jsf

library0.11.2pypypi✓ verified 21d ago

JSF (JSON Schema Faker) is a Python library designed to generate realistic-looking fake JSON data based on a given JSON schema. It supports a wide range of JSON schema features, including types, formats, patterns, and more complex structures like arrays and objects. The library is actively maintained with frequent releases, with the current stable PyPI version being 0.11.2, although more recent releases like 0.11.4 have appeared on GitHub, indicating a rapid development pace.

serialization testing data

pip install jsf

github ↗package ↗

INSTALL

IMPORT

SIG · JSF

jsf

serializationpythonv0.11.2

Install

6.7s avg

Import

1351ms

Disk

59MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.11.2 · 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 1.394s · 61.6MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 6.7s · import 1.308s · 61MB

59MB installed

● package 59MB

Code

Verified usage

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

JSF

✓ from jsf import JSF

✗ import jsf

parser

✓ from jsf import parser

schema_types

✓ from jsf import schema_types

This quickstart demonstrates how to use `jsf.resolve()` to generate a single fake JSON object based on a provided JSON schema. It showcases various schema properties like types, formats, patterns, minimum/maximum values, and required fields. For more advanced use cases, such as generating multiple items or applying specific options (e.g., preferring default values), the `JSF` class can be instantiated directly.

import jsf import os # Example JSON schema schema = { "type": "object", "properties": { "id": {"type": "string", "format": "uuid"}, "name": {"type": "string", "pattern": "^[A-Za-z ]+$"}, "age": {"type": "integer", "minimum": 18, "maximum": 99}, "email": {"type": "string", "format": "email"}, "isActive": {"type": "boolean"}, "tags": {"type": "array", "items": {"type": "string"}, "minItems": 1, "maxItems": 3} }, "required": ["id", "name", "age", "email", "isActive"] } # Generate a single fake JSON object fake_data = jsf.resolve(schema) print(fake_data) # To demonstrate with a custom generator for multiple items or specific options # from jsf import JSF # generator = JSF(schema) # print(generator.generate(prefer_default=True)) # Example of using a generator with options

jsf --version

Debug

Known issues

breakingVersion 0.8.0 introduced a breaking change by switching its internal dependency to Pydantic v2. Projects using Pydantic v1.x alongside `jsf` will need to upgrade Pydantic to v2.x or pin `jsf` to a version prior to 0.8.0.

fix

Ensure Pydantic is installed at version 2.x (`pip install 'pydantic>=2.0'`). If incompatible with other project dependencies, consider pinning `jsf` to `<0.8.0`.

affects: >=0.8.0

gotchaStarting from version 0.11.2, `jsf` supports options to `prefer_default` and `prefer_examples` during generation. If your schema includes `default` or `examples` keywords and you were previously relying on random generation for these fields, your generated data might change if these new options are enabled, potentially leading to less varied data.

fix

Be aware of the `prefer_default` and `prefer_examples` parameters when calling `jsf.resolve()` or `JSF.generate()`. If you want purely random data even when defaults/examples are present, ensure these parameters are set to `False` (their default is `False` but custom usage might enable them).

affects: >=0.11.2

gotchaPrior to version 0.10.0, non-required fields in a schema might not have been generated by default. Version 0.10.0 introduced the `force_non_required_fields` option, which, when enabled, ensures that even optional fields are always generated. Users upgrading might notice a change in data sparsity if they expected optional fields to sometimes be omitted.

fix

For versions 0.10.0 and above, use the `force_non_required_fields=True` parameter in `jsf.resolve()` or `JSF.generate()` if you always want non-required fields to be present. If you prefer sparse data, ensure this parameter is `False`.

affects: <0.10.0

gotchaIn versions prior to 0.11.0, `jsf` had limited support for recursive schemas and enums of objects, which could lead to errors or incorrect generation. These issues were addressed in version 0.11.0.

fix

If your schemas involve recursive definitions or complex enum structures with objects, ensure you are using `jsf` version 0.11.0 or newer to benefit from these fixes.

affects: <0.11.0

Upgrade

Version history

0.11.2latest on PyPI · released Mar 26, 2024

Audit

Dependencies

pydanticrequiredUsed for internal schema parsing and validation. Version 2.x is required since jsf 0.8.0.

Agent activity

7 hits · last 30 days

node

Resources

githubhttps://github.com/ghandic/jsf ↗

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