# pandas.read_excel

`pandas.``read_excel`(*args, **kwargs)

  Read an Excel table into a pandas DataFrame

  Parameters:

  io : string, path object (pathlib.Path or py._path.local.LocalPath),

  > file-like object, pandas ExcelFile, or xlrd workbook. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file://localhost/path/to/workbook.xlsx

  sheet_name : string, int, mixed list of strings/ints, 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.

  > str|int -> DataFrame is returned. list|None -> Dict of DataFrames is returned, with keys representing sheets.

  > Available Cases

  > - Defaults to 0 -> 1st sheet as a DataFrame
  > - 1 -> 2nd sheet as a DataFrame
  > - “Sheet1” -> 1st sheet as a DataFrame
  > - [0,1,”Sheet5”] -> 1st, 2nd & 5th sheet as a dictionary of DataFrames
  > - None -> All sheets as a dictionary of DataFrames

  sheetname : string, int, mixed list of strings/ints, or None, default 0

  > Deprecated since version 0.21.0: Use sheet_name instead

  header : int, list of ints, 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.

  skiprows : list-like

  > Rows to skip at the beginning (0-indexed)

  skip_footer : int, default 0

  > Rows at the end to skip (0-indexed)

  index_col : int, list of ints, 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.

  names : array-like, default None

  > List of column names to use. If file contains no header row, then you should explicitly pass header=None

  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.

  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.

  > New in version 0.20.0.

  true_values : list, default None

  > Values to consider as True

  > New in version 0.19.0.

  false_values : list, default None

  > Values to consider as False

  > New in version 0.19.0.

  parse_cols : int or list, default None

  > Deprecated since version 0.21.0: Pass in usecols instead.

  usecols : int or list, default None

  > - If None then parse all columns,
  > - If int then indicates last column to be parsed
  > - If list of ints then indicates list of column numbers to be parsed
  > - If string 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.

  squeeze : boolean, default False

  > If the parsed data only contains one column then return a Series

  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’, ‘n/a’, ‘nan’, ‘null’.

  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.

  keep_default_na : bool, default True

  > If na_values are specified and keep_default_na is False the default NaN values are overridden, otherwise they’re appended to.

  verbose : boolean, default False

  > Indicate number of NA values placed in non-numeric columns

  engine: string, default None

  > If io is not a buffer or path, this must be set to identify io. Acceptable values are None or xlrd

  convert_float : boolean, 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

  Returns:

  parsed : 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.

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