Understanding loops is one of the fundamentals of programming. As a developer, you will use loops every day, so it's important to understand all of the ways that you can control them. In this tutorial, we will discuss: What are iterators?
What are generators?
How do you create your own iterators and generators?
An iterator is an object that can be iterated (looped) upon. Technically speaking, in Python, an iterator is an object which implements the iterator protocol, which consists of the methods iter() and next().
An object is called iterable if we can get an iterator from it Such are list, tuple, string, etc.
The iter() method is used to return an iterator. For example, if you have a list, you can get an iterator from it by calling the
iter() function on it:
my_list = [4, 5, 6, 7, 8, 9] my_iter = iter(my_list) # get on iterator using iter()
In this case,
my_iter is a list and our
iter() call returned an object called “list_iterator". This is essentially an object which implements both iter and next methods. The next method returns the next element from this iterator until there are no more elements left in it:
print(next(my_iter)) print(next(my_iter)) print(next(my_iter)) next(my_iter) # calling next element without printing it print(next(my_iter)) # 4 # 5 # 6 # 8
For loops and Iterators
The for loop can iterate automatically through the list. The for loop can iterate over any iterable A for loop is implemented as:
iter_obj = iter(iterable) While True: try: element = next(iter_obj) # get the next item # do something with elements except StopIteration: # if StopIteration is raised break # for loop # break
The for loop creates an iterator object (iter_obj) by calling iter() on the iterable. Inside the loop, it calls next() to get the next element and executes the body of for loop with this value. After all the items exhaust StopIteration is raised which is internally caught and the loop ends.
Generators are functions that return an iterator to their caller.
They're not like normal functions, however - they're actually iterators themselves! A generator is an object that implements the iter and next methods of Python's iterator protocol. The function callable object then uses these methods to iterate over a series of values by calling other code inside the generator.
def first_n(n): num = 0 while num < n: yield num num += 1 sum_firs_n = sum(first_n(5)) print(sum_firs_n) # 10
The yield statement
if the function contains at least one yield statement it becomes a generator function.
return will return a value from a function.
The difference between
return is that the
return statement terminates a function entirely.
Yield however pauses the function saving all its states and later continues from there on successive calls.
Generators can be easily created using generator expressions.
Same as the
lambda function creates an anonymous generator function.
Example: Generator Expression
my_list = [1, 3, 6, 10] # Initialize the list # square each term using list comprehension print([x ** 2 for x in my_list]) # the same thing can be done using a generator expression # generator expressions are surrounded by parenthesis () print((x ** 2 for x in my_list))
One way I've found that makes me really happy about using generators is when dealing with data structures that have cycles (like lists). Usually, we would create an iterator by defining a class but with generators, we don't need classes at all!
With generators, instead of using the next() function all the time and having to call yield every once in a while, we can simply write something like:
def reverse_str(my_str): length = len(my_str) for i in range(length - 1, -1, -1): yield my_str[i] # For loop to reverse the string for char in reverse_str("hello"): print(char) # o # l # l # e # h
Iterators and Generators are objects that can cycle through a series of values. Iterators have their own methods, with next() being the most common. Generators use the yield keyword to return values one at a time when called. They're simple to implement and use much less memory than iterators because they don't store all the values in memory at once.