Im teaching myself python (3.x) and I'm trying to understand the use case for classes. I'm starting to understand what they actually do, but I'm struggling to understand why you would use a class as opposed to creating a module with functions.
For example, how does:
class cls1:
def func1(arguments...):
#do some stuff
obj1 = cls1()
obj2 = cls1()
obj1.func1(arg1,arg2...)
obj2.func1(arg1,arg2...)
Differ from:
#module.py contents
def func1(arguments...):
#do some stuff
import module
x = module.func1(arg1,arg2...)
y = module.func1(arg1,arg2...)
This is probably very simple but I just can't get my head around it.
So far, I've had quite a bit of success writing python programs, but they have all been pretty procedural, and only importing basic module functions. Classes are my next biggest hurdle.
You use class if you need multiple instance of it, and you want that instances don't interfere each other.
Module behaves like a singleton class, so you can have only one instance of them.
EDIT: for example if you have a module called example.py:
x = 0
def incr():
global x
x = x + 1
def getX():
return x
if you try to import these module twice:
import example as ex1
import example as ex2
ex1.incr()
ex1.getX()
1
ex2.getX()
1
This is why the module is imported only one time, so ex1 and ex2 points to the same object.
As long as you're only using pure functions (functions that only works on their arguments, always return the same result for the same arguments set, don't depend on any global/shared state and don't change anything - neither their arguments nor any global/shared state - IOW functions that don't have any side effects), then classes are indeed of a rather limited use. But that's functional programming, and while Python can technically be used in a functional style, it's possibly not the best choice here.
As soon has you have to share state between functions, and specially if some of these functions are supposed to change this shared state, you do have a use for OO concepts. There are mainly two ways to share state between functions: passing the state from function to function, or using globals.
The second solution - global state - is known to be troublesome, first because it makes understanding of the program flow (hence debugging) harder, but also because it prevents your code from being reentrant, which is a definitive no-no for quite a lot of now common use cases (multithreaded execution, most server-side web application code etc). Actually it makes your code practically unusable or near-unusable for anything except short simple one-shot scripts...
The second solution most often implies using half-informal complex datastructures (dicts with a given set of keys, often holding other dicts, lists, lists of dicts, sets etc), correctly initialising them and passing them from function to function - and of course have a set of functions that works on a given datastructure. IOW you are actually defining new complex datatypes (a data structure and a set of operations on that data structure), only using the lowest level tools the language provide.
Classes are actually a way to define such a data type at a higher level, grouping together the data and operations. They also offer a lot more, specially polymorphism, which makes for more generic, extensible code, and also easier unit testing.
Consider you have a file or a database with products, and each product has product id, price, availability, discount, published at web status, and more values. And you have second file with thousands of products that contain new prices and availability and discount. You want to update the values and keep control on how many products will be change and other stats. You can do it with Procedural programming and Functional programming but you will find yourself trying to discover tricks to make it work and most likely you will be lost in many different lists and sets.
On the other hand with Object-oriented programming you can create a class Product with instance variables the product-id, the old price, the old availability, the old discount, the old published status and some instance variables for the new values (new price, new availability, new discount, new published status). Than all you have to do is to read the first file/database and for every product to create a new instance of the class Product. Than you can read the second file and find the new values for your product objects. In the end every product of the first file/database will be an object and will be labeled and carry the old values and the new values. It is easier this way to track the changes, make statistics and update your database.
One more example. If you use tkinter, you can create a class for a top level window and every time you want to appear an information window or an about window (with custom color background and dimensions) you can simply create a new instance of this class.
For simple things classes are not needed. But for more complex things classes sometimes can make the solution easier.
I think the best answer is that it depends on what your indented object is supposed to be/do. But in general, there are some differences between a class and an imported module which will give each of them different features in the current module. Which the most important thing is that class has been defined to be objects, this means that they have a lot of options to act like an object which modules don't have. For example some special attributes like __getattr__, __setattr__, __iter__, etc. And the ability to create a lot of instances and even controlling the way that they are created. But for modules, the documentation describes their use-case perfectly:
If you quit from the Python interpreter and enter it again, the
definitions you have made (functions and variables) are lost.
Therefore, if you want to write a somewhat longer program, you are
better off using a text editor to prepare the input for the
interpreter and running it with that file as input instead. This is
known as creating a script. As your program gets longer, you may want
to split it into several files for easier maintenance. You may also
want to use a handy function that you’ve written in several programs
without copying its definition into each program.
To support this, Python has a way to put definitions in a file and use
them in a script or in an interactive instance of the interpreter.
Such a file is called a module; definitions from a module can be
imported into other modules or into the main module (the collection of
variables that you have access to in a script executed at the top
level and in calculator mode).
Related
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.
For start, sorry for my bad English.
Recently I read book called "elegant objects" by Yegor Bugayenko. One of the topics in this book is dedicated to using of objects instead of public constants. Author writes that public constants is a pure evil. There are a lot of reasons for this. For example, using of public constants breaks incapsulation - if we want to change this contant we need to know how classes of our package using this constant. If two classes in project are using this constant in its own way we need to change code of this two classes if we change constant.
Author suggests that instead of using global constants we need to create objects and that's allows us to change code only in one place. I will give an example below.
I already read similar topics on stackoverflow, but did not find the answer about best practices or use cases. Is the use of objects better than creating a global variable in some file, for example "settings.py"?
Is this
class WWStartDate:
date_as_string = '01.09.1939'
def as_timestamp(self):
# returns date as timestamp
def as_date_time(self):
# returns date as datetime object
better than this stored in some file inside package conf.py for example:
DATE_STRING = '01.09.1939'
if we are talking about using this date in several classes of our package?
After reading of this book I decided that objects are much better, but I see a lot of cases when developers of framework or library force us to use global variables. So It is not that simple as I can see. Why, for example, django use this approach? I am talking about file settings.py.
I think that by creating a class for a constant to offer itself in different forms to different (other) classes you are overengineering your constants. Why does the constant have to know how it's being used? Does math.pi know how it is being used? It actually has some methods because it's a float object. Nothing specific to the pi constant. Besides, when you do it, you couple the constant to other classes, and when they change their uses you may have to update the constant class. Now that's a bad idea. You want them decoupled. Each class (and only the class) should know what it is doing with the constant.
This is not to say that a wrapping class can't be useful sometimes, specially if you group related constants. But them you can use Enums too.
And globals in a module are only globals in that module. When imported you are either using copies or (qualified) linked names. But even in the linked case if you change the imported value (which you shouldn't because you say it is constant) using qualification, and a different module is importing the same value, it does not see the changes you made. They are using different namespaces. This is not the usual "global variable" concept, when we say globals are evil.
I have been programming in python for about two years; mostly data stuff (pandas, mpl, numpy), but also automation scripts and small web apps. I'm trying to become a better programmer and increase my python knowledge and one of the things that bothers me is that I have never used a class (outside of copying random flask code for small web apps). I generally understand what they are, but I can't seem to wrap my head around why I would need them over a simple function.
To add specificity to my question: I write tons of automated reports which always involve pulling data from multiple data sources (mongo, sql, postgres, apis), performing a lot or a little data munging and formatting, writing the data to csv/excel/html, send it out in an email. The scripts range from ~250 lines to ~600 lines. Would there be any reason for me to use classes to do this and why?
Classes are the pillar of Object Oriented Programming. OOP is highly concerned with code organization, reusability, and encapsulation.
First, a disclaimer: OOP is partially in contrast to Functional Programming, which is a different paradigm used a lot in Python. Not everyone who programs in Python (or surely most languages) uses OOP. You can do a lot in Java 8 that isn't very Object Oriented. If you don't want to use OOP, then don't. If you're just writing one-off scripts to process data that you'll never use again, then keep writing the way you are.
However, there are a lot of reasons to use OOP.
Some reasons:
Organization:
OOP defines well known and standard ways of describing and defining both data and procedure in code. Both data and procedure can be stored at varying levels of definition (in different classes), and there are standard ways about talking about these definitions. That is, if you use OOP in a standard way, it will help your later self and others understand, edit, and use your code. Also, instead of using a complex, arbitrary data storage mechanism (dicts of dicts or lists or dicts or lists of dicts of sets, or whatever), you can name pieces of data structures and conveniently refer to them.
State: OOP helps you define and keep track of state. For instance, in a classic example, if you're creating a program that processes students (for instance, a grade program), you can keep all the info you need about them in one spot (name, age, gender, grade level, courses, grades, teachers, peers, diet, special needs, etc.), and this data is persisted as long as the object is alive, and is easily accessible. In contrast, in pure functional programming, state is never mutated in place.
Encapsulation:
With encapsulation, procedure and data are stored together. Methods (an OOP term for functions) are defined right alongside the data that they operate on and produce. In a language like Java that allows for access control, or in Python, depending upon how you describe your public API, this means that methods and data can be hidden from the user. What this means is that if you need or want to change code, you can do whatever you want to the implementation of the code, but keep the public APIs the same.
Inheritance:
Inheritance allows you to define data and procedure in one place (in one class), and then override or extend that functionality later. For instance, in Python, I often see people creating subclasses of the dict class in order to add additional functionality. A common change is overriding the method that throws an exception when a key is requested from a dictionary that doesn't exist to give a default value based on an unknown key. This allows you to extend your own code now or later, allow others to extend your code, and allows you to extend other people's code.
Reusability: All of these reasons and others allow for greater reusability of code. Object oriented code allows you to write solid (tested) code once, and then reuse over and over. If you need to tweak something for your specific use case, you can inherit from an existing class and overwrite the existing behavior. If you need to change something, you can change it all while maintaining the existing public method signatures, and no one is the wiser (hopefully).
Again, there are several reasons not to use OOP, and you don't need to. But luckily with a language like Python, you can use just a little bit or a lot, it's up to you.
An example of the student use case (no guarantee on code quality, just an example):
Object Oriented
class Student(object):
def __init__(self, name, age, gender, level, grades=None):
self.name = name
self.age = age
self.gender = gender
self.level = level
self.grades = grades or {}
def setGrade(self, course, grade):
self.grades[course] = grade
def getGrade(self, course):
return self.grades[course]
def getGPA(self):
return sum(self.grades.values())/len(self.grades)
# Define some students
john = Student("John", 12, "male", 6, {"math":3.3})
jane = Student("Jane", 12, "female", 6, {"math":3.5})
# Now we can get to the grades easily
print(john.getGPA())
print(jane.getGPA())
Standard Dict
def calculateGPA(gradeDict):
return sum(gradeDict.values())/len(gradeDict)
students = {}
# We can set the keys to variables so we might minimize typos
name, age, gender, level, grades = "name", "age", "gender", "level", "grades"
john, jane = "john", "jane"
math = "math"
students[john] = {}
students[john][age] = 12
students[john][gender] = "male"
students[john][level] = 6
students[john][grades] = {math:3.3}
students[jane] = {}
students[jane][age] = 12
students[jane][gender] = "female"
students[jane][level] = 6
students[jane][grades] = {math:3.5}
# At this point, we need to remember who the students are and where the grades are stored. Not a huge deal, but avoided by OOP.
print(calculateGPA(students[john][grades]))
print(calculateGPA(students[jane][grades]))
Whenever you need to maintain a state of your functions and it cannot be accomplished with generators (functions which yield rather than return). Generators maintain their own state.
If you want to override any of the standard operators, you need a class.
Whenever you have a use for a Visitor pattern, you'll need classes. Every other design pattern can be accomplished more effectively and cleanly with generators, context managers (which are also better implemented as generators than as classes) and POD types (dictionaries, lists and tuples, etc.).
If you want to write "pythonic" code, you should prefer context managers and generators over classes. It will be cleaner.
If you want to extend functionality, you will almost always be able to accomplish it with containment rather than inheritance.
As every rule, this has an exception. If you want to encapsulate functionality quickly (ie, write test code rather than library-level reusable code), you can encapsulate the state in a class. It will be simple and won't need to be reusable.
If you need a C++ style destructor (RIIA), you definitely do NOT want to use classes. You want context managers.
I think you do it right. Classes are reasonable when you need to simulate some business logic or difficult real-life processes with difficult relations.
As example:
Several functions with share state
More than one copy of the same state variables
To extend the behavior of an existing functionality
I also suggest you to watch this classic video
dantiston gives a great answer on why OOP can be useful. However, it is worth noting that OOP is not necessary a better choice most cases it is used. OOP has the advantage of combining data and methods together. In terms of application, I would say that use OOP only if all the functions/methods are dealing and only dealing with a particular set of data and nothing else.
Consider a functional programming refactoring of dentiston's example:
def dictMean( nums ):
return sum(nums.values())/len(nums)
# It's good to include automatic tests for production code, to ensure that updates don't break old codes
assert( dictMean({'math':3.3,'science':3.5})==3.4 )
john = {'name':'John', 'age':12, 'gender':'male', 'level':6, 'grades':{'math':3.3}}
# setGrade
john['grades']['science']=3.5
# getGrade
print(john['grades']['math'])
# getGPA
print(dictMean(john['grades']))
At a first look, it seems like all the 3 methods exclusively deal with GPA, until you realize that Student.getGPA() can be generalized as a function to compute mean of a dict, and re-used on other problems, and the other 2 methods reinvent what dict can already do.
The functional implementation gains:
Simplicity. No boilerplate class or selfs.
Easily add automatic test code right after each
function for easy maintenance.
Easily split into several programs as your code scales.
Reusability for purposes other than computing GPA.
The functional implementation loses:
Typing in 'name', 'age', 'gender' in dict key each time is not very DRY (don't repeat yourself). It's possible to avoid that by changing dict to a list. Sure, a list is less clear than a dict, but this is a none issue if you include an automatic test code below anyway.
Issues this example doesn't cover:
OOP inheritance can be supplanted by function callback.
Calling an OOP class has to create an instance of it first. This can be boring when you don't have data in __init__(self).
A class defines a real world entity. If you are working on something that exists individually and has its own logic that is separate from others, you should create a class for it. For example, a class that encapsulates database connectivity.
If this not the case, no need to create class
It depends on your idea and design. If you are a good designer, then OOPs will come out naturally in the form of various design patterns.
For simple script-level processing, OOPs can be overhead.
Simply consider the basic benefits of OOPs like reusability and extendability and make sure if they are needed or not.
OOPs make complex things simpler and simpler things complex.
Simply keep the things simple in either way using OOPs or not using OOPs. Whichever is simpler, use that.
Let's suppose I have several functions for a RPG I'm working on...
def name_of_function():
action
and wanted to implement axe class (see below) into each function without having to rewrite each class. How would I create the class as a global class. I'm not sure if I'm using the correct terminology or not on that, but please help. This has always held me abck from creating Text based RPG games. An example of a global class would be awesome!
class axe:
attack = 5
weight = 6
description = "A lightweight battle axe."
level_required = 1
price = 10
You can't create anything that's truly global in Python - that is, something that's magically available to any module no matter what. But it hardly matters once you understand how modules and importing work.
Typically, you create classes and organize them into modules. Then you import them into whatever module needs them, which adds the class to the module's symbol table.
So for instance, you might create a module called weapons.py, and create a WeaponBase class in it, and then Axe and Broadsword classes derived from WeaponsBase. Then, in any module that needed to use weapons, you'd put
import weapons
at the top of the file. Once you do this, weapons.Axe returns the Axe class, weapons.Broadsword returns the Broadsword class, and so on. You could also use:
from weapons import Axe, Broadsword
which adds Axe and Broadsword to the module's symbol table, allowing code to do pretty much exactly what you are saying you want it to do.
You can also use
from weapons import *
but this generally is not a great idea for two reasons. First, it imports everything in the module whether you're going to use it or not - WeaponsBase, for instance. Second, you run into all kinds of confusing problems if there's a function in weapons that's got the same name as a function in the importing module.
There are a lot of subtleties in the import system. You have to be careful to make sure that modules don't try to import each other, for instance. And eventually your project gets large enough that you don't want to put all of its modules in the same directory, and you'll have to learn about things like __init__.py. But you can worry about that down the road.
i beg to differ with the view that you can't create something truly global in python. in fact, it is easy. in Python 3.1, it looks like this:
def get_builtins():
"""Due to the way Python works, ``__builtins__`` can strangely be either a module or a dictionary,
depending on whether the file is executed directly or as an import. I couldn’t care less about this
detail, so here is a method that simply returns the namespace as a dictionary."""
return getattr( __builtins__, '__dict__', __builtins__ )
like a bunch of other things, builtins are one point where Py3 differs in details from the way it used to work in Py2. read the "What's New in Python X.X" documents on python.org for details. i have no idea what the reason for the convention mentioned above might be; i just want to ignore that stuff. i think that above code should work in Py2 as well.
so the point here is there is a __builtins__ thingie which holds a lot of stuff that comes as, well, built-into Python. all the sum, max, range stuff you've come to love. well, almost everything. but you don't need the details, really. the simplest thing you could do is to say
G = get_builtins()
G[ 'G' ] = G
G[ 'axe' ] = axe
at a point in your code that is always guaranteed to execute. G stands in for the globally available namespace, and since i've registered G itself within G, G now magically transcends its existence into the background of every module. means you should use it with care. where naming collisions occur between whatever is held in G and in a module's namespace, the module's namespace should win (as it gets inspected first). also, be prepared for everybody to jump on you when you tell them you're POLLUTING THE GLOBAL NAMESPACE dammit. i'm relly surprised noone has copmplained about that as yet, here.
well, those people would be quite right, in a way. personally, however, this is one of my main application composition techniques: what you do is you take a step away from the all-purpose module (which shouldn't do such a thing) towards a fine-tuned application-specific namespace. your modules are bound not to work outside that namespace, but then they're not supposed to, either. i actually started this style of programming as an experimental rebellion against (1) established views, hah!, and (2) the desperation that befalls me whenever i want to accomplish something less than trivial using Python's import statement. these days, i only use import for standard library stuff and regularly-installed modules; for my own stuff, i use a homegrown system. what can i say, it works!
ah yes, two more points: do yourself a favor, if you like this solution, and write yourself a publish() method or the like that oversees you never publish a name that has already been taken. in most cases, you do not want that.
lastly, let me second the first commenter: i have been programming in exactly the style you show above, coz that's what you find in the textbook examples (most of the time using cars, not axes to be sure). for a rather substantial number of reasons, i've pretty much given up on that.
consider this: JSON defines but seven kinds of data: null, true, false, numbers, texts, lists, dictionaries, that's it. i claim you can model any other useful datatype with those.
there is still a lot of justification for things like sets, bags, ordered dictionaries and so on. the claim here is not that it is always convenient or appropriate to fall back on a pure, directly JSON-compatible form; the claim is only that it is possible to simulate. right now, i'm implementing a sparse list for use in a messaging system, and that data type i do implement in classical OOP. that's what it's good for.
but i never define classes that go beyond these generic datatypes. rather, i write libraries that take generic datatypes and that provide the functionality you need. all of my business data (in your case probably representations of players, scenes, implements and so on) go into generic data container (as a rule, mostly dicts). i know there are open questions with this way of architecturing things, but programming has become ever so much easier, so much more fluent since i broke thru the BS that part of OOP propaganda is (apart from the really useful and nice things that another part of OOP is).
oh yes, and did i mention that as long as you keep your business data in JSON-compatible objects you can always write them to and resurrect them from the disk? or send them over the wire so you can interact with remote players? and how incredibly twisted the serialization business can become in classical OOP if you want to do that (read this for the tip of the iceberg)? most of the technical detail you have to know in this field is completely meaningless for the rest of your life.
You can add (or change existing) Python built-in functions and classes by doing either of the following, at least in Py 2.x. Afterwards, whatever you add will available to all code by default, although not permanently.
Disclaimer: Doing this sort of thing can be dangerous due to possible name clashes and problematic due to the fact that it's extremely non-explicit. But, hey, as they say, we're all adults here, right?
class MyCLass: pass
# one way
setattr(__builtins__, 'MyCLass', MyCLass)
# another way
import __builtin__
__builtin__.MyCLass = MyCLass
Another way is to create a singleton:
class Singleton(type):
def __init__(cls, name, bases, dict):
super(Singleton, cls).__init__(name, bases, dict)
cls.instance = None
class GlobalClass(object):
__metaclass__ = Singleton
def __init__():
pinrt("I am global and whenever attributes are added in one instance, any other instance will be affected as well.")
Let's say that i have a Python module to control a videoconference system. In that module i have some global variables and functions to control the states of the videoconference, the calls, a phone book, etc.
To start the control system, the module self-executes a function to initialize the videoconference (ethernet connection, polling states and so)
Now, if i need to start controlling a second videoconference system, i'm not sure how to approach that problem: i thought about making the videoconference module a class and create two instances (one for each videoconference system) and then initialize both, but the problem is that i don't really need to have two instances of a videoconference class since i won't do anything with those objects because i only need to initialize the systems; after that i don't need to call or keep them for anything else.
example code:
Videoconference.py
class Videoconference:
def __init__(self):
self.state = 0
#Initialization code
Main.py
from Videoconference import Videoconference
vidC1 = Videoconference()
vidC2 = Videoconference()
#vidC1 and vidC2 will never be use again
So, the question is: should i convert the videoconference module to a class and create instances (like in the example), even if i'm not going to use them for anything else appart of the initialization process? Or is there another solution without creating a class?
Perhaps this is a matter of preference, but I think having a class in the above case would be the safer bet. Often I'll write a function and when it gets too complicated I'll think that I should have created a class (and often do so), but I've never created a class that was too simple and thought that this is too easy why didn't I just create a function.
Even if you have one object instead of two, it often helps readability to create a class. For example:
vid = VideoConference()
# vid.initialize_old_system() # Suppose you have an old system that you no longer use
# But want to keep its method for reference
vid.initialize_new_system()
vid.view_call_history(since=yesterday)
This sounds like the perfect use case for a VideoConferenceSystem object. You say you have globals (ew!) that govern state (yuck!) and calls functions for control.
Sounds to me like you've got the chance to convert that all to an object that has attributes that hold state and methods to mutate it. Sounds like you should be refactoring more than just the initialization code, so those vidC1 and vidC2 objects are useful.
I think you're approaching this problem the right way in your example. In this way, you can have multiple video conferences, each of which may have different attribute states (e.g. vidC1.conference_duration, etc.).