What is lxml?
lxml is the most feature-rich and easy-to-use library for processing XML and HTML in Python. Built on the proven libxml2 and libxslt C libraries, lxml combines the power of these industry-standard libraries with the ease of Python programming. It provides a Pythonic API for XML/HTML processing while delivering C-level performance.
lxml is widely used in web scraping, data processing pipelines, configuration file handling, and anywhere robust XML/HTML processing is required. It supports XPath, XSLT, XML Schema validation, and provides both SAX-style streaming parsing and DOM-style tree processing capabilities.
lxml Performance Calculator
Total Throughput: 45 docs/sec
Elements: ~5,000
Efficiency: 100%
lxml Core Features
XML Processing
Full XML 1.0 support with namespace handling and validation.
• Namespace support
• DTD and Schema validation
• XML catalogs
• Entity resolution
HTML Processing
Robust HTML parsing with automatic error correction.
• HTML5 compatibility
• Automatic cleanup
• Link manipulation
• Form processing
XPath Support
Full XPath 1.0 implementation with extension functions.
• Custom XPath functions
• Variable binding
• Namespace contexts
• Performance optimized
XSLT Transformation
Complete XSLT 1.0 processor for document transformation.
• Extension elements
• Custom functions
• Multiple output formats
• Parameter passing
lxml Parsing Methods
Tree Parsing (DOM-style)
Load entire document into memory for full random access.
from lxml import etree, html
# XML parsing
xml_doc = etree.parse('document.xml')
root = xml_doc.getroot()
# HTML parsing with automatic error correction
html_doc = html.parse('webpage.html')
# From string
root = etree.fromstring('<root><item>data</item></root>')
# With custom parser
parser = etree.XMLParser(strip_cdata=False)
doc = etree.parse('document.xml', parser)Streaming Parsing (SAX-style)
Process large documents incrementally with minimal memory usage.
from lxml import etree
def parse_large_xml(filename):
context = etree.iterparse(filename, events=('start', 'end'))
context = iter(context)
event, root = next(context) # Get root element
for event, elem in context:
if event == 'end' and elem.tag == 'record':
# Process individual record
process_record(elem)
# Clear element to free memory
elem.clear()
root.clear() # Clean up root references
# Memory-efficient large file processing
parse_large_xml('huge_dataset.xml')XPath Queries
Powerful element selection using XPath expressions.
# Simple XPath queries
titles = doc.xpath('//title/text()')
links = doc.xpath('//a/@href')
# Complex XPath with predicates
expensive_items = doc.xpath('//item[price > 100]/name/text()')
# XPath with namespaces
nsmap = {'ns': 'http://example.com/namespace'}
nodes = doc.xpath('//ns:element', namespaces=nsmap)
# Custom XPath functions
def custom_func(context, nodes):
return [node.tag.upper() for node in nodes]
ns = etree.FunctionNamespace('http://custom.com')
ns['upper_tag'] = custom_func
result = doc.xpath('custom:upper_tag(//item)', namespaces={'custom': 'http://custom.com'})Real-World lxml Implementations
Scrapy
Web scraping framework using lxml as its core HTML/XML processing engine.
- • Parses millions of web pages daily
- • XPath-based data extraction
- • HTML cleaning and normalization
- • High-performance concurrent processing
Plone CMS
Content management system using lxml for template processing and content transformation.
- • XSLT-based template engine
- • XML configuration processing
- • Content syndication (RSS/Atom)
- • Multi-language content handling
Sage ERP
Enterprise resource planning software using lxml for data exchange and reporting.
- • XML-based data interchange formats
- • Financial report generation with XSLT
- • Configuration file processing
- • Integration with external systems
OpenERP/Odoo
Business application platform leveraging lxml for view definitions and data processing.
- • XML view definition processing
- • Report template transformation
- • Data import/export pipelines
- • Processing hundreds of XML schemas
lxml Performance Optimization
Memory Optimization
- • Use iterparse() for large files to stream processing
- • Call elem.clear() to free memory during iteration
- • Use XMLParser(huge_tree=True) for very large documents
- • Avoid storing references to elements after processing
- • Use XPath instead of Python loops when possible
- • Consider using objectify for data binding scenarios
Speed Optimization
- • Compile XPath expressions for repeated use
- • Use XMLParser(remove_blank_text=True) to reduce nodes
- • Batch XPath queries instead of multiple individual queries
- • Use C14N only when canonicalization is required
- • Disable validation when not needed
- • Use etree.iterparse() with specific events only
lxml Best Practices
✅ Do
- • Use XPath for complex element selection
- • Handle XML namespaces properly with nsmap
- • Use streaming parsing for large documents
- • Compile frequently-used XPath expressions
- • Handle encoding explicitly when parsing
- • Use appropriate parser options for your use case
❌ Don't
- • Parse malformed XML without using recover mode
- • Use lxml for simple text processing tasks
- • Ignore namespace declarations in XML documents
- • Keep references to elements from cleared trees
- • Use BeautifulSoup syntax with lxml (they're different)
- • Enable validation unless specifically needed