Yet another python library to safely work with json data. It implements many useful features such as optional chaining, schema validation, type casting, safe indexing and default values.
pip install pyaddictfrom pyaddict import JDict, JList
from pyaddict.schema import Object, String, Integer, Array
jdict = JDict({
"name": "John",
"age": 30,
"cars": [
{"model": "BMW 230", "mpg": 27.5},
{"model": "Ford Edge", "mpg": 24.1}
]
})
# dicts
print(jdict.ensure("name", str)) # John
print(jdict.ensure("age", int)) # 30
print(jdict.ensure("age", str)) # ""
print(jdict.ensureCast("age", str)) # "30"
print(jdict.optionalGet("age", str)) # None
print(jdict.optionalCast("age", str)) # "30"
print(jdict.optionalGet("gender", str)) # None
print(jdict.optionalCast("gender", str)) # None
print(jdict.ensure("gender", str)) # ""
# lists
cars = jdict.ensureCast("cars", JList)
print(cars.assertGet(1, dict)) # {'model': 'Ford Edge', 'mpg': 24.1}
print(cars.assertGet(2, dict)) # AssertionError
# iterators
for car in cars.iterator().ensureCast(JDict):
print(car.ensureCast("model", str)) # BMW 230, Ford Edge
# chaining
chain = jdict.chain()
print(chain.ensureCast("cars[1].mpg", str)) # "24.1"
print(chain.ensureCast("cars[2].mpg", str)) # ""
# or via direct access (returns Optional[Any]!)
print(chain["cars[2].mpg"]) # IndexError
print(chain["cars[2]?.mpg"]) # None
# schema validation
schema = Object({
"name": String(),
"age": String().coerce(),
"dogs": Array(String()).min(1).optional()
}).withAdditionalProperties()
print(schema.error(jdict)) # None
badSchema = Object({
"name": String().min(5),
"age": Float(),
"cars": Object()
})
print(badSchema.error(jdict)) # ValidationError(expected 4 to be greater than or equal to 5, name: min)
staticSchema = Object({
"name": "John",
"age": 30,
"cars": [
{"model": "BMW 230", "mpg": 27.5},
{"model": "Ford Edge", "mpg": 24.1}
]
})
print(staticSchema.error(jdict)) # None
mixedSchema = Object({
"name": String().enum("John"),
"age": 30,
"dogs": Array(String()).min(1).optional()
}).withAdditionalProperties()
print(mixedSchema.error(jdict)) # NoneThe library is fully typed and thus can be used with mypy & pylint. Check out the wiki for more information.
When working with json data, it is common to have to deal with missing keys, wrong types, etc. This library provides a simple way to deal with these issues. Additionally, it provides easy-to-use typing support for mypy and pylint and detailed documentation. Starting with version 1.0.0, pyaddict includes a schema validation feature inspired by zod. It is especially useful when validating user input, e.g. in web applications.