signature is 'out', which can takes an ndarray, so check if the 'axis'
+    parameter is an instance of ndarray, since 'axis' itself should either be
+    an integer or None
+    """
+    if isinstance(axis, ndarray):
+        args = (axis,) + args
+        # error: Incompatible types in assignment (expression has type "None",
+        # variable has type "Union[ndarray[Any, Any], str, int]")
+        axis = None  # type: ignore[assignment]
+
+    validate_clip(args, kwargs)
+    # error: Incompatible return value type (got "Union[ndarray[Any, Any],
+    # str, int]", expected "Union[str, int, None]")
+    return axis  # type: ignore[return-value]
+
+
+CUM_FUNC_DEFAULTS: dict[str, Any] = {}
+CUM_FUNC_DEFAULTS["dtype"] = None
+CUM_FUNC_DEFAULTS["out"] = None
+validate_cum_func = CompatValidator(
+    CUM_FUNC_DEFAULTS, method="both", max_fname_arg_count=1
+)
+validate_cumsum = CompatValidator(
+    CUM_FUNC_DEFAULTS, fname="cumsum", method="both", max_fname_arg_count=1
+)
+
+
+def validate_cum_func_with_skipna(skipna: bool, args, kwargs, name) -> bool:
+    """
+    If this function is called via the 'numpy' library, the third parameter in
+    its signature is 'dtype', which takes either a 'numpy' dtype or 'None', so
+    check if the 'skipna' parameter is a boolean or not
+    """
+    if not is_bool(skipna):
+        args = (skipna,) + args
+        skipna = True
+    elif isinstance(skipna, np.bool_):
+        skipna = bool(skipna)
+
+    validate_cum_func(args, kwargs, fname=name)
+    return skipna
+
+
+ALLANY_DEFAULTS: dict[str, bool | None] = {}
+ALLANY_DEFAULTS["dtype"] = None
+ALLANY_DEFAULTS["out"] = None
+ALLANY_DEFAULTS["keepdims"] = False
+ALLANY_DEFAULTS["axis"] = None
+validate_all = CompatValidator(
+    ALLANY_DEFAULTS, fname="all", method="both", max_fname_arg_count=1
+)
+validate_any = CompatValidator(
+    ALLANY_DEFAULTS, fname="any", method="both", max_fname_arg_count=1
+)
+
+LOGICAL_FUNC_DEFAULTS = {"out": None, "keepdims": False}
+validate_logical_func = CompatValidator(LOGICAL_FUNC_DEFAULTS, method="kwargs")
+
+MINMAX_DEFAULTS = {"axis": None, "dtype": None, "out": None, "keepdims": False}
+validate_min = CompatValidator(
+    MINMAX_DEFAULTS, fname="min", method="both", max_fname_arg_count=1
+)
+validate_max = CompatValidator(
+    MINMAX_DEFAULTS, fname="max", method="both", max_fname_arg_count=1
+)
+
+RESHAPE_DEFAULTS: dict[str, str] = {"order": "C"}
+validate_reshape = CompatValidator(
+    RESHAPE_DEFAULTS, fname="reshape", method="both", max_fname_arg_count=1
+)
+
+REPEAT_DEFAULTS: dict[str, Any] = {"axis": None}
+validate_repeat = CompatValidator(
+    REPEAT_DEFAULTS, fname="repeat", method="both", max_fname_arg_count=1
+)
+
+ROUND_DEFAULTS: dict[str, Any] = {"out": None}
+validate_round = CompatValidator(
+    ROUND_DEFAULTS, fname="round", method="both", max_fname_arg_count=1
+)
+
+SORT_DEFAULTS: dict[str, int | str | None] = {}
+SORT_DEFAULTS["axis"] = -1
+SORT_DEFAULTS["kind"] = "quicksort"
+SORT_DEFAULTS["order"] = None
+validate_sort = CompatValidator(SORT_DEFAULTS, fname="sort", method="kwargs")
+
+STAT_FUNC_DEFAULTS: dict[str, Any | None] = {}
+STAT_FUNC_DEFAULTS["dtype"] = None
+STAT_FUNC_DEFAULTS["out"] = None
+
+SUM_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
+SUM_DEFAULTS["axis"] = None
+SUM_DEFAULTS["keepdims"] = False
+SUM_DEFAULTS["initial"] = None
+
+PROD_DEFAULTS = SUM_DEFAULTS.copy()
+
+MEAN_DEFAULTS = SUM_DEFAULTS.copy()
+
+MEDIAN_DEFAULTS = STAT_FUNC_DEFAULTS.copy()
+MEDIAN_DEFAULTS["overwrite_input"] = False
+MEDIAN_DEFAULTS["keepdims"] = False
+
+STAT_FUNC_DEFAULTS["keepdims"] = False
+
+validate_stat_func = CompatValidator(STAT_FUNC_DEFAULTS, method="kwargs")
+validate_sum = CompatValidator(
+    SUM_DEFAULTS, fname="sum", method="both", max_fname_arg_count=1
+)
+validate_prod = CompatValidator(
+    PROD_DEFAULTS, fname="prod", method="both", max_fname_arg_count=1
+)
+validate_mean = CompatValidator(
+    MEAN_DEFAULTS, fname="mean", method="both", max_fname_arg_count=1
+)
+validate_median = CompatValidator(
+    MEDIAN_DEFAULTS, fname="median", method="both", max_fname_arg_count=1
+)
+
+STAT_DDOF_FUNC_DEFAULTS: dict[str, bool | None] = {}
+STAT_DDOF_FUNC_DEFAULTS["dtype"] = None
+STAT_DDOF_FUNC_DEFAULTS["out"] = None
+STAT_DDOF_FUNC_DEFAULTS["keepdims"] = False
+validate_stat_ddof_func = CompatValidator(STAT_DDOF_FUNC_DEFAULTS, method="kwargs")
+
+TAKE_DEFAULTS: dict[str, str | None] = {}
+TAKE_DEFAULTS["out"] = None
+TAKE_DEFAULTS["mode"] = "raise"
+validate_take = CompatValidator(TAKE_DEFAULTS, fname="take", method="kwargs")
+
+
+def validate_take_with_convert(convert: ndarray | bool | None, args, kwargs) -> bool:
+    """
+    If this function is called via the 'numpy' library, the third parameter in
+    its signature is 'axis', which takes either an ndarray or 'None', so check
+    if the 'convert' parameter is either an instance of ndarray or is None
+    """
+    if isinstance(convert, ndarray) or convert is None:
+        args = (convert,) + args
+        convert = True
+
+    validate_take(args, kwargs, max_fname_arg_count=3, method="both")
+    return convert
+
+
+TRANSPOSE_DEFAULTS = {"axes": None}
+validate_transpose = CompatValidator(
+    TRANSPOSE_DEFAULTS, fname="transpose", method="both", max_fname_arg_count=0
+)
+
+
+def validate_groupby_func(name: str, args, kwargs, allowed=None) -> None:
+    """
+    'args' and 'kwargs' should be empty, except for allowed kwargs because all
+    of their necessary parameters are explicitly listed in the function
+    signature
+    """
+    if allowed is None:
+        allowed = []
+
+    kwargs = set(kwargs) - set(allowed)
+
+    if len(args) + len(kwargs) > 0:
+        raise UnsupportedFunctionCall(
+            "numpy operations are not valid with groupby. "
+            f"Use .groupby(...).{name}() instead"
+        )
+
+
+RESAMPLER_NUMPY_OPS = ("min", "max", "sum", "prod", "mean", "std", "var")
+
+
+def validate_resampler_func(method: str, args, kwargs) -> None:
+    """
+    'args' and 'kwargs' should be empty because all of their necessary
+    parameters are explicitly listed in the function signature
+    """
+    if len(args) + len(kwargs) > 0:
+        if method in RESAMPLER_NUMPY_OPS:
+            raise UnsupportedFunctionCall(
+                "numpy operations are not valid with resample. "
+                f"Use .resample(...).{method}() instead"
+            )
+        raise TypeError("too many arguments passed in")
+
+
+def validate_minmax_axis(axis: AxisInt | None, ndim: int = 1) -> None:
+    """
+    Ensure that the axis argument passed to min, max, argmin, or argmax is zero
+    or None, as otherwise it will be incorrectly ignored.
+
+    Parameters
+    ----------
+    axis : int or None
+    ndim : int, default 1
+
+    Raises
+    ------
+    ValueError
+    """
+    if axis is None:
+        return
+    if axis >= ndim or (axis < 0 and ndim + axis < 0):
+        raise ValueError(f"`axis` must be fewer than the number of dimensions ({ndim})")
+
+
+_validation_funcs = {
+    "median": validate_median,
+    "mean": validate_mean,
+    "min": validate_min,
+    "max": validate_max,
+    "sum": validate_sum,
+    "prod": validate_prod,
+}
+
+
+def validate_func(fname, args, kwargs) -> None:
+    if fname not in _validation_funcs:
+        return validate_stat_func(args, kwargs, fname=fname)
+
+    validation_func = _validation_funcs[fname]
+    return validation_func(args, kwargs)

venv/lib/python3.10/site-packages/pandas/compat/pickle_compat.py

@@ -0,0 +1,262 @@
+"""
+Support pre-0.12 series pickle compatibility.
+"""
+from __future__ import annotations
+
+import contextlib
+import copy
+import io
+import pickle as pkl
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs.arrays import NDArrayBacked
+from pandas._libs.tslibs import BaseOffset
+
+from pandas import Index
+from pandas.core.arrays import (
+    DatetimeArray,
+    PeriodArray,
+    TimedeltaArray,
+)
+from pandas.core.internals import BlockManager
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+
+
+def load_reduce(self) -> None:
+    stack = self.stack
+    args = stack.pop()
+    func = stack[-1]
+
+    try:
+        stack[-1] = func(*args)
+        return
+    except TypeError as err:
+        # If we have a deprecated function,
+        # try to replace and try again.
+
+        msg = "_reconstruct: First argument must be a sub-type of ndarray"
+
+        if msg in str(err):
+            try:
+                cls = args[0]
+                stack[-1] = object.__new__(cls)
+                return
+            except TypeError:
+                pass
+        elif args and isinstance(args[0], type) and issubclass(args[0], BaseOffset):
+            # TypeError: object.__new__(Day) is not safe, use Day.__new__()
+            cls = args[0]
+            stack[-1] = cls.__new__(*args)
+            return
+        elif args and issubclass(args[0], PeriodArray):
+            cls = args[0]
+            stack[-1] = NDArrayBacked.__new__(*args)
+            return
+
+        raise
+
+
+# If classes are moved, provide compat here.
+_class_locations_map = {
+    ("pandas.core.sparse.array", "SparseArray"): ("pandas.core.arrays", "SparseArray"),
+    # 15477
+    ("pandas.core.base", "FrozenNDArray"): ("numpy", "ndarray"),
+    # Re-routing unpickle block logic to go through _unpickle_block instead
+    # for pandas <= 1.3.5
+    ("pandas.core.internals.blocks", "new_block"): (
+        "pandas._libs.internals",
+        "_unpickle_block",
+    ),
+    ("pandas.core.indexes.frozen", "FrozenNDArray"): ("numpy", "ndarray"),
+    ("pandas.core.base", "FrozenList"): ("pandas.core.indexes.frozen", "FrozenList"),
+    # 10890
+    ("pandas.core.series", "TimeSeries"): ("pandas.core.series", "Series"),
+    ("pandas.sparse.series", "SparseTimeSeries"): (
+        "pandas.core.sparse.series",
+        "SparseSeries",
+    ),
+    # 12588, extensions moving
+    ("pandas._sparse", "BlockIndex"): ("pandas._libs.sparse", "BlockIndex"),
+    ("pandas.tslib", "Timestamp"): ("pandas._libs.tslib", "Timestamp"),
+    # 18543 moving period
+    ("pandas._period", "Period"): ("pandas._libs.tslibs.period", "Period"),
+    ("pandas._libs.period", "Period"): ("pandas._libs.tslibs.period", "Period"),
+    # 18014 moved __nat_unpickle from _libs.tslib-->_libs.tslibs.nattype
+    ("pandas.tslib", "__nat_unpickle"): (
+        "pandas._libs.tslibs.nattype",
+        "__nat_unpickle",
+    ),
+    ("pandas._libs.tslib", "__nat_unpickle"): (
+        "pandas._libs.tslibs.nattype",
+        "__nat_unpickle",
+    ),
+    # 15998 top-level dirs moving
+    ("pandas.sparse.array", "SparseArray"): (
+        "pandas.core.arrays.sparse",
+        "SparseArray",
+    ),
+    ("pandas.indexes.base", "_new_Index"): ("pandas.core.indexes.base", "_new_Index"),
+    ("pandas.indexes.base", "Index"): ("pandas.core.indexes.base", "Index"),
+    ("pandas.indexes.numeric", "Int64Index"): (
+        "pandas.core.indexes.base",
+        "Index",  # updated in 50775
+    ),
+    ("pandas.indexes.range", "RangeIndex"): ("pandas.core.indexes.range", "RangeIndex"),
+    ("pandas.indexes.multi", "MultiIndex"): ("pandas.core.indexes.multi", "MultiIndex"),
+    ("pandas.tseries.index", "_new_DatetimeIndex"): (
+        "pandas.core.indexes.datetimes",
+        "_new_DatetimeIndex",
+    ),
+    ("pandas.tseries.index", "DatetimeIndex"): (
+        "pandas.core.indexes.datetimes",
+        "DatetimeIndex",
+    ),
+    ("pandas.tseries.period", "PeriodIndex"): (
+        "pandas.core.indexes.period",
+        "PeriodIndex",
+    ),
+    # 19269, arrays moving
+    ("pandas.core.categorical", "Categorical"): ("pandas.core.arrays", "Categorical"),
+    # 19939, add timedeltaindex, float64index compat from 15998 move
+    ("pandas.tseries.tdi", "TimedeltaIndex"): (
+        "pandas.core.indexes.timedeltas",
+        "TimedeltaIndex",
+    ),
+    ("pandas.indexes.numeric", "Float64Index"): (
+        "pandas.core.indexes.base",
+        "Index",  # updated in 50775
+    ),
+    # 50775, remove Int64Index, UInt64Index & Float64Index from codabase
+    ("pandas.core.indexes.numeric", "Int64Index"): (
+        "pandas.core.indexes.base",
+        "Index",
+    ),
+    ("pandas.core.indexes.numeric", "UInt64Index"): (
+        "pandas.core.indexes.base",
+        "Index",
+    ),
+    ("pandas.core.indexes.numeric", "Float64Index"): (
+        "pandas.core.indexes.base",
+        "Index",
+    ),
+    ("pandas.core.arrays.sparse.dtype", "SparseDtype"): (
+        "pandas.core.dtypes.dtypes",
+        "SparseDtype",
+    ),
+}
+
+
+# our Unpickler sub-class to override methods and some dispatcher
+# functions for compat and uses a non-public class of the pickle module.
+
+
+class Unpickler(pkl._Unpickler):
+    def find_class(self, module, name):
+        # override superclass
+        key = (module, name)
+        module, name = _class_locations_map.get(key, key)
+        return super().find_class(module, name)
+
+
+Unpickler.dispatch = copy.copy(Unpickler.dispatch)
+Unpickler.dispatch[pkl.REDUCE[0]] = load_reduce
+
+
+def load_newobj(self) -> None:
+    args = self.stack.pop()
+    cls = self.stack[-1]
+
+    # compat
+    if issubclass(cls, Index):
+        obj = object.__new__(cls)
+    elif issubclass(cls, DatetimeArray) and not args:
+        arr = np.array([], dtype="M8[ns]")
+        obj = cls.__new__(cls, arr, arr.dtype)
+    elif issubclass(cls, TimedeltaArray) and not args:
+        arr = np.array([], dtype="m8[ns]")
+        obj = cls.__new__(cls, arr, arr.dtype)
+    elif cls is BlockManager and not args:
+        obj = cls.__new__(cls, (), [], False)
+    else:
+        obj = cls.__new__(cls, *args)
+
+    self.stack[-1] = obj
+
+
+Unpickler.dispatch[pkl.NEWOBJ[0]] = load_newobj
+
+
+def load_newobj_ex(self) -> None:
+    kwargs = self.stack.pop()
+    args = self.stack.pop()
+    cls = self.stack.pop()
+
+    # compat
+    if issubclass(cls, Index):
+        obj = object.__new__(cls)
+    else:
+        obj = cls.__new__(cls, *args, **kwargs)
+    self.append(obj)
+
+
+try:
+    Unpickler.dispatch[pkl.NEWOBJ_EX[0]] = load_newobj_ex
+except (AttributeError, KeyError):
+    pass
+
+
+def load(fh, encoding: str | None = None, is_verbose: bool = False):
+    """
+    Load a pickle, with a provided encoding,
+
+    Parameters
+    ----------
+    fh : a filelike object
+    encoding : an optional encoding
+    is_verbose : show exception output
+    """
+    try:
+        fh.seek(0)
+        if encoding is not None:
+            up = Unpickler(fh, encoding=encoding)
+        else:
+            up = Unpickler(fh)
+        # "Unpickler" has no attribute "is_verbose"  [attr-defined]
+        up.is_verbose = is_verbose  # type: ignore[attr-defined]
+
+        return up.load()
+    except (ValueError, TypeError):
+        raise
+
+
+def loads(
+    bytes_object: bytes,
+    *,
+    fix_imports: bool = True,
+    encoding: str = "ASCII",
+    errors: str = "strict",
+):
+    """
+    Analogous to pickle._loads.
+    """
+    fd = io.BytesIO(bytes_object)
+    return Unpickler(
+        fd, fix_imports=fix_imports, encoding=encoding, errors=errors
+    ).load()
+
+
+@contextlib.contextmanager
+def patch_pickle() -> Generator[None, None, None]:
+    """
+    Temporarily patch pickle to use our unpickler.
+    """
+    orig_loads = pkl.loads
+    try:
+        setattr(pkl, "loads", loads)
+        yield
+    finally:
+        setattr(pkl, "loads", orig_loads)

venv/lib/python3.10/site-packages/pandas/compat/pyarrow.py

@@ -0,0 +1,27 @@
+""" support pyarrow compatibility across versions """
+
+from __future__ import annotations
+
+from pandas.util.version import Version
+
+try:
+    import pyarrow as pa
+
+    _palv = Version(Version(pa.__version__).base_version)
+    pa_version_under10p1 = _palv < Version("10.0.1")
+    pa_version_under11p0 = _palv < Version("11.0.0")
+    pa_version_under12p0 = _palv < Version("12.0.0")
+    pa_version_under13p0 = _palv < Version("13.0.0")
+    pa_version_under14p0 = _palv < Version("14.0.0")
+    pa_version_under14p1 = _palv < Version("14.0.1")
+    pa_version_under15p0 = _palv < Version("15.0.0")
+    pa_version_under16p0 = _palv < Version("16.0.0")
+except ImportError:
+    pa_version_under10p1 = True
+    pa_version_under11p0 = True
+    pa_version_under12p0 = True
+    pa_version_under13p0 = True
+    pa_version_under14p0 = True
+    pa_version_under14p1 = True
+    pa_version_under15p0 = True
+    pa_version_under16p0 = True

venv/lib/python3.10/site-packages/pandas/io/__init__.py

@@ -0,0 +1,13 @@
+# ruff: noqa: TCH004
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    # import modules that have public classes/functions
+    from pandas.io import (
+        formats,
+        json,
+        stata,
+    )
+
+    # mark only those modules as public
+    __all__ = ["formats", "json", "stata"]

venv/lib/python3.10/site-packages/pandas/io/_util.py

@@ -0,0 +1,34 @@
+from __future__ import annotations
+
+from typing import Callable
+
+from pandas.compat._optional import import_optional_dependency
+
+import pandas as pd
+
+
+def _arrow_dtype_mapping() -> dict:
+    pa = import_optional_dependency("pyarrow")
+    return {
+        pa.int8(): pd.Int8Dtype(),
+        pa.int16(): pd.Int16Dtype(),
+        pa.int32(): pd.Int32Dtype(),
+        pa.int64(): pd.Int64Dtype(),
+        pa.uint8(): pd.UInt8Dtype(),
+        pa.uint16(): pd.UInt16Dtype(),
+        pa.uint32(): pd.UInt32Dtype(),
+        pa.uint64(): pd.UInt64Dtype(),
+        pa.bool_(): pd.BooleanDtype(),
+        pa.string(): pd.StringDtype(),
+        pa.float32(): pd.Float32Dtype(),
+        pa.float64(): pd.Float64Dtype(),
+    }
+
+
+def arrow_string_types_mapper() -> Callable:
+    pa = import_optional_dependency("pyarrow")
+
+    return {
+        pa.string(): pd.StringDtype(storage="pyarrow_numpy"),
+        pa.large_string(): pd.StringDtype(storage="pyarrow_numpy"),
+    }.get

venv/lib/python3.10/site-packages/pandas/io/api.py

@@ -0,0 +1,65 @@
+"""
+Data IO api
+"""
+
+from pandas.io.clipboards import read_clipboard
+from pandas.io.excel import (
+    ExcelFile,
+    ExcelWriter,
+    read_excel,
+)
+from pandas.io.feather_format import read_feather
+from pandas.io.gbq import read_gbq
+from pandas.io.html import read_html
+from pandas.io.json import read_json
+from pandas.io.orc import read_orc
+from pandas.io.parquet import read_parquet
+from pandas.io.parsers import (
+    read_csv,
+    read_fwf,
+    read_table,
+)
+from pandas.io.pickle import (
+    read_pickle,
+    to_pickle,
+)
+from pandas.io.pytables import (
+    HDFStore,
+    read_hdf,
+)
+from pandas.io.sas import read_sas
+from pandas.io.spss import read_spss
+from pandas.io.sql import (
+    read_sql,
+    read_sql_query,
+    read_sql_table,
+)
+from pandas.io.stata import read_stata
+from pandas.io.xml import read_xml
+
+__all__ = [
+    "ExcelFile",
+    "ExcelWriter",
+    "HDFStore",
+    "read_clipboard",
+    "read_csv",
+    "read_excel",
+    "read_feather",
+    "read_fwf",
+    "read_gbq",
+    "read_hdf",
+    "read_html",
+    "read_json",
+    "read_orc",
+    "read_parquet",
+    "read_pickle",
+    "read_sas",
+    "read_spss",
+    "read_sql",
+    "read_sql_query",
+    "read_sql_table",
+    "read_stata",
+    "read_table",
+    "read_xml",
+    "to_pickle",
+]

venv/lib/python3.10/site-packages/pandas/io/clipboards.py

@@ -0,0 +1,197 @@
+""" io on the clipboard """
+from __future__ import annotations
+
+from io import StringIO
+from typing import TYPE_CHECKING
+import warnings
+
+from pandas._libs import lib
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.generic import ABCDataFrame
+
+from pandas import (
+    get_option,
+    option_context,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import DtypeBackend
+
+
+def read_clipboard(
+    sep: str = r"\s+",
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    **kwargs,
+):  # pragma: no cover
+    r"""
+    Read text from clipboard and pass to :func:`~pandas.read_csv`.
+
+    Parses clipboard contents similar to how CSV files are parsed
+    using :func:`~pandas.read_csv`.
+
+    Parameters
+    ----------
+    sep : str, default '\\s+'
+        A string or regex delimiter. The default of ``'\\s+'`` denotes
+        one or more whitespace characters.
+
+    dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    **kwargs
+        See :func:`~pandas.read_csv` for the full argument list.
+
+    Returns
+    -------
+    DataFrame
+        A parsed :class:`~pandas.DataFrame` object.
+
+    See Also
+    --------
+    DataFrame.to_clipboard : Copy object to the system clipboard.
+    read_csv : Read a comma-separated values (csv) file into DataFrame.
+    read_fwf : Read a table of fixed-width formatted lines into DataFrame.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
+    >>> df.to_clipboard()  # doctest: +SKIP
+    >>> pd.read_clipboard()  # doctest: +SKIP
+         A  B  C
+    0    1  2  3
+    1    4  5  6
+    """
+    encoding = kwargs.pop("encoding", "utf-8")
+
+    # only utf-8 is valid for passed value because that's what clipboard
+    # supports
+    if encoding is not None and encoding.lower().replace("-", "") != "utf8":
+        raise NotImplementedError("reading from clipboard only supports utf-8 encoding")
+
+    check_dtype_backend(dtype_backend)
+
+    from pandas.io.clipboard import clipboard_get
+    from pandas.io.parsers import read_csv
+
+    text = clipboard_get()
+
+    # Try to decode (if needed, as "text" might already be a string here).
+    try:
+        text = text.decode(kwargs.get("encoding") or get_option("display.encoding"))
+    except AttributeError:
+        pass
+
+    # Excel copies into clipboard with \t separation
+    # inspect no more then the 10 first lines, if they
+    # all contain an equal number (>0) of tabs, infer
+    # that this came from excel and set 'sep' accordingly
+    lines = text[:10000].split("\n")[:-1][:10]
+
+    # Need to remove leading white space, since read_csv
+    # accepts:
+    #    a  b
+    # 0  1  2
+    # 1  3  4
+
+    counts = {x.lstrip(" ").count("\t") for x in lines}
+    if len(lines) > 1 and len(counts) == 1 and counts.pop() != 0:
+        sep = "\t"
+        # check the number of leading tabs in the first line
+        # to account for index columns
+        index_length = len(lines[0]) - len(lines[0].lstrip(" \t"))
+        if index_length != 0:
+            kwargs.setdefault("index_col", list(range(index_length)))
+
+    # Edge case where sep is specified to be None, return to default
+    if sep is None and kwargs.get("delim_whitespace") is None:
+        sep = r"\s+"
+
+    # Regex separator currently only works with python engine.
+    # Default to python if separator is multi-character (regex)
+    if len(sep) > 1 and kwargs.get("engine") is None:
+        kwargs["engine"] = "python"
+    elif len(sep) > 1 and kwargs.get("engine") == "c":
+        warnings.warn(
+            "read_clipboard with regex separator does not work properly with c engine.",
+            stacklevel=find_stack_level(),
+        )
+
+    return read_csv(StringIO(text), sep=sep, dtype_backend=dtype_backend, **kwargs)
+
+
+def to_clipboard(
+    obj, excel: bool | None = True, sep: str | None = None, **kwargs
+) -> None:  # pragma: no cover
+    """
+    Attempt to write text representation of object to the system clipboard
+    The clipboard can be then pasted into Excel for example.
+
+    Parameters
+    ----------
+    obj : the object to write to the clipboard
+    excel : bool, defaults to True
+            if True, use the provided separator, writing in a csv
+            format for allowing easy pasting into excel.
+            if False, write a string representation of the object
+            to the clipboard
+    sep : optional, defaults to tab
+    other keywords are passed to to_csv
+
+    Notes
+    -----
+    Requirements for your platform
+      - Linux: xclip, or xsel (with PyQt4 modules)
+      - Windows:
+      - OS X:
+    """
+    encoding = kwargs.pop("encoding", "utf-8")
+
+    # testing if an invalid encoding is passed to clipboard
+    if encoding is not None and encoding.lower().replace("-", "") != "utf8":
+        raise ValueError("clipboard only supports utf-8 encoding")
+
+    from pandas.io.clipboard import clipboard_set
+
+    if excel is None:
+        excel = True
+
+    if excel:
+        try:
+            if sep is None:
+                sep = "\t"
+            buf = StringIO()
+
+            # clipboard_set (pyperclip) expects unicode
+            obj.to_csv(buf, sep=sep, encoding="utf-8", **kwargs)
+            text = buf.getvalue()
+
+            clipboard_set(text)
+            return
+        except TypeError:
+            warnings.warn(
+                "to_clipboard in excel mode requires a single character separator.",
+                stacklevel=find_stack_level(),
+            )
+    elif sep is not None:
+        warnings.warn(
+            "to_clipboard with excel=False ignores the sep argument.",
+            stacklevel=find_stack_level(),
+        )
+
+    if isinstance(obj, ABCDataFrame):
+        # str(df) has various unhelpful defaults, like truncation
+        with option_context("display.max_colwidth", None):
+            objstr = obj.to_string(**kwargs)
+    else:
+        objstr = str(obj)
+    clipboard_set(objstr)

venv/lib/python3.10/site-packages/pandas/io/common.py

@@ -0,0 +1,1267 @@
+"""Common IO api utilities"""
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+import codecs
+from collections import defaultdict
+from collections.abc import (
+    Hashable,
+    Mapping,
+    Sequence,
+)
+import dataclasses
+import functools
+import gzip
+from io import (
+    BufferedIOBase,
+    BytesIO,
+    RawIOBase,
+    StringIO,
+    TextIOBase,
+    TextIOWrapper,
+)
+import mmap
+import os
+from pathlib import Path
+import re
+import tarfile
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+    AnyStr,
+    DefaultDict,
+    Generic,
+    Literal,
+    TypeVar,
+    cast,
+    overload,
+)
+from urllib.parse import (
+    urljoin,
+    urlparse as parse_url,
+    uses_netloc,
+    uses_params,
+    uses_relative,
+)
+import warnings
+import zipfile
+
+from pandas._typing import (
+    BaseBuffer,
+    ReadCsvBuffer,
+)
+from pandas.compat import (
+    get_bz2_file,
+    get_lzma_file,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_file_like,
+    is_integer,
+    is_list_like,
+)
+from pandas.core.dtypes.generic import ABCMultiIndex
+
+from pandas.core.shared_docs import _shared_docs
+
+_VALID_URLS = set(uses_relative + uses_netloc + uses_params)
+_VALID_URLS.discard("")
+_RFC_3986_PATTERN = re.compile(r"^[A-Za-z][A-Za-z0-9+\-+.]*://")
+
+BaseBufferT = TypeVar("BaseBufferT", bound=BaseBuffer)
+
+
+if TYPE_CHECKING:
+    from types import TracebackType
+
+    from pandas._typing import (
+        CompressionDict,
+        CompressionOptions,
+        FilePath,
+        ReadBuffer,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+    from pandas import MultiIndex
+
+
+@dataclasses.dataclass
+class IOArgs:
+    """
+    Return value of io/common.py:_get_filepath_or_buffer.
+    """
+
+    filepath_or_buffer: str | BaseBuffer
+    encoding: str
+    mode: str
+    compression: CompressionDict
+    should_close: bool = False
+
+
+@dataclasses.dataclass
+class IOHandles(Generic[AnyStr]):
+    """
+    Return value of io/common.py:get_handle
+
+    Can be used as a context manager.
+
+    This is used to easily close created buffers and to handle corner cases when
+    TextIOWrapper is inserted.
+
+    handle: The file handle to be used.
+    created_handles: All file handles that are created by get_handle
+    is_wrapped: Whether a TextIOWrapper needs to be detached.
+    """
+
+    # handle might not implement the IO-interface
+    handle: IO[AnyStr]
+    compression: CompressionDict
+    created_handles: list[IO[bytes] | IO[str]] = dataclasses.field(default_factory=list)
+    is_wrapped: bool = False
+
+    def close(self) -> None:
+        """
+        Close all created buffers.
+
+        Note: If a TextIOWrapper was inserted, it is flushed and detached to
+        avoid closing the potentially user-created buffer.
+        """
+        if self.is_wrapped:
+            assert isinstance(self.handle, TextIOWrapper)
+            self.handle.flush()
+            self.handle.detach()
+            self.created_handles.remove(self.handle)
+        for handle in self.created_handles:
+            handle.close()
+        self.created_handles = []
+        self.is_wrapped = False
+
+    def __enter__(self) -> IOHandles[AnyStr]:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+
+def is_url(url: object) -> bool:
+    """
+    Check to see if a URL has a valid protocol.
+
+    Parameters
+    ----------
+    url : str or unicode
+
+    Returns
+    -------
+    isurl : bool
+        If `url` has a valid protocol return True otherwise False.
+    """
+    if not isinstance(url, str):
+        return False
+    return parse_url(url).scheme in _VALID_URLS
+
+
+@overload
+def _expand_user(filepath_or_buffer: str) -> str:
+    ...
+
+
+@overload
+def _expand_user(filepath_or_buffer: BaseBufferT) -> BaseBufferT:
+    ...
+
+
+def _expand_user(filepath_or_buffer: str | BaseBufferT) -> str | BaseBufferT:
+    """
+    Return the argument with an initial component of ~ or ~user
+    replaced by that user's home directory.
+
+    Parameters
+    ----------
+    filepath_or_buffer : object to be converted if possible
+
+    Returns
+    -------
+    expanded_filepath_or_buffer : an expanded filepath or the
+                                  input if not expandable
+    """
+    if isinstance(filepath_or_buffer, str):
+        return os.path.expanduser(filepath_or_buffer)
+    return filepath_or_buffer
+
+
+def validate_header_arg(header: object) -> None:
+    if header is None:
+        return
+    if is_integer(header):
+        header = cast(int, header)
+        if header < 0:
+            # GH 27779
+            raise ValueError(
+                "Passing negative integer to header is invalid. "
+                "For no header, use header=None instead"
+            )
+        return
+    if is_list_like(header, allow_sets=False):
+        header = cast(Sequence, header)
+        if not all(map(is_integer, header)):
+            raise ValueError("header must be integer or list of integers")
+        if any(i < 0 for i in header):
+            raise ValueError("cannot specify multi-index header with negative integers")
+        return
+    if is_bool(header):
+        raise TypeError(
+            "Passing a bool to header is invalid. Use header=None for no header or "
+            "header=int or list-like of ints to specify "
+            "the row(s) making up the column names"
+        )
+    # GH 16338
+    raise ValueError("header must be integer or list of integers")
+
+
+@overload
+def stringify_path(filepath_or_buffer: FilePath, convert_file_like: bool = ...) -> str:
+    ...
+
+
+@overload
+def stringify_path(
+    filepath_or_buffer: BaseBufferT, convert_file_like: bool = ...
+) -> BaseBufferT:
+    ...
+
+
+def stringify_path(
+    filepath_or_buffer: FilePath | BaseBufferT,
+    convert_file_like: bool = False,
+) -> str | BaseBufferT:
+    """
+    Attempt to convert a path-like object to a string.
+
+    Parameters
+    ----------
+    filepath_or_buffer : object to be converted
+
+    Returns
+    -------
+    str_filepath_or_buffer : maybe a string version of the object
+
+    Notes
+    -----
+    Objects supporting the fspath protocol are coerced
+    according to its __fspath__ method.
+
+    Any other object is passed through unchanged, which includes bytes,
+    strings, buffers, or anything else that's not even path-like.
+    """
+    if not convert_file_like and is_file_like(filepath_or_buffer):
+        # GH 38125: some fsspec objects implement os.PathLike but have already opened a
+        # file. This prevents opening the file a second time. infer_compression calls
+        # this function with convert_file_like=True to infer the compression.
+        return cast(BaseBufferT, filepath_or_buffer)
+
+    if isinstance(filepath_or_buffer, os.PathLike):
+        filepath_or_buffer = filepath_or_buffer.__fspath__()
+    return _expand_user(filepath_or_buffer)
+
+
+def urlopen(*args, **kwargs):
+    """
+    Lazy-import wrapper for stdlib urlopen, as that imports a big chunk of
+    the stdlib.
+    """
+    import urllib.request
+
+    return urllib.request.urlopen(*args, **kwargs)
+
+
+def is_fsspec_url(url: FilePath | BaseBuffer) -> bool:
+    """
+    Returns true if the given URL looks like
+    something fsspec can handle
+    """
+    return (
+        isinstance(url, str)
+        and bool(_RFC_3986_PATTERN.match(url))
+        and not url.startswith(("http://", "https://"))
+    )
+
+
+@doc(
+    storage_options=_shared_docs["storage_options"],
+    compression_options=_shared_docs["compression_options"] % "filepath_or_buffer",
+)
+def _get_filepath_or_buffer(
+    filepath_or_buffer: FilePath | BaseBuffer,
+    encoding: str = "utf-8",
+    compression: CompressionOptions | None = None,
+    mode: str = "r",
+    storage_options: StorageOptions | None = None,
+) -> IOArgs:
+    """
+    If the filepath_or_buffer is a url, translate and return the buffer.
+    Otherwise passthrough.
+
+    Parameters
+    ----------
+    filepath_or_buffer : a url, filepath (str, py.path.local or pathlib.Path),
+                         or buffer
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    encoding : the encoding to use to decode bytes, default is 'utf-8'
+    mode : str, optional
+
+    {storage_options}
+
+
+    Returns the dataclass IOArgs.
+    """
+    filepath_or_buffer = stringify_path(filepath_or_buffer)
+
+    # handle compression dict
+    compression_method, compression = get_compression_method(compression)
+    compression_method = infer_compression(filepath_or_buffer, compression_method)
+
+    # GH21227 internal compression is not used for non-binary handles.
+    if compression_method and hasattr(filepath_or_buffer, "write") and "b" not in mode:
+        warnings.warn(
+            "compression has no effect when passing a non-binary object as input.",
+            RuntimeWarning,
+            stacklevel=find_stack_level(),
+        )
+        compression_method = None
+
+    compression = dict(compression, method=compression_method)
+
+    # bz2 and xz do not write the byte order mark for utf-16 and utf-32
+    # print a warning when writing such files
+    if (
+        "w" in mode
+        and compression_method in ["bz2", "xz"]
+        and encoding in ["utf-16", "utf-32"]
+    ):
+        warnings.warn(
+            f"{compression} will not write the byte order mark for {encoding}",
+            UnicodeWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    # Use binary mode when converting path-like objects to file-like objects (fsspec)
+    # except when text mode is explicitly requested. The original mode is returned if
+    # fsspec is not used.
+    fsspec_mode = mode
+    if "t" not in fsspec_mode and "b" not in fsspec_mode:
+        fsspec_mode += "b"
+
+    if isinstance(filepath_or_buffer, str) and is_url(filepath_or_buffer):
+        # TODO: fsspec can also handle HTTP via requests, but leaving this
+        # unchanged. using fsspec appears to break the ability to infer if the
+        # server responded with gzipped data
+        storage_options = storage_options or {}
+
+        # waiting until now for importing to match intended lazy logic of
+        # urlopen function defined elsewhere in this module
+        import urllib.request
+
+        # assuming storage_options is to be interpreted as headers
+        req_info = urllib.request.Request(filepath_or_buffer, headers=storage_options)
+        with urlopen(req_info) as req:
+            content_encoding = req.headers.get("Content-Encoding", None)
+            if content_encoding == "gzip":
+                # Override compression based on Content-Encoding header
+                compression = {"method": "gzip"}
+            reader = BytesIO(req.read())
+        return IOArgs(
+            filepath_or_buffer=reader,
+            encoding=encoding,
+            compression=compression,
+            should_close=True,
+            mode=fsspec_mode,
+        )
+
+    if is_fsspec_url(filepath_or_buffer):
+        assert isinstance(
+            filepath_or_buffer, str
+        )  # just to appease mypy for this branch
+        # two special-case s3-like protocols; these have special meaning in Hadoop,
+        # but are equivalent to just "s3" from fsspec's point of view
+        # cc #11071
+        if filepath_or_buffer.startswith("s3a://"):
+            filepath_or_buffer = filepath_or_buffer.replace("s3a://", "s3://")
+        if filepath_or_buffer.startswith("s3n://"):
+            filepath_or_buffer = filepath_or_buffer.replace("s3n://", "s3://")
+        fsspec = import_optional_dependency("fsspec")
+
+        # If botocore is installed we fallback to reading with anon=True
+        # to allow reads from public buckets
+        err_types_to_retry_with_anon: list[Any] = []
+        try:
+            import_optional_dependency("botocore")
+            from botocore.exceptions import (
+                ClientError,
+                NoCredentialsError,
+            )
+
+            err_types_to_retry_with_anon = [
+                ClientError,
+                NoCredentialsError,
+                PermissionError,
+            ]
+        except ImportError:
+            pass
+
+        try:
+            file_obj = fsspec.open(
+                filepath_or_buffer, mode=fsspec_mode, **(storage_options or {})
+            ).open()
+        # GH 34626 Reads from Public Buckets without Credentials needs anon=True
+        except tuple(err_types_to_retry_with_anon):
+            if storage_options is None:
+                storage_options = {"anon": True}
+            else:
+                # don't mutate user input.
+                storage_options = dict(storage_options)
+                storage_options["anon"] = True
+            file_obj = fsspec.open(
+                filepath_or_buffer, mode=fsspec_mode, **(storage_options or {})
+            ).open()
+
+        return IOArgs(
+            filepath_or_buffer=file_obj,
+            encoding=encoding,
+            compression=compression,
+            should_close=True,
+            mode=fsspec_mode,
+        )
+    elif storage_options:
+        raise ValueError(
+            "storage_options passed with file object or non-fsspec file path"
+        )
+
+    if isinstance(filepath_or_buffer, (str, bytes, mmap.mmap)):
+        return IOArgs(
+            filepath_or_buffer=_expand_user(filepath_or_buffer),
+            encoding=encoding,
+            compression=compression,
+            should_close=False,
+            mode=mode,
+        )
+
+    # is_file_like requires (read | write) & __iter__ but __iter__ is only
+    # needed for read_csv(engine=python)
+    if not (
+        hasattr(filepath_or_buffer, "read") or hasattr(filepath_or_buffer, "write")
+    ):
+        msg = f"Invalid file path or buffer object type: {type(filepath_or_buffer)}"
+        raise ValueError(msg)
+
+    return IOArgs(
+        filepath_or_buffer=filepath_or_buffer,
+        encoding=encoding,
+        compression=compression,
+        should_close=False,
+        mode=mode,
+    )
+
+
+def file_path_to_url(path: str) -> str:
+    """
+    converts an absolute native path to a FILE URL.
+
+    Parameters
+    ----------
+    path : a path in native format
+
+    Returns
+    -------
+    a valid FILE URL
+    """
+    # lazify expensive import (~30ms)
+    from urllib.request import pathname2url
+
+    return urljoin("file:", pathname2url(path))
+
+
+extension_to_compression = {
+    ".tar": "tar",
+    ".tar.gz": "tar",
+    ".tar.bz2": "tar",
+    ".tar.xz": "tar",
+    ".gz": "gzip",
+    ".bz2": "bz2",
+    ".zip": "zip",
+    ".xz": "xz",
+    ".zst": "zstd",
+}
+_supported_compressions = set(extension_to_compression.values())
+
+
+def get_compression_method(
+    compression: CompressionOptions,
+) -> tuple[str | None, CompressionDict]:
+    """
+    Simplifies a compression argument to a compression method string and
+    a mapping containing additional arguments.
+
+    Parameters
+    ----------
+    compression : str or mapping
+        If string, specifies the compression method. If mapping, value at key
+        'method' specifies compression method.
+
+    Returns
+    -------
+    tuple of ({compression method}, Optional[str]
+              {compression arguments}, Dict[str, Any])
+
+    Raises
+    ------
+    ValueError on mapping missing 'method' key
+    """
+    compression_method: str | None
+    if isinstance(compression, Mapping):
+        compression_args = dict(compression)
+        try:
+            compression_method = compression_args.pop("method")
+        except KeyError as err:
+            raise ValueError("If mapping, compression must have key 'method'") from err
+    else:
+        compression_args = {}
+        compression_method = compression
+    return compression_method, compression_args
+
+
+@doc(compression_options=_shared_docs["compression_options"] % "filepath_or_buffer")
+def infer_compression(
+    filepath_or_buffer: FilePath | BaseBuffer, compression: str | None
+) -> str | None:
+    """
+    Get the compression method for filepath_or_buffer. If compression='infer',
+    the inferred compression method is returned. Otherwise, the input
+    compression method is returned unchanged, unless it's invalid, in which
+    case an error is raised.
+
+    Parameters
+    ----------
+    filepath_or_buffer : str or file handle
+        File path or object.
+    {compression_options}
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    Returns
+    -------
+    string or None
+
+    Raises
+    ------
+    ValueError on invalid compression specified.
+    """
+    if compression is None:
+        return None
+
+    # Infer compression
+    if compression == "infer":
+        # Convert all path types (e.g. pathlib.Path) to strings
+        filepath_or_buffer = stringify_path(filepath_or_buffer, convert_file_like=True)
+        if not isinstance(filepath_or_buffer, str):
+            # Cannot infer compression of a buffer, assume no compression
+            return None
+
+        # Infer compression from the filename/URL extension
+        for extension, compression in extension_to_compression.items():
+            if filepath_or_buffer.lower().endswith(extension):
+                return compression
+        return None
+
+    # Compression has been specified. Check that it's valid
+    if compression in _supported_compressions:
+        return compression
+
+    valid = ["infer", None] + sorted(_supported_compressions)
+    msg = (
+        f"Unrecognized compression type: {compression}\n"
+        f"Valid compression types are {valid}"
+    )
+    raise ValueError(msg)
+
+
+def check_parent_directory(path: Path | str) -> None:
+    """
+    Check if parent directory of a file exists, raise OSError if it does not
+
+    Parameters
+    ----------
+    path: Path or str
+        Path to check parent directory of
+    """
+    parent = Path(path).parent
+    if not parent.is_dir():
+        raise OSError(rf"Cannot save file into a non-existent directory: '{parent}'")
+
+
+@overload
+def get_handle(
+    path_or_buf: FilePath | BaseBuffer,
+    mode: str,
+    *,
+    encoding: str | None = ...,
+    compression: CompressionOptions = ...,
+    memory_map: bool = ...,
+    is_text: Literal[False],
+    errors: str | None = ...,
+    storage_options: StorageOptions = ...,
+) -> IOHandles[bytes]:
+    ...
+
+
+@overload
+def get_handle(
+    path_or_buf: FilePath | BaseBuffer,
+    mode: str,
+    *,
+    encoding: str | None = ...,
+    compression: CompressionOptions = ...,
+    memory_map: bool = ...,
+    is_text: Literal[True] = ...,
+    errors: str | None = ...,
+    storage_options: StorageOptions = ...,
+) -> IOHandles[str]:
+    ...
+
+
+@overload
+def get_handle(
+    path_or_buf: FilePath | BaseBuffer,
+    mode: str,
+    *,
+    encoding: str | None = ...,
+    compression: CompressionOptions = ...,
+    memory_map: bool = ...,
+    is_text: bool = ...,
+    errors: str | None = ...,
+    storage_options: StorageOptions = ...,
+) -> IOHandles[str] | IOHandles[bytes]:
+    ...
+
+
+@doc(compression_options=_shared_docs["compression_options"] % "path_or_buf")
+def get_handle(
+    path_or_buf: FilePath | BaseBuffer,
+    mode: str,
+    *,
+    encoding: str | None = None,
+    compression: CompressionOptions | None = None,
+    memory_map: bool = False,
+    is_text: bool = True,
+    errors: str | None = None,
+    storage_options: StorageOptions | None = None,
+) -> IOHandles[str] | IOHandles[bytes]:
+    """
+    Get file handle for given path/buffer and mode.
+
+    Parameters
+    ----------
+    path_or_buf : str or file handle
+        File path or object.
+    mode : str
+        Mode to open path_or_buf with.
+    encoding : str or None
+        Encoding to use.
+    {compression_options}
+
+           May be a dict with key 'method' as compression mode
+           and other keys as compression options if compression
+           mode is 'zip'.
+
+           Passing compression options as keys in dict is
+           supported for compression modes 'gzip', 'bz2', 'zstd' and 'zip'.
+
+        .. versionchanged:: 1.4.0 Zstandard support.
+
+    memory_map : bool, default False
+        See parsers._parser_params for more information. Only used by read_csv.
+    is_text : bool, default True
+        Whether the type of the content passed to the file/buffer is string or
+        bytes. This is not the same as `"b" not in mode`. If a string content is
+        passed to a binary file/buffer, a wrapper is inserted.
+    errors : str, default 'strict'
+        Specifies how encoding and decoding errors are to be handled.
+        See the errors argument for :func:`open` for a full list
+        of options.
+    storage_options: StorageOptions = None
+        Passed to _get_filepath_or_buffer
+
+    Returns the dataclass IOHandles
+    """
+    # Windows does not default to utf-8. Set to utf-8 for a consistent behavior
+    encoding = encoding or "utf-8"
+
+    errors = errors or "strict"
+
+    # read_csv does not know whether the buffer is opened in binary/text mode
+    if _is_binary_mode(path_or_buf, mode) and "b" not in mode:
+        mode += "b"
+
+    # validate encoding and errors
+    codecs.lookup(encoding)
+    if isinstance(errors, str):
+        codecs.lookup_error(errors)
+
+    # open URLs
+    ioargs = _get_filepath_or_buffer(
+        path_or_buf,
+        encoding=encoding,
+        compression=compression,
+        mode=mode,
+        storage_options=storage_options,
+    )
+
+    handle = ioargs.filepath_or_buffer
+    handles: list[BaseBuffer]
+
+    # memory mapping needs to be the first step
+    # only used for read_csv
+    handle, memory_map, handles = _maybe_memory_map(handle, memory_map)
+
+    is_path = isinstance(handle, str)
+    compression_args = dict(ioargs.compression)
+    compression = compression_args.pop("method")
+
+    # Only for write methods
+    if "r" not in mode and is_path:
+        check_parent_directory(str(handle))
+
+    if compression:
+        if compression != "zstd":
+            # compression libraries do not like an explicit text-mode
+            ioargs.mode = ioargs.mode.replace("t", "")
+        elif compression == "zstd" and "b" not in ioargs.mode:
+            # python-zstandard defaults to text mode, but we always expect
+            # compression libraries to use binary mode.
+            ioargs.mode += "b"
+
+        # GZ Compression
+        if compression == "gzip":
+            if isinstance(handle, str):
+                # error: Incompatible types in assignment (expression has type
+                # "GzipFile", variable has type "Union[str, BaseBuffer]")
+                handle = gzip.GzipFile(  # type: ignore[assignment]
+                    filename=handle,
+                    mode=ioargs.mode,
+                    **compression_args,
+                )
+            else:
+                handle = gzip.GzipFile(
+                    # No overload variant of "GzipFile" matches argument types
+                    # "Union[str, BaseBuffer]", "str", "Dict[str, Any]"
+                    fileobj=handle,  # type: ignore[call-overload]
+                    mode=ioargs.mode,
+                    **compression_args,
+                )
+
+        # BZ Compression
+        elif compression == "bz2":
+            # Overload of "BZ2File" to handle pickle protocol 5
+            # "Union[str, BaseBuffer]", "str", "Dict[str, Any]"
+            handle = get_bz2_file()(  # type: ignore[call-overload]
+                handle,
+                mode=ioargs.mode,
+                **compression_args,
+            )
+
+        # ZIP Compression
+        elif compression == "zip":
+            # error: Argument 1 to "_BytesZipFile" has incompatible type
+            # "Union[str, BaseBuffer]"; expected "Union[Union[str, PathLike[str]],
+            # ReadBuffer[bytes], WriteBuffer[bytes]]"
+            handle = _BytesZipFile(
+                handle, ioargs.mode, **compression_args  # type: ignore[arg-type]
+            )
+            if handle.buffer.mode == "r":
+                handles.append(handle)
+                zip_names = handle.buffer.namelist()
+                if len(zip_names) == 1:
+                    handle = handle.buffer.open(zip_names.pop())
+                elif not zip_names:
+                    raise ValueError(f"Zero files found in ZIP file {path_or_buf}")
+                else:
+                    raise ValueError(
+                        "Multiple files found in ZIP file. "
+                        f"Only one file per ZIP: {zip_names}"
+                    )
+
+        # TAR Encoding
+        elif compression == "tar":
+            compression_args.setdefault("mode", ioargs.mode)
+            if isinstance(handle, str):
+                handle = _BytesTarFile(name=handle, **compression_args)
+            else:
+                # error: Argument "fileobj" to "_BytesTarFile" has incompatible
+                # type "BaseBuffer"; expected "Union[ReadBuffer[bytes],
+                # WriteBuffer[bytes], None]"
+                handle = _BytesTarFile(
+                    fileobj=handle, **compression_args  # type: ignore[arg-type]
+                )
+            assert isinstance(handle, _BytesTarFile)
+            if "r" in handle.buffer.mode:
+                handles.append(handle)
+                files = handle.buffer.getnames()
+                if len(files) == 1:
+                    file = handle.buffer.extractfile(files[0])
+                    assert file is not None
+                    handle = file
+                elif not files:
+                    raise ValueError(f"Zero files found in TAR archive {path_or_buf}")
+                else:
+                    raise ValueError(
+                        "Multiple files found in TAR archive. "
+                        f"Only one file per TAR archive: {files}"
+                    )
+
+        # XZ Compression
+        elif compression == "xz":
+            # error: Argument 1 to "LZMAFile" has incompatible type "Union[str,
+            # BaseBuffer]"; expected "Optional[Union[Union[str, bytes, PathLike[str],
+            # PathLike[bytes]], IO[bytes]], None]"
+            handle = get_lzma_file()(
+                handle, ioargs.mode, **compression_args  # type: ignore[arg-type]
+            )
+
+        # Zstd Compression
+        elif compression == "zstd":
+            zstd = import_optional_dependency("zstandard")
+            if "r" in ioargs.mode:
+                open_args = {"dctx": zstd.ZstdDecompressor(**compression_args)}
+            else:
+                open_args = {"cctx": zstd.ZstdCompressor(**compression_args)}
+            handle = zstd.open(
+                handle,
+                mode=ioargs.mode,
+                **open_args,
+            )
+
+        # Unrecognized Compression
+        else:
+            msg = f"Unrecognized compression type: {compression}"
+            raise ValueError(msg)
+
+        assert not isinstance(handle, str)
+        handles.append(handle)
+
+    elif isinstance(handle, str):
+        # Check whether the filename is to be opened in binary mode.
+        # Binary mode does not support 'encoding' and 'newline'.
+        if ioargs.encoding and "b" not in ioargs.mode:
+            # Encoding
+            handle = open(
+                handle,
+                ioargs.mode,
+                encoding=ioargs.encoding,
+                errors=errors,
+                newline="",
+            )
+        else:
+            # Binary mode
+            handle = open(handle, ioargs.mode)
+        handles.append(handle)
+
+    # Convert BytesIO or file objects passed with an encoding
+    is_wrapped = False
+    if not is_text and ioargs.mode == "rb" and isinstance(handle, TextIOBase):
+        # not added to handles as it does not open/buffer resources
+        handle = _BytesIOWrapper(
+            handle,
+            encoding=ioargs.encoding,
+        )
+    elif is_text and (
+        compression or memory_map or _is_binary_mode(handle, ioargs.mode)
+    ):
+        if (
+            not hasattr(handle, "readable")
+            or not hasattr(handle, "writable")
+            or not hasattr(handle, "seekable")
+        ):
+            handle = _IOWrapper(handle)
+        # error: Argument 1 to "TextIOWrapper" has incompatible type
+        # "_IOWrapper"; expected "IO[bytes]"
+        handle = TextIOWrapper(
+            handle,  # type: ignore[arg-type]
+            encoding=ioargs.encoding,
+            errors=errors,
+            newline="",
+        )
+        handles.append(handle)
+        # only marked as wrapped when the caller provided a handle
+        is_wrapped = not (
+            isinstance(ioargs.filepath_or_buffer, str) or ioargs.should_close
+        )
+
+    if "r" in ioargs.mode and not hasattr(handle, "read"):
+        raise TypeError(
+            "Expected file path name or file-like object, "
+            f"got {type(ioargs.filepath_or_buffer)} type"
+        )
+
+    handles.reverse()  # close the most recently added buffer first
+    if ioargs.should_close:
+        assert not isinstance(ioargs.filepath_or_buffer, str)
+        handles.append(ioargs.filepath_or_buffer)
+
+    return IOHandles(
+        # error: Argument "handle" to "IOHandles" has incompatible type
+        # "Union[TextIOWrapper, GzipFile, BaseBuffer, typing.IO[bytes],
+        # typing.IO[Any]]"; expected "pandas._typing.IO[Any]"
+        handle=handle,  # type: ignore[arg-type]
+        # error: Argument "created_handles" to "IOHandles" has incompatible type
+        # "List[BaseBuffer]"; expected "List[Union[IO[bytes], IO[str]]]"
+        created_handles=handles,  # type: ignore[arg-type]
+        is_wrapped=is_wrapped,
+        compression=ioargs.compression,
+    )
+
+
+# error: Definition of "__enter__" in base class "IOBase" is incompatible
+# with definition in base class "BinaryIO"
+class _BufferedWriter(BytesIO, ABC):  # type: ignore[misc]
+    """
+    Some objects do not support multiple .write() calls (TarFile and ZipFile).
+    This wrapper writes to the underlying buffer on close.
+    """
+
+    buffer = BytesIO()
+
+    @abstractmethod
+    def write_to_buffer(self) -> None:
+        ...
+
+    def close(self) -> None:
+        if self.closed:
+            # already closed
+            return
+        if self.getbuffer().nbytes:
+            # write to buffer
+            self.seek(0)
+            with self.buffer:
+                self.write_to_buffer()
+        else:
+            self.buffer.close()
+        super().close()
+
+
+class _BytesTarFile(_BufferedWriter):
+    def __init__(
+        self,
+        name: str | None = None,
+        mode: Literal["r", "a", "w", "x"] = "r",
+        fileobj: ReadBuffer[bytes] | WriteBuffer[bytes] | None = None,
+        archive_name: str | None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.archive_name = archive_name
+        self.name = name
+        # error: Incompatible types in assignment (expression has type "TarFile",
+        # base class "_BufferedWriter" defined the type as "BytesIO")
+        self.buffer: tarfile.TarFile = tarfile.TarFile.open(  # type: ignore[assignment]
+            name=name,
+            mode=self.extend_mode(mode),
+            fileobj=fileobj,
+            **kwargs,
+        )
+
+    def extend_mode(self, mode: str) -> str:
+        mode = mode.replace("b", "")
+        if mode != "w":
+            return mode
+        if self.name is not None:
+            suffix = Path(self.name).suffix
+            if suffix in (".gz", ".xz", ".bz2"):
+                mode = f"{mode}:{suffix[1:]}"
+        return mode
+
+    def infer_filename(self) -> str | None:
+        """
+        If an explicit archive_name is not given, we still want the file inside the zip
+        file not to be named something.tar, because that causes confusion (GH39465).
+        """
+        if self.name is None:
+            return None
+
+        filename = Path(self.name)
+        if filename.suffix == ".tar":
+            return filename.with_suffix("").name
+        elif filename.suffix in (".tar.gz", ".tar.bz2", ".tar.xz"):
+            return filename.with_suffix("").with_suffix("").name
+        return filename.name
+
+    def write_to_buffer(self) -> None:
+        # TarFile needs a non-empty string
+        archive_name = self.archive_name or self.infer_filename() or "tar"
+        tarinfo = tarfile.TarInfo(name=archive_name)
+        tarinfo.size = len(self.getvalue())
+        self.buffer.addfile(tarinfo, self)
+
+
+class _BytesZipFile(_BufferedWriter):
+    def __init__(
+        self,
+        file: FilePath | ReadBuffer[bytes] | WriteBuffer[bytes],
+        mode: str,
+        archive_name: str | None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        mode = mode.replace("b", "")
+        self.archive_name = archive_name
+
+        kwargs.setdefault("compression", zipfile.ZIP_DEFLATED)
+        # error: Incompatible types in assignment (expression has type "ZipFile",
+        # base class "_BufferedWriter" defined the type as "BytesIO")
+        self.buffer: zipfile.ZipFile = zipfile.ZipFile(  # type: ignore[assignment]
+            file, mode, **kwargs
+        )
+
+    def infer_filename(self) -> str | None:
+        """
+        If an explicit archive_name is not given, we still want the file inside the zip
+        file not to be named something.zip, because that causes confusion (GH39465).
+        """
+        if isinstance(self.buffer.filename, (os.PathLike, str)):
+            filename = Path(self.buffer.filename)
+            if filename.suffix == ".zip":
+                return filename.with_suffix("").name
+            return filename.name
+        return None
+
+    def write_to_buffer(self) -> None:
+        # ZipFile needs a non-empty string
+        archive_name = self.archive_name or self.infer_filename() or "zip"
+        self.buffer.writestr(archive_name, self.getvalue())
+
+
+class _IOWrapper:
+    # TextIOWrapper is overly strict: it request that the buffer has seekable, readable,
+    # and writable. If we have a read-only buffer, we shouldn't need writable and vice
+    # versa. Some buffers, are seek/read/writ-able but they do not have the "-able"
+    # methods, e.g., tempfile.SpooledTemporaryFile.
+    # If a buffer does not have the above "-able" methods, we simple assume they are
+    # seek/read/writ-able.
+    def __init__(self, buffer: BaseBuffer) -> None:
+        self.buffer = buffer
+
+    def __getattr__(self, name: str):
+        return getattr(self.buffer, name)
+
+    def readable(self) -> bool:
+        if hasattr(self.buffer, "readable"):
+            return self.buffer.readable()
+        return True
+
+    def seekable(self) -> bool:
+        if hasattr(self.buffer, "seekable"):
+            return self.buffer.seekable()
+        return True
+
+    def writable(self) -> bool:
+        if hasattr(self.buffer, "writable"):
+            return self.buffer.writable()
+        return True
+
+
+class _BytesIOWrapper:
+    # Wrapper that wraps a StringIO buffer and reads bytes from it
+    # Created for compat with pyarrow read_csv
+    def __init__(self, buffer: StringIO | TextIOBase, encoding: str = "utf-8") -> None:
+        self.buffer = buffer
+        self.encoding = encoding
+        # Because a character can be represented by more than 1 byte,
+        # it is possible that reading will produce more bytes than n
+        # We store the extra bytes in this overflow variable, and append the
+        # overflow to the front of the bytestring the next time reading is performed
+        self.overflow = b""
+
+    def __getattr__(self, attr: str):
+        return getattr(self.buffer, attr)
+
+    def read(self, n: int | None = -1) -> bytes:
+        assert self.buffer is not None
+        bytestring = self.buffer.read(n).encode(self.encoding)
+        # When n=-1/n greater than remaining bytes: Read entire file/rest of file
+        combined_bytestring = self.overflow + bytestring
+        if n is None or n < 0 or n >= len(combined_bytestring):
+            self.overflow = b""
+            return combined_bytestring
+        else:
+            to_return = combined_bytestring[:n]
+            self.overflow = combined_bytestring[n:]
+            return to_return
+
+
+def _maybe_memory_map(
+    handle: str | BaseBuffer, memory_map: bool
+) -> tuple[str | BaseBuffer, bool, list[BaseBuffer]]:
+    """Try to memory map file/buffer."""
+    handles: list[BaseBuffer] = []
+    memory_map &= hasattr(handle, "fileno") or isinstance(handle, str)
+    if not memory_map:
+        return handle, memory_map, handles
+
+    # mmap used by only read_csv
+    handle = cast(ReadCsvBuffer, handle)
+
+    # need to open the file first
+    if isinstance(handle, str):
+        handle = open(handle, "rb")
+        handles.append(handle)
+
+    try:
+        # open mmap and adds *-able
+        # error: Argument 1 to "_IOWrapper" has incompatible type "mmap";
+        # expected "BaseBuffer"
+        wrapped = _IOWrapper(
+            mmap.mmap(
+                handle.fileno(), 0, access=mmap.ACCESS_READ  # type: ignore[arg-type]
+            )
+        )
+    finally:
+        for handle in reversed(handles):
+            # error: "BaseBuffer" has no attribute "close"
+            handle.close()  # type: ignore[attr-defined]
+
+    return wrapped, memory_map, [wrapped]
+
+
+def file_exists(filepath_or_buffer: FilePath | BaseBuffer) -> bool:
+    """Test whether file exists."""
+    exists = False
+    filepath_or_buffer = stringify_path(filepath_or_buffer)
+    if not isinstance(filepath_or_buffer, str):
+        return exists
+    try:
+        exists = os.path.exists(filepath_or_buffer)
+        # gh-5874: if the filepath is too long will raise here
+    except (TypeError, ValueError):
+        pass
+    return exists
+
+
+def _is_binary_mode(handle: FilePath | BaseBuffer, mode: str) -> bool:
+    """Whether the handle is opened in binary mode"""
+    # specified by user
+    if "t" in mode or "b" in mode:
+        return "b" in mode
+
+    # exceptions
+    text_classes = (
+        # classes that expect string but have 'b' in mode
+        codecs.StreamWriter,
+        codecs.StreamReader,
+        codecs.StreamReaderWriter,
+    )
+    if issubclass(type(handle), text_classes):
+        return False
+
+    return isinstance(handle, _get_binary_io_classes()) or "b" in getattr(
+        handle, "mode", mode
+    )
+
+
+@functools.lru_cache
+def _get_binary_io_classes() -> tuple[type, ...]:
+    """IO classes that that expect bytes"""
+    binary_classes: tuple[type, ...] = (BufferedIOBase, RawIOBase)
+
+    # python-zstandard doesn't use any of the builtin base classes; instead we
+    # have to use the `zstd.ZstdDecompressionReader` class for isinstance checks.
+    # Unfortunately `zstd.ZstdDecompressionReader` isn't exposed by python-zstandard
+    # so we have to get it from a `zstd.ZstdDecompressor` instance.
+    # See also https://github.com/indygreg/python-zstandard/pull/165.
+    zstd = import_optional_dependency("zstandard", errors="ignore")
+    if zstd is not None:
+        with zstd.ZstdDecompressor().stream_reader(b"") as reader:
+            binary_classes += (type(reader),)
+
+    return binary_classes
+
+
+def is_potential_multi_index(
+    columns: Sequence[Hashable] | MultiIndex,
+    index_col: bool | Sequence[int] | None = None,
+) -> bool:
+    """
+    Check whether or not the `columns` parameter
+    could be converted into a MultiIndex.
+
+    Parameters
+    ----------
+    columns : array-like
+        Object which may or may not be convertible into a MultiIndex
+    index_col : None, bool or list, optional
+        Column or columns to use as the (possibly hierarchical) index
+
+    Returns
+    -------
+    bool : Whether or not columns could become a MultiIndex
+    """
+    if index_col is None or isinstance(index_col, bool):
+        index_col = []
+
+    return bool(
+        len(columns)
+        and not isinstance(columns, ABCMultiIndex)
+        and all(isinstance(c, tuple) for c in columns if c not in list(index_col))
+    )
+
+
+def dedup_names(
+    names: Sequence[Hashable], is_potential_multiindex: bool
+) -> Sequence[Hashable]:
+    """
+    Rename column names if duplicates exist.
+
+    Currently the renaming is done by appending a period and an autonumeric,
+    but a custom pattern may be supported in the future.
+
+    Examples
+    --------
+    >>> dedup_names(["x", "y", "x", "x"], is_potential_multiindex=False)
+    ['x', 'y', 'x.1', 'x.2']
+    """
+    names = list(names)  # so we can index
+    counts: DefaultDict[Hashable, int] = defaultdict(int)
+
+    for i, col in enumerate(names):
+        cur_count = counts[col]
+
+        while cur_count > 0:
+            counts[col] = cur_count + 1
+
+            if is_potential_multiindex:
+                # for mypy
+                assert isinstance(col, tuple)
+                col = col[:-1] + (f"{col[-1]}.{cur_count}",)
+            else:
+                col = f"{col}.{cur_count}"
+            cur_count = counts[col]
+
+        names[i] = col
+        counts[col] = cur_count + 1
+
+    return names

venv/lib/python3.10/site-packages/pandas/io/feather_format.py

@@ -0,0 +1,143 @@
+""" feather-format compat """
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+from pandas._config import using_pyarrow_string_dtype
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+from pandas.util._validators import check_dtype_backend
+
+import pandas as pd
+from pandas.core.api import DataFrame
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io._util import arrow_string_types_mapper
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        DtypeBackend,
+        FilePath,
+        ReadBuffer,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def to_feather(
+    df: DataFrame,
+    path: FilePath | WriteBuffer[bytes],
+    storage_options: StorageOptions | None = None,
+    **kwargs: Any,
+) -> None:
+    """
+    Write a DataFrame to the binary Feather format.
+
+    Parameters
+    ----------
+    df : DataFrame
+    path : str, path object, or file-like object
+    {storage_options}
+    **kwargs :
+        Additional keywords passed to `pyarrow.feather.write_feather`.
+
+    """
+    import_optional_dependency("pyarrow")
+    from pyarrow import feather
+
+    if not isinstance(df, DataFrame):
+        raise ValueError("feather only support IO with DataFrames")
+
+    with get_handle(
+        path, "wb", storage_options=storage_options, is_text=False
+    ) as handles:
+        feather.write_feather(df, handles.handle, **kwargs)
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def read_feather(
+    path: FilePath | ReadBuffer[bytes],
+    columns: Sequence[Hashable] | None = None,
+    use_threads: bool = True,
+    storage_options: StorageOptions | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+) -> DataFrame:
+    """
+    Load a feather-format object from the file path.
+
+    Parameters
+    ----------
+    path : str, path object, or file-like object
+        String, path object (implementing ``os.PathLike[str]``), or file-like
+        object implementing a binary ``read()`` function. The string could be a URL.
+        Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is
+        expected. A local file could be: ``file://localhost/path/to/table.feather``.
+    columns : sequence, default None
+        If not provided, all columns are read.
+    use_threads : bool, default True
+        Whether to parallelize reading using multiple threads.
+    {storage_options}
+
+    dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
+        Back-end data type applied to the resultant :class:`DataFrame`
+        (still experimental). Behaviour is as follows:
+
+        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
+          (default).
+        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
+          DataFrame.
+
+        .. versionadded:: 2.0
+
+    Returns
+    -------
+    type of object stored in file
+
+    Examples
+    --------
+    >>> df = pd.read_feather("path/to/file.feather")  # doctest: +SKIP
+    """
+    import_optional_dependency("pyarrow")
+    from pyarrow import feather
+
+    # import utils to register the pyarrow extension types
+    import pandas.core.arrays.arrow.extension_types  # pyright: ignore[reportUnusedImport] # noqa: F401
+
+    check_dtype_backend(dtype_backend)
+
+    with get_handle(
+        path, "rb", storage_options=storage_options, is_text=False
+    ) as handles:
+        if dtype_backend is lib.no_default and not using_pyarrow_string_dtype():
+            return feather.read_feather(
+                handles.handle, columns=columns, use_threads=bool(use_threads)
+            )
+
+        pa_table = feather.read_table(
+            handles.handle, columns=columns, use_threads=bool(use_threads)
+        )
+
+        if dtype_backend == "numpy_nullable":
+            from pandas.io._util import _arrow_dtype_mapping
+
+            return pa_table.to_pandas(types_mapper=_arrow_dtype_mapping().get)
+
+        elif dtype_backend == "pyarrow":
+            return pa_table.to_pandas(types_mapper=pd.ArrowDtype)
+
+        elif using_pyarrow_string_dtype():
+            return pa_table.to_pandas(types_mapper=arrow_string_types_mapper())
+        else:
+            raise NotImplementedError

venv/lib/python3.10/site-packages/pandas/io/formats/__init__.py

@@ -0,0 +1,9 @@
+# ruff: noqa: TCH004
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    # import modules that have public classes/functions
+    from pandas.io.formats import style
+
+    # and mark only those modules as public
+    __all__ = ["style"]

venv/lib/python3.10/site-packages/pandas/io/formats/_color_data.py

@@ -0,0 +1,157 @@
+# GH37967: Enable the use of CSS named colors, as defined in
+# matplotlib.colors.CSS4_COLORS, when exporting to Excel.
+# This data has been copied here, instead of being imported from matplotlib,
+# not to have ``to_excel`` methods require matplotlib.
+# source: matplotlib._color_data (3.3.3)
+from __future__ import annotations
+
+CSS4_COLORS = {
+    "aliceblue": "F0F8FF",
+    "antiquewhite": "FAEBD7",
+    "aqua": "00FFFF",
+    "aquamarine": "7FFFD4",
+    "azure": "F0FFFF",
+    "beige": "F5F5DC",
+    "bisque": "FFE4C4",
+    "black": "000000",
+    "blanchedalmond": "FFEBCD",
+    "blue": "0000FF",
+    "blueviolet": "8A2BE2",
+    "brown": "A52A2A",
+    "burlywood": "DEB887",
+    "cadetblue": "5F9EA0",
+    "chartreuse": "7FFF00",
+    "chocolate": "D2691E",
+    "coral": "FF7F50",
+    "cornflowerblue": "6495ED",
+    "cornsilk": "FFF8DC",
+    "crimson": "DC143C",
+    "cyan": "00FFFF",
+    "darkblue": "00008B",
+    "darkcyan": "008B8B",
+    "darkgoldenrod": "B8860B",
+    "darkgray": "A9A9A9",
+    "darkgreen": "006400",
+    "darkgrey": "A9A9A9",
+    "darkkhaki": "BDB76B",
+    "darkmagenta": "8B008B",
+    "darkolivegreen": "556B2F",
+    "darkorange": "FF8C00",
+    "darkorchid": "9932CC",
+    "darkred": "8B0000",
+    "darksalmon": "E9967A",
+    "darkseagreen": "8FBC8F",
+    "darkslateblue": "483D8B",
+    "darkslategray": "2F4F4F",
+    "darkslategrey": "2F4F4F",
+    "darkturquoise": "00CED1",
+    "darkviolet": "9400D3",
+    "deeppink": "FF1493",
+    "deepskyblue": "00BFFF",
+    "dimgray": "696969",
+    "dimgrey": "696969",
+    "dodgerblue": "1E90FF",
+    "firebrick": "B22222",
+    "floralwhite": "FFFAF0",
+    "forestgreen": "228B22",
+    "fuchsia": "FF00FF",
+    "gainsboro": "DCDCDC",
+    "ghostwhite": "F8F8FF",
+    "gold": "FFD700",
+    "goldenrod": "DAA520",
+    "gray": "808080",
+    "green": "008000",
+    "greenyellow": "ADFF2F",
+    "grey": "808080",
+    "honeydew": "F0FFF0",
+    "hotpink": "FF69B4",
+    "indianred": "CD5C5C",
+    "indigo": "4B0082",
+    "ivory": "FFFFF0",
+    "khaki": "F0E68C",
+    "lavender": "E6E6FA",
+    "lavenderblush": "FFF0F5",
+    "lawngreen": "7CFC00",
+    "lemonchiffon": "FFFACD",
+    "lightblue": "ADD8E6",
+    "lightcoral": "F08080",
+    "lightcyan": "E0FFFF",
+    "lightgoldenrodyellow": "FAFAD2",
+    "lightgray": "D3D3D3",
+    "lightgreen": "90EE90",
+    "lightgrey": "D3D3D3",
+    "lightpink": "FFB6C1",
+    "lightsalmon": "FFA07A",
+    "lightseagreen": "20B2AA",
+    "lightskyblue": "87CEFA",
+    "lightslategray": "778899",
+    "lightslategrey": "778899",
+    "lightsteelblue": "B0C4DE",
+    "lightyellow": "FFFFE0",
+    "lime": "00FF00",
+    "limegreen": "32CD32",
+    "linen": "FAF0E6",
+    "magenta": "FF00FF",
+    "maroon": "800000",
+    "mediumaquamarine": "66CDAA",
+    "mediumblue": "0000CD",
+    "mediumorchid": "BA55D3",
+    "mediumpurple": "9370DB",
+    "mediumseagreen": "3CB371",
+    "mediumslateblue": "7B68EE",
+    "mediumspringgreen": "00FA9A",
+    "mediumturquoise": "48D1CC",
+    "mediumvioletred": "C71585",
+    "midnightblue": "191970",
+    "mintcream": "F5FFFA",
+    "mistyrose": "FFE4E1",
+    "moccasin": "FFE4B5",
+    "navajowhite": "FFDEAD",
+    "navy": "000080",
+    "oldlace": "FDF5E6",
+    "olive": "808000",
+    "olivedrab": "6B8E23",
+    "orange": "FFA500",
+    "orangered": "FF4500",
+    "orchid": "DA70D6",
+    "palegoldenrod": "EEE8AA",
+    "palegreen": "98FB98",
+    "paleturquoise": "AFEEEE",
+    "palevioletred": "DB7093",
+    "papayawhip": "FFEFD5",
+    "peachpuff": "FFDAB9",
+    "peru": "CD853F",
+    "pink": "FFC0CB",
+    "plum": "DDA0DD",
+    "powderblue": "B0E0E6",
+    "purple": "800080",
+    "rebeccapurple": "663399",
+    "red": "FF0000",
+    "rosybrown": "BC8F8F",
+    "royalblue": "4169E1",
+    "saddlebrown": "8B4513",
+    "salmon": "FA8072",
+    "sandybrown": "F4A460",
+    "seagreen": "2E8B57",
+    "seashell": "FFF5EE",
+    "sienna": "A0522D",
+    "silver": "C0C0C0",
+    "skyblue": "87CEEB",
+    "slateblue": "6A5ACD",
+    "slategray": "708090",
+    "slategrey": "708090",
+    "snow": "FFFAFA",
+    "springgreen": "00FF7F",
+    "steelblue": "4682B4",
+    "tan": "D2B48C",
+    "teal": "008080",
+    "thistle": "D8BFD8",
+    "tomato": "FF6347",
+    "turquoise": "40E0D0",
+    "violet": "EE82EE",
+    "wheat": "F5DEB3",
+    "white": "FFFFFF",
+    "whitesmoke": "F5F5F5",
+    "yellow": "FFFF00",
+    "yellowgreen": "9ACD32",
+}

venv/lib/python3.10/site-packages/pandas/io/formats/console.py

@@ -0,0 +1,94 @@
+"""
+Internal module for console introspection
+"""
+from __future__ import annotations
+
+from shutil import get_terminal_size
+
+
+def get_console_size() -> tuple[int | None, int | None]:
+    """
+    Return console size as tuple = (width, height).
+
+    Returns (None,None) in non-interactive session.
+    """
+    from pandas import get_option
+
+    display_width = get_option("display.width")
+    display_height = get_option("display.max_rows")
+
+    # Consider
+    # interactive shell terminal, can detect term size
+    # interactive non-shell terminal (ipnb/ipqtconsole), cannot detect term
+    # size non-interactive script, should disregard term size
+
+    # in addition
+    # width,height have default values, but setting to 'None' signals
+    # should use Auto-Detection, But only in interactive shell-terminal.
+    # Simple. yeah.
+
+    if in_interactive_session():
+        if in_ipython_frontend():
+            # sane defaults for interactive non-shell terminal
+            # match default for width,height in config_init
+            from pandas._config.config import get_default_val
+
+            terminal_width = get_default_val("display.width")
+            terminal_height = get_default_val("display.max_rows")
+        else:
+            # pure terminal
+            terminal_width, terminal_height = get_terminal_size()
+    else:
+        terminal_width, terminal_height = None, None
+
+    # Note if the User sets width/Height to None (auto-detection)
+    # and we're in a script (non-inter), this will return (None,None)
+    # caller needs to deal.
+    return display_width or terminal_width, display_height or terminal_height
+
+
+# ----------------------------------------------------------------------
+# Detect our environment
+
+
+def in_interactive_session() -> bool:
+    """
+    Check if we're running in an interactive shell.
+
+    Returns
+    -------
+    bool
+        True if running under python/ipython interactive shell.
+    """
+    from pandas import get_option
+
+    def check_main():
+        try:
+            import __main__ as main
+        except ModuleNotFoundError:
+            return get_option("mode.sim_interactive")
+        return not hasattr(main, "__file__") or get_option("mode.sim_interactive")
+
+    try:
+        # error: Name '__IPYTHON__' is not defined
+        return __IPYTHON__ or check_main()  # type: ignore[name-defined]
+    except NameError:
+        return check_main()
+
+
+def in_ipython_frontend() -> bool:
+    """
+    Check if we're inside an IPython zmq frontend.
+
+    Returns
+    -------
+    bool
+    """
+    try:
+        # error: Name 'get_ipython' is not defined
+        ip = get_ipython()  # type: ignore[name-defined]
+        return "zmq" in str(type(ip)).lower()
+    except NameError:
+        pass
+
+    return False

venv/lib/python3.10/site-packages/pandas/io/formats/css.py

@@ -0,0 +1,421 @@
+"""
+Utilities for interpreting CSS from Stylers for formatting non-HTML outputs.
+"""
+from __future__ import annotations
+
+import re
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+)
+import warnings
+
+from pandas.errors import CSSWarning
+from pandas.util._exceptions import find_stack_level
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Generator,
+        Iterable,
+        Iterator,
+    )
+
+
+def _side_expander(prop_fmt: str) -> Callable:
+    """
+    Wrapper to expand shorthand property into top, right, bottom, left properties
+
+    Parameters
+    ----------
+    side : str
+        The border side to expand into properties
+
+    Returns
+    -------
+        function: Return to call when a 'border(-{side}): {value}' string is encountered
+    """
+
+    def expand(self, prop, value: str) -> Generator[tuple[str, str], None, None]:
+        """
+        Expand shorthand property into side-specific property (top, right, bottom, left)
+
+        Parameters
+        ----------
+            prop (str): CSS property name
+            value (str): String token for property
+
+        Yields
+        ------
+            Tuple (str, str): Expanded property, value
+        """
+        tokens = value.split()
+        try:
+            mapping = self.SIDE_SHORTHANDS[len(tokens)]
+        except KeyError:
+            warnings.warn(
+                f'Could not expand "{prop}: {value}"',
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+            return
+        for key, idx in zip(self.SIDES, mapping):
+            yield prop_fmt.format(key), tokens[idx]
+
+    return expand
+
+
+def _border_expander(side: str = "") -> Callable:
+    """
+    Wrapper to expand 'border' property into border color, style, and width properties
+
+    Parameters
+    ----------
+    side : str
+        The border side to expand into properties
+
+    Returns
+    -------
+        function: Return to call when a 'border(-{side}): {value}' string is encountered
+    """
+    if side != "":
+        side = f"-{side}"
+
+    def expand(self, prop, value: str) -> Generator[tuple[str, str], None, None]:
+        """
+        Expand border into color, style, and width tuples
+
+        Parameters
+        ----------
+            prop : str
+                CSS property name passed to styler
+            value : str
+                Value passed to styler for property
+
+        Yields
+        ------
+            Tuple (str, str): Expanded property, value
+        """
+        tokens = value.split()
+        if len(tokens) == 0 or len(tokens) > 3:
+            warnings.warn(
+                f'Too many tokens provided to "{prop}" (expected 1-3)',
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        # TODO: Can we use current color as initial value to comply with CSS standards?
+        border_declarations = {
+            f"border{side}-color": "black",
+            f"border{side}-style": "none",
+            f"border{side}-width": "medium",
+        }
+        for token in tokens:
+            if token.lower() in self.BORDER_STYLES:
+                border_declarations[f"border{side}-style"] = token
+            elif any(ratio in token.lower() for ratio in self.BORDER_WIDTH_RATIOS):
+                border_declarations[f"border{side}-width"] = token
+            else:
+                border_declarations[f"border{side}-color"] = token
+            # TODO: Warn user if item entered more than once (e.g. "border: red green")
+
+        # Per CSS, "border" will reset previous "border-*" definitions
+        yield from self.atomize(border_declarations.items())
+
+    return expand
+
+
+class CSSResolver:
+    """
+    A callable for parsing and resolving CSS to atomic properties.
+    """
+
+    UNIT_RATIOS = {
+        "pt": ("pt", 1),
+        "em": ("em", 1),
+        "rem": ("pt", 12),
+        "ex": ("em", 0.5),
+        # 'ch':
+        "px": ("pt", 0.75),
+        "pc": ("pt", 12),
+        "in": ("pt", 72),
+        "cm": ("in", 1 / 2.54),
+        "mm": ("in", 1 / 25.4),
+        "q": ("mm", 0.25),
+        "!!default": ("em", 0),
+    }
+
+    FONT_SIZE_RATIOS = UNIT_RATIOS.copy()
+    FONT_SIZE_RATIOS.update(
+        {
+            "%": ("em", 0.01),
+            "xx-small": ("rem", 0.5),
+            "x-small": ("rem", 0.625),
+            "small": ("rem", 0.8),
+            "medium": ("rem", 1),
+            "large": ("rem", 1.125),
+            "x-large": ("rem", 1.5),
+            "xx-large": ("rem", 2),
+            "smaller": ("em", 1 / 1.2),
+            "larger": ("em", 1.2),
+            "!!default": ("em", 1),
+        }
+    )
+
+    MARGIN_RATIOS = UNIT_RATIOS.copy()
+    MARGIN_RATIOS.update({"none": ("pt", 0)})
+
+    BORDER_WIDTH_RATIOS = UNIT_RATIOS.copy()
+    BORDER_WIDTH_RATIOS.update(
+        {
+            "none": ("pt", 0),
+            "thick": ("px", 4),
+            "medium": ("px", 2),
+            "thin": ("px", 1),
+            # Default: medium only if solid
+        }
+    )
+
+    BORDER_STYLES = [
+        "none",
+        "hidden",
+        "dotted",
+        "dashed",
+        "solid",
+        "double",
+        "groove",
+        "ridge",
+        "inset",
+        "outset",
+        "mediumdashdot",
+        "dashdotdot",
+        "hair",
+        "mediumdashdotdot",
+        "dashdot",
+        "slantdashdot",
+        "mediumdashed",
+    ]
+
+    SIDE_SHORTHANDS = {
+        1: [0, 0, 0, 0],
+        2: [0, 1, 0, 1],
+        3: [0, 1, 2, 1],
+        4: [0, 1, 2, 3],
+    }
+
+    SIDES = ("top", "right", "bottom", "left")
+
+    CSS_EXPANSIONS = {
+        **{
+            (f"border-{prop}" if prop else "border"): _border_expander(prop)
+            for prop in ["", "top", "right", "bottom", "left"]
+        },
+        **{
+            f"border-{prop}": _side_expander(f"border-{{:s}}-{prop}")
+            for prop in ["color", "style", "width"]
+        },
+        "margin": _side_expander("margin-{:s}"),
+        "padding": _side_expander("padding-{:s}"),
+    }
+
+    def __call__(
+        self,
+        declarations: str | Iterable[tuple[str, str]],
+        inherited: dict[str, str] | None = None,
+    ) -> dict[str, str]:
+        """
+        The given declarations to atomic properties.
+
+        Parameters
+        ----------
+        declarations_str : str | Iterable[tuple[str, str]]
+            A CSS string or set of CSS declaration tuples
+            e.g. "font-weight: bold; background: blue" or
+            {("font-weight", "bold"), ("background", "blue")}
+        inherited : dict, optional
+            Atomic properties indicating the inherited style context in which
+            declarations_str is to be resolved. ``inherited`` should already
+            be resolved, i.e. valid output of this method.
+
+        Returns
+        -------
+        dict
+            Atomic CSS 2.2 properties.
+
+        Examples
+        --------
+        >>> resolve = CSSResolver()
+        >>> inherited = {'font-family': 'serif', 'font-weight': 'bold'}
+        >>> out = resolve('''
+        ...               border-color: BLUE RED;
+        ...               font-size: 1em;
+        ...               font-size: 2em;
+        ...               font-weight: normal;
+        ...               font-weight: inherit;
+        ...               ''', inherited)
+        >>> sorted(out.items())  # doctest: +NORMALIZE_WHITESPACE
+        [('border-bottom-color', 'blue'),
+         ('border-left-color', 'red'),
+         ('border-right-color', 'red'),
+         ('border-top-color', 'blue'),
+         ('font-family', 'serif'),
+         ('font-size', '24pt'),
+         ('font-weight', 'bold')]
+        """
+        if isinstance(declarations, str):
+            declarations = self.parse(declarations)
+        props = dict(self.atomize(declarations))
+        if inherited is None:
+            inherited = {}
+
+        props = self._update_initial(props, inherited)
+        props = self._update_font_size(props, inherited)
+        return self._update_other_units(props)
+
+    def _update_initial(
+        self,
+        props: dict[str, str],
+        inherited: dict[str, str],
+    ) -> dict[str, str]:
+        # 1. resolve inherited, initial
+        for prop, val in inherited.items():
+            if prop not in props:
+                props[prop] = val
+
+        new_props = props.copy()
+        for prop, val in props.items():
+            if val == "inherit":
+                val = inherited.get(prop, "initial")
+
+            if val in ("initial", None):
+                # we do not define a complete initial stylesheet
+                del new_props[prop]
+            else:
+                new_props[prop] = val
+        return new_props
+
+    def _update_font_size(
+        self,
+        props: dict[str, str],
+        inherited: dict[str, str],
+    ) -> dict[str, str]:
+        # 2. resolve relative font size
+        if props.get("font-size"):
+            props["font-size"] = self.size_to_pt(
+                props["font-size"],
+                self._get_font_size(inherited),
+                conversions=self.FONT_SIZE_RATIOS,
+            )
+        return props
+
+    def _get_font_size(self, props: dict[str, str]) -> float | None:
+        if props.get("font-size"):
+            font_size_string = props["font-size"]
+            return self._get_float_font_size_from_pt(font_size_string)
+        return None
+
+    def _get_float_font_size_from_pt(self, font_size_string: str) -> float:
+        assert font_size_string.endswith("pt")
+        return float(font_size_string.rstrip("pt"))
+
+    def _update_other_units(self, props: dict[str, str]) -> dict[str, str]:
+        font_size = self._get_font_size(props)
+        # 3. TODO: resolve other font-relative units
+        for side in self.SIDES:
+            prop = f"border-{side}-width"
+            if prop in props:
+                props[prop] = self.size_to_pt(
+                    props[prop],
+                    em_pt=font_size,
+                    conversions=self.BORDER_WIDTH_RATIOS,
+                )
+
+            for prop in [f"margin-{side}", f"padding-{side}"]:
+                if prop in props:
+                    # TODO: support %
+                    props[prop] = self.size_to_pt(
+                        props[prop],
+                        em_pt=font_size,
+                        conversions=self.MARGIN_RATIOS,
+                    )
+        return props
+
+    def size_to_pt(self, in_val, em_pt=None, conversions=UNIT_RATIOS) -> str:
+        def _error():
+            warnings.warn(
+                f"Unhandled size: {repr(in_val)}",
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+            return self.size_to_pt("1!!default", conversions=conversions)
+
+        match = re.match(r"^(\S*?)([a-zA-Z%!].*)", in_val)
+        if match is None:
+            return _error()
+
+        val, unit = match.groups()
+        if val == "":
+            # hack for 'large' etc.
+            val = 1
+        else:
+            try:
+                val = float(val)
+            except ValueError:
+                return _error()
+
+        while unit != "pt":
+            if unit == "em":
+                if em_pt is None:
+                    unit = "rem"
+                else:
+                    val *= em_pt
+                    unit = "pt"
+                continue
+
+            try:
+                unit, mul = conversions[unit]
+            except KeyError:
+                return _error()
+            val *= mul
+
+        val = round(val, 5)
+        if int(val) == val:
+            size_fmt = f"{int(val):d}pt"
+        else:
+            size_fmt = f"{val:f}pt"
+        return size_fmt
+
+    def atomize(self, declarations: Iterable) -> Generator[tuple[str, str], None, None]:
+        for prop, value in declarations:
+            prop = prop.lower()
+            value = value.lower()
+            if prop in self.CSS_EXPANSIONS:
+                expand = self.CSS_EXPANSIONS[prop]
+                yield from expand(self, prop, value)
+            else:
+                yield prop, value
+
+    def parse(self, declarations_str: str) -> Iterator[tuple[str, str]]:
+        """
+        Generates (prop, value) pairs from declarations.
+
+        In a future version may generate parsed tokens from tinycss/tinycss2
+
+        Parameters
+        ----------
+        declarations_str : str
+        """
+        for decl in declarations_str.split(";"):
+            if not decl.strip():
+                continue
+            prop, sep, val = decl.partition(":")
+            prop = prop.strip().lower()
+            # TODO: don't lowercase case sensitive parts of values (strings)
+            val = val.strip().lower()
+            if sep:
+                yield prop, val
+            else:
+                warnings.warn(
+                    f"Ill-formatted attribute: expected a colon in {repr(decl)}",
+                    CSSWarning,
+                    stacklevel=find_stack_level(),
+                )

venv/lib/python3.10/site-packages/pandas/io/formats/csvs.py

@@ -0,0 +1,330 @@
+"""
+Module for formatting output data into CSV files.
+"""
+
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Iterator,
+    Sequence,
+)
+import csv as csvlib
+import os
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    cast,
+)
+
+import numpy as np
+
+from pandas._libs import writers as libwriters
+from pandas._typing import SequenceNotStr
+from pandas.util._decorators import cache_readonly
+
+from pandas.core.dtypes.generic import (
+    ABCDatetimeIndex,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCPeriodIndex,
+)
+from pandas.core.dtypes.missing import notna
+
+from pandas.core.indexes.api import Index
+
+from pandas.io.common import get_handle
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        CompressionOptions,
+        FilePath,
+        FloatFormatType,
+        IndexLabel,
+        StorageOptions,
+        WriteBuffer,
+        npt,
+    )
+
+    from pandas.io.formats.format import DataFrameFormatter
+
+
+_DEFAULT_CHUNKSIZE_CELLS = 100_000
+
+
+class CSVFormatter:
+    cols: npt.NDArray[np.object_]
+
+    def __init__(
+        self,
+        formatter: DataFrameFormatter,
+        path_or_buf: FilePath | WriteBuffer[str] | WriteBuffer[bytes] = "",
+        sep: str = ",",
+        cols: Sequence[Hashable] | None = None,
+        index_label: IndexLabel | None = None,
+        mode: str = "w",
+        encoding: str | None = None,
+        errors: str = "strict",
+        compression: CompressionOptions = "infer",
+        quoting: int | None = None,
+        lineterminator: str | None = "\n",
+        chunksize: int | None = None,
+        quotechar: str | None = '"',
+        date_format: str | None = None,
+        doublequote: bool = True,
+        escapechar: str | None = None,
+        storage_options: StorageOptions | None = None,
+    ) -> None:
+        self.fmt = formatter
+
+        self.obj = self.fmt.frame
+
+        self.filepath_or_buffer = path_or_buf
+        self.encoding = encoding
+        self.compression: CompressionOptions = compression
+        self.mode = mode
+        self.storage_options = storage_options
+
+        self.sep = sep
+        self.index_label = self._initialize_index_label(index_label)
+        self.errors = errors
+        self.quoting = quoting or csvlib.QUOTE_MINIMAL
+        self.quotechar = self._initialize_quotechar(quotechar)
+        self.doublequote = doublequote
+        self.escapechar = escapechar
+        self.lineterminator = lineterminator or os.linesep
+        self.date_format = date_format
+        self.cols = self._initialize_columns(cols)
+        self.chunksize = self._initialize_chunksize(chunksize)
+
+    @property
+    def na_rep(self) -> str:
+        return self.fmt.na_rep
+
+    @property
+    def float_format(self) -> FloatFormatType | None:
+        return self.fmt.float_format
+
+    @property
+    def decimal(self) -> str:
+        return self.fmt.decimal
+
+    @property
+    def header(self) -> bool | SequenceNotStr[str]:
+        return self.fmt.header
+
+    @property
+    def index(self) -> bool:
+        return self.fmt.index
+
+    def _initialize_index_label(self, index_label: IndexLabel | None) -> IndexLabel:
+        if index_label is not False:
+            if index_label is None:
+                return self._get_index_label_from_obj()
+            elif not isinstance(index_label, (list, tuple, np.ndarray, ABCIndex)):
+                # given a string for a DF with Index
+                return [index_label]
+        return index_label
+
+    def _get_index_label_from_obj(self) -> Sequence[Hashable]:
+        if isinstance(self.obj.index, ABCMultiIndex):
+            return self._get_index_label_multiindex()
+        else:
+            return self._get_index_label_flat()
+
+    def _get_index_label_multiindex(self) -> Sequence[Hashable]:
+        return [name or "" for name in self.obj.index.names]
+
+    def _get_index_label_flat(self) -> Sequence[Hashable]:
+        index_label = self.obj.index.name
+        return [""] if index_label is None else [index_label]
+
+    def _initialize_quotechar(self, quotechar: str | None) -> str | None:
+        if self.quoting != csvlib.QUOTE_NONE:
+            # prevents crash in _csv
+            return quotechar
+        return None
+
+    @property
+    def has_mi_columns(self) -> bool:
+        return bool(isinstance(self.obj.columns, ABCMultiIndex))
+
+    def _initialize_columns(
+        self, cols: Iterable[Hashable] | None
+    ) -> npt.NDArray[np.object_]:
+        # validate mi options
+        if self.has_mi_columns:
+            if cols is not None:
+                msg = "cannot specify cols with a MultiIndex on the columns"
+                raise TypeError(msg)
+
+        if cols is not None:
+            if isinstance(cols, ABCIndex):
+                cols = cols._get_values_for_csv(**self._number_format)
+            else:
+                cols = list(cols)
+            self.obj = self.obj.loc[:, cols]
+
+        # update columns to include possible multiplicity of dupes
+        # and make sure cols is just a list of labels
+        new_cols = self.obj.columns
+        return new_cols._get_values_for_csv(**self._number_format)
+
+    def _initialize_chunksize(self, chunksize: int | None) -> int:
+        if chunksize is None:
+            return (_DEFAULT_CHUNKSIZE_CELLS // (len(self.cols) or 1)) or 1
+        return int(chunksize)
+
+    @property
+    def _number_format(self) -> dict[str, Any]:
+        """Dictionary used for storing number formatting settings."""
+        return {
+            "na_rep": self.na_rep,
+            "float_format": self.float_format,
+            "date_format": self.date_format,
+            "quoting": self.quoting,
+            "decimal": self.decimal,
+        }
+
+    @cache_readonly
+    def data_index(self) -> Index:
+        data_index = self.obj.index
+        if (
+            isinstance(data_index, (ABCDatetimeIndex, ABCPeriodIndex))
+            and self.date_format is not None
+        ):
+            data_index = Index(
+                [x.strftime(self.date_format) if notna(x) else "" for x in data_index]
+            )
+        elif isinstance(data_index, ABCMultiIndex):
+            data_index = data_index.remove_unused_levels()
+        return data_index
+
+    @property
+    def nlevels(self) -> int:
+        if self.index:
+            return getattr(self.data_index, "nlevels", 1)
+        else:
+            return 0
+
+    @property
+    def _has_aliases(self) -> bool:
+        return isinstance(self.header, (tuple, list, np.ndarray, ABCIndex))
+
+    @property
+    def _need_to_save_header(self) -> bool:
+        return bool(self._has_aliases or self.header)
+
+    @property
+    def write_cols(self) -> SequenceNotStr[Hashable]:
+        if self._has_aliases:
+            assert not isinstance(self.header, bool)
+            if len(self.header) != len(self.cols):
+                raise ValueError(
+                    f"Writing {len(self.cols)} cols but got {len(self.header)} aliases"
+                )
+            return self.header
+        else:
+            # self.cols is an ndarray derived from Index._get_values_for_csv,
+            #  so its entries are strings, i.e. hashable
+            return cast(SequenceNotStr[Hashable], self.cols)
+
+    @property
+    def encoded_labels(self) -> list[Hashable]:
+        encoded_labels: list[Hashable] = []
+
+        if self.index and self.index_label:
+            assert isinstance(self.index_label, Sequence)
+            encoded_labels = list(self.index_label)
+
+        if not self.has_mi_columns or self._has_aliases:
+            encoded_labels += list(self.write_cols)
+
+        return encoded_labels
+
+    def save(self) -> None:
+        """
+        Create the writer & save.
+        """
+        # apply compression and byte/text conversion
+        with get_handle(
+            self.filepath_or_buffer,
+            self.mode,
+            encoding=self.encoding,
+            errors=self.errors,
+            compression=self.compression,
+            storage_options=self.storage_options,
+        ) as handles:
+            # Note: self.encoding is irrelevant here
+            self.writer = csvlib.writer(
+                handles.handle,
+                lineterminator=self.lineterminator,
+                delimiter=self.sep,
+                quoting=self.quoting,
+                doublequote=self.doublequote,
+                escapechar=self.escapechar,
+                quotechar=self.quotechar,
+            )
+
+            self._save()
+
+    def _save(self) -> None:
+        if self._need_to_save_header:
+            self._save_header()
+        self._save_body()
+
+    def _save_header(self) -> None:
+        if not self.has_mi_columns or self._has_aliases:
+            self.writer.writerow(self.encoded_labels)
+        else:
+            for row in self._generate_multiindex_header_rows():
+                self.writer.writerow(row)
+
+    def _generate_multiindex_header_rows(self) -> Iterator[list[Hashable]]:
+        columns = self.obj.columns
+        for i in range(columns.nlevels):
+            # we need at least 1 index column to write our col names
+            col_line = []
+            if self.index:
+                # name is the first column
+                col_line.append(columns.names[i])
+
+                if isinstance(self.index_label, list) and len(self.index_label) > 1:
+                    col_line.extend([""] * (len(self.index_label) - 1))
+
+            col_line.extend(columns._get_level_values(i))
+            yield col_line
+
+        # Write out the index line if it's not empty.
+        # Otherwise, we will print out an extraneous
+        # blank line between the mi and the data rows.
+        if self.encoded_labels and set(self.encoded_labels) != {""}:
+            yield self.encoded_labels + [""] * len(columns)
+
+    def _save_body(self) -> None:
+        nrows = len(self.data_index)
+        chunks = (nrows // self.chunksize) + 1
+        for i in range(chunks):
+            start_i = i * self.chunksize
+            end_i = min(start_i + self.chunksize, nrows)
+            if start_i >= end_i:
+                break
+            self._save_chunk(start_i, end_i)
+
+    def _save_chunk(self, start_i: int, end_i: int) -> None:
+        # create the data for a chunk
+        slicer = slice(start_i, end_i)
+        df = self.obj.iloc[slicer]
+
+        res = df._get_values_for_csv(**self._number_format)
+        data = list(res._iter_column_arrays())
+
+        ix = self.data_index[slicer]._get_values_for_csv(**self._number_format)
+        libwriters.write_csv_rows(
+            data,
+            ix,
+            self.nlevels,
+            self.cols,
+            self.writer,
+        )

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

@@ -0,0 +1,962 @@
+"""
+Utilities for conversion to writer-agnostic Excel representation.
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Mapping,
+    Sequence,
+)
+import functools
+import itertools
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    cast,
+)
+import warnings
+
+import numpy as np
+
+from pandas._libs.lib import is_list_like
+from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes import missing
+from pandas.core.dtypes.common import (
+    is_float,
+    is_scalar,
+)
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+)
+import pandas.core.common as com
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.formats._color_data import CSS4_COLORS
+from pandas.io.formats.css import (
+    CSSResolver,
+    CSSWarning,
+)
+from pandas.io.formats.format import get_level_lengths
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        FilePath,
+        IndexLabel,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+
+    from pandas import ExcelWriter
+
+
+class ExcelCell:
+    __fields__ = ("row", "col", "val", "style", "mergestart", "mergeend")
+    __slots__ = __fields__
+
+    def __init__(
+        self,
+        row: int,
+        col: int,
+        val,
+        style=None,
+        mergestart: int | None = None,
+        mergeend: int | None = None,
+    ) -> None:
+        self.row = row
+        self.col = col
+        self.val = val
+        self.style = style
+        self.mergestart = mergestart
+        self.mergeend = mergeend
+
+
+class CssExcelCell(ExcelCell):
+    def __init__(
+        self,
+        row: int,
+        col: int,
+        val,
+        style: dict | None,
+        css_styles: dict[tuple[int, int], list[tuple[str, Any]]] | None,
+        css_row: int,
+        css_col: int,
+        css_converter: Callable | None,
+        **kwargs,
+    ) -> None:
+        if css_styles and css_converter:
+            # Use dict to get only one (case-insensitive) declaration per property
+            declaration_dict = {
+                prop.lower(): val for prop, val in css_styles[css_row, css_col]
+            }
+            # Convert to frozenset for order-invariant caching
+            unique_declarations = frozenset(declaration_dict.items())
+            style = css_converter(unique_declarations)
+
+        super().__init__(row=row, col=col, val=val, style=style, **kwargs)
+
+
+class CSSToExcelConverter:
+    """
+    A callable for converting CSS declarations to ExcelWriter styles
+
+    Supports parts of CSS 2.2, with minimal CSS 3.0 support (e.g. text-shadow),
+    focusing on font styling, backgrounds, borders and alignment.
+
+    Operates by first computing CSS styles in a fairly generic
+    way (see :meth:`compute_css`) then determining Excel style
+    properties from CSS properties (see :meth:`build_xlstyle`).
+
+    Parameters
+    ----------
+    inherited : str, optional
+        CSS declarations understood to be the containing scope for the
+        CSS processed by :meth:`__call__`.
+    """
+
+    NAMED_COLORS = CSS4_COLORS
+
+    VERTICAL_MAP = {
+        "top": "top",
+        "text-top": "top",
+        "middle": "center",
+        "baseline": "bottom",
+        "bottom": "bottom",
+        "text-bottom": "bottom",
+        # OpenXML also has 'justify', 'distributed'
+    }
+
+    BOLD_MAP = {
+        "bold": True,
+        "bolder": True,
+        "600": True,
+        "700": True,
+        "800": True,
+        "900": True,
+        "normal": False,
+        "lighter": False,
+        "100": False,
+        "200": False,
+        "300": False,
+        "400": False,
+        "500": False,
+    }
+
+    ITALIC_MAP = {
+        "normal": False,
+        "italic": True,
+        "oblique": True,
+    }
+
+    FAMILY_MAP = {
+        "serif": 1,  # roman
+        "sans-serif": 2,  # swiss
+        "cursive": 4,  # script
+        "fantasy": 5,  # decorative
+    }
+
+    BORDER_STYLE_MAP = {
+        style.lower(): style
+        for style in [
+            "dashed",
+            "mediumDashDot",
+            "dashDotDot",
+            "hair",
+            "dotted",
+            "mediumDashDotDot",
+            "double",
+            "dashDot",
+            "slantDashDot",
+            "mediumDashed",
+        ]
+    }
+
+    # NB: Most of the methods here could be classmethods, as only __init__
+    #     and __call__ make use of instance attributes.  We leave them as
+    #     instancemethods so that users can easily experiment with extensions
+    #     without monkey-patching.
+    inherited: dict[str, str] | None
+
+    def __init__(self, inherited: str | None = None) -> None:
+        if inherited is not None:
+            self.inherited = self.compute_css(inherited)
+        else:
+            self.inherited = None
+        # We should avoid cache on the __call__ method.
+        # Otherwise once the method __call__ has been called
+        # garbage collection no longer deletes the instance.
+        self._call_cached = functools.cache(self._call_uncached)
+
+    compute_css = CSSResolver()
+
+    def __call__(
+        self, declarations: str | frozenset[tuple[str, str]]
+    ) -> dict[str, dict[str, str]]:
+        """
+        Convert CSS declarations to ExcelWriter style.
+
+        Parameters
+        ----------
+        declarations : str | frozenset[tuple[str, str]]
+            CSS string or set of CSS declaration tuples.
+            e.g. "font-weight: bold; background: blue" or
+            {("font-weight", "bold"), ("background", "blue")}
+
+        Returns
+        -------
+        xlstyle : dict
+            A style as interpreted by ExcelWriter when found in
+            ExcelCell.style.
+        """
+        return self._call_cached(declarations)
+
+    def _call_uncached(
+        self, declarations: str | frozenset[tuple[str, str]]
+    ) -> dict[str, dict[str, str]]:
+        properties = self.compute_css(declarations, self.inherited)
+        return self.build_xlstyle(properties)
+
+    def build_xlstyle(self, props: Mapping[str, str]) -> dict[str, dict[str, str]]:
+        out = {
+            "alignment": self.build_alignment(props),
+            "border": self.build_border(props),
+            "fill": self.build_fill(props),
+            "font": self.build_font(props),
+            "number_format": self.build_number_format(props),
+        }
+
+        # TODO: handle cell width and height: needs support in pandas.io.excel
+
+        def remove_none(d: dict[str, str | None]) -> None:
+            """Remove key where value is None, through nested dicts"""
+            for k, v in list(d.items()):
+                if v is None:
+                    del d[k]
+                elif isinstance(v, dict):
+                    remove_none(v)
+                    if not v:
+                        del d[k]
+
+        remove_none(out)
+        return out
+
+    def build_alignment(self, props: Mapping[str, str]) -> dict[str, bool | str | None]:
+        # TODO: text-indent, padding-left -> alignment.indent
+        return {
+            "horizontal": props.get("text-align"),
+            "vertical": self._get_vertical_alignment(props),
+            "wrap_text": self._get_is_wrap_text(props),
+        }
+
+    def _get_vertical_alignment(self, props: Mapping[str, str]) -> str | None:
+        vertical_align = props.get("vertical-align")
+        if vertical_align:
+            return self.VERTICAL_MAP.get(vertical_align)
+        return None
+
+    def _get_is_wrap_text(self, props: Mapping[str, str]) -> bool | None:
+        if props.get("white-space") is None:
+            return None
+        return bool(props["white-space"] not in ("nowrap", "pre", "pre-line"))
+
+    def build_border(
+        self, props: Mapping[str, str]
+    ) -> dict[str, dict[str, str | None]]:
+        return {
+            side: {
+                "style": self._border_style(
+                    props.get(f"border-{side}-style"),
+                    props.get(f"border-{side}-width"),
+                    self.color_to_excel(props.get(f"border-{side}-color")),
+                ),
+                "color": self.color_to_excel(props.get(f"border-{side}-color")),
+            }
+            for side in ["top", "right", "bottom", "left"]
+        }
+
+    def _border_style(self, style: str | None, width: str | None, color: str | None):
+        # convert styles and widths to openxml, one of:
+        #       'dashDot'
+        #       'dashDotDot'
+        #       'dashed'
+        #       'dotted'
+        #       'double'
+        #       'hair'
+        #       'medium'
+        #       'mediumDashDot'
+        #       'mediumDashDotDot'
+        #       'mediumDashed'
+        #       'slantDashDot'
+        #       'thick'
+        #       'thin'
+        if width is None and style is None and color is None:
+            # Return None will remove "border" from style dictionary
+            return None
+
+        if width is None and style is None:
+            # Return "none" will keep "border" in style dictionary
+            return "none"
+
+        if style in ("none", "hidden"):
+            return "none"
+
+        width_name = self._get_width_name(width)
+        if width_name is None:
+            return "none"
+
+        if style in (None, "groove", "ridge", "inset", "outset", "solid"):
+            # not handled
+            return width_name
+
+        if style == "double":
+            return "double"
+        if style == "dotted":
+            if width_name in ("hair", "thin"):
+                return "dotted"
+            return "mediumDashDotDot"
+        if style == "dashed":
+            if width_name in ("hair", "thin"):
+                return "dashed"
+            return "mediumDashed"
+        elif style in self.BORDER_STYLE_MAP:
+            # Excel-specific styles
+            return self.BORDER_STYLE_MAP[style]
+        else:
+            warnings.warn(
+                f"Unhandled border style format: {repr(style)}",
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+            return "none"
+
+    def _get_width_name(self, width_input: str | None) -> str | None:
+        width = self._width_to_float(width_input)
+        if width < 1e-5:
+            return None
+        elif width < 1.3:
+            return "thin"
+        elif width < 2.8:
+            return "medium"
+        return "thick"
+
+    def _width_to_float(self, width: str | None) -> float:
+        if width is None:
+            width = "2pt"
+        return self._pt_to_float(width)
+
+    def _pt_to_float(self, pt_string: str) -> float:
+        assert pt_string.endswith("pt")
+        return float(pt_string.rstrip("pt"))
+
+    def build_fill(self, props: Mapping[str, str]):
+        # TODO: perhaps allow for special properties
+        #       -excel-pattern-bgcolor and -excel-pattern-type
+        fill_color = props.get("background-color")
+        if fill_color not in (None, "transparent", "none"):
+            return {"fgColor": self.color_to_excel(fill_color), "patternType": "solid"}
+
+    def build_number_format(self, props: Mapping[str, str]) -> dict[str, str | None]:
+        fc = props.get("number-format")
+        fc = fc.replace("§", ";") if isinstance(fc, str) else fc
+        return {"format_code": fc}
+
+    def build_font(
+        self, props: Mapping[str, str]
+    ) -> dict[str, bool | float | str | None]:
+        font_names = self._get_font_names(props)
+        decoration = self._get_decoration(props)
+        return {
+            "name": font_names[0] if font_names else None,
+            "family": self._select_font_family(font_names),
+            "size": self._get_font_size(props),
+            "bold": self._get_is_bold(props),
+            "italic": self._get_is_italic(props),
+            "underline": ("single" if "underline" in decoration else None),
+            "strike": ("line-through" in decoration) or None,
+            "color": self.color_to_excel(props.get("color")),
+            # shadow if nonzero digit before shadow color
+            "shadow": self._get_shadow(props),
+        }
+
+    def _get_is_bold(self, props: Mapping[str, str]) -> bool | None:
+        weight = props.get("font-weight")
+        if weight:
+            return self.BOLD_MAP.get(weight)
+        return None
+
+    def _get_is_italic(self, props: Mapping[str, str]) -> bool | None:
+        font_style = props.get("font-style")
+        if font_style:
+            return self.ITALIC_MAP.get(font_style)
+        return None
+
+    def _get_decoration(self, props: Mapping[str, str]) -> Sequence[str]:
+        decoration = props.get("text-decoration")
+        if decoration is not None:
+            return decoration.split()
+        else:
+            return ()
+
+    def _get_underline(self, decoration: Sequence[str]) -> str | None:
+        if "underline" in decoration:
+            return "single"
+        return None
+
+    def _get_shadow(self, props: Mapping[str, str]) -> bool | None:
+        if "text-shadow" in props:
+            return bool(re.search("^[^#(]*[1-9]", props["text-shadow"]))
+        return None
+
+    def _get_font_names(self, props: Mapping[str, str]) -> Sequence[str]:
+        font_names_tmp = re.findall(
+            r"""(?x)
+            (
+            "(?:[^"]|\\")+"
+            |
+            '(?:[^']|\\')+'
+            |
+            [^'",]+
+            )(?=,|\s*$)
+        """,
+            props.get("font-family", ""),
+        )
+
+        font_names = []
+        for name in font_names_tmp:
+            if name[:1] == '"':
+                name = name[1:-1].replace('\\"', '"')
+            elif name[:1] == "'":
+                name = name[1:-1].replace("\\'", "'")
+            else:
+                name = name.strip()
+            if name:
+                font_names.append(name)
+        return font_names
+
+    def _get_font_size(self, props: Mapping[str, str]) -> float | None:
+        size = props.get("font-size")
+        if size is None:
+            return size
+        return self._pt_to_float(size)
+
+    def _select_font_family(self, font_names: Sequence[str]) -> int | None:
+        family = None
+        for name in font_names:
+            family = self.FAMILY_MAP.get(name)
+            if family:
+                break
+
+        return family
+
+    def color_to_excel(self, val: str | None) -> str | None:
+        if val is None:
+            return None
+
+        if self._is_hex_color(val):
+            return self._convert_hex_to_excel(val)
+
+        try:
+            return self.NAMED_COLORS[val]
+        except KeyError:
+            warnings.warn(
+                f"Unhandled color format: {repr(val)}",
+                CSSWarning,
+                stacklevel=find_stack_level(),
+            )
+        return None
+
+    def _is_hex_color(self, color_string: str) -> bool:
+        return bool(color_string.startswith("#"))
+
+    def _convert_hex_to_excel(self, color_string: str) -> str:
+        code = color_string.lstrip("#")
+        if self._is_shorthand_color(color_string):
+            return (code[0] * 2 + code[1] * 2 + code[2] * 2).upper()
+        else:
+            return code.upper()
+
+    def _is_shorthand_color(self, color_string: str) -> bool:
+        """Check if color code is shorthand.
+
+        #FFF is a shorthand as opposed to full #FFFFFF.
+        """
+        code = color_string.lstrip("#")
+        if len(code) == 3:
+            return True
+        elif len(code) == 6:
+            return False
+        else:
+            raise ValueError(f"Unexpected color {color_string}")
+
+
+class ExcelFormatter:
+    """
+    Class for formatting a DataFrame to a list of ExcelCells,
+
+    Parameters
+    ----------
+    df : DataFrame or Styler
+    na_rep: na representation
+    float_format : str, default None
+        Format string for floating point numbers
+    cols : sequence, optional
+        Columns to write
+    header : bool or sequence of str, default True
+        Write out column names. If a list of string is given it is
+        assumed to be aliases for the column names
+    index : bool, default True
+        output row names (index)
+    index_label : str or sequence, default None
+        Column label for index column(s) if desired. If None is given, and
+        `header` and `index` are True, then the index names are used. A
+        sequence should be given if the DataFrame uses MultiIndex.
+    merge_cells : bool, default False
+        Format MultiIndex and Hierarchical Rows as merged cells.
+    inf_rep : str, default `'inf'`
+        representation for np.inf values (which aren't representable in Excel)
+        A `'-'` sign will be added in front of -inf.
+    style_converter : callable, optional
+        This translates Styler styles (CSS) into ExcelWriter styles.
+        Defaults to ``CSSToExcelConverter()``.
+        It should have signature css_declarations string -> excel style.
+        This is only called for body cells.
+    """
+
+    max_rows = 2**20
+    max_cols = 2**14
+
+    def __init__(
+        self,
+        df,
+        na_rep: str = "",
+        float_format: str | None = None,
+        cols: Sequence[Hashable] | None = None,
+        header: Sequence[Hashable] | bool = True,
+        index: bool = True,
+        index_label: IndexLabel | None = None,
+        merge_cells: bool = False,
+        inf_rep: str = "inf",
+        style_converter: Callable | None = None,
+    ) -> None:
+        self.rowcounter = 0
+        self.na_rep = na_rep
+        if not isinstance(df, DataFrame):
+            self.styler = df
+            self.styler._compute()  # calculate applied styles
+            df = df.data
+            if style_converter is None:
+                style_converter = CSSToExcelConverter()
+            self.style_converter: Callable | None = style_converter
+        else:
+            self.styler = None
+            self.style_converter = None
+        self.df = df
+        if cols is not None:
+            # all missing, raise
+            if not len(Index(cols).intersection(df.columns)):
+                raise KeyError("passes columns are not ALL present dataframe")
+
+            if len(Index(cols).intersection(df.columns)) != len(set(cols)):
+                # Deprecated in GH#17295, enforced in 1.0.0
+                raise KeyError("Not all names specified in 'columns' are found")
+
+            self.df = df.reindex(columns=cols)
+
+        self.columns = self.df.columns
+        self.float_format = float_format
+        self.index = index
+        self.index_label = index_label
+        self.header = header
+        self.merge_cells = merge_cells
+        self.inf_rep = inf_rep
+
+    @property
+    def header_style(self) -> dict[str, dict[str, str | bool]]:
+        return {
+            "font": {"bold": True},
+            "borders": {
+                "top": "thin",
+                "right": "thin",
+                "bottom": "thin",
+                "left": "thin",
+            },
+            "alignment": {"horizontal": "center", "vertical": "top"},
+        }
+
+    def _format_value(self, val):
+        if is_scalar(val) and missing.isna(val):
+            val = self.na_rep
+        elif is_float(val):
+            if missing.isposinf_scalar(val):
+                val = self.inf_rep
+            elif missing.isneginf_scalar(val):
+                val = f"-{self.inf_rep}"
+            elif self.float_format is not None:
+                val = float(self.float_format % val)
+        if getattr(val, "tzinfo", None) is not None:
+            raise ValueError(
+                "Excel does not support datetimes with "
+                "timezones. Please ensure that datetimes "
+                "are timezone unaware before writing to Excel."
+            )
+        return val
+
+    def _format_header_mi(self) -> Iterable[ExcelCell]:
+        if self.columns.nlevels > 1:
+            if not self.index:
+                raise NotImplementedError(
+                    "Writing to Excel with MultiIndex columns and no "
+                    "index ('index'=False) is not yet implemented."
+                )
+
+        if not (self._has_aliases or self.header):
+            return
+
+        columns = self.columns
+        level_strs = columns._format_multi(
+            sparsify=self.merge_cells, include_names=False
+        )
+        level_lengths = get_level_lengths(level_strs)
+        coloffset = 0
+        lnum = 0
+
+        if self.index and isinstance(self.df.index, MultiIndex):
+            coloffset = len(self.df.index[0]) - 1
+
+        if self.merge_cells:
+            # Format multi-index as a merged cells.
+            for lnum, name in enumerate(columns.names):
+                yield ExcelCell(
+                    row=lnum,
+                    col=coloffset,
+                    val=name,
+                    style=self.header_style,
+                )
+
+            for lnum, (spans, levels, level_codes) in enumerate(
+                zip(level_lengths, columns.levels, columns.codes)
+            ):
+                values = levels.take(level_codes)
+                for i, span_val in spans.items():
+                    mergestart, mergeend = None, None
+                    if span_val > 1:
+                        mergestart, mergeend = lnum, coloffset + i + span_val
+                    yield CssExcelCell(
+                        row=lnum,
+                        col=coloffset + i + 1,
+                        val=values[i],
+                        style=self.header_style,
+                        css_styles=getattr(self.styler, "ctx_columns", None),
+                        css_row=lnum,
+                        css_col=i,
+                        css_converter=self.style_converter,
+                        mergestart=mergestart,
+                        mergeend=mergeend,
+                    )
+        else:
+            # Format in legacy format with dots to indicate levels.
+            for i, values in enumerate(zip(*level_strs)):
+                v = ".".join(map(pprint_thing, values))
+                yield CssExcelCell(
+                    row=lnum,
+                    col=coloffset + i + 1,
+                    val=v,
+                    style=self.header_style,
+                    css_styles=getattr(self.styler, "ctx_columns", None),
+                    css_row=lnum,
+                    css_col=i,
+                    css_converter=self.style_converter,
+                )
+
+        self.rowcounter = lnum
+
+    def _format_header_regular(self) -> Iterable[ExcelCell]:
+        if self._has_aliases or self.header:
+            coloffset = 0
+
+            if self.index:
+                coloffset = 1
+                if isinstance(self.df.index, MultiIndex):
+                    coloffset = len(self.df.index.names)
+
+            colnames = self.columns
+            if self._has_aliases:
+                self.header = cast(Sequence, self.header)
+                if len(self.header) != len(self.columns):
+                    raise ValueError(
+                        f"Writing {len(self.columns)} cols "
+                        f"but got {len(self.header)} aliases"
+                    )
+                colnames = self.header
+
+            for colindex, colname in enumerate(colnames):
+                yield CssExcelCell(
+                    row=self.rowcounter,
+                    col=colindex + coloffset,
+                    val=colname,
+                    style=self.header_style,
+                    css_styles=getattr(self.styler, "ctx_columns", None),
+                    css_row=0,
+                    css_col=colindex,
+                    css_converter=self.style_converter,
+                )
+
+    def _format_header(self) -> Iterable[ExcelCell]:
+        gen: Iterable[ExcelCell]
+
+        if isinstance(self.columns, MultiIndex):
+            gen = self._format_header_mi()
+        else:
+            gen = self._format_header_regular()
+
+        gen2: Iterable[ExcelCell] = ()
+
+        if self.df.index.names:
+            row = [x if x is not None else "" for x in self.df.index.names] + [
+                ""
+            ] * len(self.columns)
+            if functools.reduce(lambda x, y: x and y, (x != "" for x in row)):
+                gen2 = (
+                    ExcelCell(self.rowcounter, colindex, val, self.header_style)
+                    for colindex, val in enumerate(row)
+                )
+                self.rowcounter += 1
+        return itertools.chain(gen, gen2)
+
+    def _format_body(self) -> Iterable[ExcelCell]:
+        if isinstance(self.df.index, MultiIndex):
+            return self._format_hierarchical_rows()
+        else:
+            return self._format_regular_rows()
+
+    def _format_regular_rows(self) -> Iterable[ExcelCell]:
+        if self._has_aliases or self.header:
+            self.rowcounter += 1
+
+        # output index and index_label?
+        if self.index:
+            # check aliases
+            # if list only take first as this is not a MultiIndex
+            if self.index_label and isinstance(
+                self.index_label, (list, tuple, np.ndarray, Index)
+            ):
+                index_label = self.index_label[0]
+            # if string good to go
+            elif self.index_label and isinstance(self.index_label, str):
+                index_label = self.index_label
+            else:
+                index_label = self.df.index.names[0]
+
+            if isinstance(self.columns, MultiIndex):
+                self.rowcounter += 1
+
+            if index_label and self.header is not False:
+                yield ExcelCell(self.rowcounter - 1, 0, index_label, self.header_style)
+
+            # write index_values
+            index_values = self.df.index
+            if isinstance(self.df.index, PeriodIndex):
+                index_values = self.df.index.to_timestamp()
+
+            for idx, idxval in enumerate(index_values):
+                yield CssExcelCell(
+                    row=self.rowcounter + idx,
+                    col=0,
+                    val=idxval,
+                    style=self.header_style,
+                    css_styles=getattr(self.styler, "ctx_index", None),
+                    css_row=idx,
+                    css_col=0,
+                    css_converter=self.style_converter,
+                )
+            coloffset = 1
+        else:
+            coloffset = 0
+
+        yield from self._generate_body(coloffset)
+
+    def _format_hierarchical_rows(self) -> Iterable[ExcelCell]:
+        if self._has_aliases or self.header:
+            self.rowcounter += 1
+
+        gcolidx = 0
+
+        if self.index:
+            index_labels = self.df.index.names
+            # check for aliases
+            if self.index_label and isinstance(
+                self.index_label, (list, tuple, np.ndarray, Index)
+            ):
+                index_labels = self.index_label
+
+            # MultiIndex columns require an extra row
+            # with index names (blank if None) for
+            # unambiguous round-trip, unless not merging,
+            # in which case the names all go on one row Issue #11328
+            if isinstance(self.columns, MultiIndex) and self.merge_cells:
+                self.rowcounter += 1
+
+            # if index labels are not empty go ahead and dump
+            if com.any_not_none(*index_labels) and self.header is not False:
+                for cidx, name in enumerate(index_labels):
+                    yield ExcelCell(self.rowcounter - 1, cidx, name, self.header_style)
+
+            if self.merge_cells:
+                # Format hierarchical rows as merged cells.
+                level_strs = self.df.index._format_multi(
+                    sparsify=True, include_names=False
+                )
+                level_lengths = get_level_lengths(level_strs)
+
+                for spans, levels, level_codes in zip(
+                    level_lengths, self.df.index.levels, self.df.index.codes
+                ):
+                    values = levels.take(
+                        level_codes,
+                        allow_fill=levels._can_hold_na,
+                        fill_value=levels._na_value,
+                    )
+
+                    for i, span_val in spans.items():
+                        mergestart, mergeend = None, None
+                        if span_val > 1:
+                            mergestart = self.rowcounter + i + span_val - 1
+                            mergeend = gcolidx
+                        yield CssExcelCell(
+                            row=self.rowcounter + i,
+                            col=gcolidx,
+                            val=values[i],
+                            style=self.header_style,
+                            css_styles=getattr(self.styler, "ctx_index", None),
+                            css_row=i,
+                            css_col=gcolidx,
+                            css_converter=self.style_converter,
+                            mergestart=mergestart,
+                            mergeend=mergeend,
+                        )
+                    gcolidx += 1
+
+            else:
+                # Format hierarchical rows with non-merged values.
+                for indexcolvals in zip(*self.df.index):
+                    for idx, indexcolval in enumerate(indexcolvals):
+                        yield CssExcelCell(
+                            row=self.rowcounter + idx,
+                            col=gcolidx,
+                            val=indexcolval,
+                            style=self.header_style,
+                            css_styles=getattr(self.styler, "ctx_index", None),
+                            css_row=idx,
+                            css_col=gcolidx,
+                            css_converter=self.style_converter,
+                        )
+                    gcolidx += 1
+
+        yield from self._generate_body(gcolidx)
+
+    @property
+    def _has_aliases(self) -> bool:
+        """Whether the aliases for column names are present."""
+        return is_list_like(self.header)
+
+    def _generate_body(self, coloffset: int) -> Iterable[ExcelCell]:
+        # Write the body of the frame data series by series.
+        for colidx in range(len(self.columns)):
+            series = self.df.iloc[:, colidx]
+            for i, val in enumerate(series):
+                yield CssExcelCell(
+                    row=self.rowcounter + i,
+                    col=colidx + coloffset,
+                    val=val,
+                    style=None,
+                    css_styles=getattr(self.styler, "ctx", None),
+                    css_row=i,
+                    css_col=colidx,
+                    css_converter=self.style_converter,
+                )
+
+    def get_formatted_cells(self) -> Iterable[ExcelCell]:
+        for cell in itertools.chain(self._format_header(), self._format_body()):
+            cell.val = self._format_value(cell.val)
+            yield cell
+
+    @doc(storage_options=_shared_docs["storage_options"])
+    def write(
+        self,
+        writer: FilePath | WriteExcelBuffer | ExcelWriter,
+        sheet_name: str = "Sheet1",
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+        engine: str | None = None,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        writer : path-like, file-like, or ExcelWriter object
+            File path or existing ExcelWriter
+        sheet_name : str, default 'Sheet1'
+            Name of sheet which will contain DataFrame
+        startrow :
+            upper left cell row to dump data frame
+        startcol :
+            upper left cell column to dump data frame
+        freeze_panes : tuple of integer (length 2), default None
+            Specifies the one-based bottommost row and rightmost column that
+            is to be frozen
+        engine : string, default None
+            write engine to use if writer is a path - you can also set this
+            via the options ``io.excel.xlsx.writer``,
+            or ``io.excel.xlsm.writer``.
+
+        {storage_options}
+
+        engine_kwargs: dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        from pandas.io.excel import ExcelWriter
+
+        num_rows, num_cols = self.df.shape
+        if num_rows > self.max_rows or num_cols > self.max_cols:
+            raise ValueError(
+                f"This sheet is too large! Your sheet size is: {num_rows}, {num_cols} "
+                f"Max sheet size is: {self.max_rows}, {self.max_cols}"
+            )
+
+        if engine_kwargs is None:
+            engine_kwargs = {}
+
+        formatted_cells = self.get_formatted_cells()
+        if isinstance(writer, ExcelWriter):
+            need_save = False
+        else:
+            writer = ExcelWriter(
+                writer,
+                engine=engine,
+                storage_options=storage_options,
+                engine_kwargs=engine_kwargs,
+            )
+            need_save = True
+
+        try:
+            writer._write_cells(
+                formatted_cells,
+                sheet_name,
+                startrow=startrow,
+                startcol=startcol,
+                freeze_panes=freeze_panes,
+            )
+        finally:
+            # make sure to close opened file handles
+            if need_save:
+                writer.close()

venv/lib/python3.10/site-packages/pandas/io/formats/format.py

@@ -0,0 +1,2058 @@
+"""
+Internal module for formatting output data in csv, html, xml,
+and latex files. This module also applies to display formatting.
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Generator,
+    Hashable,
+    Mapping,
+    Sequence,
+)
+from contextlib import contextmanager
+from csv import QUOTE_NONE
+from decimal import Decimal
+from functools import partial
+from io import StringIO
+import math
+import re
+from shutil import get_terminal_size
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Final,
+    cast,
+)
+
+import numpy as np
+
+from pandas._config.config import (
+    get_option,
+    set_option,
+)
+
+from pandas._libs import lib
+from pandas._libs.missing import NA
+from pandas._libs.tslibs import (
+    NaT,
+    Timedelta,
+    Timestamp,
+)
+from pandas._libs.tslibs.nattype import NaTType
+
+from pandas.core.dtypes.common import (
+    is_complex_dtype,
+    is_float,
+    is_integer,
+    is_list_like,
+    is_numeric_dtype,
+    is_scalar,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+)
+from pandas.core.dtypes.missing import (
+    isna,
+    notna,
+)
+
+from pandas.core.arrays import (
+    Categorical,
+    DatetimeArray,
+    ExtensionArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.string_ import StringDtype
+from pandas.core.base import PandasObject
+import pandas.core.common as com
+from pandas.core.indexes.api import (
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    ensure_index,
+)
+from pandas.core.indexes.datetimes import DatetimeIndex
+from pandas.core.indexes.timedeltas import TimedeltaIndex
+from pandas.core.reshape.concat import concat
+
+from pandas.io.common import (
+    check_parent_directory,
+    stringify_path,
+)
+from pandas.io.formats import printing
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        Axes,
+        ColspaceArgType,
+        ColspaceType,
+        CompressionOptions,
+        FilePath,
+        FloatFormatType,
+        FormattersType,
+        IndexLabel,
+        SequenceNotStr,
+        StorageOptions,
+        WriteBuffer,
+    )
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+
+common_docstring: Final = """
+        Parameters
+        ----------
+        buf : str, Path or StringIO-like, optional, default None
+            Buffer to write to. If None, the output is returned as a string.
+        columns : array-like, optional, default None
+            The subset of columns to write. Writes all columns by default.
+        col_space : %(col_space_type)s, optional
+            %(col_space)s.
+        header : %(header_type)s, optional
+            %(header)s.
+        index : bool, optional, default True
+            Whether to print index (row) labels.
+        na_rep : str, optional, default 'NaN'
+            String representation of ``NaN`` to use.
+        formatters : list, tuple or dict of one-param. functions, optional
+            Formatter functions to apply to columns' elements by position or
+            name.
+            The result of each function must be a unicode string.
+            List/tuple must be of length equal to the number of columns.
+        float_format : one-parameter function, optional, default None
+            Formatter function to apply to columns' elements if they are
+            floats. This function must return a unicode string and will be
+            applied only to the non-``NaN`` elements, with ``NaN`` being
+            handled by ``na_rep``.
+        sparsify : bool, optional, default True
+            Set to False for a DataFrame with a hierarchical index to print
+            every multiindex key at each row.
+        index_names : bool, optional, default True
+            Prints the names of the indexes.
+        justify : str, default None
+            How to justify the column labels. If None uses the option from
+            the print configuration (controlled by set_option), 'right' out
+            of the box. Valid values are
+
+            * left
+            * right
+            * center
+            * justify
+            * justify-all
+            * start
+            * end
+            * inherit
+            * match-parent
+            * initial
+            * unset.
+        max_rows : int, optional
+            Maximum number of rows to display in the console.
+        max_cols : int, optional
+            Maximum number of columns to display in the console.
+        show_dimensions : bool, default False
+            Display DataFrame dimensions (number of rows by number of columns).
+        decimal : str, default '.'
+            Character recognized as decimal separator, e.g. ',' in Europe.
+    """
+
+VALID_JUSTIFY_PARAMETERS = (
+    "left",
+    "right",
+    "center",
+    "justify",
+    "justify-all",
+    "start",
+    "end",
+    "inherit",
+    "match-parent",
+    "initial",
+    "unset",
+)
+
+return_docstring: Final = """
+        Returns
+        -------
+        str or None
+            If buf is None, returns the result as a string. Otherwise returns
+            None.
+    """
+
+
+class SeriesFormatter:
+    """
+    Implement the main logic of Series.to_string, which underlies
+    Series.__repr__.
+    """
+
+    def __init__(
+        self,
+        series: Series,
+        *,
+        length: bool | str = True,
+        header: bool = True,
+        index: bool = True,
+        na_rep: str = "NaN",
+        name: bool = False,
+        float_format: str | None = None,
+        dtype: bool = True,
+        max_rows: int | None = None,
+        min_rows: int | None = None,
+    ) -> None:
+        self.series = series
+        self.buf = StringIO()
+        self.name = name
+        self.na_rep = na_rep
+        self.header = header
+        self.length = length
+        self.index = index
+        self.max_rows = max_rows
+        self.min_rows = min_rows
+
+        if float_format is None:
+            float_format = get_option("display.float_format")
+        self.float_format = float_format
+        self.dtype = dtype
+        self.adj = printing.get_adjustment()
+
+        self._chk_truncate()
+
+    def _chk_truncate(self) -> None:
+        self.tr_row_num: int | None
+
+        min_rows = self.min_rows
+        max_rows = self.max_rows
+        # truncation determined by max_rows, actual truncated number of rows
+        # used below by min_rows
+        is_truncated_vertically = max_rows and (len(self.series) > max_rows)
+        series = self.series
+        if is_truncated_vertically:
+            max_rows = cast(int, max_rows)
+            if min_rows:
+                # if min_rows is set (not None or 0), set max_rows to minimum
+                # of both
+                max_rows = min(min_rows, max_rows)
+            if max_rows == 1:
+                row_num = max_rows
+                series = series.iloc[:max_rows]
+            else:
+                row_num = max_rows // 2
+                series = concat((series.iloc[:row_num], series.iloc[-row_num:]))
+            self.tr_row_num = row_num
+        else:
+            self.tr_row_num = None
+        self.tr_series = series
+        self.is_truncated_vertically = is_truncated_vertically
+
+    def _get_footer(self) -> str:
+        name = self.series.name
+        footer = ""
+
+        index = self.series.index
+        if (
+            isinstance(index, (DatetimeIndex, PeriodIndex, TimedeltaIndex))
+            and index.freq is not None
+        ):
+            footer += f"Freq: {index.freqstr}"
+
+        if self.name is not False and name is not None:
+            if footer:
+                footer += ", "
+
+            series_name = printing.pprint_thing(name, escape_chars=("\t", "\r", "\n"))
+            footer += f"Name: {series_name}"
+
+        if self.length is True or (
+            self.length == "truncate" and self.is_truncated_vertically
+        ):
+            if footer:
+                footer += ", "
+            footer += f"Length: {len(self.series)}"
+
+        if self.dtype is not False and self.dtype is not None:
+            dtype_name = getattr(self.tr_series.dtype, "name", None)
+            if dtype_name:
+                if footer:
+                    footer += ", "
+                footer += f"dtype: {printing.pprint_thing(dtype_name)}"
+
+        # level infos are added to the end and in a new line, like it is done
+        # for Categoricals
+        if isinstance(self.tr_series.dtype, CategoricalDtype):
+            level_info = self.tr_series._values._get_repr_footer()
+            if footer:
+                footer += "\n"
+            footer += level_info
+
+        return str(footer)
+
+    def _get_formatted_values(self) -> list[str]:
+        return format_array(
+            self.tr_series._values,
+            None,
+            float_format=self.float_format,
+            na_rep=self.na_rep,
+            leading_space=self.index,
+        )
+
+    def to_string(self) -> str:
+        series = self.tr_series
+        footer = self._get_footer()
+
+        if len(series) == 0:
+            return f"{type(self.series).__name__}([], {footer})"
+
+        index = series.index
+        have_header = _has_names(index)
+        if isinstance(index, MultiIndex):
+            fmt_index = index._format_multi(include_names=True, sparsify=None)
+            adj = printing.get_adjustment()
+            fmt_index = adj.adjoin(2, *fmt_index).split("\n")
+        else:
+            fmt_index = index._format_flat(include_name=True)
+        fmt_values = self._get_formatted_values()
+
+        if self.is_truncated_vertically:
+            n_header_rows = 0
+            row_num = self.tr_row_num
+            row_num = cast(int, row_num)
+            width = self.adj.len(fmt_values[row_num - 1])
+            if width > 3:
+                dot_str = "..."
+            else:
+                dot_str = ".."
+            # Series uses mode=center because it has single value columns
+            # DataFrame uses mode=left
+            dot_str = self.adj.justify([dot_str], width, mode="center")[0]
+            fmt_values.insert(row_num + n_header_rows, dot_str)
+            fmt_index.insert(row_num + 1, "")
+
+        if self.index:
+            result = self.adj.adjoin(3, *[fmt_index[1:], fmt_values])
+        else:
+            result = self.adj.adjoin(3, fmt_values)
+
+        if self.header and have_header:
+            result = fmt_index[0] + "\n" + result
+
+        if footer:
+            result += "\n" + footer
+
+        return str("".join(result))
+
+
+def get_dataframe_repr_params() -> dict[str, Any]:
+    """Get the parameters used to repr(dataFrame) calls using DataFrame.to_string.
+
+    Supplying these parameters to DataFrame.to_string is equivalent to calling
+    ``repr(DataFrame)``. This is useful if you want to adjust the repr output.
+
+    .. versionadded:: 1.4.0
+
+    Example
+    -------
+    >>> import pandas as pd
+    >>>
+    >>> df = pd.DataFrame([[1, 2], [3, 4]])
+    >>> repr_params = pd.io.formats.format.get_dataframe_repr_params()
+    >>> repr(df) == df.to_string(**repr_params)
+    True
+    """
+    from pandas.io.formats import console
+
+    if get_option("display.expand_frame_repr"):
+        line_width, _ = console.get_console_size()
+    else:
+        line_width = None
+    return {
+        "max_rows": get_option("display.max_rows"),
+        "min_rows": get_option("display.min_rows"),
+        "max_cols": get_option("display.max_columns"),
+        "max_colwidth": get_option("display.max_colwidth"),
+        "show_dimensions": get_option("display.show_dimensions"),
+        "line_width": line_width,
+    }
+
+
+def get_series_repr_params() -> dict[str, Any]:
+    """Get the parameters used to repr(Series) calls using Series.to_string.
+
+    Supplying these parameters to Series.to_string is equivalent to calling
+    ``repr(series)``. This is useful if you want to adjust the series repr output.
+
+    .. versionadded:: 1.4.0
+
+    Example
+    -------
+    >>> import pandas as pd
+    >>>
+    >>> ser = pd.Series([1, 2, 3, 4])
+    >>> repr_params = pd.io.formats.format.get_series_repr_params()
+    >>> repr(ser) == ser.to_string(**repr_params)
+    True
+    """
+    width, height = get_terminal_size()
+    max_rows_opt = get_option("display.max_rows")
+    max_rows = height if max_rows_opt == 0 else max_rows_opt
+    min_rows = height if max_rows_opt == 0 else get_option("display.min_rows")
+
+    return {
+        "name": True,
+        "dtype": True,
+        "min_rows": min_rows,
+        "max_rows": max_rows,
+        "length": get_option("display.show_dimensions"),
+    }
+
+
+class DataFrameFormatter:
+    """
+    Class for processing dataframe formatting options and data.
+
+    Used by DataFrame.to_string, which backs DataFrame.__repr__.
+    """
+
+    __doc__ = __doc__ if __doc__ else ""
+    __doc__ += common_docstring + return_docstring
+
+    def __init__(
+        self,
+        frame: DataFrame,
+        columns: Axes | None = None,
+        col_space: ColspaceArgType | None = None,
+        header: bool | SequenceNotStr[str] = True,
+        index: bool = True,
+        na_rep: str = "NaN",
+        formatters: FormattersType | None = None,
+        justify: str | None = None,
+        float_format: FloatFormatType | None = None,
+        sparsify: bool | None = None,
+        index_names: bool = True,
+        max_rows: int | None = None,
+        min_rows: int | None = None,
+        max_cols: int | None = None,
+        show_dimensions: bool | str = False,
+        decimal: str = ".",
+        bold_rows: bool = False,
+        escape: bool = True,
+    ) -> None:
+        self.frame = frame
+        self.columns = self._initialize_columns(columns)
+        self.col_space = self._initialize_colspace(col_space)
+        self.header = header
+        self.index = index
+        self.na_rep = na_rep
+        self.formatters = self._initialize_formatters(formatters)
+        self.justify = self._initialize_justify(justify)
+        self.float_format = float_format
+        self.sparsify = self._initialize_sparsify(sparsify)
+        self.show_index_names = index_names
+        self.decimal = decimal
+        self.bold_rows = bold_rows
+        self.escape = escape
+        self.max_rows = max_rows
+        self.min_rows = min_rows
+        self.max_cols = max_cols
+        self.show_dimensions = show_dimensions
+
+        self.max_cols_fitted = self._calc_max_cols_fitted()
+        self.max_rows_fitted = self._calc_max_rows_fitted()
+
+        self.tr_frame = self.frame
+        self.truncate()
+        self.adj = printing.get_adjustment()
+
+    def get_strcols(self) -> list[list[str]]:
+        """
+        Render a DataFrame to a list of columns (as lists of strings).
+        """
+        strcols = self._get_strcols_without_index()
+
+        if self.index:
+            str_index = self._get_formatted_index(self.tr_frame)
+            strcols.insert(0, str_index)
+
+        return strcols
+
+    @property
+    def should_show_dimensions(self) -> bool:
+        return self.show_dimensions is True or (
+            self.show_dimensions == "truncate" and self.is_truncated
+        )
+
+    @property
+    def is_truncated(self) -> bool:
+        return bool(self.is_truncated_horizontally or self.is_truncated_vertically)
+
+    @property
+    def is_truncated_horizontally(self) -> bool:
+        return bool(self.max_cols_fitted and (len(self.columns) > self.max_cols_fitted))
+
+    @property
+    def is_truncated_vertically(self) -> bool:
+        return bool(self.max_rows_fitted and (len(self.frame) > self.max_rows_fitted))
+
+    @property
+    def dimensions_info(self) -> str:
+        return f"\n\n[{len(self.frame)} rows x {len(self.frame.columns)} columns]"
+
+    @property
+    def has_index_names(self) -> bool:
+        return _has_names(self.frame.index)
+
+    @property
+    def has_column_names(self) -> bool:
+        return _has_names(self.frame.columns)
+
+    @property
+    def show_row_idx_names(self) -> bool:
+        return all((self.has_index_names, self.index, self.show_index_names))
+
+    @property
+    def show_col_idx_names(self) -> bool:
+        return all((self.has_column_names, self.show_index_names, self.header))
+
+    @property
+    def max_rows_displayed(self) -> int:
+        return min(self.max_rows or len(self.frame), len(self.frame))
+
+    def _initialize_sparsify(self, sparsify: bool | None) -> bool:
+        if sparsify is None:
+            return get_option("display.multi_sparse")
+        return sparsify
+
+    def _initialize_formatters(
+        self, formatters: FormattersType | None
+    ) -> FormattersType:
+        if formatters is None:
+            return {}
+        elif len(self.frame.columns) == len(formatters) or isinstance(formatters, dict):
+            return formatters
+        else:
+            raise ValueError(
+                f"Formatters length({len(formatters)}) should match "
+                f"DataFrame number of columns({len(self.frame.columns)})"
+            )
+
+    def _initialize_justify(self, justify: str | None) -> str:
+        if justify is None:
+            return get_option("display.colheader_justify")
+        else:
+            return justify
+
+    def _initialize_columns(self, columns: Axes | None) -> Index:
+        if columns is not None:
+            cols = ensure_index(columns)
+            self.frame = self.frame[cols]
+            return cols
+        else:
+            return self.frame.columns
+
+    def _initialize_colspace(self, col_space: ColspaceArgType | None) -> ColspaceType:
+        result: ColspaceType
+
+        if col_space is None:
+            result = {}
+        elif isinstance(col_space, (int, str)):
+            result = {"": col_space}
+            result.update({column: col_space for column in self.frame.columns})
+        elif isinstance(col_space, Mapping):
+            for column in col_space.keys():
+                if column not in self.frame.columns and column != "":
+                    raise ValueError(
+                        f"Col_space is defined for an unknown column: {column}"
+                    )
+            result = col_space
+        else:
+            if len(self.frame.columns) != len(col_space):
+                raise ValueError(
+                    f"Col_space length({len(col_space)}) should match "
+                    f"DataFrame number of columns({len(self.frame.columns)})"
+                )
+            result = dict(zip(self.frame.columns, col_space))
+        return result
+
+    def _calc_max_cols_fitted(self) -> int | None:
+        """Number of columns fitting the screen."""
+        if not self._is_in_terminal():
+            return self.max_cols
+
+        width, _ = get_terminal_size()
+        if self._is_screen_narrow(width):
+            return width
+        else:
+            return self.max_cols
+
+    def _calc_max_rows_fitted(self) -> int | None:
+        """Number of rows with data fitting the screen."""
+        max_rows: int | None
+
+        if self._is_in_terminal():
+            _, height = get_terminal_size()
+            if self.max_rows == 0:
+                # rows available to fill with actual data
+                return height - self._get_number_of_auxiliary_rows()
+
+            if self._is_screen_short(height):
+                max_rows = height
+            else:
+                max_rows = self.max_rows
+        else:
+            max_rows = self.max_rows
+
+        return self._adjust_max_rows(max_rows)
+
+    def _adjust_max_rows(self, max_rows: int | None) -> int | None:
+        """Adjust max_rows using display logic.
+
+        See description here:
+        https://pandas.pydata.org/docs/dev/user_guide/options.html#frequently-used-options
+
+        GH #37359
+        """
+        if max_rows:
+            if (len(self.frame) > max_rows) and self.min_rows:
+                # if truncated, set max_rows showed to min_rows
+                max_rows = min(self.min_rows, max_rows)
+        return max_rows
+
+    def _is_in_terminal(self) -> bool:
+        """Check if the output is to be shown in terminal."""
+        return bool(self.max_cols == 0 or self.max_rows == 0)
+
+    def _is_screen_narrow(self, max_width) -> bool:
+        return bool(self.max_cols == 0 and len(self.frame.columns) > max_width)
+
+    def _is_screen_short(self, max_height) -> bool:
+        return bool(self.max_rows == 0 and len(self.frame) > max_height)
+
+    def _get_number_of_auxiliary_rows(self) -> int:
+        """Get number of rows occupied by prompt, dots and dimension info."""
+        dot_row = 1
+        prompt_row = 1
+        num_rows = dot_row + prompt_row
+
+        if self.show_dimensions:
+            num_rows += len(self.dimensions_info.splitlines())
+
+        if self.header:
+            num_rows += 1
+
+        return num_rows
+
+    def truncate(self) -> None:
+        """
+        Check whether the frame should be truncated. If so, slice the frame up.
+        """
+        if self.is_truncated_horizontally:
+            self._truncate_horizontally()
+
+        if self.is_truncated_vertically:
+            self._truncate_vertically()
+
+    def _truncate_horizontally(self) -> None:
+        """Remove columns, which are not to be displayed and adjust formatters.
+
+        Attributes affected:
+            - tr_frame
+            - formatters
+            - tr_col_num
+        """
+        assert self.max_cols_fitted is not None
+        col_num = self.max_cols_fitted // 2
+        if col_num >= 1:
+            left = self.tr_frame.iloc[:, :col_num]
+            right = self.tr_frame.iloc[:, -col_num:]
+            self.tr_frame = concat((left, right), axis=1)
+
+            # truncate formatter
+            if isinstance(self.formatters, (list, tuple)):
+                self.formatters = [
+                    *self.formatters[:col_num],
+                    *self.formatters[-col_num:],
+                ]
+        else:
+            col_num = cast(int, self.max_cols)
+            self.tr_frame = self.tr_frame.iloc[:, :col_num]
+        self.tr_col_num = col_num
+
+    def _truncate_vertically(self) -> None:
+        """Remove rows, which are not to be displayed.
+
+        Attributes affected:
+            - tr_frame
+            - tr_row_num
+        """
+        assert self.max_rows_fitted is not None
+        row_num = self.max_rows_fitted // 2
+        if row_num >= 1:
+            _len = len(self.tr_frame)
+            _slice = np.hstack([np.arange(row_num), np.arange(_len - row_num, _len)])
+            self.tr_frame = self.tr_frame.iloc[_slice]
+        else:
+            row_num = cast(int, self.max_rows)
+            self.tr_frame = self.tr_frame.iloc[:row_num, :]
+        self.tr_row_num = row_num
+
+    def _get_strcols_without_index(self) -> list[list[str]]:
+        strcols: list[list[str]] = []
+
+        if not is_list_like(self.header) and not self.header:
+            for i, c in enumerate(self.tr_frame):
+                fmt_values = self.format_col(i)
+                fmt_values = _make_fixed_width(
+                    strings=fmt_values,
+                    justify=self.justify,
+                    minimum=int(self.col_space.get(c, 0)),
+                    adj=self.adj,
+                )
+                strcols.append(fmt_values)
+            return strcols
+
+        if is_list_like(self.header):
+            # cast here since can't be bool if is_list_like
+            self.header = cast(list[str], self.header)
+            if len(self.header) != len(self.columns):
+                raise ValueError(
+                    f"Writing {len(self.columns)} cols "
+                    f"but got {len(self.header)} aliases"
+                )
+            str_columns = [[label] for label in self.header]
+        else:
+            str_columns = self._get_formatted_column_labels(self.tr_frame)
+
+        if self.show_row_idx_names:
+            for x in str_columns:
+                x.append("")
+
+        for i, c in enumerate(self.tr_frame):
+            cheader = str_columns[i]
+            header_colwidth = max(
+                int(self.col_space.get(c, 0)), *(self.adj.len(x) for x in cheader)
+            )
+            fmt_values = self.format_col(i)
+            fmt_values = _make_fixed_width(
+                fmt_values, self.justify, minimum=header_colwidth, adj=self.adj
+            )
+
+            max_len = max(*(self.adj.len(x) for x in fmt_values), header_colwidth)
+            cheader = self.adj.justify(cheader, max_len, mode=self.justify)
+            strcols.append(cheader + fmt_values)
+
+        return strcols
+
+    def format_col(self, i: int) -> list[str]:
+        frame = self.tr_frame
+        formatter = self._get_formatter(i)
+        return format_array(
+            frame.iloc[:, i]._values,
+            formatter,
+            float_format=self.float_format,
+            na_rep=self.na_rep,
+            space=self.col_space.get(frame.columns[i]),
+            decimal=self.decimal,
+            leading_space=self.index,
+        )
+
+    def _get_formatter(self, i: str | int) -> Callable | None:
+        if isinstance(self.formatters, (list, tuple)):
+            if is_integer(i):
+                i = cast(int, i)
+                return self.formatters[i]
+            else:
+                return None
+        else:
+            if is_integer(i) and i not in self.columns:
+                i = self.columns[i]
+            return self.formatters.get(i, None)
+
+    def _get_formatted_column_labels(self, frame: DataFrame) -> list[list[str]]:
+        from pandas.core.indexes.multi import sparsify_labels
+
+        columns = frame.columns
+
+        if isinstance(columns, MultiIndex):
+            fmt_columns = columns._format_multi(sparsify=False, include_names=False)
+            fmt_columns = list(zip(*fmt_columns))
+            dtypes = self.frame.dtypes._values
+
+            # if we have a Float level, they don't use leading space at all
+            restrict_formatting = any(level.is_floating for level in columns.levels)
+            need_leadsp = dict(zip(fmt_columns, map(is_numeric_dtype, dtypes)))
+
+            def space_format(x, y):
+                if (
+                    y not in self.formatters
+                    and need_leadsp[x]
+                    and not restrict_formatting
+                ):
+                    return " " + y
+                return y
+
+            str_columns_tuple = list(
+                zip(*([space_format(x, y) for y in x] for x in fmt_columns))
+            )
+            if self.sparsify and len(str_columns_tuple):
+                str_columns_tuple = sparsify_labels(str_columns_tuple)
+
+            str_columns = [list(x) for x in zip(*str_columns_tuple)]
+        else:
+            fmt_columns = columns._format_flat(include_name=False)
+            dtypes = self.frame.dtypes
+            need_leadsp = dict(zip(fmt_columns, map(is_numeric_dtype, dtypes)))
+            str_columns = [
+                [" " + x if not self._get_formatter(i) and need_leadsp[x] else x]
+                for i, x in enumerate(fmt_columns)
+            ]
+        # self.str_columns = str_columns
+        return str_columns
+
+    def _get_formatted_index(self, frame: DataFrame) -> list[str]:
+        # Note: this is only used by to_string() and to_latex(), not by
+        # to_html(). so safe to cast col_space here.
+        col_space = {k: cast(int, v) for k, v in self.col_space.items()}
+        index = frame.index
+        columns = frame.columns
+        fmt = self._get_formatter("__index__")
+
+        if isinstance(index, MultiIndex):
+            fmt_index = index._format_multi(
+                sparsify=self.sparsify,
+                include_names=self.show_row_idx_names,
+                formatter=fmt,
+            )
+        else:
+            fmt_index = [
+                index._format_flat(include_name=self.show_row_idx_names, formatter=fmt)
+            ]
+
+        fmt_index = [
+            tuple(
+                _make_fixed_width(
+                    list(x), justify="left", minimum=col_space.get("", 0), adj=self.adj
+                )
+            )
+            for x in fmt_index
+        ]
+
+        adjoined = self.adj.adjoin(1, *fmt_index).split("\n")
+
+        # empty space for columns
+        if self.show_col_idx_names:
+            col_header = [str(x) for x in self._get_column_name_list()]
+        else:
+            col_header = [""] * columns.nlevels
+
+        if self.header:
+            return col_header + adjoined
+        else:
+            return adjoined
+
+    def _get_column_name_list(self) -> list[Hashable]:
+        names: list[Hashable] = []
+        columns = self.frame.columns
+        if isinstance(columns, MultiIndex):
+            names.extend("" if name is None else name for name in columns.names)
+        else:
+            names.append("" if columns.name is None else columns.name)
+        return names
+
+
+class DataFrameRenderer:
+    """Class for creating dataframe output in multiple formats.
+
+    Called in pandas.core.generic.NDFrame:
+        - to_csv
+        - to_latex
+
+    Called in pandas.core.frame.DataFrame:
+        - to_html
+        - to_string
+
+    Parameters
+    ----------
+    fmt : DataFrameFormatter
+        Formatter with the formatting options.
+    """
+
+    def __init__(self, fmt: DataFrameFormatter) -> None:
+        self.fmt = fmt
+
+    def to_html(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        encoding: str | None = None,
+        classes: str | list | tuple | None = None,
+        notebook: bool = False,
+        border: int | bool | None = None,
+        table_id: str | None = None,
+        render_links: bool = False,
+    ) -> str | None:
+        """
+        Render a DataFrame to a html table.
+
+        Parameters
+        ----------
+        buf : str, path object, file-like object, or None, default None
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a string ``write()`` function. If None, the result is
+            returned as a string.
+        encoding : str, default “utf-8”
+            Set character encoding.
+        classes : str or list-like
+            classes to include in the `class` attribute of the opening
+            ``<table>`` tag, in addition to the default "dataframe".
+        notebook : {True, False}, optional, default False
+            Whether the generated HTML is for IPython Notebook.
+        border : int
+            A ``border=border`` attribute is included in the opening
+            ``<table>`` tag. Default ``pd.options.display.html.border``.
+        table_id : str, optional
+            A css id is included in the opening `<table>` tag if specified.
+        render_links : bool, default False
+            Convert URLs to HTML links.
+        """
+        from pandas.io.formats.html import (
+            HTMLFormatter,
+            NotebookFormatter,
+        )
+
+        Klass = NotebookFormatter if notebook else HTMLFormatter
+
+        html_formatter = Klass(
+            self.fmt,
+            classes=classes,
+            border=border,
+            table_id=table_id,
+            render_links=render_links,
+        )
+        string = html_formatter.to_string()
+        return save_to_buffer(string, buf=buf, encoding=encoding)
+
+    def to_string(
+        self,
+        buf: FilePath | WriteBuffer[str] | None = None,
+        encoding: str | None = None,
+        line_width: int | None = None,
+    ) -> str | None:
+        """
+        Render a DataFrame to a console-friendly tabular output.
+
+        Parameters
+        ----------
+        buf : str, path object, file-like object, or None, default None
+            String, path object (implementing ``os.PathLike[str]``), or file-like
+            object implementing a string ``write()`` function. If None, the result is
+            returned as a string.
+        encoding: str, default “utf-8”
+            Set character encoding.
+        line_width : int, optional
+            Width to wrap a line in characters.
+        """
+        from pandas.io.formats.string import StringFormatter
+
+        string_formatter = StringFormatter(self.fmt, line_width=line_width)
+        string = string_formatter.to_string()
+        return save_to_buffer(string, buf=buf, encoding=encoding)
+
+    def to_csv(
+        self,
+        path_or_buf: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None,
+        encoding: str | None = None,
+        sep: str = ",",
+        columns: Sequence[Hashable] | None = None,
+        index_label: IndexLabel | None = None,
+        mode: str = "w",
+        compression: CompressionOptions = "infer",
+        quoting: int | None = None,
+        quotechar: str = '"',
+        lineterminator: str | None = None,
+        chunksize: int | None = None,
+        date_format: str | None = None,
+        doublequote: bool = True,
+        escapechar: str | None = None,
+        errors: str = "strict",
+        storage_options: StorageOptions | None = None,
+    ) -> str | None:
+        """
+        Render dataframe as comma-separated file.
+        """
+        from pandas.io.formats.csvs import CSVFormatter
+
+        if path_or_buf is None:
+            created_buffer = True
+            path_or_buf = StringIO()
+        else:
+            created_buffer = False
+
+        csv_formatter = CSVFormatter(
+            path_or_buf=path_or_buf,
+            lineterminator=lineterminator,
+            sep=sep,
+            encoding=encoding,
+            errors=errors,
+            compression=compression,
+            quoting=quoting,
+            cols=columns,
+            index_label=index_label,
+            mode=mode,
+            chunksize=chunksize,
+            quotechar=quotechar,
+            date_format=date_format,
+            doublequote=doublequote,
+            escapechar=escapechar,
+            storage_options=storage_options,
+            formatter=self.fmt,
+        )
+        csv_formatter.save()
+
+        if created_buffer:
+            assert isinstance(path_or_buf, StringIO)
+            content = path_or_buf.getvalue()
+            path_or_buf.close()
+            return content
+
+        return None
+
+
+def save_to_buffer(
+    string: str,
+    buf: FilePath | WriteBuffer[str] | None = None,
+    encoding: str | None = None,
+) -> str | None:
+    """
+    Perform serialization. Write to buf or return as string if buf is None.
+    """
+    with _get_buffer(buf, encoding=encoding) as fd:
+        fd.write(string)
+        if buf is None:
+            # error: "WriteBuffer[str]" has no attribute "getvalue"
+            return fd.getvalue()  # type: ignore[attr-defined]
+        return None
+
+
+@contextmanager
+def _get_buffer(
+    buf: FilePath | WriteBuffer[str] | None, encoding: str | None = None
+) -> Generator[WriteBuffer[str], None, None] | Generator[StringIO, None, None]:
+    """
+    Context manager to open, yield and close buffer for filenames or Path-like
+    objects, otherwise yield buf unchanged.
+    """
+    if buf is not None:
+        buf = stringify_path(buf)
+    else:
+        buf = StringIO()
+
+    if encoding is None:
+        encoding = "utf-8"
+    elif not isinstance(buf, str):
+        raise ValueError("buf is not a file name and encoding is specified.")
+
+    if hasattr(buf, "write"):
+        # Incompatible types in "yield" (actual type "Union[str, WriteBuffer[str],
+        # StringIO]", expected type "Union[WriteBuffer[str], StringIO]")
+        yield buf  # type: ignore[misc]
+    elif isinstance(buf, str):
+        check_parent_directory(str(buf))
+        with open(buf, "w", encoding=encoding, newline="") as f:
+            # GH#30034 open instead of codecs.open prevents a file leak
+            #  if we have an invalid encoding argument.
+            # newline="" is needed to roundtrip correctly on
+            #  windows test_to_latex_filename
+            yield f
+    else:
+        raise TypeError("buf is not a file name and it has no write method")
+
+
+# ----------------------------------------------------------------------
+# Array formatters
+
+
+def format_array(
+    values: ArrayLike,
+    formatter: Callable | None,
+    float_format: FloatFormatType | None = None,
+    na_rep: str = "NaN",
+    digits: int | None = None,
+    space: str | int | None = None,
+    justify: str = "right",
+    decimal: str = ".",
+    leading_space: bool | None = True,
+    quoting: int | None = None,
+    fallback_formatter: Callable | None = None,
+) -> list[str]:
+    """
+    Format an array for printing.
+
+    Parameters
+    ----------
+    values : np.ndarray or ExtensionArray
+    formatter
+    float_format
+    na_rep
+    digits
+    space
+    justify
+    decimal
+    leading_space : bool, optional, default True
+        Whether the array should be formatted with a leading space.
+        When an array as a column of a Series or DataFrame, we do want
+        the leading space to pad between columns.
+
+        When formatting an Index subclass
+        (e.g. IntervalIndex._get_values_for_csv), we don't want the
+        leading space since it should be left-aligned.
+    fallback_formatter
+
+    Returns
+    -------
+    List[str]
+    """
+    fmt_klass: type[_GenericArrayFormatter]
+    if lib.is_np_dtype(values.dtype, "M"):
+        fmt_klass = _Datetime64Formatter
+        values = cast(DatetimeArray, values)
+    elif isinstance(values.dtype, DatetimeTZDtype):
+        fmt_klass = _Datetime64TZFormatter
+        values = cast(DatetimeArray, values)
+    elif lib.is_np_dtype(values.dtype, "m"):
+        fmt_klass = _Timedelta64Formatter
+        values = cast(TimedeltaArray, values)
+    elif isinstance(values.dtype, ExtensionDtype):
+        fmt_klass = _ExtensionArrayFormatter
+    elif lib.is_np_dtype(values.dtype, "fc"):
+        fmt_klass = FloatArrayFormatter
+    elif lib.is_np_dtype(values.dtype, "iu"):
+        fmt_klass = _IntArrayFormatter
+    else:
+        fmt_klass = _GenericArrayFormatter
+
+    if space is None:
+        space = 12
+
+    if float_format is None:
+        float_format = get_option("display.float_format")
+
+    if digits is None:
+        digits = get_option("display.precision")
+
+    fmt_obj = fmt_klass(
+        values,
+        digits=digits,
+        na_rep=na_rep,
+        float_format=float_format,
+        formatter=formatter,
+        space=space,
+        justify=justify,
+        decimal=decimal,
+        leading_space=leading_space,
+        quoting=quoting,
+        fallback_formatter=fallback_formatter,
+    )
+
+    return fmt_obj.get_result()
+
+
+class _GenericArrayFormatter:
+    def __init__(
+        self,
+        values: ArrayLike,
+        digits: int = 7,
+        formatter: Callable | None = None,
+        na_rep: str = "NaN",
+        space: str | int = 12,
+        float_format: FloatFormatType | None = None,
+        justify: str = "right",
+        decimal: str = ".",
+        quoting: int | None = None,
+        fixed_width: bool = True,
+        leading_space: bool | None = True,
+        fallback_formatter: Callable | None = None,
+    ) -> None:
+        self.values = values
+        self.digits = digits
+        self.na_rep = na_rep
+        self.space = space
+        self.formatter = formatter
+        self.float_format = float_format
+        self.justify = justify
+        self.decimal = decimal
+        self.quoting = quoting
+        self.fixed_width = fixed_width
+        self.leading_space = leading_space
+        self.fallback_formatter = fallback_formatter
+
+    def get_result(self) -> list[str]:
+        fmt_values = self._format_strings()
+        return _make_fixed_width(fmt_values, self.justify)
+
+    def _format_strings(self) -> list[str]:
+        if self.float_format is None:
+            float_format = get_option("display.float_format")
+            if float_format is None:
+                precision = get_option("display.precision")
+                float_format = lambda x: _trim_zeros_single_float(
+                    f"{x: .{precision:d}f}"
+                )
+        else:
+            float_format = self.float_format
+
+        if self.formatter is not None:
+            formatter = self.formatter
+        elif self.fallback_formatter is not None:
+            formatter = self.fallback_formatter
+        else:
+            quote_strings = self.quoting is not None and self.quoting != QUOTE_NONE
+            formatter = partial(
+                printing.pprint_thing,
+                escape_chars=("\t", "\r", "\n"),
+                quote_strings=quote_strings,
+            )
+
+        def _format(x):
+            if self.na_rep is not None and is_scalar(x) and isna(x):
+                if x is None:
+                    return "None"
+                elif x is NA:
+                    return str(NA)
+                elif lib.is_float(x) and np.isinf(x):
+                    # TODO(3.0): this will be unreachable when use_inf_as_na
+                    #  deprecation is enforced
+                    return str(x)
+                elif x is NaT or isinstance(x, (np.datetime64, np.timedelta64)):
+                    return "NaT"
+                return self.na_rep
+            elif isinstance(x, PandasObject):
+                return str(x)
+            elif isinstance(x, StringDtype):
+                return repr(x)
+            else:
+                # object dtype
+                return str(formatter(x))
+
+        vals = self.values
+        if not isinstance(vals, np.ndarray):
+            raise TypeError(
+                "ExtensionArray formatting should use _ExtensionArrayFormatter"
+            )
+        inferred = lib.map_infer(vals, is_float)
+        is_float_type = (
+            inferred
+            # vals may have 2 or more dimensions
+            & np.all(notna(vals), axis=tuple(range(1, len(vals.shape))))
+        )
+        leading_space = self.leading_space
+        if leading_space is None:
+            leading_space = is_float_type.any()
+
+        fmt_values = []
+        for i, v in enumerate(vals):
+            if (not is_float_type[i] or self.formatter is not None) and leading_space:
+                fmt_values.append(f" {_format(v)}")
+            elif is_float_type[i]:
+                fmt_values.append(float_format(v))
+            else:
+                if leading_space is False:
+                    # False specifically, so that the default is
+                    # to include a space if we get here.
+                    tpl = "{v}"
+                else:
+                    tpl = " {v}"
+                fmt_values.append(tpl.format(v=_format(v)))
+
+        return fmt_values
+
+
+class FloatArrayFormatter(_GenericArrayFormatter):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+
+        # float_format is expected to be a string
+        # formatter should be used to pass a function
+        if self.float_format is not None and self.formatter is None:
+            # GH21625, GH22270
+            self.fixed_width = False
+            if callable(self.float_format):
+                self.formatter = self.float_format
+                self.float_format = None
+
+    def _value_formatter(
+        self,
+        float_format: FloatFormatType | None = None,
+        threshold: float | None = None,
+    ) -> Callable:
+        """Returns a function to be applied on each value to format it"""
+        # the float_format parameter supersedes self.float_format
+        if float_format is None:
+            float_format = self.float_format
+
+        # we are going to compose different functions, to first convert to
+        # a string, then replace the decimal symbol, and finally chop according
+        # to the threshold
+
+        # when there is no float_format, we use str instead of '%g'
+        # because str(0.0) = '0.0' while '%g' % 0.0 = '0'
+        if float_format:
+
+            def base_formatter(v):
+                assert float_format is not None  # for mypy
+                # error: "str" not callable
+                # error: Unexpected keyword argument "value" for "__call__" of
+                # "EngFormatter"
+                return (
+                    float_format(value=v)  # type: ignore[operator,call-arg]
+                    if notna(v)
+                    else self.na_rep
+                )
+
+        else:
+
+            def base_formatter(v):
+                return str(v) if notna(v) else self.na_rep
+
+        if self.decimal != ".":
+
+            def decimal_formatter(v):
+                return base_formatter(v).replace(".", self.decimal, 1)
+
+        else:
+            decimal_formatter = base_formatter
+
+        if threshold is None:
+            return decimal_formatter
+
+        def formatter(value):
+            if notna(value):
+                if abs(value) > threshold:
+                    return decimal_formatter(value)
+                else:
+                    return decimal_formatter(0.0)
+            else:
+                return self.na_rep
+
+        return formatter
+
+    def get_result_as_array(self) -> np.ndarray:
+        """
+        Returns the float values converted into strings using
+        the parameters given at initialisation, as a numpy array
+        """
+
+        def format_with_na_rep(values: ArrayLike, formatter: Callable, na_rep: str):
+            mask = isna(values)
+            formatted = np.array(
+                [
+                    formatter(val) if not m else na_rep
+                    for val, m in zip(values.ravel(), mask.ravel())
+                ]
+            ).reshape(values.shape)
+            return formatted
+
+        def format_complex_with_na_rep(
+            values: ArrayLike, formatter: Callable, na_rep: str
+        ):
+            real_values = np.real(values).ravel()  # type: ignore[arg-type]
+            imag_values = np.imag(values).ravel()  # type: ignore[arg-type]
+            real_mask, imag_mask = isna(real_values), isna(imag_values)
+            formatted_lst = []
+            for val, real_val, imag_val, re_isna, im_isna in zip(
+                values.ravel(),
+                real_values,
+                imag_values,
+                real_mask,
+                imag_mask,
+            ):
+                if not re_isna and not im_isna:
+                    formatted_lst.append(formatter(val))
+                elif not re_isna:  # xxx+nanj
+                    formatted_lst.append(f"{formatter(real_val)}+{na_rep}j")
+                elif not im_isna:  # nan[+/-]xxxj
+                    # The imaginary part may either start with a "-" or a space
+                    imag_formatted = formatter(imag_val).strip()
+                    if imag_formatted.startswith("-"):
+                        formatted_lst.append(f"{na_rep}{imag_formatted}j")
+                    else:
+                        formatted_lst.append(f"{na_rep}+{imag_formatted}j")
+                else:  # nan+nanj
+                    formatted_lst.append(f"{na_rep}+{na_rep}j")
+            return np.array(formatted_lst).reshape(values.shape)
+
+        if self.formatter is not None:
+            return format_with_na_rep(self.values, self.formatter, self.na_rep)
+
+        if self.fixed_width:
+            threshold = get_option("display.chop_threshold")
+        else:
+            threshold = None
+
+        # if we have a fixed_width, we'll need to try different float_format
+        def format_values_with(float_format):
+            formatter = self._value_formatter(float_format, threshold)
+
+            # default formatter leaves a space to the left when formatting
+            # floats, must be consistent for left-justifying NaNs (GH #25061)
+            na_rep = " " + self.na_rep if self.justify == "left" else self.na_rep
+
+            # different formatting strategies for complex and non-complex data
+            # need to distinguish complex and float NaNs (GH #53762)
+            values = self.values
+            is_complex = is_complex_dtype(values)
+
+            # separate the wheat from the chaff
+            if is_complex:
+                values = format_complex_with_na_rep(values, formatter, na_rep)
+            else:
+                values = format_with_na_rep(values, formatter, na_rep)
+
+            if self.fixed_width:
+                if is_complex:
+                    result = _trim_zeros_complex(values, self.decimal)
+                else:
+                    result = _trim_zeros_float(values, self.decimal)
+                return np.asarray(result, dtype="object")
+
+            return values
+
+        # There is a special default string when we are fixed-width
+        # The default is otherwise to use str instead of a formatting string
+        float_format: FloatFormatType | None
+        if self.float_format is None:
+            if self.fixed_width:
+                if self.leading_space is True:
+                    fmt_str = "{value: .{digits:d}f}"
+                else:
+                    fmt_str = "{value:.{digits:d}f}"
+                float_format = partial(fmt_str.format, digits=self.digits)
+            else:
+                float_format = self.float_format
+        else:
+            float_format = lambda value: self.float_format % value
+
+        formatted_values = format_values_with(float_format)
+
+        if not self.fixed_width:
+            return formatted_values
+
+        # we need do convert to engineering format if some values are too small
+        # and would appear as 0, or if some values are too big and take too
+        # much space
+
+        if len(formatted_values) > 0:
+            maxlen = max(len(x) for x in formatted_values)
+            too_long = maxlen > self.digits + 6
+        else:
+            too_long = False
+
+        abs_vals = np.abs(self.values)
+        # this is pretty arbitrary for now
+        # large values: more that 8 characters including decimal symbol
+        # and first digit, hence > 1e6
+        has_large_values = (abs_vals > 1e6).any()
+        has_small_values = ((abs_vals < 10 ** (-self.digits)) & (abs_vals > 0)).any()
+
+        if has_small_values or (too_long and has_large_values):
+            if self.leading_space is True:
+                fmt_str = "{value: .{digits:d}e}"
+            else:
+                fmt_str = "{value:.{digits:d}e}"
+            float_format = partial(fmt_str.format, digits=self.digits)
+            formatted_values = format_values_with(float_format)
+
+        return formatted_values
+
+    def _format_strings(self) -> list[str]:
+        return list(self.get_result_as_array())
+
+
+class _IntArrayFormatter(_GenericArrayFormatter):
+    def _format_strings(self) -> list[str]:
+        if self.leading_space is False:
+            formatter_str = lambda x: f"{x:d}".format(x=x)
+        else:
+            formatter_str = lambda x: f"{x: d}".format(x=x)
+        formatter = self.formatter or formatter_str
+        fmt_values = [formatter(x) for x in self.values]
+        return fmt_values
+
+
+class _Datetime64Formatter(_GenericArrayFormatter):
+    values: DatetimeArray
+
+    def __init__(
+        self,
+        values: DatetimeArray,
+        nat_rep: str = "NaT",
+        date_format: None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(values, **kwargs)
+        self.nat_rep = nat_rep
+        self.date_format = date_format
+
+    def _format_strings(self) -> list[str]:
+        """we by definition have DO NOT have a TZ"""
+        values = self.values
+
+        if self.formatter is not None:
+            return [self.formatter(x) for x in values]
+
+        fmt_values = values._format_native_types(
+            na_rep=self.nat_rep, date_format=self.date_format
+        )
+        return fmt_values.tolist()
+
+
+class _ExtensionArrayFormatter(_GenericArrayFormatter):
+    values: ExtensionArray
+
+    def _format_strings(self) -> list[str]:
+        values = self.values
+
+        formatter = self.formatter
+        fallback_formatter = None
+        if formatter is None:
+            fallback_formatter = values._formatter(boxed=True)
+
+        if isinstance(values, Categorical):
+            # Categorical is special for now, so that we can preserve tzinfo
+            array = values._internal_get_values()
+        else:
+            array = np.asarray(values, dtype=object)
+
+        fmt_values = format_array(
+            array,
+            formatter,
+            float_format=self.float_format,
+            na_rep=self.na_rep,
+            digits=self.digits,
+            space=self.space,
+            justify=self.justify,
+            decimal=self.decimal,
+            leading_space=self.leading_space,
+            quoting=self.quoting,
+            fallback_formatter=fallback_formatter,
+        )
+        return fmt_values
+
+
+def format_percentiles(
+    percentiles: (np.ndarray | Sequence[float]),
+) -> list[str]:
+    """
+    Outputs rounded and formatted percentiles.
+
+    Parameters
+    ----------
+    percentiles : list-like, containing floats from interval [0,1]
+
+    Returns
+    -------
+    formatted : list of strings
+
+    Notes
+    -----
+    Rounding precision is chosen so that: (1) if any two elements of
+    ``percentiles`` differ, they remain different after rounding
+    (2) no entry is *rounded* to 0% or 100%.
+    Any non-integer is always rounded to at least 1 decimal place.
+
+    Examples
+    --------
+    Keeps all entries different after rounding:
+
+    >>> format_percentiles([0.01999, 0.02001, 0.5, 0.666666, 0.9999])
+    ['1.999%', '2.001%', '50%', '66.667%', '99.99%']
+
+    No element is rounded to 0% or 100% (unless already equal to it).
+    Duplicates are allowed:
+
+    >>> format_percentiles([0, 0.5, 0.02001, 0.5, 0.666666, 0.9999])
+    ['0%', '50%', '2.0%', '50%', '66.67%', '99.99%']
+    """
+    percentiles = np.asarray(percentiles)
+
+    # It checks for np.nan as well
+    if (
+        not is_numeric_dtype(percentiles)
+        or not np.all(percentiles >= 0)
+        or not np.all(percentiles <= 1)
+    ):
+        raise ValueError("percentiles should all be in the interval [0,1]")
+
+    percentiles = 100 * percentiles
+    prec = get_precision(percentiles)
+    percentiles_round_type = percentiles.round(prec).astype(int)
+
+    int_idx = np.isclose(percentiles_round_type, percentiles)
+
+    if np.all(int_idx):
+        out = percentiles_round_type.astype(str)
+        return [i + "%" for i in out]
+
+    unique_pcts = np.unique(percentiles)
+    prec = get_precision(unique_pcts)
+    out = np.empty_like(percentiles, dtype=object)
+    out[int_idx] = percentiles[int_idx].round().astype(int).astype(str)
+
+    out[~int_idx] = percentiles[~int_idx].round(prec).astype(str)
+    return [i + "%" for i in out]
+
+
+def get_precision(array: np.ndarray | Sequence[float]) -> int:
+    to_begin = array[0] if array[0] > 0 else None
+    to_end = 100 - array[-1] if array[-1] < 100 else None
+    diff = np.ediff1d(array, to_begin=to_begin, to_end=to_end)
+    diff = abs(diff)
+    prec = -np.floor(np.log10(np.min(diff))).astype(int)
+    prec = max(1, prec)
+    return prec
+
+
+def _format_datetime64(x: NaTType | Timestamp, nat_rep: str = "NaT") -> str:
+    if x is NaT:
+        return nat_rep
+
+    # Timestamp.__str__ falls back to datetime.datetime.__str__ = isoformat(sep=' ')
+    # so it already uses string formatting rather than strftime (faster).
+    return str(x)
+
+
+def _format_datetime64_dateonly(
+    x: NaTType | Timestamp,
+    nat_rep: str = "NaT",
+    date_format: str | None = None,
+) -> str:
+    if isinstance(x, NaTType):
+        return nat_rep
+
+    if date_format:
+        return x.strftime(date_format)
+    else:
+        # Timestamp._date_repr relies on string formatting (faster than strftime)
+        return x._date_repr
+
+
+def get_format_datetime64(
+    is_dates_only: bool, nat_rep: str = "NaT", date_format: str | None = None
+) -> Callable:
+    """Return a formatter callable taking a datetime64 as input and providing
+    a string as output"""
+
+    if is_dates_only:
+        return lambda x: _format_datetime64_dateonly(
+            x, nat_rep=nat_rep, date_format=date_format
+        )
+    else:
+        return lambda x: _format_datetime64(x, nat_rep=nat_rep)
+
+
+class _Datetime64TZFormatter(_Datetime64Formatter):
+    values: DatetimeArray
+
+    def _format_strings(self) -> list[str]:
+        """we by definition have a TZ"""
+        ido = self.values._is_dates_only
+        values = self.values.astype(object)
+        formatter = self.formatter or get_format_datetime64(
+            ido, date_format=self.date_format
+        )
+        fmt_values = [formatter(x) for x in values]
+
+        return fmt_values
+
+
+class _Timedelta64Formatter(_GenericArrayFormatter):
+    values: TimedeltaArray
+
+    def __init__(
+        self,
+        values: TimedeltaArray,
+        nat_rep: str = "NaT",
+        **kwargs,
+    ) -> None:
+        # TODO: nat_rep is never passed, na_rep is.
+        super().__init__(values, **kwargs)
+        self.nat_rep = nat_rep
+
+    def _format_strings(self) -> list[str]:
+        formatter = self.formatter or get_format_timedelta64(
+            self.values, nat_rep=self.nat_rep, box=False
+        )
+        return [formatter(x) for x in self.values]
+
+
+def get_format_timedelta64(
+    values: TimedeltaArray,
+    nat_rep: str | float = "NaT",
+    box: bool = False,
+) -> Callable:
+    """
+    Return a formatter function for a range of timedeltas.
+    These will all have the same format argument
+
+    If box, then show the return in quotes
+    """
+    even_days = values._is_dates_only
+
+    if even_days:
+        format = None
+    else:
+        format = "long"
+
+    def _formatter(x):
+        if x is None or (is_scalar(x) and isna(x)):
+            return nat_rep
+
+        if not isinstance(x, Timedelta):
+            x = Timedelta(x)
+
+        # Timedelta._repr_base uses string formatting (faster than strftime)
+        result = x._repr_base(format=format)
+        if box:
+            result = f"'{result}'"
+        return result
+
+    return _formatter
+
+
+def _make_fixed_width(
+    strings: list[str],
+    justify: str = "right",
+    minimum: int | None = None,
+    adj: printing._TextAdjustment | None = None,
+) -> list[str]:
+    if len(strings) == 0 or justify == "all":
+        return strings
+
+    if adj is None:
+        adjustment = printing.get_adjustment()
+    else:
+        adjustment = adj
+
+    max_len = max(adjustment.len(x) for x in strings)
+
+    if minimum is not None:
+        max_len = max(minimum, max_len)
+
+    conf_max = get_option("display.max_colwidth")
+    if conf_max is not None and max_len > conf_max:
+        max_len = conf_max
+
+    def just(x: str) -> str:
+        if conf_max is not None:
+            if (conf_max > 3) & (adjustment.len(x) > max_len):
+                x = x[: max_len - 3] + "..."
+        return x
+
+    strings = [just(x) for x in strings]
+    result = adjustment.justify(strings, max_len, mode=justify)
+    return result
+
+
+def _trim_zeros_complex(str_complexes: ArrayLike, decimal: str = ".") -> list[str]:
+    """
+    Separates the real and imaginary parts from the complex number, and
+    executes the _trim_zeros_float method on each of those.
+    """
+    real_part, imag_part = [], []
+    for x in str_complexes:
+        # Complex numbers are represented as "(-)xxx(+/-)xxxj"
+        # The split will give [{"", "-"}, "xxx", "+/-", "xxx", "j", ""]
+        # Therefore, the imaginary part is the 4th and 3rd last elements,
+        # and the real part is everything before the imaginary part
+        trimmed = re.split(r"([j+-])", x)
+        real_part.append("".join(trimmed[:-4]))
+        imag_part.append("".join(trimmed[-4:-2]))
+
+    # We want to align the lengths of the real and imaginary parts of each complex
+    # number, as well as the lengths the real (resp. complex) parts of all numbers
+    # in the array
+    n = len(str_complexes)
+    padded_parts = _trim_zeros_float(real_part + imag_part, decimal)
+    if len(padded_parts) == 0:
+        return []
+    padded_length = max(len(part) for part in padded_parts) - 1
+    padded = [
+        real_pt  # real part, possibly NaN
+        + imag_pt[0]  # +/-
+        + f"{imag_pt[1:]:>{padded_length}}"  # complex part (no sign), possibly nan
+        + "j"
+        for real_pt, imag_pt in zip(padded_parts[:n], padded_parts[n:])
+    ]
+    return padded
+
+
+def _trim_zeros_single_float(str_float: str) -> str:
+    """
+    Trims trailing zeros after a decimal point,
+    leaving just one if necessary.
+    """
+    str_float = str_float.rstrip("0")
+    if str_float.endswith("."):
+        str_float += "0"
+
+    return str_float
+
+
+def _trim_zeros_float(
+    str_floats: ArrayLike | list[str], decimal: str = "."
+) -> list[str]:
+    """
+    Trims the maximum number of trailing zeros equally from
+    all numbers containing decimals, leaving just one if
+    necessary.
+    """
+    trimmed = str_floats
+    number_regex = re.compile(rf"^\s*[\+-]?[0-9]+\{decimal}[0-9]*$")
+
+    def is_number_with_decimal(x) -> bool:
+        return re.match(number_regex, x) is not None
+
+    def should_trim(values: ArrayLike | list[str]) -> bool:
+        """
+        Determine if an array of strings should be trimmed.
+
+        Returns True if all numbers containing decimals (defined by the
+        above regular expression) within the array end in a zero, otherwise
+        returns False.
+        """
+        numbers = [x for x in values if is_number_with_decimal(x)]
+        return len(numbers) > 0 and all(x.endswith("0") for x in numbers)
+
+    while should_trim(trimmed):
+        trimmed = [x[:-1] if is_number_with_decimal(x) else x for x in trimmed]
+
+    # leave one 0 after the decimal points if need be.
+    result = [
+        x + "0" if is_number_with_decimal(x) and x.endswith(decimal) else x
+        for x in trimmed
+    ]
+    return result
+
+
+def _has_names(index: Index) -> bool:
+    if isinstance(index, MultiIndex):
+        return com.any_not_none(*index.names)
+    else:
+        return index.name is not None
+
+
+class EngFormatter:
+    """
+    Formats float values according to engineering format.
+
+    Based on matplotlib.ticker.EngFormatter
+    """
+
+    # The SI engineering prefixes
+    ENG_PREFIXES = {
+        -24: "y",
+        -21: "z",
+        -18: "a",
+        -15: "f",
+        -12: "p",
+        -9: "n",
+        -6: "u",
+        -3: "m",
+        0: "",
+        3: "k",
+        6: "M",
+        9: "G",
+        12: "T",
+        15: "P",
+        18: "E",
+        21: "Z",
+        24: "Y",
+    }
+
+    def __init__(
+        self, accuracy: int | None = None, use_eng_prefix: bool = False
+    ) -> None:
+        self.accuracy = accuracy
+        self.use_eng_prefix = use_eng_prefix
+
+    def __call__(self, num: float) -> str:
+        """
+        Formats a number in engineering notation, appending a letter
+        representing the power of 1000 of the original number. Some examples:
+        >>> format_eng = EngFormatter(accuracy=0, use_eng_prefix=True)
+        >>> format_eng(0)
+        ' 0'
+        >>> format_eng = EngFormatter(accuracy=1, use_eng_prefix=True)
+        >>> format_eng(1_000_000)
+        ' 1.0M'
+        >>> format_eng = EngFormatter(accuracy=2, use_eng_prefix=False)
+        >>> format_eng("-1e-6")
+        '-1.00E-06'
+
+        @param num: the value to represent
+        @type num: either a numeric value or a string that can be converted to
+                   a numeric value (as per decimal.Decimal constructor)
+
+        @return: engineering formatted string
+        """
+        dnum = Decimal(str(num))
+
+        if Decimal.is_nan(dnum):
+            return "NaN"
+
+        if Decimal.is_infinite(dnum):
+            return "inf"
+
+        sign = 1
+
+        if dnum < 0:  # pragma: no cover
+            sign = -1
+            dnum = -dnum
+
+        if dnum != 0:
+            pow10 = Decimal(int(math.floor(dnum.log10() / 3) * 3))
+        else:
+            pow10 = Decimal(0)
+
+        pow10 = pow10.min(max(self.ENG_PREFIXES.keys()))
+        pow10 = pow10.max(min(self.ENG_PREFIXES.keys()))
+        int_pow10 = int(pow10)
+
+        if self.use_eng_prefix:
+            prefix = self.ENG_PREFIXES[int_pow10]
+        elif int_pow10 < 0:
+            prefix = f"E-{-int_pow10:02d}"
+        else:
+            prefix = f"E+{int_pow10:02d}"
+
+        mant = sign * dnum / (10**pow10)
+
+        if self.accuracy is None:  # pragma: no cover
+            format_str = "{mant: g}{prefix}"
+        else:
+            format_str = f"{{mant: .{self.accuracy:d}f}}{{prefix}}"
+
+        formatted = format_str.format(mant=mant, prefix=prefix)
+
+        return formatted
+
+
+def set_eng_float_format(accuracy: int = 3, use_eng_prefix: bool = False) -> None:
+    """
+    Format float representation in DataFrame with SI notation.
+
+    Parameters
+    ----------
+    accuracy : int, default 3
+        Number of decimal digits after the floating point.
+    use_eng_prefix : bool, default False
+        Whether to represent a value with SI prefixes.
+
+    Returns
+    -------
+    None
+
+    Examples
+    --------
+    >>> df = pd.DataFrame([1e-9, 1e-3, 1, 1e3, 1e6])
+    >>> df
+                  0
+    0  1.000000e-09
+    1  1.000000e-03
+    2  1.000000e+00
+    3  1.000000e+03
+    4  1.000000e+06
+
+    >>> pd.set_eng_float_format(accuracy=1)
+    >>> df
+             0
+    0  1.0E-09
+    1  1.0E-03
+    2  1.0E+00
+    3  1.0E+03
+    4  1.0E+06
+
+    >>> pd.set_eng_float_format(use_eng_prefix=True)
+    >>> df
+            0
+    0  1.000n
+    1  1.000m
+    2   1.000
+    3  1.000k
+    4  1.000M
+
+    >>> pd.set_eng_float_format(accuracy=1, use_eng_prefix=True)
+    >>> df
+          0
+    0  1.0n
+    1  1.0m
+    2   1.0
+    3  1.0k
+    4  1.0M
+
+    >>> pd.set_option("display.float_format", None)  # unset option
+    """
+    set_option("display.float_format", EngFormatter(accuracy, use_eng_prefix))
+
+
+def get_level_lengths(
+    levels: Any, sentinel: bool | object | str = ""
+) -> list[dict[int, int]]:
+    """
+    For each index in each level the function returns lengths of indexes.
+
+    Parameters
+    ----------
+    levels : list of lists
+        List of values on for level.
+    sentinel : string, optional
+        Value which states that no new index starts on there.
+
+    Returns
+    -------
+    Returns list of maps. For each level returns map of indexes (key is index
+    in row and value is length of index).
+    """
+    if len(levels) == 0:
+        return []
+
+    control = [True] * len(levels[0])
+
+    result = []
+    for level in levels:
+        last_index = 0
+
+        lengths = {}
+        for i, key in enumerate(level):
+            if control[i] and key == sentinel:
+                pass
+            else:
+                control[i] = False
+                lengths[last_index] = i - last_index
+                last_index = i
+
+        lengths[last_index] = len(level) - last_index
+
+        result.append(lengths)
+
+    return result
+
+
+def buffer_put_lines(buf: WriteBuffer[str], lines: list[str]) -> None:
+    """
+    Appends lines to a buffer.
+
+    Parameters
+    ----------
+    buf
+        The buffer to write to
+    lines
+        The lines to append.
+    """
+    if any(isinstance(x, str) for x in lines):
+        lines = [str(x) for x in lines]
+    buf.write("\n".join(lines))

venv/lib/python3.10/site-packages/pandas/io/formats/html.py

@@ -0,0 +1,646 @@
+"""
+Module for formatting output data in HTML.
+"""
+from __future__ import annotations
+
+from textwrap import dedent
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Final,
+    cast,
+)
+
+from pandas._config import get_option
+
+from pandas._libs import lib
+
+from pandas import (
+    MultiIndex,
+    option_context,
+)
+
+from pandas.io.common import is_url
+from pandas.io.formats.format import (
+    DataFrameFormatter,
+    get_level_lengths,
+)
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Iterable,
+        Mapping,
+    )
+
+
+class HTMLFormatter:
+    """
+    Internal class for formatting output data in html.
+    This class is intended for shared functionality between
+    DataFrame.to_html() and DataFrame._repr_html_().
+    Any logic in common with other output formatting methods
+    should ideally be inherited from classes in format.py
+    and this class responsible for only producing html markup.
+    """
+
+    indent_delta: Final = 2
+
+    def __init__(
+        self,
+        formatter: DataFrameFormatter,
+        classes: str | list[str] | tuple[str, ...] | None = None,
+        border: int | bool | None = None,
+        table_id: str | None = None,
+        render_links: bool = False,
+    ) -> None:
+        self.fmt = formatter
+        self.classes = classes
+
+        self.frame = self.fmt.frame
+        self.columns = self.fmt.tr_frame.columns
+        self.elements: list[str] = []
+        self.bold_rows = self.fmt.bold_rows
+        self.escape = self.fmt.escape
+        self.show_dimensions = self.fmt.show_dimensions
+        if border is None or border is True:
+            border = cast(int, get_option("display.html.border"))
+        elif not border:
+            border = None
+
+        self.border = border
+        self.table_id = table_id
+        self.render_links = render_links
+
+        self.col_space = {}
+        is_multi_index = isinstance(self.columns, MultiIndex)
+        for column, value in self.fmt.col_space.items():
+            col_space_value = f"{value}px" if isinstance(value, int) else value
+            self.col_space[column] = col_space_value
+            # GH 53885: Handling case where column is index
+            # Flatten the data in the multi index and add in the map
+            if is_multi_index and isinstance(column, tuple):
+                for column_index in column:
+                    self.col_space[str(column_index)] = col_space_value
+
+    def to_string(self) -> str:
+        lines = self.render()
+        if any(isinstance(x, str) for x in lines):
+            lines = [str(x) for x in lines]
+        return "\n".join(lines)
+
+    def render(self) -> list[str]:
+        self._write_table()
+
+        if self.should_show_dimensions:
+            by = chr(215)  # ×  # noqa: RUF003
+            self.write(
+                f"<p>{len(self.frame)} rows {by} {len(self.frame.columns)} columns</p>"
+            )
+
+        return self.elements
+
+    @property
+    def should_show_dimensions(self) -> bool:
+        return self.fmt.should_show_dimensions
+
+    @property
+    def show_row_idx_names(self) -> bool:
+        return self.fmt.show_row_idx_names
+
+    @property
+    def show_col_idx_names(self) -> bool:
+        return self.fmt.show_col_idx_names
+
+    @property
+    def row_levels(self) -> int:
+        if self.fmt.index:
+            # showing (row) index
+            return self.frame.index.nlevels
+        elif self.show_col_idx_names:
+            # see gh-22579
+            # Column misalignment also occurs for
+            # a standard index when the columns index is named.
+            # If the row index is not displayed a column of
+            # blank cells need to be included before the DataFrame values.
+            return 1
+        # not showing (row) index
+        return 0
+
+    def _get_columns_formatted_values(self) -> Iterable:
+        return self.columns
+
+    @property
+    def is_truncated(self) -> bool:
+        return self.fmt.is_truncated
+
+    @property
+    def ncols(self) -> int:
+        return len(self.fmt.tr_frame.columns)
+
+    def write(self, s: Any, indent: int = 0) -> None:
+        rs = pprint_thing(s)
+        self.elements.append(" " * indent + rs)
+
+    def write_th(
+        self, s: Any, header: bool = False, indent: int = 0, tags: str | None = None
+    ) -> None:
+        """
+        Method for writing a formatted <th> cell.
+
+        If col_space is set on the formatter then that is used for
+        the value of min-width.
+
+        Parameters
+        ----------
+        s : object
+            The data to be written inside the cell.
+        header : bool, default False
+            Set to True if the <th> is for use inside <thead>.  This will
+            cause min-width to be set if there is one.
+        indent : int, default 0
+            The indentation level of the cell.
+        tags : str, default None
+            Tags to include in the cell.
+
+        Returns
+        -------
+        A written <th> cell.
+        """
+        col_space = self.col_space.get(s, None)
+
+        if header and col_space is not None:
+            tags = tags or ""
+            tags += f'style="min-width: {col_space};"'
+
+        self._write_cell(s, kind="th", indent=indent, tags=tags)
+
+    def write_td(self, s: Any, indent: int = 0, tags: str | None = None) -> None:
+        self._write_cell(s, kind="td", indent=indent, tags=tags)
+
+    def _write_cell(
+        self, s: Any, kind: str = "td", indent: int = 0, tags: str | None = None
+    ) -> None:
+        if tags is not None:
+            start_tag = f"<{kind} {tags}>"
+        else:
+            start_tag = f"<{kind}>"
+
+        if self.escape:
+            # escape & first to prevent double escaping of &
+            esc = {"&": r"&amp;", "<": r"&lt;", ">": r"&gt;"}
+        else:
+            esc = {}
+
+        rs = pprint_thing(s, escape_chars=esc).strip()
+
+        if self.render_links and is_url(rs):
+            rs_unescaped = pprint_thing(s, escape_chars={}).strip()
+            start_tag += f'<a href="{rs_unescaped}" target="_blank">'
+            end_a = "</a>"
+        else:
+            end_a = ""
+
+        self.write(f"{start_tag}{rs}{end_a}</{kind}>", indent)
+
+    def write_tr(
+        self,
+        line: Iterable,
+        indent: int = 0,
+        indent_delta: int = 0,
+        header: bool = False,
+        align: str | None = None,
+        tags: dict[int, str] | None = None,
+        nindex_levels: int = 0,
+    ) -> None:
+        if tags is None:
+            tags = {}
+
+        if align is None:
+            self.write("<tr>", indent)
+        else:
+            self.write(f'<tr style="text-align: {align};">', indent)
+        indent += indent_delta
+
+        for i, s in enumerate(line):
+            val_tag = tags.get(i, None)
+            if header or (self.bold_rows and i < nindex_levels):
+                self.write_th(s, indent=indent, header=header, tags=val_tag)
+            else:
+                self.write_td(s, indent, tags=val_tag)
+
+        indent -= indent_delta
+        self.write("</tr>", indent)
+
+    def _write_table(self, indent: int = 0) -> None:
+        _classes = ["dataframe"]  # Default class.
+        use_mathjax = get_option("display.html.use_mathjax")
+        if not use_mathjax:
+            _classes.append("tex2jax_ignore")
+        if self.classes is not None:
+            if isinstance(self.classes, str):
+                self.classes = self.classes.split()
+            if not isinstance(self.classes, (list, tuple)):
+                raise TypeError(
+                    "classes must be a string, list, "
+                    f"or tuple, not {type(self.classes)}"
+                )
+            _classes.extend(self.classes)
+
+        if self.table_id is None:
+            id_section = ""
+        else:
+            id_section = f' id="{self.table_id}"'
+
+        if self.border is None:
+            border_attr = ""
+        else:
+            border_attr = f' border="{self.border}"'
+
+        self.write(
+            f'<table{border_attr} class="{" ".join(_classes)}"{id_section}>',
+            indent,
+        )
+
+        if self.fmt.header or self.show_row_idx_names:
+            self._write_header(indent + self.indent_delta)
+
+        self._write_body(indent + self.indent_delta)
+
+        self.write("</table>", indent)
+
+    def _write_col_header(self, indent: int) -> None:
+        row: list[Hashable]
+        is_truncated_horizontally = self.fmt.is_truncated_horizontally
+        if isinstance(self.columns, MultiIndex):
+            template = 'colspan="{span:d}" halign="left"'
+
+            sentinel: lib.NoDefault | bool
+            if self.fmt.sparsify:
+                # GH3547
+                sentinel = lib.no_default
+            else:
+                sentinel = False
+            levels = self.columns._format_multi(sparsify=sentinel, include_names=False)
+            level_lengths = get_level_lengths(levels, sentinel)
+            inner_lvl = len(level_lengths) - 1
+            for lnum, (records, values) in enumerate(zip(level_lengths, levels)):
+                if is_truncated_horizontally:
+                    # modify the header lines
+                    ins_col = self.fmt.tr_col_num
+                    if self.fmt.sparsify:
+                        recs_new = {}
+                        # Increment tags after ... col.
+                        for tag, span in list(records.items()):
+                            if tag >= ins_col:
+                                recs_new[tag + 1] = span
+                            elif tag + span > ins_col:
+                                recs_new[tag] = span + 1
+                                if lnum == inner_lvl:
+                                    values = (
+                                        values[:ins_col] + ("...",) + values[ins_col:]
+                                    )
+                                else:
+                                    # sparse col headers do not receive a ...
+                                    values = (
+                                        values[:ins_col]
+                                        + (values[ins_col - 1],)
+                                        + values[ins_col:]
+                                    )
+                            else:
+                                recs_new[tag] = span
+                            # if ins_col lies between tags, all col headers
+                            # get ...
+                            if tag + span == ins_col:
+                                recs_new[ins_col] = 1
+                                values = values[:ins_col] + ("...",) + values[ins_col:]
+                        records = recs_new
+                        inner_lvl = len(level_lengths) - 1
+                        if lnum == inner_lvl:
+                            records[ins_col] = 1
+                    else:
+                        recs_new = {}
+                        for tag, span in list(records.items()):
+                            if tag >= ins_col:
+                                recs_new[tag + 1] = span
+                            else:
+                                recs_new[tag] = span
+                        recs_new[ins_col] = 1
+                        records = recs_new
+                        values = values[:ins_col] + ["..."] + values[ins_col:]
+
+                # see gh-22579
+                # Column Offset Bug with to_html(index=False) with
+                # MultiIndex Columns and Index.
+                # Initially fill row with blank cells before column names.
+                # TODO: Refactor to remove code duplication with code
+                # block below for standard columns index.
+                row = [""] * (self.row_levels - 1)
+                if self.fmt.index or self.show_col_idx_names:
+                    # see gh-22747
+                    # If to_html(index_names=False) do not show columns
+                    # index names.
+                    # TODO: Refactor to use _get_column_name_list from
+                    # DataFrameFormatter class and create a
+                    # _get_formatted_column_labels function for code
+                    # parity with DataFrameFormatter class.
+                    if self.fmt.show_index_names:
+                        name = self.columns.names[lnum]
+                        row.append(pprint_thing(name or ""))
+                    else:
+                        row.append("")
+
+                tags = {}
+                j = len(row)
+                for i, v in enumerate(values):
+                    if i in records:
+                        if records[i] > 1:
+                            tags[j] = template.format(span=records[i])
+                    else:
+                        continue
+                    j += 1
+                    row.append(v)
+                self.write_tr(row, indent, self.indent_delta, tags=tags, header=True)
+        else:
+            # see gh-22579
+            # Column misalignment also occurs for
+            # a standard index when the columns index is named.
+            # Initially fill row with blank cells before column names.
+            # TODO: Refactor to remove code duplication with code block
+            # above for columns MultiIndex.
+            row = [""] * (self.row_levels - 1)
+            if self.fmt.index or self.show_col_idx_names:
+                # see gh-22747
+                # If to_html(index_names=False) do not show columns
+                # index names.
+                # TODO: Refactor to use _get_column_name_list from
+                # DataFrameFormatter class.
+                if self.fmt.show_index_names:
+                    row.append(self.columns.name or "")
+                else:
+                    row.append("")
+            row.extend(self._get_columns_formatted_values())
+            align = self.fmt.justify
+
+            if is_truncated_horizontally:
+                ins_col = self.row_levels + self.fmt.tr_col_num
+                row.insert(ins_col, "...")
+
+            self.write_tr(row, indent, self.indent_delta, header=True, align=align)
+
+    def _write_row_header(self, indent: int) -> None:
+        is_truncated_horizontally = self.fmt.is_truncated_horizontally
+        row = [x if x is not None else "" for x in self.frame.index.names] + [""] * (
+            self.ncols + (1 if is_truncated_horizontally else 0)
+        )
+        self.write_tr(row, indent, self.indent_delta, header=True)
+
+    def _write_header(self, indent: int) -> None:
+        self.write("<thead>", indent)
+
+        if self.fmt.header:
+            self._write_col_header(indent + self.indent_delta)
+
+        if self.show_row_idx_names:
+            self._write_row_header(indent + self.indent_delta)
+
+        self.write("</thead>", indent)
+
+    def _get_formatted_values(self) -> dict[int, list[str]]:
+        with option_context("display.max_colwidth", None):
+            fmt_values = {i: self.fmt.format_col(i) for i in range(self.ncols)}
+        return fmt_values
+
+    def _write_body(self, indent: int) -> None:
+        self.write("<tbody>", indent)
+        fmt_values = self._get_formatted_values()
+
+        # write values
+        if self.fmt.index and isinstance(self.frame.index, MultiIndex):
+            self._write_hierarchical_rows(fmt_values, indent + self.indent_delta)
+        else:
+            self._write_regular_rows(fmt_values, indent + self.indent_delta)
+
+        self.write("</tbody>", indent)
+
+    def _write_regular_rows(
+        self, fmt_values: Mapping[int, list[str]], indent: int
+    ) -> None:
+        is_truncated_horizontally = self.fmt.is_truncated_horizontally
+        is_truncated_vertically = self.fmt.is_truncated_vertically
+
+        nrows = len(self.fmt.tr_frame)
+
+        if self.fmt.index:
+            fmt = self.fmt._get_formatter("__index__")
+            if fmt is not None:
+                index_values = self.fmt.tr_frame.index.map(fmt)
+            else:
+                # only reached with non-Multi index
+                index_values = self.fmt.tr_frame.index._format_flat(include_name=False)
+
+        row: list[str] = []
+        for i in range(nrows):
+            if is_truncated_vertically and i == (self.fmt.tr_row_num):
+                str_sep_row = ["..."] * len(row)
+                self.write_tr(
+                    str_sep_row,
+                    indent,
+                    self.indent_delta,
+                    tags=None,
+                    nindex_levels=self.row_levels,
+                )
+
+            row = []
+            if self.fmt.index:
+                row.append(index_values[i])
+            # see gh-22579
+            # Column misalignment also occurs for
+            # a standard index when the columns index is named.
+            # Add blank cell before data cells.
+            elif self.show_col_idx_names:
+                row.append("")
+            row.extend(fmt_values[j][i] for j in range(self.ncols))
+
+            if is_truncated_horizontally:
+                dot_col_ix = self.fmt.tr_col_num + self.row_levels
+                row.insert(dot_col_ix, "...")
+            self.write_tr(
+                row, indent, self.indent_delta, tags=None, nindex_levels=self.row_levels
+            )
+
+    def _write_hierarchical_rows(
+        self, fmt_values: Mapping[int, list[str]], indent: int
+    ) -> None:
+        template = 'rowspan="{span}" valign="top"'
+
+        is_truncated_horizontally = self.fmt.is_truncated_horizontally
+        is_truncated_vertically = self.fmt.is_truncated_vertically
+        frame = self.fmt.tr_frame
+        nrows = len(frame)
+
+        assert isinstance(frame.index, MultiIndex)
+        idx_values = frame.index._format_multi(sparsify=False, include_names=False)
+        idx_values = list(zip(*idx_values))
+
+        if self.fmt.sparsify:
+            # GH3547
+            sentinel = lib.no_default
+            levels = frame.index._format_multi(sparsify=sentinel, include_names=False)
+
+            level_lengths = get_level_lengths(levels, sentinel)
+            inner_lvl = len(level_lengths) - 1
+            if is_truncated_vertically:
+                # Insert ... row and adjust idx_values and
+                # level_lengths to take this into account.
+                ins_row = self.fmt.tr_row_num
+                inserted = False
+                for lnum, records in enumerate(level_lengths):
+                    rec_new = {}
+                    for tag, span in list(records.items()):
+                        if tag >= ins_row:
+                            rec_new[tag + 1] = span
+                        elif tag + span > ins_row:
+                            rec_new[tag] = span + 1
+
+                            # GH 14882 - Make sure insertion done once
+                            if not inserted:
+                                dot_row = list(idx_values[ins_row - 1])
+                                dot_row[-1] = "..."
+                                idx_values.insert(ins_row, tuple(dot_row))
+                                inserted = True
+                            else:
+                                dot_row = list(idx_values[ins_row])
+                                dot_row[inner_lvl - lnum] = "..."
+                                idx_values[ins_row] = tuple(dot_row)
+                        else:
+                            rec_new[tag] = span
+                        # If ins_row lies between tags, all cols idx cols
+                        # receive ...
+                        if tag + span == ins_row:
+                            rec_new[ins_row] = 1
+                            if lnum == 0:
+                                idx_values.insert(
+                                    ins_row, tuple(["..."] * len(level_lengths))
+                                )
+
+                            # GH 14882 - Place ... in correct level
+                            elif inserted:
+                                dot_row = list(idx_values[ins_row])
+                                dot_row[inner_lvl - lnum] = "..."
+                                idx_values[ins_row] = tuple(dot_row)
+                    level_lengths[lnum] = rec_new
+
+                level_lengths[inner_lvl][ins_row] = 1
+                for ix_col in fmt_values:
+                    fmt_values[ix_col].insert(ins_row, "...")
+                nrows += 1
+
+            for i in range(nrows):
+                row = []
+                tags = {}
+
+                sparse_offset = 0
+                j = 0
+                for records, v in zip(level_lengths, idx_values[i]):
+                    if i in records:
+                        if records[i] > 1:
+                            tags[j] = template.format(span=records[i])
+                    else:
+                        sparse_offset += 1
+                        continue
+
+                    j += 1
+                    row.append(v)
+
+                row.extend(fmt_values[j][i] for j in range(self.ncols))
+                if is_truncated_horizontally:
+                    row.insert(
+                        self.row_levels - sparse_offset + self.fmt.tr_col_num, "..."
+                    )
+                self.write_tr(
+                    row,
+                    indent,
+                    self.indent_delta,
+                    tags=tags,
+                    nindex_levels=len(levels) - sparse_offset,
+                )
+        else:
+            row = []
+            for i in range(len(frame)):
+                if is_truncated_vertically and i == (self.fmt.tr_row_num):
+                    str_sep_row = ["..."] * len(row)
+                    self.write_tr(
+                        str_sep_row,
+                        indent,
+                        self.indent_delta,
+                        tags=None,
+                        nindex_levels=self.row_levels,
+                    )
+
+                idx_values = list(
+                    zip(*frame.index._format_multi(sparsify=False, include_names=False))
+                )
+                row = []
+                row.extend(idx_values[i])
+                row.extend(fmt_values[j][i] for j in range(self.ncols))
+                if is_truncated_horizontally:
+                    row.insert(self.row_levels + self.fmt.tr_col_num, "...")
+                self.write_tr(
+                    row,
+                    indent,
+                    self.indent_delta,
+                    tags=None,
+                    nindex_levels=frame.index.nlevels,
+                )
+
+
+class NotebookFormatter(HTMLFormatter):
+    """
+    Internal class for formatting output data in html for display in Jupyter
+    Notebooks. This class is intended for functionality specific to
+    DataFrame._repr_html_() and DataFrame.to_html(notebook=True)
+    """
+
+    def _get_formatted_values(self) -> dict[int, list[str]]:
+        return {i: self.fmt.format_col(i) for i in range(self.ncols)}
+
+    def _get_columns_formatted_values(self) -> list[str]:
+        # only reached with non-Multi Index
+        return self.columns._format_flat(include_name=False)
+
+    def write_style(self) -> None:
+        # We use the "scoped" attribute here so that the desired
+        # style properties for the data frame are not then applied
+        # throughout the entire notebook.
+        template_first = """\
+            <style scoped>"""
+        template_last = """\
+            </style>"""
+        template_select = """\
+                .dataframe %s {
+                    %s: %s;
+                }"""
+        element_props = [
+            ("tbody tr th:only-of-type", "vertical-align", "middle"),
+            ("tbody tr th", "vertical-align", "top"),
+        ]
+        if isinstance(self.columns, MultiIndex):
+            element_props.append(("thead tr th", "text-align", "left"))
+            if self.show_row_idx_names:
+                element_props.append(
+                    ("thead tr:last-of-type th", "text-align", "right")
+                )
+        else:
+            element_props.append(("thead th", "text-align", "right"))
+        template_mid = "\n\n".join(template_select % t for t in element_props)
+        template = dedent(f"{template_first}\n{template_mid}\n{template_last}")
+        self.write(template)
+
+    def render(self) -> list[str]:
+        self.write("<div>")
+        self.write_style()
+        super().render()
+        self.write("</div>")
+        return self.elements

venv/lib/python3.10/site-packages/pandas/io/formats/info.py

@@ -0,0 +1,1101 @@
+from __future__ import annotations
+
+from abc import (
+    ABC,
+    abstractmethod,
+)
+import sys
+from textwrap import dedent
+from typing import TYPE_CHECKING
+
+from pandas._config import get_option
+
+from pandas.io.formats import format as fmt
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Iterable,
+        Iterator,
+        Mapping,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        Dtype,
+        WriteBuffer,
+    )
+
+    from pandas import (
+        DataFrame,
+        Index,
+        Series,
+    )
+
+
+frame_max_cols_sub = dedent(
+    """\
+    max_cols : int, optional
+        When to switch from the verbose to the truncated output. If the
+        DataFrame has more than `max_cols` columns, the truncated output
+        is used. By default, the setting in
+        ``pandas.options.display.max_info_columns`` is used."""
+)
+
+
+show_counts_sub = dedent(
+    """\
+    show_counts : bool, optional
+        Whether to show the non-null counts. By default, this is shown
+        only if the DataFrame is smaller than
+        ``pandas.options.display.max_info_rows`` and
+        ``pandas.options.display.max_info_columns``. A value of True always
+        shows the counts, and False never shows the counts."""
+)
+
+
+frame_examples_sub = dedent(
+    """\
+    >>> int_values = [1, 2, 3, 4, 5]
+    >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
+    >>> float_values = [0.0, 0.25, 0.5, 0.75, 1.0]
+    >>> df = pd.DataFrame({"int_col": int_values, "text_col": text_values,
+    ...                   "float_col": float_values})
+    >>> df
+        int_col text_col  float_col
+    0        1    alpha       0.00
+    1        2     beta       0.25
+    2        3    gamma       0.50
+    3        4    delta       0.75
+    4        5  epsilon       1.00
+
+    Prints information of all columns:
+
+    >>> df.info(verbose=True)
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 5 entries, 0 to 4
+    Data columns (total 3 columns):
+     #   Column     Non-Null Count  Dtype
+    ---  ------     --------------  -----
+     0   int_col    5 non-null      int64
+     1   text_col   5 non-null      object
+     2   float_col  5 non-null      float64
+    dtypes: float64(1), int64(1), object(1)
+    memory usage: 248.0+ bytes
+
+    Prints a summary of columns count and its dtypes but not per column
+    information:
+
+    >>> df.info(verbose=False)
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 5 entries, 0 to 4
+    Columns: 3 entries, int_col to float_col
+    dtypes: float64(1), int64(1), object(1)
+    memory usage: 248.0+ bytes
+
+    Pipe output of DataFrame.info to buffer instead of sys.stdout, get
+    buffer content and writes to a text file:
+
+    >>> import io
+    >>> buffer = io.StringIO()
+    >>> df.info(buf=buffer)
+    >>> s = buffer.getvalue()
+    >>> with open("df_info.txt", "w",
+    ...           encoding="utf-8") as f:  # doctest: +SKIP
+    ...     f.write(s)
+    260
+
+    The `memory_usage` parameter allows deep introspection mode, specially
+    useful for big DataFrames and fine-tune memory optimization:
+
+    >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
+    >>> df = pd.DataFrame({
+    ...     'column_1': np.random.choice(['a', 'b', 'c'], 10 ** 6),
+    ...     'column_2': np.random.choice(['a', 'b', 'c'], 10 ** 6),
+    ...     'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
+    ... })
+    >>> df.info()
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 1000000 entries, 0 to 999999
+    Data columns (total 3 columns):
+     #   Column    Non-Null Count    Dtype
+    ---  ------    --------------    -----
+     0   column_1  1000000 non-null  object
+     1   column_2  1000000 non-null  object
+     2   column_3  1000000 non-null  object
+    dtypes: object(3)
+    memory usage: 22.9+ MB
+
+    >>> df.info(memory_usage='deep')
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 1000000 entries, 0 to 999999
+    Data columns (total 3 columns):
+     #   Column    Non-Null Count    Dtype
+    ---  ------    --------------    -----
+     0   column_1  1000000 non-null  object
+     1   column_2  1000000 non-null  object
+     2   column_3  1000000 non-null  object
+    dtypes: object(3)
+    memory usage: 165.9 MB"""
+)
+
+
+frame_see_also_sub = dedent(
+    """\
+    DataFrame.describe: Generate descriptive statistics of DataFrame
+        columns.
+    DataFrame.memory_usage: Memory usage of DataFrame columns."""
+)
+
+
+frame_sub_kwargs = {
+    "klass": "DataFrame",
+    "type_sub": " and columns",
+    "max_cols_sub": frame_max_cols_sub,
+    "show_counts_sub": show_counts_sub,
+    "examples_sub": frame_examples_sub,
+    "see_also_sub": frame_see_also_sub,
+    "version_added_sub": "",
+}
+
+
+series_examples_sub = dedent(
+    """\
+    >>> int_values = [1, 2, 3, 4, 5]
+    >>> text_values = ['alpha', 'beta', 'gamma', 'delta', 'epsilon']
+    >>> s = pd.Series(text_values, index=int_values)
+    >>> s.info()
+    <class 'pandas.core.series.Series'>
+    Index: 5 entries, 1 to 5
+    Series name: None
+    Non-Null Count  Dtype
+    --------------  -----
+    5 non-null      object
+    dtypes: object(1)
+    memory usage: 80.0+ bytes
+
+    Prints a summary excluding information about its values:
+
+    >>> s.info(verbose=False)
+    <class 'pandas.core.series.Series'>
+    Index: 5 entries, 1 to 5
+    dtypes: object(1)
+    memory usage: 80.0+ bytes
+
+    Pipe output of Series.info to buffer instead of sys.stdout, get
+    buffer content and writes to a text file:
+
+    >>> import io
+    >>> buffer = io.StringIO()
+    >>> s.info(buf=buffer)
+    >>> s = buffer.getvalue()
+    >>> with open("df_info.txt", "w",
+    ...           encoding="utf-8") as f:  # doctest: +SKIP
+    ...     f.write(s)
+    260
+
+    The `memory_usage` parameter allows deep introspection mode, specially
+    useful for big Series and fine-tune memory optimization:
+
+    >>> random_strings_array = np.random.choice(['a', 'b', 'c'], 10 ** 6)
+    >>> s = pd.Series(np.random.choice(['a', 'b', 'c'], 10 ** 6))
+    >>> s.info()
+    <class 'pandas.core.series.Series'>
+    RangeIndex: 1000000 entries, 0 to 999999
+    Series name: None
+    Non-Null Count    Dtype
+    --------------    -----
+    1000000 non-null  object
+    dtypes: object(1)
+    memory usage: 7.6+ MB
+
+    >>> s.info(memory_usage='deep')
+    <class 'pandas.core.series.Series'>
+    RangeIndex: 1000000 entries, 0 to 999999
+    Series name: None
+    Non-Null Count    Dtype
+    --------------    -----
+    1000000 non-null  object
+    dtypes: object(1)
+    memory usage: 55.3 MB"""
+)
+
+
+series_see_also_sub = dedent(
+    """\
+    Series.describe: Generate descriptive statistics of Series.
+    Series.memory_usage: Memory usage of Series."""
+)
+
+
+series_sub_kwargs = {
+    "klass": "Series",
+    "type_sub": "",
+    "max_cols_sub": "",
+    "show_counts_sub": show_counts_sub,
+    "examples_sub": series_examples_sub,
+    "see_also_sub": series_see_also_sub,
+    "version_added_sub": "\n.. versionadded:: 1.4.0\n",
+}
+
+
+INFO_DOCSTRING = dedent(
+    """
+    Print a concise summary of a {klass}.
+
+    This method prints information about a {klass} including
+    the index dtype{type_sub}, non-null values and memory usage.
+    {version_added_sub}\
+
+    Parameters
+    ----------
+    verbose : bool, optional
+        Whether to print the full summary. By default, the setting in
+        ``pandas.options.display.max_info_columns`` is followed.
+    buf : writable buffer, defaults to sys.stdout
+        Where to send the output. By default, the output is printed to
+        sys.stdout. Pass a writable buffer if you need to further process
+        the output.
+    {max_cols_sub}
+    memory_usage : bool, str, optional
+        Specifies whether total memory usage of the {klass}
+        elements (including the index) should be displayed. By default,
+        this follows the ``pandas.options.display.memory_usage`` setting.
+
+        True always show memory usage. False never shows memory usage.
+        A value of 'deep' is equivalent to "True with deep introspection".
+        Memory usage is shown in human-readable units (base-2
+        representation). Without deep introspection a memory estimation is
+        made based in column dtype and number of rows assuming values
+        consume the same memory amount for corresponding dtypes. With deep
+        memory introspection, a real memory usage calculation is performed
+        at the cost of computational resources. See the
+        :ref:`Frequently Asked Questions <df-memory-usage>` for more
+        details.
+    {show_counts_sub}
+
+    Returns
+    -------
+    None
+        This method prints a summary of a {klass} and returns None.
+
+    See Also
+    --------
+    {see_also_sub}
+
+    Examples
+    --------
+    {examples_sub}
+    """
+)
+
+
+def _put_str(s: str | Dtype, space: int) -> str:
+    """
+    Make string of specified length, padding to the right if necessary.
+
+    Parameters
+    ----------
+    s : Union[str, Dtype]
+        String to be formatted.
+    space : int
+        Length to force string to be of.
+
+    Returns
+    -------
+    str
+        String coerced to given length.
+
+    Examples
+    --------
+    >>> pd.io.formats.info._put_str("panda", 6)
+    'panda '
+    >>> pd.io.formats.info._put_str("panda", 4)
+    'pand'
+    """
+    return str(s)[:space].ljust(space)
+
+
+def _sizeof_fmt(num: float, size_qualifier: str) -> str:
+    """
+    Return size in human readable format.
+
+    Parameters
+    ----------
+    num : int
+        Size in bytes.
+    size_qualifier : str
+        Either empty, or '+' (if lower bound).
+
+    Returns
+    -------
+    str
+        Size in human readable format.
+
+    Examples
+    --------
+    >>> _sizeof_fmt(23028, '')
+    '22.5 KB'
+
+    >>> _sizeof_fmt(23028, '+')
+    '22.5+ KB'
+    """
+    for x in ["bytes", "KB", "MB", "GB", "TB"]:
+        if num < 1024.0:
+            return f"{num:3.1f}{size_qualifier} {x}"
+        num /= 1024.0
+    return f"{num:3.1f}{size_qualifier} PB"
+
+
+def _initialize_memory_usage(
+    memory_usage: bool | str | None = None,
+) -> bool | str:
+    """Get memory usage based on inputs and display options."""
+    if memory_usage is None:
+        memory_usage = get_option("display.memory_usage")
+    return memory_usage
+
+
+class _BaseInfo(ABC):
+    """
+    Base class for DataFrameInfo and SeriesInfo.
+
+    Parameters
+    ----------
+    data : DataFrame or Series
+        Either dataframe or series.
+    memory_usage : bool or str, optional
+        If "deep", introspect the data deeply by interrogating object dtypes
+        for system-level memory consumption, and include it in the returned
+        values.
+    """
+
+    data: DataFrame | Series
+    memory_usage: bool | str
+
+    @property
+    @abstractmethod
+    def dtypes(self) -> Iterable[Dtype]:
+        """
+        Dtypes.
+
+        Returns
+        -------
+        dtypes : sequence
+            Dtype of each of the DataFrame's columns (or one series column).
+        """
+
+    @property
+    @abstractmethod
+    def dtype_counts(self) -> Mapping[str, int]:
+        """Mapping dtype - number of counts."""
+
+    @property
+    @abstractmethod
+    def non_null_counts(self) -> Sequence[int]:
+        """Sequence of non-null counts for all columns or column (if series)."""
+
+    @property
+    @abstractmethod
+    def memory_usage_bytes(self) -> int:
+        """
+        Memory usage in bytes.
+
+        Returns
+        -------
+        memory_usage_bytes : int
+            Object's total memory usage in bytes.
+        """
+
+    @property
+    def memory_usage_string(self) -> str:
+        """Memory usage in a form of human readable string."""
+        return f"{_sizeof_fmt(self.memory_usage_bytes, self.size_qualifier)}\n"
+
+    @property
+    def size_qualifier(self) -> str:
+        size_qualifier = ""
+        if self.memory_usage:
+            if self.memory_usage != "deep":
+                # size_qualifier is just a best effort; not guaranteed to catch
+                # all cases (e.g., it misses categorical data even with object
+                # categories)
+                if (
+                    "object" in self.dtype_counts
+                    or self.data.index._is_memory_usage_qualified()
+                ):
+                    size_qualifier = "+"
+        return size_qualifier
+
+    @abstractmethod
+    def render(
+        self,
+        *,
+        buf: WriteBuffer[str] | None,
+        max_cols: int | None,
+        verbose: bool | None,
+        show_counts: bool | None,
+    ) -> None:
+        pass
+
+
+class DataFrameInfo(_BaseInfo):
+    """
+    Class storing dataframe-specific info.
+    """
+
+    def __init__(
+        self,
+        data: DataFrame,
+        memory_usage: bool | str | None = None,
+    ) -> None:
+        self.data: DataFrame = data
+        self.memory_usage = _initialize_memory_usage(memory_usage)
+
+    @property
+    def dtype_counts(self) -> Mapping[str, int]:
+        return _get_dataframe_dtype_counts(self.data)
+
+    @property
+    def dtypes(self) -> Iterable[Dtype]:
+        """
+        Dtypes.
+
+        Returns
+        -------
+        dtypes
+            Dtype of each of the DataFrame's columns.
+        """
+        return self.data.dtypes
+
+    @property
+    def ids(self) -> Index:
+        """
+        Column names.
+
+        Returns
+        -------
+        ids : Index
+            DataFrame's column names.
+        """
+        return self.data.columns
+
+    @property
+    def col_count(self) -> int:
+        """Number of columns to be summarized."""
+        return len(self.ids)
+
+    @property
+    def non_null_counts(self) -> Sequence[int]:
+        """Sequence of non-null counts for all columns or column (if series)."""
+        return self.data.count()
+
+    @property
+    def memory_usage_bytes(self) -> int:
+        deep = self.memory_usage == "deep"
+        return self.data.memory_usage(index=True, deep=deep).sum()
+
+    def render(
+        self,
+        *,
+        buf: WriteBuffer[str] | None,
+        max_cols: int | None,
+        verbose: bool | None,
+        show_counts: bool | None,
+    ) -> None:
+        printer = _DataFrameInfoPrinter(
+            info=self,
+            max_cols=max_cols,
+            verbose=verbose,
+            show_counts=show_counts,
+        )
+        printer.to_buffer(buf)
+
+
+class SeriesInfo(_BaseInfo):
+    """
+    Class storing series-specific info.
+    """
+
+    def __init__(
+        self,
+        data: Series,
+        memory_usage: bool | str | None = None,
+    ) -> None:
+        self.data: Series = data
+        self.memory_usage = _initialize_memory_usage(memory_usage)
+
+    def render(
+        self,
+        *,
+        buf: WriteBuffer[str] | None = None,
+        max_cols: int | None = None,
+        verbose: bool | None = None,
+        show_counts: bool | None = None,
+    ) -> None:
+        if max_cols is not None:
+            raise ValueError(
+                "Argument `max_cols` can only be passed "
+                "in DataFrame.info, not Series.info"
+            )
+        printer = _SeriesInfoPrinter(
+            info=self,
+            verbose=verbose,
+            show_counts=show_counts,
+        )
+        printer.to_buffer(buf)
+
+    @property
+    def non_null_counts(self) -> Sequence[int]:
+        return [self.data.count()]
+
+    @property
+    def dtypes(self) -> Iterable[Dtype]:
+        return [self.data.dtypes]
+
+    @property
+    def dtype_counts(self) -> Mapping[str, int]:
+        from pandas.core.frame import DataFrame
+
+        return _get_dataframe_dtype_counts(DataFrame(self.data))
+
+    @property
+    def memory_usage_bytes(self) -> int:
+        """Memory usage in bytes.
+
+        Returns
+        -------
+        memory_usage_bytes : int
+            Object's total memory usage in bytes.
+        """
+        deep = self.memory_usage == "deep"
+        return self.data.memory_usage(index=True, deep=deep)
+
+
+class _InfoPrinterAbstract:
+    """
+    Class for printing dataframe or series info.
+    """
+
+    def to_buffer(self, buf: WriteBuffer[str] | None = None) -> None:
+        """Save dataframe info into buffer."""
+        table_builder = self._create_table_builder()
+        lines = table_builder.get_lines()
+        if buf is None:  # pragma: no cover
+            buf = sys.stdout
+        fmt.buffer_put_lines(buf, lines)
+
+    @abstractmethod
+    def _create_table_builder(self) -> _TableBuilderAbstract:
+        """Create instance of table builder."""
+
+
+class _DataFrameInfoPrinter(_InfoPrinterAbstract):
+    """
+    Class for printing dataframe info.
+
+    Parameters
+    ----------
+    info : DataFrameInfo
+        Instance of DataFrameInfo.
+    max_cols : int, optional
+        When to switch from the verbose to the truncated output.
+    verbose : bool, optional
+        Whether to print the full summary.
+    show_counts : bool, optional
+        Whether to show the non-null counts.
+    """
+
+    def __init__(
+        self,
+        info: DataFrameInfo,
+        max_cols: int | None = None,
+        verbose: bool | None = None,
+        show_counts: bool | None = None,
+    ) -> None:
+        self.info = info
+        self.data = info.data
+        self.verbose = verbose
+        self.max_cols = self._initialize_max_cols(max_cols)
+        self.show_counts = self._initialize_show_counts(show_counts)
+
+    @property
+    def max_rows(self) -> int:
+        """Maximum info rows to be displayed."""
+        return get_option("display.max_info_rows", len(self.data) + 1)
+
+    @property
+    def exceeds_info_cols(self) -> bool:
+        """Check if number of columns to be summarized does not exceed maximum."""
+        return bool(self.col_count > self.max_cols)
+
+    @property
+    def exceeds_info_rows(self) -> bool:
+        """Check if number of rows to be summarized does not exceed maximum."""
+        return bool(len(self.data) > self.max_rows)
+
+    @property
+    def col_count(self) -> int:
+        """Number of columns to be summarized."""
+        return self.info.col_count
+
+    def _initialize_max_cols(self, max_cols: int | None) -> int:
+        if max_cols is None:
+            return get_option("display.max_info_columns", self.col_count + 1)
+        return max_cols
+
+    def _initialize_show_counts(self, show_counts: bool | None) -> bool:
+        if show_counts is None:
+            return bool(not self.exceeds_info_cols and not self.exceeds_info_rows)
+        else:
+            return show_counts
+
+    def _create_table_builder(self) -> _DataFrameTableBuilder:
+        """
+        Create instance of table builder based on verbosity and display settings.
+        """
+        if self.verbose:
+            return _DataFrameTableBuilderVerbose(
+                info=self.info,
+                with_counts=self.show_counts,
+            )
+        elif self.verbose is False:  # specifically set to False, not necessarily None
+            return _DataFrameTableBuilderNonVerbose(info=self.info)
+        elif self.exceeds_info_cols:
+            return _DataFrameTableBuilderNonVerbose(info=self.info)
+        else:
+            return _DataFrameTableBuilderVerbose(
+                info=self.info,
+                with_counts=self.show_counts,
+            )
+
+
+class _SeriesInfoPrinter(_InfoPrinterAbstract):
+    """Class for printing series info.
+
+    Parameters
+    ----------
+    info : SeriesInfo
+        Instance of SeriesInfo.
+    verbose : bool, optional
+        Whether to print the full summary.
+    show_counts : bool, optional
+        Whether to show the non-null counts.
+    """
+
+    def __init__(
+        self,
+        info: SeriesInfo,
+        verbose: bool | None = None,
+        show_counts: bool | None = None,
+    ) -> None:
+        self.info = info
+        self.data = info.data
+        self.verbose = verbose
+        self.show_counts = self._initialize_show_counts(show_counts)
+
+    def _create_table_builder(self) -> _SeriesTableBuilder:
+        """
+        Create instance of table builder based on verbosity.
+        """
+        if self.verbose or self.verbose is None:
+            return _SeriesTableBuilderVerbose(
+                info=self.info,
+                with_counts=self.show_counts,
+            )
+        else:
+            return _SeriesTableBuilderNonVerbose(info=self.info)
+
+    def _initialize_show_counts(self, show_counts: bool | None) -> bool:
+        if show_counts is None:
+            return True
+        else:
+            return show_counts
+
+
+class _TableBuilderAbstract(ABC):
+    """
+    Abstract builder for info table.
+    """
+
+    _lines: list[str]
+    info: _BaseInfo
+
+    @abstractmethod
+    def get_lines(self) -> list[str]:
+        """Product in a form of list of lines (strings)."""
+
+    @property
+    def data(self) -> DataFrame | Series:
+        return self.info.data
+
+    @property
+    def dtypes(self) -> Iterable[Dtype]:
+        """Dtypes of each of the DataFrame's columns."""
+        return self.info.dtypes
+
+    @property
+    def dtype_counts(self) -> Mapping[str, int]:
+        """Mapping dtype - number of counts."""
+        return self.info.dtype_counts
+
+    @property
+    def display_memory_usage(self) -> bool:
+        """Whether to display memory usage."""
+        return bool(self.info.memory_usage)
+
+    @property
+    def memory_usage_string(self) -> str:
+        """Memory usage string with proper size qualifier."""
+        return self.info.memory_usage_string
+
+    @property
+    def non_null_counts(self) -> Sequence[int]:
+        return self.info.non_null_counts
+
+    def add_object_type_line(self) -> None:
+        """Add line with string representation of dataframe to the table."""
+        self._lines.append(str(type(self.data)))
+
+    def add_index_range_line(self) -> None:
+        """Add line with range of indices to the table."""
+        self._lines.append(self.data.index._summary())
+
+    def add_dtypes_line(self) -> None:
+        """Add summary line with dtypes present in dataframe."""
+        collected_dtypes = [
+            f"{key}({val:d})" for key, val in sorted(self.dtype_counts.items())
+        ]
+        self._lines.append(f"dtypes: {', '.join(collected_dtypes)}")
+
+
+class _DataFrameTableBuilder(_TableBuilderAbstract):
+    """
+    Abstract builder for dataframe info table.
+
+    Parameters
+    ----------
+    info : DataFrameInfo.
+        Instance of DataFrameInfo.
+    """
+
+    def __init__(self, *, info: DataFrameInfo) -> None:
+        self.info: DataFrameInfo = info
+
+    def get_lines(self) -> list[str]:
+        self._lines = []
+        if self.col_count == 0:
+            self._fill_empty_info()
+        else:
+            self._fill_non_empty_info()
+        return self._lines
+
+    def _fill_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to empty dataframe."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self._lines.append(f"Empty {type(self.data).__name__}\n")
+
+    @abstractmethod
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty dataframe."""
+
+    @property
+    def data(self) -> DataFrame:
+        """DataFrame."""
+        return self.info.data
+
+    @property
+    def ids(self) -> Index:
+        """Dataframe columns."""
+        return self.info.ids
+
+    @property
+    def col_count(self) -> int:
+        """Number of dataframe columns to be summarized."""
+        return self.info.col_count
+
+    def add_memory_usage_line(self) -> None:
+        """Add line containing memory usage."""
+        self._lines.append(f"memory usage: {self.memory_usage_string}")
+
+
+class _DataFrameTableBuilderNonVerbose(_DataFrameTableBuilder):
+    """
+    Dataframe info table builder for non-verbose output.
+    """
+
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty dataframe."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self.add_columns_summary_line()
+        self.add_dtypes_line()
+        if self.display_memory_usage:
+            self.add_memory_usage_line()
+
+    def add_columns_summary_line(self) -> None:
+        self._lines.append(self.ids._summary(name="Columns"))
+
+
+class _TableBuilderVerboseMixin(_TableBuilderAbstract):
+    """
+    Mixin for verbose info output.
+    """
+
+    SPACING: str = " " * 2
+    strrows: Sequence[Sequence[str]]
+    gross_column_widths: Sequence[int]
+    with_counts: bool
+
+    @property
+    @abstractmethod
+    def headers(self) -> Sequence[str]:
+        """Headers names of the columns in verbose table."""
+
+    @property
+    def header_column_widths(self) -> Sequence[int]:
+        """Widths of header columns (only titles)."""
+        return [len(col) for col in self.headers]
+
+    def _get_gross_column_widths(self) -> Sequence[int]:
+        """Get widths of columns containing both headers and actual content."""
+        body_column_widths = self._get_body_column_widths()
+        return [
+            max(*widths)
+            for widths in zip(self.header_column_widths, body_column_widths)
+        ]
+
+    def _get_body_column_widths(self) -> Sequence[int]:
+        """Get widths of table content columns."""
+        strcols: Sequence[Sequence[str]] = list(zip(*self.strrows))
+        return [max(len(x) for x in col) for col in strcols]
+
+    def _gen_rows(self) -> Iterator[Sequence[str]]:
+        """
+        Generator function yielding rows content.
+
+        Each element represents a row comprising a sequence of strings.
+        """
+        if self.with_counts:
+            return self._gen_rows_with_counts()
+        else:
+            return self._gen_rows_without_counts()
+
+    @abstractmethod
+    def _gen_rows_with_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data with counts."""
+
+    @abstractmethod
+    def _gen_rows_without_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data without counts."""
+
+    def add_header_line(self) -> None:
+        header_line = self.SPACING.join(
+            [
+                _put_str(header, col_width)
+                for header, col_width in zip(self.headers, self.gross_column_widths)
+            ]
+        )
+        self._lines.append(header_line)
+
+    def add_separator_line(self) -> None:
+        separator_line = self.SPACING.join(
+            [
+                _put_str("-" * header_colwidth, gross_colwidth)
+                for header_colwidth, gross_colwidth in zip(
+                    self.header_column_widths, self.gross_column_widths
+                )
+            ]
+        )
+        self._lines.append(separator_line)
+
+    def add_body_lines(self) -> None:
+        for row in self.strrows:
+            body_line = self.SPACING.join(
+                [
+                    _put_str(col, gross_colwidth)
+                    for col, gross_colwidth in zip(row, self.gross_column_widths)
+                ]
+            )
+            self._lines.append(body_line)
+
+    def _gen_non_null_counts(self) -> Iterator[str]:
+        """Iterator with string representation of non-null counts."""
+        for count in self.non_null_counts:
+            yield f"{count} non-null"
+
+    def _gen_dtypes(self) -> Iterator[str]:
+        """Iterator with string representation of column dtypes."""
+        for dtype in self.dtypes:
+            yield pprint_thing(dtype)
+
+
+class _DataFrameTableBuilderVerbose(_DataFrameTableBuilder, _TableBuilderVerboseMixin):
+    """
+    Dataframe info table builder for verbose output.
+    """
+
+    def __init__(
+        self,
+        *,
+        info: DataFrameInfo,
+        with_counts: bool,
+    ) -> None:
+        self.info = info
+        self.with_counts = with_counts
+        self.strrows: Sequence[Sequence[str]] = list(self._gen_rows())
+        self.gross_column_widths: Sequence[int] = self._get_gross_column_widths()
+
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty dataframe."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self.add_columns_summary_line()
+        self.add_header_line()
+        self.add_separator_line()
+        self.add_body_lines()
+        self.add_dtypes_line()
+        if self.display_memory_usage:
+            self.add_memory_usage_line()
+
+    @property
+    def headers(self) -> Sequence[str]:
+        """Headers names of the columns in verbose table."""
+        if self.with_counts:
+            return [" # ", "Column", "Non-Null Count", "Dtype"]
+        return [" # ", "Column", "Dtype"]
+
+    def add_columns_summary_line(self) -> None:
+        self._lines.append(f"Data columns (total {self.col_count} columns):")
+
+    def _gen_rows_without_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data without counts."""
+        yield from zip(
+            self._gen_line_numbers(),
+            self._gen_columns(),
+            self._gen_dtypes(),
+        )
+
+    def _gen_rows_with_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data with counts."""
+        yield from zip(
+            self._gen_line_numbers(),
+            self._gen_columns(),
+            self._gen_non_null_counts(),
+            self._gen_dtypes(),
+        )
+
+    def _gen_line_numbers(self) -> Iterator[str]:
+        """Iterator with string representation of column numbers."""
+        for i, _ in enumerate(self.ids):
+            yield f" {i}"
+
+    def _gen_columns(self) -> Iterator[str]:
+        """Iterator with string representation of column names."""
+        for col in self.ids:
+            yield pprint_thing(col)
+
+
+class _SeriesTableBuilder(_TableBuilderAbstract):
+    """
+    Abstract builder for series info table.
+
+    Parameters
+    ----------
+    info : SeriesInfo.
+        Instance of SeriesInfo.
+    """
+
+    def __init__(self, *, info: SeriesInfo) -> None:
+        self.info: SeriesInfo = info
+
+    def get_lines(self) -> list[str]:
+        self._lines = []
+        self._fill_non_empty_info()
+        return self._lines
+
+    @property
+    def data(self) -> Series:
+        """Series."""
+        return self.info.data
+
+    def add_memory_usage_line(self) -> None:
+        """Add line containing memory usage."""
+        self._lines.append(f"memory usage: {self.memory_usage_string}")
+
+    @abstractmethod
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty series."""
+
+
+class _SeriesTableBuilderNonVerbose(_SeriesTableBuilder):
+    """
+    Series info table builder for non-verbose output.
+    """
+
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty series."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self.add_dtypes_line()
+        if self.display_memory_usage:
+            self.add_memory_usage_line()
+
+
+class _SeriesTableBuilderVerbose(_SeriesTableBuilder, _TableBuilderVerboseMixin):
+    """
+    Series info table builder for verbose output.
+    """
+
+    def __init__(
+        self,
+        *,
+        info: SeriesInfo,
+        with_counts: bool,
+    ) -> None:
+        self.info = info
+        self.with_counts = with_counts
+        self.strrows: Sequence[Sequence[str]] = list(self._gen_rows())
+        self.gross_column_widths: Sequence[int] = self._get_gross_column_widths()
+
+    def _fill_non_empty_info(self) -> None:
+        """Add lines to the info table, pertaining to non-empty series."""
+        self.add_object_type_line()
+        self.add_index_range_line()
+        self.add_series_name_line()
+        self.add_header_line()
+        self.add_separator_line()
+        self.add_body_lines()
+        self.add_dtypes_line()
+        if self.display_memory_usage:
+            self.add_memory_usage_line()
+
+    def add_series_name_line(self) -> None:
+        self._lines.append(f"Series name: {self.data.name}")
+
+    @property
+    def headers(self) -> Sequence[str]:
+        """Headers names of the columns in verbose table."""
+        if self.with_counts:
+            return ["Non-Null Count", "Dtype"]
+        return ["Dtype"]
+
+    def _gen_rows_without_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data without counts."""
+        yield from self._gen_dtypes()
+
+    def _gen_rows_with_counts(self) -> Iterator[Sequence[str]]:
+        """Iterator with string representation of body data with counts."""
+        yield from zip(
+            self._gen_non_null_counts(),
+            self._gen_dtypes(),
+        )
+
+
+def _get_dataframe_dtype_counts(df: DataFrame) -> Mapping[str, int]:
+    """
+    Create mapping between datatypes and their number of occurrences.
+    """
+    # groupby dtype.name to collect e.g. Categorical columns
+    return df.dtypes.value_counts().groupby(lambda x: x.name).sum()

venv/lib/python3.10/site-packages/pandas/io/formats/printing.py

@@ -0,0 +1,572 @@
+"""
+Printing tools.
+"""
+from __future__ import annotations
+
+from collections.abc import (
+    Iterable,
+    Mapping,
+    Sequence,
+)
+import sys
+from typing import (
+    Any,
+    Callable,
+    TypeVar,
+    Union,
+)
+from unicodedata import east_asian_width
+
+from pandas._config import get_option
+
+from pandas.core.dtypes.inference import is_sequence
+
+from pandas.io.formats.console import get_console_size
+
+EscapeChars = Union[Mapping[str, str], Iterable[str]]
+_KT = TypeVar("_KT")
+_VT = TypeVar("_VT")
+
+
+def adjoin(space: int, *lists: list[str], **kwargs) -> str:
+    """
+    Glues together two sets of strings using the amount of space requested.
+    The idea is to prettify.
+
+    ----------
+    space : int
+        number of spaces for padding
+    lists : str
+        list of str which being joined
+    strlen : callable
+        function used to calculate the length of each str. Needed for unicode
+        handling.
+    justfunc : callable
+        function used to justify str. Needed for unicode handling.
+    """
+    strlen = kwargs.pop("strlen", len)
+    justfunc = kwargs.pop("justfunc", _adj_justify)
+
+    newLists = []
+    lengths = [max(map(strlen, x)) + space for x in lists[:-1]]
+    # not the last one
+    lengths.append(max(map(len, lists[-1])))
+    maxLen = max(map(len, lists))
+    for i, lst in enumerate(lists):
+        nl = justfunc(lst, lengths[i], mode="left")
+        nl = ([" " * lengths[i]] * (maxLen - len(lst))) + nl
+        newLists.append(nl)
+    toJoin = zip(*newLists)
+    return "\n".join("".join(lines) for lines in toJoin)
+
+
+def _adj_justify(texts: Iterable[str], max_len: int, mode: str = "right") -> list[str]:
+    """
+    Perform ljust, center, rjust against string or list-like
+    """
+    if mode == "left":
+        return [x.ljust(max_len) for x in texts]
+    elif mode == "center":
+        return [x.center(max_len) for x in texts]
+    else:
+        return [x.rjust(max_len) for x in texts]
+
+
+# Unicode consolidation
+# ---------------------
+#
+# pprinting utility functions for generating Unicode text or
+# bytes(3.x)/str(2.x) representations of objects.
+# Try to use these as much as possible rather than rolling your own.
+#
+# When to use
+# -----------
+#
+# 1) If you're writing code internal to pandas (no I/O directly involved),
+#    use pprint_thing().
+#
+#    It will always return unicode text which can handled by other
+#    parts of the package without breakage.
+#
+# 2) if you need to write something out to file, use
+#    pprint_thing_encoded(encoding).
+#
+#    If no encoding is specified, it defaults to utf-8. Since encoding pure
+#    ascii with utf-8 is a no-op you can safely use the default utf-8 if you're
+#    working with straight ascii.
+
+
+def _pprint_seq(
+    seq: Sequence, _nest_lvl: int = 0, max_seq_items: int | None = None, **kwds
+) -> str:
+    """
+    internal. pprinter for iterables. you should probably use pprint_thing()
+    rather than calling this directly.
+
+    bounds length of printed sequence, depending on options
+    """
+    if isinstance(seq, set):
+        fmt = "{{{body}}}"
+    else:
+        fmt = "[{body}]" if hasattr(seq, "__setitem__") else "({body})"
+
+    if max_seq_items is False:
+        nitems = len(seq)
+    else:
+        nitems = max_seq_items or get_option("max_seq_items") or len(seq)
+
+    s = iter(seq)
+    # handle sets, no slicing
+    r = [
+        pprint_thing(next(s), _nest_lvl + 1, max_seq_items=max_seq_items, **kwds)
+        for i in range(min(nitems, len(seq)))
+    ]
+    body = ", ".join(r)
+
+    if nitems < len(seq):
+        body += ", ..."
+    elif isinstance(seq, tuple) and len(seq) == 1:
+        body += ","
+
+    return fmt.format(body=body)
+
+
+def _pprint_dict(
+    seq: Mapping, _nest_lvl: int = 0, max_seq_items: int | None = None, **kwds
+) -> str:
+    """
+    internal. pprinter for iterables. you should probably use pprint_thing()
+    rather than calling this directly.
+    """
+    fmt = "{{{things}}}"
+    pairs = []
+
+    pfmt = "{key}: {val}"
+
+    if max_seq_items is False:
+        nitems = len(seq)
+    else:
+        nitems = max_seq_items or get_option("max_seq_items") or len(seq)
+
+    for k, v in list(seq.items())[:nitems]:
+        pairs.append(
+            pfmt.format(
+                key=pprint_thing(k, _nest_lvl + 1, max_seq_items=max_seq_items, **kwds),
+                val=pprint_thing(v, _nest_lvl + 1, max_seq_items=max_seq_items, **kwds),
+            )
+        )
+
+    if nitems < len(seq):
+        return fmt.format(things=", ".join(pairs) + ", ...")
+    else:
+        return fmt.format(things=", ".join(pairs))
+
+
+def pprint_thing(
+    thing: Any,
+    _nest_lvl: int = 0,
+    escape_chars: EscapeChars | None = None,
+    default_escapes: bool = False,
+    quote_strings: bool = False,
+    max_seq_items: int | None = None,
+) -> str:
+    """
+    This function is the sanctioned way of converting objects
+    to a string representation and properly handles nested sequences.
+
+    Parameters
+    ----------
+    thing : anything to be formatted
+    _nest_lvl : internal use only. pprint_thing() is mutually-recursive
+        with pprint_sequence, this argument is used to keep track of the
+        current nesting level, and limit it.
+    escape_chars : list or dict, optional
+        Characters to escape. If a dict is passed the values are the
+        replacements
+    default_escapes : bool, default False
+        Whether the input escape characters replaces or adds to the defaults
+    max_seq_items : int or None, default None
+        Pass through to other pretty printers to limit sequence printing
+
+    Returns
+    -------
+    str
+    """
+
+    def as_escaped_string(
+        thing: Any, escape_chars: EscapeChars | None = escape_chars
+    ) -> str:
+        translate = {"\t": r"\t", "\n": r"\n", "\r": r"\r"}
+        if isinstance(escape_chars, dict):
+            if default_escapes:
+                translate.update(escape_chars)
+            else:
+                translate = escape_chars
+            escape_chars = list(escape_chars.keys())
+        else:
+            escape_chars = escape_chars or ()
+
+        result = str(thing)
+        for c in escape_chars:
+            result = result.replace(c, translate[c])
+        return result
+
+    if hasattr(thing, "__next__"):
+        return str(thing)
+    elif isinstance(thing, dict) and _nest_lvl < get_option(
+        "display.pprint_nest_depth"
+    ):
+        result = _pprint_dict(
+            thing, _nest_lvl, quote_strings=True, max_seq_items=max_seq_items
+        )
+    elif is_sequence(thing) and _nest_lvl < get_option("display.pprint_nest_depth"):
+        result = _pprint_seq(
+            thing,
+            _nest_lvl,
+            escape_chars=escape_chars,
+            quote_strings=quote_strings,
+            max_seq_items=max_seq_items,
+        )
+    elif isinstance(thing, str) and quote_strings:
+        result = f"'{as_escaped_string(thing)}'"
+    else:
+        result = as_escaped_string(thing)
+
+    return result
+
+
+def pprint_thing_encoded(
+    object, encoding: str = "utf-8", errors: str = "replace"
+) -> bytes:
+    value = pprint_thing(object)  # get unicode representation of object
+    return value.encode(encoding, errors)
+
+
+def enable_data_resource_formatter(enable: bool) -> None:
+    if "IPython" not in sys.modules:
+        # definitely not in IPython
+        return
+    from IPython import get_ipython
+
+    ip = get_ipython()
+    if ip is None:
+        # still not in IPython
+        return
+
+    formatters = ip.display_formatter.formatters
+    mimetype = "application/vnd.dataresource+json"
+
+    if enable:
+        if mimetype not in formatters:
+            # define tableschema formatter
+            from IPython.core.formatters import BaseFormatter
+            from traitlets import ObjectName
+
+            class TableSchemaFormatter(BaseFormatter):
+                print_method = ObjectName("_repr_data_resource_")
+                _return_type = (dict,)
+
+            # register it:
+            formatters[mimetype] = TableSchemaFormatter()
+        # enable it if it's been disabled:
+        formatters[mimetype].enabled = True
+    # unregister tableschema mime-type
+    elif mimetype in formatters:
+        formatters[mimetype].enabled = False
+
+
+def default_pprint(thing: Any, max_seq_items: int | None = None) -> str:
+    return pprint_thing(
+        thing,
+        escape_chars=("\t", "\r", "\n"),
+        quote_strings=True,
+        max_seq_items=max_seq_items,
+    )
+
+
+def format_object_summary(
+    obj,
+    formatter: Callable,
+    is_justify: bool = True,
+    name: str | None = None,
+    indent_for_name: bool = True,
+    line_break_each_value: bool = False,
+) -> str:
+    """
+    Return the formatted obj as a unicode string
+
+    Parameters
+    ----------
+    obj : object
+        must be iterable and support __getitem__
+    formatter : callable
+        string formatter for an element
+    is_justify : bool
+        should justify the display
+    name : name, optional
+        defaults to the class name of the obj
+    indent_for_name : bool, default True
+        Whether subsequent lines should be indented to
+        align with the name.
+    line_break_each_value : bool, default False
+        If True, inserts a line break for each value of ``obj``.
+        If False, only break lines when the a line of values gets wider
+        than the display width.
+
+    Returns
+    -------
+    summary string
+    """
+    display_width, _ = get_console_size()
+    if display_width is None:
+        display_width = get_option("display.width") or 80
+    if name is None:
+        name = type(obj).__name__
+
+    if indent_for_name:
+        name_len = len(name)
+        space1 = f'\n{(" " * (name_len + 1))}'
+        space2 = f'\n{(" " * (name_len + 2))}'
+    else:
+        space1 = "\n"
+        space2 = "\n "  # space for the opening '['
+
+    n = len(obj)
+    if line_break_each_value:
+        # If we want to vertically align on each value of obj, we need to
+        # separate values by a line break and indent the values
+        sep = ",\n " + " " * len(name)
+    else:
+        sep = ","
+    max_seq_items = get_option("display.max_seq_items") or n
+
+    # are we a truncated display
+    is_truncated = n > max_seq_items
+
+    # adj can optionally handle unicode eastern asian width
+    adj = get_adjustment()
+
+    def _extend_line(
+        s: str, line: str, value: str, display_width: int, next_line_prefix: str
+    ) -> tuple[str, str]:
+        if adj.len(line.rstrip()) + adj.len(value.rstrip()) >= display_width:
+            s += line.rstrip()
+            line = next_line_prefix
+        line += value
+        return s, line
+
+    def best_len(values: list[str]) -> int:
+        if values:
+            return max(adj.len(x) for x in values)
+        else:
+            return 0
+
+    close = ", "
+
+    if n == 0:
+        summary = f"[]{close}"
+    elif n == 1 and not line_break_each_value:
+        first = formatter(obj[0])
+        summary = f"[{first}]{close}"
+    elif n == 2 and not line_break_each_value:
+        first = formatter(obj[0])
+        last = formatter(obj[-1])
+        summary = f"[{first}, {last}]{close}"
+    else:
+        if max_seq_items == 1:
+            # If max_seq_items=1 show only last element
+            head = []
+            tail = [formatter(x) for x in obj[-1:]]
+        elif n > max_seq_items:
+            n = min(max_seq_items // 2, 10)
+            head = [formatter(x) for x in obj[:n]]
+            tail = [formatter(x) for x in obj[-n:]]
+        else:
+            head = []
+            tail = [formatter(x) for x in obj]
+
+        # adjust all values to max length if needed
+        if is_justify:
+            if line_break_each_value:
+                # Justify each string in the values of head and tail, so the
+                # strings will right align when head and tail are stacked
+                # vertically.
+                head, tail = _justify(head, tail)
+            elif is_truncated or not (
+                len(", ".join(head)) < display_width
+                and len(", ".join(tail)) < display_width
+            ):
+                # Each string in head and tail should align with each other
+                max_length = max(best_len(head), best_len(tail))
+                head = [x.rjust(max_length) for x in head]
+                tail = [x.rjust(max_length) for x in tail]
+            # If we are not truncated and we are only a single
+            # line, then don't justify
+
+        if line_break_each_value:
+            # Now head and tail are of type List[Tuple[str]]. Below we
+            # convert them into List[str], so there will be one string per
+            # value. Also truncate items horizontally if wider than
+            # max_space
+            max_space = display_width - len(space2)
+            value = tail[0]
+            max_items = 1
+            for num_items in reversed(range(1, len(value) + 1)):
+                pprinted_seq = _pprint_seq(value, max_seq_items=num_items)
+                if len(pprinted_seq) < max_space:
+                    max_items = num_items
+                    break
+            head = [_pprint_seq(x, max_seq_items=max_items) for x in head]
+            tail = [_pprint_seq(x, max_seq_items=max_items) for x in tail]
+
+        summary = ""
+        line = space2
+
+        for head_value in head:
+            word = head_value + sep + " "
+            summary, line = _extend_line(summary, line, word, display_width, space2)
+
+        if is_truncated:
+            # remove trailing space of last line
+            summary += line.rstrip() + space2 + "..."
+            line = space2
+
+        for tail_item in tail[:-1]:
+            word = tail_item + sep + " "
+            summary, line = _extend_line(summary, line, word, display_width, space2)
+
+        # last value: no sep added + 1 space of width used for trailing ','
+        summary, line = _extend_line(summary, line, tail[-1], display_width - 2, space2)
+        summary += line
+
+        # right now close is either '' or ', '
+        # Now we want to include the ']', but not the maybe space.
+        close = "]" + close.rstrip(" ")
+        summary += close
+
+        if len(summary) > (display_width) or line_break_each_value:
+            summary += space1
+        else:  # one row
+            summary += " "
+
+        # remove initial space
+        summary = "[" + summary[len(space2) :]
+
+    return summary
+
+
+def _justify(
+    head: list[Sequence[str]], tail: list[Sequence[str]]
+) -> tuple[list[tuple[str, ...]], list[tuple[str, ...]]]:
+    """
+    Justify items in head and tail, so they are right-aligned when stacked.
+
+    Parameters
+    ----------
+    head : list-like of list-likes of strings
+    tail : list-like of list-likes of strings
+
+    Returns
+    -------
+    tuple of list of tuples of strings
+        Same as head and tail, but items are right aligned when stacked
+        vertically.
+
+    Examples
+    --------
+    >>> _justify([['a', 'b']], [['abc', 'abcd']])
+    ([('  a', '   b')], [('abc', 'abcd')])
+    """
+    combined = head + tail
+
+    # For each position for the sequences in ``combined``,
+    # find the length of the largest string.
+    max_length = [0] * len(combined[0])
+    for inner_seq in combined:
+        length = [len(item) for item in inner_seq]
+        max_length = [max(x, y) for x, y in zip(max_length, length)]
+
+    # justify each item in each list-like in head and tail using max_length
+    head_tuples = [
+        tuple(x.rjust(max_len) for x, max_len in zip(seq, max_length)) for seq in head
+    ]
+    tail_tuples = [
+        tuple(x.rjust(max_len) for x, max_len in zip(seq, max_length)) for seq in tail
+    ]
+    return head_tuples, tail_tuples
+
+
+class PrettyDict(dict[_KT, _VT]):
+    """Dict extension to support abbreviated __repr__"""
+
+    def __repr__(self) -> str:
+        return pprint_thing(self)
+
+
+class _TextAdjustment:
+    def __init__(self) -> None:
+        self.encoding = get_option("display.encoding")
+
+    def len(self, text: str) -> int:
+        return len(text)
+
+    def justify(self, texts: Any, max_len: int, mode: str = "right") -> list[str]:
+        """
+        Perform ljust, center, rjust against string or list-like
+        """
+        if mode == "left":
+            return [x.ljust(max_len) for x in texts]
+        elif mode == "center":
+            return [x.center(max_len) for x in texts]
+        else:
+            return [x.rjust(max_len) for x in texts]
+
+    def adjoin(self, space: int, *lists, **kwargs) -> str:
+        return adjoin(space, *lists, strlen=self.len, justfunc=self.justify, **kwargs)
+
+
+class _EastAsianTextAdjustment(_TextAdjustment):
+    def __init__(self) -> None:
+        super().__init__()
+        if get_option("display.unicode.ambiguous_as_wide"):
+            self.ambiguous_width = 2
+        else:
+            self.ambiguous_width = 1
+
+        # Definition of East Asian Width
+        # https://unicode.org/reports/tr11/
+        # Ambiguous width can be changed by option
+        self._EAW_MAP = {"Na": 1, "N": 1, "W": 2, "F": 2, "H": 1}
+
+    def len(self, text: str) -> int:
+        """
+        Calculate display width considering unicode East Asian Width
+        """
+        if not isinstance(text, str):
+            return len(text)
+
+        return sum(
+            self._EAW_MAP.get(east_asian_width(c), self.ambiguous_width) for c in text
+        )
+
+    def justify(
+        self, texts: Iterable[str], max_len: int, mode: str = "right"
+    ) -> list[str]:
+        # re-calculate padding space per str considering East Asian Width
+        def _get_pad(t):
+            return max_len - self.len(t) + len(t)
+
+        if mode == "left":
+            return [x.ljust(_get_pad(x)) for x in texts]
+        elif mode == "center":
+            return [x.center(_get_pad(x)) for x in texts]
+        else:
+            return [x.rjust(_get_pad(x)) for x in texts]
+
+
+def get_adjustment() -> _TextAdjustment:
+    use_east_asian_width = get_option("display.unicode.east_asian_width")
+    if use_east_asian_width:
+        return _EastAsianTextAdjustment()
+    else:
+        return _TextAdjustment()

venv/lib/python3.10/site-packages/pandas/io/formats/string.py

@@ -0,0 +1,206 @@
+"""
+Module for formatting output data in console (to string).
+"""
+from __future__ import annotations
+
+from shutil import get_terminal_size
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.io.formats.printing import pprint_thing
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+    from pandas.io.formats.format import DataFrameFormatter
+
+
+class StringFormatter:
+    """Formatter for string representation of a dataframe."""
+
+    def __init__(self, fmt: DataFrameFormatter, line_width: int | None = None) -> None:
+        self.fmt = fmt
+        self.adj = fmt.adj
+        self.frame = fmt.frame
+        self.line_width = line_width
+
+    def to_string(self) -> str:
+        text = self._get_string_representation()
+        if self.fmt.should_show_dimensions:
+            text = f"{text}{self.fmt.dimensions_info}"
+        return text
+
+    def _get_strcols(self) -> list[list[str]]:
+        strcols = self.fmt.get_strcols()
+        if self.fmt.is_truncated:
+            strcols = self._insert_dot_separators(strcols)
+        return strcols
+
+    def _get_string_representation(self) -> str:
+        if self.fmt.frame.empty:
+            return self._empty_info_line
+
+        strcols = self._get_strcols()
+
+        if self.line_width is None:
+            # no need to wrap around just print the whole frame
+            return self.adj.adjoin(1, *strcols)
+
+        if self._need_to_wrap_around:
+            return self._join_multiline(strcols)
+
+        return self._fit_strcols_to_terminal_width(strcols)
+
+    @property
+    def _empty_info_line(self) -> str:
+        return (
+            f"Empty {type(self.frame).__name__}\n"
+            f"Columns: {pprint_thing(self.frame.columns)}\n"
+            f"Index: {pprint_thing(self.frame.index)}"
+        )
+
+    @property
+    def _need_to_wrap_around(self) -> bool:
+        return bool(self.fmt.max_cols is None or self.fmt.max_cols > 0)
+
+    def _insert_dot_separators(self, strcols: list[list[str]]) -> list[list[str]]:
+        str_index = self.fmt._get_formatted_index(self.fmt.tr_frame)
+        index_length = len(str_index)
+
+        if self.fmt.is_truncated_horizontally:
+            strcols = self._insert_dot_separator_horizontal(strcols, index_length)
+
+        if self.fmt.is_truncated_vertically:
+            strcols = self._insert_dot_separator_vertical(strcols, index_length)
+
+        return strcols
+
+    @property
+    def _adjusted_tr_col_num(self) -> int:
+        return self.fmt.tr_col_num + 1 if self.fmt.index else self.fmt.tr_col_num
+
+    def _insert_dot_separator_horizontal(
+        self, strcols: list[list[str]], index_length: int
+    ) -> list[list[str]]:
+        strcols.insert(self._adjusted_tr_col_num, [" ..."] * index_length)
+        return strcols
+
+    def _insert_dot_separator_vertical(
+        self, strcols: list[list[str]], index_length: int
+    ) -> list[list[str]]:
+        n_header_rows = index_length - len(self.fmt.tr_frame)
+        row_num = self.fmt.tr_row_num
+        for ix, col in enumerate(strcols):
+            cwidth = self.adj.len(col[row_num])
+
+            if self.fmt.is_truncated_horizontally:
+                is_dot_col = ix == self._adjusted_tr_col_num
+            else:
+                is_dot_col = False
+
+            if cwidth > 3 or is_dot_col:
+                dots = "..."
+            else:
+                dots = ".."
+
+            if ix == 0 and self.fmt.index:
+                dot_mode = "left"
+            elif is_dot_col:
+                cwidth = 4
+                dot_mode = "right"
+            else:
+                dot_mode = "right"
+
+            dot_str = self.adj.justify([dots], cwidth, mode=dot_mode)[0]
+            col.insert(row_num + n_header_rows, dot_str)
+        return strcols
+
+    def _join_multiline(self, strcols_input: Iterable[list[str]]) -> str:
+        lwidth = self.line_width
+        adjoin_width = 1
+        strcols = list(strcols_input)
+
+        if self.fmt.index:
+            idx = strcols.pop(0)
+            lwidth -= np.array([self.adj.len(x) for x in idx]).max() + adjoin_width
+
+        col_widths = [
+            np.array([self.adj.len(x) for x in col]).max() if len(col) > 0 else 0
+            for col in strcols
+        ]
+
+        assert lwidth is not None
+        col_bins = _binify(col_widths, lwidth)
+        nbins = len(col_bins)
+
+        str_lst = []
+        start = 0
+        for i, end in enumerate(col_bins):
+            row = strcols[start:end]
+            if self.fmt.index:
+                row.insert(0, idx)
+            if nbins > 1:
+                nrows = len(row[-1])
+                if end <= len(strcols) and i < nbins - 1:
+                    row.append([" \\"] + ["  "] * (nrows - 1))
+                else:
+                    row.append([" "] * nrows)
+            str_lst.append(self.adj.adjoin(adjoin_width, *row))
+            start = end
+        return "\n\n".join(str_lst)
+
+    def _fit_strcols_to_terminal_width(self, strcols: list[list[str]]) -> str:
+        from pandas import Series
+
+        lines = self.adj.adjoin(1, *strcols).split("\n")
+        max_len = Series(lines).str.len().max()
+        # plus truncate dot col
+        width, _ = get_terminal_size()
+        dif = max_len - width
+        # '+ 1' to avoid too wide repr (GH PR #17023)
+        adj_dif = dif + 1
+        col_lens = Series([Series(ele).str.len().max() for ele in strcols])
+        n_cols = len(col_lens)
+        counter = 0
+        while adj_dif > 0 and n_cols > 1:
+            counter += 1
+            mid = round(n_cols / 2)
+            mid_ix = col_lens.index[mid]
+            col_len = col_lens[mid_ix]
+            # adjoin adds one
+            adj_dif -= col_len + 1
+            col_lens = col_lens.drop(mid_ix)
+            n_cols = len(col_lens)
+
+        # subtract index column
+        max_cols_fitted = n_cols - self.fmt.index
+        # GH-21180. Ensure that we print at least two.
+        max_cols_fitted = max(max_cols_fitted, 2)
+        self.fmt.max_cols_fitted = max_cols_fitted
+
+        # Call again _truncate to cut frame appropriately
+        # and then generate string representation
+        self.fmt.truncate()
+        strcols = self._get_strcols()
+        return self.adj.adjoin(1, *strcols)
+
+
+def _binify(cols: list[int], line_width: int) -> list[int]:
+    adjoin_width = 1
+    bins = []
+    curr_width = 0
+    i_last_column = len(cols) - 1
+    for i, w in enumerate(cols):
+        w_adjoined = w + adjoin_width
+        curr_width += w_adjoined
+        if i_last_column == i:
+            wrap = curr_width + 1 > line_width and i > 0
+        else:
+            wrap = curr_width + 2 > line_width and i > 0
+        if wrap:
+            bins.append(i)
+            curr_width = w_adjoined
+
+    bins.append(len(cols))
+    return bins

venv/lib/python3.10/site-packages/pandas/io/formats/style_render.py

@@ -0,0 +1,2497 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from collections.abc import Sequence
+from functools import partial
+import re
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    DefaultDict,
+    Optional,
+    TypedDict,
+    Union,
+)
+from uuid import uuid4
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import lib
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.dtypes.common import (
+    is_complex,
+    is_float,
+    is_integer,
+)
+from pandas.core.dtypes.generic import ABCSeries
+
+from pandas import (
+    DataFrame,
+    Index,
+    IndexSlice,
+    MultiIndex,
+    Series,
+    isna,
+)
+from pandas.api.types import is_list_like
+import pandas.core.common as com
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        Axis,
+        Level,
+    )
+jinja2 = import_optional_dependency("jinja2", extra="DataFrame.style requires jinja2.")
+from markupsafe import escape as escape_html  # markupsafe is jinja2 dependency
+
+BaseFormatter = Union[str, Callable]
+ExtFormatter = Union[BaseFormatter, dict[Any, Optional[BaseFormatter]]]
+CSSPair = tuple[str, Union[str, float]]
+CSSList = list[CSSPair]
+CSSProperties = Union[str, CSSList]
+
+
+class CSSDict(TypedDict):
+    selector: str
+    props: CSSProperties
+
+
+CSSStyles = list[CSSDict]
+Subset = Union[slice, Sequence, Index]
+
+
+class StylerRenderer:
+    """
+    Base class to process rendering a Styler with a specified jinja2 template.
+    """
+
+    loader = jinja2.PackageLoader("pandas", "io/formats/templates")
+    env = jinja2.Environment(loader=loader, trim_blocks=True)
+    template_html = env.get_template("html.tpl")
+    template_html_table = env.get_template("html_table.tpl")
+    template_html_style = env.get_template("html_style.tpl")
+    template_latex = env.get_template("latex.tpl")
+    template_string = env.get_template("string.tpl")
+
+    def __init__(
+        self,
+        data: DataFrame | Series,
+        uuid: str | None = None,
+        uuid_len: int = 5,
+        table_styles: CSSStyles | None = None,
+        table_attributes: str | None = None,
+        caption: str | tuple | list | None = None,
+        cell_ids: bool = True,
+        precision: int | None = None,
+    ) -> None:
+        # validate ordered args
+        if isinstance(data, Series):
+            data = data.to_frame()
+        if not isinstance(data, DataFrame):
+            raise TypeError("``data`` must be a Series or DataFrame")
+        self.data: DataFrame = data
+        self.index: Index = data.index
+        self.columns: Index = data.columns
+        if not isinstance(uuid_len, int) or uuid_len < 0:
+            raise TypeError("``uuid_len`` must be an integer in range [0, 32].")
+        self.uuid = uuid or uuid4().hex[: min(32, uuid_len)]
+        self.uuid_len = len(self.uuid)
+        self.table_styles = table_styles
+        self.table_attributes = table_attributes
+        self.caption = caption
+        self.cell_ids = cell_ids
+        self.css = {
+            "row_heading": "row_heading",
+            "col_heading": "col_heading",
+            "index_name": "index_name",
+            "col": "col",
+            "row": "row",
+            "col_trim": "col_trim",
+            "row_trim": "row_trim",
+            "level": "level",
+            "data": "data",
+            "blank": "blank",
+            "foot": "foot",
+        }
+        self.concatenated: list[StylerRenderer] = []
+        # add rendering variables
+        self.hide_index_names: bool = False
+        self.hide_column_names: bool = False
+        self.hide_index_: list = [False] * self.index.nlevels
+        self.hide_columns_: list = [False] * self.columns.nlevels
+        self.hidden_rows: Sequence[int] = []  # sequence for specific hidden rows/cols
+        self.hidden_columns: Sequence[int] = []
+        self.ctx: DefaultDict[tuple[int, int], CSSList] = defaultdict(list)
+        self.ctx_index: DefaultDict[tuple[int, int], CSSList] = defaultdict(list)
+        self.ctx_columns: DefaultDict[tuple[int, int], CSSList] = defaultdict(list)
+        self.cell_context: DefaultDict[tuple[int, int], str] = defaultdict(str)
+        self._todo: list[tuple[Callable, tuple, dict]] = []
+        self.tooltips: Tooltips | None = None
+        precision = (
+            get_option("styler.format.precision") if precision is None else precision
+        )
+        self._display_funcs: DefaultDict[  # maps (row, col) -> format func
+            tuple[int, int], Callable[[Any], str]
+        ] = defaultdict(lambda: partial(_default_formatter, precision=precision))
+        self._display_funcs_index: DefaultDict[  # maps (row, level) -> format func
+            tuple[int, int], Callable[[Any], str]
+        ] = defaultdict(lambda: partial(_default_formatter, precision=precision))
+        self._display_funcs_columns: DefaultDict[  # maps (level, col) -> format func
+            tuple[int, int], Callable[[Any], str]
+        ] = defaultdict(lambda: partial(_default_formatter, precision=precision))
+
+    def _render(
+        self,
+        sparse_index: bool,
+        sparse_columns: bool,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        blank: str = "",
+    ):
+        """
+        Computes and applies styles and then generates the general render dicts.
+
+        Also extends the `ctx` and `ctx_index` attributes with those of concatenated
+        stylers for use within `_translate_latex`
+        """
+        self._compute()
+        dxs = []
+        ctx_len = len(self.index)
+        for i, concatenated in enumerate(self.concatenated):
+            concatenated.hide_index_ = self.hide_index_
+            concatenated.hidden_columns = self.hidden_columns
+            foot = f"{self.css['foot']}{i}"
+            concatenated.css = {
+                **self.css,
+                "data": f"{foot}_data",
+                "row_heading": f"{foot}_row_heading",
+                "row": f"{foot}_row",
+                "foot": f"{foot}_foot",
+            }
+            dx = concatenated._render(
+                sparse_index, sparse_columns, max_rows, max_cols, blank
+            )
+            dxs.append(dx)
+
+            for (r, c), v in concatenated.ctx.items():
+                self.ctx[(r + ctx_len, c)] = v
+            for (r, c), v in concatenated.ctx_index.items():
+                self.ctx_index[(r + ctx_len, c)] = v
+
+            ctx_len += len(concatenated.index)
+
+        d = self._translate(
+            sparse_index, sparse_columns, max_rows, max_cols, blank, dxs
+        )
+        return d
+
+    def _render_html(
+        self,
+        sparse_index: bool,
+        sparse_columns: bool,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        **kwargs,
+    ) -> str:
+        """
+        Renders the ``Styler`` including all applied styles to HTML.
+        Generates a dict with necessary kwargs passed to jinja2 template.
+        """
+        d = self._render(sparse_index, sparse_columns, max_rows, max_cols, "&nbsp;")
+        d.update(kwargs)
+        return self.template_html.render(
+            **d,
+            html_table_tpl=self.template_html_table,
+            html_style_tpl=self.template_html_style,
+        )
+
+    def _render_latex(
+        self, sparse_index: bool, sparse_columns: bool, clines: str | None, **kwargs
+    ) -> str:
+        """
+        Render a Styler in latex format
+        """
+        d = self._render(sparse_index, sparse_columns, None, None)
+        self._translate_latex(d, clines=clines)
+        self.template_latex.globals["parse_wrap"] = _parse_latex_table_wrapping
+        self.template_latex.globals["parse_table"] = _parse_latex_table_styles
+        self.template_latex.globals["parse_cell"] = _parse_latex_cell_styles
+        self.template_latex.globals["parse_header"] = _parse_latex_header_span
+        d.update(kwargs)
+        return self.template_latex.render(**d)
+
+    def _render_string(
+        self,
+        sparse_index: bool,
+        sparse_columns: bool,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        **kwargs,
+    ) -> str:
+        """
+        Render a Styler in string format
+        """
+        d = self._render(sparse_index, sparse_columns, max_rows, max_cols)
+        d.update(kwargs)
+        return self.template_string.render(**d)
+
+    def _compute(self):
+        """
+        Execute the style functions built up in `self._todo`.
+
+        Relies on the conventions that all style functions go through
+        .apply or .map. The append styles to apply as tuples of
+
+        (application method, *args, **kwargs)
+        """
+        self.ctx.clear()
+        self.ctx_index.clear()
+        self.ctx_columns.clear()
+        r = self
+        for func, args, kwargs in self._todo:
+            r = func(self)(*args, **kwargs)
+        return r
+
+    def _translate(
+        self,
+        sparse_index: bool,
+        sparse_cols: bool,
+        max_rows: int | None = None,
+        max_cols: int | None = None,
+        blank: str = "&nbsp;",
+        dxs: list[dict] | None = None,
+    ):
+        """
+        Process Styler data and settings into a dict for template rendering.
+
+        Convert data and settings from ``Styler`` attributes such as ``self.data``,
+        ``self.tooltips`` including applying any methods in ``self._todo``.
+
+        Parameters
+        ----------
+        sparse_index : bool
+            Whether to sparsify the index or print all hierarchical index elements.
+            Upstream defaults are typically to `pandas.options.styler.sparse.index`.
+        sparse_cols : bool
+            Whether to sparsify the columns or print all hierarchical column elements.
+            Upstream defaults are typically to `pandas.options.styler.sparse.columns`.
+        max_rows, max_cols : int, optional
+            Specific max rows and cols. max_elements always take precedence in render.
+        blank : str
+            Entry to top-left blank cells.
+        dxs : list[dict]
+            The render dicts of the concatenated Stylers.
+
+        Returns
+        -------
+        d : dict
+            The following structure: {uuid, table_styles, caption, head, body,
+            cellstyle, table_attributes}
+        """
+        if dxs is None:
+            dxs = []
+        self.css["blank_value"] = blank
+
+        # construct render dict
+        d = {
+            "uuid": self.uuid,
+            "table_styles": format_table_styles(self.table_styles or []),
+            "caption": self.caption,
+        }
+
+        max_elements = get_option("styler.render.max_elements")
+        max_rows = max_rows if max_rows else get_option("styler.render.max_rows")
+        max_cols = max_cols if max_cols else get_option("styler.render.max_columns")
+        max_rows, max_cols = _get_trimming_maximums(
+            len(self.data.index),
+            len(self.data.columns),
+            max_elements,
+            max_rows,
+            max_cols,
+        )
+
+        self.cellstyle_map_columns: DefaultDict[
+            tuple[CSSPair, ...], list[str]
+        ] = defaultdict(list)
+        head = self._translate_header(sparse_cols, max_cols)
+        d.update({"head": head})
+
+        # for sparsifying a MultiIndex and for use with latex clines
+        idx_lengths = _get_level_lengths(
+            self.index, sparse_index, max_rows, self.hidden_rows
+        )
+        d.update({"index_lengths": idx_lengths})
+
+        self.cellstyle_map: DefaultDict[tuple[CSSPair, ...], list[str]] = defaultdict(
+            list
+        )
+        self.cellstyle_map_index: DefaultDict[
+            tuple[CSSPair, ...], list[str]
+        ] = defaultdict(list)
+        body: list = self._translate_body(idx_lengths, max_rows, max_cols)
+        d.update({"body": body})
+
+        ctx_maps = {
+            "cellstyle": "cellstyle_map",
+            "cellstyle_index": "cellstyle_map_index",
+            "cellstyle_columns": "cellstyle_map_columns",
+        }  # add the cell_ids styles map to the render dictionary in right format
+        for k, attr in ctx_maps.items():
+            map = [
+                {"props": list(props), "selectors": selectors}
+                for props, selectors in getattr(self, attr).items()
+            ]
+            d.update({k: map})
+
+        for dx in dxs:  # self.concatenated is not empty
+            d["body"].extend(dx["body"])  # type: ignore[union-attr]
+            d["cellstyle"].extend(dx["cellstyle"])  # type: ignore[union-attr]
+            d["cellstyle_index"].extend(  # type: ignore[union-attr]
+                dx["cellstyle_index"]
+            )
+
+        table_attr = self.table_attributes
+        if not get_option("styler.html.mathjax"):
+            table_attr = table_attr or ""
+            if 'class="' in table_attr:
+                table_attr = table_attr.replace('class="', 'class="tex2jax_ignore ')
+            else:
+                table_attr += ' class="tex2jax_ignore"'
+        d.update({"table_attributes": table_attr})
+
+        if self.tooltips:
+            d = self.tooltips._translate(self, d)
+
+        return d
+
+    def _translate_header(self, sparsify_cols: bool, max_cols: int):
+        """
+        Build each <tr> within table <head> as a list
+
+        Using the structure:
+             +----------------------------+---------------+---------------------------+
+             |  index_blanks ...          | column_name_0 |  column_headers (level_0) |
+          1) |       ..                   |       ..      |             ..            |
+             |  index_blanks ...          | column_name_n |  column_headers (level_n) |
+             +----------------------------+---------------+---------------------------+
+          2) |  index_names (level_0 to level_n) ...      | column_blanks ...         |
+             +----------------------------+---------------+---------------------------+
+
+        Parameters
+        ----------
+        sparsify_cols : bool
+            Whether column_headers section will add colspan attributes (>1) to elements.
+        max_cols : int
+            Maximum number of columns to render. If exceeded will contain `...` filler.
+
+        Returns
+        -------
+        head : list
+            The associated HTML elements needed for template rendering.
+        """
+        # for sparsifying a MultiIndex
+        col_lengths = _get_level_lengths(
+            self.columns, sparsify_cols, max_cols, self.hidden_columns
+        )
+
+        clabels = self.data.columns.tolist()
+        if self.data.columns.nlevels == 1:
+            clabels = [[x] for x in clabels]
+        clabels = list(zip(*clabels))
+
+        head = []
+        # 1) column headers
+        for r, hide in enumerate(self.hide_columns_):
+            if hide or not clabels:
+                continue
+
+            header_row = self._generate_col_header_row(
+                (r, clabels), max_cols, col_lengths
+            )
+            head.append(header_row)
+
+        # 2) index names
+        if (
+            self.data.index.names
+            and com.any_not_none(*self.data.index.names)
+            and not all(self.hide_index_)
+            and not self.hide_index_names
+        ):
+            index_names_row = self._generate_index_names_row(
+                clabels, max_cols, col_lengths
+            )
+            head.append(index_names_row)
+
+        return head
+
+    def _generate_col_header_row(
+        self, iter: Sequence, max_cols: int, col_lengths: dict
+    ):
+        """
+        Generate the row containing column headers:
+
+         +----------------------------+---------------+---------------------------+
+         |  index_blanks ...          | column_name_i |  column_headers (level_i) |
+         +----------------------------+---------------+---------------------------+
+
+        Parameters
+        ----------
+        iter : tuple
+            Looping variables from outer scope
+        max_cols : int
+            Permissible number of columns
+        col_lengths :
+            c
+
+        Returns
+        -------
+        list of elements
+        """
+
+        r, clabels = iter
+
+        # number of index blanks is governed by number of hidden index levels
+        index_blanks = [
+            _element("th", self.css["blank"], self.css["blank_value"], True)
+        ] * (self.index.nlevels - sum(self.hide_index_) - 1)
+
+        name = self.data.columns.names[r]
+        column_name = [
+            _element(
+                "th",
+                (
+                    f"{self.css['blank']} {self.css['level']}{r}"
+                    if name is None
+                    else f"{self.css['index_name']} {self.css['level']}{r}"
+                ),
+                name
+                if (name is not None and not self.hide_column_names)
+                else self.css["blank_value"],
+                not all(self.hide_index_),
+            )
+        ]
+
+        column_headers: list = []
+        visible_col_count: int = 0
+        for c, value in enumerate(clabels[r]):
+            header_element_visible = _is_visible(c, r, col_lengths)
+            if header_element_visible:
+                visible_col_count += col_lengths.get((r, c), 0)
+            if self._check_trim(
+                visible_col_count,
+                max_cols,
+                column_headers,
+                "th",
+                f"{self.css['col_heading']} {self.css['level']}{r} "
+                f"{self.css['col_trim']}",
+            ):
+                break
+
+            header_element = _element(
+                "th",
+                (
+                    f"{self.css['col_heading']} {self.css['level']}{r} "
+                    f"{self.css['col']}{c}"
+                ),
+                value,
+                header_element_visible,
+                display_value=self._display_funcs_columns[(r, c)](value),
+                attributes=(
+                    f'colspan="{col_lengths.get((r, c), 0)}"'
+                    if col_lengths.get((r, c), 0) > 1
+                    else ""
+                ),
+            )
+
+            if self.cell_ids:
+                header_element["id"] = f"{self.css['level']}{r}_{self.css['col']}{c}"
+            if (
+                header_element_visible
+                and (r, c) in self.ctx_columns
+                and self.ctx_columns[r, c]
+            ):
+                header_element["id"] = f"{self.css['level']}{r}_{self.css['col']}{c}"
+                self.cellstyle_map_columns[tuple(self.ctx_columns[r, c])].append(
+                    f"{self.css['level']}{r}_{self.css['col']}{c}"
+                )
+
+            column_headers.append(header_element)
+
+        return index_blanks + column_name + column_headers
+
+    def _generate_index_names_row(
+        self, iter: Sequence, max_cols: int, col_lengths: dict
+    ):
+        """
+        Generate the row containing index names
+
+         +----------------------------+---------------+---------------------------+
+         |  index_names (level_0 to level_n) ...      | column_blanks ...         |
+         +----------------------------+---------------+---------------------------+
+
+        Parameters
+        ----------
+        iter : tuple
+            Looping variables from outer scope
+        max_cols : int
+            Permissible number of columns
+
+        Returns
+        -------
+        list of elements
+        """
+
+        clabels = iter
+
+        index_names = [
+            _element(
+                "th",
+                f"{self.css['index_name']} {self.css['level']}{c}",
+                self.css["blank_value"] if name is None else name,
+                not self.hide_index_[c],
+            )
+            for c, name in enumerate(self.data.index.names)
+        ]
+
+        column_blanks: list = []
+        visible_col_count: int = 0
+        if clabels:
+            last_level = self.columns.nlevels - 1  # use last level since never sparsed
+            for c, value in enumerate(clabels[last_level]):
+                header_element_visible = _is_visible(c, last_level, col_lengths)
+                if header_element_visible:
+                    visible_col_count += 1
+                if self._check_trim(
+                    visible_col_count,
+                    max_cols,
+                    column_blanks,
+                    "th",
+                    f"{self.css['blank']} {self.css['col']}{c} {self.css['col_trim']}",
+                    self.css["blank_value"],
+                ):
+                    break
+
+                column_blanks.append(
+                    _element(
+                        "th",
+                        f"{self.css['blank']} {self.css['col']}{c}",
+                        self.css["blank_value"],
+                        c not in self.hidden_columns,
+                    )
+                )
+
+        return index_names + column_blanks
+
+    def _translate_body(self, idx_lengths: dict, max_rows: int, max_cols: int):
+        """
+        Build each <tr> within table <body> as a list
+
+        Use the following structure:
+          +--------------------------------------------+---------------------------+
+          |  index_header_0    ...    index_header_n   |  data_by_column   ...     |
+          +--------------------------------------------+---------------------------+
+
+        Also add elements to the cellstyle_map for more efficient grouped elements in
+        <style></style> block
+
+        Parameters
+        ----------
+        sparsify_index : bool
+            Whether index_headers section will add rowspan attributes (>1) to elements.
+
+        Returns
+        -------
+        body : list
+            The associated HTML elements needed for template rendering.
+        """
+        rlabels = self.data.index.tolist()
+        if not isinstance(self.data.index, MultiIndex):
+            rlabels = [[x] for x in rlabels]
+
+        body: list = []
+        visible_row_count: int = 0
+        for r, row_tup in [
+            z for z in enumerate(self.data.itertuples()) if z[0] not in self.hidden_rows
+        ]:
+            visible_row_count += 1
+            if self._check_trim(
+                visible_row_count,
+                max_rows,
+                body,
+                "row",
+            ):
+                break
+
+            body_row = self._generate_body_row(
+                (r, row_tup, rlabels), max_cols, idx_lengths
+            )
+            body.append(body_row)
+        return body
+
+    def _check_trim(
+        self,
+        count: int,
+        max: int,
+        obj: list,
+        element: str,
+        css: str | None = None,
+        value: str = "...",
+    ) -> bool:
+        """
+        Indicates whether to break render loops and append a trimming indicator
+
+        Parameters
+        ----------
+        count : int
+            The loop count of previous visible items.
+        max : int
+            The allowable rendered items in the loop.
+        obj : list
+            The current render collection of the rendered items.
+        element : str
+            The type of element to append in the case a trimming indicator is needed.
+        css : str, optional
+            The css to add to the trimming indicator element.
+        value : str, optional
+            The value of the elements display if necessary.
+
+        Returns
+        -------
+        result : bool
+            Whether a trimming element was required and appended.
+        """
+        if count > max:
+            if element == "row":
+                obj.append(self._generate_trimmed_row(max))
+            else:
+                obj.append(_element(element, css, value, True, attributes=""))
+            return True
+        return False
+
+    def _generate_trimmed_row(self, max_cols: int) -> list:
+        """
+        When a render has too many rows we generate a trimming row containing "..."
+
+        Parameters
+        ----------
+        max_cols : int
+            Number of permissible columns
+
+        Returns
+        -------
+        list of elements
+        """
+        index_headers = [
+            _element(
+                "th",
+                (
+                    f"{self.css['row_heading']} {self.css['level']}{c} "
+                    f"{self.css['row_trim']}"
+                ),
+                "...",
+                not self.hide_index_[c],
+                attributes="",
+            )
+            for c in range(self.data.index.nlevels)
+        ]
+
+        data: list = []
+        visible_col_count: int = 0
+        for c, _ in enumerate(self.columns):
+            data_element_visible = c not in self.hidden_columns
+            if data_element_visible:
+                visible_col_count += 1
+            if self._check_trim(
+                visible_col_count,
+                max_cols,
+                data,
+                "td",
+                f"{self.css['data']} {self.css['row_trim']} {self.css['col_trim']}",
+            ):
+                break
+
+            data.append(
+                _element(
+                    "td",
+                    f"{self.css['data']} {self.css['col']}{c} {self.css['row_trim']}",
+                    "...",
+                    data_element_visible,
+                    attributes="",
+                )
+            )
+
+        return index_headers + data
+
+    def _generate_body_row(
+        self,
+        iter: tuple,
+        max_cols: int,
+        idx_lengths: dict,
+    ):
+        """
+        Generate a regular row for the body section of appropriate format.
+
+          +--------------------------------------------+---------------------------+
+          |  index_header_0    ...    index_header_n   |  data_by_column   ...     |
+          +--------------------------------------------+---------------------------+
+
+        Parameters
+        ----------
+        iter : tuple
+            Iterable from outer scope: row number, row data tuple, row index labels.
+        max_cols : int
+            Number of permissible columns.
+        idx_lengths : dict
+            A map of the sparsification structure of the index
+
+        Returns
+        -------
+            list of elements
+        """
+        r, row_tup, rlabels = iter
+
+        index_headers = []
+        for c, value in enumerate(rlabels[r]):
+            header_element_visible = (
+                _is_visible(r, c, idx_lengths) and not self.hide_index_[c]
+            )
+            header_element = _element(
+                "th",
+                (
+                    f"{self.css['row_heading']} {self.css['level']}{c} "
+                    f"{self.css['row']}{r}"
+                ),
+                value,
+                header_element_visible,
+                display_value=self._display_funcs_index[(r, c)](value),
+                attributes=(
+                    f'rowspan="{idx_lengths.get((c, r), 0)}"'
+                    if idx_lengths.get((c, r), 0) > 1
+                    else ""
+                ),
+            )
+
+            if self.cell_ids:
+                header_element[
+                    "id"
+                ] = f"{self.css['level']}{c}_{self.css['row']}{r}"  # id is given
+            if (
+                header_element_visible
+                and (r, c) in self.ctx_index
+                and self.ctx_index[r, c]
+            ):
+                # always add id if a style is specified
+                header_element["id"] = f"{self.css['level']}{c}_{self.css['row']}{r}"
+                self.cellstyle_map_index[tuple(self.ctx_index[r, c])].append(
+                    f"{self.css['level']}{c}_{self.css['row']}{r}"
+                )
+
+            index_headers.append(header_element)
+
+        data: list = []
+        visible_col_count: int = 0
+        for c, value in enumerate(row_tup[1:]):
+            data_element_visible = (
+                c not in self.hidden_columns and r not in self.hidden_rows
+            )
+            if data_element_visible:
+                visible_col_count += 1
+            if self._check_trim(
+                visible_col_count,
+                max_cols,
+                data,
+                "td",
+                f"{self.css['data']} {self.css['row']}{r} {self.css['col_trim']}",
+            ):
+                break
+
+            # add custom classes from cell context
+            cls = ""
+            if (r, c) in self.cell_context:
+                cls = " " + self.cell_context[r, c]
+
+            data_element = _element(
+                "td",
+                (
+                    f"{self.css['data']} {self.css['row']}{r} "
+                    f"{self.css['col']}{c}{cls}"
+                ),
+                value,
+                data_element_visible,
+                attributes="",
+                display_value=self._display_funcs[(r, c)](value),
+            )
+
+            if self.cell_ids:
+                data_element["id"] = f"{self.css['row']}{r}_{self.css['col']}{c}"
+            if data_element_visible and (r, c) in self.ctx and self.ctx[r, c]:
+                # always add id if needed due to specified style
+                data_element["id"] = f"{self.css['row']}{r}_{self.css['col']}{c}"
+                self.cellstyle_map[tuple(self.ctx[r, c])].append(
+                    f"{self.css['row']}{r}_{self.css['col']}{c}"
+                )
+
+            data.append(data_element)
+
+        return index_headers + data
+
+    def _translate_latex(self, d: dict, clines: str | None) -> None:
+        r"""
+        Post-process the default render dict for the LaTeX template format.
+
+        Processing items included are:
+          - Remove hidden columns from the non-headers part of the body.
+          - Place cellstyles directly in td cells rather than use cellstyle_map.
+          - Remove hidden indexes or reinsert missing th elements if part of multiindex
+            or multirow sparsification (so that \multirow and \multicol work correctly).
+        """
+        index_levels = self.index.nlevels
+        visible_index_level_n = index_levels - sum(self.hide_index_)
+        d["head"] = [
+            [
+                {**col, "cellstyle": self.ctx_columns[r, c - visible_index_level_n]}
+                for c, col in enumerate(row)
+                if col["is_visible"]
+            ]
+            for r, row in enumerate(d["head"])
+        ]
+
+        def _concatenated_visible_rows(obj, n, row_indices):
+            """
+            Extract all visible row indices recursively from concatenated stylers.
+            """
+            row_indices.extend(
+                [r + n for r in range(len(obj.index)) if r not in obj.hidden_rows]
+            )
+            n += len(obj.index)
+            for concatenated in obj.concatenated:
+                n = _concatenated_visible_rows(concatenated, n, row_indices)
+            return n
+
+        def concatenated_visible_rows(obj):
+            row_indices: list[int] = []
+            _concatenated_visible_rows(obj, 0, row_indices)
+            # TODO try to consolidate the concat visible rows
+            # methods to a single function / recursion for simplicity
+            return row_indices
+
+        body = []
+        for r, row in zip(concatenated_visible_rows(self), d["body"]):
+            # note: cannot enumerate d["body"] because rows were dropped if hidden
+            # during _translate_body so must zip to acquire the true r-index associated
+            # with the ctx obj which contains the cell styles.
+            if all(self.hide_index_):
+                row_body_headers = []
+            else:
+                row_body_headers = [
+                    {
+                        **col,
+                        "display_value": col["display_value"]
+                        if col["is_visible"]
+                        else "",
+                        "cellstyle": self.ctx_index[r, c],
+                    }
+                    for c, col in enumerate(row[:index_levels])
+                    if (col["type"] == "th" and not self.hide_index_[c])
+                ]
+
+            row_body_cells = [
+                {**col, "cellstyle": self.ctx[r, c]}
+                for c, col in enumerate(row[index_levels:])
+                if (col["is_visible"] and col["type"] == "td")
+            ]
+
+            body.append(row_body_headers + row_body_cells)
+        d["body"] = body
+
+        # clines are determined from info on index_lengths and hidden_rows and input
+        # to a dict defining which row clines should be added in the template.
+        if clines not in [
+            None,
+            "all;data",
+            "all;index",
+            "skip-last;data",
+            "skip-last;index",
+        ]:
+            raise ValueError(
+                f"`clines` value of {clines} is invalid. Should either be None or one "
+                f"of 'all;data', 'all;index', 'skip-last;data', 'skip-last;index'."
+            )
+        if clines is not None:
+            data_len = len(row_body_cells) if "data" in clines and d["body"] else 0
+
+            d["clines"] = defaultdict(list)
+            visible_row_indexes: list[int] = [
+                r for r in range(len(self.data.index)) if r not in self.hidden_rows
+            ]
+            visible_index_levels: list[int] = [
+                i for i in range(index_levels) if not self.hide_index_[i]
+            ]
+            for rn, r in enumerate(visible_row_indexes):
+                for lvln, lvl in enumerate(visible_index_levels):
+                    if lvl == index_levels - 1 and "skip-last" in clines:
+                        continue
+                    idx_len = d["index_lengths"].get((lvl, r), None)
+                    if idx_len is not None:  # i.e. not a sparsified entry
+                        d["clines"][rn + idx_len].append(
+                            f"\\cline{{{lvln+1}-{len(visible_index_levels)+data_len}}}"
+                        )
+
+    def format(
+        self,
+        formatter: ExtFormatter | None = None,
+        subset: Subset | None = None,
+        na_rep: str | None = None,
+        precision: int | None = None,
+        decimal: str = ".",
+        thousands: str | None = None,
+        escape: str | None = None,
+        hyperlinks: str | None = None,
+    ) -> StylerRenderer:
+        r"""
+        Format the text display value of cells.
+
+        Parameters
+        ----------
+        formatter : str, callable, dict or None
+            Object to define how values are displayed. See notes.
+        subset : label, array-like, IndexSlice, optional
+            A valid 2d input to `DataFrame.loc[<subset>]`, or, in the case of a 1d input
+            or single key, to `DataFrame.loc[:, <subset>]` where the columns are
+            prioritised, to limit ``data`` to *before* applying the function.
+        na_rep : str, optional
+            Representation for missing values.
+            If ``na_rep`` is None, no special formatting is applied.
+        precision : int, optional
+            Floating point precision to use for display purposes, if not determined by
+            the specified ``formatter``.
+
+            .. versionadded:: 1.3.0
+
+        decimal : str, default "."
+            Character used as decimal separator for floats, complex and integers.
+
+            .. versionadded:: 1.3.0
+
+        thousands : str, optional, default None
+            Character used as thousands separator for floats, complex and integers.
+
+            .. versionadded:: 1.3.0
+
+        escape : str, optional
+            Use 'html' to replace the characters ``&``, ``<``, ``>``, ``'``, and ``"``
+            in cell display string with HTML-safe sequences.
+            Use 'latex' to replace the characters ``&``, ``%``, ``$``, ``#``, ``_``,
+            ``{``, ``}``, ``~``, ``^``, and ``\`` in the cell display string with
+            LaTeX-safe sequences.
+            Use 'latex-math' to replace the characters the same way as in 'latex' mode,
+            except for math substrings, which either are surrounded
+            by two characters ``$`` or start with the character ``\(`` and
+            end with ``\)``. Escaping is done before ``formatter``.
+
+            .. versionadded:: 1.3.0
+
+        hyperlinks : {"html", "latex"}, optional
+            Convert string patterns containing https://, http://, ftp:// or www. to
+            HTML <a> tags as clickable URL hyperlinks if "html", or LaTeX \href
+            commands if "latex".
+
+            .. versionadded:: 1.4.0
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.format_index: Format the text display value of index labels.
+
+        Notes
+        -----
+        This method assigns a formatting function, ``formatter``, to each cell in the
+        DataFrame. If ``formatter`` is ``None``, then the default formatter is used.
+        If a callable then that function should take a data value as input and return
+        a displayable representation, such as a string. If ``formatter`` is
+        given as a string this is assumed to be a valid Python format specification
+        and is wrapped to a callable as ``string.format(x)``. If a ``dict`` is given,
+        keys should correspond to column names, and values should be string or
+        callable, as above.
+
+        The default formatter currently expresses floats and complex numbers with the
+        pandas display precision unless using the ``precision`` argument here. The
+        default formatter does not adjust the representation of missing values unless
+        the ``na_rep`` argument is used.
+
+        The ``subset`` argument defines which region to apply the formatting function
+        to. If the ``formatter`` argument is given in dict form but does not include
+        all columns within the subset then these columns will have the default formatter
+        applied. Any columns in the formatter dict excluded from the subset will
+        be ignored.
+
+        When using a ``formatter`` string the dtypes must be compatible, otherwise a
+        `ValueError` will be raised.
+
+        When instantiating a Styler, default formatting can be applied be setting the
+        ``pandas.options``:
+
+          - ``styler.format.formatter``: default None.
+          - ``styler.format.na_rep``: default None.
+          - ``styler.format.precision``: default 6.
+          - ``styler.format.decimal``: default ".".
+          - ``styler.format.thousands``: default None.
+          - ``styler.format.escape``: default None.
+
+        .. warning::
+           `Styler.format` is ignored when using the output format `Styler.to_excel`,
+           since Excel and Python have inherrently different formatting structures.
+           However, it is possible to use the `number-format` pseudo CSS attribute
+           to force Excel permissible formatting. See examples.
+
+        Examples
+        --------
+        Using ``na_rep`` and ``precision`` with the default ``formatter``
+
+        >>> df = pd.DataFrame([[np.nan, 1.0, 'A'], [2.0, np.nan, 3.0]])
+        >>> df.style.format(na_rep='MISS', precision=3)  # doctest: +SKIP
+                0       1       2
+        0    MISS   1.000       A
+        1   2.000    MISS   3.000
+
+        Using a ``formatter`` specification on consistent column dtypes
+
+        >>> df.style.format('{:.2f}', na_rep='MISS', subset=[0,1])  # doctest: +SKIP
+                0      1          2
+        0    MISS   1.00          A
+        1    2.00   MISS   3.000000
+
+        Using the default ``formatter`` for unspecified columns
+
+        >>> df.style.format({0: '{:.2f}', 1: '£ {:.1f}'}, na_rep='MISS', precision=1)
+        ...  # doctest: +SKIP
+                 0      1     2
+        0    MISS   £ 1.0     A
+        1    2.00    MISS   3.0
+
+        Multiple ``na_rep`` or ``precision`` specifications under the default
+        ``formatter``.
+
+        >>> (df.style.format(na_rep='MISS', precision=1, subset=[0])
+        ...     .format(na_rep='PASS', precision=2, subset=[1, 2]))  # doctest: +SKIP
+                0      1      2
+        0    MISS   1.00      A
+        1     2.0   PASS   3.00
+
+        Using a callable ``formatter`` function.
+
+        >>> func = lambda s: 'STRING' if isinstance(s, str) else 'FLOAT'
+        >>> df.style.format({0: '{:.1f}', 2: func}, precision=4, na_rep='MISS')
+        ...  # doctest: +SKIP
+                0        1        2
+        0    MISS   1.0000   STRING
+        1     2.0     MISS    FLOAT
+
+        Using a ``formatter`` with HTML ``escape`` and ``na_rep``.
+
+        >>> df = pd.DataFrame([['<div></div>', '"A&B"', None]])
+        >>> s = df.style.format(
+        ...     '<a href="a.com/{0}">{0}</a>', escape="html", na_rep="NA"
+        ...     )
+        >>> s.to_html()  # doctest: +SKIP
+        ...
+        <td .. ><a href="a.com/&lt;div&gt;&lt;/div&gt;">&lt;div&gt;&lt;/div&gt;</a></td>
+        <td .. ><a href="a.com/&#34;A&amp;B&#34;">&#34;A&amp;B&#34;</a></td>
+        <td .. >NA</td>
+        ...
+
+        Using a ``formatter`` with ``escape`` in 'latex' mode.
+
+        >>> df = pd.DataFrame([["123"], ["~ ^"], ["$%#"]])
+        >>> df.style.format("\\textbf{{{}}}", escape="latex").to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{ll}
+         & 0 \\
+        0 & \textbf{123} \\
+        1 & \textbf{\textasciitilde \space \textasciicircum } \\
+        2 & \textbf{\$\%\#} \\
+        \end{tabular}
+
+        Applying ``escape`` in 'latex-math' mode. In the example below
+        we enter math mode using the character ``$``.
+
+        >>> df = pd.DataFrame([[r"$\sum_{i=1}^{10} a_i$ a~b $\alpha \
+        ...     = \frac{\beta}{\zeta^2}$"], ["%#^ $ \$x^2 $"]])
+        >>> df.style.format(escape="latex-math").to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{ll}
+         & 0 \\
+        0 & $\sum_{i=1}^{10} a_i$ a\textasciitilde b $\alpha = \frac{\beta}{\zeta^2}$ \\
+        1 & \%\#\textasciicircum \space $ \$x^2 $ \\
+        \end{tabular}
+
+        We can use the character ``\(`` to enter math mode and the character ``\)``
+        to close math mode.
+
+        >>> df = pd.DataFrame([[r"\(\sum_{i=1}^{10} a_i\) a~b \(\alpha \
+        ...     = \frac{\beta}{\zeta^2}\)"], ["%#^ \( \$x^2 \)"]])
+        >>> df.style.format(escape="latex-math").to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{ll}
+         & 0 \\
+        0 & \(\sum_{i=1}^{10} a_i\) a\textasciitilde b \(\alpha
+        = \frac{\beta}{\zeta^2}\) \\
+        1 & \%\#\textasciicircum \space \( \$x^2 \) \\
+        \end{tabular}
+
+        If we have in one DataFrame cell a combination of both shorthands
+        for math formulas, the shorthand with the sign ``$`` will be applied.
+
+        >>> df = pd.DataFrame([[r"\( x^2 \)  $x^2$"], \
+        ...     [r"$\frac{\beta}{\zeta}$ \(\frac{\beta}{\zeta}\)"]])
+        >>> df.style.format(escape="latex-math").to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{ll}
+         & 0 \\
+        0 & \textbackslash ( x\textasciicircum 2 \textbackslash )  $x^2$ \\
+        1 & $\frac{\beta}{\zeta}$ \textbackslash (\textbackslash
+        frac\{\textbackslash beta\}\{\textbackslash zeta\}\textbackslash ) \\
+        \end{tabular}
+
+        Pandas defines a `number-format` pseudo CSS attribute instead of the `.format`
+        method to create `to_excel` permissible formatting. Note that semi-colons are
+        CSS protected characters but used as separators in Excel's format string.
+        Replace semi-colons with the section separator character (ASCII-245) when
+        defining the formatting here.
+
+        >>> df = pd.DataFrame({"A": [1, 0, -1]})
+        >>> pseudo_css = "number-format: 0§[Red](0)§-§@;"
+        >>> filename = "formatted_file.xlsx"
+        >>> df.style.map(lambda v: pseudo_css).to_excel(filename) # doctest: +SKIP
+
+        .. figure:: ../../_static/style/format_excel_css.png
+        """
+        if all(
+            (
+                formatter is None,
+                subset is None,
+                precision is None,
+                decimal == ".",
+                thousands is None,
+                na_rep is None,
+                escape is None,
+                hyperlinks is None,
+            )
+        ):
+            self._display_funcs.clear()
+            return self  # clear the formatter / revert to default and avoid looping
+
+        subset = slice(None) if subset is None else subset
+        subset = non_reducing_slice(subset)
+        data = self.data.loc[subset]
+
+        if not isinstance(formatter, dict):
+            formatter = {col: formatter for col in data.columns}
+
+        cis = self.columns.get_indexer_for(data.columns)
+        ris = self.index.get_indexer_for(data.index)
+        for ci in cis:
+            format_func = _maybe_wrap_formatter(
+                formatter.get(self.columns[ci]),
+                na_rep=na_rep,
+                precision=precision,
+                decimal=decimal,
+                thousands=thousands,
+                escape=escape,
+                hyperlinks=hyperlinks,
+            )
+            for ri in ris:
+                self._display_funcs[(ri, ci)] = format_func
+
+        return self
+
+    def format_index(
+        self,
+        formatter: ExtFormatter | None = None,
+        axis: Axis = 0,
+        level: Level | list[Level] | None = None,
+        na_rep: str | None = None,
+        precision: int | None = None,
+        decimal: str = ".",
+        thousands: str | None = None,
+        escape: str | None = None,
+        hyperlinks: str | None = None,
+    ) -> StylerRenderer:
+        r"""
+        Format the text display value of index labels or column headers.
+
+        .. versionadded:: 1.4.0
+
+        Parameters
+        ----------
+        formatter : str, callable, dict or None
+            Object to define how values are displayed. See notes.
+        axis : {0, "index", 1, "columns"}
+            Whether to apply the formatter to the index or column headers.
+        level : int, str, list
+            The level(s) over which to apply the generic formatter.
+        na_rep : str, optional
+            Representation for missing values.
+            If ``na_rep`` is None, no special formatting is applied.
+        precision : int, optional
+            Floating point precision to use for display purposes, if not determined by
+            the specified ``formatter``.
+        decimal : str, default "."
+            Character used as decimal separator for floats, complex and integers.
+        thousands : str, optional, default None
+            Character used as thousands separator for floats, complex and integers.
+        escape : str, optional
+            Use 'html' to replace the characters ``&``, ``<``, ``>``, ``'``, and ``"``
+            in cell display string with HTML-safe sequences.
+            Use 'latex' to replace the characters ``&``, ``%``, ``$``, ``#``, ``_``,
+            ``{``, ``}``, ``~``, ``^``, and ``\`` in the cell display string with
+            LaTeX-safe sequences.
+            Escaping is done before ``formatter``.
+        hyperlinks : {"html", "latex"}, optional
+            Convert string patterns containing https://, http://, ftp:// or www. to
+            HTML <a> tags as clickable URL hyperlinks if "html", or LaTeX \href
+            commands if "latex".
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.format: Format the text display value of data cells.
+
+        Notes
+        -----
+        This method assigns a formatting function, ``formatter``, to each level label
+        in the DataFrame's index or column headers. If ``formatter`` is ``None``,
+        then the default formatter is used.
+        If a callable then that function should take a label value as input and return
+        a displayable representation, such as a string. If ``formatter`` is
+        given as a string this is assumed to be a valid Python format specification
+        and is wrapped to a callable as ``string.format(x)``. If a ``dict`` is given,
+        keys should correspond to MultiIndex level numbers or names, and values should
+        be string or callable, as above.
+
+        The default formatter currently expresses floats and complex numbers with the
+        pandas display precision unless using the ``precision`` argument here. The
+        default formatter does not adjust the representation of missing values unless
+        the ``na_rep`` argument is used.
+
+        The ``level`` argument defines which levels of a MultiIndex to apply the
+        method to. If the ``formatter`` argument is given in dict form but does
+        not include all levels within the level argument then these unspecified levels
+        will have the default formatter applied. Any levels in the formatter dict
+        specifically excluded from the level argument will be ignored.
+
+        When using a ``formatter`` string the dtypes must be compatible, otherwise a
+        `ValueError` will be raised.
+
+        .. warning::
+           `Styler.format_index` is ignored when using the output format
+           `Styler.to_excel`, since Excel and Python have inherrently different
+           formatting structures.
+           However, it is possible to use the `number-format` pseudo CSS attribute
+           to force Excel permissible formatting. See documentation for `Styler.format`.
+
+        Examples
+        --------
+        Using ``na_rep`` and ``precision`` with the default ``formatter``
+
+        >>> df = pd.DataFrame([[1, 2, 3]], columns=[2.0, np.nan, 4.0])
+        >>> df.style.format_index(axis=1, na_rep='MISS', precision=3)  # doctest: +SKIP
+            2.000    MISS   4.000
+        0       1       2       3
+
+        Using a ``formatter`` specification on consistent dtypes in a level
+
+        >>> df.style.format_index('{:.2f}', axis=1, na_rep='MISS')  # doctest: +SKIP
+             2.00   MISS    4.00
+        0       1      2       3
+
+        Using the default ``formatter`` for unspecified levels
+
+        >>> df = pd.DataFrame([[1, 2, 3]],
+        ...     columns=pd.MultiIndex.from_arrays([["a", "a", "b"],[2, np.nan, 4]]))
+        >>> df.style.format_index({0: lambda v: v.upper()}, axis=1, precision=1)
+        ...  # doctest: +SKIP
+                       A       B
+              2.0    nan     4.0
+        0       1      2       3
+
+        Using a callable ``formatter`` function.
+
+        >>> func = lambda s: 'STRING' if isinstance(s, str) else 'FLOAT'
+        >>> df.style.format_index(func, axis=1, na_rep='MISS')
+        ...  # doctest: +SKIP
+                  STRING  STRING
+            FLOAT   MISS   FLOAT
+        0       1      2       3
+
+        Using a ``formatter`` with HTML ``escape`` and ``na_rep``.
+
+        >>> df = pd.DataFrame([[1, 2, 3]], columns=['"A"', 'A&B', None])
+        >>> s = df.style.format_index('$ {0}', axis=1, escape="html", na_rep="NA")
+        ...  # doctest: +SKIP
+        <th .. >$ &#34;A&#34;</th>
+        <th .. >$ A&amp;B</th>
+        <th .. >NA</td>
+        ...
+
+        Using a ``formatter`` with LaTeX ``escape``.
+
+        >>> df = pd.DataFrame([[1, 2, 3]], columns=["123", "~", "$%#"])
+        >>> df.style.format_index("\\textbf{{{}}}", escape="latex", axis=1).to_latex()
+        ...  # doctest: +SKIP
+        \begin{tabular}{lrrr}
+        {} & {\textbf{123}} & {\textbf{\textasciitilde }} & {\textbf{\$\%\#}} \\
+        0 & 1 & 2 & 3 \\
+        \end{tabular}
+        """
+        axis = self.data._get_axis_number(axis)
+        if axis == 0:
+            display_funcs_, obj = self._display_funcs_index, self.index
+        else:
+            display_funcs_, obj = self._display_funcs_columns, self.columns
+        levels_ = refactor_levels(level, obj)
+
+        if all(
+            (
+                formatter is None,
+                level is None,
+                precision is None,
+                decimal == ".",
+                thousands is None,
+                na_rep is None,
+                escape is None,
+                hyperlinks is None,
+            )
+        ):
+            display_funcs_.clear()
+            return self  # clear the formatter / revert to default and avoid looping
+
+        if not isinstance(formatter, dict):
+            formatter = {level: formatter for level in levels_}
+        else:
+            formatter = {
+                obj._get_level_number(level): formatter_
+                for level, formatter_ in formatter.items()
+            }
+
+        for lvl in levels_:
+            format_func = _maybe_wrap_formatter(
+                formatter.get(lvl),
+                na_rep=na_rep,
+                precision=precision,
+                decimal=decimal,
+                thousands=thousands,
+                escape=escape,
+                hyperlinks=hyperlinks,
+            )
+
+            for idx in [(i, lvl) if axis == 0 else (lvl, i) for i in range(len(obj))]:
+                display_funcs_[idx] = format_func
+
+        return self
+
+    def relabel_index(
+        self,
+        labels: Sequence | Index,
+        axis: Axis = 0,
+        level: Level | list[Level] | None = None,
+    ) -> StylerRenderer:
+        r"""
+        Relabel the index, or column header, keys to display a set of specified values.
+
+        .. versionadded:: 1.5.0
+
+        Parameters
+        ----------
+        labels : list-like or Index
+            New labels to display. Must have same length as the underlying values not
+            hidden.
+        axis : {"index", 0, "columns", 1}
+            Apply to the index or columns.
+        level : int, str, list, optional
+            The level(s) over which to apply the new labels. If `None` will apply
+            to all levels of an Index or MultiIndex which are not hidden.
+
+        Returns
+        -------
+        Styler
+
+        See Also
+        --------
+        Styler.format_index: Format the text display value of index or column headers.
+        Styler.hide: Hide the index, column headers, or specified data from display.
+
+        Notes
+        -----
+        As part of Styler, this method allows the display of an index to be
+        completely user-specified without affecting the underlying DataFrame data,
+        index, or column headers. This means that the flexibility of indexing is
+        maintained whilst the final display is customisable.
+
+        Since Styler is designed to be progressively constructed with method chaining,
+        this method is adapted to react to the **currently specified hidden elements**.
+        This is useful because it means one does not have to specify all the new
+        labels if the majority of an index, or column headers, have already been hidden.
+        The following produce equivalent display (note the length of ``labels`` in
+        each case).
+
+        .. code-block:: python
+
+            # relabel first, then hide
+            df = pd.DataFrame({"col": ["a", "b", "c"]})
+            df.style.relabel_index(["A", "B", "C"]).hide([0,1])
+            # hide first, then relabel
+            df = pd.DataFrame({"col": ["a", "b", "c"]})
+            df.style.hide([0,1]).relabel_index(["C"])
+
+        This method should be used, rather than :meth:`Styler.format_index`, in one of
+        the following cases (see examples):
+
+          - A specified set of labels are required which are not a function of the
+            underlying index keys.
+          - The function of the underlying index keys requires a counter variable,
+            such as those available upon enumeration.
+
+        Examples
+        --------
+        Basic use
+
+        >>> df = pd.DataFrame({"col": ["a", "b", "c"]})
+        >>> df.style.relabel_index(["A", "B", "C"])  # doctest: +SKIP
+             col
+        A      a
+        B      b
+        C      c
+
+        Chaining with pre-hidden elements
+
+        >>> df.style.hide([0,1]).relabel_index(["C"])  # doctest: +SKIP
+             col
+        C      c
+
+        Using a MultiIndex
+
+        >>> midx = pd.MultiIndex.from_product([[0, 1], [0, 1], [0, 1]])
+        >>> df = pd.DataFrame({"col": list(range(8))}, index=midx)
+        >>> styler = df.style  # doctest: +SKIP
+                  col
+        0  0  0     0
+              1     1
+           1  0     2
+              1     3
+        1  0  0     4
+              1     5
+           1  0     6
+              1     7
+        >>> styler.hide((midx.get_level_values(0)==0)|(midx.get_level_values(1)==0))
+        ...  # doctest: +SKIP
+        >>> styler.hide(level=[0,1])  # doctest: +SKIP
+        >>> styler.relabel_index(["binary6", "binary7"])  # doctest: +SKIP
+                  col
+        binary6     6
+        binary7     7
+
+        We can also achieve the above by indexing first and then re-labeling
+
+        >>> styler = df.loc[[(1,1,0), (1,1,1)]].style
+        >>> styler.hide(level=[0,1]).relabel_index(["binary6", "binary7"])
+        ...  # doctest: +SKIP
+                  col
+        binary6     6
+        binary7     7
+
+        Defining a formatting function which uses an enumeration counter. Also note
+        that the value of the index key is passed in the case of string labels so it
+        can also be inserted into the label, using curly brackets (or double curly
+        brackets if the string if pre-formatted),
+
+        >>> df = pd.DataFrame({"samples": np.random.rand(10)})
+        >>> styler = df.loc[np.random.randint(0,10,3)].style
+        >>> styler.relabel_index([f"sample{i+1} ({{}})" for i in range(3)])
+        ...  # doctest: +SKIP
+                         samples
+        sample1 (5)     0.315811
+        sample2 (0)     0.495941
+        sample3 (2)     0.067946
+        """
+        axis = self.data._get_axis_number(axis)
+        if axis == 0:
+            display_funcs_, obj = self._display_funcs_index, self.index
+            hidden_labels, hidden_lvls = self.hidden_rows, self.hide_index_
+        else:
+            display_funcs_, obj = self._display_funcs_columns, self.columns
+            hidden_labels, hidden_lvls = self.hidden_columns, self.hide_columns_
+        visible_len = len(obj) - len(set(hidden_labels))
+        if len(labels) != visible_len:
+            raise ValueError(
+                "``labels`` must be of length equal to the number of "
+                f"visible labels along ``axis`` ({visible_len})."
+            )
+
+        if level is None:
+            level = [i for i in range(obj.nlevels) if not hidden_lvls[i]]
+        levels_ = refactor_levels(level, obj)
+
+        def alias_(x, value):
+            if isinstance(value, str):
+                return value.format(x)
+            return value
+
+        for ai, i in enumerate([i for i in range(len(obj)) if i not in hidden_labels]):
+            if len(levels_) == 1:
+                idx = (i, levels_[0]) if axis == 0 else (levels_[0], i)
+                display_funcs_[idx] = partial(alias_, value=labels[ai])
+            else:
+                for aj, lvl in enumerate(levels_):
+                    idx = (i, lvl) if axis == 0 else (lvl, i)
+                    display_funcs_[idx] = partial(alias_, value=labels[ai][aj])
+
+        return self
+
+
+def _element(
+    html_element: str,
+    html_class: str | None,
+    value: Any,
+    is_visible: bool,
+    **kwargs,
+) -> dict:
+    """
+    Template to return container with information for a <td></td> or <th></th> element.
+    """
+    if "display_value" not in kwargs:
+        kwargs["display_value"] = value
+    return {
+        "type": html_element,
+        "value": value,
+        "class": html_class,
+        "is_visible": is_visible,
+        **kwargs,
+    }
+
+
+def _get_trimming_maximums(
+    rn,
+    cn,
+    max_elements,
+    max_rows=None,
+    max_cols=None,
+    scaling_factor: float = 0.8,
+) -> tuple[int, int]:
+    """
+    Recursively reduce the number of rows and columns to satisfy max elements.
+
+    Parameters
+    ----------
+    rn, cn : int
+        The number of input rows / columns
+    max_elements : int
+        The number of allowable elements
+    max_rows, max_cols : int, optional
+        Directly specify an initial maximum rows or columns before compression.
+    scaling_factor : float
+        Factor at which to reduce the number of rows / columns to fit.
+
+    Returns
+    -------
+    rn, cn : tuple
+        New rn and cn values that satisfy the max_elements constraint
+    """
+
+    def scale_down(rn, cn):
+        if cn >= rn:
+            return rn, int(cn * scaling_factor)
+        else:
+            return int(rn * scaling_factor), cn
+
+    if max_rows:
+        rn = max_rows if rn > max_rows else rn
+    if max_cols:
+        cn = max_cols if cn > max_cols else cn
+
+    while rn * cn > max_elements:
+        rn, cn = scale_down(rn, cn)
+
+    return rn, cn
+
+
+def _get_level_lengths(
+    index: Index,
+    sparsify: bool,
+    max_index: int,
+    hidden_elements: Sequence[int] | None = None,
+):
+    """
+    Given an index, find the level length for each element.
+
+    Parameters
+    ----------
+    index : Index
+        Index or columns to determine lengths of each element
+    sparsify : bool
+        Whether to hide or show each distinct element in a MultiIndex
+    max_index : int
+        The maximum number of elements to analyse along the index due to trimming
+    hidden_elements : sequence of int
+        Index positions of elements hidden from display in the index affecting
+        length
+
+    Returns
+    -------
+    Dict :
+        Result is a dictionary of (level, initial_position): span
+    """
+    if isinstance(index, MultiIndex):
+        levels = index._format_multi(sparsify=lib.no_default, include_names=False)
+    else:
+        levels = index._format_flat(include_name=False)
+
+    if hidden_elements is None:
+        hidden_elements = []
+
+    lengths = {}
+    if not isinstance(index, MultiIndex):
+        for i, value in enumerate(levels):
+            if i not in hidden_elements:
+                lengths[(0, i)] = 1
+        return lengths
+
+    for i, lvl in enumerate(levels):
+        visible_row_count = 0  # used to break loop due to display trimming
+        for j, row in enumerate(lvl):
+            if visible_row_count > max_index:
+                break
+            if not sparsify:
+                # then lengths will always equal 1 since no aggregation.
+                if j not in hidden_elements:
+                    lengths[(i, j)] = 1
+                    visible_row_count += 1
+            elif (row is not lib.no_default) and (j not in hidden_elements):
+                # this element has not been sparsified so must be the start of section
+                last_label = j
+                lengths[(i, last_label)] = 1
+                visible_row_count += 1
+            elif row is not lib.no_default:
+                # even if the above is hidden, keep track of it in case length > 1 and
+                # later elements are visible
+                last_label = j
+                lengths[(i, last_label)] = 0
+            elif j not in hidden_elements:
+                # then element must be part of sparsified section and is visible
+                visible_row_count += 1
+                if visible_row_count > max_index:
+                    break  # do not add a length since the render trim limit reached
+                if lengths[(i, last_label)] == 0:
+                    # if previous iteration was first-of-section but hidden then offset
+                    last_label = j
+                    lengths[(i, last_label)] = 1
+                else:
+                    # else add to previous iteration
+                    lengths[(i, last_label)] += 1
+
+    non_zero_lengths = {
+        element: length for element, length in lengths.items() if length >= 1
+    }
+
+    return non_zero_lengths
+
+
+def _is_visible(idx_row, idx_col, lengths) -> bool:
+    """
+    Index -> {(idx_row, idx_col): bool}).
+    """
+    return (idx_col, idx_row) in lengths
+
+
+def format_table_styles(styles: CSSStyles) -> CSSStyles:
+    """
+    looks for multiple CSS selectors and separates them:
+    [{'selector': 'td, th', 'props': 'a:v;'}]
+        ---> [{'selector': 'td', 'props': 'a:v;'},
+              {'selector': 'th', 'props': 'a:v;'}]
+    """
+    return [
+        {"selector": selector, "props": css_dict["props"]}
+        for css_dict in styles
+        for selector in css_dict["selector"].split(",")
+    ]
+
+
+def _default_formatter(x: Any, precision: int, thousands: bool = False) -> Any:
+    """
+    Format the display of a value
+
+    Parameters
+    ----------
+    x : Any
+        Input variable to be formatted
+    precision : Int
+        Floating point precision used if ``x`` is float or complex.
+    thousands : bool, default False
+        Whether to group digits with thousands separated with ",".
+
+    Returns
+    -------
+    value : Any
+        Matches input type, or string if input is float or complex or int with sep.
+    """
+    if is_float(x) or is_complex(x):
+        return f"{x:,.{precision}f}" if thousands else f"{x:.{precision}f}"
+    elif is_integer(x):
+        return f"{x:,}" if thousands else str(x)
+    return x
+
+
+def _wrap_decimal_thousands(
+    formatter: Callable, decimal: str, thousands: str | None
+) -> Callable:
+    """
+    Takes a string formatting function and wraps logic to deal with thousands and
+    decimal parameters, in the case that they are non-standard and that the input
+    is a (float, complex, int).
+    """
+
+    def wrapper(x):
+        if is_float(x) or is_integer(x) or is_complex(x):
+            if decimal != "." and thousands is not None and thousands != ",":
+                return (
+                    formatter(x)
+                    .replace(",", "§_§-")  # rare string to avoid "," <-> "." clash.
+                    .replace(".", decimal)
+                    .replace("§_§-", thousands)
+                )
+            elif decimal != "." and (thousands is None or thousands == ","):
+                return formatter(x).replace(".", decimal)
+            elif decimal == "." and thousands is not None and thousands != ",":
+                return formatter(x).replace(",", thousands)
+        return formatter(x)
+
+    return wrapper
+
+
+def _str_escape(x, escape):
+    """if escaping: only use on str, else return input"""
+    if isinstance(x, str):
+        if escape == "html":
+            return escape_html(x)
+        elif escape == "latex":
+            return _escape_latex(x)
+        elif escape == "latex-math":
+            return _escape_latex_math(x)
+        else:
+            raise ValueError(
+                f"`escape` only permitted in {{'html', 'latex', 'latex-math'}}, \
+got {escape}"
+            )
+    return x
+
+
+def _render_href(x, format):
+    """uses regex to detect a common URL pattern and converts to href tag in format."""
+    if isinstance(x, str):
+        if format == "html":
+            href = '<a href="{0}" target="_blank">{0}</a>'
+        elif format == "latex":
+            href = r"\href{{{0}}}{{{0}}}"
+        else:
+            raise ValueError("``hyperlinks`` format can only be 'html' or 'latex'")
+        pat = r"((http|ftp)s?:\/\/|www.)[\w/\-?=%.:@]+\.[\w/\-&?=%.,':;~!@#$*()\[\]]+"
+        return re.sub(pat, lambda m: href.format(m.group(0)), x)
+    return x
+
+
+def _maybe_wrap_formatter(
+    formatter: BaseFormatter | None = None,
+    na_rep: str | None = None,
+    precision: int | None = None,
+    decimal: str = ".",
+    thousands: str | None = None,
+    escape: str | None = None,
+    hyperlinks: str | None = None,
+) -> Callable:
+    """
+    Allows formatters to be expressed as str, callable or None, where None returns
+    a default formatting function. wraps with na_rep, and precision where they are
+    available.
+    """
+    # Get initial func from input string, input callable, or from default factory
+    if isinstance(formatter, str):
+        func_0 = lambda x: formatter.format(x)
+    elif callable(formatter):
+        func_0 = formatter
+    elif formatter is None:
+        precision = (
+            get_option("styler.format.precision") if precision is None else precision
+        )
+        func_0 = partial(
+            _default_formatter, precision=precision, thousands=(thousands is not None)
+        )
+    else:
+        raise TypeError(f"'formatter' expected str or callable, got {type(formatter)}")
+
+    # Replace chars if escaping
+    if escape is not None:
+        func_1 = lambda x: func_0(_str_escape(x, escape=escape))
+    else:
+        func_1 = func_0
+
+    # Replace decimals and thousands if non-standard inputs detected
+    if decimal != "." or (thousands is not None and thousands != ","):
+        func_2 = _wrap_decimal_thousands(func_1, decimal=decimal, thousands=thousands)
+    else:
+        func_2 = func_1
+
+    # Render links
+    if hyperlinks is not None:
+        func_3 = lambda x: func_2(_render_href(x, format=hyperlinks))
+    else:
+        func_3 = func_2
+
+    # Replace missing values if na_rep
+    if na_rep is None:
+        return func_3
+    else:
+        return lambda x: na_rep if (isna(x) is True) else func_3(x)
+
+
+def non_reducing_slice(slice_: Subset):
+    """
+    Ensure that a slice doesn't reduce to a Series or Scalar.
+
+    Any user-passed `subset` should have this called on it
+    to make sure we're always working with DataFrames.
+    """
+    # default to column slice, like DataFrame
+    # ['A', 'B'] -> IndexSlices[:, ['A', 'B']]
+    kinds = (ABCSeries, np.ndarray, Index, list, str)
+    if isinstance(slice_, kinds):
+        slice_ = IndexSlice[:, slice_]
+
+    def pred(part) -> bool:
+        """
+        Returns
+        -------
+        bool
+            True if slice does *not* reduce,
+            False if `part` is a tuple.
+        """
+        # true when slice does *not* reduce, False when part is a tuple,
+        # i.e. MultiIndex slice
+        if isinstance(part, tuple):
+            # GH#39421 check for sub-slice:
+            return any((isinstance(s, slice) or is_list_like(s)) for s in part)
+        else:
+            return isinstance(part, slice) or is_list_like(part)
+
+    if not is_list_like(slice_):
+        if not isinstance(slice_, slice):
+            # a 1-d slice, like df.loc[1]
+            slice_ = [[slice_]]
+        else:
+            # slice(a, b, c)
+            slice_ = [slice_]  # to tuplize later
+    else:
+        # error: Item "slice" of "Union[slice, Sequence[Any]]" has no attribute
+        # "__iter__" (not iterable) -> is specifically list_like in conditional
+        slice_ = [p if pred(p) else [p] for p in slice_]  # type: ignore[union-attr]
+    return tuple(slice_)
+
+
+def maybe_convert_css_to_tuples(style: CSSProperties) -> CSSList:
+    """
+    Convert css-string to sequence of tuples format if needed.
+    'color:red; border:1px solid black;' -> [('color', 'red'),
+                                             ('border','1px solid red')]
+    """
+    if isinstance(style, str):
+        s = style.split(";")
+        try:
+            return [
+                (x.split(":")[0].strip(), x.split(":")[1].strip())
+                for x in s
+                if x.strip() != ""
+            ]
+        except IndexError:
+            raise ValueError(
+                "Styles supplied as string must follow CSS rule formats, "
+                f"for example 'attr: val;'. '{style}' was given."
+            )
+    return style
+
+
+def refactor_levels(
+    level: Level | list[Level] | None,
+    obj: Index,
+) -> list[int]:
+    """
+    Returns a consistent levels arg for use in ``hide_index`` or ``hide_columns``.
+
+    Parameters
+    ----------
+    level : int, str, list
+        Original ``level`` arg supplied to above methods.
+    obj:
+        Either ``self.index`` or ``self.columns``
+
+    Returns
+    -------
+    list : refactored arg with a list of levels to hide
+    """
+    if level is None:
+        levels_: list[int] = list(range(obj.nlevels))
+    elif isinstance(level, int):
+        levels_ = [level]
+    elif isinstance(level, str):
+        levels_ = [obj._get_level_number(level)]
+    elif isinstance(level, list):
+        levels_ = [
+            obj._get_level_number(lev) if not isinstance(lev, int) else lev
+            for lev in level
+        ]
+    else:
+        raise ValueError("`level` must be of type `int`, `str` or list of such")
+    return levels_
+
+
+class Tooltips:
+    """
+    An extension to ``Styler`` that allows for and manipulates tooltips on hover
+    of ``<td>`` cells in the HTML result.
+
+    Parameters
+    ----------
+    css_name: str, default "pd-t"
+        Name of the CSS class that controls visualisation of tooltips.
+    css_props: list-like, default; see Notes
+        List of (attr, value) tuples defining properties of the CSS class.
+    tooltips: DataFrame, default empty
+        DataFrame of strings aligned with underlying Styler data for tooltip
+        display.
+
+    Notes
+    -----
+    The default properties for the tooltip CSS class are:
+
+        - visibility: hidden
+        - position: absolute
+        - z-index: 1
+        - background-color: black
+        - color: white
+        - transform: translate(-20px, -20px)
+
+    Hidden visibility is a key prerequisite to the hover functionality, and should
+    always be included in any manual properties specification.
+    """
+
+    def __init__(
+        self,
+        css_props: CSSProperties = [
+            ("visibility", "hidden"),
+            ("position", "absolute"),
+            ("z-index", 1),
+            ("background-color", "black"),
+            ("color", "white"),
+            ("transform", "translate(-20px, -20px)"),
+        ],
+        css_name: str = "pd-t",
+        tooltips: DataFrame = DataFrame(),
+    ) -> None:
+        self.class_name = css_name
+        self.class_properties = css_props
+        self.tt_data = tooltips
+        self.table_styles: CSSStyles = []
+
+    @property
+    def _class_styles(self):
+        """
+        Combine the ``_Tooltips`` CSS class name and CSS properties to the format
+        required to extend the underlying ``Styler`` `table_styles` to allow
+        tooltips to render in HTML.
+
+        Returns
+        -------
+        styles : List
+        """
+        return [
+            {
+                "selector": f".{self.class_name}",
+                "props": maybe_convert_css_to_tuples(self.class_properties),
+            }
+        ]
+
+    def _pseudo_css(self, uuid: str, name: str, row: int, col: int, text: str):
+        """
+        For every table data-cell that has a valid tooltip (not None, NaN or
+        empty string) must create two pseudo CSS entries for the specific
+        <td> element id which are added to overall table styles:
+        an on hover visibility change and a content change
+        dependent upon the user's chosen display string.
+
+        For example:
+            [{"selector": "T__row1_col1:hover .pd-t",
+             "props": [("visibility", "visible")]},
+            {"selector": "T__row1_col1 .pd-t::after",
+             "props": [("content", "Some Valid Text String")]}]
+
+        Parameters
+        ----------
+        uuid: str
+            The uuid of the Styler instance
+        name: str
+            The css-name of the class used for styling tooltips
+        row : int
+            The row index of the specified tooltip string data
+        col : int
+            The col index of the specified tooltip string data
+        text : str
+            The textual content of the tooltip to be displayed in HTML.
+
+        Returns
+        -------
+        pseudo_css : List
+        """
+        selector_id = "#T_" + uuid + "_row" + str(row) + "_col" + str(col)
+        return [
+            {
+                "selector": selector_id + f":hover .{name}",
+                "props": [("visibility", "visible")],
+            },
+            {
+                "selector": selector_id + f" .{name}::after",
+                "props": [("content", f'"{text}"')],
+            },
+        ]
+
+    def _translate(self, styler: StylerRenderer, d: dict):
+        """
+        Mutate the render dictionary to allow for tooltips:
+
+        - Add ``<span>`` HTML element to each data cells ``display_value``. Ignores
+          headers.
+        - Add table level CSS styles to control pseudo classes.
+
+        Parameters
+        ----------
+        styler_data : DataFrame
+            Underlying ``Styler`` DataFrame used for reindexing.
+        uuid : str
+            The underlying ``Styler`` uuid for CSS id.
+        d : dict
+            The dictionary prior to final render
+
+        Returns
+        -------
+        render_dict : Dict
+        """
+        self.tt_data = self.tt_data.reindex_like(styler.data)
+        if self.tt_data.empty:
+            return d
+
+        name = self.class_name
+        mask = (self.tt_data.isna()) | (self.tt_data.eq(""))  # empty string = no ttip
+        self.table_styles = [
+            style
+            for sublist in [
+                self._pseudo_css(styler.uuid, name, i, j, str(self.tt_data.iloc[i, j]))
+                for i in range(len(self.tt_data.index))
+                for j in range(len(self.tt_data.columns))
+                if not (
+                    mask.iloc[i, j]
+                    or i in styler.hidden_rows
+                    or j in styler.hidden_columns
+                )
+            ]
+            for style in sublist
+        ]
+
+        if self.table_styles:
+            # add span class to every cell only if at least 1 non-empty tooltip
+            for row in d["body"]:
+                for item in row:
+                    if item["type"] == "td":
+                        item["display_value"] = (
+                            str(item["display_value"])
+                            + f'<span class="{self.class_name}"></span>'
+                        )
+            d["table_styles"].extend(self._class_styles)
+            d["table_styles"].extend(self.table_styles)
+
+        return d
+
+
+def _parse_latex_table_wrapping(table_styles: CSSStyles, caption: str | None) -> bool:
+    """
+    Indicate whether LaTeX {tabular} should be wrapped with a {table} environment.
+
+    Parses the `table_styles` and detects any selectors which must be included outside
+    of {tabular}, i.e. indicating that wrapping must occur, and therefore return True,
+    or if a caption exists and requires similar.
+    """
+    IGNORED_WRAPPERS = ["toprule", "midrule", "bottomrule", "column_format"]
+    # ignored selectors are included with {tabular} so do not need wrapping
+    return (
+        table_styles is not None
+        and any(d["selector"] not in IGNORED_WRAPPERS for d in table_styles)
+    ) or caption is not None
+
+
+def _parse_latex_table_styles(table_styles: CSSStyles, selector: str) -> str | None:
+    """
+    Return the first 'props' 'value' from ``tables_styles`` identified by ``selector``.
+
+    Examples
+    --------
+    >>> table_styles = [{'selector': 'foo', 'props': [('attr','value')]},
+    ...                 {'selector': 'bar', 'props': [('attr', 'overwritten')]},
+    ...                 {'selector': 'bar', 'props': [('a1', 'baz'), ('a2', 'ignore')]}]
+    >>> _parse_latex_table_styles(table_styles, selector='bar')
+    'baz'
+
+    Notes
+    -----
+    The replacement of "§" with ":" is to avoid the CSS problem where ":" has structural
+    significance and cannot be used in LaTeX labels, but is often required by them.
+    """
+    for style in table_styles[::-1]:  # in reverse for most recently applied style
+        if style["selector"] == selector:
+            return str(style["props"][0][1]).replace("§", ":")
+    return None
+
+
+def _parse_latex_cell_styles(
+    latex_styles: CSSList, display_value: str, convert_css: bool = False
+) -> str:
+    r"""
+    Mutate the ``display_value`` string including LaTeX commands from ``latex_styles``.
+
+    This method builds a recursive latex chain of commands based on the
+    CSSList input, nested around ``display_value``.
+
+    If a CSS style is given as ('<command>', '<options>') this is translated to
+    '\<command><options>{display_value}', and this value is treated as the
+    display value for the next iteration.
+
+    The most recent style forms the inner component, for example for styles:
+    `[('c1', 'o1'), ('c2', 'o2')]` this returns: `\c1o1{\c2o2{display_value}}`
+
+    Sometimes latex commands have to be wrapped with curly braces in different ways:
+    We create some parsing flags to identify the different behaviours:
+
+     - `--rwrap`        : `\<command><options>{<display_value>}`
+     - `--wrap`         : `{\<command><options> <display_value>}`
+     - `--nowrap`       : `\<command><options> <display_value>`
+     - `--lwrap`        : `{\<command><options>} <display_value>`
+     - `--dwrap`        : `{\<command><options>}{<display_value>}`
+
+    For example for styles:
+    `[('c1', 'o1--wrap'), ('c2', 'o2')]` this returns: `{\c1o1 \c2o2{display_value}}
+    """
+    if convert_css:
+        latex_styles = _parse_latex_css_conversion(latex_styles)
+    for command, options in latex_styles[::-1]:  # in reverse for most recent style
+        formatter = {
+            "--wrap": f"{{\\{command}--to_parse {display_value}}}",
+            "--nowrap": f"\\{command}--to_parse {display_value}",
+            "--lwrap": f"{{\\{command}--to_parse}} {display_value}",
+            "--rwrap": f"\\{command}--to_parse{{{display_value}}}",
+            "--dwrap": f"{{\\{command}--to_parse}}{{{display_value}}}",
+        }
+        display_value = f"\\{command}{options} {display_value}"
+        for arg in ["--nowrap", "--wrap", "--lwrap", "--rwrap", "--dwrap"]:
+            if arg in str(options):
+                display_value = formatter[arg].replace(
+                    "--to_parse", _parse_latex_options_strip(value=options, arg=arg)
+                )
+                break  # only ever one purposeful entry
+    return display_value
+
+
+def _parse_latex_header_span(
+    cell: dict[str, Any],
+    multirow_align: str,
+    multicol_align: str,
+    wrap: bool = False,
+    convert_css: bool = False,
+) -> str:
+    r"""
+    Refactor the cell `display_value` if a 'colspan' or 'rowspan' attribute is present.
+
+    'rowspan' and 'colspan' do not occur simultaneouly. If they are detected then
+    the `display_value` is altered to a LaTeX `multirow` or `multicol` command
+    respectively, with the appropriate cell-span.
+
+    ``wrap`` is used to enclose the `display_value` in braces which is needed for
+    column headers using an siunitx package.
+
+    Requires the package {multirow}, whereas multicol support is usually built in
+    to the {tabular} environment.
+
+    Examples
+    --------
+    >>> cell = {'cellstyle': '', 'display_value':'text', 'attributes': 'colspan="3"'}
+    >>> _parse_latex_header_span(cell, 't', 'c')
+    '\\multicolumn{3}{c}{text}'
+    """
+    display_val = _parse_latex_cell_styles(
+        cell["cellstyle"], cell["display_value"], convert_css
+    )
+    if "attributes" in cell:
+        attrs = cell["attributes"]
+        if 'colspan="' in attrs:
+            colspan = attrs[attrs.find('colspan="') + 9 :]  # len('colspan="') = 9
+            colspan = int(colspan[: colspan.find('"')])
+            if "naive-l" == multicol_align:
+                out = f"{{{display_val}}}" if wrap else f"{display_val}"
+                blanks = " & {}" if wrap else " &"
+                return out + blanks * (colspan - 1)
+            elif "naive-r" == multicol_align:
+                out = f"{{{display_val}}}" if wrap else f"{display_val}"
+                blanks = "{} & " if wrap else "& "
+                return blanks * (colspan - 1) + out
+            return f"\\multicolumn{{{colspan}}}{{{multicol_align}}}{{{display_val}}}"
+        elif 'rowspan="' in attrs:
+            if multirow_align == "naive":
+                return display_val
+            rowspan = attrs[attrs.find('rowspan="') + 9 :]
+            rowspan = int(rowspan[: rowspan.find('"')])
+            return f"\\multirow[{multirow_align}]{{{rowspan}}}{{*}}{{{display_val}}}"
+    if wrap:
+        return f"{{{display_val}}}"
+    else:
+        return display_val
+
+
+def _parse_latex_options_strip(value: str | float, arg: str) -> str:
+    """
+    Strip a css_value which may have latex wrapping arguments, css comment identifiers,
+    and whitespaces, to a valid string for latex options parsing.
+
+    For example: 'red /* --wrap */  ' --> 'red'
+    """
+    return str(value).replace(arg, "").replace("/*", "").replace("*/", "").strip()
+
+
+def _parse_latex_css_conversion(styles: CSSList) -> CSSList:
+    """
+    Convert CSS (attribute,value) pairs to equivalent LaTeX (command,options) pairs.
+
+    Ignore conversion if tagged with `--latex` option, skipped if no conversion found.
+    """
+
+    def font_weight(value, arg):
+        if value in ("bold", "bolder"):
+            return "bfseries", f"{arg}"
+        return None
+
+    def font_style(value, arg):
+        if value == "italic":
+            return "itshape", f"{arg}"
+        if value == "oblique":
+            return "slshape", f"{arg}"
+        return None
+
+    def color(value, user_arg, command, comm_arg):
+        """
+        CSS colors have 5 formats to process:
+
+         - 6 digit hex code: "#ff23ee"     --> [HTML]{FF23EE}
+         - 3 digit hex code: "#f0e"        --> [HTML]{FF00EE}
+         - rgba: rgba(128, 255, 0, 0.5)    --> [rgb]{0.502, 1.000, 0.000}
+         - rgb: rgb(128, 255, 0,)          --> [rbg]{0.502, 1.000, 0.000}
+         - string: red                     --> {red}
+
+        Additionally rgb or rgba can be expressed in % which is also parsed.
+        """
+        arg = user_arg if user_arg != "" else comm_arg
+
+        if value[0] == "#" and len(value) == 7:  # color is hex code
+            return command, f"[HTML]{{{value[1:].upper()}}}{arg}"
+        if value[0] == "#" and len(value) == 4:  # color is short hex code
+            val = f"{value[1].upper()*2}{value[2].upper()*2}{value[3].upper()*2}"
+            return command, f"[HTML]{{{val}}}{arg}"
+        elif value[:3] == "rgb":  # color is rgb or rgba
+            r = re.findall("(?<=\\()[0-9\\s%]+(?=,)", value)[0].strip()
+            r = float(r[:-1]) / 100 if "%" in r else int(r) / 255
+            g = re.findall("(?<=,)[0-9\\s%]+(?=,)", value)[0].strip()
+            g = float(g[:-1]) / 100 if "%" in g else int(g) / 255
+            if value[3] == "a":  # color is rgba
+                b = re.findall("(?<=,)[0-9\\s%]+(?=,)", value)[1].strip()
+            else:  # color is rgb
+                b = re.findall("(?<=,)[0-9\\s%]+(?=\\))", value)[0].strip()
+            b = float(b[:-1]) / 100 if "%" in b else int(b) / 255
+            return command, f"[rgb]{{{r:.3f}, {g:.3f}, {b:.3f}}}{arg}"
+        else:
+            return command, f"{{{value}}}{arg}"  # color is likely string-named
+
+    CONVERTED_ATTRIBUTES: dict[str, Callable] = {
+        "font-weight": font_weight,
+        "background-color": partial(color, command="cellcolor", comm_arg="--lwrap"),
+        "color": partial(color, command="color", comm_arg=""),
+        "font-style": font_style,
+    }
+
+    latex_styles: CSSList = []
+    for attribute, value in styles:
+        if isinstance(value, str) and "--latex" in value:
+            # return the style without conversion but drop '--latex'
+            latex_styles.append((attribute, value.replace("--latex", "")))
+        if attribute in CONVERTED_ATTRIBUTES:
+            arg = ""
+            for x in ["--wrap", "--nowrap", "--lwrap", "--dwrap", "--rwrap"]:
+                if x in str(value):
+                    arg, value = x, _parse_latex_options_strip(value, x)
+                    break
+            latex_style = CONVERTED_ATTRIBUTES[attribute](value, arg)
+            if latex_style is not None:
+                latex_styles.extend([latex_style])
+    return latex_styles
+
+
+def _escape_latex(s: str) -> str:
+    r"""
+    Replace the characters ``&``, ``%``, ``$``, ``#``, ``_``, ``{``, ``}``,
+    ``~``, ``^``, and ``\`` in the string with LaTeX-safe sequences.
+
+    Use this if you need to display text that might contain such characters in LaTeX.
+
+    Parameters
+    ----------
+    s : str
+        Input to be escaped
+
+    Return
+    ------
+    str :
+        Escaped string
+    """
+    return (
+        s.replace("\\", "ab2§=§8yz")  # rare string for final conversion: avoid \\ clash
+        .replace("ab2§=§8yz ", "ab2§=§8yz\\space ")  # since \backslash gobbles spaces
+        .replace("&", "\\&")
+        .replace("%", "\\%")
+        .replace("$", "\\$")
+        .replace("#", "\\#")
+        .replace("_", "\\_")
+        .replace("{", "\\{")
+        .replace("}", "\\}")
+        .replace("~ ", "~\\space ")  # since \textasciitilde gobbles spaces
+        .replace("~", "\\textasciitilde ")
+        .replace("^ ", "^\\space ")  # since \textasciicircum gobbles spaces
+        .replace("^", "\\textasciicircum ")
+        .replace("ab2§=§8yz", "\\textbackslash ")
+    )
+
+
+def _math_mode_with_dollar(s: str) -> str:
+    r"""
+    All characters in LaTeX math mode are preserved.
+
+    The substrings in LaTeX math mode, which start with
+    the character ``$`` and end with ``$``, are preserved
+    without escaping. Otherwise regular LaTeX escaping applies.
+
+    Parameters
+    ----------
+    s : str
+        Input to be escaped
+
+    Return
+    ------
+    str :
+        Escaped string
+    """
+    s = s.replace(r"\$", r"rt8§=§7wz")
+    pattern = re.compile(r"\$.*?\$")
+    pos = 0
+    ps = pattern.search(s, pos)
+    res = []
+    while ps:
+        res.append(_escape_latex(s[pos : ps.span()[0]]))
+        res.append(ps.group())
+        pos = ps.span()[1]
+        ps = pattern.search(s, pos)
+
+    res.append(_escape_latex(s[pos : len(s)]))
+    return "".join(res).replace(r"rt8§=§7wz", r"\$")
+
+
+def _math_mode_with_parentheses(s: str) -> str:
+    r"""
+    All characters in LaTeX math mode are preserved.
+
+    The substrings in LaTeX math mode, which start with
+    the character ``\(`` and end with ``\)``, are preserved
+    without escaping. Otherwise regular LaTeX escaping applies.
+
+    Parameters
+    ----------
+    s : str
+        Input to be escaped
+
+    Return
+    ------
+    str :
+        Escaped string
+    """
+    s = s.replace(r"\(", r"LEFT§=§6yzLEFT").replace(r"\)", r"RIGHTab5§=§RIGHT")
+    res = []
+    for item in re.split(r"LEFT§=§6yz|ab5§=§RIGHT", s):
+        if item.startswith("LEFT") and item.endswith("RIGHT"):
+            res.append(item.replace("LEFT", r"\(").replace("RIGHT", r"\)"))
+        elif "LEFT" in item and "RIGHT" in item:
+            res.append(
+                _escape_latex(item).replace("LEFT", r"\(").replace("RIGHT", r"\)")
+            )
+        else:
+            res.append(
+                _escape_latex(item)
+                .replace("LEFT", r"\textbackslash (")
+                .replace("RIGHT", r"\textbackslash )")
+            )
+    return "".join(res)
+
+
+def _escape_latex_math(s: str) -> str:
+    r"""
+    All characters in LaTeX math mode are preserved.
+
+    The substrings in LaTeX math mode, which either are surrounded
+    by two characters ``$`` or start with the character ``\(`` and end with ``\)``,
+    are preserved without escaping. Otherwise regular LaTeX escaping applies.
+
+    Parameters
+    ----------
+    s : str
+        Input to be escaped
+
+    Return
+    ------
+    str :
+        Escaped string
+    """
+    s = s.replace(r"\$", r"rt8§=§7wz")
+    ps_d = re.compile(r"\$.*?\$").search(s, 0)
+    ps_p = re.compile(r"\(.*?\)").search(s, 0)
+    mode = []
+    if ps_d:
+        mode.append(ps_d.span()[0])
+    if ps_p:
+        mode.append(ps_p.span()[0])
+    if len(mode) == 0:
+        return _escape_latex(s.replace(r"rt8§=§7wz", r"\$"))
+    if s[mode[0]] == r"$":
+        return _math_mode_with_dollar(s.replace(r"rt8§=§7wz", r"\$"))
+    if s[mode[0] - 1 : mode[0] + 1] == r"\(":
+        return _math_mode_with_parentheses(s.replace(r"rt8§=§7wz", r"\$"))
+    else:
+        return _escape_latex(s.replace(r"rt8§=§7wz", r"\$"))