¿Cómo leer un archivo .xlsx usando la biblioteca de pandas en iPython?

Quiero leer un archivo .xlsx utilizando la Biblioteca de Pandas de python y trasladar los datos a una tabla postgreSQL.

Todo lo que pude hacer hasta ahora es:

import pandas as pd data = pd.ExcelFile("*File Name*") 

Ahora sé que el paso se ejecutó con éxito, pero quiero saber cómo puedo analizar el archivo de Excel que se ha leído para que pueda entender cómo los datos de Excel se asignan a los datos de los datos variables.
Aprendí que los datos son un objeto Dataframe si no estoy equivocado. Entonces, ¿cómo puedo analizar este objeto de dataframe para extraer cada línea fila por fila.

Normalmente creo un diccionario que contiene un DataFrame para cada hoja:

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

Actualización: en la versión 0.21.0+ de pandas obtendrá este comportamiento de forma más limpia al pasar sheet_name=None a read_excel :

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

En 0.20 y anteriores, este era un nombre de sheetname lugar de sheet_name (ahora está en desuso en favor de lo anterior):

 dfs = pd.read_excel(file_name, sheetname=None) 
 from pandas import read_excel # find your sheet name at the bottom left of your excel file and assign # it to sheet_name my_sheet = 'Sheet1' file_name = 'products_and_categories.xlsx' # name of your excel file df = read_excel(file_name, sheet_name = my_sheet) print(df.head()) # shows headers with top 5 rows 

El método read_excel de read_excel es como el método read_csv :

 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 (eg "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/AN/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 (ie, 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. 

Si usa read_excel() en un archivo abierto con la función open() , asegúrese de agregar rb a la función open para evitar errores de encoding