import abc import datetime from io import BufferedIOBase, BytesIO, RawIOBase import os from textwrap import fill from typing import Union from pandas._config import config from pandas._libs.parsers import STR_NA_VALUES from pandas.errors import EmptyDataError from pandas.util._decorators import Appender, deprecate_nonkeyword_arguments from pandas.core.dtypes.common import is_bool, is_float, is_integer, is_list_like from pandas.core.frame import DataFrame from pandas.io.common import ( get_filepath_or_buffer, is_url, stringify_path, urlopen, validate_header_arg, ) from pandas.io.excel._util import ( _fill_mi_header, _get_default_writer, _maybe_convert_usecols, _pop_header_name, get_writer, ) from pandas.io.parsers import TextParser _read_excel_doc = ( """ Read an Excel file into a pandas DataFrame. Supports `xls`, `xlsx`, `xlsm`, `xlsb`, `odf`, `ods` and `odt` file extensions read from a local filesystem or URL. Supports an option to read a single sheet or a list of sheets. Parameters ---------- io : str, bytes, ExcelFile, xlrd.Book, path object, or file-like object Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: ``file://localhost/path/to/table.xlsx``. If you want to pass in a path object, pandas accepts any ``os.PathLike``. By file-like object, we refer to objects with a ``read()`` method, such as a file handler (e.g. via builtin ``open`` function) or ``StringIO``. sheet_name : str, int, list, or None, default 0 Strings are used for sheet names. Integers are used in zero-indexed sheet positions. Lists of strings/integers are used to request multiple sheets. Specify None to get all sheets. Available cases: * Defaults to ``0``: 1st sheet as a `DataFrame` * ``1``: 2nd sheet as a `DataFrame` * ``"Sheet1"``: Load sheet with name "Sheet1" * ``[0, 1, "Sheet5"]``: Load first, second and sheet named "Sheet5" as a dict of `DataFrame` * None: All sheets. header : int, list of int, default 0 Row (0-indexed) to use for the column labels of the parsed DataFrame. If a list of integers is passed those row positions will be combined into a ``MultiIndex``. Use None if there is no header. names : array-like, default None List of column names to use. If file contains no header row, then you should explicitly pass header=None. index_col : int, list of int, default None Column (0-indexed) to use as the row labels of the DataFrame. Pass None if there is no such column. If a list is passed, those columns will be combined into a ``MultiIndex``. If a subset of data is selected with ``usecols``, index_col is based on the subset. usecols : int, str, list-like, or callable default None * If None, then parse all columns. * If str, then indicates comma separated list of Excel column letters and column ranges (e.g. "A:E" or "A,C,E:F"). Ranges are inclusive of both sides. * If list of int, then indicates list of column numbers to be parsed. * If list of string, then indicates list of column names to be parsed. .. versionadded:: 0.24.0 * If callable, then evaluate each column name against it and parse the column if the callable returns ``True``. Returns a subset of the columns according to behavior above. .. versionadded:: 0.24.0 squeeze : bool, default False If the parsed data only contains one column then return a Series. dtype : Type name or dict of column -> type, default None Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32} Use `object` to preserve data as stored in Excel and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion. engine : str, default None If io is not a buffer or path, this must be set to identify io. Supported engines: "xlrd", "openpyxl", "odf", "pyxlsb", default "xlrd". Engine compatibility : - "xlrd" supports most old/new Excel file formats. - "openpyxl" supports newer Excel file formats. - "odf" supports OpenDocument file formats (.odf, .ods, .odt). - "pyxlsb" supports Binary Excel files. 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 Rows to skip at the beginning (0-indexed). nrows : int, default None Number of rows to parse. .. versionadded:: 0.23.0 na_values : scalar, str, list-like, or dict, default None Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following values are interpreted as NaN: '""" + fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent=" ") + """'. keep_default_na : bool, default True Whether or not to include the default NaN values when parsing the data. Depending on whether `na_values` is passed in, the behavior is as follows: * If `keep_default_na` is True, and `na_values` are specified, `na_values` is appended to the default NaN values used for parsing. * If `keep_default_na` is True, and `na_values` are not specified, only the default NaN values are used for parsing. * If `keep_default_na` is False, and `na_values` are specified, only the NaN values specified `na_values` are used for parsing. * If `keep_default_na` is False, and `na_values` are not specified, no strings will be parsed as NaN. Note that if `na_filter` is passed in as False, the `keep_default_na` and `na_values` parameters will be ignored. na_filter : bool, default True Detect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file. verbose : bool, default False Indicate number of NA values placed in non-numeric columns. parse_dates : bool, list-like, or dict, default False The behavior is as follows: * bool. If True -> try parsing the index. * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column. * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column. * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result 'foo' If a column or index contains an unparseable date, the entire column or index will be returned unaltered as an object data type. If you don`t want to parse some cells as date just change their type in Excel to "Text". For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_excel``. Note: A fast-path exists for iso8601-formatted dates. date_parser : function, optional Function to use for converting a sequence of string columns to an array of datetime instances. The default uses ``dateutil.parser.parser`` to do the conversion. Pandas will try to call `date_parser` in three different ways, advancing to the next if an exception occurs: 1) Pass one or more arrays (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the string values from the columns defined by `parse_dates` into a single array and pass that; and 3) call `date_parser` once for each row using one or more strings (corresponding to the columns defined by `parse_dates`) as arguments. thousands : str, default None Thousands separator for parsing string columns to numeric. Note that this parameter is only necessary for columns stored as TEXT in Excel, any numeric columns will automatically be parsed, regardless of display format. comment : str, default None Comments out remainder of line. Pass a character or characters to this argument to indicate comments in the input file. Any data between the comment string and the end of the current line is ignored. skipfooter : int, default 0 Rows at the end to skip (0-indexed). convert_float : bool, default True Convert integral floats to int (i.e., 1.0 --> 1). If False, all numeric data will be read in as floats: Excel stores all numbers as floats internally. mangle_dupe_cols : bool, default True Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than 'X'...'X'. Passing in False will cause data to be overwritten if there are duplicate names in the columns. Returns ------- DataFrame or dict of DataFrames DataFrame from the passed in Excel file. See notes in sheet_name argument for more information on when a dict of DataFrames is returned. See Also -------- DataFrame.to_excel : Write DataFrame to an Excel file. DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file. read_csv : Read a comma-separated values (csv) file into DataFrame. read_fwf : Read a table of fixed-width formatted lines into DataFrame. Examples -------- The file can be read using the file name as string or an open file object: >>> pd.read_excel('tmp.xlsx', index_col=0) # doctest: +SKIP Name Value 0 string1 1 1 string2 2 2 #Comment 3 >>> pd.read_excel(open('tmp.xlsx', 'rb'), ... sheet_name='Sheet3') # doctest: +SKIP Unnamed: 0 Name Value 0 0 string1 1 1 1 string2 2 2 2 #Comment 3 Index and header can be specified via the `index_col` and `header` arguments >>> pd.read_excel('tmp.xlsx', index_col=None, header=None) # doctest: +SKIP 0 1 2 0 NaN Name Value 1 0.0 string1 1 2 1.0 string2 2 3 2.0 #Comment 3 Column types are inferred but can be explicitly specified >>> pd.read_excel('tmp.xlsx', index_col=0, ... dtype={'Name': str, 'Value': float}) # doctest: +SKIP Name Value 0 string1 1.0 1 string2 2.0 2 #Comment 3.0 True, False, and NA values, and thousands separators have defaults, but can be explicitly specified, too. Supply the values you would like as strings or lists of strings! >>> pd.read_excel('tmp.xlsx', index_col=0, ... na_values=['string1', 'string2']) # doctest: +SKIP Name Value 0 NaN 1 1 NaN 2 2 #Comment 3 Comment lines in the excel input file can be skipped using the `comment` kwarg >>> pd.read_excel('tmp.xlsx', index_col=0, comment='#') # doctest: +SKIP Name Value 0 string1 1.0 1 string2 2.0 2 None NaN """ ) @deprecate_nonkeyword_arguments(allowed_args=2, version="2.0") @Appender(_read_excel_doc) def read_excel( io, sheet_name=0, header=0, names=None, index_col=None, usecols=None, squeeze=False, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, parse_dates=False, date_parser=None, thousands=None, comment=None, skipfooter=0, convert_float=True, mangle_dupe_cols=True, ): if not isinstance(io, ExcelFile): io = ExcelFile(io, engine=engine) elif engine and engine != io.engine: raise ValueError( "Engine should not be specified when passing " "an ExcelFile - ExcelFile already has the engine set" ) return io.parse( sheet_name=sheet_name, header=header, names=names, index_col=index_col, usecols=usecols, squeeze=squeeze, dtype=dtype, converters=converters, true_values=true_values, false_values=false_values, skiprows=skiprows, nrows=nrows, na_values=na_values, keep_default_na=keep_default_na, na_filter=na_filter, verbose=verbose, parse_dates=parse_dates, date_parser=date_parser, thousands=thousands, comment=comment, skipfooter=skipfooter, convert_float=convert_float, mangle_dupe_cols=mangle_dupe_cols, ) class _BaseExcelReader(metaclass=abc.ABCMeta): def __init__(self, filepath_or_buffer): # If filepath_or_buffer is a url, load the data into a BytesIO if is_url(filepath_or_buffer): filepath_or_buffer = BytesIO(urlopen(filepath_or_buffer).read()) elif not isinstance(filepath_or_buffer, (ExcelFile, self._workbook_class)): filepath_or_buffer, _, _, _ = get_filepath_or_buffer(filepath_or_buffer) if isinstance(filepath_or_buffer, self._workbook_class): self.book = filepath_or_buffer elif hasattr(filepath_or_buffer, "read"): # N.B. xlrd.Book has a read attribute too filepath_or_buffer.seek(0) self.book = self.load_workbook(filepath_or_buffer) elif isinstance(filepath_or_buffer, str): self.book = self.load_workbook(filepath_or_buffer) elif isinstance(filepath_or_buffer, bytes): self.book = self.load_workbook(BytesIO(filepath_or_buffer)) else: raise ValueError( "Must explicitly set engine if not passing in buffer or path for io." ) @property @abc.abstractmethod def _workbook_class(self): pass @abc.abstractmethod def load_workbook(self, filepath_or_buffer): pass def close(self): pass @property @abc.abstractmethod def sheet_names(self): pass @abc.abstractmethod def get_sheet_by_name(self, name): pass @abc.abstractmethod def get_sheet_by_index(self, index): pass @abc.abstractmethod def get_sheet_data(self, sheet, convert_float): pass def parse( self, sheet_name=0, header=0, names=None, index_col=None, usecols=None, squeeze=False, dtype=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, verbose=False, parse_dates=False, date_parser=None, thousands=None, comment=None, skipfooter=0, convert_float=True, mangle_dupe_cols=True, **kwds, ): validate_header_arg(header) ret_dict = False # Keep sheetname to maintain backwards compatibility. if isinstance(sheet_name, list): sheets = sheet_name ret_dict = True elif sheet_name is None: sheets = self.sheet_names ret_dict = True else: sheets = [sheet_name] # handle same-type duplicates. sheets = list(dict.fromkeys(sheets).keys()) output = {} for asheetname in sheets: if verbose: print(f"Reading sheet {asheetname}") if isinstance(asheetname, str): sheet = self.get_sheet_by_name(asheetname) else: # assume an integer if not a string sheet = self.get_sheet_by_index(asheetname) data = self.get_sheet_data(sheet, convert_float) usecols = _maybe_convert_usecols(usecols) if not data: output[asheetname] = DataFrame() continue if is_list_like(header) and len(header) == 1: header = header[0] # forward fill and pull out names for MultiIndex column header_names = None if header is not None and is_list_like(header): header_names = [] control_row = [True] * len(data[0]) for row in header: if is_integer(skiprows): row += skiprows data[row], control_row = _fill_mi_header(data[row], control_row) if index_col is not None: header_name, _ = _pop_header_name(data[row], index_col) header_names.append(header_name) if is_list_like(index_col): # Forward fill values for MultiIndex index. if header is None: offset = 0 elif not is_list_like(header): offset = 1 + header else: offset = 1 + max(header) # Check if we have an empty dataset # before trying to collect data. if offset < len(data): for col in index_col: last = data[offset][col] for row in range(offset + 1, len(data)): if data[row][col] == "" or data[row][col] is None: data[row][col] = last else: last = data[row][col] has_index_names = is_list_like(header) and len(header) > 1 # GH 12292 : error when read one empty column from excel file try: parser = TextParser( data, names=names, header=header, index_col=index_col, has_index_names=has_index_names, squeeze=squeeze, dtype=dtype, true_values=true_values, false_values=false_values, skiprows=skiprows, nrows=nrows, na_values=na_values, parse_dates=parse_dates, date_parser=date_parser, thousands=thousands, comment=comment, skipfooter=skipfooter, usecols=usecols, mangle_dupe_cols=mangle_dupe_cols, **kwds, ) output[asheetname] = parser.read(nrows=nrows) if not squeeze or isinstance(output[asheetname], DataFrame): if header_names: output[asheetname].columns = output[ asheetname ].columns.set_names(header_names) except EmptyDataError: # No Data, return an empty DataFrame output[asheetname] = DataFrame() if ret_dict: return output else: return output[asheetname] class ExcelWriter(metaclass=abc.ABCMeta): """ Class for writing DataFrame objects into excel sheets. Default is to use xlwt for xls, openpyxl for xlsx, odf for ods. See DataFrame.to_excel for typical usage. Parameters ---------- path : str 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). .. versionadded:: 0.24.0 Attributes ---------- None Methods ------- None Notes ----- None of the methods and properties are considered public. For compatibility with CSV writers, ExcelWriter serializes lists and dicts to strings before writing. Examples -------- Default usage: >>> with ExcelWriter('path_to_file.xlsx') as writer: ... df.to_excel(writer) To write to separate sheets in a single file: >>> with ExcelWriter('path_to_file.xlsx') as writer: ... df1.to_excel(writer, sheet_name='Sheet1') ... df2.to_excel(writer, sheet_name='Sheet2') You can set the date format or datetime format: >>> with ExcelWriter('path_to_file.xlsx', date_format='YYYY-MM-DD', datetime_format='YYYY-MM-DD HH:MM:SS') as writer: ... df.to_excel(writer) You can also append to an existing Excel file: >>> with ExcelWriter('path_to_file.xlsx', mode='a') as writer: ... df.to_excel(writer, sheet_name='Sheet3') """ # Defining an ExcelWriter implementation (see abstract methods for more...) # - Mandatory # - ``write_cells(self, cells, sheet_name=None, startrow=0, startcol=0)`` # --> called to write additional DataFrames to disk # - ``supported_extensions`` (tuple of supported extensions), used to # check that engine supports the given extension. # - ``engine`` - string that gives the engine name. Necessary to # instantiate class directly and bypass ``ExcelWriterMeta`` engine # lookup. # - ``save(self)`` --> called to save file to disk # - Mostly mandatory (i.e. should at least exist) # - book, cur_sheet, path # - Optional: # - ``__init__(self, path, engine=None, **kwargs)`` --> always called # with path as first argument. # You also need to register the class with ``register_writer()``. # Technically, ExcelWriter implementations don't need to subclass # ExcelWriter. def __new__(cls, path, engine=None, **kwargs): # only switch class if generic(ExcelWriter) if cls is ExcelWriter: if engine is None or (isinstance(engine, str) and engine == "auto"): if isinstance(path, str): ext = os.path.splitext(path)[-1][1:] else: ext = "xlsx" try: engine = config.get_option(f"io.excel.{ext}.writer") if engine == "auto": engine = _get_default_writer(ext) except KeyError as err: raise ValueError(f"No engine for filetype: '{ext}'") from err cls = get_writer(engine) return object.__new__(cls) # declare external properties you can count on book = None curr_sheet = None path = None @property @abc.abstractmethod def supported_extensions(self): """Extensions that writer engine supports.""" pass @property @abc.abstractmethod def engine(self): """Name of engine.""" pass @abc.abstractmethod def write_cells( self, cells, sheet_name=None, startrow=0, startcol=0, freeze_panes=None ): """ Write given formatted cells into Excel an excel sheet Parameters ---------- cells : generator cell of formatted data to save to Excel sheet sheet_name : str, default None Name of Excel sheet, if None, then use self.cur_sheet startrow : upper left cell row to dump data frame startcol : upper left cell column to dump data frame freeze_panes: int tuple of length 2 contains the bottom-most row and right-most column to freeze """ pass @abc.abstractmethod def save(self): """ Save workbook to disk. """ pass def __init__( self, path, engine=None, date_format=None, datetime_format=None, mode="w", **engine_kwargs, ): # validate that this engine can handle the extension if isinstance(path, str): ext = os.path.splitext(path)[-1] self.check_extension(ext) self.path = path self.sheets = {} self.cur_sheet = None if date_format is None: self.date_format = "YYYY-MM-DD" else: self.date_format = date_format if datetime_format is None: self.datetime_format = "YYYY-MM-DD HH:MM:SS" else: self.datetime_format = datetime_format self.mode = mode def __fspath__(self): return stringify_path(self.path) def _get_sheet_name(self, sheet_name): if sheet_name is None: sheet_name = self.cur_sheet if sheet_name is None: # pragma: no cover raise ValueError("Must pass explicit sheet_name or set cur_sheet property") return sheet_name def _value_with_fmt(self, val): """ Convert numpy types to Python types for the Excel writers. Parameters ---------- val : object Value to be written into cells Returns ------- Tuple with the first element being the converted value and the second being an optional format """ fmt = None if is_integer(val): val = int(val) elif is_float(val): val = float(val) elif is_bool(val): val = bool(val) elif isinstance(val, datetime.datetime): fmt = self.datetime_format elif isinstance(val, datetime.date): fmt = self.date_format elif isinstance(val, datetime.timedelta): val = val.total_seconds() / float(86400) fmt = "0" else: val = str(val) return val, fmt @classmethod def check_extension(cls, ext): """ 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}'") else: return True # Allow use as a contextmanager def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def close(self): """synonym for save, to make it more file-like""" return self.save() def _is_ods_stream(stream: Union[BufferedIOBase, RawIOBase]) -> bool: """ Check if the stream is an OpenDocument Spreadsheet (.ods) file It uses magic values inside the stream Parameters ---------- stream : Union[BufferedIOBase, RawIOBase] IO stream with data which might be an ODS file Returns ------- is_ods : bool Boolean indication that this is indeed an ODS file or not """ stream.seek(0) is_ods = False if stream.read(4) == b"PK\003\004": stream.seek(30) is_ods = ( stream.read(54) == b"mimetype" b"application/vnd.oasis.opendocument.spreadsheet" ) stream.seek(0) return is_ods class ExcelFile: """ Class for parsing tabular excel sheets into DataFrame objects. Uses xlrd engine by default. See read_excel for more documentation Parameters ---------- path_or_buffer : str, path object (pathlib.Path or py._path.local.LocalPath), a file-like object, xlrd workbook or openpypl workbook. If a string or path object, expected to be a path to a .xls, .xlsx, .xlsb, .xlsm, .odf, .ods, or .odt file. engine : str, default None If io is not a buffer or path, this must be set to identify io. Supported engines: ``xlrd``, ``openpyxl``, ``odf``, ``pyxlsb``, default ``xlrd``. Engine compatibility : - ``xlrd`` supports most old/new Excel file formats. - ``openpyxl`` supports newer Excel file formats. - ``odf`` supports OpenDocument file formats (.odf, .ods, .odt). - ``pyxlsb`` supports Binary Excel files. """ 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 = { "xlrd": _XlrdReader, "openpyxl": _OpenpyxlReader, "odf": _ODFReader, "pyxlsb": _PyxlsbReader, } def __init__(self, path_or_buffer, engine=None): if engine is None: engine = "xlrd" if isinstance(path_or_buffer, (BufferedIOBase, RawIOBase)): if _is_ods_stream(path_or_buffer): engine = "odf" else: ext = os.path.splitext(str(path_or_buffer))[-1] if ext == ".ods": engine = "odf" if engine not in self._engines: raise ValueError(f"Unknown engine: {engine}") self.engine = engine # Could be a str, ExcelFile, Book, etc. self.io = path_or_buffer # Always a string self._io = stringify_path(path_or_buffer) self._reader = self._engines[engine](self._io) def __fspath__(self): return self._io def parse( self, sheet_name=0, header=0, names=None, index_col=None, usecols=None, squeeze=False, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, parse_dates=False, date_parser=None, thousands=None, comment=None, skipfooter=0, convert_float=True, mangle_dupe_cols=True, **kwds, ): """ Parse specified sheet(s) into a DataFrame. Equivalent to read_excel(ExcelFile, ...) See the read_excel docstring for more info on accepted parameters. Returns ------- DataFrame or dict of DataFrames DataFrame from the passed in Excel file. """ return self._reader.parse( sheet_name=sheet_name, header=header, names=names, index_col=index_col, usecols=usecols, squeeze=squeeze, converters=converters, true_values=true_values, false_values=false_values, skiprows=skiprows, nrows=nrows, na_values=na_values, parse_dates=parse_dates, date_parser=date_parser, thousands=thousands, comment=comment, skipfooter=skipfooter, convert_float=convert_float, mangle_dupe_cols=mangle_dupe_cols, **kwds, ) @property def book(self): return self._reader.book @property def sheet_names(self): return self._reader.sheet_names def close(self): """close io if necessary""" self._reader.close() def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): self.close() def __del__(self): # Ensure we don't leak file descriptors, but put in try/except in case # attributes are already deleted try: self.close() except AttributeError: pass