This tutorial will go through some of the major aspects of inheritance in Python, including how parent classes and child classes work, how to override methods and attributes, how to use the super function, and how to make use of multiple inheritance. You should have Python 3 installed and a programming environment set up on your computer or server.
Inheritance is when a class uses code constructed within another class. If we think of inheritance in terms of biology, we can think of a child inheriting certain traits from their parent. Children also may share the same last name with their parents. Classes called child classes or subclasses inherit methods and variables from parent classes or base classes.
Because the Child subclass is inheriting from the Parent base class, the Child class can reuse the code of Parent , allowing the programmer to use fewer lines of code and decrease redundancy. Parent or base classes create a pattern out of which child or subclasses can be based on. Parent classes allow us to create child classes through inheritance without having to write the same code over again each time. Any class can be made into a parent class, so they are each fully functional classes in their own right, rather than just templates.
Similarly, an Animal class may have eating and sleeping methods, and a Snake subclass may include its own specific hissing and slithering methods. Each of these fish will have first names and last names in addition to characteristics. Info: To follow along with the example code in this tutorial, open a Python interactive shell on your local system by running the python3 command.
Building a parent class follows the same methodology as building any other class, except we are thinking about what methods the child classes will be able to make use of once we create those. Child or subclasses are classes that will inherit from the parent class.
That means that each child class will be able to make use of the methods and variables of the parent class. For example, a Goldfish child class that subclasses the Fish class will be able to make use of the swim method declared in Fish without needing to declare it. We can think of each child class as being a class of the parent class. That is, if we have a child class called Rhombus and a parent class called Parallelogram , we can say that a Rhombus is a Parallelogram , just as a Goldfish is a Fish.
The first line of a child class looks a little different than non-child classes as you must pass the parent class into the child class as a parameter:. The Trout class is a child of the Fish class. We know this because of the inclusion of the word Fish in parentheses. We have created a Trout object terry that makes use of each of the methods of the Fish class even though we did not define those methods in the Trout child class.
Child classes inherit the methods of the parent class it belongs to, so each child class can make use of those methods within programs. So far, we have looked at the child class Trout that made use of the pass keyword to inherit all of the parent class Fish behaviors, and another child class Clownfish that inherited all of the parent class behaviors and also created its own unique method that is specific to the child class.
Sometimes, however, we will want to make use of some of the parent class behaviors but not all of them. When we change parent class methods we override them. When constructing parent and child classes, it is important to keep program design in mind so that overriding does not produce unnecessary or redundant code. In terms of program design, if we had more than one non-bony fish, we would most likely want to make separate classes for each of these two types of fish. Sharks, unlike bony fish, have skeletons made of cartilage instead of bone.
They also have eyelids and are unable to swim backwards. Note that this only works if the base class is accessible as BaseClassName in the global scope.
Use issubclass to check class inheritance: issubclass bool, int is True since bool is a subclass of int. However, issubclass float, int is False since float is not a subclass of int.
Python supports a form of multiple inheritance as well. A class definition with multiple base classes looks like this:. For most purposes, in the simplest cases, you can think of the search for attributes inherited from a parent class as depth-first, left-to-right, not searching twice in the same class where there is an overlap in the hierarchy.
Thus, if an attribute is not found in DerivedClassName , it is searched for in Base1 , then recursively in the base classes of Base1 , and if it was not found there, it was searched for in Base2 , and so on. In fact, it is slightly more complex than that; the method resolution order changes dynamically to support cooperative calls to super.
This approach is known in some other multiple-inheritance languages as call-next-method and is more powerful than the super call found in single-inheritance languages. Dynamic ordering is necessary because all cases of multiple inheritance exhibit one or more diamond relationships where at least one of the parent classes can be accessed through multiple paths from the bottommost class.
For example, all classes inherit from object , so any case of multiple inheritance provides more than one path to reach object. To keep the base classes from being accessed more than once, the dynamic algorithm linearizes the search order in a way that preserves the left-to-right ordering specified in each class, that calls each parent only once, and that is monotonic meaning that a class can be subclassed without affecting the precedence order of its parents.
Taken together, these properties make it possible to design reliable and extensible classes with multiple inheritance. However, there is a convention that is followed by most Python code: a name prefixed with an underscore e. It should be considered an implementation detail and subject to change without notice. Since there is a valid use-case for class-private members namely to avoid name clashes of names with names defined by subclasses , there is limited support for such a mechanism, called name mangling.
This mangling is done without regard to the syntactic position of the identifier, as long as it occurs within the definition of a class. Name mangling is helpful for letting subclasses override methods without breaking intraclass method calls. Note that the mangling rules are designed mostly to avoid accidents; it still is possible to access or modify a variable that is considered private.
This can even be useful in special circumstances, such as in the debugger. Notice that code passed to exec or eval does not consider the classname of the invoking class to be the current class; this is similar to the effect of the global statement, the effect of which is likewise restricted to code that is byte-compiled together.
An empty class definition will do nicely:. A piece of Python code that expects a particular abstract data type can often be passed a class that emulates the methods of that data type instead.
For instance, if you have a function that formats some data from a file object, you can define a class with methods read and readline that get the data from a string buffer instead, and pass it as an argument. Instance method objects have attributes, too: m. By now you have probably noticed that most container objects can be looped over using a for statement:. This style of access is clear, concise, and convenient.
The use of iterators pervades and unifies Python. Behind the scenes, the for statement calls iter on the container object. Having seen the mechanics behind the iterator protocol, it is easy to add iterator behavior to your classes.
Generators are a simple and powerful tool for creating iterators. They are written like regular functions but use the yield statement whenever they want to return data. Each time next is called on it, the generator resumes where it left off it remembers all the data values and which statement was last executed. An example shows that generators can be trivially easy to create:. Anything that can be done with generators can also be done with class-based iterators as described in the previous section.
Another key feature is that the local variables and execution state are automatically saved between calls. This made the function easier to write and much more clear than an approach using instance variables like self. In addition to automatic method creation and saving program state, when generators terminate, they automatically raise StopIteration. In combination, these features make it easy to create iterators with no more effort than writing a regular function.
Some simple generators can be coded succinctly as expressions using a syntax similar to list comprehensions but with parentheses instead of square brackets. These expressions are designed for situations where the generator is used right away by an enclosing function. Generator expressions are more compact but less versatile than full generator definitions and tend to be more memory friendly than equivalent list comprehensions. Except for one thing. Obviously, using this violates the abstraction of namespace implementation, and should be restricted to things like post-mortem debuggers.
Navigation index modules next previous Python ». After local assignment: test spam After nonlocal assignment: nonlocal spam After global assignment: nonlocal spam In global scope: global spam. 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: x. BaseClassName :. An example shows that generators can be trivially easy to create: def reverse data : for index in range len data - 1 , - 1 , - 1 : yield data [ index ].
Table of Contents 9. Classes 9. A Word About Names and Objects 9. Python Scopes and Namespaces 9. Scopes and Namespaces Example 9. A First Look at Classes 9. Class Definition Syntax 9. Class Objects 9. Instance Objects 9. Method Objects 9. Class and Instance Variables 9. Random Remarks 9. Inheritance 9. Multiple Inheritance 9. Private Variables 9. Odds and Ends 9.
Iterators 9. Generators 9. Without it, it creates an old-style class. If you use type on an old-style object, you just get "instance". On a new-style object you get its class. In the absence of any other superclasses that you specifically want to inherit from, the superclass should always be object , which is the root of all classes in Python.
But the new-style classes today are as good as being the only style of classes. But, if you don't explicitly use the word object when creating classes, then as others mentioned, Python 3.
But I guess explicit is always better than implicit hell. Is there any reason for a class declaration to inherit from object? I just found some code that does this and I can't find a good reason why.
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