Modern XML & JSON Bindings for Python 3.12+

pyxsdata is a complete, modern data binding library for Python 3.12+ allowing developers to access and use XML and JSON documents as simple objects rather than using DOM.

The code generator supports XML schemas, DTD, WSDL definitions, XML & JSON documents. It produces simple dataclasses or Pydantic v2 models with type hints and binding metadata.

The included XML and JSON parser/serializer are highly optimized and adaptable, with multiple handlers and configuration properties.

About pyxsdata (Modern Fork of xsdata)

pyxsdata is a modernized successor and fork of xsdata designed exclusively for Python 3.12+ and actively maintained with modern tooling.

Key Enhancements over Legacy xsdata

• Significantly Faster Deserialization: Up to 54% faster in pure Python (over 2x throughput) and up to 15x faster with native Rust acceleration (pyxsdata[core]) compared to legacy xsdata.
• Unified Native Pydantic v2: Consolidates xsdata-pydantic directly into the core library under pyxsdata.pydantic with dedicated --output pydantic generation and drop-in parsers/serializers.
• Python 3.12+ Architecture: Exclusively leverages PEP 695 generics (class Foo[T]: ...), type union syntax (X | Y), pattern matching, and kw_only dataclasses.
• Native Rust Acceleration (pyxsdata-core): First-class zero-copy Rust parser backend achieving ~300,000+ objects/sec (pip install "pyxsdata[core]").
• Ultra-Fast C++ pugixml Support: First-class support for constant-memory pull-parsing via pugixml (pip install "pyxsdata[pugixml]").
• Modern Packaging & Tooling: Managed and built with Astral uv, statically type checked with Astral ty with zero diagnostics, and formatted with ruff.
• Active & Responsive Maintenance: Regular dependency updates, modern CI packaging, and active community maintenance.

Performance & Deserializer Benchmarks

pyxsdata provides a decoupled, event-driven deserialization architecture supporting multiple parser backends. You can freely choose between zero-dependency standard library execution, C/C++ acceleration, or native Rust parsing via pyxsdata-core.

Deserialization Benchmarks (Standard Python @dataclass)

Parsing 10,000 complex XML items (3.36 MB payload) into nested Python @dataclass structures:

(Benchmark run on Linux x86_64, CPython 3.12.14, lowest of 5 runs over 10,000 items)

Deserialization Benchmarks (Pydantic v2 BaseModel)

Parsing 1,000 complex XML items into Pydantic v2 BaseModel instances:

Which Deserializer Should You Use?

• CoreXmlParser / CoreEventHandler (pip install "pyxsdata[core]"): Recommended for high-throughput production systems, real-time APIs, webhooks, and big data feeds. Driven by native Rust (quick-xml), it bypasses intermediate Python DOM objects and maps tokens directly to Python dataclasses or Pydantic models via CPython C-API at ~300,000 objects/second.
• NativeEventHandler (Built-in standard library): Recommended for zero-dependency deployments, lightweight microservices, and serverless environments (AWS Lambda, Google Cloud Run) where installing C/Rust compilers is undesirable.
• LxmlEventHandler (pip install "pyxsdata[lxml]"): Ideal for legacy XML workflows requiring schema DTD validation (load_dtd=True), XInclude resolution (process_xinclude=True), or direct parsing from lxml.etree.Element trees.
• PugixmlEventHandler (pip install "pyxsdata[pugixml]"): Fast C++ pull-parser offering constant-memory streaming for large XML payloads.

Getting started

$ # Install all dependencies including CLI, LXML, SOAP, Pydantic, and native Rust core acceleration
$ pip install "pyxsdata[cli,core,lxml,soap,pydantic]"

$ # Generate models
$ pyxsdata generate tests/fixtures/primer/order.xsd --package tests.fixtures.primer

>>> from tests.fixtures.primer import PurchaseOrder
>>> from pyxsdata.formats.dataclass.parsers import XmlParser, CoreXmlParser
>>>
>>> # Standard pure Python parser:
>>> parser = XmlParser()
>>> order = parser.parse("tests/fixtures/primer/sample.xml", PurchaseOrder)
>>> order.bill_to
Usaddress(name='Robert Smith', street='8 Oak Avenue', city='Old Town', state='PA', zip=Decimal('95819'), country='US')
>>>
>>> # Or ultra-fast Rust-accelerated parser (~290,000+ objs/sec):
>>> core_parser = CoreXmlParser()
>>> order = core_parser.parse("tests/fixtures/primer/sample.xml", PurchaseOrder)

Pydantic Support

Generate Pydantic v2 models directly with --output pydantic:

$ pyxsdata generate tests/fixtures/primer/order.xsd --output pydantic --package myapp.models

>>> from pyxsdata.pydantic.bindings import XmlParser, CoreXmlParser
>>>
>>> # Standard pure Python parser:
>>> parser = XmlParser()
>>> order = parser.from_string(xml_text, PurchaseOrder)
>>> order.model_dump()
>>>
>>> # Or ultra-fast Rust-accelerated parser (~310,000+ objs/sec):
>>> core_parser = CoreXmlParser()
>>> order = core_parser.from_string(xml_text, PurchaseOrder)
>>> order.model_dump()

Check the documentation for more ✨✨✨

Features

Code Generator

• XML Schemas 1.0 & 1.1
• WSDL 1.1 definitions with SOAP 1.1 bindings
• DTD external definitions
• Directly from XML and JSON Documents
• Extensive configuration to customize output
• Pluggable code writer for custom output formats (Standard Dataclasses, Pydantic v2)

Default Output

• Pure Python 3.12+ dataclasses or Pydantic models with metadata
• Modern type hints with support for forward references and unions
• Enumerations and inner classes
• Support namespace qualified elements and attributes

Data Binding

• XML and JSON parser, serializer
• PyCode serializer
• Multiple parser handlers: Native xml.etree, C lxml, C++ pugixml, and Rust pyxsdata-core
• Native Rust zero-copy acceleration (pyxsdata-core) for ~300k objs/sec deserialization
• Support wildcard elements and attributes
• Support xinclude statements and unknown properties
• Native Pydantic v2 support (pyxsdata.pydantic)
• Fully type-checked with Astral ty

Changelog: 0.0.0

• Modernized for Python 3.12+ minimum.
• Consolidated xsdata-pydantic into core library as pyxsdata.pydantic.
• Replaced mypy with Astral's static type checker ty.
• Standardized CLI tool to pyxsdata.
• Documentation powered by Zensical.