JSON in data science projects: tips & tricks

1. orjson

   1 22 7,137 8.0 Python

   Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy

   

   orjson is the fastest JSON library available for python. It natively manages dataclass objects, datetime, numpy and UUID objects.

2. Stream

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   Stream - Scalable APIs for Chat, Feeds, Moderation, & Video. Stream helps developers build engaging apps that scale to millions with performant and flexible Chat, Feeds, Moderation, and Video APIs and SDKs powered by a global edge network and enterprise-grade infrastructure.

   

3. NumPy

   2 303 29,886 10.0 Python

   The fundamental package for scientific computing with Python.

   

   Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

4. jsonlines

   3 13 150 7.2 CSS

   Documentation for the JSON Lines text file format

   

   This can be remedied by using the [JSON Lines] format (https://jsonlines.org/). This involves nothing more and nothing less than placing one JSON object per line, so that you can browse the objects without having to parse the entire collection all at once.

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