How to read a .xlsx file using the pandas Library in iPython? How to read a .xlsx file using the pandas Library in iPython? python python

How to read a .xlsx file using the pandas Library in iPython?


I usually create a dictionary containing a DataFrame for every sheet:

xl_file = pd.ExcelFile(file_name)dfs = {sheet_name: xl_file.parse(sheet_name)           for sheet_name in xl_file.sheet_names}

Update: In pandas version 0.21.0+ you will get this behavior more cleanly by passing sheet_name=None to read_excel:

dfs = pd.read_excel(file_name, sheet_name=None)

In 0.20 and prior, this was sheetname rather than sheet_name (this is now deprecated in favor of the above):

dfs = pd.read_excel(file_name, sheetname=None)


The following worked for me:

from pandas import read_excelmy_sheet = 'Sheet1' # change it to your sheet name, you can find your sheet name at the bottom left of your excel filefile_name = 'products_and_categories.xlsx' # change it to the name of your excel filedf = read_excel(file_name, sheet_name = my_sheet)print(df.head()) # shows headers with top 5 rows


DataFrame's read_excel method is like read_csv method:

dfs = pd.read_excel(xlsx_file, sheetname="sheet1")Help on function read_excel in module pandas.io.excel:read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, index_col=None, names=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, convert_float=True, has_index_names=None, converters=None, true_values=None, false_values=None, engine=None, squeeze=False, **kwds)    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    sheetname : 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    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``    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``    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.    true_values : list, default None        Values to consider as True        .. versionadded:: 0.19.0    false_values : list, default None        Values to consider as False        .. versionadded:: 0.19.0    parse_cols : 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 column names and          column ranges (e.g. "A:E" or "A,C,E:F")    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', 'nan'.    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    has_index_names : boolean, default None        DEPRECATED: for version 0.17+ index names will be automatically        inferred based on index_col.  To read Excel output from 0.16.2 and        prior that had saved index names, use True.    Returns    -------    parsed : DataFrame or Dict of DataFrames        DataFrame from the passed in Excel file.  See notes in sheetname        argument for more information on when a Dict of Dataframes is returned.