Iterators, generators and decorators

In this chapter we will learn about iterators, generators and decorators.


Python iterator objects required to support two methods while following the iterator protocol.

__iter__ returns the iterator object itself. This is used in for and in statements.

__next__ method returns the next value from the iterator. If there is no more items to return then it should raise StopIteration exception.

class Counter(object):
    def __init__(self, low, high):
        self.current = low
        self.high = high

    def __iter__(self):
        'Returns itself as an iterator object'
        return self

    def __next__(self):
        'Returns the next value till current is lower than high'
        if self.current > self.high:
            raise StopIteration
            self.current += 1
            return self.current - 1

Now we can use this iterator in our code.

>>> c = Counter(5,10)
>>> for i in c:
...   print(i, end=' ')
5 6 7 8 9 10

Remember that an iterator object can be used only once. It means after it raises StopIteration once, it will keep raising the same exception.

>>> c = Counter(5,6)
>>> next(c)
>>> next(c)
>>> next(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 11, in next
>>> next(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 11, in next

Using the iterator in for loop example we saw, the following example tries to show the code behind the scenes.

>>> iterator = iter(c)
>>> while True:
...     try:
...         x = iterator.__next__()
...         print(x, end=' ')
...     except StopIteration as e:
...         break
5 6 7 8 9 10


In this section we learn about Python generators. They were introduced in Python 2.3. It is an easier way to create iterators using a keyword yield from a function.

>>> def my_generator():
...     print("Inside my generator")
...     yield 'a'
...     yield 'b'
...     yield 'c'
>>> my_generator()
<generator object my_generator at 0x7fbcfa0a6aa0>

In the above example we create a simple generator using the yield statements. We can use it in a for loop just like we use any other iterators.

>>> for char in my_generator():
...     print(char)
Inside my generator

In the next example we will create the same Counter class using a generator function and use it in a for loop.

def counter_generator(low, high):
    while low <= high:
       yield low
       low += 1

>>> for i in counter_generator(5,10):
...     print(i, end=' ')
5 6 7 8 9 10

Inside the while loop when it reaches to the yield statement, the value of low is returned and the generator state is suspended. During the second next call the generator resumed where it freeze-ed before and then the value of low is increased by one. It continues with the while loop and comes to the yield statement again.

When you call an generator function it returns a *generator* object. If you call *dir* on this object you will find that it contains __iter__ and *__next__* methods among the other methods.

   >>> c = counter_generator(5,10)
   >>> dir(c)
   ['__class__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__',
'__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__iter__',
'__le__', '__lt__', '__name__', '__ne__', '__new__', '__next__', '__reduce__',
'__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__',
'close', 'gi_code', 'gi_frame', 'gi_running', 'send', 'throw']

We mostly use generators for laze evaluations. This way generators become a good approach to work with lots of data. If you don’t want to load all the data in the memory, you can use a generator which will pass you each piece of data at a time.

One of the biggest example of such example is os.path.walk() function which uses a callback function and current os.walk generator. Using the generator implementation saves memory.

We can have generators which produces infinite values. The following is a one such example.

>>> def infinite_generator(start=0):
...     while True:
...         yield start
...         start += 1
>>> for num in infinite_generator(4):
...     print(num, end=' ')
...     if num > 20:
...         break
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

If we go back to the example of my_generator we will find one feature of generators. They are not re-usable.

>>> g = my_generator()
>>> for c in g:
...     print(c)
Inside my generator
>>> for c in g:
...     print(c)

One way to create a reusable generator is Object based generators which does not hold any state. Any class with a __iter__ method which yields data can be used as a object generator. In the following example we will recreate out counter generator.

>>> class Counter(object):
...     def __init__(self, low, high):
...         self.low = low
...         self.high = high
...     def __iter__(self):
...          counter = self.low
...          while self.high >= counter:
...              yield counter
...              counter += 1
>>> gobj = Counter(5, 10)
>>> for num in gobj:
...     print(num, end=' ')
5 6 7 8 9 10
>>> for num in gobj:
...     print(num, end=' ')
5 6 7 8 9 10

Generator expressions

Generator expressionsGenerator expressions

In this section we will learn about generator expressions which is a high performance, memory efficient generalization of list comprehensions and generators.

For example we will try to sum the squares of all numbers from 1 to 99.

>>> sum([x*x for x in range(1,10)])

The example actually first creates a list of the square values in memory and then it iterates over it and finally after sum it frees the memory. You can understand the memory usage in case of a big list.

We can save memory usage by using a generator expression.

sum(x*x for x in range(1,10))

The syntax of generator expression says that always needs to be directly inside a set of parentheses and cannot have a comma on either side. Which basically means both the examples below are valid generator expression usage example.

>>> sum(x*x for x in range(1,10))
>>> g = (x*x for x in range(1,10))
>>> g
<generator object <genexpr> at 0x7fc559516b90>

We can have chaining of generators or generator expressions. In the following example we will read the file */var/log/cron* and will find if any particular job (in the example we are searching for anacron) is running successfully or not.

We can do the same using a shell command tail -f /var/log/cron |grep anacron

>>> jobtext = 'anacron'
>>> all = (line for line in open('/var/log/cron', 'r') )
>>> job = ( line for line in all if line.find(jobtext) != -1)
>>> text = next(job)
>>> text
"May  6 12:17:15 dhcp193-104 anacron[23052]: Job `cron.daily' terminated\n"
>>> text = next(job)
>>> text
'May  6 12:17:15 dhcp193-104 anacron[23052]: Normal exit (1 job run)\n'
>>> text = next(job)
>>> text
'May  6 13:01:01 dhcp193-104 run-parts(/etc/cron.hourly)[25907]: starting 0anacron\n'

You can write a for loop to the lines.


Closures are nothing but functions that are returned by another function. We use closures to remove code duplication. In the following example we create a simple closure for adding numbers.

>>> def add_number(num):
...     def adder(number):
...         'adder is a closure'
...         return num + number
...     return adder
>>> a_10 = add_number(10)
>>> a_10(21)
>>> a_10(34)
>>> a_5 = add_number(5)
>>> a_5(3)

adder is a closure which adds a given number to a pre-defined one.


Decorator is way to dynamically add some new behavior to some objects. We achieve the same in Python by using closures.

In the example we will create a simple example which will print some statement before and after the execution of a function.

>>> def my_decorator(func):
...     def wrapper(*args, **kwargs):
...         print("Before call")
...         result = func(*args, **kwargs)
...         print("After call")
...         return result
...     return wrapper
>>> @my_decorator
... def add(a, b):
...     "Our add function"
...     return a + b
>>> add(1, 3)
Before call
After call