Python is an object-oriented programming language, which means that it provides features that support object-oriented programming.
It is not easy to define object-oriented programming, but we have already seen some of its characteristics:
For example, the Time class defined in Chapter 16 corresponds to the way people record the time of day, and the functions we defined correspond to the kinds of things people do with times. Similarly, the Point and Rectangle classes correspond to the mathematical concepts of a point and a rectangle.
So far, we have not taken advantage of the features Python provides to support object-oriented programming. Strictly speaking, these features are not necessary. For the most part, they provide an alternative syntax for things we have already done, but in many cases, the alternative is more concise and more accurately conveys the structure of the program.
For example, in the Time program, there is no obvious connection between the class definition and the function definitions that follow. With some examination, it is apparent that every function takes at least one Time object as an argument.
This observation is the motivation for methods; a method is a function that is associated with a particular class. For example, we have seen methods for strings, lists, dictionaries and tuples. In this chapter, we will define methods for user-defined types.
Methods are semantically the same as functions, but there are two syntactic differences:
In the next few sections, we will take the functions from the previous two chapters and transform them into methods. This transformation is purely mechanical; you can do it simply by following a sequence of steps. If you are comfortable converting from one form to another, you will be able to choose the best form for whatever you are doing.
In Chapter 16, we defined a class named Time and in Exercise 16.1, you wrote a function named print_time:
class Time:
"""represents the time of day
attributes: hour, minute, second"""
def print_time(time):
print '%.2d:%.2d:%.2d' % (time.hour, time.minute, time.second)
To call this function, you have to pass a Time object as an argument:
>>> start = Time() >>> start.hour = 9 >>> start.minute = 45 >>> start.second = 00 >>> print_time(start) 09:45:00
To make print_time a method, all we have to do is move the function definition inside the class definition. Notice the change in indentation.
class Time:
def print_time(time):
print '%.2d:%.2d:%.2d' % (time.hour, time.minute, time.second)
Now there are two ways to call print_time. The first (and less common) way is to use function syntax:
>>> Time.print_time(start) 09:45:00
In this use of dot notation, Time is the name of the class, and print_time is the name of the method. start is passed as a parameter.
The second (and more concise) way is to use method syntax:
>>> start.print_time() 09:45:00
In this use of dot notation, print_time is the name of the method (again), and start is the object the method is invoked on, which is called the subject. Just as the subject of a sentence is what the sentence is about, the subject of a method invocation is what the method is about.
Inside the method, the subject is assigned to the first parameter, so in this case start is assigned to time.
By convention, the first parameter of a method is called self, so it would be more common to write print_time like this:
class Time:
def print_time(self):
print '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)
The reason for this convention is convoluted, but it is based on a useful metaphor:
The syntax for a function call, print_time(start), suggests that the function is the active agent. It says something like, “Hey print_time! Here's an object for you to print.”
In object-oriented programming, the objects are the active agents. A method invocation like start.print_time() says “Hey start! Please print yourself.”
This change in perspective might be more polite, but it is not obvious that it is useful. In the examples we have seen so far, it may not be. But sometimes shifting responsibility from the functions onto the objects makes it possible to write more versatile functions, and makes it easier to maintain and reuse code.
Here's a version of increment (from Section 16.3) rewritten as a method:
# inside class Time:
def increment(self, seconds):
seconds += self.time_to_int()
return int_to_time(seconds)
This version assumes that time_to_int is written as a method, as in Exercise 17.1. Also, note that it is a pure function, not a modifier.
Here's how you would invoke increment:
>>> start.print_time() 09:45:00 >>> end = start.increment(1337) >>> end.print_time() 10:07:17
The subject, start, gets assigned to the first parameter, self. The argument, 1337, gets assigned to the second parameter, seconds.
This mechanism can be confusing, especially if you make an error. For example, if you invoke increment and omit the argument, you get:
>>> end = start.increment(1337, 460) TypeError: increment() takes exactly 2 arguments (3 given)
The error message is initially confusing, because there are only two arguments in parentheses. But the subject is also considered an argument, so all together that's three.
after (from Exercise 16.2) is slightly more complicated because it takes two Time objects as parameters. In this case it is conventional to name the first parameter self and the second parameter other:
# inside class Time:
def after(self, other):
return self.time_to_int() > other.time_to_int()
To use this method, you have to invoke it on one object and pass the other as an argument:
>>> end.after(start) True
One nice thing about this syntax is that it has the same word order as English, subject-verb-object.
The init method (short for “initialization”) is a special method that gets invoked when an object is instantiated. Its full name is __init__ (two underscore characters, followed by init, and then two more underscores). An init method for the Time class might look like this:
# inside class Time:
def __init__(self, hour=0, minute=0, second=0):
self.hour = hour
self.minute = minute
self.second = second
It is common for the parameters of __init__ to have the same names as the attributes. The statement
self.hour = hour
stores the value of the parameter hour as an attribute in the new Time object self.
The parameters are optional, so if you call Time with no arguments, you get the default values.
>>> time = Time() >>> time.print_time() 00:00:00
If you provide one argument, it overrides hour:
>>> time = Time (9) >>> time.print_time() 09:00:00
If you provide two arguments, they override hour and minute.
>>> time = Time(9, 45) >>> time.print_time() 09:45:00
And if you provide three arguments, they override all three default values.
__str__ is a special method name, like __init__, that is supposed to return a string representation of an object.
For example, here is a str method for Time objects:
# inside class Time:
def __str__(self):
return '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)
When you print an object, Python invokes the str method:
>>> time = Time(9, 45) >>> print time 09:45:00
When I write a new class, I almost always start by writing __init__, which makes it easier to instantiate objects, and __str__, which is almost always useful for debugging.
By defining other special methods, you can specify the behavior of operators on user-defined types. For example, if you define an add method for the Time class, you can use the + operator on Time objects.
Here is what the definition might look like:
# inside class Time:
def __add__(self, other):
seconds = self.time_to_int() + other.time_to_int()
return int_to_time(seconds)
And here is how you could use it:
>>> start = Time(9, 45) >>> duration = Time(1, 35) >>> print start + duration 11:20:00
When you apply the + operator to Time objects, Python invokes __add__. When you print the result, Python invokes __str__. So there is quite a lot happening behind the scenes!
Changing the behavior of an operator so that it works with user-defined types is called operator overloading. For every operator in Python there is a corresponding special method, like __add__.
In the previous section we added two Time objects, but you also might want to add an integer to a Time object. The following is an alternative version of __add__ that checks the type of other and invokes either add_time or increment:
# inside class Time:
def __add__(self, other):
if isinstance(other, Time):
return self.add_time(other)
else:
return self.increment(other)
def add_time(self, other):
seconds = self.time_to_int() + other.time_to_int()
return int_to_time(seconds)
def increment(self, seconds):
seconds += self.time_to_int()
return int_to_time(seconds)
The built-in function isinstance takes a value and a class object, and returns True if the value is an instance of the class.
If other is a Time object, __add__ invokes add_time. Otherwise it assumes that the seconds parameter is a number and invokes increment. This operation is called a type-based dispatch because it dispatches the computation to different methods based on the type of the arguments.
Here are examples that use the + operator with different types:
>>> start = Time(9, 45) >>> duration = Time(1, 35) >>> print start + duration 11:20:00 >>> print start + 1337 10:07:17
Unfortunately, this implementation of addition is not commutative. If the integer is the first operand, you get
>>> print 1337 + start TypeError: unsupported operand type(s) for +: 'int' and 'instance'
The problem is, instead of asking the Time object to add an integer, Python is asking an integer to add a Time object, and it doesn't know how to do that. But there is a clever solution for this problem, the radd method, which stands for “right-side add.” This method is invoked when a Time object appears on the right side of the + operator. Here's the definition:
# inside class Time:
def __radd__(self, other):
return self.__add__(other)
And here's how it's used:
>>> print 1337 + start 10:07:17
Type-based dispatch is useful when it is necessary, but (fortunately) it is not always necessary. Often you can avoid it by writing functions that they work correctly for arguments with different types.
Many of the functions we wrote for strings will actually work for any kind of sequence. For example, in Section 11.1 we used histogram to count the number of times each letter appears in a word.
def histogram(s):
d = {}
for c in s:
if c not in d:
d[c] = 1
else:
d[c] = d[c]+1
return d
This function also works for lists, tuples, and even dictionaries, as long as the elements of s are hashable, so they can be used as keys in d.
>>> t = ['spam', 'egg', 'spam', 'spam', 'bacon', 'spam']
>>> histogram(t)
{'bacon': 1, 'egg': 1, 'spam': 4}
Functions that can work with several types are called polymorphic.
Many of the built-in functions are polymorphic. For example, sum works with any kind of sequence, as long as the elements support the addition operator.
>>> t = [1, 2.0, 42L] >>> print sum(t) 45.0
Since Time objects provide an add method, they work with sum:
>>> t1 = Time(7, 43) >>> t2 = Time(7, 41) >>> t3 = Time(7, 37) >>> total = sum([t1, t2, t3]) >>> print total 23:01:00
In general, if all of the operations inside a function work with a given type, then the function works with that type.
The best kind of polymorphism is the unintentional kind, where you discover that a function you have already written can be applied to a type you never planned for.
Minimum debuggable units (instantiate and print).
Build debugging scaffolding as part of the program.