When you need to iterate over files in folder with similar names one of the ways could be make a pattern and use glob. To make sure that returned path is file you can use isfile().

Regarding XML, I see that basically you need to write values of every terminal tag in column with name of this tag. As you have various files you can create tag-value dictionaries from each file and store them into ChainMap. After all files processed you can use DictWriter to write all data into final csv file.

This method is much more safe and flexible then use static column names. Firstly program will collect all possible tag(column) names from all files, so in case if XML doesn't have such a tag or have some extra tags it won't throw an exception and all data will be saved.

Code:

import xml.etree.ElementTree as ET
from glob import iglob
from os.path import isfile, join
from csv import DictWriter
from collections import ChainMap

xml_root = r"C:\data\Desktop\Blue\XML-files"
pattern = "xmlfile_*"
data = ChainMap()
for filename in iglob(join(xml_root, pattern)):
    if isfile(filename):
        tree = ET.parse(filename)
        root = tree.getroot()
        temp = {node.tag: node.text for node in root.iter() if not node}
        data = data.new_child(temp)

with open(join(xml_root, "data.csv"), "w", newline="") as f:
    writer = DictWriter(f, data)
    writer.writeheader()
    writer.writerows(data.maps[:-1])  # last is empty dict

Upd. If you want to use xlsx format instead of csv you have to use third-party library (e.g. openpyxl). Example of usage:

from openpyxl import Workbook

...

wb = Workbook(write_only=True)
ws = wb.create_sheet()
ws.append(list(data))  # write header
for row in data.maps[:-1]:
    ws.append([row.get(key, "") for key in data])
wb.save(join(xml_root, "data.xlsx"))

As of v1.3, you can simply use:

pandas.read_xml(path_or_file)