pykwalify - JSON/YAML Schema Validator

1.8.0 · active · verified Thu Apr 09

pykwalify is a Python library and CLI tool for validating JSON and YAML data against a schema, based on the Kwalify schema specification. The current version is 1.8.0, with releases occurring periodically to add features, fix bugs, and update dependencies, though major development seems to have slowed since late 2020.

Warnings

breaking Python 2.7 and 3.5 support has been dropped. pykwalify v1.8.0 and later require Python 3.6 or newer.
Fix: Upgrade your Python environment to 3.6 or later.
breaking Support for the `PyYAML` parser has been completely removed in favor of `ruamel.yaml`. Attempts to use `PyYAML` will fail.
Fix: Ensure `ruamel.yaml` (minimum 0.16.0) is installed and used. No code change is typically needed as `pykwalify` defaults to `ruamel.yaml` since v1.8.0.
gotcha `True` and `False` are no longer considered valid integers. This change affects schemas where boolean values might have previously passed integer validation.
Fix: Update schemas to use `type: bool` for boolean values or ensure data correctly uses `type: int` for numbers.
gotcha The default YAML parser was changed to `ruamel.yaml` from `PyYAML` (which was deprecated in 1.5.2 and removed in 1.8.0). Ensure `ruamel.yaml>=0.16.0` and `python-dateutil>=2.8.0` are installed to avoid dependency conflicts or parser issues.
Fix: Install the required versions: `pip install 'ruamel.yaml>=0.16.0' 'python-dateutil>=2.8.0'`.

Install

pip install pykwalify Install latest stable version

Imports

Core
```
from pykwalify.core import Core
```
The primary validation class 'Core' is typically imported directly from the pykwalify.core submodule for convenience.

SchemaError

from pykwalify.errors import SchemaError

ValidationError

from pykwalify.errors import ValidationError

Quickstart

This quickstart demonstrates how to define a schema and validate YAML data using pykwalify. It uses `io.StringIO` to simulate file input, making the example self-contained. It also shows how to catch `ValidationError` exceptions for invalid data.

import io
from pykwalify.core import Core, ValidationError

# Define your schema in YAML
schema_yaml = """
type: map
mapping:
  name:
    type: str
    required: true
  age:
    type: int
    range: {min: 0, max: 120}
    required: false
"""

# Define your data in YAML
data_yaml = """
name: Alice
age: 30
"""

# Create Core instance with StringIO objects for schema and data
c = Core(source_data=io.StringIO(data_yaml), schema_data=io.StringIO(schema_yaml))

try:
    c.validate()
    print("Validation successful!")
except ValidationError as e:
    print(f"Validation failed: {e}")

# Example of invalid data
invalid_data_yaml = """
name: Bob
age: 150
"""
c_invalid = Core(source_data=io.StringIO(invalid_data_yaml), schema_data=io.StringIO(schema_yaml))

try:
    c_invalid.validate()
except ValidationError as e:
    print(f"Invalid data validation failed as expected: {e}")

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