How to Parse XML in Python (10 Methods + Examples)

Parsing XML is a common task in Python, whether you are working with configuration files, web scraping, or APIs. This guide provides 10 different solutions with code examples, use cases, comparison tables, and FAQs. By the end, you’ll know which method fits your project best.

🔹 What is XML Parsing?

XML (eXtensible Markup Language) is widely used to store and transport data. Parsing XML means reading the XML structure and extracting useful information. In Python, you have multiple ways to achieve this, ranging from built-in libraries to advanced frameworks.

🔹 Solution 1: Using xml.etree.ElementTree (Standard Library)

import xml.etree.ElementTree as ET

xml_data = '''<root><item>Apple</item><item>Banana</item></root>'''
root = ET.fromstring(xml_data)

for child in root:
    print(child.tag, child.text)

✅ Pros: Built-in, fast, no dependencies.
⚠️ Cons: Limited validation, weaker for complex XML.

🔹 Solution 2: Using xml.dom.minidom

from xml.dom import minidom

doc = minidom.parseString('<root><item>Apple</item></root>')
items = doc.getElementsByTagName("item")

for item in items:
    print(item.firstChild.data)

✅ Pros: Pretty printing, simple to use.
⚠️ Cons: Memory-heavy for large XML.

🔹 Solution 3: Using lxml

from lxml import etree

xml_data = '''<root><item>Apple</item><item>Banana</item></root>'''
root = etree.fromstring(xml_data)

for item in root.findall("item"):
    print(item.text)

✅ Pros: Very fast, XPath support, validation.
⚠️ Cons: Requires installation (pip install lxml).

🔹 Solution 4: Using BeautifulSoup

from bs4 import BeautifulSoup

xml_data = """<root><item>Apple</item><item>Banana</item></root>"""
soup = BeautifulSoup(xml_data, "xml")

for item in soup.find_all("item"):
    print(item.text)

✅ Pros: Beginner-friendly, flexible parsing.
⚠️ Cons: Slower than lxml.

🔹 Solution 5: Using defusedxml (Secure Parsing)

from defusedxml.ElementTree import fromstring

xml_data = '<root><item>Apple</item></root>'
root = fromstring(xml_data)

for child in root:
    print(child.text)

✅ Pros: Prevents XML vulnerabilities (XXE, Billion Laughs).
⚠️ Cons: Limited features.

🔹 Solution 6: Using xmltodict

import xmltodict

xml_data = """<root><item>Apple</item><item>Banana</item></root>"""
parsed = xmltodict.parse(xml_data)

print(parsed["root"]["item"])

✅ Pros: Converts XML → Python dict directly.
⚠️ Cons: Not ideal for streaming large XML.

🔹 Solution 7: Using pandas

import pandas as pd

xml_data = """<root><row><name>John</name></row><row><name>Jane</name></row></root>"""
df = pd.read_xml(xml_data)
print(df)

✅ Pros: Perfect for data analysis.
⚠️ Cons: Requires structured XML.

🔹 Solution 8: Using Regex (Not Recommended)

import re

xml_data = '<root><item>Apple</item><item>Banana</item></root>'
items = re.findall(r'<item>(.*?)</item>', xml_data)
print(items)

✅ Pros: Quick hacks.
⚠️ Cons: Fragile, breaks on nested/complex XML.

🔹 Solution 9: Using Scrapy (For Web Crawling)

import scrapy

class XMLSpider(scrapy.Spider):
    name = "xml_spider"
    start_urls = ["https://example.com/data.xml"]

    def parse(self, response):
        for item in response.xpath("//item/text()").getall():
            yield {"item": item}

✅ Pros: Scalable, great for scraping XML feeds.
⚠️ Cons: Overkill for simple parsing.

🔹 Solution 10: Using Scrapeless API (Best Alternative)

Instead of maintaining parsing logic yourself, you can use Scrapeless Scraping Browser. It automatically:

• Handles dynamic content
• Extracts structured data (JSON, XML)
• Bypasses anti-bot protection

import requests

url = "https://api.scrapeless.com/xml-extract"
payload = {"url": "https://example.com/data.xml"}

response = requests.post(url, json=payload)
print(response.json())

✅ Pros: No setup, robust, scalable.
⚠️ Cons: Paid service.

🔹 Comparison Table

🔹 Real-World Scenarios

• Config files → ElementTree
• Large datasets → lxml
• APIs → xmltodict
• Data science → pandas
• Secure apps → defusedxml
• Web scraping → Scrapy or Scrapeless

🔹 FAQ

Q1: What is the fastest way to parse XML in Python?
👉 lxml is the fastest open-source solution. Scrapeless API is faster for production-grade tasks.

Q2: How do I prevent XML security issues?
👉 Use defusedxml or Scrapeless API, which sanitize inputs.

Q3: Can I convert XML directly into JSON?
👉 Yes, xmltodict or Scrapeless API can do this.

Q4: Which method is best for web scraping?
👉 Use Scrapy for small projects, Scrapeless for enterprise needs.

🔹 References

• Python Official XML Docs
• W3C XML Standards
• Real Python XML Tutorial

🔹 Conclusion

Python offers many ways to parse XML, from built-in libraries like ElementTree to advanced solutions like lxml and Scrapy. If you need scalable, secure, and maintenance-free parsing, consider using Scrapeless Scraping Browser.