jsonpickle
jsonpickle is a library for the two-way conversion of complex Python objects and JSON. jsonpickle builds upon the existing JSON encoders, such as simplejson, json, and demjson.
For complete documentation, please visit the jsonpickle documentation.
Bug reports and merge requests are encouraged at the jsonpickle repository on github.
jsonpickle supports Python 2.7 and Python 3.4 or greater.
WARNING: jsonpickle can execute arbitrary Python code. Do not load jsonpickles from untrusted / unauthenticated sources.
Why jsonpickle?
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.
Install
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
If you have the files checked out for development:
git clone https://github.com/jsonpickle/jsonpickle.git cd jsonpickle python setup.py develop
Numpy Support
jsonpickle includes a built-in numpy extension. If would like to encode sklearn models, numpy arrays, and other numpy-based data then you must enable the numpy extension by registering its handlers:
>>> import jsonpickle.ext.numpy as jsonpickle_numpy >>> jsonpickle_numpy.register_handlers()
Pandas Support
jsonpickle includes a built-in pandas extension. If would like to encode pandas DataFrame or Series objects then you must enable the pandas extension by registering its handlers:
>>> import jsonpickle.ext.pandas as jsonpickle_pandas >>> jsonpickle_pandas.register_handlers()
jsonpickleJS
jsonpickleJS is a javascript implementation of jsonpickle by Michael Scott Cuthbert. jsonpickleJS can be extremely useful for projects that have parallel data structures between Python and Javascript.
License
Licensed under the BSD License. See COPYING for details. See jsonpickleJS/LICENSE for details about the jsonpickleJS license.
Development
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 against multiple python versions using tox:
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 requirements-dev.txt. It is recommended to create a virtualenv and install the requirements there.:
python3 -mvenv env3x vx env3x pip install --requirement requirements-dev.txt
You can then execute tests inside the virtualenv:
vx env3x make test
vx is a simple script that allows you to eschew the typical virtualenv source activate / deactivate dance.
The following steps clone vx to ~/src/vx and symlinks to the script from ~/bin/vx. This assumes that $HOME/bin is in your $PATH.:
mkdir -p ~/bin ~/src cd ~/src && git clone git://github.com/davvid/vx.git cd ~/bin && ln -s ../src/vx/vx
You don't need vx to run the jsonpickle's tests -- you can always use the activate and deactivate virtualenv workflow instead. vx is convenient when testing against multiple virtualenvs because it does not mutate your shell environment.