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

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…

如果你读excel文件

dfs = pd.ExcelFile('file')

然后使用

dfs.sheet_names
dfs.parse('sheetname')

另一种变体

df = pd.read_excel('file', sheet_name='sheetname')

With the load_workbook readonly option, what was earlier seen as a execution seen visibly waiting for many seconds happened with milliseconds. The solution could however be still improved. import pandas as pd from openpyxl import load_workbook class ExcelFile: def __init__(self, **kwargs): ........ ..... self._SheetNames = list(load_workbook(self._name,read_only=True,keep_links=False).sheetnames) The Excelfile.parse takes the same time as reading the complete xls in order of 10s of sec. This result was obtained with windows 10 operating system with below package versions C:\>python -V Python 3.9.1 C:\>pip list Package Version --------------- ------- et-xmlfile 1.0.1 numpy 1.20.2 openpyxl 3.0.7 pandas 1.2.3 pip 21.0.1 python-dateutil 2.8.1 pytz 2021.1 pyxlsb 1.0.8 setuptools 49.2.1 six 1.15.0 xlrd 2.0.1

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

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")]