我需要创建一个子例程，循环遍历outlook消息集合，打开附件，并将zip文件夹中的任何表格数据提取到pandas数据框中。为了获取表格数据，我创建了一个名为zip_to_dfs的函数，它接受outlook MailItem附件作为参数。

#function to extract tabluar data within zip file to pandas dataframe. returns dictionary object(key=filename;value=pandas df)
import pandas as pd, zipfile, tempfile, os

def zip_to_dfs(attachment, extract_fn=None):
    #returns diciontary object with filename for key and dataframes from attached files as values
    df_objects = {}
    tmp=tempfile.TemporaryFile().name
    attachment.SaveAsFile(tmp)
    if zipfile.is_zipfile(tmp)==True:
        zf = zipfile.ZipFile(tmp)
        #below subroutine could be made to separate function (read tablular to df) to make more readable
        for file in zf.infolist():
            extension = os.path.splitext(file.filename)[1]
            if extension in ['.xls','.xlsx','.xlsm']:
                temp_df = pd.read_excel(zf.open(file.filename), header=None)
                df_objects.update({file.filename:temp_df})
            elif file.filename.endswith(".csv"):
                temp_df = pd.read_csv(zf.open(file.filename), header=None)
                df_objects.update({file.filename:temp_df})
            else:
                raise NotImplementedError('Unexpected filetype: '+str(file.filename))
    else: 
        raise NotImplementedError('Expected zip file')
    return(df_objects)

该函数按预期工作，但可能效率不高。有人使用过tempfile或zip文件库吗？如果是这样，你知道Zipfile和TemporaryFile方法是否会自动清理吗？或者这些文件在磁盘上是打开的？您是否发现这种方法存在其他明显的问题？
编辑的代码版本：

def zipattach_to_dfs(attachment, extract_fn=None):
    #evaluates zip file attachments and returns dictionary with file name as key and dataframes as values
    df_objects = {}
    with NamedTemporaryFile(suffix='.tmp', delete=False) as tmp:
        attachment.SaveAsFile(tmp.name)
        zf = ZipFile(tmp)
        for file in zf.infolist():
            datetime = (file.date_time)
            key = (f'{file.filename}({datetime[0]}-{datetime[1]}-{datetime[2]})')
            if isexcel(file) ==True:
                temp_df = pd.read_excel(zf.open(file.filename), header=None)
                df_objects.update({key:temp_df})
            elif file.filename.endswith(".csv"):
                temp_df = pd.read_csv(zf.open(file.filename), header=None)
                df_objects.update({key:temp_df})
            else:
                raise NotImplementedError('Unexpected filetype: '+str(file.filename))
    return (df_objects)