stereotype 1.5.2

Models for conversion and validation of rich data structures.

stereotype

Stereotype is a performance-focused Python 3.8+ library for providing a structure for your data and validating it. The models allow fast & easy conversion between primitive data and well-typed Python classes.

Stereotype is heavily influenced by the beauty of dataclasses and versatility of Schematics, while having much better performance - both in terms of CPU usage and memory footprint. While it wasn't an influence, it is somewhat similar to Pydantic, but also beats it in benchmarks and provides easier validation.

Stereotype supports Python 3.8 and above (future support for older versions of Python is highly unlikely) and has 100% test coverage.

Features

• Fields
  • All JSON atomic types - bool, int, float, str, Optional[*]
  • Compound fields - List[*] of any type or a Dict[*, *] of atomic types to any type
  • Model nesting - Model subclass fields, including recursive definitions
  • Dynamic model fields - Model subclass fields resolved using a string type key
  • Free-form fields using Any
  • Calculated serializable fields - a property present also in serialized data
  • Schematics compatibility field, custom fields can be defined
• Validation
  • Basic built-in validation helpers for most fields
  • Custom field validator callbacks
  • Custom Model instance validation methods
  • Validation separate from conversion, multiple validation errors reported with paths
• Conversion & serialization
  • Optional field defaults using atomic values or callables
  • Renaming or disabling fields for purposes of input/output/both
  • Optional hiding of None values from output
  • Serialization roles using field blacklists or whitelists, with inheritance or overriding

Documentation

Full documentation of stereotype

Brief usage example

from typing import Optional, List
from stereotype import Model, StrField, FloatField


class Movie(Model):
    name: str
    genre: str = StrField(choices=("Comedy", "Action", "Family", "Drama"))
    ratings: Optional[float] = FloatField(min_value=1, max_value=10, default=None)
    cast: List[CastMember] = []


class CastMember(Model):
    name: str


movie = Movie({"name": "Monty Python and the Holy Grail", "genre": "Comedy", "ratings": 8.2})
movie.validate()
movie.cast.append(CastMember({"name": "John Cleese"}))
print(movie.serialize())

See the Tutorial for more examples with detailed explanations.

Issues & contributing

Please see the Contribution guide