text: import abc
  import datetime
  from distutils.version import LooseVersion
  import inspect
  from io import BytesIO
  import os
  from textwrap import fill
  from typing import Any, Dict, Mapping, Optional, Union, cast
  import warnings
  import zipfile
  
  from pandas._config import config
  
  from pandas._libs.parsers import STR_NA_VALUES
  from pandas._typing import Buffer, DtypeArg, FilePathOrBuffer, StorageOptions
  from pandas.compat._optional import import_optional_dependency
  from pandas.errors import EmptyDataError
  from pandas.util._decorators import Appender, deprecate_nonkeyword_arguments, doc
  
  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.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
  
  _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``.
  sheet_name : str, int, list, or None, default 0
      Strings are used for sheet names. Integers are used in zero-indexed
      sheet positions. Lists of strings/integers are used to request
      multiple sheets. Specify None to get all sheets.
  
      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 sheets.
  
  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, 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.
  usecols : int, 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.
      * If list of string, then indicates list of column names to be parsed.
  
        .. versionadded:: 0.24.0
  
      * 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.
  
        .. versionadded:: 0.24.0
  
  squeeze : bool, default False
      If the parsed data only contains one column then return a Series.
  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.
      If converters are specified, they will be applied INSTEAD
      of dtype conversion.
  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".
      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.
  
      .. 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`` 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.
         - Otherwise ``xlrd`` will be used and a ``FutureWarning`` will be raised. This
           case will raise a ``ValueError`` in a future version of pandas.
  
  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 unparseable 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.
  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.
  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).
  convert_float : bool, default True
      Convert integral floats to int (i.e., 1.0 --> 1). If False, all numeric
      data will be read in as floats: Excel stores all numbers as floats
      internally.
  mangle_dupe_cols : bool, default True
      Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
      'X'...'X'. Passing in False will cause data to be overwritten if there
      are duplicate names in the columns.
  storage_options : dict, optional
      Extra options that make sense for a particular storage connection, e.g.
      host, port, username, password, etc., if using a URL that will
      be parsed by ``fsspec``, e.g., starting "s3://", "gcs://". An error
      will be raised if providing this argument with a local path or
      a file-like buffer. See the fsspec and backend storage implementation
      docs for the set of allowed keys and values.
  
      .. versionadded:: 1.2.0
  
  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.
  
  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
  """
  )
  
  
  @deprecate_nonkeyword_arguments(allowed_args=2, version="2.0")
  @Appender(_read_excel_doc)
  def read_excel(
      io,
      sheet_name=0,
      header=0,
      names=None,
      index_col=None,
      usecols=None,
      squeeze=False,
      dtype: Optional[DtypeArg] = None,
      engine=None,
      converters=None,
      true_values=None,
      false_values=None,
      skiprows=None,
      nrows=None,
      na_values=None,
      keep_default_na=True,
      na_filter=True,
      verbose=False,
      parse_dates=False,
      date_parser=None,
      thousands=None,
      comment=None,
      skipfooter=0,
      convert_float=True,
      mangle_dupe_cols=True,
      storage_options: StorageOptions = None,
  ):
  
      should_close = False
      if not isinstance(io, ExcelFile):
          should_close = True
          io = ExcelFile(io, storage_options=storage_options, engine=engine)
      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,
              squeeze=squeeze,
              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,
              thousands=thousands,
              comment=comment,
              skipfooter=skipfooter,
              convert_float=convert_float,
              mangle_dupe_cols=mangle_dupe_cols,
          )
      finally:
          # make sure to close opened file handles
          if should_close:
              io.close()
      return data
  
  
  class BaseExcelReader(metaclass=abc.ABCMeta):
      def __init__(self, filepath_or_buffer, storage_options: StorageOptions = None):
          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)
              self.book = self.load_workbook(self.handles.handle)
          elif isinstance(self.handles.handle, bytes):
              self.book = self.load_workbook(BytesIO(self.handles.handle))
          else:
              raise ValueError(
                  "Must explicitly set engine if not passing in buffer or path for io."
              )
  
      @property
      @abc.abstractmethod
      def _workbook_class(self):
          pass
  
      @abc.abstractmethod
      def load_workbook(self, filepath_or_buffer):
          pass
  
      def close(self):
          if hasattr(self.book, "close"):
              # pyxlsb opens a TemporaryFile
              self.book.close()
          self.handles.close()
  
      @property
      @abc.abstractmethod
      def sheet_names(self):
          pass
  
      @abc.abstractmethod
      def get_sheet_by_name(self, name):
          pass
  
      @abc.abstractmethod
      def get_sheet_by_index(self, index):
          pass
  
      @abc.abstractmethod
      def get_sheet_data(self, sheet, convert_float):
          pass
  
      def parse(
          self,
          sheet_name=0,
          header=0,
          names=None,
          index_col=None,
          usecols=None,
          squeeze=False,
          dtype: Optional[DtypeArg] = None,
          true_values=None,
          false_values=None,
          skiprows=None,
          nrows=None,
          na_values=None,
          verbose=False,
          parse_dates=False,
          date_parser=None,
          thousands=None,
          comment=None,
          skipfooter=0,
          convert_float=True,
          mangle_dupe_cols=True,
          **kwds,
      ):
  
          validate_header_arg(header)
  
          ret_dict = False
  
          # Keep sheetname to maintain backwards compatibility.
          if isinstance(sheet_name, list):
              sheets = sheet_name
              ret_dict = True
          elif sheet_name is None:
              sheets = self.sheet_names
              ret_dict = True
          else:
              sheets = [sheet_name]
  
          # handle same-type duplicates.
          sheets = list(dict.fromkeys(sheets).keys())
  
          output = {}
  
          for asheetname in sheets:
              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)
  
              data = self.get_sheet_data(sheet, convert_float)
              if hasattr(sheet, "close"):
                  # pyxlsb opens two TemporaryFiles
                  sheet.close()
              usecols = maybe_convert_usecols(usecols)
  
              if not data:
                  output[asheetname] = DataFrame()
                  continue
  
              if is_list_like(header) and len(header) == 1:
                  header = header[0]
  
              # forward fill and pull out names for MultiIndex column
              header_names = None
              if header is not None and is_list_like(header):
                  header_names = []
                  control_row = [True] * len(data[0])
  
                  for row in header:
                      if is_integer(skiprows):
                          row += skiprows
  
                      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)
  
              has_index_names = is_list_like(header) and len(header) > 1
  
              if is_list_like(index_col):
                  # Forward fill values for MultiIndex index.
                  if header is None:
                      offset = 0
                  elif not is_list_like(header):
                      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):
                      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,
                      squeeze=squeeze,
                      dtype=dtype,
                      true_values=true_values,
                      false_values=false_values,
                      skiprows=skiprows,
                      nrows=nrows,
                      na_values=na_values,
                      parse_dates=parse_dates,
                      date_parser=date_parser,
                      thousands=thousands,
                      comment=comment,
                      skipfooter=skipfooter,
                      usecols=usecols,
                      mangle_dupe_cols=mangle_dupe_cols,
                      **kwds,
                  )
  
                  output[asheetname] = parser.read(nrows=nrows)
  
                  if not squeeze or isinstance(output[asheetname], DataFrame):
                      if header_names:
                          output[asheetname].columns = output[
                              asheetname
                          ].columns.set_names(header_names)
  
              except EmptyDataError:
                  # No Data, return an empty DataFrame
                  output[asheetname] = DataFrame()
  
          if ret_dict:
              return output
          else:
              return output[asheetname]
  
  
  class ExcelWriter(metaclass=abc.ABCMeta):
      """
      Class for writing DataFrame objects into excel sheets.
  
      Default is to use xlwt for xls, openpyxl for xlsx, odf for ods.
      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.
  
          .. deprecated:: 1.2.0
  
              As the `xlwt <https://pypi.org/project/xlwt/>`__ package is no longer
              maintained, the ``xlwt`` engine will be removed in a future
              version of pandas.
  
      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.
  
          .. versionadded:: 0.24.0
      storage_options : dict, optional
          Extra options that make sense for a particular storage connection, e.g.
          host, port, username, password, etc., if using a URL that will
          be parsed by ``fsspec``, e.g., starting "s3://", "gcs://".
  
          .. versionadded:: 1.2.0
  
      Attributes
      ----------
      None
  
      Methods
      -------
      None
  
      Notes
      -----
      None of the methods and properties are considered public.
  
      For compatibility with CSV writers, ExcelWriter serializes lists
      and dicts to strings before writing.
  
      Examples
      --------
      Default usage:
  
      >>> with ExcelWriter('path_to_file.xlsx') as writer:
      ...     df.to_excel(writer)
  
      To write to separate sheets in a single file:
  
      >>> with ExcelWriter('path_to_file.xlsx') as writer:
      ...     df1.to_excel(writer, sheet_name='Sheet1')
      ...     df2.to_excel(writer, sheet_name='Sheet2')
  
      You can set the date format or datetime format:
  
      >>> with ExcelWriter('path_to_file.xlsx',
      ...                   date_format='YYYY-MM-DD',
      ...                   datetime_format='YYYY-MM-DD HH:MM:SS') as writer:
      ...     df.to_excel(writer)
  
      You can also append to an existing Excel file:
  
      >>> with ExcelWriter('path_to_file.xlsx', mode='a') as writer:
      ...     df.to_excel(writer, sheet_name='Sheet3')
  
      You can store Excel file in RAM:
  
      >>> import io
      >>> buffer = io.BytesIO()
      >>> with pd.ExcelWriter(buffer) as writer:
      ...     df.to_excel(writer)
  
      You can pack Excel file into zip archive:
  
      >>> import zipfile
      >>> 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)
      """
  
      # 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.
      def __new__(cls, path, engine=None, **kwargs):
          # 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
  
              if engine == "xlwt":
                  xls_config_engine = config.get_option(
                      "io.excel.xls.writer", silent=True
                  )
                  # Don't warn a 2nd time if user has changed the default engine for xls
                  if xls_config_engine != "xlwt":
                      warnings.warn(
                          "As the xlwt package is no longer maintained, the xlwt "
                          "engine will be removed in a future version of pandas. "
                          "This is the only engine in pandas that supports writing "
                          "in the xls format. Install openpyxl and write to an xlsx "
                          "file instead. You can set the option io.excel.xls.writer "
                          "to 'xlwt' to silence this warning. While this option is "
                          "deprecated and will also raise a warning, it can "
                          "be globally set and the warning suppressed.",
                          FutureWarning,
                          stacklevel=4,
                      )
  
              cls = get_writer(engine)
  
          return object.__new__(cls)
  
      # declare external properties you can count on
      curr_sheet = None
      path = None
  
      @property
      @abc.abstractmethod
      def supported_extensions(self):
          """Extensions that writer engine supports."""
          pass
  
      @property
      @abc.abstractmethod
      def engine(self):
          """Name of engine."""
          pass
  
      @abc.abstractmethod
      def write_cells(
          self, cells, sheet_name=None, startrow=0, startcol=0, freeze_panes=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
          """
          pass
  
      @abc.abstractmethod
      def save(self):
          """
          Save workbook to disk.
          """
          pass
  
      def __init__(
          self,
          path: Union[FilePathOrBuffer, "ExcelWriter"],
          engine=None,
          date_format=None,
          datetime_format=None,
          mode: str = "w",
          storage_options: StorageOptions = None,
          **engine_kwargs,
      ):
          # 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+")
  
          # cast ExcelWriter to avoid adding 'if self.handles is not None'
          self.handles = IOHandles(cast(Buffer, path), compression={"copression": None})
          if not isinstance(path, ExcelWriter):
              self.handles = get_handle(
                  path, mode, storage_options=storage_options, is_text=False
              )
          self.sheets: Dict[str, Any] = {}
          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
  
      def __fspath__(self):
          return getattr(self.handles.handle, "name", "")
  
      def _get_sheet_name(self, sheet_name):
          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):
          """
          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):
          """
          checks that path's extension against the Writer's supported
          extensions.  If it isn't supported, raises UnsupportedFiletypeError.
          """
          if ext.startswith("."):
              ext = ext[1:]
          # error: "Callable[[ExcelWriter], Any]" has no attribute "__iter__"
          #  (not iterable)  [attr-defined]
          if not any(
              ext in extension
              for extension in cls.supported_extensions  # type: ignore[attr-defined]
          ):
              raise ValueError(f"Invalid extension for engine '{cls.engine}': '{ext}'")
          else:
              return True
  
      # Allow use as a contextmanager
      def __enter__(self):
          return self
  
      def __exit__(self, exc_type, exc_value, traceback):
          self.close()
  
      def close(self):
          """synonym for save, to make it more file-like"""
          content = self.save()
          self.handles.close()
          return content
  
  
  XLS_SIGNATURE = b"\xD0\xCF\x11\xE0\xA1\xB1\x1A\xE1"
  ZIP_SIGNATURE = b"PK\x03\x04"
  PEEK_SIZE = max(len(XLS_SIGNATURE), len(ZIP_SIGNATURE))
  
  
  @doc(storage_options=_shared_docs["storage_options"])
  def inspect_excel_format(
      content_or_path: FilePathOrBuffer,
      storage_options: StorageOptions = None,
  ) -> str:
      """
      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
          Format of file.
  
      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")
          else:
              assert isinstance(buf, bytes)
              peek = buf
          stream.seek(0)
  
          if peek.startswith(XLS_SIGNATURE):
              return "xls"
          elif not peek.startswith(ZIP_SIGNATURE):
              raise ValueError("File is not a recognized excel file")
  
          # ZipFile typing is overly-strict
          # https://github.com/python/typeshed/issues/4212
          zf = zipfile.ZipFile(stream)  # type: ignore[arg-type]
  
          # 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, path object (pathlib.Path or py._path.local.LocalPath),
          a file-like object, xlrd workbook or openpypl 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``
          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.
  
          .. 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.
             - Otherwise ``xlrd`` will be used and a ``FutureWarning`` will be raised.
               This case will raise a ``ValueError`` in a future version of pandas.
  
             .. warning::
  
              Please do not report issues when using ``xlrd`` to read ``.xlsx`` files.
              This is not supported, switch to using ``openpyxl`` instead.
      """
  
      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,
      }
  
      def __init__(
          self, path_or_buffer, engine=None, storage_options: StorageOptions = None
      ):
          if engine is not None and engine not in self._engines:
              raise ValueError(f"Unknown engine: {engine}")
  
          # 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 = LooseVersion(xlrd.__version__)
  
          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 engine is None:
              # ext will always be valid, otherwise inspect_excel_format would raise
              engine = config.get_option(f"io.excel.{ext}.reader", silent=True)
              if engine == "auto":
                  engine = get_default_engine(ext, mode="reader")
  
          if engine == "xlrd" and ext != "xls" and xlrd_version is not None:
              if xlrd_version >= "2":
                  raise ValueError(
                      f"Your version of xlrd is {xlrd_version}. In xlrd >= 2.0, "
                      f"only the xls format is supported. Install openpyxl instead."
                  )
              else:
                  caller = inspect.stack()[1]
                  if (
                      caller.filename.endswith(
                          os.path.join("pandas", "io", "excel", "_base.py")
                      )
                      and caller.function == "read_excel"
                  ):
                      stacklevel = 4
                  else:
                      stacklevel = 2
                  warnings.warn(
                      f"Your version of xlrd is {xlrd_version}. In xlrd >= 2.0, "
                      f"only the xls format is supported. As a result, the "
                      f"openpyxl engine will be used if it is installed and the "
                      f"engine argument is not specified. Install "
                      f"openpyxl instead.",
                      FutureWarning,
                      stacklevel=stacklevel,
                  )
  
          self.engine = engine
          self.storage_options = storage_options
  
          self._reader = self._engines[engine](self._io, storage_options=storage_options)
  
      def __fspath__(self):
          return self._io
  
      def parse(
          self,
          sheet_name=0,
          header=0,
          names=None,
          index_col=None,
          usecols=None,
          squeeze=False,
          converters=None,
          true_values=None,
          false_values=None,
          skiprows=None,
          nrows=None,
          na_values=None,
          parse_dates=False,
          date_parser=None,
          thousands=None,
          comment=None,
          skipfooter=0,
          convert_float=True,
          mangle_dupe_cols=True,
          **kwds,
      ):
          """
          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.
          """
          return self._reader.parse(
              sheet_name=sheet_name,
              header=header,
              names=names,
              index_col=index_col,
              usecols=usecols,
              squeeze=squeeze,
              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,
              thousands=thousands,
              comment=comment,
              skipfooter=skipfooter,
              convert_float=convert_float,
              mangle_dupe_cols=mangle_dupe_cols,
              **kwds,
          )
  
      @property
      def book(self):
          return self._reader.book
  
      @property
      def sheet_names(self):
          return self._reader.sheet_names
  
      def close(self):
          """close io if necessary"""
          self._reader.close()
  
      def __enter__(self):
          return self
  
      def __exit__(self, exc_type, exc_value, traceback):
          self.close()
  
      def __del__(self):
          # Ensure we don't leak file descriptors, but put in try/except in case
          # attributes are already deleted
          try:
              self.close()
          except AttributeError:
              pass
  