Introduction

Installation

This library can be installed with pip:

We recommend a virtual environment (https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto)

Quickstart

To start, we import the module into our workspace:

There are different ways to load a JSON object in our interpreter:

• By directly entering the corresponding Python object in the constructor

json_object = js.JSONObject(
    {
        "data": [
            {
                "name": "Daniel",
                "age": 30,
                "hobbies": ["music", "reading", "football"]
            },
            {
                "name": "Gloria",
                "age": 25,
                "hobbies": ["tennis", "music", "programming"]
            }
        ]
    }
)

• By calling the method open of the JSONObject class:

json_object = js.JSONObject.open("<path_of_json_file>") # to open a local json file

json_object = js.JSONObject.open("<url_of_json_file>") # to open a remote url json file

Note

The method automatically detects if the argument is a local path or a remote URL. Remote URLs must contain the protocol (http/https).

Once the json data has been loaded as a JSONNode instance, we will be able to perform some useful things, such as browsing the nested object by attribute access with autocompletion features:

json_object.data._0.hobbies._1
# 'reading'

>> json_object.data._1.name
    'Gloria'

Or changing node elements as we want:

json_object.data._0.hobbies._1 = "SLEEPING"

>> json_object
    {
        "data": [
            {
                "name": "Daniel",
                "age": 30,
                "hobbies": ["music", "SLEEPING", "football"]
            },
            {
                "name": "Gloria",
                "age": 25,
                "hobbies": ["tennis", "music", "programming"]
            }
        ]
    }

But probably the most important feature is the ability to make queries, following a syntax similar to the one offered by Django’s ORM. Let’s see some examples:

>> json_object.query(hobbies__contains="football")
    <QuerySet [['music', 'SLEEPING', 'football']]>

>> json_object.query(age__lt=30, include_parent_=True).first() # retrieving the first query result including parent object (the inner dict)
    {'name': 'Gloria', 'age': 25, 'hobbies': ['tennis', 'music', 'programming']}

>> json_object.query(name__regex=r"(?:Daniel|Gloria)")
    <QuerySet ['Daniel', 'Gloria']>

>> json_object.query(hobbies__contains="music").count() # counting the number of nodes with 'music' as hobbie
    2

>> json_object.query(hobbies=js.All).update(None) # updating all hobbies nodes to null values

>> json_object
    {
        "data": [
            {
                "name": "Daniel",
                "age": 30,
                "hobbies": None
            },
            {
                "name": "Gloria",
                "age": 25,
                "hobbies": None
            }
        ]
    }