Python 3: Object-Oriented Design

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Python 3 Object Oriented Programming

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Harness the power of Python 3 objects

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by Dusty Phillips | December 2010 | Open Source

In software development, design is often considered the step done before programming. This isn't true; in reality, analysis, programming, and design tend to overlap, combine, and interweave. In this article by Dusty Phillips, author of Python 3 Object Oriented Programming, we will learn:

  • What object-oriented means
  • The difference between object-oriented design and object-oriented programming
  • The basic principles of object-oriented design
  • Basic Unified Modeling Language and when it isn't evil

 

Python 3 Object Oriented Programming

Python 3 Object Oriented Programming

Harness the power of Python 3 objects

  • Learn how to do Object Oriented Programming in Python using this step-by-step tutorial
  • Design public interfaces using abstraction, encapsulation, and information hiding
  • Turn your designs into working software by studying the Python syntax
  • Raise, handle, define, and manipulate exceptions using special error objects
  • Implement Object Oriented Programming in Python using practical examples
        Read more about this book      

(For more resources on Python, see here.)

Object-oriented?

Everyone knows what an object is: a tangible "something" that we can sense, feel, and manipulate. The earliest objects we interact with are typically baby toys. Wooden blocks, plastic shapes, and over-sized puzzle pieces are common first objects. Babies learn quickly that certain objects do certain things. Triangles fit in triangle-shaped holes. Bells ring, buttons press, and levers pull.

The definition of an object in software development is not so very different. Objects are not typically tangible somethings that you can pick up, sense, or feel, but they are models of somethings that can do certain things and have certain things done to them. Formally, an object is a collection of data and associated behaviors.

So knowing what an object is, what does it mean to be object-oriented? Oriented simply means directed toward. So object-oriented simply means, "functionally directed toward modeling objects". It is one of many techniques used for modeling complex systems by describing a collection of interacting objects via their data and behavior.

If you've read any hype, you've probably come across the terms object-oriented analysis, object-oriented design, object-oriented analysis and design, and object-oriented programming. These are all highly related concepts under the general object-oriented umbrella.

In fact, analysis, design, and programming are all stages of software development. Calling them object-oriented simply specifies what style of software development is being pursued.

Object-oriented Analysis (OOA) is the process of looking at a problem, system, or task that somebody wants to turn into an application and identifying the objects and interactions between those objects. The analysis stage is all about what needs to be done. The output of the analysis stage is a set of requirements. If we were to complete the analysis stage in one step, we would have turned a task, such as, "I need a website", into a set of requirements, such as:

Visitors to the website need to be able to (italic represents actions, bold represents objects):

  • review our history
  • apply for jobs
  • browse, compare, and order our products

Object-oriented Design (OOD) is the process of converting such requirements into an implementation specification. The designer must name the objects, define the behaviors, and formally specify what objects can activate specific behaviors on other objects. The design stage is all about how things should be done. The output of the design stage is an implementation specification. If we were to complete the design stage in one step, we would have turned the requirements into a set of classes and interfaces that could be implemented in (ideally) any object-oriented programming language.

Object-oriented Programming (OOP) is the process of converting this perfectly defined design into a working program that does exactly what the CEO originally requested.

Yeah, right! It would be lovely if the world met this ideal and we could follow these stages one by one, in perfect order like all the old textbooks told us to. As usual, the real world is much murkier. No matter how hard we try to separate these stages, we'll always find things that need further analysis while we're designing. When we're programming, we find features that need clarification in the design. In the fast-paced modern world, most development happens in an iterative development model. In iterative development, a small part of the task is modeled, designed, and programmed, then the program is reviewed and expanded to improve each feature and include new features in a series of short cycles.

In this article we will cover the basic object-oriented principles in the context of design. This allows us to understand these rather simple concepts without having to argue with software syntax or interpreters.

Objects and classes

So, an object is a collection of data with associated behaviors. How do we tell two types of objects apart? Apples and oranges are both objects, but it is a common adage that they cannot be compared. Apples and oranges aren't modeled very often in computer programming, but let's pretend we're doing an inventory application for a fruit farm! As an example, we can assume that apples go in barrels and oranges go in baskets.

Now, we have four kinds of objects: apples, oranges, baskets, and barrels. In object-oriented modeling, the term used for kinds of objects is class. So, in technical terms, we now have four classes of objects.

What's the difference between an object and a class? Classes describe objects. They are like blueprints for creating an object. You might have three oranges sitting on the table in front of you. Each orange is a distinct object, but all three have the attributes and behaviors associated with one class: the general class of oranges.

The relationship between the four classes of objects in our inventory system can be described using a Unified Modeling Language (invariably referred to as UML, because three letter acronyms are cool) class diagram. Here is our first class diagram:

Python 3: Object-Oriented Design

This diagram simply shows that an Orange is somehow associated with a Basket and that an Apple is also somehow associated with a Barrel. Association is the most basic way for two classes to be related.

UML is very popular among managers, and occasionally disparaged by programmers. The syntax of a UML diagram is generally pretty obvious; you don't have to read a tutorial to (mostly) understand what is going on when you see one. UML is also fairly easy to draw, and quite intuitive. After all, many people, when describing classes and their relationships, will naturally draw boxes with lines between them. Having a standard based on these intuitive diagrams makes it easy for programmers to communicate with designers, managers, and each other.

However, some programmers think UML is a waste of time. Citing iterative development, they will argue that formal specifications done up in fancy UML diagrams are going to be redundant before they're implemented, and that maintaining those formal diagrams will only waste time and not benefit anyone.

This is true of some organizations, and hogwash in other corporate cultures. However, every programming team consisting of more than one person will occasionally have to sit down and hash out the details of part of the system they are currently working on. UML is extremely useful, in these brainstorming sessions, for quick and easy communication. Even those organizations that scoff at formal class diagrams tend to use some informal version of UML in their design meetings, or team discussions.

Further, the most important person you ever have to communicate with is yourself. We all think we can remember the design decisions we've made, but there are always, "Why did I do that?" moments hiding in our future. If we keep the scraps of paper we did our initial diagramming on when we started a design, we'll eventually find that they are a useful reference.

UML covers far more than class and object diagrams; it also has syntax for use cases, deployment, state changes, and activities. We'll be dealing with some common class diagram syntax in this discussion of object-oriented design. You'll find you can pick up the structure by example, and you'll subconsciously choose UML-inspired syntax in your own team or personal design sessions.

Our initial diagram, while correct, does not remind us that apples go in barrels or how many barrels a single apple can go in. It only tells us that apples are somehow associated with barrels. The association between classes is often obvious and needs no further explanation, but the option to add further clarification is always there. The beauty of UML is that most things are optional. We only need to specify as much information in a diagram as makes sense for the current situation. In a quick whiteboard session, we might just quickly draw lines between boxes. In a formal document that needs to make sense in six months, we might go into more detail. In the case of apples and barrels, we can be fairly confident that the association is, "many apples go in one barrel", but just to make sure nobody confuses it with, "one apple spoils one barrel", we can enhance the diagram as shown:

Python 3: Object-Oriented Design

This diagram tells us that oranges go in baskets with a little arrow showing what goes in what. It also tells us the multiplicity (number of that object that can be used in the association) on both sides of the relationship. One Basket can hold many (represented by a *) Orange objects. Any one Orange can go in exactly one Basket.

It can be easy to confuse which side of a relationship the multiplicity goes on. The multiplicity is the number of objects of that class that can be associated with any one object at the other end of the association. For the apple goes in barrel association, reading from left to right, many instances of the Apple class (that is many Apple objects) can go in any one Barrel. Reading from right to left, exactly one Barrel can be associated with any one Apple.

Python 3 Object Oriented Programming Harness the power of Python 3 objects
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Specifying attributes and behaviors

We now have a grasp on some basic object-oriented terminology. Objects are instances of classes that can be associated with each other. An object instance is a specific object with its own set of data and behaviors; a specific orange on the table in front of us is said to be an instance of the general class of oranges. That's simple enough, but what are these data and behaviors that are associated with each object?

Data describes objects

Let's start with data. Data typically represents the individual characteristics of a certain object. A class of objects can define specific characteristics that are shared by all instances of that class. Each instance can then have different data values for the given characteristics. For example, our three oranges on the table (if we haven't eaten any) could each have a different weight. The orange class could then have a weight attribute. All instances of the orange class have a weight attribute, but each orange might have a different value for this weight. Attributes don't have to be unique though; any two oranges may weigh the same amount. As a more realistic example, two objects representing different customers might have the same value for a first name attribute. Attributes are frequently referred to as properties.

In our fruit inventory application, the fruit farmer may want to know what orchard the orange came from, when it was picked, and how much it weighs. They might also want to keep track of where each basket is stored. Apples might have a color attribute and barrels might come in different sizes. Some of these properties may also belong to multiple classes (we may want to know when apples are picked, too), but for this first example, let's just add a few different attributes to our class diagram:

Python 3: Object-Oriented Design

Depending on how detailed our design needs to be, we can also specify the type for each attribute. Attribute types are often primitives that are standard to most programming languages, such as integer, floating-point number, string, byte, or boolean. However, they can also represent data structures such as lists, trees, or graphs, or, most notably, other classes. This is one area where the design stage can overlap with the programming stage. The various primitives or objects available in one programming language may be somewhat different from what is available in other languages. Usually we don't need to concern ourselves with this at the design stage, as implementation-specific details are chosen during the programming stage. Use generic names and we'll be fine. If our design calls for a list container type, the Java programmers can choose to use a LinkedList or an ArrayList when implementing it, while the Python programmers (that's us!) can choose between the list built-in and a tuple.

In our fruit farming example, so far, our attributes are all basic primitives. But there are implicit attributes that we can make explicit: the associations. For a given orange, we might have an attribute containing the basket that holds that orange. Alternatively, one basket might contain a list of the oranges it holds. The next diagram adds these attributes as well as including type descriptions for our current properties:

Python 3: Object-Oriented Design

Behaviors are actions

Now we know what data is, but what are behaviors? Behaviors are actions that can occur on an object. The behaviors that can be performed on a specific class of objects are called methods. At the programming level, methods are like functions in structured programming, but they magically have access to all the data associated with that object. Like functions, methods can also accept parameters, and return values.

Parameters to a method are a list of objects that need to be passed into the method that is being called. These objects are used by the method to perform whatever behavior or task it is meant to do. Return values are the results of that task. Here's a concrete example; if our objects are numbers, the number class might have an add method that accepts a second number as a parameter. The first number object's add method will return the sum when the second number is passed to it. Given an object and it's method name, a calling object can call, or invoke the method on the target object. Invoking a method, at the programming level, is the process of telling the method to execute itself by passing it the required parameters as arguments.

We've stretched our, "comparing apples and oranges" example into a basic (if far-fetched) inventory application. Let's stretch it a little further and see if it breaks. One action that can be associated with oranges is the pick action. If you think about implementation, pick would place the orange in a basket by updating the basket attribute on the orange, and by adding the orange to the oranges list on the Basket. So pick needs to know what basket it is dealing with. We do this by giving the pick method a basket parameter. Since our fruit farmer also sells juice, we can add a squeeze method to Orange. When squeezed, squeeze might return the amount of juice retrieved, while also removing the Orange from the basket it was in.

Basket can have a sell action. When a basket is sold, our inventory system might update some data on as-yet unspecified objects for accounting and profit calculations. Alternatively, our basket of oranges might go bad before we can sell them, so we add a discard method. Let's add these methods to our diagram:

Python 3: Object-Oriented Design

Adding models and methods to individual objects allows us to create a system of interacting objects. Each object in the system is a member of a certain class. These classes specify what types of data the object can hold and what methods can be invoked on it. The data in each object can be in a different state from other objects of the same class, and each object may react to method calls differently because of the differences in state.

Object-oriented analysis and design is all about figuring out what those objects are and how they should interact. The next section describes principles that can be used to make those interactions as simple and intuitive as possible.

Hiding details and creating the public interface

The key purpose of modeling an object in object-oriented design is to determine what the public interface of that object will be. The interface is the collection of attributes and methods that other objects can use to interact with that object. They do not need, and are often not allowed, to access the internal workings of the object. A common real-world example is the television. Our interface to the television is the remote control. Each button on the remote control represents a method that can be called on the television object. When we, as the calling object, access these methods, we do not know or care if the television is getting its signal from an antenna, a cable connection, or a satellite dish. We don't care what electronic signals are being sent to adjust the volume, or whether that volume is being output to speakers or a set of headphones. If we open the television to access the internal workings, for example to split the output signal to both external speakers and a set of headphones, we will void the warranty.

This process of hiding the implementation, or functional details, of an object is suitably called information hiding. It is also sometimes referred to as encapsulation, but encapsulation is actually a more all-encompassing term. Encapsulated data is not necessarily hidden. Encapsulation is, literally, creating a capsule, so think of creating a time capsule. If you put a bunch of information into a time capsule, lock and bury it, it is both encapsulated and the information is hidden. On the other hand, if the time capsule has not been buried and is unlocked or made of clear plastic, the items inside it are still encapsulated, but there is no information hiding.

The distinction between encapsulation and information hiding is largely irrelevant, especially at the design level. Many practical references use the terms interchangeably. As Python programmers, we don't actually have or need true information hiding, so the more encompassing definition for encapsulation is suitable.

The public interface, however, is very important. It needs to be carefully designed as it is difficult to change it in the future. Changing the interface will break any client objects that are calling it. We can change the internals all we like, for example, to make it more efficient, or to access data over the network as well as locally, and the client objects will still be able to talk to it, unmodified, using the public interface. On the other hand, if we change the interface, by changing attribute names that are publicly accessed or by altering the order or types of arguments that a method can accept, all client objects will also have to be modified.

Remember, program objects represent real objects, but they are not real objects. They are models. One of the greatest gifts of modeling is the ability to ignore details that are irrelevant. A model car may look like a real 1956 Thunderbird on the outside, but it doesn't run and the driveshaft doesn't turn, as these details are overly complex and irrelevant to the youngster assembling the model. The model is an abstraction of a real concept.

Abstraction is another object-oriented buzzword that ties in with encapsulation and information hiding. Simply put, abstraction means dealing with the level of detail that is most appropriate to a given task. It is the process of extracting a public interface from the inner details. A driver of a car needs to interact with steering, gas pedal, and brakes. The workings of the motor, drive train, and brake subsystem don't matter to the driver. A mechanic, on the other hand works at a different level of abstraction, tuning the engine and bleeding the breaks. Here's an example of two abstraction levels for a car:

Python 3: Object-Oriented Design

Now we have several new terms that refer to similar concepts. Condensing all this jargon into a single sentence, abstraction is the process of encapsulating information with separate public and private interfaces. The private interfaces can be subject to information hiding.

The important thing to bring from all these definitions is to make our models understandable to the other objects that have to interact with them. This means paying careful attention to small details. Ensure methods and properties have sensible names. When analyzing a system, objects typically represent nouns in the original problem, while methods are normally verbs. Attributes can often be picked up as adjectives, although if the attribute refers to another object that is part of the current object, it will still likely be a noun. Name classes, attributes, and methods accordingly. Don't try to model objects or actions that might be useful in the future. Model exactly those tasks that the system needs to perform and the design will naturally gravitate towards one that has an appropriate level of abstraction. This is not to say we should not think about possible future design modifications. Our designs should be open ended so that future requirements can be satisfied. However, when abstracting interfaces, try to model exactly what needs to be modeled and nothing more.

When designing the interface, try placing yourself in the object's shoes and imagine that the object has a strong preference for privacy. Don't let other objects have access to data about you unless you feel it is in your best interest for them to have it. Don't give them an interface to force you to perform a specific task unless you are certain you want them to be able to do that to you.

This is also a good practice for ensuring privacy on your social networking accounts!

Composition and inheritance

So far, we've learned to design systems as a group of interacting objects, where each interaction is viewing the objects involved at an appropriate level of abstraction. But we don't know yet how to create those levels of abstraction. There are a variety of ways to do this. But even most design patterns rely on two basic principles known as composition and inheritance.

Composition is the act of collecting together several objects to compose a new one. Composition is usually a good choice when one object is part of another object. We've already seen a first hint of composition in the mechanic example. A car is composed of an engine, transmission, starter, headlights, and windshield, among numerous other parts. The engine, in turn, is composed of pistons, a crank shaft, and valves. In this example, composition is a good way to provide levels of abstraction. The car object can provide the interface required by a driver, while also providing access to its component parts, which offers a deeper level of abstraction suitable for a mechanic. Those component parts can, of course, be further broken down if the mechanic needs more information to diagnose a problem or tune the engine.

This is a common first example of composition, bit it's not a very good one when it comes to designing computer systems. Physical objects are easy to break into component objects. People have been doing it at least since the ancient Greeks originally postulated that atoms were the smallest unit of matter (they, of course, didn't have access to particle accelerators). Computer systems are generally less complicated than physical objects, yet identifying the component objects in such systems does not happen as naturally. The objects in an object-oriented system occasionally represent physical objects like people, books, or telephones. More often, however, they represent abstract ideas. People have names, books have titles, and telephones are used to make calls. Calls, titles, accounts, names, appointments, and payments are not usually considered objects in the physical world, but they are all frequently modeled components in computer systems.

Let's try modeling a more computer-oriented example to see composition in action. We'll be looking at the design of a computerized chess game. This was a very popular pastime among academics in the '80s and '90s. People were predicting that computers would one day be able to defeat a human chess master. When this happened in 1997 (IBM's Deep Blue defeated world chess champion, Gary Kasparov), interest in the problem waned, although there are still contests between computer and human chess players, and the program has not yet been written that can defeat a human chess master 100% of the time.

As a basic, high-level analysis: a game of chess is played between two players, using a chess set featuring a board containing sixty-four positions in an 8x8 grid. The board can have two sets of sixteen pieces that can be moved, in alternating turns by the two players in different ways. Each piece can take other pieces. The board will be required to draw itself on the computer screen after each turn.

I've identified some of the possible objects in the description using italics, and a few key methods using bold. This is a common first step in turning an object-oriented analysis into a design. At this point, to emphasize composition, we'll focus on the board, without worrying too much about the players or the different types of pieces.

Let's start at the highest level of abstraction possible. We have two players interacting with a chess set by taking turns making moves.

Python 3: Object-Oriented Design

What is that? It doesn't quite look like our earlier class diagrams. That's because it isn't a class diagram! This is an object diagram, also called an instance diagram. It describes the system at a specific state in time, and is describing specific instances of objects, not the interaction between classes. Remember, both players are members of the same class, so the class diagram looks a little different:

Python 3: Object-Oriented Design

The diagram shows that exactly two players can interact with one chess set. It also indicates that any one player can be playing with only one chess set at a time.

But we're discussing composition, not UML, so let's think about what the Chess Set is composed of. We don't care what the player is composed of at this time. We can assume that the player has a heart and brain, among other organs, but these are irrelevant to our model. Indeed, there is nothing stopping said player from being Deep Blue itself, which has neither a heart nor brain.

The chess set, then, is composed of a board and thirty-two pieces. The board is further comprised of sixty-four positions. You could argue that pieces are not part of the chess set because you could replace the pieces in a chess set with a different set of pieces. While this is unlikely or impossible in a computerized version of chess, it introduces us to aggregation. Aggregation is almost exactly like composition. The difference is that aggregate objects can exist independently. It would be impossible for a position to be associated with a different chess board, so we say the board is composed of positions. But the pieces, which might exist independently of the chess set, are said to be in an aggregate relationship with that set.

Another way to differentiate between aggregation and composition is to think about the lifespan of the object. If the composite (outside) object controls when the related (inside) objects are created and destroyed, composition is most suitable. If the related object is created independently of the composite object, or can outlast that object, an aggregate relationship makes more sense. Also keep in mind that composition is aggregation; aggregation is simply a more general form of composition. Any composite relationship is also an aggregate relationship, but not vice versa.

Let's describe our current chess set composition and add some attributes to the objects to hold the composite relationships:

Python 3: Object-Oriented Design

The composition relationship is represented in UML as a solid diamond. The hollow diamond represents the aggregate relationship. You'll notice that the board and pieces are stored as part of the chess set in exactly the same way a reference to them is stored as an attribute on the chess set. This shows that once again, in practice, the distinction between aggregation and composition is often irrelevant once you get past the design stage. When implemented, they behave in much the same way. However, it can help to differentiate between the two when your team is discussing how the different objects interact. Often you can treat them as the same thing, but when you need to distinguish between them, it's great to know the difference (this is abstraction at work).

Inheritance

We have discussed three types of relationships between objects: association, composition, and aggregation. But we have not fully specified our chess set, and these tools don't seem to give us all the power we need. We discussed the possibility that a player might be a human or it might be a piece of software featuring artificial intelligence. It doesn't seem right to say that a Player is associated with a human, or that the artificial intelligence implementation is part of the Player object. What we really need is the ability to say that "Deep Blue is a player" or that "Gary Kasparov is a player".

The is a relationship is formed by inheritance. Inheritance is the most famous, well-known, and over-used relationship in object-oriented programming. Inheritance is sort of like a family tree. My grandfather's last name was Phillips and my father inherited that name. I inherited it from him (along with blue eyes and a penchant for writing). In object-oriented programming, instead of inheriting features and behaviors from a person, one class can inherit attributes and methods from another class.

For example, there are thirty-two chess pieces in our chess set, but there are only six different types of pieces (pawns, rooks, bishops, knights, king, and queen), each of which behaves differently when it is moved. All of these classes of piece have properties, like color and the chess set they are part of, but they also have unique shapes when drawn on the chess board, and make different moves. See how the six types of pieces can inherit from a Piece class:

Python 3: Object-Oriented Design

The hollow arrows, of course, indicate that the individual classes of pieces inherit from the Piece class. All the subtypes automatically have a chess_set and color attribute inherited from the base class. Each piece provides a different shape property (to be drawn on the screen when rendering the board), and a different move method to move the piece to a new position on the board at each turn.

We actually know that all subclasses of the Piece class need to have a move method, otherwise when the board tries to move the piece it will get confused. It is possible we want to create a new version of the game of chess that has one additional piece (the wizard). Our current design would allow us to design this piece without giving it a move method. The board would then choke when it asked the piece to move itself.

We can implement this by creating a dummy move method on the Piece class. The subclasses can then override this method with a more specific implementation. The default implementation might, for example, pop up an error message that says, That piece cannot be moved. Overriding methods in subtypes allows very powerful object-oriented systems to be developed. For example, if we wanted to implement a player class with artificial intelligence, we might provide a calculate_move method that takes a Board object and decides which piece to move where. A very basic class might randomly choose a piece and direction and move it. We could then override this method in a subclass with the Deep Blue implementation. The first class would be suitable for play against a raw beginner, the latter would challenge a grand master. The important thing is that other methods on the class, such as the ones that inform the board as to which move was chosen would not need to be changed; this implementation can be shared between the two classes.

In the case of chess pieces, it doesn't really make sense to provide a default implementation of the move method. All we need to do is specify that the move method is required in any subclasses. This can be done by making Piece an abstract class with the move method declared abstract. Abstract methods basically say "We need this method in a subclass, but we are declining to specify an implementation in this class."

Indeed, it is possible to make a class that does not implement any methods at all. Such a class would simply tell us what the class should do, but provides absolutely no advice on how to do it. In object-oriented parlance, such classes are called interfaces.

Inheritance provides abstraction

Now it's time for another long buzzword. Polymorphism is the ability to treat a class differently depending on which subclass is implemented. We've already seen it in action with the pieces system we've described. If we took the design a bit further, we'd probably see that the Board object can accept a move from the player and call the move function on the piece. The board need not ever know what type of piece it is dealing with. All it has to do is call the move method and the proper subclass will take care of moving it as a Knight or a Pawn.

Polymorphism is pretty cool, but it is a word that is rarely used in Python programming. Python goes an extra step past allowing a subclass of an object to be treated like a parent class. A board implemented in Python could take any object that has a move method, whether it is a Bishop piece, a car, or a duck. When move is called, the Bishop will move diagonally on the board, the car will drive someplace, and the duck will swim or fly, depending on its mood.

This sort of polymorphism in Python is typically referred to as duck typing: "If it walks like a duck or swims like a duck, it's a duck". We don't care if it really is a duck (inheritance), only that it swims or walks. Geese and swans might easily be able to provide the duck-like behavior we are looking for. This allows future designers to create new types of birds without actually specifying an inheritance hierarchy for aquatic birds. It also allows them to create completely different drop-in behaviors that the original designers never planned for. For example, future designers might be able to make a walking, swimming penguin that works with the same interface without ever suggesting that penguins are ducks.

Multiple inheritance

When we think of inheritance in our own family tree, we can see that we inherit features from more than just one parent. When strangers tell a proud mother that her son has, "his fathers eyes", she will typically respond along the lines of, "yes, but he got my nose".

Object-oriented design can also feature such multiple inheritance, which allows a subclass to inherit functionality from multiple parent classes. In practice, multiple inheritance can be tricky business, and some programming languages, (most notably, Java) strictly prohibit it. But multiple inheritance can have its uses. Most often, it can be used to create objects that have two distinct sets of behaviors. For example, an object designed to connect to a scanner and send a fax of the scanned document might be created by inheriting from two separate scanner and faxer objects.

As long as two classes have distinct interfaces, it is not normally harmful for a subclass to inherit from both of them. But it gets messy if we inherit from two classes that provide overlapping interfaces. For example, if we have a motorcycle class that has a move method, and a boat class also featuring a move method, and we want to merge them into the ultimate amphibious vehicle, how does the resulting class know what to do when we call move? At the design level, this needs to be explained, and at the implementation level, each programming language has different ways of deciding which parent class's method is called, or in what order.

Often, the best way to deal with it is to avoid it. If you have a design showing up like this, you're probably doing it wrong. Take a step back, analyze the system again, and see if you can remove the multiple inheritance relationship in favor of some other association or composite design.

Inheritance is a very powerful tool for extending behavior. It is also one of the most exciting advancements of object-oriented design over earlier paradigms. Therefore, it is often the first tool that object-oriented programmers reach for. However, it is important to recognize that owning a hammer does not turn screws into nails. Inheritance is the perfect solution for obvious is a relationships but it can be abused. Programmers often use inheritance to share code between two kinds of objects that are only distantly related, with no is a relationship in sight. While this is not necessarily a bad design, it is a terrific opportunity to ask just why they decided to design it that way, and if a different relationship or design pattern would have been more suitable.

Summary

In this article, we took a whirlwind tour through the terminology of the object-oriented paradigm, focusing on object-oriented design. We learned how to separate different objects into a taxonomy of different classes and to describe the attributes and behaviors of those objects via the class interface. In particular, we covered:

  • Classes and objects
  • Abstraction, encapsulation, and information hiding
  • Designing a public interface
  • Object relations: association, composition, and inheritance

Further resources on this subject:


Python 3 Object Oriented Programming Harness the power of Python 3 objects
Published: July 2010
eBook Price: $29.99
Book Price: $49.99
See more
Select your format and quantity:

About the Author :


Dusty Phillips

Dusty Phillips is a Canadian freelance software developer, teacher, martial artist, and open source aficionado. He is closely affiliated with the Arch Linux community and other open source projects. He maintains the Arch Linux storefronts and has compiled the Arch Linux Handbook. Dusty holds a master's degree in computer science, with specialization in Human Computer Interaction. He currently has six different Python interpreters installed on his computer.

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Python 2.6 Graphics Cookbook
Python 2.6 Graphics Cookbook

Python Testing: Beginner's Guide
Python Testing: Beginner's Guide

MySQL for Python
MySQL for Python

Expert Python Programming
Expert Python Programming

Python Geo-Spatial Development
Python Geo-Spatial Development

Spring Python 1.1
Spring Python 1.1

Inkscape 0.48 Essentials for Web Designers
Inkscape 0.48 Essentials for Web Designers

jQuery 1.4 Reference Guide
jQuery 1.4 Reference Guide


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