jsonbourne

Dynamic Graphics Python

Install: pip install jsonbourne

---

Who? What? Where? When? Why?

WHO??? Secret agent Json Bourne

WHAT??? Python json lib/pkg that makes json feel a little bit more like the JSON module in javascript/typescript

WHERE??? Dynamic Graphics Inc

WHEN??? 9 => 5 on workdays

WHY??? Three reasons: 1) Why not? 2) Also I (jessekrubin) wanted a python object that was a hybrid between a dictionary and an python-object that worked kinda like a js object. 3) Was able to get jsonbourne on pip

---

Usage:

JSON ~ from jsonbourne import JSON

JSON basics:

from jsonbourne import JSON
string_stringify = JSON.stringify({"a":1, "b":2, "c":3})  # '{"a": 1, "b": 2, "c": 3}'
string_dumps = JSON.dumps({"a":1, "b":2, "c":3})  # '{"a": 1, "b": 2, "c": 3}'
string_dumps

'{"a":1,"b":2,"c":3}'

JSON option kwargs ~ pretty & sort_keys

pretty:

string_dumps = JSON.stringify({"b":2, "a":1, "c":3}, pretty=True)  # '{"a": 1, "b": 2, "c": 3}'
print(string_dumps)

{
  "b": 2,
  "a": 1,
  "c": 3
}

sort_keys:

string_dumps = JSON.stringify({"b":2, "a":1, "c":3}, pretty=True, sort_keys=True)  # '{"a": 1, "b": 2, "c": 3}'
print(string_dumps)

{
  "a": 1,
  "b": 2,
  "c": 3
}

JsonObj & JSON

• Python dictionary/object with dot access
• Create a jsonbourne.JsonObj with jsonbourne.JSON
• Recurses into sub lists/dictionaries
• Works with pydantic.BaseModel and attrs
• Allows for kwarging (**json_obj)

import datetime

data = {
    "key": "value",
    "list": [1, 2, 3, 4, 5],
    "dt": datetime.datetime(1970, 1, 1, 0, 0, 0, 1),
    "sub": {'b': 3, 'key': 'val', 'a': 1,},
    "timedelta": datetime.timedelta(days=2),
}

JSON(data)

JsonObj(**{
    'dt': datetime.datetime(1970, 1, 1, 0, 0, 0, 1),
    'key': 'value',
    'list': [1, 2, 3, 4, 5],
    'sub': {'a': 1, 'b': 3, 'key': 'val'},
    'timedelta': datetime.timedelta(days=2)
})

Dot access

JSON(data).sub.b

3

stringified_data = JSON(data).stringify(pretty=True)
print(stringified_data)

{
  "key": "value",
  "list": [
    1,
    2,
    3,
    4,
    5
  ],
  "dt": "1970-01-01T00:00:00.000001",
  "sub": {
    "b": 3,
    "key": "val",
    "a": 1
  },
  "timedelta": 172800.0
}

parsed_data = JSON(stringified_data)
parsed_data

JsonObj(**{
    'dt': '1970-01-01T00:00:00.000001',
    'key': 'value',
    'list': [1, 2, 3, 4, 5],
    'sub': {'a': 1, 'b': 3, 'key': 'val'},
    'timedelta': 172800.0
})

list(parsed_data.keys())

['key', 'list', 'dt', 'sub', 'timedelta']

list(parsed_data.items())

[('key', 'value'),
 ('list', [1, 2, 3, 4, 5]),
 ('dt', '1970-01-01T00:00:00.000001'),
 ('sub', JsonObj(**{'b': 3, 'key': 'val', 'a': 1})),
 ('timedelta', 172800.0)]

list(parsed_data.dot_keys())

['key', 'list', 'dt', 'sub.b', 'sub.key', 'sub.a', 'timedelta']

list(parsed_data.dot_items())

[('key', 'value'),
 ('list', [1, 2, 3, 4, 5]),
 ('dt', '1970-01-01T00:00:00.000001'),
 ('sub.b', 3),
 ('sub.key', 'val'),
 ('sub.a', 1),
 ('timedelta', 172800.0)]

parsed_data['sub.key']

'val'

parsed_data.dot_lookup('sub.key')

'val'

{**parsed_data}

{'key': 'value',
 'list': [1, 2, 3, 4, 5],
 'dt': '1970-01-01T00:00:00.000001',
 'sub': JsonObj(**{'b': 3, 'key': 'val', 'a': 1}),
 'timedelta': 172800.0}

# fully eject
parsed_data.eject()

{'key': 'value',
 'list': [1, 2, 3, 4, 5],
 'dt': '1970-01-01T00:00:00.000001',
 'sub': {'b': 3, 'key': 'val', 'a': 1},
 'timedelta': 172800.0}

Usage with pydantic

from jsonbourne import JsonObj
from jsonbourne.pydantic import JsonBaseModel
class JsonSubObj(JsonBaseModel):
    herm: int

    def to_dict(self):
        return self.dict()

    def to_json(self, *args, **kwargs):
        return self.json()

    @classmethod
    def from_json(cls, json_string: str):
        return JsonSubObj(json.loads(json_string))

class JsonObjModel(JsonBaseModel):
    a: int
    b: int
    c: str
    d: JsonObj
    e: JsonSubObj

    #
    @property
    def a_property(self) -> str:
        return "prop_value"

    def to_json(self, *args, **kwargs):
        return self.json()

    @classmethod
    def from_json(cls, json_string: str):
        return cls(**json.loads(json_string))

obj = JsonObjModel(
    **{"a": 1, "b": 2, "c": "herm", "d": {"nested": "nestedval"}, "e": {"herm": 2}}
)
obj

JsonObjModel(**{
    'a': 1, 'b': 2, 'c': 'herm', 'd': JsonObj(**{'nested': 'nestedval'}), 'e': JsonSubObj(herm=2)
})

# respects properties (which I don't think pydantic does(currently))
obj.a_property

'prop_value'

---

JSON backend/lib

jsonbourne finds the best json-lib (python-rapidjson/orjson) it can! On import jsonbourne gets to work spying on your python env. jsonbourne, the most highly qualified json-CIA-agent, will import the best python-json library it can find; if jsonbourne's cover is blown (meaning: the only json library found is the python stdlib json), jsonbourne will fallback to the python stdlib json.

jsonbourne will look for the following json-packages in the following order:

1. rapidjson
2. orjson

Custom lib preferences

from jsonbourne import import_json
json = import_json(("rapidjson", "orjson"))  # prefer rapidjson over orjson
string = json.dumps({"a":1, "b":2, "c":3})
print(json)
print(string)

<class 'jsonbourne.jsonlib._rapidjson.RAPIDJSON'>
{"a":1,"b":2,"c":3}

Installing better JSON lib:

orjson

• pip install orjson [pip]

rapidjson/python-rapidjson

• pip install python-rapidjson [pip]
• conda install -c anaconda python-rapidjson [conda anaconda/defaults]
• conda install -c conda-forge python-rapidjson [conda-forge]