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)

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)

基于@dhwanil_shah的回答，您不需要提取整个文件。zf。打开它可以直接从压缩文件中读取。

import xml.etree.ElementTree as ET
import zipfile

def xlsxSheets(f):
    zf = zipfile.ZipFile(f)

    f = zf.open(r'xl/workbook.xml')

    l = f.readline()
    l = f.readline()
    root = ET.fromstring(l)
    sheets=[]
    for c in root.findall('{http://schemas.openxmlformats.org/spreadsheetml/2006/main}sheets/*'):
        sheets.append(c.attrib['name'])
    return sheets

连续的两个readline很难看，但是内容只在文本的第二行。不需要解析整个文件。

这个解决方案似乎比read_excel版本快得多，而且很可能也比完整的提取版本快得多。

这是我发现的最快的方法，灵感来自@divingTobi的答案。所有基于xlrd、openpyxl或pandas的答案对我来说都很慢，因为它们都先加载整个文件。

from zipfile import ZipFile
from bs4 import BeautifulSoup  # you also need to install "lxml" for the XML parser

with ZipFile(file) as zipped_file:
    summary = zipped_file.open(r'xl/workbook.xml').read()
soup = BeautifulSoup(summary, "xml")
sheets = [sheet.get("name") for sheet in soup.find_all("sheet")]

#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