pykwalify

library1.8.0pypypi✓ verified 26d ago

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.

serialization

pip install pykwalify

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · PYKWALIFY

pykwalify

serializationpythonv1.8.0

Install

2.9s avg

Import

122ms

Disk

19MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.8.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.124s · 21.4MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 2.9s · import 0.120s · 22MB

19MB installed

● package 19MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

Core

✓ from pykwalify.core import Core

✗ import pykwalify.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

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}")

pykwalify --version

Debug

Known issues

breakingPython 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.

affects: >=1.8.0

breakingSupport 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.

affects: >=1.8.0

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.

affects: >=1.5.2

gotchaThe 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'`.

affects: >=1.8.0

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'pykwalify'

The 'pykwalify' library is not installed in the Python environment or is not accessible in the current PYTHONPATH.

fix

Install the library using pip: `pip install pykwalify`

NotMappingError: error code ...

The data being validated does not match the expected type 'map' (dictionary) as defined in the schema for a given node. This error can also appear as 'NotSequenceError' if a sequence (list) was expected.

fix

Adjust the data to conform to the schema's type definition; for 'NotMappingError', ensure a dictionary is provided where `type: map` is specified in the schema, and for 'NotSequenceError', provide a list where `type: seq` is specified.

AttributeError: 'generator' object has no attribute 'get'

This error occurs when `yaml.safe_load_all()` is used to load a YAML file containing a single document, as it returns a generator, but `pykwalify.core.Core` expects a dictionary or list directly for its `schema_data` or `source_data` parameters.

fix

Use `yaml.safe_load()` to load a single YAML document into a dictionary or list, which can then be passed to `pykwalify.core.Core`.

AttributeError: 'int' object has no attribute 'startswith'

A non-string value (e.g., an integer) is present in the data where the schema specifies a rule like `regex` or `pattern`, which attempts to call string methods like `startswith` on the incompatible type.

fix

Ensure that data values conform to the expected string type when using string-specific validation rules like `regex` or `pattern` in the schema. Convert non-string data to strings if appropriate for the schema definition.

Upgrade

Version history

1.8.0latest on PyPI · released Dec 30, 2020

Audit

Dependencies

ruamel.yamlrequiredDefault and preferred YAML parser; minimum version 0.16.0 required since v1.8.0.

python-dateutilrequiredRequired for date/datetime validations; minimum version 2.8.0 required since v1.8.0.

Agent activity

10 hits · last 30 days

node

Resources

githubhttp://github.com/grokzen/pykwalify ↗

packagehttps://pypi.org/project/pykwalify/ ↗

homepagehttps://pykwalify.readthedocs.io ↗