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Selecting executed method of class at runtime in python?

This question is very generic but I don't think it is opinion based. It is about software design and the example prototype is in python:
I am writing a program which goal it is to simulate some behaviour (doesn't matter). The data on which the simulation works is fixed, but the simulated behaviour I want to change at every startup time. The simulation behaviour can't be changed at runtime.
Example:
Simulation behaviour is defined like:
usedMethod = static
The program than looks something like this:
while(true)
result = static(object) # static is the method specified in the behaviour
# do something with result
The question is, how is the best way to deal with exchangeable defined functions? So another run of the simulation could look like this
while(true)
result = dynamic(object)
if dynamic is specified as usedMethod. The first thing that came in my mind was an if-else block, where I ask, which is the used method and then execute this on. This solution would not be very good, because every time I add new behaviour I have to change the if-else block and the if-else block itself would maybe cost performance, which is important, too. The simulations should be fast.
So a solution I could think of was using a function pointer (output and input of all usedMethods should be well defined and so it should not be a problem). Then I initalize the function pointer at startup, where the used method is defined.
The problem I currently have, that the used method is not a function per-se, but is a method of a class, which depends heavily on the intern members of this class, so the code is more looking like this:
balance = BalancerClass()
while(true)
result = balance.static(object)
...
balance.doSomething(input)
So my question is, what is a good solution to deal with this problem?
I thought about inheriting from the balancerClass (this would then be an abstract class, I don't know if this conecpt exists in python) and add a derived class for every used method. Then I create the correct derived object which is specified in the simulation behaviour an run-time.
In my eyes, this is a good solution, because it encapsulates the methods from the base class itself. And every used method is managed by its own class, so it can add new internal behaviour if needed.
Furthermore the doSomething method shouldn't change, so therefore it is implemented the base class, but depends on the intern changed members of the derived class.
I don't know in general if this software design is good to solve my problem or if I am missing a very basic and easy concept.
If you have a another/better solution please tell me and it would be good, if you provide the advantages/disadvantages. Also could you tell me advantages/disadvantages of my solution, which I didn't think of?

Hey I can be wrong but what you are looking for boils down to either dependency injection or strategy design pattern both of which solve the problem of executing dynamic code at runtime via a common interface without worrying about the actual implementations. There are also much simpler ways just like u desrcibed creating an abstract class(Interface) and having all the classes implement this interface.
I am giving brief examples fo which here for your reference:
Dependecy Injection(From wikipedia):
In software engineering, dependency injection is a technique whereby one object supplies the dependencies of another object. A "dependency" is an object that can be used, for example as a service. Instead of a client specifying which service it will use, something tells the client what service to use. The "injection" refers to the passing of a dependency (a service) into the object (a client) that would use it. The service is made part of the client's state.
Passing the service to the client, rather than allowing a client to build or find the service, is the fundamental requirement of the pattern.
Python does not have such a conecpt inbuilt in the language itself but there are packages out there that implements this pattern.
Here is a nice article about this in python(All credits to the original author):
Dependency Injection in Python
Strategy Pattern: This is an anti-pattern to inheritance and is an example of composition which basically means instead of inheriting from a base class we pass the required class's object to the constructor of classes we want to have the functionality in. For example:
Suppose you want to have a common add() operation but it can be implemented in different ways(add two numbers or add two strings)
Class XYZ():
def __constructor__(adder):
self.adder = adder
The only condition being all adders passed to the XYZ class should have a common Interface.
Here is a more detailed example:
Strategy Pattern in Python
Interfaces:
Interfaces are the simplest, they define a set of common attributes and methods(with or without a default implementation). Any class then can implement an interface with its own functionality or some shared common functionality. In python Interfaces are implemented via abc package.

Python/Django and services as classes

Are there any conventions on how to implement services in Django? Coming from a Java background, we create services for business logic and we "inject" them wherever we need them.
Not sure if I'm using python/django the wrong way, but I need to connect to a 3rd party API, so I'm using an api_service.py file to do that. The question is, I want to define this service as a class, and in Java, I can inject this class wherever I need it and it acts more or less like a singleton. Is there something like this I can use with Django or should I build the service as a singleton and get the instance somewhere or even have just separate functions and no classes?

TL;DR It's hard to tell without more details but chances are you only need a mere module with a couple plain functions or at most just a couple simple classes.
Longest answer:
Python is not Java. You can of course (technically I mean) use Java-ish designs, but this is usually not the best thing to do.
Your description of the problem to solve is a bit too vague to come with a concrete answer, but we can at least give you a few hints and pointers (no pun intended):
1/ Everything is an object
In python, everything (well, everything you can find on the RHS of an assignment that is) is an object, including modules, classes, functions and methods.
One of the consequences is that you don't need any complex framework for dependency injection - you just pass the desired object (module, class, function, method, whatever) as argument and you're done.
Another consequence is that you don't necessarily need classes for everything - a plain function or module can be just enough.
A typical use case is the strategy pattern, which, in Python, is most often implemented using a mere callback function (or any other callable FWIW).
2/ a python module is a singleton.
As stated above, at runtime a python module is an object (of type module) whose attributes are the names defined at the module's top-level.
Except for some (pathological) corner cases, a python module is only imported once for a given process and is garanteed to be unique. Combined with the fact that python's "global" scope is really only "module-level" global, this make modules proper singletons, so this design pattern is actually already builtin.
3/ a python class is (almost) a singleton
Python classes are objects too (instance of type type, directly or indirectly), and python has classmethods (methods that act on the class itself instead of acting on the current instance) and class-level attributes (attributes that belong to the class object itself, not to it's instances), so if you write a class that only has classmethods and class attributes, you technically have a singleton - and you can use this class either directly or thru instances without any difference since classmethods can be called on instances too.
The main difference here wrt/ "modules as singletons" is that with classes you can use inheritance...
4/ python has callables
Python has the concept of "callable" objects. A "callable" is an object whose class implements the __call__() operator), and each such object can be called as if it was a function.
This means that you can not only use functions as objects but also use objects as functions - IOW, the "functor" pattern is builtin. This makes it very easy to "capture" some context in one part of the code and use this context for computations in another part.
5/ a python class is a factory
Python has no new keyword. Pythonc classes are callables, and instanciation is done by just calling the class.
This means that you can actually use a class or function the same way to get an instance, so the "factory" pattern is also builtin.
6/ python has computed attributes
and beside the most obvious application (replacing a public attribute by a pair of getter/setter without breaking client code), this - combined with other features like callables etc - can prove to be very powerful. As a matter of fact, that's how functions defined in a class become methods
7/ Python is dynamic
Python's objects are (usually) dict-based (there are exceptions but those are few and mostly low-level C-coded classes), which means you can dynamically add / replace (and even remove) attributes and methods (since methods are attributes) on a per-instance or per-class basis.
While this is not a feature you want to use without reasons, it's still a very powerful one as it allows to dynamically customize an object (remember that classes are objects too), allowing for more complex objects and classes creation schemes than what you can do in a static language.
But Python's dynamic nature goes even further - you can use class decorators and/or metaclasses to taylor the creation of a class object (you may want to have a look at Django models source code for a concrete example), or even just dynamically create a new class using it's metaclass and a dict of functions and other class-level attributes.
Here again, this can really make seemingly complex issues a breeze to solve (and avoid a lot of boilerplate code).
Actually, Python exposes and lets you hook into most of it's inners (object model, attribute resolution rules, import mechanism etc), so once you understand the whole design and how everything fits together you really have the hand on most aspects of your code at runtime.
Python is not Java
Now I understand that all of this looks a bit like a vendor's catalog, but the point is highlight how Python differs from Java and why canonical Java solutions - or (at least) canonical Java implementations of those solutions - usually don't port well to the Python world. It's not that they don't work at all, just that Python usually has more straightforward (and much simpler IMHO) ways to implement common (and less common) design patterns.
wrt/ your concrete use case, you will have to post a much more detailed description, but "connecting to a 3rd part API" (I assume a REST api ?) from a Django project is so trivial that it really doesn't warrant much design considerations by itself.

In Python you can write the same as Java program structure. You don't need to be so strongly typed but you can. I'm using types when creating common classes and libraries that are used across multiple scripts.
Here you can read about Python typing
You can do the same here in Python. Define your class in package (folder) called services
Then if you want singleton you can do like that:
class Service(object):
instance = None
def __new__(cls):
if cls.instance is not None:
return cls.instance
else:
inst = cls.instance = super(Service, cls).__new__()
return inst
And now you import it wherever you want in the rest of the code
from services import Service
Service().do_action()

Adding to the answer given by bruno desthuilliers and TreantBG.
There are certain questions that you can ask about the requirements.
For example one question could be, does the api being called change with different type of objects ?
If the api doesn't change, you will probably be okay with keeping it as a method in some file or class.
If it does change, such that you are calling API 1 for some scenario, API 2 for some and so on and so forth, you will likely be better off with moving/abstracting this logic out to some class (from a better code organisation point of view).
PS: Python allows you to be as flexible as you want when it comes to code organisation. It's really upto you to decide on how you want to organise the code.

Parameterized skipping for Python unittests: Best practices?

I have the following scenario:
I have a list of "dangerous patterns"; each is a string that contains dangerous characters, such as "%s with embedded ' single quote", "%s with embedded \t horizontal tab" and similar (it's about a dozen patterns).
Each test is to be run
once with a vanilla pattern that does not inject dangerous characters (i.e. "%s"), and
once for each dangerous pattern.
If the vanilla test fails, we skip the dangerous pattern tests on the assumption that they don't make much sense if the vanilla case fails.
Various other constraints:
Stick with the batteries included in Python as far as possible (i.e. unittest is hopefully enough, even if nose would work).
I'd like to keep the contract of unittest.TestCase as far as possible. I.e. the solution should not affect test discovery (which might find everything that starts with test, but then there's also runTest which may be overridden in the construction, and more variation).
I tried a few solutions, and I am not happy with any of them:
Writing an abstract class causes unittest to try and run it as a test case (because it quacks like a test case). This can be worked around, but the code is getting ugly fast. Also, a whole lot of functions needs to be overridden, and for several of them the documentation is a bit unclear about what properties need to be implemented in the base class. Plus test discovery would have to be replicated, which means duplicating code from inside unittest.
Writing a function that executes the tests as SubTests, to be called from each test function. Requires boilerplate in every test function, and gives just a single test result for the entire series of tests.
Write a decorator. Avoids the test case discovery problem of the abstract class approach, but has all the other problems.
Write a decorator that takes a TestCase and returns a TestSuite. This worked best so far, but I don't know whether I can add the same TestCase object multiple times. Or whether TestCase objects can be copied or not (I have control over all of them but I don't know what the base class does or expects in this regard).
What's the best approach?

Need advice how to decouple logging functionality from data processing in Python

In my project, I have a set of classes that do some job by calling external commands, and return their results.
Let's call them "reports".
I want to add logging to these reports, however, a concrete logger object will be defined at runtime rather than
at the time when classes are defined.
So, for now I see two variants how to implement logging:
I. At runtime, instantiate some ReportLogger class instance, that can monkey-patch all given report instances with logging functionality using given concrete logger.
Pros:
It is possible to apply logging to any child report class I really need, and not touch other classes.
Cons:
Magic! Monkey-patching is not explicit way to do things.
Logging is actually applied at runtime, so it's less clear to understand that there is some logging when looking to report classes code.
II. Singleton ReportLogger class, that wraps all reports at creation time via decorators, but accepts concrete logger at runtime.
Pros:
Explicit and clean way to mark that these reports require logging (as well as apply it actually).
Cons:
It's harder to deal with child classes that inherit from basic Report class. If, for example, in base Report class, some method like collect_data() is decorated with #log_collect_data, then for child classes logging will be tightly coupled with collect_data(). Or, maybe, I have to split actual code from collect_data() to, say, _collect_data() to modify it in child classes, call _collect_data() from collect_data(), and then wrap collect_data() with #log_collect_data.
I like second method, but I want better way to deal with child classes rather than using _collect_data(). Any advices are welcome!

Unittest in Django. What is relationship between TestCase class and method

I am doing some unit testing stuff in Django. What is the relationship between TestCase class and the actual method in this class? What is the best practice for organizing these stuff?
For example, I have
class Test(TestCase):
def __init__(self):
...
def testTestA(self):
#test code
def testTestB(self):
#test code
If I organize in this way:
class Test1(TestCase):
def __init__(self):
...
def testTestA(self):
#test code
class Test2(TestCase):
def __init__(self):
...
def testTestB(self):
...
Which is better and what is the difference?
Thanks!

You rarely write __init__ for a TestCase. So strike that from your mental model of unit testing.
You sometimes write a setUp and tearDown. Django automates much of this, however, and you often merely provide a static fixtures= variable that's used to populate the test database.
More fundamentally, what's a test case?
A test case is a "fixture" -- a configuration of a unit under test -- that you can then exercise. Ideally each TestCase has a setUp method that creates one fixture. Each method will perform a manipulation on that fixture and assert that the manipulation worked.
However. There's nothing dogmatic about that.
In many cases -- particularly when exercising Django models -- where there just aren't that many interesting manipulations.
If you don't override save in a model, you don't really need to do CRUD testing. You can (and should) trust the ORM. [If you don't trust it, then get a new framework that you do trust.]
If you have a few properties in a models class, you might not want to create a distinct method to test each property. You might want to simply test them sequentially in a single method of a single TestCase.
If, OTOH, you have really complex class with lots of state changes, you will need a distinct TestCase to configure an object is one state, manipulate it into another state and assert that the changes all behaved correctly.
View Functions, since they aren't -- technically -- stateful, don't match the Unit Test philosophy perfectly. When doing setUp to create a unit in a known state, you're using the client interface to step through some interactions to create a session in a known state. Once the session has reached as desired state, then your various test methods will exercise that session, and assert that things worked.
Summary
Think of TestCase as a "Setup" or "Context" in which tests will be run.
Think of each method as "when_X_should_Y" statement. Some folks suggest that kind of name ("test_when_x_should_y") So the method will perform "X" and assert that "Y" was the response.

It's kind of hard to answer this question regarding the proper organization of cases A and B and test methods 1, 2 and 3...
However splitting the tests to test cases serves two major purposes:
1) Organizing the tests around some logical groups, such as CustomerViewTests, OrdersAggregationTests, etc.
2) Sharing the same setUp() and tearDown() methods, for tests which require the same, well, setup and tear down.
More information and examples can be found at unitTest documentation.