如果不需要分析，请避免使用panda，并考虑使用**csv.DictWriter来构建CSV，在csv.DictWriter**中可以指定写入上下文的附加模式。下面将分析<ClinicalData>的所有底层后代，并将每个集移植到CSV行中。

from csv import DictWriter
from xml.etree import ElementTree

maintree = ElementTree.parse('FILE_XML.xml')
parentroot = maintree.getroot()
nmsp = {"doc": "http://www.cdisc.org/ns/odm/v1.3"}

# RETRIEVE ALL ELEMENT TAGS
all_tags = list(set([elem.tag for elem in parentroot.iter()]))

# RETRIEVE ALL ATTRIB KEYS
all_keys = [list(elem.attrib.keys()) for elem in maintree.iter()]
# UNNEST AND DE-DEDUPE
all_keys = set(sum([key for key in all_keys], []))

# COMBINE ELEM AND ATTRIB NAMES
all_tags = all_tags + list(all_keys)
all_tags = [(tag.split('}')[1] if '}' in tag else tag) for tag in all_tags]

# APPEND TO EXISTING DATA WIH 'a'
with open('FILE_TABLE_CMD.csv', 'a') as f:
    writer = DictWriter(f, fieldnames = all_tags)
    writer.writeheader()

    # ITERATE THROUGH ALL ClincalData ELEMENTS
    for cd in parentroot.findall('doc:ClinicalData', namespaces=nmsp):
        temp_dict = {}

        # ITERATE THROUGH ALL DESCENDANTS
        for elem in cd.iter():
            # UPDATE DICT FOR ELEMENT TAG/TEXT
            temp_dict[elem.tag.split("}", 1)[1]] = elem.text
            # MERGE ELEM DICT WITH ATTRIB DICT
            temp_dict = {**temp_dict, **elem.attrib}

            # REMOVE NAMESPACES IN KEYS
            temp_dict = {
                (k.split('}')[1] if '}' in k else k):v 
                for k,v
                in temp_dict.items()
             }

        # WRITE ROW TO CSV
        writer.writerow(temp_dict)

实际上，您可以使用最新v1.5中**pandas.read_xml**的 newiterparse特性。尽管该特性旨在用于非常大的XML，但它允许解析任何基础元素或属性，而不受XPath所需的关系限制。
您仍然需要找到所有元素和属性名称。CSV输出与上面的不同，因为方法会删除所有全空列，并保留XML中元素/属性的顺序。此外，pandas.DataFrame.csv确实支持附加模式，但可能需要条件逻辑来写入标题。

import os
from xml.etree import ElementTree
import pandas as pd  # VERSION 1.5+

maintree = ElementTree.parse('FILE_XML.xml')
parentroot = maintree.getroot()

# RETRIEVE ALL ELEMENT TAGS
all_tags = list(set([elem.tag for elem in parentroot.iter()]))

# RETRIEVE ALL ATTRIB KEYS
all_keys = [list(elem.attrib.keys()) for elem in maintree.iter()]
# UNNEST AND DE-DEDUPE
all_keys = set(sum([key for key in all_keys], []))

# COMBINE ELEM AND ATTRIB NAMES
all_tags = all_tags + list(all_keys)
all_tags = [(tag.split('}')[1] if '}' in tag else tag) for tag in all_tags]

clinical_data_df = pd.read_xml(
    "FILE_XML.xml", iterparse = {"ClinicalData": all_tags}, parser="etree"
)

if os.path.exists("FILE_TABLE_CMD.csv"):
    # CREATE CSV WITH HEADERS
    clinical_data_df.to_csv("FILE_TABLE_CMD.csv", index=False)
else:
    # APPEND TO CSV WITHOUT HEADERS
    clinical_data_df.to_csv("FILE_TABLE_CMD.csv", index=False, mode="a", header=False)