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Classes & Iterators

❝ East is East, and West is West, and never the twain shall meet. ❞
— Rudyard Kipling

Diving In

Iterators are the “secret sauce” of Python 3. They’re everywhere, underlying everything, always just out of sight. Comprehensions are just a simple form of iterators. Generators are just a simple form of iterators. A function that yields values is a nice, compact way of building an iterator without building an iterator. Let me show you what I mean by that.

Remember the Fibonacci generator? Here it is as a built-from-scratch iterator:

[download fibonacci2.py]

class Fib:
    '''iterator that yields numbers in the Fibonacci sequence'''

    def __init__(self, max):
        self.max = max

    def __iter__(self):
        self.a = 0
        self.b = 1
        return self

    def __next__(self):
        fib = self.a
        if fib > self.max:
            raise StopIteration
        self.a, self.b = self.b, self.a + self.b
        return fib

Let’s take that one line at a time.

class Fib:

class? What’s a class?

⁂

Defining Classes

Python is fully object-oriented: you can define your own classes, inherit from your own or built-in classes, and instantiate the classes you’ve defined.

Defining a class in Python is simple. As with functions, there is no separate interface definition. Just define the class and start coding. A Python class starts with the reserved word class, followed by the class name. Technically, that’s all that’s required, since a class doesn’t need to inherit from any other class.

class PapayaWhip:  ①
    pass           ②

The name of this class is PapayaWhip, and it doesn’t inherit from any other class. Class names are usually capitalized, EachWordLikeThis, but this is only a convention, not a requirement.
You probably guessed this, but everything in a class is indented, just like the code within a function, if statement, for loop, or any other block of code. The first line not indented is outside the class.

This PapayaWhip class doesn’t define any methods or attributes, but syntactically, there needs to be something in the definition, thus the pass statement. This is a Python reserved word that just means “move along, nothing to see here”. It’s a statement that does nothing, and it’s a good placeholder when you’re stubbing out functions or classes.

☞The pass statement in Python is like a empty set of curly braces ({}) in Java or C.

Many classes are inherited from other classes, but this one is not. Many classes define methods, but this one does not. There is nothing that a Python class absolutely must have, other than a name. In particular, C++ programmers may find it odd that Python classes don’t have explicit constructors and destructors. Although it’s not required, Python classes can have something similar to a constructor: the __init__() method.

The `init()` Method

This example shows the initialization of the Fib class using the __init__ method.

class Fib:
    '''iterator that yields numbers in the Fibonacci sequence'''  ①

    def __init__(self, max):                                      ②

Classes can (and should) have docstrings too, just like modules and functions.
The __init__() method is called immediately after an instance of the class is created. It would be tempting — but technically incorrect — to call this the “constructor” of the class. It’s tempting, because it looks like a C++ constructor (by convention, the __init__() method is the first method defined for the class), acts like one (it’s the first piece of code executed in a newly created instance of the class), and even sounds like one. Incorrect, because the object has already been constructed by the time the __init__() method is called, and you already have a valid reference to the new instance of the class.

The first argument of every class method, including the __init__() method, is always a reference to the current instance of the class. By convention, this argument is named self. This argument fills the role of the reserved word this in C++ or Java, but self is not a reserved word in Python, merely a naming convention. Nonetheless, please don’t call it anything but self; this is a very strong convention.

In all class methods, self refers to the instance whose method was called. But in the specific case of the __init__() method, the instance whose method was called is also the newly created object. Although you need to specify self explicitly when defining the method, you do not specify it when calling the method; Python will add it for you automatically.

⁂

Instantiating Classes

Instantiating classes in Python is straightforward. To instantiate a class, simply call the class as if it were a function, passing the arguments that the __init__() method requires. The return value will be the newly created object.

>>> import fibonacci2
>>> fib = fibonacci2.Fib(100) ①
>>> fib ②
<fibonacci2.Fib object at 0x00DB8810>
>>> fib.__class__ ③
<class 'fibonacci2.Fib'>
>>> fib.__doc__ ④
'iterator that yields numbers in the Fibonacci sequence'

You are creating an instance of the Fib class (defined in the fibonacci2 module) and assigning the newly created instance to the variable fib. You are passing one parameter, 100, which will end up as the max argument in Fib’s __init__() method.
fib is now an instance of the Fib class.
Every class instance has a built-in attribute, __class__, which is the object’s class. Java programmers may be familiar with the Class class, which contains methods like getName() and getSuperclass() to get metadata information about an object. In Python, this kind of metadata is available through attributes, but the idea is the same.
You can access the instance’s docstring just as with a function or a module. All instances of a class share the same docstring.

☞In Python, simply call a class as if it were a function to create a new instance of the class. There is no explicit new operator like there is in C++ or Java.

⁂

Instance Variables

On to the next line:

class Fib:
    def __init__(self, max):
        self.max = max        ①

What is self.max? It’s an instance variable. It is completely separate from max, which was passed into the __init__() method as an argument. self.max is “global” to the instance. That means that you can access it from other methods.

class Fib:
    def __init__(self, max):
        self.max = max        ①
    .
    .
    .
    def __next__(self):
        fib = self.a
        if fib > self.max:    ②

self.max is defined in the __init__() method…
…and referenced in the __next__() method.

Instance variables are specific to one instance of a class. For example, if you create two Fib instances with different maximum values, they will each remember their own values.

>>> import fibonacci2
>>> fib1 = fibonacci2.Fib(100)
>>> fib2 = fibonacci2.Fib(200)
>>> fib1.max
100
>>> fib2.max
200

⁂

A Fibonacci Iterator

Now you’re ready to learn how to build an iterator. An iterator is just a class that defines an __iter__() method.

All three of these class methods, __init__, __iter__, and __next__, begin and end with a pair of underscore (_) characters. Why is that? There’s nothing magical about it, but it usually indicates that these are “special methods.” The only thing “special” about special methods is that they aren’t called directly; Python calls them when you use some other syntax on the class or an instance of the class. More about special methods.

[download fibonacci2.py]

class Fib:                                        ①
    def __init__(self, max):                      ②
        self.max = max

    def __iter__(self):                           ③
        self.a = 0
        self.b = 1
        return self

    def __next__(self):                           ④
        fib = self.a
        if fib > self.max:
            raise StopIteration                   ⑤
        self.a, self.b = self.b, self.a + self.b
        return fib                                ⑥

To build an iterator from scratch, Fib needs to be a class, not a function.
“Calling” Fib(max) is really creating an instance of this class and calling its __init__() method with max. The __init__() method saves the maximum value as an instance variable so other methods can refer to it later.
The __iter__() method is called whenever someone calls iter(fib). (As you’ll see in a minute, a for loop will call this automatically, but you can also call it yourself manually.) After performing beginning-of-iteration initialization (in this case, resetting self.a and self.b, our two counters), the __iter__() method can return any object that implements a __next__() method. In this case (and in most cases), __iter__() simply returns self, since this class implements its own __next__() method.
The __next__() method is called whenever someone calls next() on an iterator of an instance of a class. That will make more sense in a minute.
When the __next__() method raises a StopIteration exception, this signals to the caller that the iteration is exhausted. Unlike most exceptions, this is not an error; it’s a normal condition that just means that the iterator has no more values to generate. If the caller is a for loop, it will notice this StopIteration exception and gracefully exit the loop. (In other words, it will swallow the exception.) This little bit of magic is actually the key to using iterators in for loops.
To spit out the next value, an iterator’s __next__() method simply returns the value. Do not use yield here; that’s a bit of syntactic sugar that only applies when you’re using generators. Here you’re creating your own iterator from scratch; use return instead.

Thoroughly confused yet? Excellent. Let’s see how to call this iterator:

>>> from fibonacci2 import Fib
>>> for n in Fib(1000):
...     print(n, end=' ')
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987

Why, it’s exactly the same! Byte for byte identical to how you called Fibonacci-as-a-generator (modulo one capital letter). But how?

There’s a bit of magic involved in for loops. Here’s what happens:

The for loop calls Fib(1000), as shown. This returns an instance of the Fib class. Call this fib_inst.
Secretly, and quite cleverly, the for loop calls iter(fib_inst), which returns an iterator object. Call this fib_iter. In this case, fib_iter == fib_inst, because the __iter__() method returns self, but the for loop doesn’t know (or care) about that.
To “loop through” the iterator, the for loop calls next(fib_iter), which calls the __next__() method on the fib_iter object, which does the next-Fibonacci-number calculations and returns a value. The for loop takes this value and assigns it to n, then executes the body of the for loop for that value of n.
How does the for loop know when to stop? I’m glad you asked! When next(fib_iter) raises a StopIteration exception, the for loop will swallow the exception and gracefully exit. (Any other exception will pass through and be raised as usual.) And where have you seen a StopIteration exception? In the __next__() method, of course!

⁂

A Plural Rule Iterator

Now it’s time for the finale. Let’s rewrite the plural rules generator as an iterator.

[download plural6.py]

class LazyRules:
    rules_filename = 'plural6-rules.txt'

    def __init__(self):
        self.pattern_file = open(self.rules_filename, encoding='utf-8')
        self.cache = []

    def __iter__(self):
        self.cache_index = 0
        return self

    def __next__(self):
        self.cache_index += 1
        if len(self.cache) >= self.cache_index:
            return self.cache[self.cache_index - 1]

        if self.pattern_file.closed:
            raise StopIteration

        line = self.pattern_file.readline()
        if not line:
            self.pattern_file.close()
            raise StopIteration

        pattern, search, replace = line.split(None, 3)
        funcs = build_match_and_apply_functions(
            pattern, search, replace)
        self.cache.append(funcs)
        return funcs

rules = LazyRules()

So this is a class that implements __iter__() and __next__(), so it can be used as an iterator. Then, you instantiate the class and assign it to rules. This happens just once, on import.

Let’s take the class one bite at a time.

class LazyRules:
    rules_filename = 'plural6-rules.txt'

    def __init__(self):
        self.pattern_file = open(self.rules_filename, encoding='utf-8')  ①
        self.cache = []                                                  ②

When we instantiate the LazyRules class, open the pattern file but don’t read anything from it. (That comes later.)
After opening the patterns file, initialize the cache. You’ll use this cache later (in the __next__() method) as you read lines from the pattern file.

Before we continue, let’s take a closer look at rules_filename. It’s not defined within the __iter__() method. In fact, it’s not defined within any method. It’s defined at the class level. It’s a class variable, and although you can access it just like an instance variable (self.rules_filename), it is shared across all instances of the LazyRules class.

>>> import plural6
>>> r1 = plural6.LazyRules()
>>> r2 = plural6.LazyRules()
>>> r1.rules_filename ①
'plural6-rules.txt'
>>> r2.rules_filename
'plural6-rules.txt'
>>> r2.rules_filename = 'r2-override.txt' ②
>>> r2.rules_filename
'r2-override.txt'
>>> r1.rules_filename
'plural6-rules.txt'
>>> r2.__class__.rules_filename ③
'plural6-rules.txt'
>>> r2.__class__.rules_filename = 'papayawhip.txt' ④
>>> r1.rules_filename
'papayawhip.txt'
>>> r2.rules_filename ⑤
'r2-overridetxt'

Each instance of the class inherits the rules_filename attribute with the value defined by the class.
Changing the attribute’s value in one instance does not affect other instances…
…nor does it change the class attribute. You can access the class attribute (as opposed to an individual instance’s attribute) by using the special __class__ attribute to access the class itself.
If you change the class attribute, all instances that are still inheriting that value (like r1 here) will be affected.
Instances that have overridden that attribute (like r2 here) will not be affected.

And now back to our show.

    def __iter__(self):       ①
        self.cache_index = 0
        return self           ②

The __iter__() method will be called every time someone — say, a for loop — calls iter(rules).
The one thing that every __iter__() method must do is return an iterator. In this case, it returns self, which signals that this class defines a __next__() method which will take care of returning values throughout the iteration.

    def __next__(self):                                 ①
        .
        .
        .
        pattern, search, replace = line.split(None, 3)
        funcs = build_match_and_apply_functions(        ②
            pattern, search, replace)
        self.cache.append(funcs)                        ③
        return funcs

The __next__() method gets called whenever someone — say, a for loop — calls next(rules). This method will only make sense if we start at the end and work backwards. So let’s do that.
The last part of this function should look familiar, at least. The build_match_and_apply_functions() function hasn’t changed; it’s the same as it ever was.
The only difference is that, before returning the match and apply functions (which are stored in the tuple funcs), we’re going to save them in self.cache.

Moving backwards…

    def __next__(self):
        .
        .
        .
        line = self.pattern_file.readline()  ①
        if not line:                         ②
            self.pattern_file.close()
            raise StopIteration              ③
        .
        .
        .

A bit of advanced file trickery here. The readline() method (note: singular, not the plural readlines()) reads exactly one line from an open file. Specifically, the next line. (File objects are iterators too! It’s iterators all the way down…)
If there was a line for readline() to read, line will not be an empty string. Even if the file contained a blank line, line would end up as the one-character string '\n' (a carriage return). If line is really an empty string, that means there are no more lines to read from the file.
When we reach the end of the file, we should close the file and raise the magic StopIteration exception. Remember, we got to this point because we needed a match and apply function for the next rule. The next rule comes from the next line of the file… but there is no next line! Therefore, we have no value to return. The iteration is over. (♫ The party’s over… ♫)

Moving backwards all the way to the start of the __next__() method…

    def __next__(self):
        self.cache_index += 1
        if len(self.cache) >= self.cache_index:
            return self.cache[self.cache_index - 1]     ①

        if self.pattern_file.closed:
            raise StopIteration                         ②
        .
        .
        .

self.cache will be a list of the functions we need to match and apply individual rules. (At least that should sound familiar!) self.cache_index keeps track of which cached item we should return next. If we haven’t exhausted the cache yet (i.e. if the length of self.cache is greater than self.cache_index), then we have a cache hit! Hooray! We can return the match and apply functions from the cache instead of building them from scratch.
On the other hand, if we don’t get a hit from the cache, and the file object has been closed (which could happen, further down the method, as you saw in the previous code snippet), then there’s nothing more we can do. If the file is closed, it means we’ve exhausted it — we’ve already read through every line from the pattern file, and we’ve already built and cached the match and apply functions for each pattern. The file is exhausted; the cache is exhausted; I’m exhausted. Wait, what? Hang in there, we’re almost done.

Putting it all together, here’s what happens when:

When the module is imported, it creates a single instance of the LazyRules class, called rules, which opens the pattern file but does not read from it.
When asked for the first match and apply function, it checks its cache but finds the cache is empty. So it reads a single line from the pattern file, builds the match and apply functions from those patterns, and caches them.
Let’s say, for the sake of argument, that the very first rule matched. If so, no further match and apply functions are built, and no further lines are read from the pattern file.
Furthermore, for the sake of argument, suppose that the caller calls the plural() function again to pluralize a different word. The for loop in the plural() function will call iter(rules), which will reset the cache index but will not reset the open file object.
The first time through, the for loop will ask for a value from rules, which will invoke its __next__() method. This time, however, the cache is primed with a single pair of match and apply functions, corresponding to the patterns in the first line of the pattern file. Since they were built and cached in the course of pluralizing the previous word, they’re retrieved from the cache. The cache index increments, and the open file is never touched.
Let’s say, for the sake of argument, that the first rule does not match this time around. So the for loop comes around again and asks for another value from rules. This invokes the __next__() method a second time. This time, the cache is exhausted — it only contained one item, and we’re asking for a second — so the __next__() method continues. It reads another line from the open file, builds match and apply functions out of the patterns, and caches them.
This read-build-and-cache process will continue as long as the rules being read from the pattern file don’t match the word we’re trying to pluralize. If we do find a matching rule before the end of the file, we simply use it and stop, with the file still open. The file pointer will stay wherever we stopped reading, waiting for the next readline() command. In the meantime, the cache now has more items in it, and if we start all over again trying to pluralize a new word, each of those items in the cache will be tried before reading the next line from the pattern file.

We have achieved pluralization nirvana.

Minimal startup cost. The only thing that happens on import is instantiating a single class and opening a file (but not reading from it).
Maximum performance. The previous example would read through the file and build functions dynamically every time you wanted to pluralize a word. This version will cache functions as soon as they’re built, and in the worst case, it will only read through the pattern file once, no matter how many words you pluralize.
Separation of code and data. All the patterns are stored in a separate file. Code is code, and data is data, and never the twain shall meet.

☞Is this really nirvana? Well, yes and no. Here’s something to consider with the LazyRules example: the pattern file is opened (during __init__()), and it remains open until the final rule is reached. Python will eventually close the file when it exits, or after the last instantiation of the LazyRules class is destroyed, but still, that could be a long time. If this class is part of a long-running Python process, the Python interpreter may never exit, and the LazyRules object may never get destroyed.

There are ways around this. Instead of opening the file during __init__() and leaving it open while you read rules one line at a time, you could open the file, read all the rules, and immediately close the file. Or you could open the file, read one rule, save the file position with the tell() method, close the file, and later re-open it and use the seek() method to continue reading where you left off. Or you could not worry about it and just leave the file open, like this example code does. Programming is design, and design is all about trade-offs and constraints. Leaving a file open too long might be a problem; making your code more complicated might be a problem. Which one is the bigger problem depends on your development team, your application, and your runtime environment.

⁂

Classes & Iterators

Diving In

Defining Classes

The __init__() Method

Instantiating Classes

Instance Variables

A Fibonacci Iterator

A Plural Rule Iterator

Further Reading

The `init()` Method