Here’s one way:

import xmltodict

d = xmltodict.parse("""
<D1>
    <RECORD>
            <ELEC>EL-13</ELEC>
            <VAL>10</VAL>
            <POWER>Max</POWER>
            <WIRING>2.3</WIRING>
            <ENABLED>Yes</ENABLED>
    </RECORD>       
    <RECORD>
            <ELEC>EL-14</ELEC>
            <VAL>30</VAL>
            <POWER>Max</POWER>
            <WIRING>1.1</WIRING>
            <ENABLED>Yes</ENABLED>
    </RECORD>       
</D1>
""")

pd.DataFrame(d).iloc[:,0].explode().apply(pd.Series).reset_index(drop=True).to_csv('out.csv’)

# Alternative:
pd.json_normalize(d).stack().explode().apply(pd.Series)

Explanation ->

1. Convert the XML to dict.
2. load the result into a dataframe.
3. use explode to extract the values from the list of dict into multiple roes.
4. Apply pd.series to generate the required columns from the dict
5. Save the output to csv.

Updated Answer:

df1 = pd.json_normalize(d).stack().explode().apply(pd.Series)
pd.concat([df1.pop('DATA').apply(pd.Series), df1], 1)

I recommend just using libraries because they're usually very optimised. I'll talk about that later. For now, here's a way that utilises the xml.dom.minidom module, which is a part of the Python standard library, so no additional libraries are required.

Edit: rewrote the last part using the standard CSV library instead of manually writing the file, as suggested by a comment. That makes for 2 Python built-in modules, not 1. The original code for the CSV writing will be at the end of the reply, if you're interested.

from xml.dom import minidom
from csv import DictWriter

# Step 1: Read and parse the XML file
# Write it as a string, or open the file and read it
xml_file = open('employees.xml', 'r')
xml_data = xml_file.read()

dom = minidom.parseString(xml_data)
employees = dom.getElementsByTagName('employee')

xml_file.close()

# Step 2: Extract the required information
data = []
for employee in employees:
    emp_data = {}
    for child in employee.childNodes:
        if child.nodeType == minidom.Node.ELEMENT_NODE:
            emp_data[child.tagName] = child.firstChild.data
    data.append(emp_data)

# Step 3: Write the extracted information to a CSV file
with open('output.csv', 'w', newline = '') as csv_file:
    fieldnames = ['id', 'name', 'age', 'salary', 'division']
    writer = DictWriter(csv_file, fieldnames = fieldnames)

    writer.writeheader()
    for emp_data in data:
        writer.writerow(emp_data)

Don't reinvent the wheel, just realign it.

— Anthony J. D'Angelo, I think

I recommend NOT using this code. You should really just use lxml. It's extremely simple and easy to use and can handle complex XML structures with nested elements and attributes. Let me know how everything goes!

Original CSV write code without CSV library

# Step 3: Write the extracted information to a CSV file
with open('output.csv', 'w') as f:
    f.write('id,name,age,salary,division\n')
    for emp_data in data:
        f.write(f"{emp_data['id']},{emp_data['name']},{emp_data['age']},{emp_data['salary']},{emp_data['division']}\n")

Using Pandas, parsing all xml fields.

import xml.etree.ElementTree as ET
import pandas as pd

tree = ET.parse("file.xml")
root = tree.getroot()

get_range = lambda col: range(len(col))
l = [{r[i].tag:r[i].text for i in get_range(r)} for r in root]

df = pd.DataFrame.from_dict(l)
df.to_csv('file.csv')

To use lxml, you can simply pass the string as html(). By using the xpath //record/ts (starting with double backslash), you can fetch all your ts results. The main id can be accessed by calling .getparent() and then the attribute.

To convert xml to csv, I would recommend using the python package csv. You can use normal file writer. However csv write handles a lot of issues and it's cleaner.

In general, you have one method that handles everything. I would recommend splitting the logic into functions. Think Single Responsibility. Also the solution below I've converted the xml nodes into a NamedTupple and then write the namedTupple to csv. It's a lot easier to maintain/ read. (i.e Theres one place that sets the header text and one place populate the data).

from lxml import etree
import csv #py -m pip install python-csv
import collections
from collections import namedtuple

Record = namedtuple('Record', ['id', 'date', 'time', 'value']) # Model to store records.

def CreateCsvFile(results):
    with open('results.csv', 'w', newline='') as csvfile:
        writer = csv.DictWriter(csvfile, fieldnames=list(Record._fields)) # use the namedtuple fields for the headers 
        writer.writeheader()
        writer.writerows([x._asdict() for x in results]) # To use DictWriter, the namedtuple has to be converted to dictionary

def FormatRecord(xmlNode):
    return Record(xmlNode.getparent().attrib['id'], xmlNode.attrib["date"], xmlNode.attrib["time"], xmlNode.text)

def Main(html):
    xmlTree = etree.HTML(html)
    results = [FormatRecord(xmlNode) for xmlNode in xmlTree.xpath('//record/ts')] # the double backslash will retrieve all nodes for record.
    CreateCsvFile(results)

if __name__ == '__main__':
    Main("""<record id="idOne">
            <ts date="2019-07-03" time="15:28:41.720440">5</ts>
            <ts date="2019-07-03" time="15:28:42.629959">10</ts>
            <ts date="2019-07-03" time="15:28:43.552677">15</ts>
            <ts date="2019-07-03" time="15:28:43.855345">20</ts>
        </record>

        <record id="idTwo">
            <ts date="2019-07-03" time="15:28:45.072922">30</ts>
            <ts date="2019-07-03" time="15:28:45.377087">35</ts>
            <ts date="2019-07-03" time="15:28:46.316321">40</ts>
            <ts date="2019-07-03" time="15:28:47.527960">45</ts>
        </record>""")