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Lifter is a generic query engine, inspired by Django ORM and SQLAlchemy.
Its goal is to provide a unique interface to query any type of data, regardless of the underlying query language or data backend (SQL, REST, Python objects...).
Warning: This package is still in alpha state and a lot of work is still needed to make queries faster and efficient. Contributions are welcome :)
Useful links:
python-lifter <http://stackoverflow.com/questions/tagged/python-lifter>_.. _Django: https://docs.djangoproject.com/en/1.9/ref/models/querysets/ .. _SQLAlchemy: http://docs.sqlalchemy.org/en/rel_1_0/orm/tutorial.html#common-filter-operators
Consider the following list of users, returned from a REST API endpoint:
.. code-block:: python
users = [
{
"is_active": True,
"age": 35,
"eye_color": "brown",
"name": "Bernard",
"gender": "male",
"email": "bernard@blackbooks.com",
},
{
"is_active": True,
"age": 34,
"eye_color": "brown",
"name": "Manny",
"gender": "male",
"email": "manny@blackbooks.com",
},
{
"is_active": True,
"age": 35,
"eye_color": "brown",
"name": "Fran",
"gender": "female",
"email": "fran@blackbooks.com",
},
# And so on ...
]Now, imagine you have to extract data from this list. Let's compare how you can do this using regular Python and lifter.
To use lifter in your project, you'll only need the following instructions:
.. code-block:: python
import lifter.models
from lifter.backends.python import IterableStore
class User(lifter.models.Model):
pass
store = IterableStore(users)
manager = store.query(User)Getting all active 26 years old users:
.. code-block:: python
# vanilla Python
results = [
user for user in users
if user['age'] == 26 and user['is_active']
]
# lifter
results = manager.filter(User.age == 26, User.is_active == True)Getting names and emails of inactive users under 56:
.. code-block:: python
# vanilla Python
results = [
(user['name'], user['email']) for user in users
if not user['is_active'] and user['age'] < 56
]
# lifter
results = manager.filter(User.is_active == False, User.age < 56)\
.values_list('name', 'email')Getting all active users except the one with brown eyes and sort them by age:
.. code-block:: python
# vanilla Python
raw_results = [
user for user in users
if user['is_active'] and not user['eye_color'] == 'brown'
]
results = sorted(raw_results, key=lambda v: v['age'])
# lifter
results = manager.filter(User.is_active == True)\
.exclude(User.eye_color == 'brown')\
.order_by('age')Getting minimum and average women age:
.. code-block:: python
# vanilla Python
from statistics import mean # Only in Python >=3.4
women_ages = [
user['age'] for user in users
if user['gender'] == 'female'
]
women_average_age = mean(women_ages)
minimum_woman_age = min(women_ages)
# lifter
results = manager.filter(User.gender == 'female')\
.aggregate((User.age, mean), (User.age, min))As you can see, lifter's version is shorter and more readable than vanilla Python. It's also less error prone, especially if you're writing really complex queries, and quite familiar if you've already used an ORM.
Wanna know more? Have a look at the documentation_!
.. _documentation: http://lifter.readthedocs.org