Quickstart

Install django-predicate:

pip install django-predicate

Then use the P object just as you would Q objects:

from predicate import P

p = P(some_field__startswith="hello", age__gt=20)

You can then call the eval method with a model instance to check whether it passes the conditions:

model_instance = MyModel(some_field="hello there", age=21)
other_model_instance = MyModel(some_field="hello there", age=10)
p.eval(model_instance)
>>> True
p.eval(other_model_instance)
>>> False

or you can use Python’s in operator.

model_instance in p
>>> True

Even though a predicate is not a true container class - it can be used as (and was designed as being) a virtual “set” of objects that meets some condiiton.

Like Q objects, P objects can be &’ed and |’ed together to form more complex logic groupings.

In fact, P objects are actually a subclass of Q objects, so you can use them in queryset filter statements:

qs = MyModel.objects.filter(p)

P objects also support QuerySet-like filtering operations that can be applied to an arbitrary iterable: P.get(iterable), P.filter(iterable), and P.exclude(iterable):

model_instance = MyModel(some_field="hello there", age=21)
other_model_instance = MyModel(some_field="hello there", age=10)
p.filter([model_instance, other_model_instance]) == [model_instance]
>>> True
p.get([model_instance, other_model_instance]) == model_instance
>>> True
p.exclude([model_instance, other_model_instance]) == [other_model_instance]
>>> True

If you have a situation where you want to use querysets and predicates based on the same conditions, it is far better to start with the predicate. Because of the way querysets assume a SQL context, it is non-trivial to reverse engineer them back into a predicate. However as seen above, it is very straightforward to create a queryset based on a predicate.

Development

These instructions assume you have Postgres installed and all referenced Python versions installed and activated, e.g. with Pyenv.

To run the tests locally, do the following:

1. Clone this repo.

2. Start Postgres in the background for the Postgres tests with postgres -D ~/postgres.

3. Activate all tested Python versions used in this repo: pyenv local 3.6 3.7 3.10.

4. (Optional) Create a virtualenv and activate it: python -m venv .venv, source .venv/bin/activate.

5. (Optional) Upgrade pip to prevent platform issues pip install --upgrade pip

6. Install Tox for running tests: pip install tox.

7. Run all tests by issuing command tox with no arguments.

Changelog