PiddlmZddlmZmZmZmZmZddlZddl m Z ddl m Z ddl Z ddlmZmZmZmZmZmZmZmZmZmZddlZddlZddlmZddlmZdd lm Z m!Z!dd l"m#Z#dd l$m%Z%dd l&m'Z'dd l(m)Z)ddl*m+Z+m,Z,m-Z-m.Z.m/Z/m0Z0ddl1m2Z2ddl3m4Z4ddl5m6Z6m7Z7m8Z8m9Z9ddl:m;Z;mZ>m?Z?ddl@mAZAddlBmCZCerddlDmEZEddlFmGZGmHZHmIZImJZJmKZKmLZLmMZMmNZNmOZOmPZPe d`dddddddddddddddddddddddddadEZQedddddddddddddddddddddddddbdHZQe%dI dcdddddddddddddJdJdKdKdddLdddejRddMdddSZQedTZSGdUdVeeSZTe%dIGdWdXeeSZUdYZVdZZWeXeYeZgeVeWRZ[ dedfd]Z\e%dIGd^d_Z]dS)g) annotations)CallableHashableIterableMappingSequenceN)Decimal)partial) IO TYPE_CHECKINGAnyGenericLiteralSelfTypeVarUnioncastoverload)config)lib) get_versionimport_optional_dependency)EmptyDataError) set_module)find_stack_level)check_dtype_backend)is_bool is_decimal is_file_likeis_float is_integer is_list_like) DataFrame)Version) IOHandles get_handlestringify_pathvalidate_header_arg)fill_mi_headerget_default_engine get_writermaybe_convert_usecolspop_header_name) TextParser)validate_integer) TracebackType) DtypeArg DtypeBackendExcelWriterIfSheetExistsFilePath HashableTIntStrT ReadBufferSequenceNotStrStorageOptionsWriteExcelBuffer.)headernames index_colusecolsdtypeengine converters true_values false_valuesskiprowsnrows na_valueskeep_default_na na_filterverbose parse_dates date_format thousandsdecimalcomment skipfooterstorage_options dtype_backend sheet_name str | intr;int | Sequence[int] | Noner<'SequenceNotStr[Hashable] | range | Noner= int | str | Sequence[int] | Noner>Nint | str | Sequence[int] | Sequence[str] | Callable[[HashableT], bool] | Noner?DtypeArg | Noner@?Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | NonerA0dict[str, Callable] | dict[int, Callable] | NonerBIterable[Hashable] | NonerCrD4Sequence[int] | int | Callable[[int], object] | NonerE int | NonerGboolrHrIrJlist | dict | boolrK dict[Hashable, str] | str | NonerL str | NonerMstrrNrOintrPr9rQDtypeBackend | lib.NoDefaultreturnr#cdSNiorRr;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLrMrNrOrPrQs i/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/pandas/io/excel/_base.py read_excelrlXs Dlist[IntStrT] | Nonedict[IntStrT, DataFrame]cdSrgrhris rkrlrl}s D #srmpandasTF.)r;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLrMrNrOrPrQ engine_kwargs str | int | list[IntStrT] | NoneStorageOptions | Noners dict | None$DataFrame | dict[IntStrT, DataFrame]c|t|d}|i}t|tsd}t||||}n|r||jkrt d ||||||||| | | | | ||||||||||}|r|n#|r|wwxYw|S)a13 Read an Excel file into a ``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, 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 (chart sheets do not count as a sheet position). Lists of strings/integers are used to request multiple sheets. When ``None``, will return a dictionary containing DataFrames for each sheet. 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``: Returns a dictionary containing DataFrames for each sheet. 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 `_ 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. Does not include header rows. 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', '', '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_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). 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 `_. dtype_backend : {'numpy_nullable', 'pyarrow'} Back-end data type applied to the resultant :class:`DataFrame` (still experimental). If not specified, the default behavior is to not use nullable data types. If specified, the behavior is as follows: * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame` * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype` :class:`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 ` 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 FNT)rPr@rsz_Engine should not be specified when passing an ExcelFile - ExcelFile already has the engine set)rRr;r<r=r>r?rArBrCrDrErFrGrHrIrJrKrLrMrNrOrQ)r isinstance ExcelFiler@ ValueErrorparseclose)rjrRr;r<r=r>r?r@rArBrCrDrErFrGrHrIrJrKrLrMrNrOrPrQrs should_closedatas rkrlrls(p  &&&L b) $ $    +'      Fbi'' B   xx!!#%+##!'-  4   HHJJJ   HHJJJJ  Ks +B B9 _WorkbookTceZdZUded< dGdHd ZedId ZdJd ZdKdZedLdZ dMdZ dNdZ dOdPdZ dQdZ dRdZdSdZdTd%Zd&d&dddddddddd'd'ddd(dd&ejfdUd?Zdd&dddddddddd'ddd(dd&ejfdVdFZdS)WBaseExcelReaderrbookNrPrursrvreNonec,|i}t|ddi|_t|t|jfst |d|d|_t|jj|jr|jj|_dSt|jjdrj|jj d | |jj||_dS#t$r| wxYwtd) Nmethod)handle compressionrbFrPis_textreadrzCMust explicitly set engine if not passing in buffer or path for io.)r%handlesryrz_workbook_classr&rrhasattrseek load_workbook Exceptionr}r{)selffilepath_or_bufferrPrss rk__init__zBaseExcelReader.__init__s/  M %Hd3C    ,y$:N.OPP %"D/SXDL dl)4+? @ @  +DIII T\(& 1 1  L  $ $Q ' ' '  ..t|/BMRR      U s =%C$$ Dtype[_WorkbookT]ctrgNotImplementedErrorrs rkrzBaseExcelReader._workbook_class3!!rmctrgr)rrrss rkrzBaseExcelReader.load_workbook7!!rmct|dr]t|jdr|jn.t|jdr|j|jdS)Nrr}release_resources)rrr}rrrs rkr}zBaseExcelReader.close:s 4 .ty'** . !!!!$788 . ++--- rm list[str]ctrgrrs rk sheet_nameszBaseExcelReader.sheet_namesGrrmnamerbctrgrrrs rkget_sheet_by_namez!BaseExcelReader.get_sheet_by_nameKrrmindexrcctrgr)rrs rkget_sheet_by_indexz"BaseExcelReader.get_sheet_by_indexNrrmrowsr]ctrgr)rsheetrs rkget_sheet_datazBaseExcelReader.get_sheet_dataQrrmcft|j}||krtd|d|ddS)NzWorksheet index z is invalid, z worksheets found)lenrr{)rrn_sheetss rkraise_if_bad_sheet_by_indexz+BaseExcelReader.raise_if_bad_sheet_by_indexTsMt'(( H  R5RRxRRR   rmc>||jvrtd|ddS)NzWorksheet named 'z ' not found)rr{rs rkraise_if_bad_sheet_by_namez*BaseExcelReader.raise_if_bad_sheet_by_name[s2 t' ' 'BBBBCC C ( 'rmrDr rows_to_usecPd}d}||kr||s|dz }|dz }||k|S)a 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 rrh)rrDrirows_used_so_fars rk_check_skiprows_funcz$BaseExcelReader._check_skiprows_func_sR* ,,8A;; & A% FA,,rmr;rTr=r\rEc||dS|d}nJt|rtt|}d|z}n tt|}d|dz}t |r/|-tt|}t |dkr|dz }|||zSt|rtt|}||z|zSt |r@d d }tt|}|t||||zSt|r||||zSdS) a 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 NrrDrxrcrer^c ||vSrgrh)rDrs rkfz%BaseExcelReader._calc_rows..fs H}$rm)rDrrrcrer^) r!rrcrr"rrr callable)rr;r=rDrE header_rowsrs rk _calc_rowszBaseExcelReader._calc_rows|su6 =4 >KK    )#v&&Ff*KK(F++FfRj.K    !I$9(F++F6{{Qq  & & h   2C**H&1 1  ! ! X % % % %Hh//H,,WQ-A-A;QVCVWW W H   ,,e#  trmrFrrrR(str | int | list[int] | list[str] | Noner<rUr?rXrBr[rCrIr^rJr_rKr`rLrarMrNrOrQrdc (t|td| d}t|tr|}d}n(| |j}d}nt|t r|g}n|g}t ttttt ftt | }i}d}|D]}|}| rtd|t|t r| |}n||}|||| | }|||}t#|dr|t'|}|st)||<|jdid|d|d|d |d |d |d |d |d| d| d|d|d| d| d|d|d|d|d|d||}|t-d|r|S||S)NrEFTzReading sheet r}routput asheetnamer;r<r=r>r?rDrBrCrFrJrKrLrMrNrOrQzSheet name is an empty listrh)r(r/rylistrrbrrrcdictfromkeyskeysprintrrrrrr}r,r# _parse_sheetr{)rrRr;r<r=r>r?rBrCrDrErFrIrJrKrLrMrNrOrQkwdsret_dictsheetsrlast_sheetnamerrfile_rows_neededrs rkr|zBaseExcelReader.parses. F###%((( j$ ' ' "FHH  %FHH  C ( ( " \FF \FeDItCy014 f8M8M8R8R8T8T3U3UVV + + J'N 53z33444*c** <..z::// ;;#vy(ERR &&u.>??Dug&&  +G44G %.[[z"&T&Tv&:v  e  $)  e"e(K*\$)(K(K $)!" #$ %&&:'(,m+FF0  !:;; ;  *M.) )rmrrrrrstr | int | Nonec #$d}d}t|r.t|tsJd}t|dkrd}|r&t tt |d}d}|t|rt|tsJg}dgt|dz#|D]}t | rt| t sJ|| z }|t|dz kr&td|dt|dz dt||#\||<#|.t|||\}}| |d}|r|s|t|t r|h$n&t|tsJt|$t|tsJt|t|kr>|t|}t#$fdt|D}t|r|d}n-t|t rd|z}ndt|z}|r|dz }|t|krt|tsJ|D]m}|||} t|dzt|D]<}|||d ks||| | |||<.|||} =n t!|fid |d |d |d |d|d| d| d| d| d| ddd|d|d|d|d|d|d|d||}!|!| ||<|r+||j|||_nY#t($rt+||<Ynz/BaseExcelReader._parse_sheet..lsY&&1&q>&/0}.D.DG(qDy.D.D.D.D&&rmrr<r;r=has_index_namesr?rBrCrDrErFskip_blank_linesrJrKrLrMrNrOr>rQ)rEz (sheet: ))r"ryrrrrcr!r{r)r-appendsetall enumeratemaxranger.rcolumns set_namesrr#rargs)%rrrrr;r<r=r>r?rDrErBrCrFrJrKrLrMrNrOrQris_list_headeris_len_one_list_header header_namesrow header_name_rpotential_index_namesoffsetcollastparsererrrrs% @@rkrzBaseExcelReader._parse_sheet s[0!&    .fh// / //!N6{{a)-& ! 4(3-003F  ,v"6"6 fh// / //L&3tAw<</K 5 5h''$%h444448OCTQ&&$444t99q=444 *8S ;)O)O&S ;(%4T#Y %J%JNK '' 444   "8 Y=R)S)) /!* !)X66666 #I fh// / //6{{SYY&&(,S[[(9%"%&&&&& )*? @ @&&&##  " " 2~FC(( )VS[[  ! D !!!)X66666$22C<,D$VaZT;;229S>R//49S>3I-1DIcNN#'9S>DD 2& ev$)  !0  e (K*\"e$)"'(K(K$)  !" #$&:%& '(,m+F0"(5!9!9F:  -3J-?-G-Q-Q ..z"* - - -!*F:      8A;>>>>>N!""NNCHI  sB NO+2 O+;+O&&O+)NN)rPrursrvrer)rerrerrer)rer)rrb)rrcrg)rr])rrcrer)rrbrer)rDrrrcrerc) r;rTr=rTrDr\rEr]rer])"rRrr;rTr<rUr=rTr?rXrBr[rCr[rDr\rEr]rIr^rJr_rKr`rLrarMrbrNrarOrcrQrd)$rrrrrrr;rTr<rUr=rTr?rXrDr\rEr]rBr[rCr[rJr_rKr`rLrarMrbrNrarOrcrQrd)__name__ __module__ __qualname____annotations__rpropertyrrr}rrrrrrrrr no_defaultr|rrhrmrkrrs 26%) @"""X"""""    """X""""""""""""""DDDD:>>>>D@A-.9=04!%1526IM */8< $"69n)b*b*b*b*b*P(,-.9=04!%IM 1526*/8< $"69n+XXXXXXXrmrcdeZdZUdZded<ded< d>d?dZed@dZedAdZedBdZ edCdZ dDdEd'Z dFd(Z d>dGd*Z edAd+ZedAd,ZedAd-ZdAd.ZdHd/ZdId1ZedJd4ZdKd5ZdLd<ZdFd=ZdS)M ExcelWritera Class for writing DataFrame objects into excel sheets. Default is to use: * `xlsxwriter `__ for xlsx files if xlsxwriter is installed otherwise `openpyxl `__ * `odf `__ for ods files See :meth:`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..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 : 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 `_. 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. 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)`` * odf: ``odf.opendocument.OpenDocumentSpreadsheet(**engine_kwargs)`` See Also -------- read_excel : Read an Excel sheet values (xlsx) file into DataFrame. 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 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 pd.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 pd.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 rb_enginetuple[str, ...]_supported_extensionsNwpath)FilePath | WriteExcelBuffer | ExcelWriterr@rarKdatetime_formatmoderPruif_sheet_existsExcelWriterIfSheetExists | Nonersrvrerc |tur|t|tr|dkrt|tr.tj|ddd} nd} t jd| d}|dkrt| d}n&#t$r} td | d | d} ~ wwxYw|Jt|}t |S) Nautorrxlsx io.excel.z.writerwriterrzNo engine for filetype: '')rryrbosrsplitextr get_optionr*KeyErrorr{r+object__new__) clsrr@rKrrrPrrsextrs rkrzExcelWriter.__new__s +  ~*VS"9"9~f>N>NdC((!'**4004QRR8CC CR#./G3/G/G/GHHF''!3Ch!G!G!GRRR$%G%G%G%GHHcQR%%%V$$C~~c"""s-/B C'B;;Cc|jS)z'Extensions that writer engine supports.)rrs rksupported_extensionsz ExcelWriter.supported_extensionss ))rmc|jS)zName of engine.)rrs rkr@zExcelWriter.engines |rmdict[str, Any]ct)z(Mapping of sheet names to sheet objects.rrs rkrzExcelWriter.sheetss "!rmrct)z Book instance. 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This attribute can be used to access engine-specific features. rrs rkrzExcelWriter.books "!rmrrRstartrowrcstartcol freeze_panestuple[int, int] | Noneautofilter_rangerct)a 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 autofilter_range: str, default None column ranges to add automatic filters to, for example "A1:D5" r)rcellsrRrrrrs rk _write_cellszExcelWriter._write_cellss 2"!rmct)z( Save workbook to disk. rrs rk_savezExcelWriter._saves "!rmdict[str, Any] | Nonec vt|tr:tj|d} || d|vr|dz }|dd}|dvrtd|d|rd|vrtd|d }||_tttt|d di |_ t|tst|||d |_ d|_|d|_n||_|d|_n||_||_dS)Nrbazr+)Nerrornewreplaceoverlayr z^' is not valid for if_sheet_exists. Valid options are 'error', 'new', 'replace' and 'overlay'.z7if_sheet_exists is only valid in append mode (mode='a')r(r)rFrz YYYY-MM-DDzYYYY-MM-DD HH:MM:SS)ryrbr rr check_extensionr*r{_if_sheet_existsr%rr bytes_handlesrr& _cur_sheet _date_format_datetime_format_mode) rrr@rKrrrPrrsrs rkrzExcelWriter.__init__s dC  &'""4((,C   % % % d?? CKD||C&& "N N NMOMMM   Xt4//VWW W  "%O /" ED ! ! t/D    $ ,, &dOUDM   ,D   +D   "$9D ! !$3D ! rmc|jSzW Format string for dates written into Excel files (e.g. 'YYYY-MM-DD'). )r1rs rkrKzExcelWriter.date_formats   rmc|jSr5)r2rs rkrzExcelWriter.datetime_format $$rmc|jS)z[ How to behave when writing to a sheet that already exists in append mode. )r-rs rkrzExcelWriter.if_sheet_existsr7rmc8t|jjddS)Nrr)getattrr/rrs rk __fspath__zExcelWriter.__fspath__&st}+VR888rmc:||j}|td|S)Nz8Must pass explicit sheet_name or set _cur_sheet property)r0r{)rrRs rk_get_sheet_namezExcelWriter._get_sheet_name)s*  J  WXX XrmOtuple[int | float | bool | str | datetime.datetime | datetime.date, str | None]cd}t|rt|}n1t|rt|}nt |rt |}nt |rt|}nt|tj r|j }nt|tj r|j }nt|tj r|dz }d}n[t|}t!|dkr9t#jdt!|dt&t)||fS)aG 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 NiQ0izCell contents too long (z ), truncated to 32767 characters) stacklevel)r!rcr floatrr^rr rydatetimer2dater1 timedelta total_secondsrbrwarningswarn UserWarningr)rvalfmts rk_value_with_fmtzExcelWriter._value_with_fmt0sN$ c?? c((CC c]] **CC S\\ s))CC __ #,,CC X. / / 'CC X] + + #CC X/ 0 0 ##%%-CCCc((C3xx% 4s3xx444/11  Cxrmr Literal[True]cdr ddtfd|jDstd|jdddS) z checks that path's extension against the Writer's supported extensions. If it isn't supported, raises UnsupportedFiletypeError. rrrNc3 K|]}|vV dSrgrh)r extensionrs rkrz.ExcelWriter.check_extension..is(OO 3)#OOOOOOrmzInvalid extension for engine 'z': 'r T) startswithanyrr{r@)rrs `rkr,zExcelWriter.check_extensionasx >>#   abb'COOOOS5NOOOOO VTcjTTcTTTUU Utrmc|Srgrhrs rk __enter__zExcelWriter.__enter__n rmexc_typetype[BaseException] | None exc_valueBaseException | None tracebackTracebackType | Nonec.|dSrgr}rrVrXrZs rk__exit__zExcelWriter.__exit__q rmc`||jdS)z+synonym for save, to make it more file-likeN)r#r/r}rs rkr}zExcelWriter.closeys)  rm)NNNrNNN)rrr@rarKrarrarrbrPrurrrsrvrer)rer)rerb)rerr)NrrNN) rRrarrcrrcrrrrarerr)rrr@rarKrarrarrbrPrurrrsr$rer)rRrarerb)rer>)rrbrerMrerrVrWrXrYrZr[rer)rrr__doc__rrrrr@rrr!r#rrKrrr;r=rL classmethodr,rTr_r}rhrmrkrrsSgg~LLL**** ""&&*15;?%) # # # # #D***X*X"""X""""X""&/3'+"""""6""""""&&*15;?/355555n!!!X! %%%X% %%%X% 9999////b   [ rmr)s s s sࡱsPKcontent_or_pathFilePath | ReadBuffer[bytes]ct|d|d5}|j}|d|t}|t dt |tsJ||dtfdtDr ddddS ts ddddStj |5}d |D}dddn #1swxYwYd |vr dddd Sd |vr dddd Sd|vr ddddS ddddS#1swxYwYdS)a 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 : 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 `_. 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. rFrrNzstream is emptyc3BK|]}|VdSrg)rQ)rsigpeeks rkrz'inspect_excel_format..s/>>ts##>>>>>>rmxlsc^h|]*}|dd+S)\/)r*lower)rrs rk z'inspect_excel_format..s?48 T3''--//rmzxl/workbook.xmlrzxl/workbook.binxlsbz content.xmlodszip)r&rrr PEEK_SIZEr{ryr.rRXLS_SIGNATURESrQ ZIP_SIGNATUREzipfileZipFilenamelist)rfrPrstreambufzfcomponent_namesrks @rkinspect_excel_formatrsF      Akk)$$ ;.// /#u%%%%% A >>>>~>>> > > // "_V $ $ <>KKMMO                 / /34  / /78 O + +;<=sTBE/9E/!E/5D  E/ D$ $E/'D$ (E/=E/E/!E//E36E3ceZdZUdZddlmZddlmZddlm Z ddl m Z ddl m Z e e ee edZd ed < d=d>dZdZddd d d d d d d d d dd d d dejfd?d0Zed1Zed2Zd@d3ZdAd5ZdBd<Zd S)Crza Class for parsing tabular Excel sheets into DataFrame objects. See read_excel for more documentation. Parameters ---------- path_or_buffer : str, bytes, pathlib.Path, 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. The engine `xlrd `_ 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 `_ 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 if `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. 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 `_. engine_kwargs : dict, optional Arbitrary keyword arguments passed to excel engine. 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 -------- >>> file = pd.ExcelFile("myfile.xlsx") # doctest: +SKIP >>> with pd.ExcelFile("myfile.xls") as xls: # doctest: +SKIP ... df1 = pd.read_excel(xls, "Sheet1") # doctest: +SKIP r)CalamineReader) ODFReader)OpenpyxlReader) PyxlsbReader) XlrdReader)xlrdopenpyxlodfpyxlsbcalaminezMapping[str, Any]_enginesNr@rarPrursrvrerc|i}|||jvrtd|t||_|d}t |t t jtfs\t|sMtddd}n ddl }tt|}|t ||jrd}|"t||}|tdt!jd |d }|d krt%|d }|J||_||_|j||j|||_dS)NzUnknown engine: rignore)errorsrrl)rfrPzLExcel file format cannot be determined, you must specify an engine manually.rz.readerrreaderr )rPrs)rr{r'_ioryrbr PathLikerzrrrr$rBookrrrr*r@rP_reader)rpath_or_bufferr@rPrsr xlrd_versionrs rkrzExcelFile.__init__!s  M  & "="=88899 9".11 >"Cbk9 = ">22 .fXFFFN#'LLKKK#*;t+<+<#=#=L+ >490U0U+C{*$2O;$. &'?3'?'?'?@@F+Ch???!!! .,t}V, H+'    rmc|jSrg)rrs rkr;zExcelFile.__fspath__[s xrmFrRrr;rTr<rUr=rBr[rCrDr\rEr]rJr_rK str | dict[Hashable, str] | NonerLrNrOrcrQrd7DataFrame | dict[str, DataFrame] | dict[int, DataFrame]c |jjdid|d|d|d|d|d|d|d|d | d | d | d | d | d|d|d|d||S)a~ Parse specified sheet(s) into a DataFrame. Equivalent to read_excel(ExcelFile, ...) See the read_excel docstring for more info on accepted parameters. Parameters ---------- 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. When ``None``, will return a dictionary containing DataFrames for each sheet. 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. 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. 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_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. 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). dtype_backend : {{'numpy_nullable', 'pyarrow'}} Back-end data type applied to the resultant :class:`DataFrame` (still experimental). If not specified, the default behavior is to not use nullable data types. If specified, the behavior is as follows: * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame` * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype` :class:`DataFrame` .. versionadded:: 2.0 **kwds : dict, optional Arbitrary keyword arguments passed to excel engine. Returns ------- DataFrame or dict of DataFrames DataFrame from the passed in Excel file. See Also -------- read_excel : Read an Excel sheet values (xlsx) file into DataFrame. read_csv : Read a comma-separated values (csv) file into DataFrame. read_fwf : Read a table of fixed-width formatted lines into DataFrame. Examples -------- >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"]) >>> df.to_excel("myfile.xlsx") # doctest: +SKIP >>> file = pd.ExcelFile("myfile.xlsx") # doctest: +SKIP >>> file.parse() # doctest: +SKIP rRr;r<r=r>rArBrCrDrErFrJrKrLrNrOrQrh)rr|)rrRr;r<r=r>rArBrCrDrErFrJrKrLrNrOrQrs rkr|zExcelFile.parse^sl"t|!   !z 6 %  i  G  "z  $  & X %  i $  $   i G "z! "(-%   rmc|jjS)aF Gets the Excel workbook. Workbook is the top-level container for all document information. Returns ------- Excel Workbook The workbook object of the type defined by the engine being used. See Also -------- read_excel : Read an Excel file into a pandas DataFrame. Examples -------- >>> file = pd.ExcelFile("myfile.xlsx") # doctest: +SKIP >>> file.book # doctest: +SKIP >>> file.book.path # doctest: +SKIP '/xl/workbook.xml' >>> file.book.active # doctest: +SKIP >>> file.book.sheetnames # doctest: +SKIP ['Sheet1', 'Sheet2'] )rrrs rkrzExcelFile.book s8|  rmc|jjS)a Names of the sheets in the document. This is particularly useful for loading a specific sheet into a DataFrame when you do not know the sheet names beforehand. Returns ------- list of str List of sheet names in the document. See Also -------- ExcelFile.parse : Parse a sheet into a DataFrame. read_excel : Read an Excel file into a pandas DataFrame. If you know the sheet names, it may be easier to specify them directly to read_excel. Examples -------- >>> file = pd.ExcelFile("myfile.xlsx") # doctest: +SKIP >>> file.sheet_names # doctest: +SKIP ["Sheet1", "Sheet2"] )rrrs rkrzExcelFile.sheet_names's2|''rmc8|jdS)zclose io if necessaryN)rr}rs rkr}zExcelFile.closeBs rmrc|Srgrhrs rkrTzExcelFile.__enter__FrUrmrVrWrXrYrZr[c.|dSrgr]r^s rkr_zExcelFile.__exit__Ir`rm)NNN)r@rarPrursrvrer)rRrr;rTr<rUr=rTrBr[rCr[rDr\rEr]rJr_rKrrLrarNrarOrcrQrdrerrrbrc)rrrrdpandas.io.excel._calaminerpandas.io.excel._odfreaderrpandas.io.excel._openpyxlrpandas.io.excel._pyxlsbrpandas.io.excel._xlrdrrrrr;rrr|rrrr}rTr_rhrmrkrzrzsBBH988888444444888888444444000000"" ##H"15%) 8 8 8 8 8 t @A-.9=041526IM */8< $"69n%i i i i i V!!X!:((X(4rmrz).)0rRrSr;rTr<rUr=rVr>rWr?rXr@rYrArZrBr[rCr[rDr\rEr]rGr^rHr^rIr^rJr_rKr`rLrarMrbrNrarOrcrPr9rQrdrer#)0rRrnr;rTr<rUr=rVr>rWr?rXr@rYrArZrBr[rCr[rDr\rEr]rGr^rHr^rIr^rJr_rKr`rLrarMrbrNrarOrcrPr9rQrdrero)r)2rRrtr;rTr<rUr=rVr>rWr?rXr@rYrArZrBr[rCr[rDr\rEr]rGr^rHr^rIr^rJr_rKr`rLrarMrbrNrarOrcrPrurQrdrsrvrerwrg)rfrgrPrurera)^ __future__rcollections.abcrrrrrrCrMr functoolsr r typingr r r rrrrrrrrGrxpandas._configr pandas._libsrpandas.compat._optionalrr pandas.errorsrpandas.util._decoratorsrpandas.util._exceptionsrpandas.util._validatorsrpandas.core.dtypes.commonrrrr r!r"pandas.core.framer#pandas.util.versionr$pandas.io.commonr%r&r'r(pandas.io.excel._utilr)r*r+r,r-pandas.io.parsersr.pandas.io.parsers.readersr/typesr0pandas._typingr1r2r3r4r5r6r7r8r9r:rlrrrrrvrwrmaprrurrzrhrmrkrsE""""""                         !!!!!!)(((((544444777777(''''''''''' )(((((666666######                          ! *-5825  NQCF-0.1EH&)47&)25A!!!!! !H *-5825  NQCF-0.1EH&)47&)25A!#!#!#!#!# !#H H45g*+5926 !NRCG-1.2EI &+48 -125.!%AggggggT W\ " " hhhhhgj)hhhV  H@@@@@'*%@@@F  CC9>9=99:: ; ; .2AAAAAH HAAAAAAAAAArm