from __future__ import annotations from collections.abc import ( Callable, Hashable, Iterable, Mapping, Sequence, ) import datetime from decimal import Decimal from functools import partial import os from typing import ( IO, TYPE_CHECKING, Any, Generic, Literal, Self, TypeVar, Union, cast, overload, ) import warnings import zipfile from pandas._config import config from pandas._libs import lib from pandas.compat._optional import ( get_version, import_optional_dependency, ) from pandas.errors import EmptyDataError from pandas.util._decorators import ( set_module, ) from pandas.util._exceptions import find_stack_level from pandas.util._validators import check_dtype_backend from pandas.core.dtypes.common import ( is_bool, is_decimal, is_file_like, is_float, is_integer, is_list_like, ) from pandas.core.frame import DataFrame from pandas.util.version import Version from pandas.io.common import ( IOHandles, get_handle, stringify_path, validate_header_arg, ) from pandas.io.excel._util import ( fill_mi_header, get_default_engine, get_writer, maybe_convert_usecols, pop_header_name, ) from pandas.io.parsers import TextParser from pandas.io.parsers.readers import validate_integer if TYPE_CHECKING: from types import TracebackType from pandas._typing import ( DtypeArg, DtypeBackend, ExcelWriterIfSheetExists, FilePath, HashableT, IntStrT, ReadBuffer, SequenceNotStr, StorageOptions, WriteExcelBuffer, ) @overload def read_excel( io, # sheet name is str or int -> DataFrame sheet_name: str | int = ..., *, header: int | Sequence[int] | None = ..., names: SequenceNotStr[Hashable] | range | None = ..., index_col: int | str | Sequence[int] | None = ..., usecols: int | str | Sequence[int] | Sequence[str] | Callable[[HashableT], bool] | None = ..., dtype: DtypeArg | None = ..., engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb", "calamine"] | None = ..., converters: dict[str, Callable] | dict[int, Callable] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: Sequence[int] | int | Callable[[int], object] | None = ..., nrows: int | None = ..., na_values=..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: list | dict | bool = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: StorageOptions = ..., dtype_backend: DtypeBackend | lib.NoDefault = ..., ) -> DataFrame: ... @overload def read_excel( io, # sheet name is list or None -> dict[IntStrT, DataFrame] sheet_name: list[IntStrT] | None, *, header: int | Sequence[int] | None = ..., names: SequenceNotStr[Hashable] | range | None = ..., index_col: int | str | Sequence[int] | None = ..., usecols: int | str | Sequence[int] | Sequence[str] | Callable[[HashableT], bool] | None = ..., dtype: DtypeArg | None = ..., engine: Literal["xlrd", "openpyxl", "odf", "pyxlsb", "calamine"] | None = ..., converters: dict[str, Callable] | dict[int, Callable] | None = ..., true_values: Iterable[Hashable] | None = ..., false_values: Iterable[Hashable] | None = ..., skiprows: Sequence[int] | int | Callable[[int], object] | None = ..., nrows: int | None = ..., na_values=..., keep_default_na: bool = ..., na_filter: bool = ..., verbose: bool = ..., parse_dates: list | dict | bool = ..., date_format: dict[Hashable, str] | str | None = ..., thousands: str | None = ..., decimal: str = ..., comment: str | None = ..., skipfooter: int = ..., storage_options: StorageOptions = ..., dtype_backend: DtypeBackend | lib.NoDefault = ..., ) -> dict[IntStrT, DataFrame]: ... @set_module("pandas") def read_excel( io, sheet_name: str | int | list[IntStrT] | None = 0, *, header: int | Sequence[int] | None = 0, names: SequenceNotStr[Hashable] | range | None = None, index_col: int | str | Sequence[int] | None = None, usecols: int | str | Sequence[int] | Sequence[str] | Callable[[HashableT], 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_format: dict[Hashable, str] | str | None = None, thousands: str | None = None, decimal: str = ".", comment: str | None = None, skipfooter: int = 0, storage_options: StorageOptions | None = None, dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default, engine_kwargs: dict | None = None, ) -> DataFrame | dict[IntStrT, DataFrame]: """ 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 """ check_dtype_backend(dtype_backend) should_close = False if engine_kwargs is None: engine_kwargs = {} if not isinstance(io, ExcelFile): should_close = True io = ExcelFile( io, storage_options=storage_options, engine=engine, engine_kwargs=engine_kwargs, ) elif engine and engine != io.engine: raise ValueError( "Engine should not be specified when passing " "an ExcelFile - ExcelFile already has the engine set" ) try: data = io.parse( sheet_name=sheet_name, header=header, names=names, index_col=index_col, usecols=usecols, dtype=dtype, converters=converters, true_values=true_values, false_values=false_values, skiprows=skiprows, nrows=nrows, na_values=na_values, keep_default_na=keep_default_na, na_filter=na_filter, verbose=verbose, parse_dates=parse_dates, date_format=date_format, thousands=thousands, decimal=decimal, comment=comment, skipfooter=skipfooter, dtype_backend=dtype_backend, ) finally: # make sure to close opened file handles if should_close: io.close() return data _WorkbookT = TypeVar("_WorkbookT") class BaseExcelReader(Generic[_WorkbookT]): book: _WorkbookT def __init__( self, filepath_or_buffer, storage_options: StorageOptions | None = None, engine_kwargs: dict | None = None, ) -> None: if engine_kwargs is None: engine_kwargs = {} self.handles = IOHandles( handle=filepath_or_buffer, compression={"method": None} ) if not isinstance(filepath_or_buffer, (ExcelFile, self._workbook_class)): self.handles = get_handle( filepath_or_buffer, "rb", storage_options=storage_options, is_text=False ) if isinstance(self.handles.handle, self._workbook_class): self.book = self.handles.handle elif hasattr(self.handles.handle, "read"): # N.B. xlrd.Book has a read attribute too self.handles.handle.seek(0) try: self.book = self.load_workbook(self.handles.handle, engine_kwargs) except Exception: self.close() raise else: raise ValueError( "Must explicitly set engine if not passing in buffer or path for io." ) @property def _workbook_class(self) -> type[_WorkbookT]: raise NotImplementedError def load_workbook(self, filepath_or_buffer, engine_kwargs) -> _WorkbookT: raise NotImplementedError def close(self) -> None: if hasattr(self, "book"): if hasattr(self.book, "close"): # pyxlsb: opens a TemporaryFile # openpyxl: https://stackoverflow.com/questions/31416842/ # openpyxl-does-not-close-excel-workbook-in-read-only-mode self.book.close() elif hasattr(self.book, "release_resources"): # xlrd # https://github.com/python-excel/xlrd/blob/2.0.1/xlrd/book.py#L548 self.book.release_resources() self.handles.close() @property def sheet_names(self) -> list[str]: raise NotImplementedError def get_sheet_by_name(self, name: str): raise NotImplementedError def get_sheet_by_index(self, index: int): raise NotImplementedError def get_sheet_data(self, sheet, rows: int | None = None): raise NotImplementedError def raise_if_bad_sheet_by_index(self, index: int) -> None: n_sheets = len(self.sheet_names) if index >= n_sheets: raise ValueError( f"Worksheet index {index} is invalid, {n_sheets} worksheets found" ) def raise_if_bad_sheet_by_name(self, name: str) -> None: if name not in self.sheet_names: raise ValueError(f"Worksheet named '{name}' not found") def _check_skiprows_func( self, skiprows: Callable, rows_to_use: int, ) -> int: """ Determine how many file rows are required to obtain `nrows` data rows when `skiprows` is a function. Parameters ---------- skiprows : function The function passed to read_excel by the user. rows_to_use : int The number of rows that will be needed for the header and the data. Returns ------- int """ i = 0 rows_used_so_far = 0 while rows_used_so_far < rows_to_use: if not skiprows(i): rows_used_so_far += 1 i += 1 return i def _calc_rows( self, header: int | Sequence[int] | None, index_col: int | Sequence[int] | None, skiprows: Sequence[int] | int | Callable[[int], object] | None, nrows: int | None, ) -> int | None: """ If nrows specified, find the number of rows needed from the file, otherwise return None. Parameters ---------- header : int, list of int, or None See read_excel docstring. index_col : int, str, list of int, or None See read_excel docstring. skiprows : list-like, int, callable, or None See read_excel docstring. nrows : int or None See read_excel docstring. Returns ------- int or None """ if nrows is None: return None if header is None: header_rows = 1 elif is_integer(header): header = cast(int, header) header_rows = 1 + header else: header = cast(Sequence, header) header_rows = 1 + header[-1] # If there is a MultiIndex header and an index then there is also # a row containing just the index name(s) if is_list_like(header) and index_col is not None: header = cast(Sequence, header) if len(header) > 1: header_rows += 1 if skiprows is None: return header_rows + nrows if is_integer(skiprows): skiprows = cast(int, skiprows) return header_rows + nrows + skiprows if is_list_like(skiprows): def f(skiprows: Sequence, x: int) -> bool: return x in skiprows skiprows = cast(Sequence, skiprows) return self._check_skiprows_func(partial(f, skiprows), header_rows + nrows) if callable(skiprows): return self._check_skiprows_func( skiprows, header_rows + nrows, ) # else unexpected skiprows type: read_excel will not optimize # the number of rows read from file return None def parse( self, sheet_name: str | int | list[int] | list[str] | None = 0, header: int | Sequence[int] | None = 0, names: SequenceNotStr[Hashable] | range | None = None, index_col: int | Sequence[int] | None = None, usecols=None, dtype: DtypeArg | None = None, true_values: Iterable[Hashable] | None = None, false_values: Iterable[Hashable] | None = None, skiprows: Sequence[int] | int | Callable[[int], object] | None = None, nrows: int | None = None, na_values=None, verbose: bool = False, parse_dates: list | dict | bool = False, date_format: dict[Hashable, str] | str | None = None, thousands: str | None = None, decimal: str = ".", comment: str | None = None, skipfooter: int = 0, dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default, **kwds, ): validate_header_arg(header) validate_integer("nrows", nrows) ret_dict = False # Keep sheetname to maintain backwards compatibility. sheets: list[int] | list[str] if isinstance(sheet_name, list): sheets = sheet_name ret_dict = True elif sheet_name is None: sheets = self.sheet_names ret_dict = True elif isinstance(sheet_name, str): sheets = [sheet_name] else: sheets = [sheet_name] # handle same-type duplicates. sheets = cast(Union[list[int], list[str]], list(dict.fromkeys(sheets).keys())) output = {} last_sheetname = None for asheetname in sheets: last_sheetname = asheetname if verbose: print(f"Reading sheet {asheetname}") if isinstance(asheetname, str): sheet = self.get_sheet_by_name(asheetname) else: # assume an integer if not a string sheet = self.get_sheet_by_index(asheetname) file_rows_needed = self._calc_rows(header, index_col, skiprows, nrows) data = self.get_sheet_data(sheet, file_rows_needed) if hasattr(sheet, "close"): # pyxlsb opens two TemporaryFiles sheet.close() usecols = maybe_convert_usecols(usecols) if not data: output[asheetname] = DataFrame() continue output = self._parse_sheet( data=data, output=output, asheetname=asheetname, header=header, names=names, index_col=index_col, usecols=usecols, dtype=dtype, skiprows=skiprows, nrows=nrows, true_values=true_values, false_values=false_values, na_values=na_values, parse_dates=parse_dates, date_format=date_format, thousands=thousands, decimal=decimal, comment=comment, skipfooter=skipfooter, dtype_backend=dtype_backend, **kwds, ) if last_sheetname is None: raise ValueError("Sheet name is an empty list") if ret_dict: return output else: return output[last_sheetname] def _parse_sheet( self, data: list, output: dict, asheetname: str | int | None = None, header: int | Sequence[int] | None = 0, names: SequenceNotStr[Hashable] | range | None = None, index_col: int | Sequence[int] | None = None, usecols=None, dtype: DtypeArg | None = None, skiprows: Sequence[int] | int | Callable[[int], object] | None = None, nrows: int | None = None, true_values: Iterable[Hashable] | None = None, false_values: Iterable[Hashable] | None = None, na_values=None, parse_dates: list | dict | bool = False, date_format: dict[Hashable, str] | str | None = None, thousands: str | None = None, decimal: str = ".", comment: str | None = None, skipfooter: int = 0, dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default, **kwds, ): is_list_header = False is_len_one_list_header = False if is_list_like(header): assert isinstance(header, Sequence) is_list_header = True if len(header) == 1: is_len_one_list_header = True if is_len_one_list_header: header = cast(Sequence[int], header)[0] # forward fill and pull out names for MultiIndex column header_names = None if header is not None and is_list_like(header): assert isinstance(header, Sequence) header_names = [] control_row = [True] * len(data[0]) for row in header: if is_integer(skiprows): assert isinstance(skiprows, int) row += skiprows if row > len(data) - 1: raise ValueError( f"header index {row} exceeds maximum index " f"{len(data) - 1} of data.", ) data[row], control_row = fill_mi_header(data[row], control_row) if index_col is not None: header_name, _ = pop_header_name(data[row], index_col) header_names.append(header_name) # If there is a MultiIndex header and an index then there is also # a row containing just the index name(s) has_index_names = False if is_list_header and not is_len_one_list_header and index_col is not None: index_col_set: set[int] if isinstance(index_col, int): index_col_set = {index_col} else: assert isinstance(index_col, Sequence) index_col_set = set(index_col) # We have to handle mi without names. If any of the entries in the data # columns are not empty, this is a regular row assert isinstance(header, Sequence) if len(header) < len(data): potential_index_names = data[len(header)] has_index_names = all( x == "" or x is None for i, x in enumerate(potential_index_names) if not control_row[i] and i not in index_col_set ) if is_list_like(index_col): # Forward fill values for MultiIndex index. if header is None: offset = 0 elif isinstance(header, int): offset = 1 + header else: offset = 1 + max(header) # GH34673: if MultiIndex names present and not defined in the header, # offset needs to be incremented so that forward filling starts # from the first MI value instead of the name if has_index_names: offset += 1 # Check if we have an empty dataset # before trying to collect data. if offset < len(data): assert isinstance(index_col, Sequence) for col in index_col: last = data[offset][col] for row in range(offset + 1, len(data)): if data[row][col] == "" or data[row][col] is None: data[row][col] = last else: last = data[row][col] # GH 12292 : error when read one empty column from excel file try: parser = TextParser( data, names=names, header=header, index_col=index_col, has_index_names=has_index_names, dtype=dtype, true_values=true_values, false_values=false_values, skiprows=skiprows, nrows=nrows, na_values=na_values, skip_blank_lines=False, # GH 39808 parse_dates=parse_dates, date_format=date_format, thousands=thousands, decimal=decimal, comment=comment, skipfooter=skipfooter, usecols=usecols, dtype_backend=dtype_backend, **kwds, ) output[asheetname] = parser.read(nrows=nrows) if header_names: output[asheetname].columns = output[asheetname].columns.set_names( header_names ) except EmptyDataError: # No Data, return an empty DataFrame output[asheetname] = DataFrame() except Exception as err: err.args = (f"{err.args[0]} (sheet: {asheetname})", *err.args[1:]) raise err return output @set_module("pandas") class ExcelWriter(Generic[_WorkbookT]): """ 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 """ # Defining an ExcelWriter implementation (see abstract methods for more...) # - Mandatory # - ``write_cells(self, cells, sheet_name=None, startrow=0, startcol=0)`` # --> called to write additional DataFrames to disk # - ``_supported_extensions`` (tuple of supported extensions), used to # check that engine supports the given extension. # - ``_engine`` - string that gives the engine name. Necessary to # instantiate class directly and bypass ``ExcelWriterMeta`` engine # lookup. # - ``save(self)`` --> called to save file to disk # - Mostly mandatory (i.e. should at least exist) # - book, cur_sheet, path # - Optional: # - ``__init__(self, path, engine=None, **kwargs)`` --> always called # with path as first argument. # You also need to register the class with ``register_writer()``. # Technically, ExcelWriter implementations don't need to subclass # ExcelWriter. _engine: str _supported_extensions: tuple[str, ...] def __new__( cls, path: FilePath | WriteExcelBuffer | ExcelWriter, engine: str | None = None, date_format: str | None = None, datetime_format: str | None = None, mode: str = "w", storage_options: StorageOptions | None = None, if_sheet_exists: ExcelWriterIfSheetExists | None = None, engine_kwargs: dict | None = None, ) -> Self: # only switch class if generic(ExcelWriter) if cls is ExcelWriter: if engine is None or (isinstance(engine, str) and engine == "auto"): if isinstance(path, str): ext = os.path.splitext(path)[-1][1:] else: ext = "xlsx" try: engine = config.get_option(f"io.excel.{ext}.writer") if engine == "auto": engine = get_default_engine(ext, mode="writer") except KeyError as err: raise ValueError(f"No engine for filetype: '{ext}'") from err # for mypy assert engine is not None # error: Incompatible types in assignment (expression has type # "type[ExcelWriter[Any]]", variable has type "type[Self]") cls = get_writer(engine) # type: ignore[assignment] return object.__new__(cls) @property def supported_extensions(self) -> tuple[str, ...]: """Extensions that writer engine supports.""" return self._supported_extensions @property def engine(self) -> str: """Name of engine.""" return self._engine @property def sheets(self) -> dict[str, Any]: """Mapping of sheet names to sheet objects.""" raise NotImplementedError @property def book(self) -> _WorkbookT: """ Book instance. Class type will depend on the engine used. This attribute can be used to access engine-specific features. """ raise NotImplementedError def _write_cells( self, cells, sheet_name: str | None = None, startrow: int = 0, startcol: int = 0, freeze_panes: tuple[int, int] | None = None, autofilter_range: str | None = None, ) -> None: """ Write given formatted cells into Excel an excel sheet Parameters ---------- cells : generator cell of formatted data to save to Excel sheet sheet_name : str, default None Name of Excel sheet, if None, then use self.cur_sheet startrow : upper left cell row to dump data frame startcol : upper left cell column to dump data frame freeze_panes: int tuple of length 2 contains the bottom-most row and right-most column to freeze autofilter_range: str, default None column ranges to add automatic filters to, for example "A1:D5" """ raise NotImplementedError def _save(self) -> None: """ Save workbook to disk. """ raise NotImplementedError def __init__( self, path: FilePath | WriteExcelBuffer | ExcelWriter, engine: str | None = None, date_format: str | None = None, datetime_format: str | None = None, mode: str = "w", storage_options: StorageOptions | None = None, if_sheet_exists: ExcelWriterIfSheetExists | None = None, engine_kwargs: dict[str, Any] | None = None, ) -> None: # validate that this engine can handle the extension if isinstance(path, str): ext = os.path.splitext(path)[-1] self.check_extension(ext) # use mode to open the file if "b" not in mode: mode += "b" # use "a" for the user to append data to excel but internally use "r+" to let # the excel backend first read the existing file and then write any data to it mode = mode.replace("a", "r+") if if_sheet_exists not in (None, "error", "new", "replace", "overlay"): raise ValueError( f"'{if_sheet_exists}' is not valid for if_sheet_exists. " "Valid options are 'error', 'new', 'replace' and 'overlay'." ) if if_sheet_exists and "r+" not in mode: raise ValueError("if_sheet_exists is only valid in append mode (mode='a')") if if_sheet_exists is None: if_sheet_exists = "error" self._if_sheet_exists = if_sheet_exists # cast ExcelWriter to avoid adding 'if self._handles is not None' self._handles = IOHandles( cast(IO[bytes], path), compression={"compression": None} ) if not isinstance(path, ExcelWriter): self._handles = get_handle( path, mode, storage_options=storage_options, is_text=False ) self._cur_sheet = None if date_format is None: self._date_format = "YYYY-MM-DD" else: self._date_format = date_format if datetime_format is None: self._datetime_format = "YYYY-MM-DD HH:MM:SS" else: self._datetime_format = datetime_format self._mode = mode @property def date_format(self) -> str: """ Format string for dates written into Excel files (e.g. 'YYYY-MM-DD'). """ return self._date_format @property def datetime_format(self) -> str: """ Format string for dates written into Excel files (e.g. 'YYYY-MM-DD'). """ return self._datetime_format @property def if_sheet_exists(self) -> str: """ How to behave when writing to a sheet that already exists in append mode. """ return self._if_sheet_exists def __fspath__(self) -> str: return getattr(self._handles.handle, "name", "") def _get_sheet_name(self, sheet_name: str | None) -> str: if sheet_name is None: sheet_name = self._cur_sheet if sheet_name is None: # pragma: no cover raise ValueError("Must pass explicit sheet_name or set _cur_sheet property") return sheet_name def _value_with_fmt( self, val ) -> tuple[ int | float | bool | str | datetime.datetime | datetime.date, str | None ]: """ Convert numpy types to Python types for the Excel writers. Parameters ---------- val : object Value to be written into cells Returns ------- Tuple with the first element being the converted value and the second being an optional format """ fmt = None if is_integer(val): val = int(val) elif is_float(val): val = float(val) elif is_bool(val): val = bool(val) elif is_decimal(val): val = Decimal(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) # GH#56954 # Excel's limitation on cell contents is 32767 characters # xref https://support.microsoft.com/en-au/office/excel-specifications-and-limits-1672b34d-7043-467e-8e27-269d656771c3 if len(val) > 32767: warnings.warn( f"Cell contents too long ({len(val)}), " "truncated to 32767 characters", UserWarning, stacklevel=find_stack_level(), ) return val, fmt @classmethod def check_extension(cls, ext: str) -> Literal[True]: """ checks that path's extension against the Writer's supported extensions. If it isn't supported, raises UnsupportedFiletypeError. """ if ext.startswith("."): ext = ext[1:] if not any(ext in extension for extension in cls._supported_extensions): raise ValueError(f"Invalid extension for engine '{cls.engine}': '{ext}'") return True # Allow use as a contextmanager def __enter__(self) -> Self: return self def __exit__( self, exc_type: type[BaseException] | None, exc_value: BaseException | None, traceback: TracebackType | None, ) -> None: self.close() def close(self) -> None: """synonym for save, to make it more file-like""" self._save() self._handles.close() XLS_SIGNATURES = ( b"\x09\x00\x04\x00\x07\x00\x10\x00", # BIFF2 b"\x09\x02\x06\x00\x00\x00\x10\x00", # BIFF3 b"\x09\x04\x06\x00\x00\x00\x10\x00", # BIFF4 b"\xd0\xcf\x11\xe0\xa1\xb1\x1a\xe1", # Compound File Binary ) ZIP_SIGNATURE = b"PK\x03\x04" PEEK_SIZE = max(map(len, (*XLS_SIGNATURES, ZIP_SIGNATURE))) def inspect_excel_format( content_or_path: FilePath | ReadBuffer[bytes], storage_options: StorageOptions | None = None, ) -> str | None: """ Inspect the path or content of an excel file and get its format. Adopted from xlrd: https://github.com/python-excel/xlrd. Parameters ---------- content_or_path : str or file-like object Path to file or content of file to inspect. May be a URL. storage_options : 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. """ with get_handle( content_or_path, "rb", storage_options=storage_options, is_text=False ) as handle: stream = handle.handle stream.seek(0) buf = stream.read(PEEK_SIZE) if buf is None: raise ValueError("stream is empty") assert isinstance(buf, bytes) peek = buf stream.seek(0) if any(peek.startswith(sig) for sig in XLS_SIGNATURES): return "xls" elif not peek.startswith(ZIP_SIGNATURE): return None with zipfile.ZipFile(stream) as zf: # Workaround for some third party files that use forward slashes and # lower case names. component_names = { name.replace("\\", "/").lower() for name in zf.namelist() } if "xl/workbook.xml" in component_names: return "xlsx" if "xl/workbook.bin" in component_names: return "xlsb" if "content.xml" in component_names: return "ods" return "zip" @set_module("pandas") class ExcelFile: """ 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 """ from pandas.io.excel._calamine import CalamineReader from pandas.io.excel._odfreader import ODFReader from pandas.io.excel._openpyxl import OpenpyxlReader from pandas.io.excel._pyxlsb import PyxlsbReader from pandas.io.excel._xlrd import XlrdReader _engines: Mapping[str, Any] = { "xlrd": XlrdReader, "openpyxl": OpenpyxlReader, "odf": ODFReader, "pyxlsb": PyxlsbReader, "calamine": CalamineReader, } def __init__( self, path_or_buffer, engine: str | None = None, storage_options: StorageOptions | None = None, engine_kwargs: dict | None = None, ) -> None: if engine_kwargs is None: engine_kwargs = {} if engine is not None and engine not in self._engines: raise ValueError(f"Unknown engine: {engine}") # Always a string self._io = stringify_path(path_or_buffer) if engine is None: # Only determine ext if it is needed ext: str | None = None if not isinstance( path_or_buffer, (str, os.PathLike, ExcelFile) ) and not is_file_like(path_or_buffer): # GH#56692 - avoid importing xlrd if possible if import_optional_dependency("xlrd", errors="ignore") is None: xlrd_version = None else: import xlrd xlrd_version = Version(get_version(xlrd)) if xlrd_version is not None and isinstance(path_or_buffer, xlrd.Book): ext = "xls" if ext is None: ext = inspect_excel_format( content_or_path=path_or_buffer, storage_options=storage_options ) if ext is None: raise ValueError( "Excel file format cannot be determined, you must specify " "an engine manually." ) engine = config.get_option(f"io.excel.{ext}.reader") if engine == "auto": engine = get_default_engine(ext, mode="reader") assert engine is not None self.engine = engine self.storage_options = storage_options self._reader = self._engines[engine]( self._io, storage_options=storage_options, engine_kwargs=engine_kwargs, ) def __fspath__(self): return self._io def parse( self, sheet_name: str | int | list[int] | list[str] | None = 0, header: int | Sequence[int] | None = 0, names: SequenceNotStr[Hashable] | range | None = None, index_col: int | Sequence[int] | None = None, usecols=None, converters=None, true_values: Iterable[Hashable] | None = None, false_values: Iterable[Hashable] | None = None, skiprows: Sequence[int] | int | Callable[[int], object] | None = None, nrows: int | None = None, na_values=None, parse_dates: list | dict | bool = False, date_format: str | dict[Hashable, str] | None = None, thousands: str | None = None, comment: str | None = None, skipfooter: int = 0, dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default, **kwds, ) -> DataFrame | dict[str, DataFrame] | dict[int, DataFrame]: """ Parse specified sheet(s) into a DataFrame. Equivalent to read_excel(ExcelFile, ...) See the read_excel docstring for more info on accepted parameters. 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 """ return self._reader.parse( sheet_name=sheet_name, header=header, names=names, index_col=index_col, usecols=usecols, converters=converters, true_values=true_values, false_values=false_values, skiprows=skiprows, nrows=nrows, na_values=na_values, parse_dates=parse_dates, date_format=date_format, thousands=thousands, comment=comment, skipfooter=skipfooter, dtype_backend=dtype_backend, **kwds, ) @property def book(self): """ 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'] """ return self._reader.book @property def sheet_names(self): """ 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"] """ return self._reader.sheet_names def close(self) -> None: """close io if necessary""" self._reader.close() def __enter__(self) -> Self: return self def __exit__( self, exc_type: type[BaseException] | None, exc_value: BaseException | None, traceback: TracebackType | None, ) -> None: self.close()