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Difficulty level: ♦♦♦♢♢

# Classes & Iterators

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

## Diving In

Generators are really just a special case of iterators. A function that `yield`s 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

1. 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.
2. 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

1. Classes can (and should) have `docstring`s too, just like modules and functions.
2. The `__init__()` method is called immediately after an instance of the class is created. It would be tempting but incorrect to call this the constructor of the class. It’s tempting, because it looks like a 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 the `__init__()` method, self refers to the newly created object; in other class methods, it refers to the instance whose method was called. 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'

1. 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.
2. fib is now an instance of the `Fib` class.
3. 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.
4. 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 C++ or Java.

⁂

## Instance Variables

On to the next line:

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

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

1. self.max is defined in the `__init__()` method…
2. …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.

[download `fibonacci2.py`]

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

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

1. To build an iterator from scratch, `fib` needs to be a class, not a function.
2. “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.
3. 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.
4. 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.
5. When the `__next__()` method raises a `StopIteration` exception, this signals to the caller that the iteration is over; no more values are available. 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.
6. To spit out the next value, an iterator’s `__next__()` method simply `return`s 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

iter(f) calls f.__iter__
next(f) calls f.__next__

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)
            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):                                ①
            self.pattern_file = open(self.rules_filename)  ③
            self.cache = []                                ②

1. The `__init__()` method is only going to be called once, when you instantiate the class and assign it to rules.
2. Since this is only going to get called once, it’s the perfect place to open the pattern file. You’ll read it later; no point doing more than you absolutely have to until absolutely necessary!
3. Also, this is a good place to initialize the cache, which you’ll use later as you read the patterns from the pattern file.

Before we continue, let’s take a closer look at rules_filename. It’s not defined within the `__init__()` 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'
    >>> r1.__class__.rules_filename                     ②
    'plural6-rules.txt'
    >>> r1.__class__.rules_filename = 'papayawhip.txt'  ③
    >>> r1.rules_filename
    'papayawhip.txt'
    >>> r2.rules_filename                               ④
    'papayawhip.txt'

1. FIXME

And now back to our show.

        def __iter__(self):       ①
            self.cache_index = 0  ②
            return self           ③

1. The `__iter__()` method will be called every time someone — say, a `for` loop — calls `iter(rules)`.
2. This is the place to reset the counter that we’re going to use to retrieve items from the cache (that we haven’t built yet — patience, grasshopper).
3. Finally, the `__iter__()` method returns self, which signals that this class will take care of returning its own values throughout an 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

1. 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.
2. 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. Each line of the pattern file will be read exactly once, as late as possible.
3. The only difference is that, before returning the match and apply functions (which are stored in the tuple funcs), we’ve going to save them in `self.cache`. Each match and apply function will be built exactly once, as late as possible, then cached.

Moving backwards…

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

1. 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…)
2. 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.
3. 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
            .
            .
            .

1. `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.
2. 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.

Thus, we have achieved our combined goal [FIXME xref]:

1. Minimal startup cost. The only thing that happens on `import` is instantiating a single class and opening a file (but not reading from it).
2. 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.
3. 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.

⁂

## Further Reading

- PEP 234: Iterators
- PEP 255: Simple Generators

☜ ☞

© 2001–9 Mark Pilgrim