validatedata

An easier way to validate data in python.

Two validation modes, one simple syntax.

• High‑performance mode – use validator() to compile rules into fast boolean callables. Ideal for data streams, and anywhere you need maximum throughput.

• General‑purpose mode – use validate_data or decorators (@validate, @validate_types) to get detailed error messages, nested validation, and optional mutation. Perfect for light APIs, CLI tools, scripts, and forms.

Validatedata gives you expressive, inline validation rules without defining model classes. It fits naturally into any Python workflow – from lightweight scripts to high‑volume data processing.

New in v0.5: The validator() fast path for dramatic performance gains (see benchmarks below).

Benchmarks (1 million repetitions)

Test	validatedata	manual	pydantic v2	msgspec	beartype	fastjsonschema
Scalar: type (int)	0.1109s	0.0842s	0.4254s	0.0793s	0.3594s	0.1478s
Scalar: type + range	0.1508s	0.1286s	0.1314s	0.1353s	0.3841s	0.1493s
Dict (valid)	1.9438s	1.1996s	1.8246s	1.2350s	3.8948s	2.8658s
Dict (invalid)	0.2644s	0.5856s	2.1661s	1.1895s	2.0818s	2.7938s

Key features

• Compact pipe-syntax shorthand: 'str|strip|min:3|max:32'

• Mirror-structure rules that match the shape of your data

• Rich built-in types: email, url, ip, uuid, semver, slug, color, phone, and more

• Function and method decorators with async support

• Conditional validation, transforms, and custom error messages

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Getting started

• Quick Start
  • Installation
  • Your first validation
  • The keys wrapper
  • Reading the result
  • Three ways to validate
  • Parameters

Guides

• Rules Reference
  • Types
  • Dict rule form
  • Shorthand rule strings
  • Nested rules
  • Transforms
  • Conditional validation
  • Custom error messages
  • Validating rule dicts
• Mirror-Structure Rules
  • The problem with explicit nested rules
  • Mirror-structure shorthand
  • Error paths
  • Multi-level nesting
  • Mixing flat and nested rules
  • Using the keys wrapper
  • Transforms on nested fields
  • Mutate and data reconstruction
  • Depth limit
  • Mixing with explicit dict rules
  • Reference: how expansion works
• Decorators
  • @validate
  • @validate_types
  • Parameters (both decorators)
  • Return values
• Examples
  • Fast validation with validator()
  • User registration
  • Flask route with the decorator
  • Application config file
  • Bulk data import
  • Conditional fields on a checkout form
  • Normalising data before saving

Project

• Changelog