### `openpyxl.load_workbook(filename, read_only=False, keep_vba=False, data_only=False, keep_links=True, rich_text=False)`

Open the given filename and return the workbook

:param filename: the path to open or a file-like object
:type filename: string or a file-like object open in binary mode c.f., :class:`zipfile.ZipFile`

:param read_only: optimised for reading, content cannot be edited
:type read_only: bool

:param keep_vba: preserve vba content (this does NOT mean you can use it)
:type keep_vba: bool

:param data_only: controls whether cells with formulae have either the formula (default) or the value stored the last time Excel read the sheet
:type data_only: bool

:param keep_links: whether links to external workbooks should be preserved. The default is True
:type keep_links: bool

:param rich_text: if set to True openpyxl will preserve any rich text formatting in cells. The default is False
:type rich_text: bool

:rtype: :class:`openpyxl.workbook.Workbook`

.. note::

    When using lazy load, all worksheets will be :class:`openpyxl.worksheet.iter_worksheet.IterableWorksheet`
    and the returned workbook will be read-only.

---
### `pandas.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' = <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' = <no_default>, engine_kwargs: 'dict | None' = None) -> 'DataFrame | dict[IntStrT, DataFrame]'`

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: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',
    '1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'None',
    'n/a', 'nan', 'null'.
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 : dict, optional
    Extra options that make sense for a particular storage connection, e.g.
    host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
    are forwarded to ``urllib.request.Request`` as header options. For other
    URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
    forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
    details, and for more examples on storage options refer `here
    <https://pandas.pydata.org/docs/user_guide/io.html?
    highlight=storage_options#reading-writing-remote-files>`_.

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

---
### `pandas.DataFrame.to_excel(self, excel_writer: 'FilePath | WriteExcelBuffer | ExcelWriter', *, sheet_name: 'str' = 'Sheet1', na_rep: 'str' = '', float_format: 'str | None' = None, columns: 'Sequence[Hashable] | None' = None, header: 'Sequence[Hashable] | bool_t' = True, index: 'bool_t' = True, index_label: 'IndexLabel | None' = None, startrow: 'int' = 0, startcol: 'int' = 0, engine: "Literal['openpyxl', 'xlsxwriter'] | None" = None, merge_cells: 'bool_t' = True, inf_rep: 'str' = 'inf', freeze_panes: 'tuple[int, int] | None' = None, storage_options: 'StorageOptions | None' = None, engine_kwargs: 'dict[str, Any] | None' = None) -> 'None'`

Write object to an Excel sheet.

To write a single object to an Excel .xlsx file it is only necessary to
specify a target file name. To write to multiple sheets it is necessary to
create an `ExcelWriter` object with a target file name, and specify a sheet
in the file to write to.

Multiple sheets may be written to by specifying unique `sheet_name`.
With all data written to the file it is necessary to save the changes.
Note that creating an `ExcelWriter` object with a file name that already
exists will result in the contents of the existing file being erased.

Parameters
----------
excel_writer : path-like, file-like, or ExcelWriter object
    File path or existing ExcelWriter.
sheet_name : str, default 'Sheet1'
    Name of sheet which will contain DataFrame.
na_rep : str, default ''
    Missing data representation.
float_format : str, optional
    Format string for floating point numbers. For example
    ``float_format="%.2f"`` will format 0.1234 to 0.12.
columns : sequence or list of str, optional
    Columns to write.
header : bool or list of str, default True
    Write out the column names. If a list of string is given it is
    assumed to be aliases for the column names.
index : bool, default True
    Write row names (index).
index_label : str or sequence, optional
    Column label for index column(s) if desired. If not specified, and
    `header` and `index` are True, then the index names are used. A
    sequence should be given if the DataFrame uses MultiIndex.
startrow : int, default 0
    Upper left cell row to dump data frame.
startcol : int, default 0
    Upper left cell column to dump data frame.
engine : str, optional
    Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this
    via the options ``io.excel.xlsx.writer`` or
    ``io.excel.xlsm.writer``.

merge_cells : bool, default True
    Write MultiIndex and Hierarchical Rows as merged cells.
inf_rep : str, default 'inf'
    Representation for infinity (there is no native representation for
    infinity in Excel).
freeze_panes : tuple of int (length 2), optional
    Specifies the one-based bottommost row and rightmost column that
    is to be frozen.
storage_options : dict, optional
    Extra options that make sense for a particular storage connection, e.g.
    host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
    are forwarded to ``urllib.request.Request`` as header options. For other
    URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
    forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
    details, and for more examples on storage options refer `here
    <https://pandas.pydata.org/docs/user_guide/io.html?
    highlight=storage_options#reading-writing-remote-files>`_.

    .. versionadded:: 1.2.0
engine_kwargs : dict, optional
    Arbitrary keyword arguments passed to excel engine.

See Also
--------
to_csv : Write DataFrame to a comma-separated values (csv) file.
ExcelWriter : Class for writing DataFrame objects into excel sheets.
read_excel : Read an Excel file into a pandas DataFrame.
read_csv : Read a comma-separated values (csv) file into DataFrame.
io.formats.style.Styler.to_excel : Add styles to Excel sheet.

Notes
-----
For compatibility with :meth:`~DataFrame.to_csv`,
to_excel serializes lists and dicts to strings before writing.

Once a workbook has been saved it is not possible to write further
data without rewriting the whole workbook.

Examples
--------

Create, write to and save a workbook:

>>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
...                    index=['row 1', 'row 2'],
...                    columns=['col 1', 'col 2'])
>>> df1.to_excel("output.xlsx")  # doctest: +SKIP

To specify the sheet name:

>>> df1.to_excel("output.xlsx",
...              sheet_name='Sheet_name_1')  # doctest: +SKIP

If you wish to write to more than one sheet in the workbook, it is
necessary to specify an ExcelWriter object:

>>> df2 = df1.copy()
>>> with pd.ExcelWriter('output.xlsx') as writer:  # doctest: +SKIP
...     df1.to_excel(writer, sheet_name='Sheet_name_1')
...     df2.to_excel(writer, sheet_name='Sheet_name_2')

ExcelWriter can also be used to append to an existing Excel file:

>>> with pd.ExcelWriter('output.xlsx',
...                     mode='a') as writer:  # doctest: +SKIP
...     df1.to_excel(writer, sheet_name='Sheet_name_3')

To set the library that is used to write the Excel file,
you can pass the `engine` keyword (the default engine is
automatically chosen depending on the file extension):

>>> df1.to_excel('output1.xlsx', engine='xlsxwriter')  # doctest: +SKIP

---
