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jsonpickle is a library for the two-way conversion of complex Python objectsandJSON. jsonpickle builds upon existing JSONencoders, such as simplejson, json, and ujson.

For complete documentation, please visit thejsonpickle documentation.

Bug reports and merge requests are encouraged at thejsonpickle repository on github.

The following is a very simple example of how one can use jsonpickle in their scripts/projects. Note the usage of jsonpickle.encode and decode, and how the data is written/encoded to a file and then read/decoded from the file.

importjsonpicklefromdataclassesimportdataclass@dataclassclassExample:data:strex=Example("value1")encoded_instance=jsonpickle.encode(ex)assertencoded_instance=='{"py/object": "__main__.Example", "data": "value1"}'withopen("example.json","w+")asf:f.write(encoded_instance)withopen("example.json","r+")asf:written_instance=f.read()decoded_instance=jsonpickle.decode(written_instance)assertdecoded_instance==ex

For more examples, see theexamples directory on GitHub for example scripts. These can be run on your local machine to see how jsonpickle works and behaves, and how to use it. Contributions from users regarding how they use jsonpickle are welcome!

Data serialized with python's pickle (or cPickle or dill) is not easily readable outside of python. Using the json format, jsonpickle allows simple data types to be stored in a human-readable format, and more complex data types such as numpy arrays and pandas dataframes, to be machine-readable on any platform that supports json. E.g., unlike pickled data, jsonpickled data stored in an Amazon S3 bucket is indexible by Amazon's Athena.

jsonpickle should be treated the same as thePython stdlib pickle modulefrom a security perspective.

Install from pip for the latest stable release:

pip install jsonpickle

Install from github for the latest changes:

pip install git+https://github.com/jsonpickle/jsonpickle.git

jsonpickle includes built-in numpy and pandas extensions. If you wouldlike to encode sklearn models, numpy arrays, pandas DataFrames, and othernumpy/pandas-based data, then you must enable the numpy and/or pandasextensions by registering their handlers:

>>> import jsonpickle.ext.numpy as jsonpickle_numpy>>> import jsonpickle.ext.pandas as jsonpickle_pandas>>> jsonpickle_numpy.register_handlers()>>> jsonpickle_pandas.register_handlers()

Use make to run the unit tests:

make test

pytest is used to run unit tests internally.

A tox target is provided to run tests using all installed and supported Python versions:

make tox

jsonpickle itself has no dependencies beyond the Python stdlib.tox is required for testing when using the tox test runner only.

The testing requirements are specified in setup.cfg.It is recommended to create a virtualenv and run tests from within thevirtualenv.:

python3 -mvenv env3source env3/bin/activatepip install --editable '.[dev]'make test

You can also use a tool such asvxto activate the virtualenv without polluting your shell environment:

python3 -mvenv env3vx env3 pip install --editable '.[dev]'vx env3 make test

If you can't use a venv, you can install the testing packages as follows:

pip install .[testing]

jsonpickle supports multiple Python versions, so using a combination ofmultiple virtualenvs and tox is useful in order to catch compatibilityissues when developing.

Unfortunately, while versions of jsonpickle before 3.0.1 should still be signed,GPG signing support was removed from PyPi(https://blog.pypi.org/posts/2023-05-23-removing-pgp/) back in May 2023.

Licensed under the BSD License. See the LICENSE file for more details.