In Java IoC / DI is a very common practice which is extensively used in web applications, nearly all available frameworks and Java EE. On the other hand, there are also lots of big Python web applications, but beside of Zope (which I’ve heard should be really horrible to code) IoC doesn’t seem to be very common in the Python world. (Please name some examples if you think that I’m wrong).

There are of course several clones of popular Java IoC frameworks available for Python, springpython for example. But none of them seems to get used practically. At least, I’ve never stumpled upon a Django or sqlalchemy+<insert your favorite wsgi toolkit here> based web application which uses something like that.

In my opinion IoC has reasonable advantages and would make it easy to replace the django-default-user-model for example, but extensive usage of interface classes and IoC in Python looks a bit odd and not »pythonic«. But maybe someone has a better explanation, why IoC isn’t widely used in Python.

I don’t actually think that DI/IoC are that uncommon in Python. What is uncommon, however, are DI/IoC frameworks/containers.

Think about it: what does a DI container do? It allows you to

1. wire together independent components into a complete application …
2. … at runtime.

We have names for “wiring together” and “at runtime”:

1. scripting
2. dynamic

So, a DI container is nothing but an interpreter for a dynamic scripting language. Actually, let me rephrase that: a typical Java/.NET DI container is nothing but a crappy interpreter for a really bad dynamic scripting language with butt-ugly, sometimes XML-based, syntax.

When you program in Python, why would you want to use an ugly, bad scripting language when you have a beautiful, brilliant scripting language at your disposal? Actually, that’s a more general question: when you program in pretty much any language, why would you want to use an ugly, bad scripting language when you have Jython and IronPython at your disposal?

So, to recap: the practice of DI/IoC is just as important in Python as it is in Java, for exactly the same reasons. The implementation of DI/IoC however, is built into the language and often so lightweight that it completely vanishes.

(Here’s a brief aside for an analogy: in assembly, a subroutine call is a pretty major deal – you have to save your local variables and registers to memory, save your return address somewhere, change the instruction pointer to the subroutine you are calling, arrange for it to somehow jump back into your subroutine when it is finished, put the arguments somewhere where the callee can find them, and so on. IOW: in assembly, “subroutine call” is a Design Pattern, and before there were languages like Fortran which had subroutine calls built in, people were building their own “subroutine frameworks”. Would you say that subroutine calls are “uncommon” in Python, just because you don’t use subroutine frameworks?)

BTW: for an example of what it looks like to take DI to its logical conclusion, take a look at Gilad Bracha‘s Newspeak Programming Language and his writings on the subject:

• Constructors Considered Harmful
• Lethal Injection
• A Ban on Imports (continued)

Part of it is the way the module system works in Python. You can get a sort of “singleton” for free, just by importing it from a module. Define an actual instance of an object in a module, and then any client code can import it and actually get a working, fully constructed / populated object.

This is in contrast to Java, where you don’t import actual instances of objects. This means you are always having to instantiate them yourself, (or use some sort of IoC/DI style approach). You can mitigate the hassle of having to instantiate everything yourself by having static factory methods (or actual factory classes), but then you still incur the resource overhead of actually creating new ones each time.

IoC and DI are super common in mature Python code. You just don’t need a framework to implement DI thanks to duck typing.

The best example is how you set up a Django application using settings.py:

# settings.py
CACHES = {
    'default': {
        'BACKEND': 'django_redis.cache.RedisCache',
        'LOCATION': REDIS_URL + '/1',
    },
    'local': {
        'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',
        'LOCATION': 'snowflake',
    }
}

Django Rest Framework utilizes DI heavily:

class FooView(APIView):
    # The "injected" dependencies:
    permission_classes = (IsAuthenticated, )
    throttle_classes = (ScopedRateThrottle, )
    parser_classes = (parsers.FormParser, parsers.JSONParser, parsers.MultiPartParser)
    renderer_classes = (renderers.JSONRenderer,)

    def get(self, request, *args, **kwargs):
        pass

    def post(self, request, *args, **kwargs):
        pass

Let me remind (source):

“Dependency Injection” is a 25-dollar term for a 5-cent concept. […] Dependency injection means giving an object its instance variables. […].

Django makes great use of inversion of control. For instance, the database server is selected by the configuration file, then the framework provides appropriate database wrapper instances to database clients.

The difference is that Python has first-class types. Data types, including classes, are themselves objects. If you want something to use a particular class, simply name the class. For example:

if config_dbms_name == 'postgresql':
    import psycopg
    self.database_interface = psycopg
elif config_dbms_name == 'mysql':
    ...

Later code can then create a database interface by writing:

my_db_connection = self.database_interface()
# Do stuff with database.

Instead of the boilerplate factory functions that Java and C++ need, Python does it with one or two lines of ordinary code. This is the strength of functional versus imperative programming.

It seens that people really dont get what Dependency injection and inversion of control means anymore.

The practice of using inversion of control is to have classes or function that depends of another classes or functions, but instead of creating the instances whithin the class of function code it is better to receive it as a parameter, so loose coupling can be archieved. That has many benefits as more testability and to archieve the liskov substitution principle.

You see, by working with interfaces and injections, your code gets more maintanable, since you can change the behavior easily, because you won’t have to rewrite a single line of code (maybe a line or two on the DI configuration) of your class to change it’s behavior, since the classes that implements the interface your class is waiting for can vary independently as long as they follow the interface. One of the best strategies to keep code decoupled and easy to maintain is to follow at least the single responsability, substitution and dependency inversion principles.

Whats a DI library good for if you can instantiate a object yourself inside a package and import it to inject it yourself? The chosen answer is right, since java has no procedural sections (code outside of classes), all that goes into boring configuration xml’s, hence the need of a class to instantiate and inject dependencies on a lazy load fashion so you don’t blow away your performance, while on python you just code the injections on the “procedural” (code outside classes) sections of your code

Haven’t used Python in several years, but I would say that it has more to do with it being a dynamically typed language than anything else. For a simple example, in Java, if I wanted to test that something wrote to standard out appropriately I could use DI and pass in any PrintStream to capture the text being written and verify it. When I’m working in Ruby, however, I can dynamically replace the ‘puts’ method on STDOUT to do the verify, leaving DI completely out of the picture. If the only reason I’m creating an abstraction is to test the class that’s using it (think File system operations or the clock in Java) then DI/IoC creates unnecessary complexity in the solution.

Actually, it is quite easy to write sufficiently clean and compact code with DI (I wonder, will it be/stay pythonic then, but anyway :) ), for example I actually perefer this way of coding:

def polite(name_str):
    return "dear " + name_str

def rude(name_str):
    return name_str + ", you, moron"

def greet(name_str, call=polite):
    print "Hello, " + call(name_str) + "!"

_

>>greet("Peter")
Hello, dear Peter!
>>greet("Jack", rude)
Hello, Jack, you, moron!

Yes, this can be viewed as just a simple form of parameterizing functions/classes, but it does its work. So, maybe Python’s default-included batteries are enough here too.

P.S. I have also posted a larger example of this naive approach at Dynamically evaluating simple boolean logic in Python.

IoC/DI is a design concept, but unfortunately it’s often taken as a concept that applies to certain languages (or typing systems). I’d love to see dependency injection containers become far more popular in Python. There’s Spring, but that’s a super-framework and seems to be a direct port of the Java concepts without much consideration for “The Python Way.”

Given Annotations in Python 3, I decided to have a crack at a full featured, but simple, dependency injection container: https://github.com/zsims/dic . It’s based on some concepts from a .NET dependency injection container (which IMO is fantastic if you’re ever playing in that space), but mutated with Python concepts.

I think due to the dynamic nature of python people don’t often see the need for another dynamic framework. When a class inherits from the new-style ‘object’ you can create a new variable dynamically (https://wiki.python.org/moin/NewClassVsClassicClass).

i.e. In plain python:

#application.py
class Application(object):
    def __init__(self):
        pass

#main.py
Application.postgres_connection = PostgresConnection()

#other.py
postgres_connection = Application.postgres_connection
db_data = postgres_connection.fetchone()

However have a look at https://github.com/noodleflake/pyioc this might be what you are looking for.

i.e. In pyioc

from libs.service_locator import ServiceLocator

#main.py
ServiceLocator.register(PostgresConnection)

#other.py
postgres_connection = ServiceLocator.resolve(PostgresConnection)
db_data = postgres_connection.fetchone()

I back “Jörg W Mittag” answer: “The Python implementation of DI/IoC is so lightweight that it completely vanishes”.

To back up this statement, take a look at the famous Martin Fowler’s example ported from Java to Python: Python:Design_Patterns:Inversion_of_Control

As you can see from the above link, a “Container” in Python can be written in 8 lines of code:

class Container:
    def __init__(self, system_data):
        for component_name, component_class, component_args in system_data:
            if type(component_class) == types.ClassType:
                args = [self.__dict__[arg] for arg in component_args]
                self.__dict__[component_name] = component_class(*args)
            else:
                self.__dict__[component_name] = component_class

My 2cents is that in most Python applications you don’t need it and, even if you needed it, chances are that many Java haters (and incompetent fiddlers who believe to be developers) consider it as something bad, just because it’s popular in Java.

An IoC system is actually useful when you have complex networks of objects, where each object may be a dependency for several others and, in turn, be itself a dependant on other objects. In such a case you’ll want to define all these objects once and have a mechanism to put them together automatically, based on as many implicit rules as possible. If you also have configuration to be defined in a simple way by the application user/administrator, that’s an additional reason to desire an IoC system that can read its components from something like a simple XML file (which would be the configuration).

The typical Python application is much simpler, just a bunch of scripts, without such a complex architecture. Personally I’m aware of what an IoC actually is (contrary to those who wrote certain answers here) and I’ve never felt the need for it in my limited Python experience (also I don’t use Spring everywhere, not when the advantages it gives don’t justify its development overhead).

That said, there are Python situations where the IoC approach is actually useful and, in fact, I read here that Django uses it.

The same reasoning above could be applied to Aspect Oriented Programming in the Java world, with the difference that the number of cases where AOP is really worthwhile is even more limited.

pytest fixtures all based on DI (source)

I agree with @Jorg in the point that DI/IoC is possible, easier and even more beautiful in Python. What’s missing is the frameworks supporting it, but there are a few exceptions. To point a couple of examples that come to my mind:

• Django comments let you wire your own Comment class with your custom logic and forms. [More Info]

• Django let you use a custom Profile object to attach to your User model. This is not completely IoC but is a good approach. Personally I’d like to replace the hole User model as the comments framework does. [More Info]

In my opinion, things like dependency injection are symptoms of a rigid and over-complex framework. When the main body of code becomes much too weighty to change easily, you find yourself having to pick small parts of it, define interfaces for them, and then allowing people to change behaviour via the objects that plug into those interfaces. That’s all well and good, but it’s better to avoid that sort of complexity in the first place.

It’s also the symptom of a statically-typed language. When the only tool you have to express abstraction is inheritance, then that’s pretty much what you use everywhere. Having said that, C++ is pretty similar but never picked up the fascination with Builders and Interfaces everywhere that Java developers did. It is easy to get over-exuberant with the dream of being flexible and extensible at the cost of writing far too much generic code with little real benefit. I think it’s a cultural thing.

Typically I think Python people are used to picking the right tool for the job, which is a coherent and simple whole, rather than the One True Tool (With A Thousand Possible Plugins) that can do anything but offers a bewildering array of possible configuration permutations. There are still interchangeable parts where necessary, but with no need for the big formalism of defining fixed interfaces, due to the flexibility of duck-typing and the relative simplicity of the language.

Unlike the strong typed nature in Java. Python’s duck typing behavior makes it so easy to pass objects around.

Java developers are focusing on the constructing the class strcuture and relation between objects, while keeping things flexible. IoC is extremely important for achieving this.

Python developers are focusing on getting the work done. They just wire up classes when they need it. They don’t even have to worry about the type of the class. As long as it can quack, it’s a duck! This nature leaves no room for IoC.