A library for dealing with JSON as python objects
Project description
[](https://travis-ci.org/dannyroberts/jsonobject)
A python library for handling deeply nested JSON objects
as well-schema'd python objects.
It is supposed to be a simpler, more standalone, and faster version
of the DocumentSchema portion of `couchdbkit`
This is just in the hacking stages and the API is not stable.
##Example##
The code below defines a simple user model, and it's natural mapping to JSON.
```python
from jsonobject import *
class User(JsonObject):
username = StringProperty()
name = StringProperty()
active = BooleanProperty(default=False)
date_joined = DateTimeProperty()
tags = ListProperty(unicode)
```
Once it is defined, it can be used to wrap or produce deserialized JSON.
```python
>>> user1 = User(
name='John Doe',
username='jdoe',
date_joined=datetime.datetime.utcnow(),
tags=['generic', 'anonymous']
)
>>> user1.to_json()
{
'name': 'John Doe',
'username': 'jdoe',
'active': False,
'date_joined': '2013-08-05T02:46:58Z',
'tags': ['generic', 'anonymous']
}
```
Notice that the datetime is converted to an ISO format string in JSON, but is a real datetime on the object:
```python
>>> user1.date_joined
datetime.datetime(2013, 8, 5, 2, 46, 58, 451286)
```
##Performance Comparison with Couchdbkit##
In order to do a direct comparison with couchdbkit, the test suite includes a large sample schema originally written with couchdbkit. It is easy to swap in jsonobject for couchdbkit and run the tests with each. Here are the results:
```
$ python -m unittest test.test_couchdbkit
....
----------------------------------------------------------------------
Ran 4 tests in 1.403s
OK
$ python -m unittest test.test_couchdbkit
....
----------------------------------------------------------------------
Ran 4 tests in 0.153s
OK
```
A python library for handling deeply nested JSON objects
as well-schema'd python objects.
It is supposed to be a simpler, more standalone, and faster version
of the DocumentSchema portion of `couchdbkit`
This is just in the hacking stages and the API is not stable.
##Example##
The code below defines a simple user model, and it's natural mapping to JSON.
```python
from jsonobject import *
class User(JsonObject):
username = StringProperty()
name = StringProperty()
active = BooleanProperty(default=False)
date_joined = DateTimeProperty()
tags = ListProperty(unicode)
```
Once it is defined, it can be used to wrap or produce deserialized JSON.
```python
>>> user1 = User(
name='John Doe',
username='jdoe',
date_joined=datetime.datetime.utcnow(),
tags=['generic', 'anonymous']
)
>>> user1.to_json()
{
'name': 'John Doe',
'username': 'jdoe',
'active': False,
'date_joined': '2013-08-05T02:46:58Z',
'tags': ['generic', 'anonymous']
}
```
Notice that the datetime is converted to an ISO format string in JSON, but is a real datetime on the object:
```python
>>> user1.date_joined
datetime.datetime(2013, 8, 5, 2, 46, 58, 451286)
```
##Performance Comparison with Couchdbkit##
In order to do a direct comparison with couchdbkit, the test suite includes a large sample schema originally written with couchdbkit. It is easy to swap in jsonobject for couchdbkit and run the tests with each. Here are the results:
```
$ python -m unittest test.test_couchdbkit
....
----------------------------------------------------------------------
Ran 4 tests in 1.403s
OK
$ python -m unittest test.test_couchdbkit
....
----------------------------------------------------------------------
Ran 4 tests in 0.153s
OK
```
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