mysdq 0.4

Installation

pip install mysdq

Quickstart

Data used in here can be found in here

In [1]: import json
In [2]: data = json.load(open('tests/users.json'))
In [3]: from mysdq import DictQuerer
In [4]: qs = DictQuerer(data)
In [5]: qs.count() == 7
Out[5]: True
In [8]: qs.get(nickname='yloking')
Out[8]:
{'address': {'city': 'Paris',
  'name': 'rue du chatea',
  'num': 169,
  'zipcode': '75014'},
 'age': 25,
 'firstname': 'yosuke',
 'lastname': 'loking',
 'nickname': 'yloking',
 'profiles': [{'name': 'twitter',
   'url': 'https://twitter.com/yloking/',
   'username': 'yloking'},
  {'name': 'github',
   'url': 'https://github.com/yloking/',
   'username': 'yloking'},
  {'name': 'reddit',
   'url': 'https://reddit.com/yloking/',
   'username': 'yloking'}]}
# Querying non matching entry returns None
In [9]: qs.get(lastname='young', age__le=20)
# Querying an entry and requesting only 2 attributes
In [11]: qs.filter(lastname='young', age__gt=20).values('nickname', 'age')
Out[11]: [{'age': 35, 'nickname': 'kyoung'}]
# Querying a sub key
In [12]: qs.filter(address__zipcode='44000').values('nickname', 'age', 'address')
Out[12]:
[{'address': {'city': 'Nantes',
   'name': 'cheval blanc',
   'num': 12,
   'zipcode': '44000'},
  'age': 35,
  'nickname': 'kyoung'}]
# Querying a item in a list
In [13]: qs.filter(profiles__0__url__contains='kwame')
Out[13]:
[{'age': 24,
  'nickname': 'kkwame',
  'profiles': [{'name': 'twitter',
    'url': 'https://twitter.com/kkwame/',
    'username': 'kkwame'},
   {'name': 'github',
    'url': 'https://github.com/kkwame/',
    'username': 'kkwame'},
   {'name': 'reddit',
    'url': 'https://reddit.com/kkwame/',
    'username': 'kkwame'}]}]
# Ordering by attribute
In [14]: qs.order_by('age').values('nickname', 'age')
Out[14]:
[{'age': 15, 'nickname': 'tblack'},
 {'age': 24, 'nickname': 'kkwame'},
 {'age': 25, 'nickname': 'yloking'},
 {'age': 25, 'nickname': 'jrodriguez'},
 {'age': 28, 'nickname': 'jkouka'},
 {'age': 32, 'nickname': 'dmccrey'},
 {'age': 35, 'nickname': 'kyoung'}]
# Grouping by attribute
In [16]: res = qs.group_by('age')
In [17]: assert len(res[25]) == 2
In [18]: len(res[25])
Out[18]: 2
# Apply a function to an attribute
In [19]: qs.apply(lambda x: x*2, 'age').values('nickname', 'age')
Out[19]:
[{'age': 30, 'nickname': 'tblack'},
 {'age': 48, 'nickname': 'kkwame'},
 {'age': 50, 'nickname': 'yloking'},
 {'age': 50, 'nickname': 'jrodriguez'},
 {'age': 56, 'nickname': 'jkouka'},
 {'age': 64, 'nickname': 'dmccrey'},
 {'age': 70, 'nickname': 'kyoung'}]

That’s pretty much it. For more filter attribute, just check the code :wink:.