PyPI version Supported Python versions Build status ReadTheDocs status pre-commit.ci status Zenodo DOI jsonschema is an implementation of the JSON Schema specification for Python. It can also be us...

>>> validate(instance={"name" : "Eggs", "price" : 34.99}, schema=schema) >>> validate( ... instance={"name" : "Eggs", "price" : "Invalid"}, schema=schema, ... ) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... ...

July 23, 2025 - A JSON Schema is made up of several ... the jsonschema library. Here's a simple example: This code defines a schema requiring an object with name (string) and age (integer)....

We now know how to define the schema of a tuple field with different drafts and how to validate multiple JSON documents against the same schema more efficiently with a validator using a specific draft. The primary library used in Python to validate a JSON document against a schema is jsonschema, which can be installed using pip install jsonschema.

May 9, 2024 - We can now extend our test and use the jsonschema library. In the example below is a test that is validating that the response from our API is fulfilling our contract (JSON schema) by using the validate function.

The TypeAdapter class lets you create an object with methods for validating, serializing, and producing JSON schemas for arbitrary types. This serves as a complete replacement for schema_of in Pydantic V1 (which is now deprecated). Here's an example of generating JSON schema from a TypeAdapter:

Support only for Python 3.3 and higher. fastjsonschema implements validation of JSON documents by JSON schema. The library implements JSON schema drafts 04, 06, and 07. The main purpose is to have a really fast implementation.

>>> from jsonschema import validate ... is raised by validate(), the instance is valid. >>> validate(instance={"name" : "Eggs", "price" : 34.99}, schema=schema) >>> validate( ... instance={"name" : "Eggs", "price" : "Invalid"}, ...

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You can use additionalProperties with a combination of properties and patternProperties. In the following example, based on the example from patternProperties, we add a "builtin" property, which must be a number, and declare that all additional properties (that are neither defined by properties nor matched by patternProperties) must be strings:

Literal values are wrapped when constructed to support validation and other schema-related operations. However, you can still use them just as you would other literals. >>> import python_jsonschema_objects as pjs >>> builder = pjs.ObjectBuilder(examples['Example Schema']) >>> ns = builder.build_classes() >>> Person = ns.ExampleSchema >>> james = Person(firstName="James", lastName="Bond") >>> str(james.lastName) 'Bond' >>> james.lastName += "ing" >>> str(james.lastName) 'Bonding' >>> james.age = 4 >>> james.age - 1 3 >>> 3 + james.age 7 >>> james.lastName / 4 Traceback (most recent call last): ...

October 20, 2023 - To create a JSON schema in Python, you can define it as a Python dictionary. Each key in the dictionary corresponds to a property in the JSON object, and the values define the constraints for that property. Here's an example of a simple JSON schema:

It was originally built to describe the common structure of a large number of JSON objects, and it uses its merging ability to generate a single schema from any number of JSON objects and/or schemas. GenSON’s schema builder follows these three rules: Every object it is given must validate under the generated schema.

fastjsonschema: Fastest Python implementation of JSON schema · fflas_ffpack: Dense linear algebra over word-size finite fields · ffmpeg: ffmpeg video converter · filelock: Platform independent file lock · flint: Fast Library for Number Theory · flit_core: Distribution-building parts of Flit.

When you define properties, you create an object where each property represents a key in the JSON data that’s being validated. You can also specify which properties defined in the object are required. Using the product catalog example, productId is a numeric value that uniquely identifies a product.

The public API consists of just one function: hypothesis_jsonschema.from_schema, which takes a JSON schema and returns a strategy for allowed JSON objects.

August 21, 2020 - Let’s see what they are and how you can leverage them in your testing setup. Let’s take an example endpoint GET /recordings/:id which returns a JSON response for a recording of a classical music piece: