#It will work for Both '.xls' and '.xlsx' by using pandas

import pandas as pd
excel_Sheet_names = (pd.ExcelFile(excelFilePath)).sheet_names

#for '.xlsx' use only  openpyxl

from openpyxl import load_workbook
excel_Sheet_names = (load_workbook(excelFilePath, read_only=True)).sheet_names
                                      

#It will work for Both '.xls' and '.xlsx' by using pandas

import pandas as pd
excel_Sheet_names = (pd.ExcelFile(excelFilePath)).sheet_names

#for '.xlsx' use only  openpyxl

from openpyxl import load_workbook
excel_Sheet_names = (load_workbook(excelFilePath, read_only=True)).sheet_names
                                      

你仍然可以使用ExcelFile类(和sheet_names属性):

xl = pd.ExcelFile('foo.xls')

xl.sheet_names  # see all sheet names

xl.parse(sheet_name)  # read a specific sheet to DataFrame

更多选项参见文档解析…

import pandas as pd

path = "\\DB\\Expense\\reconcile\\"

file_name = "202209-v01.xlsx"

df = pd.read_excel(path + file_name, None)
print(df)

sheet_names = list(df.keys())

# print last sheet name
print(sheet_names[len(sheet_names)-1])

last_month = df.get(sheet_names[len(sheet_names)-1])
print(last_month)

from openpyxl import load_workbook

sheets = load_workbook(excel_file, read_only=True).sheetnames

对于我正在使用的5MB Excel文件，没有read_only标志的load_workbook花了8.24秒。对于read_only标志，只需要39.6 ms。如果您仍然希望使用Excel库而不使用xml解决方案，那么这比解析整个文件的方法要快得多。

您应该显式地将第二个参数(sheetname)指定为None。是这样的:

 df = pandas.read_excel("/yourPath/FileName.xlsx", None);

"df"都是一个数据帧字典，你可以通过运行这个来验证:

df.keys()

结果是这样的:

[u'201610', u'201601', u'201701', u'201702', u'201703', u'201704', u'201705', u'201706', u'201612', u'fund', u'201603', u'201602', u'201605', u'201607', u'201606', u'201608', u'201512', u'201611', u'201604']

详情请参考熊猫文档:https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_excel.html