# JData for Python - a lightweight and portable data annotation method
Copyright: (C) Qianqian Fang (2019) <q.fang at neu.edu>
License: Apache License, Version 2.0
Version: 0.2
The [JData Specification](https://github.com/fangq/jdata/) defines a lightweight language-independent data annotation interface targetted at storing and sharing complex data structures across different programming languages such as MATLAB, JavaScript, Python etc. Using JData formats, a complex Python data structure can be encoded as a dict object that is easily serialized as a JSON/binary JSON file and share such data between programs of different languages.
## How to install
Github: download from https://github.com/fangq/pyjdata
PIP: run pip install jdata see https://pypi.org/project/jdata/
If you download a local copy via git clone or github download, after unzipping the package, you can
## How to use
The PyJData module is easy to use. You can use the encode()/decode() functions to encode Python data into JData annotation format, or decode JData structures into native Python data, for example
` import jdata as jd import numpy as np a={'str':'test','num':1.2,'list':[1.1,[2.1]],'nan':float('nan'),'np':np.arange(1,5,dtype=np.uint8)} jd.encode(a) jd.decode(jd.encode(a)) d1=jd.encode(a,{'compression':'zlib','base64':1}) d1 jd.decode(d1,{'base64':1}) `
One can further save the JData annotated data into JSON or binary JSON (UBJSON) files using the jdata.save function, or loading JData-formatted data to Python using jdata.load
` import jdata as jd import numpy as np a={'str':'test','num':1.2,'list':[1.1,[2.1]],'nan':float('nan'),'np':np.arange(1,5,dtype=np.uint8)} jd.save(a,'test.json') newdata=jd.load('test.json') newdata `
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