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llmeval-env/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"]

llmeval-env/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",
+]

llmeval-env/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

llmeval-env/lib/python3.10/site-packages/pandas/io/excel/__init__.py

@@ -0,0 +1,19 @@
+from pandas.io.excel._base import (
+    ExcelFile,
+    ExcelWriter,
+    read_excel,
+)
+from pandas.io.excel._odswriter import ODSWriter as _ODSWriter
+from pandas.io.excel._openpyxl import OpenpyxlWriter as _OpenpyxlWriter
+from pandas.io.excel._util import register_writer
+from pandas.io.excel._xlsxwriter import XlsxWriter as _XlsxWriter
+
+__all__ = ["read_excel", "ExcelWriter", "ExcelFile"]
+
+
+register_writer(_OpenpyxlWriter)
+
+register_writer(_XlsxWriter)
+
+
+register_writer(_ODSWriter)

llmeval-env/lib/python3.10/site-packages/pandas/io/excel/_base.py

@@ -0,0 +1,1659 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    Mapping,
+    Sequence,
+)
+import datetime
+from functools import partial
+from io import BytesIO
+import os
+from textwrap import fill
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Generic,
+    Literal,
+    TypeVar,
+    Union,
+    cast,
+    overload,
+)
+import warnings
+import zipfile
+
+from pandas._config import config
+
+from pandas._libs import lib
+from pandas._libs.parsers import STR_NA_VALUES
+from pandas.compat._optional import (
+    get_version,
+    import_optional_dependency,
+)
+from pandas.errors import EmptyDataError
+from pandas.util._decorators import (
+    Appender,
+    doc,
+)
+from pandas.util._exceptions import find_stack_level
+from pandas.util._validators import check_dtype_backend
+
+from pandas.core.dtypes.common import (
+    is_bool,
+    is_float,
+    is_integer,
+    is_list_like,
+)
+
+from pandas.core.frame import DataFrame
+from pandas.core.shared_docs import _shared_docs
+from pandas.util.version import Version
+
+from pandas.io.common import (
+    IOHandles,
+    get_handle,
+    stringify_path,
+    validate_header_arg,
+)
+from pandas.io.excel._util import (
+    fill_mi_header,
+    get_default_engine,
+    get_writer,
+    maybe_convert_usecols,
+    pop_header_name,
+)
+from pandas.io.parsers import TextParser
+from pandas.io.parsers.readers import validate_integer
+
+if TYPE_CHECKING:
+    from types import TracebackType
+
+    from pandas._typing import (
+        DtypeArg,
+        DtypeBackend,
+        ExcelWriterIfSheetExists,
+        FilePath,
+        IntStrT,
+        ReadBuffer,
+        Self,
+        SequenceNotStr,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+_read_excel_doc = (
+    """
+Read an Excel file into a ``pandas`` ``DataFrame``.
+
+Supports `xls`, `xlsx`, `xlsm`, `xlsb`, `odf`, `ods` and `odt` file extensions
+read from a local filesystem or URL. Supports an option to read
+a single sheet or a list of sheets.
+
+Parameters
+----------
+io : str, bytes, ExcelFile, xlrd.Book, path object, or file-like object
+    Any valid string path is acceptable. 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.xlsx``.
+
+    If you want to pass in a path object, pandas accepts any ``os.PathLike``.
+
+    By file-like object, we refer to objects with a ``read()`` method,
+    such as a file handle (e.g. via builtin ``open`` function)
+    or ``StringIO``.
+
+    .. deprecated:: 2.1.0
+        Passing byte strings is deprecated. To read from a
+        byte string, wrap it in a ``BytesIO`` object.
+sheet_name : str, int, list, or None, default 0
+    Strings are used for sheet names. Integers are used in zero-indexed
+    sheet positions (chart sheets do not count as a sheet position).
+    Lists of strings/integers are used to request multiple sheets.
+    Specify ``None`` to get all worksheets.
+
+    Available cases:
+
+    * Defaults to ``0``: 1st sheet as a `DataFrame`
+    * ``1``: 2nd sheet as a `DataFrame`
+    * ``"Sheet1"``: Load sheet with name "Sheet1"
+    * ``[0, 1, "Sheet5"]``: Load first, second and sheet named "Sheet5"
+      as a dict of `DataFrame`
+    * ``None``: All worksheets.
+
+header : int, list of int, default 0
+    Row (0-indexed) to use for the column labels of the parsed
+    DataFrame. If a list of integers is passed those row positions will
+    be combined into a ``MultiIndex``. Use None if there is no header.
+names : array-like, default None
+    List of column names to use. If file contains no header row,
+    then you should explicitly pass header=None.
+index_col : int, str, list of int, default None
+    Column (0-indexed) to use as the row labels of the DataFrame.
+    Pass None if there is no such column.  If a list is passed,
+    those columns will be combined into a ``MultiIndex``.  If a
+    subset of data is selected with ``usecols``, index_col
+    is based on the subset.
+
+    Missing values will be forward filled to allow roundtripping with
+    ``to_excel`` for ``merged_cells=True``. To avoid forward filling the
+    missing values use ``set_index`` after reading the data instead of
+    ``index_col``.
+usecols : str, list-like, or callable, default None
+    * If None, then parse all columns.
+    * If str, then indicates comma separated list of Excel column letters
+      and column ranges (e.g. "A:E" or "A,C,E:F"). Ranges are inclusive of
+      both sides.
+    * If list of int, then indicates list of column numbers to be parsed
+      (0-indexed).
+    * If list of string, then indicates list of column names to be parsed.
+    * If callable, then evaluate each column name against it and parse the
+      column if the callable returns ``True``.
+
+    Returns a subset of the columns according to behavior above.
+dtype : Type name or dict of column -> type, default None
+    Data type for data or columns. E.g. {{'a': np.float64, 'b': np.int32}}
+    Use ``object`` to preserve data as stored in Excel and not interpret dtype,
+    which will necessarily result in ``object`` dtype.
+    If converters are specified, they will be applied INSTEAD
+    of dtype conversion.
+    If you use ``None``, it will infer the dtype of each column based on the data.
+engine : {{'openpyxl', 'calamine', 'odf', 'pyxlsb', 'xlrd'}}, default None
+    If io is not a buffer or path, this must be set to identify io.
+    Engine compatibility :
+
+    - ``openpyxl`` supports newer Excel file formats.
+    - ``calamine`` supports Excel (.xls, .xlsx, .xlsm, .xlsb)
+      and OpenDocument (.ods) file formats.
+    - ``odf`` supports OpenDocument file formats (.odf, .ods, .odt).
+    - ``pyxlsb`` supports Binary Excel files.
+    - ``xlrd`` supports old-style Excel files (.xls).
+
+    When ``engine=None``, the following logic will be used to determine the engine:
+
+    - If ``path_or_buffer`` is an OpenDocument format (.odf, .ods, .odt),
+      then `odf <https://pypi.org/project/odfpy/>`_ will be used.
+    - Otherwise if ``path_or_buffer`` is an xls format, ``xlrd`` will be used.
+    - Otherwise if ``path_or_buffer`` is in xlsb format, ``pyxlsb`` will be used.
+    - Otherwise ``openpyxl`` will be used.
+converters : dict, default None
+    Dict of functions for converting values in certain columns. Keys can
+    either be integers or column labels, values are functions that take one
+    input argument, the Excel cell content, and return the transformed
+    content.
+true_values : list, default None
+    Values to consider as True.
+false_values : list, default None
+    Values to consider as False.
+skiprows : list-like, int, or callable, optional
+    Line numbers to skip (0-indexed) or number of lines to skip (int) at the
+    start of the file. If callable, the callable function will be evaluated
+    against the row indices, returning True if the row should be skipped and
+    False otherwise. An example of a valid callable argument would be ``lambda
+    x: x in [0, 2]``.
+nrows : int, default None
+    Number of rows to parse.
+na_values : scalar, str, list-like, or dict, default None
+    Additional strings to recognize as NA/NaN. If dict passed, specific
+    per-column NA values. By default the following values are interpreted
+    as NaN: '"""
+    + fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent="    ")
+    + """'.
+keep_default_na : bool, default True
+    Whether or not to include the default NaN values when parsing the data.
+    Depending on whether ``na_values`` is passed in, the behavior is as follows:
+
+    * If ``keep_default_na`` is True, and ``na_values`` are specified,
+      ``na_values`` is appended to the default NaN values used for parsing.
+    * If ``keep_default_na`` is True, and ``na_values`` are not specified, only
+      the default NaN values are used for parsing.
+    * If ``keep_default_na`` is False, and ``na_values`` are specified, only
+      the NaN values specified ``na_values`` are used for parsing.
+    * If ``keep_default_na`` is False, and ``na_values`` are not specified, no
+      strings will be parsed as NaN.
+
+    Note that if `na_filter` is passed in as False, the ``keep_default_na`` and
+    ``na_values`` parameters will be ignored.
+na_filter : bool, default True
+    Detect missing value markers (empty strings and the value of na_values). In
+    data without any NAs, passing ``na_filter=False`` can improve the
+    performance of reading a large file.
+verbose : bool, default False
+    Indicate number of NA values placed in non-numeric columns.
+parse_dates : bool, list-like, or dict, default False
+    The behavior is as follows:
+
+    * ``bool``. If True -> try parsing the index.
+    * ``list`` of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
+      each as a separate date column.
+    * ``list`` of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
+      a single date column.
+    * ``dict``, e.g. {{'foo' : [1, 3]}} -> parse columns 1, 3 as date and call
+      result 'foo'
+
+    If a column or index contains an unparsable date, the entire column or
+    index will be returned unaltered as an object data type. If you don`t want to
+    parse some cells as date just change their type in Excel to "Text".
+    For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_excel``.
+
+    Note: A fast-path exists for iso8601-formatted dates.
+date_parser : function, optional
+    Function to use for converting a sequence of string columns to an array of
+    datetime instances. The default uses ``dateutil.parser.parser`` to do the
+    conversion. Pandas will try to call `date_parser` in three different ways,
+    advancing to the next if an exception occurs: 1) Pass one or more arrays
+    (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
+    string values from the columns defined by `parse_dates` into a single array
+    and pass that; and 3) call `date_parser` once for each row using one or
+    more strings (corresponding to the columns defined by `parse_dates`) as
+    arguments.
+
+    .. deprecated:: 2.0.0
+       Use ``date_format`` instead, or read in as ``object`` and then apply
+       :func:`to_datetime` as-needed.
+date_format : str or dict of column -> format, default ``None``
+   If used in conjunction with ``parse_dates``, will parse dates according to this
+   format. For anything more complex,
+   please read in as ``object`` and then apply :func:`to_datetime` as-needed.
+
+   .. versionadded:: 2.0.0
+thousands : str, default None
+    Thousands separator for parsing string columns to numeric.  Note that
+    this parameter is only necessary for columns stored as TEXT in Excel,
+    any numeric columns will automatically be parsed, regardless of display
+    format.
+decimal : str, default '.'
+    Character to recognize as decimal point for parsing string columns to numeric.
+    Note that this parameter is only necessary for columns stored as TEXT in Excel,
+    any numeric columns will automatically be parsed, regardless of display
+    format.(e.g. use ',' for European data).
+
+    .. versionadded:: 1.4.0
+
+comment : str, default None
+    Comments out remainder of line. Pass a character or characters to this
+    argument to indicate comments in the input file. Any data between the
+    comment string and the end of the current line is ignored.
+skipfooter : int, default 0
+    Rows at the end to skip (0-indexed).
+{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
+
+engine_kwargs : dict, optional
+    Arbitrary keyword arguments passed to excel engine.
+
+Returns
+-------
+DataFrame or dict of DataFrames
+    DataFrame from the passed in Excel file. See notes in sheet_name
+    argument for more information on when a dict of DataFrames is returned.
+
+See Also
+--------
+DataFrame.to_excel : Write DataFrame to an Excel file.
+DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
+read_csv : Read a comma-separated values (csv) file into DataFrame.
+read_fwf : Read a table of fixed-width formatted lines into DataFrame.
+
+Notes
+-----
+For specific information on the methods used for each Excel engine, refer to the pandas
+:ref:`user guide <io.excel_reader>`
+
+Examples
+--------
+The file can be read using the file name as string or an open file object:
+
+>>> pd.read_excel('tmp.xlsx', index_col=0)  # doctest: +SKIP
+       Name  Value
+0   string1      1
+1   string2      2
+2  #Comment      3
+
+>>> pd.read_excel(open('tmp.xlsx', 'rb'),
+...               sheet_name='Sheet3')  # doctest: +SKIP
+   Unnamed: 0      Name  Value
+0           0   string1      1
+1           1   string2      2
+2           2  #Comment      3
+
+Index and header can be specified via the `index_col` and `header` arguments
+
+>>> pd.read_excel('tmp.xlsx', index_col=None, header=None)  # doctest: +SKIP
+     0         1      2
+0  NaN      Name  Value
+1  0.0   string1      1
+2  1.0   string2      2
+3  2.0  #Comment      3
+
+Column types are inferred but can be explicitly specified
+
+>>> pd.read_excel('tmp.xlsx', index_col=0,
+...               dtype={{'Name': str, 'Value': float}})  # doctest: +SKIP
+       Name  Value
+0   string1    1.0
+1   string2    2.0
+2  #Comment    3.0
+
+True, False, and NA values, and thousands separators have defaults,
+but can be explicitly specified, too. Supply the values you would like
+as strings or lists of strings!
+
+>>> pd.read_excel('tmp.xlsx', index_col=0,
+...               na_values=['string1', 'string2'])  # doctest: +SKIP
+       Name  Value
+0       NaN      1
+1       NaN      2
+2  #Comment      3
+
+Comment lines in the excel input file can be skipped using the
+``comment`` kwarg.
+
+>>> pd.read_excel('tmp.xlsx', index_col=0, comment='#')  # doctest: +SKIP
+      Name  Value
+0  string1    1.0
+1  string2    2.0
+2     None    NaN
+"""
+)
+
+
+@overload
+def read_excel(
+    io,
+    # sheet name is str or int -> DataFrame
+    sheet_name: str | int = ...,
+    *,
+    header: int | Sequence[int] | None = ...,
+    names: SequenceNotStr[Hashable] | range | None = ...,
+    index_col: int | str | Sequence[int] | None = ...,
+    usecols: int
+    | str
+    | Sequence[int]
+    | Sequence[str]
+    | Callable[[str], bool]
+    | None = ...,
+    dtype: DtypeArg | None = ...,
+    engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb", "calamine"] | None = ...,
+    converters: dict[str, Callable] | dict[int, Callable] | None = ...,
+    true_values: Iterable[Hashable] | None = ...,
+    false_values: Iterable[Hashable] | None = ...,
+    skiprows: Sequence[int] | int | Callable[[int], object] | None = ...,
+    nrows: int | None = ...,
+    na_values=...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool = ...,
+    parse_dates: list | dict | bool = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: dict[Hashable, str] | str | None = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    comment: str | None = ...,
+    skipfooter: int = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> DataFrame:
+    ...
+
+
+@overload
+def read_excel(
+    io,
+    # sheet name is list or None -> dict[IntStrT, DataFrame]
+    sheet_name: list[IntStrT] | None,
+    *,
+    header: int | Sequence[int] | None = ...,
+    names: SequenceNotStr[Hashable] | range | None = ...,
+    index_col: int | str | Sequence[int] | None = ...,
+    usecols: int
+    | str
+    | Sequence[int]
+    | Sequence[str]
+    | Callable[[str], bool]
+    | None = ...,
+    dtype: DtypeArg | None = ...,
+    engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb", "calamine"] | None = ...,
+    converters: dict[str, Callable] | dict[int, Callable] | None = ...,
+    true_values: Iterable[Hashable] | None = ...,
+    false_values: Iterable[Hashable] | None = ...,
+    skiprows: Sequence[int] | int | Callable[[int], object] | None = ...,
+    nrows: int | None = ...,
+    na_values=...,
+    keep_default_na: bool = ...,
+    na_filter: bool = ...,
+    verbose: bool = ...,
+    parse_dates: list | dict | bool = ...,
+    date_parser: Callable | lib.NoDefault = ...,
+    date_format: dict[Hashable, str] | str | None = ...,
+    thousands: str | None = ...,
+    decimal: str = ...,
+    comment: str | None = ...,
+    skipfooter: int = ...,
+    storage_options: StorageOptions = ...,
+    dtype_backend: DtypeBackend | lib.NoDefault = ...,
+) -> dict[IntStrT, DataFrame]:
+    ...
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+@Appender(_read_excel_doc)
+def read_excel(
+    io,
+    sheet_name: str | int | list[IntStrT] | None = 0,
+    *,
+    header: int | Sequence[int] | None = 0,
+    names: SequenceNotStr[Hashable] | range | None = None,
+    index_col: int | str | Sequence[int] | None = None,
+    usecols: int
+    | str
+    | Sequence[int]
+    | Sequence[str]
+    | Callable[[str], bool]
+    | None = None,
+    dtype: DtypeArg | None = None,
+    engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb", "calamine"] | None = None,
+    converters: dict[str, Callable] | dict[int, Callable] | None = None,
+    true_values: Iterable[Hashable] | None = None,
+    false_values: Iterable[Hashable] | None = None,
+    skiprows: Sequence[int] | int | Callable[[int], object] | None = None,
+    nrows: int | None = None,
+    na_values=None,
+    keep_default_na: bool = True,
+    na_filter: bool = True,
+    verbose: bool = False,
+    parse_dates: list | dict | bool = False,
+    date_parser: Callable | lib.NoDefault = lib.no_default,
+    date_format: dict[Hashable, str] | str | None = None,
+    thousands: str | None = None,
+    decimal: str = ".",
+    comment: str | None = None,
+    skipfooter: int = 0,
+    storage_options: StorageOptions | None = None,
+    dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+    engine_kwargs: dict | None = None,
+) -> DataFrame | dict[IntStrT, DataFrame]:
+    check_dtype_backend(dtype_backend)
+    should_close = False
+    if engine_kwargs is None:
+        engine_kwargs = {}
+
+    if not isinstance(io, ExcelFile):
+        should_close = True
+        io = ExcelFile(
+            io,
+            storage_options=storage_options,
+            engine=engine,
+            engine_kwargs=engine_kwargs,
+        )
+    elif engine and engine != io.engine:
+        raise ValueError(
+            "Engine should not be specified when passing "
+            "an ExcelFile - ExcelFile already has the engine set"
+        )
+
+    try:
+        data = io.parse(
+            sheet_name=sheet_name,
+            header=header,
+            names=names,
+            index_col=index_col,
+            usecols=usecols,
+            dtype=dtype,
+            converters=converters,
+            true_values=true_values,
+            false_values=false_values,
+            skiprows=skiprows,
+            nrows=nrows,
+            na_values=na_values,
+            keep_default_na=keep_default_na,
+            na_filter=na_filter,
+            verbose=verbose,
+            parse_dates=parse_dates,
+            date_parser=date_parser,
+            date_format=date_format,
+            thousands=thousands,
+            decimal=decimal,
+            comment=comment,
+            skipfooter=skipfooter,
+            dtype_backend=dtype_backend,
+        )
+    finally:
+        # make sure to close opened file handles
+        if should_close:
+            io.close()
+    return data
+
+
+_WorkbookT = TypeVar("_WorkbookT")
+
+
+class BaseExcelReader(Generic[_WorkbookT]):
+    book: _WorkbookT
+
+    def __init__(
+        self,
+        filepath_or_buffer,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        if engine_kwargs is None:
+            engine_kwargs = {}
+
+        # First argument can also be bytes, so create a buffer
+        if isinstance(filepath_or_buffer, bytes):
+            filepath_or_buffer = BytesIO(filepath_or_buffer)
+
+        self.handles = IOHandles(
+            handle=filepath_or_buffer, compression={"method": None}
+        )
+        if not isinstance(filepath_or_buffer, (ExcelFile, self._workbook_class)):
+            self.handles = get_handle(
+                filepath_or_buffer, "rb", storage_options=storage_options, is_text=False
+            )
+
+        if isinstance(self.handles.handle, self._workbook_class):
+            self.book = self.handles.handle
+        elif hasattr(self.handles.handle, "read"):
+            # N.B. xlrd.Book has a read attribute too
+            self.handles.handle.seek(0)
+            try:
+                self.book = self.load_workbook(self.handles.handle, engine_kwargs)
+            except Exception:
+                self.close()
+                raise
+        else:
+            raise ValueError(
+                "Must explicitly set engine if not passing in buffer or path for io."
+            )
+
+    @property
+    def _workbook_class(self) -> type[_WorkbookT]:
+        raise NotImplementedError
+
+    def load_workbook(self, filepath_or_buffer, engine_kwargs) -> _WorkbookT:
+        raise NotImplementedError
+
+    def close(self) -> None:
+        if hasattr(self, "book"):
+            if hasattr(self.book, "close"):
+                # pyxlsb: opens a TemporaryFile
+                # openpyxl: https://stackoverflow.com/questions/31416842/
+                #     openpyxl-does-not-close-excel-workbook-in-read-only-mode
+                self.book.close()
+            elif hasattr(self.book, "release_resources"):
+                # xlrd
+                # https://github.com/python-excel/xlrd/blob/2.0.1/xlrd/book.py#L548
+                self.book.release_resources()
+        self.handles.close()
+
+    @property
+    def sheet_names(self) -> list[str]:
+        raise NotImplementedError
+
+    def get_sheet_by_name(self, name: str):
+        raise NotImplementedError
+
+    def get_sheet_by_index(self, index: int):
+        raise NotImplementedError
+
+    def get_sheet_data(self, sheet, rows: int | None = None):
+        raise NotImplementedError
+
+    def raise_if_bad_sheet_by_index(self, index: int) -> None:
+        n_sheets = len(self.sheet_names)
+        if index >= n_sheets:
+            raise ValueError(
+                f"Worksheet index {index} is invalid, {n_sheets} worksheets found"
+            )
+
+    def raise_if_bad_sheet_by_name(self, name: str) -> None:
+        if name not in self.sheet_names:
+            raise ValueError(f"Worksheet named '{name}' not found")
+
+    def _check_skiprows_func(
+        self,
+        skiprows: Callable,
+        rows_to_use: int,
+    ) -> int:
+        """
+        Determine how many file rows are required to obtain `nrows` data
+        rows when `skiprows` is a function.
+
+        Parameters
+        ----------
+        skiprows : function
+            The function passed to read_excel by the user.
+        rows_to_use : int
+            The number of rows that will be needed for the header and
+            the data.
+
+        Returns
+        -------
+        int
+        """
+        i = 0
+        rows_used_so_far = 0
+        while rows_used_so_far < rows_to_use:
+            if not skiprows(i):
+                rows_used_so_far += 1
+            i += 1
+        return i
+
+    def _calc_rows(
+        self,
+        header: int | Sequence[int] | None,
+        index_col: int | Sequence[int] | None,
+        skiprows: Sequence[int] | int | Callable[[int], object] | None,
+        nrows: int | None,
+    ) -> int | None:
+        """
+        If nrows specified, find the number of rows needed from the
+        file, otherwise return None.
+
+
+        Parameters
+        ----------
+        header : int, list of int, or None
+            See read_excel docstring.
+        index_col : int, str, list of int, or None
+            See read_excel docstring.
+        skiprows : list-like, int, callable, or None
+            See read_excel docstring.
+        nrows : int or None
+            See read_excel docstring.
+
+        Returns
+        -------
+        int or None
+        """
+        if nrows is None:
+            return None
+        if header is None:
+            header_rows = 1
+        elif is_integer(header):
+            header = cast(int, header)
+            header_rows = 1 + header
+        else:
+            header = cast(Sequence, header)
+            header_rows = 1 + header[-1]
+        # If there is a MultiIndex header and an index then there is also
+        # a row containing just the index name(s)
+        if is_list_like(header) and index_col is not None:
+            header = cast(Sequence, header)
+            if len(header) > 1:
+                header_rows += 1
+        if skiprows is None:
+            return header_rows + nrows
+        if is_integer(skiprows):
+            skiprows = cast(int, skiprows)
+            return header_rows + nrows + skiprows
+        if is_list_like(skiprows):
+
+            def f(skiprows: Sequence, x: int) -> bool:
+                return x in skiprows
+
+            skiprows = cast(Sequence, skiprows)
+            return self._check_skiprows_func(partial(f, skiprows), header_rows + nrows)
+        if callable(skiprows):
+            return self._check_skiprows_func(
+                skiprows,
+                header_rows + nrows,
+            )
+        # else unexpected skiprows type: read_excel will not optimize
+        # the number of rows read from file
+        return None
+
+    def parse(
+        self,
+        sheet_name: str | int | list[int] | list[str] | None = 0,
+        header: int | Sequence[int] | None = 0,
+        names: SequenceNotStr[Hashable] | range | None = None,
+        index_col: int | Sequence[int] | None = None,
+        usecols=None,
+        dtype: DtypeArg | None = None,
+        true_values: Iterable[Hashable] | None = None,
+        false_values: Iterable[Hashable] | None = None,
+        skiprows: Sequence[int] | int | Callable[[int], object] | None = None,
+        nrows: int | None = None,
+        na_values=None,
+        verbose: bool = False,
+        parse_dates: list | dict | bool = False,
+        date_parser: Callable | lib.NoDefault = lib.no_default,
+        date_format: dict[Hashable, str] | str | None = None,
+        thousands: str | None = None,
+        decimal: str = ".",
+        comment: str | None = None,
+        skipfooter: int = 0,
+        dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+        **kwds,
+    ):
+        validate_header_arg(header)
+        validate_integer("nrows", nrows)
+
+        ret_dict = False
+
+        # Keep sheetname to maintain backwards compatibility.
+        sheets: list[int] | list[str]
+        if isinstance(sheet_name, list):
+            sheets = sheet_name
+            ret_dict = True
+        elif sheet_name is None:
+            sheets = self.sheet_names
+            ret_dict = True
+        elif isinstance(sheet_name, str):
+            sheets = [sheet_name]
+        else:
+            sheets = [sheet_name]
+
+        # handle same-type duplicates.
+        sheets = cast(Union[list[int], list[str]], list(dict.fromkeys(sheets).keys()))
+
+        output = {}
+
+        last_sheetname = None
+        for asheetname in sheets:
+            last_sheetname = asheetname
+            if verbose:
+                print(f"Reading sheet {asheetname}")
+
+            if isinstance(asheetname, str):
+                sheet = self.get_sheet_by_name(asheetname)
+            else:  # assume an integer if not a string
+                sheet = self.get_sheet_by_index(asheetname)
+
+            file_rows_needed = self._calc_rows(header, index_col, skiprows, nrows)
+            data = self.get_sheet_data(sheet, file_rows_needed)
+            if hasattr(sheet, "close"):
+                # pyxlsb opens two TemporaryFiles
+                sheet.close()
+            usecols = maybe_convert_usecols(usecols)
+
+            if not data:
+                output[asheetname] = DataFrame()
+                continue
+
+            is_list_header = False
+            is_len_one_list_header = False
+            if is_list_like(header):
+                assert isinstance(header, Sequence)
+                is_list_header = True
+                if len(header) == 1:
+                    is_len_one_list_header = True
+
+            if is_len_one_list_header:
+                header = cast(Sequence[int], header)[0]
+
+            # forward fill and pull out names for MultiIndex column
+            header_names = None
+            if header is not None and is_list_like(header):
+                assert isinstance(header, Sequence)
+
+                header_names = []
+                control_row = [True] * len(data[0])
+
+                for row in header:
+                    if is_integer(skiprows):
+                        assert isinstance(skiprows, int)
+                        row += skiprows
+
+                    if row > len(data) - 1:
+                        raise ValueError(
+                            f"header index {row} exceeds maximum index "
+                            f"{len(data) - 1} of data.",
+                        )
+
+                    data[row], control_row = fill_mi_header(data[row], control_row)
+
+                    if index_col is not None:
+                        header_name, _ = pop_header_name(data[row], index_col)
+                        header_names.append(header_name)
+
+            # If there is a MultiIndex header and an index then there is also
+            # a row containing just the index name(s)
+            has_index_names = False
+            if is_list_header and not is_len_one_list_header and index_col is not None:
+                index_col_list: Sequence[int]
+                if isinstance(index_col, int):
+                    index_col_list = [index_col]
+                else:
+                    assert isinstance(index_col, Sequence)
+                    index_col_list = index_col
+
+                # We have to handle mi without names. If any of the entries in the data
+                # columns are not empty, this is a regular row
+                assert isinstance(header, Sequence)
+                if len(header) < len(data):
+                    potential_index_names = data[len(header)]
+                    potential_data = [
+                        x
+                        for i, x in enumerate(potential_index_names)
+                        if not control_row[i] and i not in index_col_list
+                    ]
+                    has_index_names = all(x == "" or x is None for x in potential_data)
+
+            if is_list_like(index_col):
+                # Forward fill values for MultiIndex index.
+                if header is None:
+                    offset = 0
+                elif isinstance(header, int):
+                    offset = 1 + header
+                else:
+                    offset = 1 + max(header)
+
+                # GH34673: if MultiIndex names present and not defined in the header,
+                # offset needs to be incremented so that forward filling starts
+                # from the first MI value instead of the name
+                if has_index_names:
+                    offset += 1
+
+                # Check if we have an empty dataset
+                # before trying to collect data.
+                if offset < len(data):
+                    assert isinstance(index_col, Sequence)
+
+                    for col in index_col:
+                        last = data[offset][col]
+
+                        for row in range(offset + 1, len(data)):
+                            if data[row][col] == "" or data[row][col] is None:
+                                data[row][col] = last
+                            else:
+                                last = data[row][col]
+
+            # GH 12292 : error when read one empty column from excel file
+            try:
+                parser = TextParser(
+                    data,
+                    names=names,
+                    header=header,
+                    index_col=index_col,
+                    has_index_names=has_index_names,
+                    dtype=dtype,
+                    true_values=true_values,
+                    false_values=false_values,
+                    skiprows=skiprows,
+                    nrows=nrows,
+                    na_values=na_values,
+                    skip_blank_lines=False,  # GH 39808
+                    parse_dates=parse_dates,
+                    date_parser=date_parser,
+                    date_format=date_format,
+                    thousands=thousands,
+                    decimal=decimal,
+                    comment=comment,
+                    skipfooter=skipfooter,
+                    usecols=usecols,
+                    dtype_backend=dtype_backend,
+                    **kwds,
+                )
+
+                output[asheetname] = parser.read(nrows=nrows)
+
+                if header_names:
+                    output[asheetname].columns = output[asheetname].columns.set_names(
+                        header_names
+                    )
+
+            except EmptyDataError:
+                # No Data, return an empty DataFrame
+                output[asheetname] = DataFrame()
+
+            except Exception as err:
+                err.args = (f"{err.args[0]} (sheet: {asheetname})", *err.args[1:])
+                raise err
+
+        if last_sheetname is None:
+            raise ValueError("Sheet name is an empty list")
+
+        if ret_dict:
+            return output
+        else:
+            return output[last_sheetname]
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+class ExcelWriter(Generic[_WorkbookT]):
+    """
+    Class for writing DataFrame objects into excel sheets.
+
+    Default is to use:
+
+    * `xlsxwriter <https://pypi.org/project/XlsxWriter/>`__ for xlsx files if xlsxwriter
+      is installed otherwise `openpyxl <https://pypi.org/project/openpyxl/>`__
+    * `odswriter <https://pypi.org/project/odswriter/>`__ for ods files
+
+    See ``DataFrame.to_excel`` for typical usage.
+
+    The writer should be used as a context manager. Otherwise, call `close()` to save
+    and close any opened file handles.
+
+    Parameters
+    ----------
+    path : str or typing.BinaryIO
+        Path to xls or xlsx or ods file.
+    engine : str (optional)
+        Engine to use for writing. If None, defaults to
+        ``io.excel.<extension>.writer``.  NOTE: can only be passed as a keyword
+        argument.
+    date_format : str, default None
+        Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
+    datetime_format : str, default None
+        Format string for datetime objects written into Excel files.
+        (e.g. 'YYYY-MM-DD HH:MM:SS').
+    mode : {{'w', 'a'}}, default 'w'
+        File mode to use (write or append). Append does not work with fsspec URLs.
+    {storage_options}
+
+    if_sheet_exists : {{'error', 'new', 'replace', 'overlay'}}, default 'error'
+        How to behave when trying to write to a sheet that already
+        exists (append mode only).
+
+        * error: raise a ValueError.
+        * new: Create a new sheet, with a name determined by the engine.
+        * replace: Delete the contents of the sheet before writing to it.
+        * overlay: Write contents to the existing sheet without first removing,
+          but possibly over top of, the existing contents.
+
+        .. versionadded:: 1.3.0
+
+        .. versionchanged:: 1.4.0
+
+           Added ``overlay`` option
+
+    engine_kwargs : dict, optional
+        Keyword arguments to be passed into the engine. These will be passed to
+        the following functions of the respective engines:
+
+        * xlsxwriter: ``xlsxwriter.Workbook(file, **engine_kwargs)``
+        * openpyxl (write mode): ``openpyxl.Workbook(**engine_kwargs)``
+        * openpyxl (append mode): ``openpyxl.load_workbook(file, **engine_kwargs)``
+        * odswriter: ``odf.opendocument.OpenDocumentSpreadsheet(**engine_kwargs)``
+
+        .. versionadded:: 1.3.0
+
+    Notes
+    -----
+    For compatibility with CSV writers, ExcelWriter serializes lists
+    and dicts to strings before writing.
+
+    Examples
+    --------
+    Default usage:
+
+    >>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])  # doctest: +SKIP
+    >>> with pd.ExcelWriter("path_to_file.xlsx") as writer:
+    ...     df.to_excel(writer)  # doctest: +SKIP
+
+    To write to separate sheets in a single file:
+
+    >>> df1 = pd.DataFrame([["AAA", "BBB"]], columns=["Spam", "Egg"])  # doctest: +SKIP
+    >>> df2 = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])  # doctest: +SKIP
+    >>> with pd.ExcelWriter("path_to_file.xlsx") as writer:
+    ...     df1.to_excel(writer, sheet_name="Sheet1")  # doctest: +SKIP
+    ...     df2.to_excel(writer, sheet_name="Sheet2")  # doctest: +SKIP
+
+    You can set the date format or datetime format:
+
+    >>> from datetime import date, datetime  # doctest: +SKIP
+    >>> df = pd.DataFrame(
+    ...     [
+    ...         [date(2014, 1, 31), date(1999, 9, 24)],
+    ...         [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)],
+    ...     ],
+    ...     index=["Date", "Datetime"],
+    ...     columns=["X", "Y"],
+    ... )  # doctest: +SKIP
+    >>> with pd.ExcelWriter(
+    ...     "path_to_file.xlsx",
+    ...     date_format="YYYY-MM-DD",
+    ...     datetime_format="YYYY-MM-DD HH:MM:SS"
+    ... ) as writer:
+    ...     df.to_excel(writer)  # doctest: +SKIP
+
+    You can also append to an existing Excel file:
+
+    >>> with pd.ExcelWriter("path_to_file.xlsx", mode="a", engine="openpyxl") as writer:
+    ...     df.to_excel(writer, sheet_name="Sheet3")  # doctest: +SKIP
+
+    Here, the `if_sheet_exists` parameter can be set to replace a sheet if it
+    already exists:
+
+    >>> with ExcelWriter(
+    ...     "path_to_file.xlsx",
+    ...     mode="a",
+    ...     engine="openpyxl",
+    ...     if_sheet_exists="replace",
+    ... ) as writer:
+    ...     df.to_excel(writer, sheet_name="Sheet1")  # doctest: +SKIP
+
+    You can also write multiple DataFrames to a single sheet. Note that the
+    ``if_sheet_exists`` parameter needs to be set to ``overlay``:
+
+    >>> with ExcelWriter("path_to_file.xlsx",
+    ...     mode="a",
+    ...     engine="openpyxl",
+    ...     if_sheet_exists="overlay",
+    ... ) as writer:
+    ...     df1.to_excel(writer, sheet_name="Sheet1")
+    ...     df2.to_excel(writer, sheet_name="Sheet1", startcol=3)  # doctest: +SKIP
+
+    You can store Excel file in RAM:
+
+    >>> import io
+    >>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])
+    >>> buffer = io.BytesIO()
+    >>> with pd.ExcelWriter(buffer) as writer:
+    ...     df.to_excel(writer)
+
+    You can pack Excel file into zip archive:
+
+    >>> import zipfile  # doctest: +SKIP
+    >>> df = pd.DataFrame([["ABC", "XYZ"]], columns=["Foo", "Bar"])  # doctest: +SKIP
+    >>> with zipfile.ZipFile("path_to_file.zip", "w") as zf:
+    ...     with zf.open("filename.xlsx", "w") as buffer:
+    ...         with pd.ExcelWriter(buffer) as writer:
+    ...             df.to_excel(writer)  # doctest: +SKIP
+
+    You can specify additional arguments to the underlying engine:
+
+    >>> with pd.ExcelWriter(
+    ...     "path_to_file.xlsx",
+    ...     engine="xlsxwriter",
+    ...     engine_kwargs={{"options": {{"nan_inf_to_errors": True}}}}
+    ... ) as writer:
+    ...     df.to_excel(writer)  # doctest: +SKIP
+
+    In append mode, ``engine_kwargs`` are passed through to
+    openpyxl's ``load_workbook``:
+
+    >>> with pd.ExcelWriter(
+    ...     "path_to_file.xlsx",
+    ...     engine="openpyxl",
+    ...     mode="a",
+    ...     engine_kwargs={{"keep_vba": True}}
+    ... ) as writer:
+    ...     df.to_excel(writer, sheet_name="Sheet2")  # doctest: +SKIP
+    """
+
+    # Defining an ExcelWriter implementation (see abstract methods for more...)
+
+    # - Mandatory
+    #   - ``write_cells(self, cells, sheet_name=None, startrow=0, startcol=0)``
+    #     --> called to write additional DataFrames to disk
+    #   - ``_supported_extensions`` (tuple of supported extensions), used to
+    #      check that engine supports the given extension.
+    #   - ``_engine`` - string that gives the engine name. Necessary to
+    #     instantiate class directly and bypass ``ExcelWriterMeta`` engine
+    #     lookup.
+    #   - ``save(self)`` --> called to save file to disk
+    # - Mostly mandatory (i.e. should at least exist)
+    #   - book, cur_sheet, path
+
+    # - Optional:
+    #   - ``__init__(self, path, engine=None, **kwargs)`` --> always called
+    #     with path as first argument.
+
+    # You also need to register the class with ``register_writer()``.
+    # Technically, ExcelWriter implementations don't need to subclass
+    # ExcelWriter.
+
+    _engine: str
+    _supported_extensions: tuple[str, ...]
+
+    def __new__(
+        cls,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format: str | None = None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> Self:
+        # only switch class if generic(ExcelWriter)
+        if cls is ExcelWriter:
+            if engine is None or (isinstance(engine, str) and engine == "auto"):
+                if isinstance(path, str):
+                    ext = os.path.splitext(path)[-1][1:]
+                else:
+                    ext = "xlsx"
+
+                try:
+                    engine = config.get_option(f"io.excel.{ext}.writer", silent=True)
+                    if engine == "auto":
+                        engine = get_default_engine(ext, mode="writer")
+                except KeyError as err:
+                    raise ValueError(f"No engine for filetype: '{ext}'") from err
+
+            # for mypy
+            assert engine is not None
+            #  error: Incompatible types in assignment (expression has type
+            #  "type[ExcelWriter[Any]]", variable has type "type[Self]")
+            cls = get_writer(engine)  # type: ignore[assignment]
+
+        return object.__new__(cls)
+
+    # declare external properties you can count on
+    _path = None
+
+    @property
+    def supported_extensions(self) -> tuple[str, ...]:
+        """Extensions that writer engine supports."""
+        return self._supported_extensions
+
+    @property
+    def engine(self) -> str:
+        """Name of engine."""
+        return self._engine
+
+    @property
+    def sheets(self) -> dict[str, Any]:
+        """Mapping of sheet names to sheet objects."""
+        raise NotImplementedError
+
+    @property
+    def book(self) -> _WorkbookT:
+        """
+        Book instance. Class type will depend on the engine used.
+
+        This attribute can be used to access engine-specific features.
+        """
+        raise NotImplementedError
+
+    def _write_cells(
+        self,
+        cells,
+        sheet_name: str | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+    ) -> None:
+        """
+        Write given formatted cells into Excel an excel sheet
+
+        Parameters
+        ----------
+        cells : generator
+            cell of formatted data to save to Excel sheet
+        sheet_name : str, default None
+            Name of Excel sheet, if None, then use self.cur_sheet
+        startrow : upper left cell row to dump data frame
+        startcol : upper left cell column to dump data frame
+        freeze_panes: int tuple of length 2
+            contains the bottom-most row and right-most column to freeze
+        """
+        raise NotImplementedError
+
+    def _save(self) -> None:
+        """
+        Save workbook to disk.
+        """
+        raise NotImplementedError
+
+    def __init__(
+        self,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format: str | None = None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+    ) -> None:
+        # validate that this engine can handle the extension
+        if isinstance(path, str):
+            ext = os.path.splitext(path)[-1]
+            self.check_extension(ext)
+
+        # use mode to open the file
+        if "b" not in mode:
+            mode += "b"
+        # use "a" for the user to append data to excel but internally use "r+" to let
+        # the excel backend first read the existing file and then write any data to it
+        mode = mode.replace("a", "r+")
+
+        if if_sheet_exists not in (None, "error", "new", "replace", "overlay"):
+            raise ValueError(
+                f"'{if_sheet_exists}' is not valid for if_sheet_exists. "
+                "Valid options are 'error', 'new', 'replace' and 'overlay'."
+            )
+        if if_sheet_exists and "r+" not in mode:
+            raise ValueError("if_sheet_exists is only valid in append mode (mode='a')")
+        if if_sheet_exists is None:
+            if_sheet_exists = "error"
+        self._if_sheet_exists = if_sheet_exists
+
+        # cast ExcelWriter to avoid adding 'if self._handles is not None'
+        self._handles = IOHandles(
+            cast(IO[bytes], path), compression={"compression": None}
+        )
+        if not isinstance(path, ExcelWriter):
+            self._handles = get_handle(
+                path, mode, storage_options=storage_options, is_text=False
+            )
+        self._cur_sheet = None
+
+        if date_format is None:
+            self._date_format = "YYYY-MM-DD"
+        else:
+            self._date_format = date_format
+        if datetime_format is None:
+            self._datetime_format = "YYYY-MM-DD HH:MM:SS"
+        else:
+            self._datetime_format = datetime_format
+
+        self._mode = mode
+
+    @property
+    def date_format(self) -> str:
+        """
+        Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
+        """
+        return self._date_format
+
+    @property
+    def datetime_format(self) -> str:
+        """
+        Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
+        """
+        return self._datetime_format
+
+    @property
+    def if_sheet_exists(self) -> str:
+        """
+        How to behave when writing to a sheet that already exists in append mode.
+        """
+        return self._if_sheet_exists
+
+    def __fspath__(self) -> str:
+        return getattr(self._handles.handle, "name", "")
+
+    def _get_sheet_name(self, sheet_name: str | None) -> str:
+        if sheet_name is None:
+            sheet_name = self._cur_sheet
+        if sheet_name is None:  # pragma: no cover
+            raise ValueError("Must pass explicit sheet_name or set _cur_sheet property")
+        return sheet_name
+
+    def _value_with_fmt(
+        self, val
+    ) -> tuple[
+        int | float | bool | str | datetime.datetime | datetime.date, str | None
+    ]:
+        """
+        Convert numpy types to Python types for the Excel writers.
+
+        Parameters
+        ----------
+        val : object
+            Value to be written into cells
+
+        Returns
+        -------
+        Tuple with the first element being the converted value and the second
+            being an optional format
+        """
+        fmt = None
+
+        if is_integer(val):
+            val = int(val)
+        elif is_float(val):
+            val = float(val)
+        elif is_bool(val):
+            val = bool(val)
+        elif isinstance(val, datetime.datetime):
+            fmt = self._datetime_format
+        elif isinstance(val, datetime.date):
+            fmt = self._date_format
+        elif isinstance(val, datetime.timedelta):
+            val = val.total_seconds() / 86400
+            fmt = "0"
+        else:
+            val = str(val)
+
+        return val, fmt
+
+    @classmethod
+    def check_extension(cls, ext: str) -> Literal[True]:
+        """
+        checks that path's extension against the Writer's supported
+        extensions.  If it isn't supported, raises UnsupportedFiletypeError.
+        """
+        if ext.startswith("."):
+            ext = ext[1:]
+        if not any(ext in extension for extension in cls._supported_extensions):
+            raise ValueError(f"Invalid extension for engine '{cls.engine}': '{ext}'")
+        return True
+
+    # Allow use as a contextmanager
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()
+
+    def close(self) -> None:
+        """synonym for save, to make it more file-like"""
+        self._save()
+        self._handles.close()
+
+
+XLS_SIGNATURES = (
+    b"\x09\x00\x04\x00\x07\x00\x10\x00",  # BIFF2
+    b"\x09\x02\x06\x00\x00\x00\x10\x00",  # BIFF3
+    b"\x09\x04\x06\x00\x00\x00\x10\x00",  # BIFF4
+    b"\xD0\xCF\x11\xE0\xA1\xB1\x1A\xE1",  # Compound File Binary
+)
+ZIP_SIGNATURE = b"PK\x03\x04"
+PEEK_SIZE = max(map(len, XLS_SIGNATURES + (ZIP_SIGNATURE,)))
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+def inspect_excel_format(
+    content_or_path: FilePath | ReadBuffer[bytes],
+    storage_options: StorageOptions | None = None,
+) -> str | None:
+    """
+    Inspect the path or content of an excel file and get its format.
+
+    Adopted from xlrd: https://github.com/python-excel/xlrd.
+
+    Parameters
+    ----------
+    content_or_path : str or file-like object
+        Path to file or content of file to inspect. May be a URL.
+    {storage_options}
+
+    Returns
+    -------
+    str or None
+        Format of file if it can be determined.
+
+    Raises
+    ------
+    ValueError
+        If resulting stream is empty.
+    BadZipFile
+        If resulting stream does not have an XLS signature and is not a valid zipfile.
+    """
+    if isinstance(content_or_path, bytes):
+        content_or_path = BytesIO(content_or_path)
+
+    with get_handle(
+        content_or_path, "rb", storage_options=storage_options, is_text=False
+    ) as handle:
+        stream = handle.handle
+        stream.seek(0)
+        buf = stream.read(PEEK_SIZE)
+        if buf is None:
+            raise ValueError("stream is empty")
+        assert isinstance(buf, bytes)
+        peek = buf
+        stream.seek(0)
+
+        if any(peek.startswith(sig) for sig in XLS_SIGNATURES):
+            return "xls"
+        elif not peek.startswith(ZIP_SIGNATURE):
+            return None
+
+        with zipfile.ZipFile(stream) as zf:
+            # Workaround for some third party files that use forward slashes and
+            # lower case names.
+            component_names = [
+                name.replace("\\", "/").lower() for name in zf.namelist()
+            ]
+
+        if "xl/workbook.xml" in component_names:
+            return "xlsx"
+        if "xl/workbook.bin" in component_names:
+            return "xlsb"
+        if "content.xml" in component_names:
+            return "ods"
+        return "zip"
+
+
+class ExcelFile:
+    """
+    Class for parsing tabular Excel sheets into DataFrame objects.
+
+    See read_excel for more documentation.
+
+    Parameters
+    ----------
+    path_or_buffer : str, bytes, path object (pathlib.Path or py._path.local.LocalPath),
+        A file-like object, xlrd workbook or openpyxl workbook.
+        If a string or path object, expected to be a path to a
+        .xls, .xlsx, .xlsb, .xlsm, .odf, .ods, or .odt file.
+    engine : str, default None
+        If io is not a buffer or path, this must be set to identify io.
+        Supported engines: ``xlrd``, ``openpyxl``, ``odf``, ``pyxlsb``, ``calamine``
+        Engine compatibility :
+
+        - ``xlrd`` supports old-style Excel files (.xls).
+        - ``openpyxl`` supports newer Excel file formats.
+        - ``odf`` supports OpenDocument file formats (.odf, .ods, .odt).
+        - ``pyxlsb`` supports Binary Excel files.
+        - ``calamine`` supports Excel (.xls, .xlsx, .xlsm, .xlsb)
+          and OpenDocument (.ods) file formats.
+
+        .. versionchanged:: 1.2.0
+
+           The engine `xlrd <https://xlrd.readthedocs.io/en/latest/>`_
+           now only supports old-style ``.xls`` files.
+           When ``engine=None``, the following logic will be
+           used to determine the engine:
+
+           - If ``path_or_buffer`` is an OpenDocument format (.odf, .ods, .odt),
+             then `odf <https://pypi.org/project/odfpy/>`_ will be used.
+           - Otherwise if ``path_or_buffer`` is an xls format,
+             ``xlrd`` will be used.
+           - Otherwise if ``path_or_buffer`` is in xlsb format,
+             `pyxlsb <https://pypi.org/project/pyxlsb/>`_ will be used.
+
+           .. versionadded:: 1.3.0
+
+           - Otherwise if `openpyxl <https://pypi.org/project/openpyxl/>`_ is installed,
+             then ``openpyxl`` will be used.
+           - Otherwise if ``xlrd >= 2.0`` is installed, a ``ValueError`` will be raised.
+
+           .. warning::
+
+            Please do not report issues when using ``xlrd`` to read ``.xlsx`` files.
+            This is not supported, switch to using ``openpyxl`` instead.
+    engine_kwargs : dict, optional
+        Arbitrary keyword arguments passed to excel engine.
+
+    Examples
+    --------
+    >>> file = pd.ExcelFile('myfile.xlsx')  # doctest: +SKIP
+    >>> with pd.ExcelFile("myfile.xls") as xls:  # doctest: +SKIP
+    ...     df1 = pd.read_excel(xls, "Sheet1")  # doctest: +SKIP
+    """
+
+    from pandas.io.excel._calamine import CalamineReader
+    from pandas.io.excel._odfreader import ODFReader
+    from pandas.io.excel._openpyxl import OpenpyxlReader
+    from pandas.io.excel._pyxlsb import PyxlsbReader
+    from pandas.io.excel._xlrd import XlrdReader
+
+    _engines: Mapping[str, Any] = {
+        "xlrd": XlrdReader,
+        "openpyxl": OpenpyxlReader,
+        "odf": ODFReader,
+        "pyxlsb": PyxlsbReader,
+        "calamine": CalamineReader,
+    }
+
+    def __init__(
+        self,
+        path_or_buffer,
+        engine: str | None = None,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        if engine_kwargs is None:
+            engine_kwargs = {}
+
+        if engine is not None and engine not in self._engines:
+            raise ValueError(f"Unknown engine: {engine}")
+
+        # First argument can also be bytes, so create a buffer
+        if isinstance(path_or_buffer, bytes):
+            path_or_buffer = BytesIO(path_or_buffer)
+            warnings.warn(
+                "Passing bytes to 'read_excel' is deprecated and "
+                "will be removed in a future version. To read from a "
+                "byte string, wrap it in a `BytesIO` object.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+
+        # Could be a str, ExcelFile, Book, etc.
+        self.io = path_or_buffer
+        # Always a string
+        self._io = stringify_path(path_or_buffer)
+
+        # Determine xlrd version if installed
+        if import_optional_dependency("xlrd", errors="ignore") is None:
+            xlrd_version = None
+        else:
+            import xlrd
+
+            xlrd_version = Version(get_version(xlrd))
+
+        if engine is None:
+            # Only determine ext if it is needed
+            ext: str | None
+            if xlrd_version is not None and isinstance(path_or_buffer, xlrd.Book):
+                ext = "xls"
+            else:
+                ext = inspect_excel_format(
+                    content_or_path=path_or_buffer, storage_options=storage_options
+                )
+                if ext is None:
+                    raise ValueError(
+                        "Excel file format cannot be determined, you must specify "
+                        "an engine manually."
+                    )
+
+            engine = config.get_option(f"io.excel.{ext}.reader", silent=True)
+            if engine == "auto":
+                engine = get_default_engine(ext, mode="reader")
+
+        assert engine is not None
+        self.engine = engine
+        self.storage_options = storage_options
+
+        self._reader = self._engines[engine](
+            self._io,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    def __fspath__(self):
+        return self._io
+
+    def parse(
+        self,
+        sheet_name: str | int | list[int] | list[str] | None = 0,
+        header: int | Sequence[int] | None = 0,
+        names: SequenceNotStr[Hashable] | range | None = None,
+        index_col: int | Sequence[int] | None = None,
+        usecols=None,
+        converters=None,
+        true_values: Iterable[Hashable] | None = None,
+        false_values: Iterable[Hashable] | None = None,
+        skiprows: Sequence[int] | int | Callable[[int], object] | None = None,
+        nrows: int | None = None,
+        na_values=None,
+        parse_dates: list | dict | bool = False,
+        date_parser: Callable | lib.NoDefault = lib.no_default,
+        date_format: str | dict[Hashable, str] | None = None,
+        thousands: str | None = None,
+        comment: str | None = None,
+        skipfooter: int = 0,
+        dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
+        **kwds,
+    ) -> DataFrame | dict[str, DataFrame] | dict[int, DataFrame]:
+        """
+        Parse specified sheet(s) into a DataFrame.
+
+        Equivalent to read_excel(ExcelFile, ...)  See the read_excel
+        docstring for more info on accepted parameters.
+
+        Returns
+        -------
+        DataFrame or dict of DataFrames
+            DataFrame from the passed in Excel file.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
+        >>> df.to_excel('myfile.xlsx')  # doctest: +SKIP
+        >>> file = pd.ExcelFile('myfile.xlsx')  # doctest: +SKIP
+        >>> file.parse()  # doctest: +SKIP
+        """
+        return self._reader.parse(
+            sheet_name=sheet_name,
+            header=header,
+            names=names,
+            index_col=index_col,
+            usecols=usecols,
+            converters=converters,
+            true_values=true_values,
+            false_values=false_values,
+            skiprows=skiprows,
+            nrows=nrows,
+            na_values=na_values,
+            parse_dates=parse_dates,
+            date_parser=date_parser,
+            date_format=date_format,
+            thousands=thousands,
+            comment=comment,
+            skipfooter=skipfooter,
+            dtype_backend=dtype_backend,
+            **kwds,
+        )
+
+    @property
+    def book(self):
+        return self._reader.book
+
+    @property
+    def sheet_names(self):
+        return self._reader.sheet_names
+
+    def close(self) -> None:
+        """close io if necessary"""
+        self._reader.close()
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self.close()

llmeval-env/lib/python3.10/site-packages/pandas/io/excel/_calamine.py

@@ -0,0 +1,121 @@
+from __future__ import annotations
+
+from datetime import (
+    date,
+    datetime,
+    time,
+    timedelta,
+)
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Union,
+)
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+import pandas as pd
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from python_calamine import (
+        CalamineSheet,
+        CalamineWorkbook,
+    )
+
+    from pandas._typing import (
+        FilePath,
+        NaTType,
+        ReadBuffer,
+        Scalar,
+        StorageOptions,
+    )
+
+_CellValue = Union[int, float, str, bool, time, date, datetime, timedelta]
+
+
+class CalamineReader(BaseExcelReader["CalamineWorkbook"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using calamine engine (xlsx/xls/xlsb/ods).
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path to be parsed or
+            an open readable stream.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        import_optional_dependency("python_calamine")
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[CalamineWorkbook]:
+        from python_calamine import CalamineWorkbook
+
+        return CalamineWorkbook
+
+    def load_workbook(
+        self, filepath_or_buffer: FilePath | ReadBuffer[bytes], engine_kwargs: Any
+    ) -> CalamineWorkbook:
+        from python_calamine import load_workbook
+
+        return load_workbook(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def sheet_names(self) -> list[str]:
+        from python_calamine import SheetTypeEnum
+
+        return [
+            sheet.name
+            for sheet in self.book.sheets_metadata
+            if sheet.typ == SheetTypeEnum.WorkSheet
+        ]
+
+    def get_sheet_by_name(self, name: str) -> CalamineSheet:
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book.get_sheet_by_name(name)
+
+    def get_sheet_by_index(self, index: int) -> CalamineSheet:
+        self.raise_if_bad_sheet_by_index(index)
+        return self.book.get_sheet_by_index(index)
+
+    def get_sheet_data(
+        self, sheet: CalamineSheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar | NaTType | time]]:
+        def _convert_cell(value: _CellValue) -> Scalar | NaTType | time:
+            if isinstance(value, float):
+                val = int(value)
+                if val == value:
+                    return val
+                else:
+                    return value
+            elif isinstance(value, date):
+                return pd.Timestamp(value)
+            elif isinstance(value, timedelta):
+                return pd.Timedelta(value)
+            elif isinstance(value, time):
+                return value
+
+            return value
+
+        rows: list[list[_CellValue]] = sheet.to_python(
+            skip_empty_area=False, nrows=file_rows_needed
+        )
+        data = [[_convert_cell(cell) for cell in row] for row in rows]
+
+        return data

llmeval-env/lib/python3.10/site-packages/pandas/io/excel/_odfreader.py

@@ -0,0 +1,253 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    cast,
+)
+
+import numpy as np
+
+from pandas._typing import (
+    FilePath,
+    ReadBuffer,
+    Scalar,
+    StorageOptions,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+import pandas as pd
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from odf.opendocument import OpenDocument
+
+    from pandas._libs.tslibs.nattype import NaTType
+
+
+@doc(storage_options=_shared_docs["storage_options"])
+class ODFReader(BaseExcelReader["OpenDocument"]):
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Read tables out of OpenDocument formatted files.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path to be parsed or
+            an open readable stream.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        import_optional_dependency("odf")
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[OpenDocument]:
+        from odf.opendocument import OpenDocument
+
+        return OpenDocument
+
+    def load_workbook(
+        self, filepath_or_buffer: FilePath | ReadBuffer[bytes], engine_kwargs
+    ) -> OpenDocument:
+        from odf.opendocument import load
+
+        return load(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def empty_value(self) -> str:
+        """Property for compat with other readers."""
+        return ""
+
+    @property
+    def sheet_names(self) -> list[str]:
+        """Return a list of sheet names present in the document"""
+        from odf.table import Table
+
+        tables = self.book.getElementsByType(Table)
+        return [t.getAttribute("name") for t in tables]
+
+    def get_sheet_by_index(self, index: int):
+        from odf.table import Table
+
+        self.raise_if_bad_sheet_by_index(index)
+        tables = self.book.getElementsByType(Table)
+        return tables[index]
+
+    def get_sheet_by_name(self, name: str):
+        from odf.table import Table
+
+        self.raise_if_bad_sheet_by_name(name)
+        tables = self.book.getElementsByType(Table)
+
+        for table in tables:
+            if table.getAttribute("name") == name:
+                return table
+
+        self.close()
+        raise ValueError(f"sheet {name} not found")
+
+    def get_sheet_data(
+        self, sheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar | NaTType]]:
+        """
+        Parse an ODF Table into a list of lists
+        """
+        from odf.table import (
+            CoveredTableCell,
+            TableCell,
+            TableRow,
+        )
+
+        covered_cell_name = CoveredTableCell().qname
+        table_cell_name = TableCell().qname
+        cell_names = {covered_cell_name, table_cell_name}
+
+        sheet_rows = sheet.getElementsByType(TableRow)
+        empty_rows = 0
+        max_row_len = 0
+
+        table: list[list[Scalar | NaTType]] = []
+
+        for sheet_row in sheet_rows:
+            sheet_cells = [
+                x
+                for x in sheet_row.childNodes
+                if hasattr(x, "qname") and x.qname in cell_names
+            ]
+            empty_cells = 0
+            table_row: list[Scalar | NaTType] = []
+
+            for sheet_cell in sheet_cells:
+                if sheet_cell.qname == table_cell_name:
+                    value = self._get_cell_value(sheet_cell)
+                else:
+                    value = self.empty_value
+
+                column_repeat = self._get_column_repeat(sheet_cell)
+
+                # Queue up empty values, writing only if content succeeds them
+                if value == self.empty_value:
+                    empty_cells += column_repeat
+                else:
+                    table_row.extend([self.empty_value] * empty_cells)
+                    empty_cells = 0
+                    table_row.extend([value] * column_repeat)
+
+            if max_row_len < len(table_row):
+                max_row_len = len(table_row)
+
+            row_repeat = self._get_row_repeat(sheet_row)
+            if len(table_row) == 0:
+                empty_rows += row_repeat
+            else:
+                # add blank rows to our table
+                table.extend([[self.empty_value]] * empty_rows)
+                empty_rows = 0
+                table.extend(table_row for _ in range(row_repeat))
+            if file_rows_needed is not None and len(table) >= file_rows_needed:
+                break
+
+        # Make our table square
+        for row in table:
+            if len(row) < max_row_len:
+                row.extend([self.empty_value] * (max_row_len - len(row)))
+
+        return table
+
+    def _get_row_repeat(self, row) -> int:
+        """
+        Return number of times this row was repeated
+        Repeating an empty row appeared to be a common way
+        of representing sparse rows in the table.
+        """
+        from odf.namespaces import TABLENS
+
+        return int(row.attributes.get((TABLENS, "number-rows-repeated"), 1))
+
+    def _get_column_repeat(self, cell) -> int:
+        from odf.namespaces import TABLENS
+
+        return int(cell.attributes.get((TABLENS, "number-columns-repeated"), 1))
+
+    def _get_cell_value(self, cell) -> Scalar | NaTType:
+        from odf.namespaces import OFFICENS
+
+        if str(cell) == "#N/A":
+            return np.nan
+
+        cell_type = cell.attributes.get((OFFICENS, "value-type"))
+        if cell_type == "boolean":
+            if str(cell) == "TRUE":
+                return True
+            return False
+        if cell_type is None:
+            return self.empty_value
+        elif cell_type == "float":
+            # GH5394
+            cell_value = float(cell.attributes.get((OFFICENS, "value")))
+            val = int(cell_value)
+            if val == cell_value:
+                return val
+            return cell_value
+        elif cell_type == "percentage":
+            cell_value = cell.attributes.get((OFFICENS, "value"))
+            return float(cell_value)
+        elif cell_type == "string":
+            return self._get_cell_string_value(cell)
+        elif cell_type == "currency":
+            cell_value = cell.attributes.get((OFFICENS, "value"))
+            return float(cell_value)
+        elif cell_type == "date":
+            cell_value = cell.attributes.get((OFFICENS, "date-value"))
+            return pd.Timestamp(cell_value)
+        elif cell_type == "time":
+            stamp = pd.Timestamp(str(cell))
+            # cast needed here because Scalar doesn't include datetime.time
+            return cast(Scalar, stamp.time())
+        else:
+            self.close()
+            raise ValueError(f"Unrecognized type {cell_type}")
+
+    def _get_cell_string_value(self, cell) -> str:
+        """
+        Find and decode OpenDocument text:s tags that represent
+        a run length encoded sequence of space characters.
+        """
+        from odf.element import Element
+        from odf.namespaces import TEXTNS
+        from odf.office import Annotation
+        from odf.text import S
+
+        office_annotation = Annotation().qname
+        text_s = S().qname
+
+        value = []
+
+        for fragment in cell.childNodes:
+            if isinstance(fragment, Element):
+                if fragment.qname == text_s:
+                    spaces = int(fragment.attributes.get((TEXTNS, "c"), 1))
+                    value.append(" " * spaces)
+                elif fragment.qname == office_annotation:
+                    continue
+                else:
+                    # recursive impl needed in case of nested fragments
+                    # with multiple spaces
+                    # https://github.com/pandas-dev/pandas/pull/36175#discussion_r484639704
+                    value.append(self._get_cell_string_value(fragment))
+            else:
+                value.append(str(fragment).strip("\n"))
+        return "".join(value)

llmeval-env/lib/python3.10/site-packages/pandas/io/excel/_odswriter.py

@@ -0,0 +1,357 @@
+from __future__ import annotations
+
+from collections import defaultdict
+import datetime
+import json
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    DefaultDict,
+    cast,
+    overload,
+)
+
+from pandas.io.excel._base import ExcelWriter
+from pandas.io.excel._util import (
+    combine_kwargs,
+    validate_freeze_panes,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ExcelWriterIfSheetExists,
+        FilePath,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+
+    from pandas.io.formats.excel import ExcelCell
+
+
+class ODSWriter(ExcelWriter):
+    _engine = "odf"
+    _supported_extensions = (".ods",)
+
+    def __init__(
+        self,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format=None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+        **kwargs,
+    ) -> None:
+        from odf.opendocument import OpenDocumentSpreadsheet
+
+        if mode == "a":
+            raise ValueError("Append mode is not supported with odf!")
+
+        engine_kwargs = combine_kwargs(engine_kwargs, kwargs)
+        self._book = OpenDocumentSpreadsheet(**engine_kwargs)
+
+        super().__init__(
+            path,
+            mode=mode,
+            storage_options=storage_options,
+            if_sheet_exists=if_sheet_exists,
+            engine_kwargs=engine_kwargs,
+        )
+
+        self._style_dict: dict[str, str] = {}
+
+    @property
+    def book(self):
+        """
+        Book instance of class odf.opendocument.OpenDocumentSpreadsheet.
+
+        This attribute can be used to access engine-specific features.
+        """
+        return self._book
+
+    @property
+    def sheets(self) -> dict[str, Any]:
+        """Mapping of sheet names to sheet objects."""
+        from odf.table import Table
+
+        result = {
+            sheet.getAttribute("name"): sheet
+            for sheet in self.book.getElementsByType(Table)
+        }
+        return result
+
+    def _save(self) -> None:
+        """
+        Save workbook to disk.
+        """
+        for sheet in self.sheets.values():
+            self.book.spreadsheet.addElement(sheet)
+        self.book.save(self._handles.handle)
+
+    def _write_cells(
+        self,
+        cells: list[ExcelCell],
+        sheet_name: str | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+    ) -> None:
+        """
+        Write the frame cells using odf
+        """
+        from odf.table import (
+            Table,
+            TableCell,
+            TableRow,
+        )
+        from odf.text import P
+
+        sheet_name = self._get_sheet_name(sheet_name)
+        assert sheet_name is not None
+
+        if sheet_name in self.sheets:
+            wks = self.sheets[sheet_name]
+        else:
+            wks = Table(name=sheet_name)
+            self.book.spreadsheet.addElement(wks)
+
+        if validate_freeze_panes(freeze_panes):
+            freeze_panes = cast(tuple[int, int], freeze_panes)
+            self._create_freeze_panes(sheet_name, freeze_panes)
+
+        for _ in range(startrow):
+            wks.addElement(TableRow())
+
+        rows: DefaultDict = defaultdict(TableRow)
+        col_count: DefaultDict = defaultdict(int)
+
+        for cell in sorted(cells, key=lambda cell: (cell.row, cell.col)):
+            # only add empty cells if the row is still empty
+            if not col_count[cell.row]:
+                for _ in range(startcol):
+                    rows[cell.row].addElement(TableCell())
+
+            # fill with empty cells if needed
+            for _ in range(cell.col - col_count[cell.row]):
+                rows[cell.row].addElement(TableCell())
+                col_count[cell.row] += 1
+
+            pvalue, tc = self._make_table_cell(cell)
+            rows[cell.row].addElement(tc)
+            col_count[cell.row] += 1
+            p = P(text=pvalue)
+            tc.addElement(p)
+
+        # add all rows to the sheet
+        if len(rows) > 0:
+            for row_nr in range(max(rows.keys()) + 1):
+                wks.addElement(rows[row_nr])
+
+    def _make_table_cell_attributes(self, cell) -> dict[str, int | str]:
+        """Convert cell attributes to OpenDocument attributes
+
+        Parameters
+        ----------
+        cell : ExcelCell
+            Spreadsheet cell data
+
+        Returns
+        -------
+        attributes : Dict[str, Union[int, str]]
+            Dictionary with attributes and attribute values
+        """
+        attributes: dict[str, int | str] = {}
+        style_name = self._process_style(cell.style)
+        if style_name is not None:
+            attributes["stylename"] = style_name
+        if cell.mergestart is not None and cell.mergeend is not None:
+            attributes["numberrowsspanned"] = max(1, cell.mergestart)
+            attributes["numbercolumnsspanned"] = cell.mergeend
+        return attributes
+
+    def _make_table_cell(self, cell) -> tuple[object, Any]:
+        """Convert cell data to an OpenDocument spreadsheet cell
+
+        Parameters
+        ----------
+        cell : ExcelCell
+            Spreadsheet cell data
+
+        Returns
+        -------
+        pvalue, cell : Tuple[str, TableCell]
+            Display value, Cell value
+        """
+        from odf.table import TableCell
+
+        attributes = self._make_table_cell_attributes(cell)
+        val, fmt = self._value_with_fmt(cell.val)
+        pvalue = value = val
+        if isinstance(val, bool):
+            value = str(val).lower()
+            pvalue = str(val).upper()
+            return (
+                pvalue,
+                TableCell(
+                    valuetype="boolean",
+                    booleanvalue=value,
+                    attributes=attributes,
+                ),
+            )
+        elif isinstance(val, datetime.datetime):
+            # Fast formatting
+            value = val.isoformat()
+            # Slow but locale-dependent
+            pvalue = val.strftime("%c")
+            return (
+                pvalue,
+                TableCell(valuetype="date", datevalue=value, attributes=attributes),
+            )
+        elif isinstance(val, datetime.date):
+            # Fast formatting
+            value = f"{val.year}-{val.month:02d}-{val.day:02d}"
+            # Slow but locale-dependent
+            pvalue = val.strftime("%x")
+            return (
+                pvalue,
+                TableCell(valuetype="date", datevalue=value, attributes=attributes),
+            )
+        elif isinstance(val, str):
+            return (
+                pvalue,
+                TableCell(
+                    valuetype="string",
+                    stringvalue=value,
+                    attributes=attributes,
+                ),
+            )
+        else:
+            return (
+                pvalue,
+                TableCell(
+                    valuetype="float",
+                    value=value,
+                    attributes=attributes,
+                ),
+            )
+
+    @overload
+    def _process_style(self, style: dict[str, Any]) -> str:
+        ...
+
+    @overload
+    def _process_style(self, style: None) -> None:
+        ...
+
+    def _process_style(self, style: dict[str, Any] | None) -> str | None:
+        """Convert a style dictionary to a OpenDocument style sheet
+
+        Parameters
+        ----------
+        style : Dict
+            Style dictionary
+
+        Returns
+        -------
+        style_key : str
+            Unique style key for later reference in sheet
+        """
+        from odf.style import (
+            ParagraphProperties,
+            Style,
+            TableCellProperties,
+            TextProperties,
+        )
+
+        if style is None:
+            return None
+        style_key = json.dumps(style)
+        if style_key in self._style_dict:
+            return self._style_dict[style_key]
+        name = f"pd{len(self._style_dict)+1}"
+        self._style_dict[style_key] = name
+        odf_style = Style(name=name, family="table-cell")
+        if "font" in style:
+            font = style["font"]
+            if font.get("bold", False):
+                odf_style.addElement(TextProperties(fontweight="bold"))
+        if "borders" in style:
+            borders = style["borders"]
+            for side, thickness in borders.items():
+                thickness_translation = {"thin": "0.75pt solid #000000"}
+                odf_style.addElement(
+                    TableCellProperties(
+                        attributes={f"border{side}": thickness_translation[thickness]}
+                    )
+                )
+        if "alignment" in style:
+            alignment = style["alignment"]
+            horizontal = alignment.get("horizontal")
+            if horizontal:
+                odf_style.addElement(ParagraphProperties(textalign=horizontal))
+            vertical = alignment.get("vertical")
+            if vertical:
+                odf_style.addElement(TableCellProperties(verticalalign=vertical))
+        self.book.styles.addElement(odf_style)
+        return name
+
+    def _create_freeze_panes(
+        self, sheet_name: str, freeze_panes: tuple[int, int]
+    ) -> None:
+        """
+        Create freeze panes in the sheet.
+
+        Parameters
+        ----------
+        sheet_name : str
+            Name of the spreadsheet
+        freeze_panes : tuple of (int, int)
+            Freeze pane location x and y
+        """
+        from odf.config import (
+            ConfigItem,
+            ConfigItemMapEntry,
+            ConfigItemMapIndexed,
+            ConfigItemMapNamed,
+            ConfigItemSet,
+        )
+
+        config_item_set = ConfigItemSet(name="ooo:view-settings")
+        self.book.settings.addElement(config_item_set)
+
+        config_item_map_indexed = ConfigItemMapIndexed(name="Views")
+        config_item_set.addElement(config_item_map_indexed)
+
+        config_item_map_entry = ConfigItemMapEntry()
+        config_item_map_indexed.addElement(config_item_map_entry)
+
+        config_item_map_named = ConfigItemMapNamed(name="Tables")
+        config_item_map_entry.addElement(config_item_map_named)
+
+        config_item_map_entry = ConfigItemMapEntry(name=sheet_name)
+        config_item_map_named.addElement(config_item_map_entry)
+
+        config_item_map_entry.addElement(
+            ConfigItem(name="HorizontalSplitMode", type="short", text="2")
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(name="VerticalSplitMode", type="short", text="2")
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(
+                name="HorizontalSplitPosition", type="int", text=str(freeze_panes[0])
+            )
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(
+                name="VerticalSplitPosition", type="int", text=str(freeze_panes[1])
+            )
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(name="PositionRight", type="int", text=str(freeze_panes[0]))
+        )
+        config_item_map_entry.addElement(
+            ConfigItem(name="PositionBottom", type="int", text=str(freeze_panes[1]))
+        )

llmeval-env/lib/python3.10/site-packages/pandas/io/excel/_openpyxl.py

@@ -0,0 +1,639 @@
+from __future__ import annotations
+
+import mmap
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    cast,
+)
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import (
+    BaseExcelReader,
+    ExcelWriter,
+)
+from pandas.io.excel._util import (
+    combine_kwargs,
+    validate_freeze_panes,
+)
+
+if TYPE_CHECKING:
+    from openpyxl import Workbook
+    from openpyxl.descriptors.serialisable import Serialisable
+
+    from pandas._typing import (
+        ExcelWriterIfSheetExists,
+        FilePath,
+        ReadBuffer,
+        Scalar,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+
+
+class OpenpyxlWriter(ExcelWriter):
+    _engine = "openpyxl"
+    _supported_extensions = (".xlsx", ".xlsm")
+
+    def __init__(
+        self,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format: str | None = None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+        **kwargs,
+    ) -> None:
+        # Use the openpyxl module as the Excel writer.
+        from openpyxl.workbook import Workbook
+
+        engine_kwargs = combine_kwargs(engine_kwargs, kwargs)
+
+        super().__init__(
+            path,
+            mode=mode,
+            storage_options=storage_options,
+            if_sheet_exists=if_sheet_exists,
+            engine_kwargs=engine_kwargs,
+        )
+
+        # ExcelWriter replaced "a" by "r+" to allow us to first read the excel file from
+        # the file and later write to it
+        if "r+" in self._mode:  # Load from existing workbook
+            from openpyxl import load_workbook
+
+            try:
+                self._book = load_workbook(self._handles.handle, **engine_kwargs)
+            except TypeError:
+                self._handles.handle.close()
+                raise
+            self._handles.handle.seek(0)
+        else:
+            # Create workbook object with default optimized_write=True.
+            try:
+                self._book = Workbook(**engine_kwargs)
+            except TypeError:
+                self._handles.handle.close()
+                raise
+
+            if self.book.worksheets:
+                self.book.remove(self.book.worksheets[0])
+
+    @property
+    def book(self) -> Workbook:
+        """
+        Book instance of class openpyxl.workbook.Workbook.
+
+        This attribute can be used to access engine-specific features.
+        """
+        return self._book
+
+    @property
+    def sheets(self) -> dict[str, Any]:
+        """Mapping of sheet names to sheet objects."""
+        result = {name: self.book[name] for name in self.book.sheetnames}
+        return result
+
+    def _save(self) -> None:
+        """
+        Save workbook to disk.
+        """
+        self.book.save(self._handles.handle)
+        if "r+" in self._mode and not isinstance(self._handles.handle, mmap.mmap):
+            # truncate file to the written content
+            self._handles.handle.truncate()
+
+    @classmethod
+    def _convert_to_style_kwargs(cls, style_dict: dict) -> dict[str, Serialisable]:
+        """
+        Convert a style_dict to a set of kwargs suitable for initializing
+        or updating-on-copy an openpyxl v2 style object.
+
+        Parameters
+        ----------
+        style_dict : dict
+            A dict with zero or more of the following keys (or their synonyms).
+                'font'
+                'fill'
+                'border' ('borders')
+                'alignment'
+                'number_format'
+                'protection'
+
+        Returns
+        -------
+        style_kwargs : dict
+            A dict with the same, normalized keys as ``style_dict`` but each
+            value has been replaced with a native openpyxl style object of the
+            appropriate class.
+        """
+        _style_key_map = {"borders": "border"}
+
+        style_kwargs: dict[str, Serialisable] = {}
+        for k, v in style_dict.items():
+            k = _style_key_map.get(k, k)
+            _conv_to_x = getattr(cls, f"_convert_to_{k}", lambda x: None)
+            new_v = _conv_to_x(v)
+            if new_v:
+                style_kwargs[k] = new_v
+
+        return style_kwargs
+
+    @classmethod
+    def _convert_to_color(cls, color_spec):
+        """
+        Convert ``color_spec`` to an openpyxl v2 Color object.
+
+        Parameters
+        ----------
+        color_spec : str, dict
+            A 32-bit ARGB hex string, or a dict with zero or more of the
+            following keys.
+                'rgb'
+                'indexed'
+                'auto'
+                'theme'
+                'tint'
+                'index'
+                'type'
+
+        Returns
+        -------
+        color : openpyxl.styles.Color
+        """
+        from openpyxl.styles import Color
+
+        if isinstance(color_spec, str):
+            return Color(color_spec)
+        else:
+            return Color(**color_spec)
+
+    @classmethod
+    def _convert_to_font(cls, font_dict):
+        """
+        Convert ``font_dict`` to an openpyxl v2 Font object.
+
+        Parameters
+        ----------
+        font_dict : dict
+            A dict with zero or more of the following keys (or their synonyms).
+                'name'
+                'size' ('sz')
+                'bold' ('b')
+                'italic' ('i')
+                'underline' ('u')
+                'strikethrough' ('strike')
+                'color'
+                'vertAlign' ('vertalign')
+                'charset'
+                'scheme'
+                'family'
+                'outline'
+                'shadow'
+                'condense'
+
+        Returns
+        -------
+        font : openpyxl.styles.Font
+        """
+        from openpyxl.styles import Font
+
+        _font_key_map = {
+            "sz": "size",
+            "b": "bold",
+            "i": "italic",
+            "u": "underline",
+            "strike": "strikethrough",
+            "vertalign": "vertAlign",
+        }
+
+        font_kwargs = {}
+        for k, v in font_dict.items():
+            k = _font_key_map.get(k, k)
+            if k == "color":
+                v = cls._convert_to_color(v)
+            font_kwargs[k] = v
+
+        return Font(**font_kwargs)
+
+    @classmethod
+    def _convert_to_stop(cls, stop_seq):
+        """
+        Convert ``stop_seq`` to a list of openpyxl v2 Color objects,
+        suitable for initializing the ``GradientFill`` ``stop`` parameter.
+
+        Parameters
+        ----------
+        stop_seq : iterable
+            An iterable that yields objects suitable for consumption by
+            ``_convert_to_color``.
+
+        Returns
+        -------
+        stop : list of openpyxl.styles.Color
+        """
+        return map(cls._convert_to_color, stop_seq)
+
+    @classmethod
+    def _convert_to_fill(cls, fill_dict: dict[str, Any]):
+        """
+        Convert ``fill_dict`` to an openpyxl v2 Fill object.
+
+        Parameters
+        ----------
+        fill_dict : dict
+            A dict with one or more of the following keys (or their synonyms),
+                'fill_type' ('patternType', 'patterntype')
+                'start_color' ('fgColor', 'fgcolor')
+                'end_color' ('bgColor', 'bgcolor')
+            or one or more of the following keys (or their synonyms).
+                'type' ('fill_type')
+                'degree'
+                'left'
+                'right'
+                'top'
+                'bottom'
+                'stop'
+
+        Returns
+        -------
+        fill : openpyxl.styles.Fill
+        """
+        from openpyxl.styles import (
+            GradientFill,
+            PatternFill,
+        )
+
+        _pattern_fill_key_map = {
+            "patternType": "fill_type",
+            "patterntype": "fill_type",
+            "fgColor": "start_color",
+            "fgcolor": "start_color",
+            "bgColor": "end_color",
+            "bgcolor": "end_color",
+        }
+
+        _gradient_fill_key_map = {"fill_type": "type"}
+
+        pfill_kwargs = {}
+        gfill_kwargs = {}
+        for k, v in fill_dict.items():
+            pk = _pattern_fill_key_map.get(k)
+            gk = _gradient_fill_key_map.get(k)
+            if pk in ["start_color", "end_color"]:
+                v = cls._convert_to_color(v)
+            if gk == "stop":
+                v = cls._convert_to_stop(v)
+            if pk:
+                pfill_kwargs[pk] = v
+            elif gk:
+                gfill_kwargs[gk] = v
+            else:
+                pfill_kwargs[k] = v
+                gfill_kwargs[k] = v
+
+        try:
+            return PatternFill(**pfill_kwargs)
+        except TypeError:
+            return GradientFill(**gfill_kwargs)
+
+    @classmethod
+    def _convert_to_side(cls, side_spec):
+        """
+        Convert ``side_spec`` to an openpyxl v2 Side object.
+
+        Parameters
+        ----------
+        side_spec : str, dict
+            A string specifying the border style, or a dict with zero or more
+            of the following keys (or their synonyms).
+                'style' ('border_style')
+                'color'
+
+        Returns
+        -------
+        side : openpyxl.styles.Side
+        """
+        from openpyxl.styles import Side
+
+        _side_key_map = {"border_style": "style"}
+
+        if isinstance(side_spec, str):
+            return Side(style=side_spec)
+
+        side_kwargs = {}
+        for k, v in side_spec.items():
+            k = _side_key_map.get(k, k)
+            if k == "color":
+                v = cls._convert_to_color(v)
+            side_kwargs[k] = v
+
+        return Side(**side_kwargs)
+
+    @classmethod
+    def _convert_to_border(cls, border_dict):
+        """
+        Convert ``border_dict`` to an openpyxl v2 Border object.
+
+        Parameters
+        ----------
+        border_dict : dict
+            A dict with zero or more of the following keys (or their synonyms).
+                'left'
+                'right'
+                'top'
+                'bottom'
+                'diagonal'
+                'diagonal_direction'
+                'vertical'
+                'horizontal'
+                'diagonalUp' ('diagonalup')
+                'diagonalDown' ('diagonaldown')
+                'outline'
+
+        Returns
+        -------
+        border : openpyxl.styles.Border
+        """
+        from openpyxl.styles import Border
+
+        _border_key_map = {"diagonalup": "diagonalUp", "diagonaldown": "diagonalDown"}
+
+        border_kwargs = {}
+        for k, v in border_dict.items():
+            k = _border_key_map.get(k, k)
+            if k == "color":
+                v = cls._convert_to_color(v)
+            if k in ["left", "right", "top", "bottom", "diagonal"]:
+                v = cls._convert_to_side(v)
+            border_kwargs[k] = v
+
+        return Border(**border_kwargs)
+
+    @classmethod
+    def _convert_to_alignment(cls, alignment_dict):
+        """
+        Convert ``alignment_dict`` to an openpyxl v2 Alignment object.
+
+        Parameters
+        ----------
+        alignment_dict : dict
+            A dict with zero or more of the following keys (or their synonyms).
+                'horizontal'
+                'vertical'
+                'text_rotation'
+                'wrap_text'
+                'shrink_to_fit'
+                'indent'
+        Returns
+        -------
+        alignment : openpyxl.styles.Alignment
+        """
+        from openpyxl.styles import Alignment
+
+        return Alignment(**alignment_dict)
+
+    @classmethod
+    def _convert_to_number_format(cls, number_format_dict):
+        """
+        Convert ``number_format_dict`` to an openpyxl v2.1.0 number format
+        initializer.
+
+        Parameters
+        ----------
+        number_format_dict : dict
+            A dict with zero or more of the following keys.
+                'format_code' : str
+
+        Returns
+        -------
+        number_format : str
+        """
+        return number_format_dict["format_code"]
+
+    @classmethod
+    def _convert_to_protection(cls, protection_dict):
+        """
+        Convert ``protection_dict`` to an openpyxl v2 Protection object.
+
+        Parameters
+        ----------
+        protection_dict : dict
+            A dict with zero or more of the following keys.
+                'locked'
+                'hidden'
+
+        Returns
+        -------
+        """
+        from openpyxl.styles import Protection
+
+        return Protection(**protection_dict)
+
+    def _write_cells(
+        self,
+        cells,
+        sheet_name: str | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+    ) -> None:
+        # Write the frame cells using openpyxl.
+        sheet_name = self._get_sheet_name(sheet_name)
+
+        _style_cache: dict[str, dict[str, Serialisable]] = {}
+
+        if sheet_name in self.sheets and self._if_sheet_exists != "new":
+            if "r+" in self._mode:
+                if self._if_sheet_exists == "replace":
+                    old_wks = self.sheets[sheet_name]
+                    target_index = self.book.index(old_wks)
+                    del self.book[sheet_name]
+                    wks = self.book.create_sheet(sheet_name, target_index)
+                elif self._if_sheet_exists == "error":
+                    raise ValueError(
+                        f"Sheet '{sheet_name}' already exists and "
+                        f"if_sheet_exists is set to 'error'."
+                    )
+                elif self._if_sheet_exists == "overlay":
+                    wks = self.sheets[sheet_name]
+                else:
+                    raise ValueError(
+                        f"'{self._if_sheet_exists}' is not valid for if_sheet_exists. "
+                        "Valid options are 'error', 'new', 'replace' and 'overlay'."
+                    )
+            else:
+                wks = self.sheets[sheet_name]
+        else:
+            wks = self.book.create_sheet()
+            wks.title = sheet_name
+
+        if validate_freeze_panes(freeze_panes):
+            freeze_panes = cast(tuple[int, int], freeze_panes)
+            wks.freeze_panes = wks.cell(
+                row=freeze_panes[0] + 1, column=freeze_panes[1] + 1
+            )
+
+        for cell in cells:
+            xcell = wks.cell(
+                row=startrow + cell.row + 1, column=startcol + cell.col + 1
+            )
+            xcell.value, fmt = self._value_with_fmt(cell.val)
+            if fmt:
+                xcell.number_format = fmt
+
+            style_kwargs: dict[str, Serialisable] | None = {}
+            if cell.style:
+                key = str(cell.style)
+                style_kwargs = _style_cache.get(key)
+                if style_kwargs is None:
+                    style_kwargs = self._convert_to_style_kwargs(cell.style)
+                    _style_cache[key] = style_kwargs
+
+            if style_kwargs:
+                for k, v in style_kwargs.items():
+                    setattr(xcell, k, v)
+
+            if cell.mergestart is not None and cell.mergeend is not None:
+                wks.merge_cells(
+                    start_row=startrow + cell.row + 1,
+                    start_column=startcol + cell.col + 1,
+                    end_column=startcol + cell.mergeend + 1,
+                    end_row=startrow + cell.mergestart + 1,
+                )
+
+                # When cells are merged only the top-left cell is preserved
+                # The behaviour of the other cells in a merged range is
+                # undefined
+                if style_kwargs:
+                    first_row = startrow + cell.row + 1
+                    last_row = startrow + cell.mergestart + 1
+                    first_col = startcol + cell.col + 1
+                    last_col = startcol + cell.mergeend + 1
+
+                    for row in range(first_row, last_row + 1):
+                        for col in range(first_col, last_col + 1):
+                            if row == first_row and col == first_col:
+                                # Ignore first cell. It is already handled.
+                                continue
+                            xcell = wks.cell(column=col, row=row)
+                            for k, v in style_kwargs.items():
+                                setattr(xcell, k, v)
+
+
+class OpenpyxlReader(BaseExcelReader["Workbook"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using openpyxl engine.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path object or Workbook
+            Object to be parsed.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        import_optional_dependency("openpyxl")
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[Workbook]:
+        from openpyxl import Workbook
+
+        return Workbook
+
+    def load_workbook(
+        self, filepath_or_buffer: FilePath | ReadBuffer[bytes], engine_kwargs
+    ) -> Workbook:
+        from openpyxl import load_workbook
+
+        default_kwargs = {"read_only": True, "data_only": True, "keep_links": False}
+
+        return load_workbook(
+            filepath_or_buffer,
+            **(default_kwargs | engine_kwargs),
+        )
+
+    @property
+    def sheet_names(self) -> list[str]:
+        return [sheet.title for sheet in self.book.worksheets]
+
+    def get_sheet_by_name(self, name: str):
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book[name]
+
+    def get_sheet_by_index(self, index: int):
+        self.raise_if_bad_sheet_by_index(index)
+        return self.book.worksheets[index]
+
+    def _convert_cell(self, cell) -> Scalar:
+        from openpyxl.cell.cell import (
+            TYPE_ERROR,
+            TYPE_NUMERIC,
+        )
+
+        if cell.value is None:
+            return ""  # compat with xlrd
+        elif cell.data_type == TYPE_ERROR:
+            return np.nan
+        elif cell.data_type == TYPE_NUMERIC:
+            val = int(cell.value)
+            if val == cell.value:
+                return val
+            return float(cell.value)
+
+        return cell.value
+
+    def get_sheet_data(
+        self, sheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar]]:
+        if self.book.read_only:
+            sheet.reset_dimensions()
+
+        data: list[list[Scalar]] = []
+        last_row_with_data = -1
+        for row_number, row in enumerate(sheet.rows):
+            converted_row = [self._convert_cell(cell) for cell in row]
+            while converted_row and converted_row[-1] == "":
+                # trim trailing empty elements
+                converted_row.pop()
+            if converted_row:
+                last_row_with_data = row_number
+            data.append(converted_row)
+            if file_rows_needed is not None and len(data) >= file_rows_needed:
+                break
+
+        # Trim trailing empty rows
+        data = data[: last_row_with_data + 1]
+
+        if len(data) > 0:
+            # extend rows to max width
+            max_width = max(len(data_row) for data_row in data)
+            if min(len(data_row) for data_row in data) < max_width:
+                empty_cell: list[Scalar] = [""]
+                data = [
+                    data_row + (max_width - len(data_row)) * empty_cell
+                    for data_row in data
+                ]
+
+        return data

llmeval-env/lib/python3.10/site-packages/pandas/io/excel/_pyxlsb.py

@@ -0,0 +1,127 @@
+# pyright: reportMissingImports=false
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from pyxlsb import Workbook
+
+    from pandas._typing import (
+        FilePath,
+        ReadBuffer,
+        Scalar,
+        StorageOptions,
+    )
+
+
+class PyxlsbReader(BaseExcelReader["Workbook"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer: FilePath | ReadBuffer[bytes],
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using pyxlsb engine.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path object, or Workbook
+            Object to be parsed.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        import_optional_dependency("pyxlsb")
+        # This will call load_workbook on the filepath or buffer
+        # And set the result to the book-attribute
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[Workbook]:
+        from pyxlsb import Workbook
+
+        return Workbook
+
+    def load_workbook(
+        self, filepath_or_buffer: FilePath | ReadBuffer[bytes], engine_kwargs
+    ) -> Workbook:
+        from pyxlsb import open_workbook
+
+        # TODO: hack in buffer capability
+        # This might need some modifications to the Pyxlsb library
+        # Actual work for opening it is in xlsbpackage.py, line 20-ish
+
+        return open_workbook(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def sheet_names(self) -> list[str]:
+        return self.book.sheets
+
+    def get_sheet_by_name(self, name: str):
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book.get_sheet(name)
+
+    def get_sheet_by_index(self, index: int):
+        self.raise_if_bad_sheet_by_index(index)
+        # pyxlsb sheets are indexed from 1 onwards
+        # There's a fix for this in the source, but the pypi package doesn't have it
+        return self.book.get_sheet(index + 1)
+
+    def _convert_cell(self, cell) -> Scalar:
+        # TODO: there is no way to distinguish between floats and datetimes in pyxlsb
+        # This means that there is no way to read datetime types from an xlsb file yet
+        if cell.v is None:
+            return ""  # Prevents non-named columns from not showing up as Unnamed: i
+        if isinstance(cell.v, float):
+            val = int(cell.v)
+            if val == cell.v:
+                return val
+            else:
+                return float(cell.v)
+
+        return cell.v
+
+    def get_sheet_data(
+        self,
+        sheet,
+        file_rows_needed: int | None = None,
+    ) -> list[list[Scalar]]:
+        data: list[list[Scalar]] = []
+        previous_row_number = -1
+        # When sparse=True the rows can have different lengths and empty rows are
+        # not returned. The cells are namedtuples of row, col, value (r, c, v).
+        for row in sheet.rows(sparse=True):
+            row_number = row[0].r
+            converted_row = [self._convert_cell(cell) for cell in row]
+            while converted_row and converted_row[-1] == "":
+                # trim trailing empty elements
+                converted_row.pop()
+            if converted_row:
+                data.extend([[]] * (row_number - previous_row_number - 1))
+                data.append(converted_row)
+                previous_row_number = row_number
+            if file_rows_needed is not None and len(data) >= file_rows_needed:
+                break
+        if data:
+            # extend rows to max_width
+            max_width = max(len(data_row) for data_row in data)
+            if min(len(data_row) for data_row in data) < max_width:
+                empty_cell: list[Scalar] = [""]
+                data = [
+                    data_row + (max_width - len(data_row)) * empty_cell
+                    for data_row in data
+                ]
+        return data

llmeval-env/lib/python3.10/site-packages/pandas/io/excel/_util.py

@@ -0,0 +1,334 @@
+from __future__ import annotations
+
+from collections.abc import (
+    Hashable,
+    Iterable,
+    MutableMapping,
+    Sequence,
+)
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    TypeVar,
+    overload,
+)
+
+from pandas.compat._optional import import_optional_dependency
+
+from pandas.core.dtypes.common import (
+    is_integer,
+    is_list_like,
+)
+
+if TYPE_CHECKING:
+    from pandas.io.excel._base import ExcelWriter
+
+    ExcelWriter_t = type[ExcelWriter]
+    usecols_func = TypeVar("usecols_func", bound=Callable[[Hashable], object])
+
+_writers: MutableMapping[str, ExcelWriter_t] = {}
+
+
+def register_writer(klass: ExcelWriter_t) -> None:
+    """
+    Add engine to the excel writer registry.io.excel.
+
+    You must use this method to integrate with ``to_excel``.
+
+    Parameters
+    ----------
+    klass : ExcelWriter
+    """
+    if not callable(klass):
+        raise ValueError("Can only register callables as engines")
+    engine_name = klass._engine
+    _writers[engine_name] = klass
+
+
+def get_default_engine(ext: str, mode: Literal["reader", "writer"] = "reader") -> str:
+    """
+    Return the default reader/writer for the given extension.
+
+    Parameters
+    ----------
+    ext : str
+        The excel file extension for which to get the default engine.
+    mode : str {'reader', 'writer'}
+        Whether to get the default engine for reading or writing.
+        Either 'reader' or 'writer'
+
+    Returns
+    -------
+    str
+        The default engine for the extension.
+    """
+    _default_readers = {
+        "xlsx": "openpyxl",
+        "xlsm": "openpyxl",
+        "xlsb": "pyxlsb",
+        "xls": "xlrd",
+        "ods": "odf",
+    }
+    _default_writers = {
+        "xlsx": "openpyxl",
+        "xlsm": "openpyxl",
+        "xlsb": "pyxlsb",
+        "ods": "odf",
+    }
+    assert mode in ["reader", "writer"]
+    if mode == "writer":
+        # Prefer xlsxwriter over openpyxl if installed
+        xlsxwriter = import_optional_dependency("xlsxwriter", errors="warn")
+        if xlsxwriter:
+            _default_writers["xlsx"] = "xlsxwriter"
+        return _default_writers[ext]
+    else:
+        return _default_readers[ext]
+
+
+def get_writer(engine_name: str) -> ExcelWriter_t:
+    try:
+        return _writers[engine_name]
+    except KeyError as err:
+        raise ValueError(f"No Excel writer '{engine_name}'") from err
+
+
+def _excel2num(x: str) -> int:
+    """
+    Convert Excel column name like 'AB' to 0-based column index.
+
+    Parameters
+    ----------
+    x : str
+        The Excel column name to convert to a 0-based column index.
+
+    Returns
+    -------
+    num : int
+        The column index corresponding to the name.
+
+    Raises
+    ------
+    ValueError
+        Part of the Excel column name was invalid.
+    """
+    index = 0
+
+    for c in x.upper().strip():
+        cp = ord(c)
+
+        if cp < ord("A") or cp > ord("Z"):
+            raise ValueError(f"Invalid column name: {x}")
+
+        index = index * 26 + cp - ord("A") + 1
+
+    return index - 1
+
+
+def _range2cols(areas: str) -> list[int]:
+    """
+    Convert comma separated list of column names and ranges to indices.
+
+    Parameters
+    ----------
+    areas : str
+        A string containing a sequence of column ranges (or areas).
+
+    Returns
+    -------
+    cols : list
+        A list of 0-based column indices.
+
+    Examples
+    --------
+    >>> _range2cols('A:E')
+    [0, 1, 2, 3, 4]
+    >>> _range2cols('A,C,Z:AB')
+    [0, 2, 25, 26, 27]
+    """
+    cols: list[int] = []
+
+    for rng in areas.split(","):
+        if ":" in rng:
+            rngs = rng.split(":")
+            cols.extend(range(_excel2num(rngs[0]), _excel2num(rngs[1]) + 1))
+        else:
+            cols.append(_excel2num(rng))
+
+    return cols
+
+
+@overload
+def maybe_convert_usecols(usecols: str | list[int]) -> list[int]:
+    ...
+
+
+@overload
+def maybe_convert_usecols(usecols: list[str]) -> list[str]:
+    ...
+
+
+@overload
+def maybe_convert_usecols(usecols: usecols_func) -> usecols_func:
+    ...
+
+
+@overload
+def maybe_convert_usecols(usecols: None) -> None:
+    ...
+
+
+def maybe_convert_usecols(
+    usecols: str | list[int] | list[str] | usecols_func | None,
+) -> None | list[int] | list[str] | usecols_func:
+    """
+    Convert `usecols` into a compatible format for parsing in `parsers.py`.
+
+    Parameters
+    ----------
+    usecols : object
+        The use-columns object to potentially convert.
+
+    Returns
+    -------
+    converted : object
+        The compatible format of `usecols`.
+    """
+    if usecols is None:
+        return usecols
+
+    if is_integer(usecols):
+        raise ValueError(
+            "Passing an integer for `usecols` is no longer supported.  "
+            "Please pass in a list of int from 0 to `usecols` inclusive instead."
+        )
+
+    if isinstance(usecols, str):
+        return _range2cols(usecols)
+
+    return usecols
+
+
+@overload
+def validate_freeze_panes(freeze_panes: tuple[int, int]) -> Literal[True]:
+    ...
+
+
+@overload
+def validate_freeze_panes(freeze_panes: None) -> Literal[False]:
+    ...
+
+
+def validate_freeze_panes(freeze_panes: tuple[int, int] | None) -> bool:
+    if freeze_panes is not None:
+        if len(freeze_panes) == 2 and all(
+            isinstance(item, int) for item in freeze_panes
+        ):
+            return True
+
+        raise ValueError(
+            "freeze_panes must be of form (row, column) "
+            "where row and column are integers"
+        )
+
+    # freeze_panes wasn't specified, return False so it won't be applied
+    # to output sheet
+    return False
+
+
+def fill_mi_header(
+    row: list[Hashable], control_row: list[bool]
+) -> tuple[list[Hashable], list[bool]]:
+    """
+    Forward fill blank entries in row but only inside the same parent index.
+
+    Used for creating headers in Multiindex.
+
+    Parameters
+    ----------
+    row : list
+        List of items in a single row.
+    control_row : list of bool
+        Helps to determine if particular column is in same parent index as the
+        previous value. Used to stop propagation of empty cells between
+        different indexes.
+
+    Returns
+    -------
+    Returns changed row and control_row
+    """
+    last = row[0]
+    for i in range(1, len(row)):
+        if not control_row[i]:
+            last = row[i]
+
+        if row[i] == "" or row[i] is None:
+            row[i] = last
+        else:
+            control_row[i] = False
+            last = row[i]
+
+    return row, control_row
+
+
+def pop_header_name(
+    row: list[Hashable], index_col: int | Sequence[int]
+) -> tuple[Hashable | None, list[Hashable]]:
+    """
+    Pop the header name for MultiIndex parsing.
+
+    Parameters
+    ----------
+    row : list
+        The data row to parse for the header name.
+    index_col : int, list
+        The index columns for our data. Assumed to be non-null.
+
+    Returns
+    -------
+    header_name : str
+        The extracted header name.
+    trimmed_row : list
+        The original data row with the header name removed.
+    """
+    # Pop out header name and fill w/blank.
+    if is_list_like(index_col):
+        assert isinstance(index_col, Iterable)
+        i = max(index_col)
+    else:
+        assert not isinstance(index_col, Iterable)
+        i = index_col
+
+    header_name = row[i]
+    header_name = None if header_name == "" else header_name
+
+    return header_name, row[:i] + [""] + row[i + 1 :]
+
+
+def combine_kwargs(engine_kwargs: dict[str, Any] | None, kwargs: dict) -> dict:
+    """
+    Used to combine two sources of kwargs for the backend engine.
+
+    Use of kwargs is deprecated, this function is solely for use in 1.3 and should
+    be removed in 1.4/2.0. Also _base.ExcelWriter.__new__ ensures either engine_kwargs
+    or kwargs must be None or empty respectively.
+
+    Parameters
+    ----------
+    engine_kwargs: dict
+        kwargs to be passed through to the engine.
+    kwargs: dict
+        kwargs to be psased through to the engine (deprecated)
+
+    Returns
+    -------
+    engine_kwargs combined with kwargs
+    """
+    if engine_kwargs is None:
+        result = {}
+    else:
+        result = engine_kwargs.copy()
+    result.update(kwargs)
+    return result

llmeval-env/lib/python3.10/site-packages/pandas/io/excel/_xlrd.py

@@ -0,0 +1,143 @@
+from __future__ import annotations
+
+from datetime import time
+import math
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._decorators import doc
+
+from pandas.core.shared_docs import _shared_docs
+
+from pandas.io.excel._base import BaseExcelReader
+
+if TYPE_CHECKING:
+    from xlrd import Book
+
+    from pandas._typing import (
+        Scalar,
+        StorageOptions,
+    )
+
+
+class XlrdReader(BaseExcelReader["Book"]):
+    @doc(storage_options=_shared_docs["storage_options"])
+    def __init__(
+        self,
+        filepath_or_buffer,
+        storage_options: StorageOptions | None = None,
+        engine_kwargs: dict | None = None,
+    ) -> None:
+        """
+        Reader using xlrd engine.
+
+        Parameters
+        ----------
+        filepath_or_buffer : str, path object or Workbook
+            Object to be parsed.
+        {storage_options}
+        engine_kwargs : dict, optional
+            Arbitrary keyword arguments passed to excel engine.
+        """
+        err_msg = "Install xlrd >= 2.0.1 for xls Excel support"
+        import_optional_dependency("xlrd", extra=err_msg)
+        super().__init__(
+            filepath_or_buffer,
+            storage_options=storage_options,
+            engine_kwargs=engine_kwargs,
+        )
+
+    @property
+    def _workbook_class(self) -> type[Book]:
+        from xlrd import Book
+
+        return Book
+
+    def load_workbook(self, filepath_or_buffer, engine_kwargs) -> Book:
+        from xlrd import open_workbook
+
+        if hasattr(filepath_or_buffer, "read"):
+            data = filepath_or_buffer.read()
+            return open_workbook(file_contents=data, **engine_kwargs)
+        else:
+            return open_workbook(filepath_or_buffer, **engine_kwargs)
+
+    @property
+    def sheet_names(self):
+        return self.book.sheet_names()
+
+    def get_sheet_by_name(self, name):
+        self.raise_if_bad_sheet_by_name(name)
+        return self.book.sheet_by_name(name)
+
+    def get_sheet_by_index(self, index):
+        self.raise_if_bad_sheet_by_index(index)
+        return self.book.sheet_by_index(index)
+
+    def get_sheet_data(
+        self, sheet, file_rows_needed: int | None = None
+    ) -> list[list[Scalar]]:
+        from xlrd import (
+            XL_CELL_BOOLEAN,
+            XL_CELL_DATE,
+            XL_CELL_ERROR,
+            XL_CELL_NUMBER,
+            xldate,
+        )
+
+        epoch1904 = self.book.datemode
+
+        def _parse_cell(cell_contents, cell_typ):
+            """
+            converts the contents of the cell into a pandas appropriate object
+            """
+            if cell_typ == XL_CELL_DATE:
+                # Use the newer xlrd datetime handling.
+                try:
+                    cell_contents = xldate.xldate_as_datetime(cell_contents, epoch1904)
+                except OverflowError:
+                    return cell_contents
+
+                # Excel doesn't distinguish between dates and time,
+                # so we treat dates on the epoch as times only.
+                # Also, Excel supports 1900 and 1904 epochs.
+                year = (cell_contents.timetuple())[0:3]
+                if (not epoch1904 and year == (1899, 12, 31)) or (
+                    epoch1904 and year == (1904, 1, 1)
+                ):
+                    cell_contents = time(
+                        cell_contents.hour,
+                        cell_contents.minute,
+                        cell_contents.second,
+                        cell_contents.microsecond,
+                    )
+
+            elif cell_typ == XL_CELL_ERROR:
+                cell_contents = np.nan
+            elif cell_typ == XL_CELL_BOOLEAN:
+                cell_contents = bool(cell_contents)
+            elif cell_typ == XL_CELL_NUMBER:
+                # GH5394 - Excel 'numbers' are always floats
+                # it's a minimal perf hit and less surprising
+                if math.isfinite(cell_contents):
+                    # GH54564 - don't attempt to convert NaN/Inf
+                    val = int(cell_contents)
+                    if val == cell_contents:
+                        cell_contents = val
+            return cell_contents
+
+        data = []
+
+        nrows = sheet.nrows
+        if file_rows_needed is not None:
+            nrows = min(nrows, file_rows_needed)
+        for i in range(nrows):
+            row = [
+                _parse_cell(value, typ)
+                for value, typ in zip(sheet.row_values(i), sheet.row_types(i))
+            ]
+            data.append(row)
+
+        return data

llmeval-env/lib/python3.10/site-packages/pandas/io/excel/_xlsxwriter.py

@@ -0,0 +1,284 @@
+from __future__ import annotations
+
+import json
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+from pandas.io.excel._base import ExcelWriter
+from pandas.io.excel._util import (
+    combine_kwargs,
+    validate_freeze_panes,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ExcelWriterIfSheetExists,
+        FilePath,
+        StorageOptions,
+        WriteExcelBuffer,
+    )
+
+
+class _XlsxStyler:
+    # Map from openpyxl-oriented styles to flatter xlsxwriter representation
+    # Ordering necessary for both determinism and because some are keyed by
+    # prefixes of others.
+    STYLE_MAPPING: dict[str, list[tuple[tuple[str, ...], str]]] = {
+        "font": [
+            (("name",), "font_name"),
+            (("sz",), "font_size"),
+            (("size",), "font_size"),
+            (("color", "rgb"), "font_color"),
+            (("color",), "font_color"),
+            (("b",), "bold"),
+            (("bold",), "bold"),
+            (("i",), "italic"),
+            (("italic",), "italic"),
+            (("u",), "underline"),
+            (("underline",), "underline"),
+            (("strike",), "font_strikeout"),
+            (("vertAlign",), "font_script"),
+            (("vertalign",), "font_script"),
+        ],
+        "number_format": [(("format_code",), "num_format"), ((), "num_format")],
+        "protection": [(("locked",), "locked"), (("hidden",), "hidden")],
+        "alignment": [
+            (("horizontal",), "align"),
+            (("vertical",), "valign"),
+            (("text_rotation",), "rotation"),
+            (("wrap_text",), "text_wrap"),
+            (("indent",), "indent"),
+            (("shrink_to_fit",), "shrink"),
+        ],
+        "fill": [
+            (("patternType",), "pattern"),
+            (("patterntype",), "pattern"),
+            (("fill_type",), "pattern"),
+            (("start_color", "rgb"), "fg_color"),
+            (("fgColor", "rgb"), "fg_color"),
+            (("fgcolor", "rgb"), "fg_color"),
+            (("start_color",), "fg_color"),
+            (("fgColor",), "fg_color"),
+            (("fgcolor",), "fg_color"),
+            (("end_color", "rgb"), "bg_color"),
+            (("bgColor", "rgb"), "bg_color"),
+            (("bgcolor", "rgb"), "bg_color"),
+            (("end_color",), "bg_color"),
+            (("bgColor",), "bg_color"),
+            (("bgcolor",), "bg_color"),
+        ],
+        "border": [
+            (("color", "rgb"), "border_color"),
+            (("color",), "border_color"),
+            (("style",), "border"),
+            (("top", "color", "rgb"), "top_color"),
+            (("top", "color"), "top_color"),
+            (("top", "style"), "top"),
+            (("top",), "top"),
+            (("right", "color", "rgb"), "right_color"),
+            (("right", "color"), "right_color"),
+            (("right", "style"), "right"),
+            (("right",), "right"),
+            (("bottom", "color", "rgb"), "bottom_color"),
+            (("bottom", "color"), "bottom_color"),
+            (("bottom", "style"), "bottom"),
+            (("bottom",), "bottom"),
+            (("left", "color", "rgb"), "left_color"),
+            (("left", "color"), "left_color"),
+            (("left", "style"), "left"),
+            (("left",), "left"),
+        ],
+    }
+
+    @classmethod
+    def convert(cls, style_dict, num_format_str=None):
+        """
+        converts a style_dict to an xlsxwriter format dict
+
+        Parameters
+        ----------
+        style_dict : style dictionary to convert
+        num_format_str : optional number format string
+        """
+        # Create a XlsxWriter format object.
+        props = {}
+
+        if num_format_str is not None:
+            props["num_format"] = num_format_str
+
+        if style_dict is None:
+            return props
+
+        if "borders" in style_dict:
+            style_dict = style_dict.copy()
+            style_dict["border"] = style_dict.pop("borders")
+
+        for style_group_key, style_group in style_dict.items():
+            for src, dst in cls.STYLE_MAPPING.get(style_group_key, []):
+                # src is a sequence of keys into a nested dict
+                # dst is a flat key
+                if dst in props:
+                    continue
+                v = style_group
+                for k in src:
+                    try:
+                        v = v[k]
+                    except (KeyError, TypeError):
+                        break
+                else:
+                    props[dst] = v
+
+        if isinstance(props.get("pattern"), str):
+            # TODO: support other fill patterns
+            props["pattern"] = 0 if props["pattern"] == "none" else 1
+
+        for k in ["border", "top", "right", "bottom", "left"]:
+            if isinstance(props.get(k), str):
+                try:
+                    props[k] = [
+                        "none",
+                        "thin",
+                        "medium",
+                        "dashed",
+                        "dotted",
+                        "thick",
+                        "double",
+                        "hair",
+                        "mediumDashed",
+                        "dashDot",
+                        "mediumDashDot",
+                        "dashDotDot",
+                        "mediumDashDotDot",
+                        "slantDashDot",
+                    ].index(props[k])
+                except ValueError:
+                    props[k] = 2
+
+        if isinstance(props.get("font_script"), str):
+            props["font_script"] = ["baseline", "superscript", "subscript"].index(
+                props["font_script"]
+            )
+
+        if isinstance(props.get("underline"), str):
+            props["underline"] = {
+                "none": 0,
+                "single": 1,
+                "double": 2,
+                "singleAccounting": 33,
+                "doubleAccounting": 34,
+            }[props["underline"]]
+
+        # GH 30107 - xlsxwriter uses different name
+        if props.get("valign") == "center":
+            props["valign"] = "vcenter"
+
+        return props
+
+
+class XlsxWriter(ExcelWriter):
+    _engine = "xlsxwriter"
+    _supported_extensions = (".xlsx",)
+
+    def __init__(
+        self,
+        path: FilePath | WriteExcelBuffer | ExcelWriter,
+        engine: str | None = None,
+        date_format: str | None = None,
+        datetime_format: str | None = None,
+        mode: str = "w",
+        storage_options: StorageOptions | None = None,
+        if_sheet_exists: ExcelWriterIfSheetExists | None = None,
+        engine_kwargs: dict[str, Any] | None = None,
+        **kwargs,
+    ) -> None:
+        # Use the xlsxwriter module as the Excel writer.
+        from xlsxwriter import Workbook
+
+        engine_kwargs = combine_kwargs(engine_kwargs, kwargs)
+
+        if mode == "a":
+            raise ValueError("Append mode is not supported with xlsxwriter!")
+
+        super().__init__(
+            path,
+            engine=engine,
+            date_format=date_format,
+            datetime_format=datetime_format,
+            mode=mode,
+            storage_options=storage_options,
+            if_sheet_exists=if_sheet_exists,
+            engine_kwargs=engine_kwargs,
+        )
+
+        try:
+            self._book = Workbook(self._handles.handle, **engine_kwargs)
+        except TypeError:
+            self._handles.handle.close()
+            raise
+
+    @property
+    def book(self):
+        """
+        Book instance of class xlsxwriter.Workbook.
+
+        This attribute can be used to access engine-specific features.
+        """
+        return self._book
+
+    @property
+    def sheets(self) -> dict[str, Any]:
+        result = self.book.sheetnames
+        return result
+
+    def _save(self) -> None:
+        """
+        Save workbook to disk.
+        """
+        self.book.close()
+
+    def _write_cells(
+        self,
+        cells,
+        sheet_name: str | None = None,
+        startrow: int = 0,
+        startcol: int = 0,
+        freeze_panes: tuple[int, int] | None = None,
+    ) -> None:
+        # Write the frame cells using xlsxwriter.
+        sheet_name = self._get_sheet_name(sheet_name)
+
+        wks = self.book.get_worksheet_by_name(sheet_name)
+        if wks is None:
+            wks = self.book.add_worksheet(sheet_name)
+
+        style_dict = {"null": None}
+
+        if validate_freeze_panes(freeze_panes):
+            wks.freeze_panes(*(freeze_panes))
+
+        for cell in cells:
+            val, fmt = self._value_with_fmt(cell.val)
+
+            stylekey = json.dumps(cell.style)
+            if fmt:
+                stylekey += fmt
+
+            if stylekey in style_dict:
+                style = style_dict[stylekey]
+            else:
+                style = self.book.add_format(_XlsxStyler.convert(cell.style, fmt))
+                style_dict[stylekey] = style
+
+            if cell.mergestart is not None and cell.mergeend is not None:
+                wks.merge_range(
+                    startrow + cell.row,
+                    startcol + cell.col,
+                    startrow + cell.mergestart,
+                    startcol + cell.mergeend,
+                    val,
+                    style,
+                )
+            else:
+                wks.write(startrow + cell.row, startcol + cell.col, val, style)

llmeval-env/lib/python3.10/site-packages/pandas/io/gbq.py

@@ -0,0 +1,255 @@
+""" Google BigQuery support """
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+import warnings
+
+from pandas.compat._optional import import_optional_dependency
+from pandas.util._exceptions import find_stack_level
+
+if TYPE_CHECKING:
+    import google.auth
+
+    from pandas import DataFrame
+
+
+def _try_import():
+    # since pandas is a dependency of pandas-gbq
+    # we need to import on first use
+    msg = (
+        "pandas-gbq is required to load data from Google BigQuery. "
+        "See the docs: https://pandas-gbq.readthedocs.io."
+    )
+    pandas_gbq = import_optional_dependency("pandas_gbq", extra=msg)
+    return pandas_gbq
+
+
+def read_gbq(
+    query: str,
+    project_id: str | None = None,
+    index_col: str | None = None,
+    col_order: list[str] | None = None,
+    reauth: bool = False,
+    auth_local_webserver: bool = True,
+    dialect: str | None = None,
+    location: str | None = None,
+    configuration: dict[str, Any] | None = None,
+    credentials: google.auth.credentials.Credentials | None = None,
+    use_bqstorage_api: bool | None = None,
+    max_results: int | None = None,
+    progress_bar_type: str | None = None,
+) -> DataFrame:
+    """
+    Load data from Google BigQuery.
+
+    .. deprecated:: 2.2.0
+
+       Please use ``pandas_gbq.read_gbq`` instead.
+
+    This function requires the `pandas-gbq package
+    <https://pandas-gbq.readthedocs.io>`__.
+
+    See the `How to authenticate with Google BigQuery
+    <https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
+    guide for authentication instructions.
+
+    Parameters
+    ----------
+    query : str
+        SQL-Like Query to return data values.
+    project_id : str, optional
+        Google BigQuery Account project ID. Optional when available from
+        the environment.
+    index_col : str, optional
+        Name of result column to use for index in results DataFrame.
+    col_order : list(str), optional
+        List of BigQuery column names in the desired order for results
+        DataFrame.
+    reauth : bool, default False
+        Force Google BigQuery to re-authenticate the user. This is useful
+        if multiple accounts are used.
+    auth_local_webserver : bool, default True
+        Use the `local webserver flow`_ instead of the `console flow`_
+        when getting user credentials.
+
+        .. _local webserver flow:
+            https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
+        .. _console flow:
+            https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
+
+        *New in version 0.2.0 of pandas-gbq*.
+
+        .. versionchanged:: 1.5.0
+           Default value is changed to ``True``. Google has deprecated the
+           ``auth_local_webserver = False`` `"out of band" (copy-paste)
+           flow
+           <https://developers.googleblog.com/2022/02/making-oauth-flows-safer.html?m=1#disallowed-oob>`_.
+    dialect : str, default 'legacy'
+        Note: The default value is changing to 'standard' in a future version.
+
+        SQL syntax dialect to use. Value can be one of:
+
+        ``'legacy'``
+            Use BigQuery's legacy SQL dialect. For more information see
+            `BigQuery Legacy SQL Reference
+            <https://cloud.google.com/bigquery/docs/reference/legacy-sql>`__.
+        ``'standard'``
+            Use BigQuery's standard SQL, which is
+            compliant with the SQL 2011 standard. For more information
+            see `BigQuery Standard SQL Reference
+            <https://cloud.google.com/bigquery/docs/reference/standard-sql/>`__.
+    location : str, optional
+        Location where the query job should run. See the `BigQuery locations
+        documentation
+        <https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
+        list of available locations. The location must match that of any
+        datasets used in the query.
+
+        *New in version 0.5.0 of pandas-gbq*.
+    configuration : dict, optional
+        Query config parameters for job processing.
+        For example:
+
+            configuration = {'query': {'useQueryCache': False}}
+
+        For more information see `BigQuery REST API Reference
+        <https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query>`__.
+    credentials : google.auth.credentials.Credentials, optional
+        Credentials for accessing Google APIs. Use this parameter to override
+        default credentials, such as to use Compute Engine
+        :class:`google.auth.compute_engine.Credentials` or Service Account
+        :class:`google.oauth2.service_account.Credentials` directly.
+
+        *New in version 0.8.0 of pandas-gbq*.
+    use_bqstorage_api : bool, default False
+        Use the `BigQuery Storage API
+        <https://cloud.google.com/bigquery/docs/reference/storage/>`__ to
+        download query results quickly, but at an increased cost. To use this
+        API, first `enable it in the Cloud Console
+        <https://console.cloud.google.com/apis/library/bigquerystorage.googleapis.com>`__.
+        You must also have the `bigquery.readsessions.create
+        <https://cloud.google.com/bigquery/docs/access-control#roles>`__
+        permission on the project you are billing queries to.
+
+        This feature requires version 0.10.0 or later of the ``pandas-gbq``
+        package. It also requires the ``google-cloud-bigquery-storage`` and
+        ``fastavro`` packages.
+
+    max_results : int, optional
+        If set, limit the maximum number of rows to fetch from the query
+        results.
+
+    progress_bar_type : Optional, str
+        If set, use the `tqdm <https://tqdm.github.io/>`__ library to
+        display a progress bar while the data downloads. Install the
+        ``tqdm`` package to use this feature.
+
+        Possible values of ``progress_bar_type`` include:
+
+        ``None``
+            No progress bar.
+        ``'tqdm'``
+            Use the :func:`tqdm.tqdm` function to print a progress bar
+            to :data:`sys.stderr`.
+        ``'tqdm_notebook'``
+            Use the :func:`tqdm.tqdm_notebook` function to display a
+            progress bar as a Jupyter notebook widget.
+        ``'tqdm_gui'``
+            Use the :func:`tqdm.tqdm_gui` function to display a
+            progress bar as a graphical dialog box.
+
+    Returns
+    -------
+    df: DataFrame
+        DataFrame representing results of query.
+
+    See Also
+    --------
+    pandas_gbq.read_gbq : This function in the pandas-gbq library.
+    DataFrame.to_gbq : Write a DataFrame to Google BigQuery.
+
+    Examples
+    --------
+    Example taken from `Google BigQuery documentation
+    <https://cloud.google.com/bigquery/docs/pandas-gbq-migration>`_
+
+    >>> sql = "SELECT name FROM table_name WHERE state = 'TX' LIMIT 100;"
+    >>> df = pd.read_gbq(sql, dialect="standard")  # doctest: +SKIP
+    >>> project_id = "your-project-id"  # doctest: +SKIP
+    >>> df = pd.read_gbq(sql,
+    ...                  project_id=project_id,
+    ...                  dialect="standard"
+    ...                  )  # doctest: +SKIP
+    """
+    warnings.warn(
+        "read_gbq is deprecated and will be removed in a future version. "
+        "Please use pandas_gbq.read_gbq instead: "
+        "https://pandas-gbq.readthedocs.io/en/latest/api.html#pandas_gbq.read_gbq",
+        FutureWarning,
+        stacklevel=find_stack_level(),
+    )
+    pandas_gbq = _try_import()
+
+    kwargs: dict[str, str | bool | int | None] = {}
+
+    # START: new kwargs.  Don't populate unless explicitly set.
+    if use_bqstorage_api is not None:
+        kwargs["use_bqstorage_api"] = use_bqstorage_api
+    if max_results is not None:
+        kwargs["max_results"] = max_results
+
+    kwargs["progress_bar_type"] = progress_bar_type
+    # END: new kwargs
+
+    return pandas_gbq.read_gbq(
+        query,
+        project_id=project_id,
+        index_col=index_col,
+        col_order=col_order,
+        reauth=reauth,
+        auth_local_webserver=auth_local_webserver,
+        dialect=dialect,
+        location=location,
+        configuration=configuration,
+        credentials=credentials,
+        **kwargs,
+    )
+
+
+def to_gbq(
+    dataframe: DataFrame,
+    destination_table: str,
+    project_id: str | None = None,
+    chunksize: int | None = None,
+    reauth: bool = False,
+    if_exists: str = "fail",
+    auth_local_webserver: bool = True,
+    table_schema: list[dict[str, str]] | None = None,
+    location: str | None = None,
+    progress_bar: bool = True,
+    credentials: google.auth.credentials.Credentials | None = None,
+) -> None:
+    warnings.warn(
+        "to_gbq is deprecated and will be removed in a future version. "
+        "Please use pandas_gbq.to_gbq instead: "
+        "https://pandas-gbq.readthedocs.io/en/latest/api.html#pandas_gbq.to_gbq",
+        FutureWarning,
+        stacklevel=find_stack_level(),
+    )
+    pandas_gbq = _try_import()
+    pandas_gbq.to_gbq(
+        dataframe,
+        destination_table,
+        project_id=project_id,
+        chunksize=chunksize,
+        reauth=reauth,
+        if_exists=if_exists,
+        auth_local_webserver=auth_local_webserver,
+        table_schema=table_schema,
+        location=location,
+        progress_bar=progress_bar,
+        credentials=credentials,
+    )

llmeval-env/lib/python3.10/site-packages/pandas/io/json/__init__.py

@@ -0,0 +1,15 @@
+from pandas.io.json._json import (
+    read_json,
+    to_json,
+    ujson_dumps,
+    ujson_loads,
+)
+from pandas.io.json._table_schema import build_table_schema
+
+__all__ = [
+    "ujson_dumps",
+    "ujson_loads",
+    "read_json",
+    "to_json",
+    "build_table_schema",
+]