Знакомство с классами в Python
Python — это высокоуровневый язык объектно-ориентированного программирования, созданный специально для того, чтобы помочь программистам в написании ясного логичного кода для проектов любого размера.
Чёткость, лёгкость в понимании и в то же время мощь — нигде больше не проявляются эти принципы так, как в классах Python.
Класс Python
Почти всё в Python можно назвать объектом. И у каждого объекта, естественно, есть свои характеристики, свойства и функции.
Мы можем считать класс неким «макетом» для создания объектов. Точнее сказать — нашим собственным специально настраиваемым макетом. А раз мы сами под себя его настраиваем, то и задавать ему можем любые характеристики, свойства и функции!
Начнём с простого примера. Создадим класс со свойством «x», где x=10. Вот определение нашего класса:
Вот и всё! Мы создали наш первый класс Python my_class со свойством x и значением 10. Чтобы использовать класс, вызываем нашу функцию. Дальше мы можем обращаться к любым свойствам класса по отдельности.
Данный код выводит число 10. Всё просто!
Ещё мы можем поменять переменную, просто присвоив ей новое значение. Вместо того, чтобы x был равен 10, пусть он равняется строке «Bob».
Функция __init__()
У всех классов есть функция __init__() . Её можно менять в любое время. Функция __init__() выполняется всякий раз, когда из класса создаётся объект. Она может использоваться для инициализации переменных класса. __init__() становится очень полезной, когда нам нужно, чтобы класс Python всегда начинался с тех или иных свойств.
В качестве примера возьмём следующий код:
Здесь у нас есть два человека с классом Person и именами Bob и Kate (типа мы их создали). В обоих классах исполнялась функция __init__() , инициализируя переменные класса для имени, пола и страны человека.
Что касается имени и пола, мы передали классу свои переменные, которые требовались в __init__() . Переменная «страна» инициализировалась при создании объекта в этой же функции, но с одним отличием: по причине того, что она не является переменной функции __init__() , значение не может быть задано извне. Поэтому у всех будет одна страна — США.
Результат этого кода будет такой:
Функции класса
Как любой объект, классы Python могут содержать функции! Находясь внутри класса, функции ведут себя точно так же, как вне его. Единственное отличие — способность функций класса обращаться непосредственно к переменным класса, не принимая их в качестве аргументов.
Первая функция класса встречается в строке 9 print_info() , которая выводит всю информацию о нашем объекте Person. Заметьте, что теперь, используя переменные класса с self , мы можем получить информацию о Бобе из любого места класса! Теперь, когда у нас есть прямой доступ ко всей информации, применять функции к объектам Python стало намного удобнее.
С другой стороны, трудно не заметить, как много кода нам требуется для отображения информации о Бобе с использованием print() в Python. Ну и наличие функций, специально предназначенных для того, чтобы конкретный тип-класс определялся в рамках этого класса, лучше организует код.
Вторая функция, которую мы здесь написали, называется grow_person() . Она увеличивает возраст человека на заданное пользователем количество лет. Логично сделать её функцией класса, так как она связана с нашим классом Person. Код в конечном итоге выглядит гораздо более чистым и удобным для восприятия!
Заключение
Вот и всё. Вы прошли вводный курс, посвящённый классам Python.
Если вам этого мало, займитесь изучением программирования. Отлично подойдёт для начала вот этот сайт GeeksForGeeks website (Eng). Если вам нравятся приложения или вы предпочли бы записаться на какие-нибудь курсы, в Coursera есть курс Python for Everybody (Eng), в котором больше внимания уделяется приложениям.
9. Classes¶
Classes provide a means of bundling data and functionality together. Creating a new class creates a new type of object, allowing new instances of that type to be made. Each class instance can have attributes attached to it for maintaining its state. Class instances can also have methods (defined by its class) for modifying its state.
Compared with other programming languages, Python’s class mechanism adds classes with a minimum of new syntax and semantics. It is a mixture of the class mechanisms found in C++ and Modula-3. Python classes provide all the standard features of Object Oriented Programming: the class inheritance mechanism allows multiple base classes, a derived class can override any methods of its base class or classes, and a method can call the method of a base class with the same name. Objects can contain arbitrary amounts and kinds of data. As is true for modules, classes partake of the dynamic nature of Python: they are created at runtime, and can be modified further after creation.
In C++ terminology, normally class members (including the data members) are public (except see below Private Variables ), and all member functions are virtual. As in Modula-3, there are no shorthands for referencing the object’s members from its methods: the method function is declared with an explicit first argument representing the object, which is provided implicitly by the call. As in Smalltalk, classes themselves are objects. This provides semantics for importing and renaming. Unlike C++ and Modula-3, built-in types can be used as base classes for extension by the user. Also, like in C++, most built-in operators with special syntax (arithmetic operators, subscripting etc.) can be redefined for class instances.
(Lacking universally accepted terminology to talk about classes, I will make occasional use of Smalltalk and C++ terms. I would use Modula-3 terms, since its object-oriented semantics are closer to those of Python than C++, but I expect that few readers have heard of it.)
9.1. A Word About Names and Objects¶
Objects have individuality, and multiple names (in multiple scopes) can be bound to the same object. This is known as aliasing in other languages. This is usually not appreciated on a first glance at Python, and can be safely ignored when dealing with immutable basic types (numbers, strings, tuples). However, aliasing has a possibly surprising effect on the semantics of Python code involving mutable objects such as lists, dictionaries, and most other types. This is usually used to the benefit of the program, since aliases behave like pointers in some respects. For example, passing an object is cheap since only a pointer is passed by the implementation; and if a function modifies an object passed as an argument, the caller will see the change — this eliminates the need for two different argument passing mechanisms as in Pascal.
9.2. Python Scopes and Namespaces¶
Before introducing classes, I first have to tell you something about Python’s scope rules. Class definitions play some neat tricks with namespaces, and you need to know how scopes and namespaces work to fully understand what’s going on. Incidentally, knowledge about this subject is useful for any advanced Python programmer.
Let’s begin with some definitions.
A namespace is a mapping from names to objects. Most namespaces are currently implemented as Python dictionaries, but that’s normally not noticeable in any way (except for performance), and it may change in the future. Examples of namespaces are: the set of built-in names (containing functions such as abs() , and built-in exception names); the global names in a module; and the local names in a function invocation. In a sense the set of attributes of an object also form a namespace. The important thing to know about namespaces is that there is absolutely no relation between names in different namespaces; for instance, two different modules may both define a function maximize without confusion — users of the modules must prefix it with the module name.
By the way, I use the word attribute for any name following a dot — for example, in the expression z.real , real is an attribute of the object z . Strictly speaking, references to names in modules are attribute references: in the expression modname.funcname , modname is a module object and funcname is an attribute of it. In this case there happens to be a straightforward mapping between the module’s attributes and the global names defined in the module: they share the same namespace! 1
Attributes may be read-only or writable. In the latter case, assignment to attributes is possible. Module attributes are writable: you can write modname.the_answer = 42 . Writable attributes may also be deleted with the del statement. For example, del modname.the_answer will remove the attribute the_answer from the object named by modname .
Namespaces are created at different moments and have different lifetimes. The namespace containing the built-in names is created when the Python interpreter starts up, and is never deleted. The global namespace for a module is created when the module definition is read in; normally, module namespaces also last until the interpreter quits. The statements executed by the top-level invocation of the interpreter, either read from a script file or interactively, are considered part of a module called __main__ , so they have their own global namespace. (The built-in names actually also live in a module; this is called builtins .)
The local namespace for a function is created when the function is called, and deleted when the function returns or raises an exception that is not handled within the function. (Actually, forgetting would be a better way to describe what actually happens.) Of course, recursive invocations each have their own local namespace.
A scope is a textual region of a Python program where a namespace is directly accessible. “Directly accessible” here means that an unqualified reference to a name attempts to find the name in the namespace.
Although scopes are determined statically, they are used dynamically. At any time during execution, there are 3 or 4 nested scopes whose namespaces are directly accessible:
the innermost scope, which is searched first, contains the local names
the scopes of any enclosing functions, which are searched starting with the nearest enclosing scope, contain non-local, but also non-global names
the next-to-last scope contains the current module’s global names
the outermost scope (searched last) is the namespace containing built-in names
If a name is declared global, then all references and assignments go directly to the next-to-last scope containing the module’s global names. To rebind variables found outside of the innermost scope, the nonlocal statement can be used; if not declared nonlocal, those variables are read-only (an attempt to write to such a variable will simply create a new local variable in the innermost scope, leaving the identically named outer variable unchanged).
Usually, the local scope references the local names of the (textually) current function. Outside functions, the local scope references the same namespace as the global scope: the module’s namespace. Class definitions place yet another namespace in the local scope.
It is important to realize that scopes are determined textually: the global scope of a function defined in a module is that module’s namespace, no matter from where or by what alias the function is called. On the other hand, the actual search for names is done dynamically, at run time — however, the language definition is evolving towards static name resolution, at “compile” time, so don’t rely on dynamic name resolution! (In fact, local variables are already determined statically.)
A special quirk of Python is that – if no global or nonlocal statement is in effect – assignments to names always go into the innermost scope. Assignments do not copy data — they just bind names to objects. The same is true for deletions: the statement del x removes the binding of x from the namespace referenced by the local scope. In fact, all operations that introduce new names use the local scope: in particular, import statements and function definitions bind the module or function name in the local scope.
The global statement can be used to indicate that particular variables live in the global scope and should be rebound there; the nonlocal statement indicates that particular variables live in an enclosing scope and should be rebound there.
9.2.1. Scopes and Namespaces Example¶
This is an example demonstrating how to reference the different scopes and namespaces, and how global and nonlocal affect variable binding:
The output of the example code is:
Note how the local assignment (which is default) didn’t change scope_test‘s binding of spam. The nonlocal assignment changed scope_test‘s binding of spam, and the global assignment changed the module-level binding.
You can also see that there was no previous binding for spam before the global assignment.
9.3. A First Look at Classes¶
Classes introduce a little bit of new syntax, three new object types, and some new semantics.
9.3.1. Class Definition Syntax¶
The simplest form of class definition looks like this:
Class definitions, like function definitions ( def statements) must be executed before they have any effect. (You could conceivably place a class definition in a branch of an if statement, or inside a function.)
In practice, the statements inside a class definition will usually be function definitions, but other statements are allowed, and sometimes useful — we’ll come back to this later. The function definitions inside a class normally have a peculiar form of argument list, dictated by the calling conventions for methods — again, this is explained later.
When a class definition is entered, a new namespace is created, and used as the local scope — thus, all assignments to local variables go into this new namespace. In particular, function definitions bind the name of the new function here.
When a class definition is left normally (via the end), a class object is created. This is basically a wrapper around the contents of the namespace created by the class definition; we’ll learn more about class objects in the next section. The original local scope (the one in effect just before the class definition was entered) is reinstated, and the class object is bound here to the class name given in the class definition header ( ClassName in the example).
9.3.2. Class Objects¶
Class objects support two kinds of operations: attribute references and instantiation.
Attribute references use the standard syntax used for all attribute references in Python: obj.name . Valid attribute names are all the names that were in the class’s namespace when the class object was created. So, if the class definition looked like this:
then MyClass.i and MyClass.f are valid attribute references, returning an integer and a function object, respectively. Class attributes can also be assigned to, so you can change the value of MyClass.i by assignment. __doc__ is also a valid attribute, returning the docstring belonging to the class: "A simple example class" .
Class instantiation uses function notation. Just pretend that the class object is a parameterless function that returns a new instance of the class. For example (assuming the above class):
creates a new instance of the class and assigns this object to the local variable x .
The instantiation operation (“calling” a class object) creates an empty object. Many classes like to create objects with instances customized to a specific initial state. Therefore a class may define a special method named __init__() , like this:
When a class defines an __init__() method, class instantiation automatically invokes __init__() for the newly created class instance. So in this example, a new, initialized instance can be obtained by:
Of course, the __init__() method may have arguments for greater flexibility. In that case, arguments given to the class instantiation operator are passed on to __init__() . For example,
9.3.3. Instance Objects¶
Now what can we do with instance objects? The only operations understood by instance objects are attribute references. There are two kinds of valid attribute names: data attributes and methods.
data attributes correspond to “instance variables” in Smalltalk, and to “data members” in C++. Data attributes need not be declared; like local variables, they spring into existence when they are first assigned to. For example, if x is the instance of MyClass created above, the following piece of code will print the value 16 , without leaving a trace:
The other kind of instance attribute reference is a method. A method is a function that “belongs to” an object. (In Python, the term method is not unique to class instances: other object types can have methods as well. For example, list objects have methods called append, insert, remove, sort, and so on. However, in the following discussion, we’ll use the term method exclusively to mean methods of class instance objects, unless explicitly stated otherwise.)
Valid method names of an instance object depend on its class. By definition, all attributes of a class that are function objects define corresponding methods of its instances. So in our example, x.f is a valid method reference, since MyClass.f is a function, but x.i is not, since MyClass.i is not. But x.f is not the same thing as MyClass.f — it is a method object, not a function object.
9.3.4. Method Objects¶
Usually, a method is called right after it is bound:
In the MyClass example, this will return the string ‘hello world’ . However, it is not necessary to call a method right away: x.f is a method object, and can be stored away and called at a later time. For example:
will continue to print hello world until the end of time.
What exactly happens when a method is called? You may have noticed that x.f() was called without an argument above, even though the function definition for f() specified an argument. What happened to the argument? Surely Python raises an exception when a function that requires an argument is called without any — even if the argument isn’t actually used…
Actually, you may have guessed the answer: the special thing about methods is that the instance object is passed as the first argument of the function. In our example, the call x.f() is exactly equivalent to MyClass.f(x) . In general, calling a method with a list of n arguments is equivalent to calling the corresponding function with an argument list that is created by inserting the method’s instance object before the first argument.
If you still don’t understand how methods work, a look at the implementation can perhaps clarify matters. When a non-data attribute of an instance is referenced, the instance’s class is searched. If the name denotes a valid class attribute that is a function object, a method object is created by packing (pointers to) the instance object and the function object just found together in an abstract object: this is the method object. When the method object is called with an argument list, a new argument list is constructed from the instance object and the argument list, and the function object is called with this new argument list.
9.3.5. Class and Instance Variables¶
Generally speaking, instance variables are for data unique to each instance and class variables are for attributes and methods shared by all instances of the class:
As discussed in A Word About Names and Objects , shared data can have possibly surprising effects with involving mutable objects such as lists and dictionaries. For example, the tricks list in the following code should not be used as a class variable because just a single list would be shared by all Dog instances:
Classes in Python
Classes and objects are the two main aspecs of object oriented programming. A class creates a new type where objects are instances of the class.
Objects can store data using ordinary variables that belong to the object. Variables that belong to an object or class are called as fields. Objects can also have functionality by using functions that belong to a class. Such functions are called methods of the class. This terminology is important because it helps us to differentiate between functions and variables which are separate by itself and those which belong to a class or object. Collectively, the fields and methods can be referred to as the attributes of that class.
1 The self
Class methods have only one specific difference from ordinary functions — they must have an extra first name that has to be added to the beginning of the parameter list, but you do not give a value for this parameter when you call the method, Python will provide it. This particular variable refers to the object itself, and by convention, it is given the name self.
2 The init method
There are many method names which have special significance in Python classes. We will see the significance of the __init__ method now. The __init__ method is run as soon as an object of a class is instantiated. The method is useful to do any initialization you want to do with your object.
3 accees control
We see from last example name attribute can be easily modified. We can add two underline __name to control access.
4 Class variables and Object variables
There are two types of fields — which are classified depending on whether the class or the object owns the variables respectively.
Class variables are shared in the sense that they are accessed by all objects (instances) of that class. There is only copy of the class variable and when any one object makes a change to a class variable, the change is reflected in all the other instances as well.
Object variables are owned by each individual object/instance of the class. In this case, each object has its own copy of the field i.e. they are not shared and are not related in any way to the field by the samen name in a different instance of the same class. An example will make this easy to understand.
5 Inheritance
One of the major benefits of object oriented programming is reuse of code and one of the ways this is achieved is through the inheritance mechanism. Inheritance can be best imagined as implementing a type and subtype relationship between classes.
Инстанцирование в Python
Какой метод вызывается первым при этом вызове Foo? Большинство новичков, да и, возможно, немало опытных питонистов тут же ответят: «метод __init__». Но если внимательно приглядеться к сниппетам выше, вскоре станет понятно, что такой ответ неверен.
__init__ не возвращает никакого результата, а Foo(1, y=2), напротив, возвращает экземпляр класса. К тому же __init__ принимает self в качестве первого параметра, чего не происходит при вызове Foo(1, y=2). Создание экземпляра происходит немного сложнее, о чём мы и поговорим в этой статье.
Порядок создания объекта
Инстанцирование в Python состоит из нескольких стадий. Понимание каждого шага делает нас чуть ближе к пониманию языка в целом. Foo — это класс, но в Питоне классы это тоже объекты! Классы, функции, методы и экземпляры — всё это объекты, и всякий раз, когда вы ставите скобки после их имени, вы вызываете их метод __call__. Так что Foo(1, y=2) — это эквивалент Foo.__call__(1, y=2). Причём метод __call__ объявлен в классе объекта Foo. Какой же класс у объекта Foo?
Так что класс Foo — это экземпляр класса type и вызов метода __call__ последнего возвращает класс Foo. Теперь давайте разберём, что из себя представляет метод __call__ класса type. Ниже находятся его реализации на C в CPython и в PyPy. Если надоест их смотреть, прокручивайте чуть дальше, чтобы найти упрощённую версию:
CPython
Если забыть про всевозможные проверки на ошибки, то коды выше примерно эквивалентны такому:
__new__ выделяет память под «пустой» объект и вызывает __init__, чтобы его инициализировать.
- Foo(*args, **kwargs) эквивалентно Foo.__call__(*args, **kwargs).
- Так как объект Foo — это экземпляр класса type, то вызов Foo.__call__(*args, **kwargs) эквивалентен type.__call__(Foo, *args, **kwargs).
- type.__call__(Foo, *args, **kwargs) вызывает метод type.__new__(Foo, *args, **kwargs), возвращающий obj.
- obj инициализируется при вызове obj.__init__(*args, **kwargs).
- Результат всего процесса — инициализированный obj.
Кастомизация
Теперь давайте переключим наше внимание на __new__. Этот метод выделяет память под объект и возвращает его. Вы вольны кастомизировать этот процесс множеством разных способов. Следует отметить, что, хотя __new__ и является статическим методом, вам не нужно объявлять его используя @staticmethod: интерпретатор обрабатывает __new__ как специальный случай.
Распространённый пример переопределения __new__ — создание Синглтона:
Обратите внимание, что __init__ будет вызываться каждый раз при вызове Singleton(), поэтому следует соблюдать осторожность.
Другой пример переопределения __new__ — реализация паттерна Борг («Borg»):
Учтите, что хотя примеры выше и демонстрируют возможности переопределения __new__, это ещё не значит что его обязательно нужно использовать:
__new__ — одна из самых частых жертв злоупотреблений. То, что может быть сделано переопределением этого метода, чаще всего лучше достигается другими средствами. Тем не менее, когда это действительно необходимо, __new__ — крайне полезный и мощный инструмент.
— Арион Спрэг, Хорошо забытое старое в Python
Редко можно встретить проблему в Python, где лучшим решением было использование __new__. Но когда у вас есть молоток, каждая проблема начинает выглядеть как гвоздь, поэтому всегда предпочитайте использованию нового мощного инструмента использование наиболее подходящего.