Python Iterables with Step-By-Step Examples


Python Iterables with Step-By-Step Examples

Python Iterables is One of the fundamental concepts that every Python developer should master is iterables. In this blog, we'll dive into Python iterables, understand what they are, and walk through step-by-step examples to grasp their practical applications.

What Are Iterables in Python?

An iterable is an object in Python that can be looped over. It allows you to iterate through its elements one at a time. Python provides several built-in iterables, including lists, tuples, strings, dictionaries, and sets. Additionally, you can create custom iterable objects using classes. The concept of iterables is fundamental to understanding loops and how you can work with collections of data. 

Understanding Python Iterables

In Python, an iterable is an object capable of returning its elements one at a time. This allows you to loop through its elements, accessing and processing them sequentially.The key feature of iterables is their capability to return an iterator when 'iter()' function is called upon them. This iterator then allows us to traverse the elements within the iterable using the 'next()' function.

In the same above, I mentioned in common Built-In Iterables:

Python provides several built-in iterables, which include:

Lists: Ordered collections of elements

One of the most commonly used data structures in Python, lists are ordered collections that can hold a variety of data types. They are mutable, allowing for easy modification of elements.

Strings: Sequences of characters

Strings in Python are sequences of characters. They are a fundamental data type used to represent and manipulate text, making them an essential part of virtually every Python program. Strings can be created using single or double quotes and offer a wide range of methods for manipulation.

Tuples: Immutable ordered collections

Similar to lists, tuples are also ordered collections, but they are immutable, meaning their elements cannot be changed once assigned. Tuples are particularly useful when you need a collection that should not be altered.

Sets: Unordered collections of unique elements

Sets are unordered collections of unique elements that are used for mathematical set operations such as union, intersection, difference, and symmetric difference. They provide a powerful way to handle data with unique characteristics.

Dictionaries: Collections of key-value pairs

Dictionaries are unordered collections of data in a key-value pair format. They provide a convenient way to store and retrieve data based on unique keys, enabling efficient data management and retrieval.

How Iteration Works

Python uses the concept of iteration to traverse through an iterable object. When you use a for loop, while loop, or even the 'iter()' and 'next()' functions, Python follows a specific process:

  • Creation of Iterator: When you start iterating over an iterable, Python creates an iterator object using the iterable's '__iter__' method.
  • Iteration: The iterator's '__next__' method is repeatedly called, retrieving items one by one. The current position in the iterable is tracked internally.
  • End of Iteration: When there are no more items to retrieve, the '__next__' method raises a StopIteration exception, signaling the end of the iteration.

Iterating Through Python Iterables

Now, let's explore how to iterate through various types of Python iterables using different methods and techniques. Understanding the iteration process is fundamental in comprehending how to effectively manipulate data within these structures.

Iterating through Lists

Lists can be easily traversed using a for loop or a list comprehension. Here is an example illustrating how to iterate through a list of elements:

my_list = [1, 2, 3, 4, 5]

for item in my_list:



This code snippet will print each element of the list on a new line, allowing you to access and process each item individually.

Iterating through Strings

Strings are also iterables in Python. You can iterate through the characters in a string using a for loop:

my_string = "care"

for char in my_string:



Iterating through Tuples

Similarly, you can iterate through tuples using a for loop. Since tuples are immutable, their elements cannot be changed, but you can still access and utilize each element during the iteration process.

my_tuple = (6, 7, 8, 9, 10)

for item in my_tuple:



This code will print each element of the tuple, showcasing how iteration works with immutable data structures.

Iterating through Dictionaries

Dictionaries require a slightly different approach for iteration due to their key-value structure. You can loop through either the keys, values, or both using the appropriate methods. Here's an example demonstrating iteration through dictionary keys and values:

my_dict = {'name': 'chandra', 'age': 30, 'country': 'India'}

# Iterating through keys
for key in my_dict:

# Iterating through values
for value in my_dict.values():

# Iterating through both keys and values
for key, value in my_dict.items():
    print(f"{key}: {value}")


name: chandra
age: 30
country: India

This code snippet showcases various ways to iterate through dictionary elements, providing flexibility in handling key-value pairs efficiently.

Iterating through Sets

Sets can be iterated similarly to lists and tuples. Here's a simple example demonstrating iteration through a set:

my_set = {11, 12, 13, 14, 15}

for item in my_set:



This code will print each element of the set, demonstrating how to work with unique and unordered data structures effectively.

Creating Custom Iterables

Python allows you to create your own custom iterable objects using classes. To do this, you need to implement two special methods: __iter__ and __next__.

Custom Iterable Class

Let's create a custom iterable class that generates a sequence of fruits from apple to elderberry :

class CustomFruitIterable:
    def __init__(self, fruits):
        self.fruits = fruits
        self.index = 0

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

    def __next__(self):
        if self.index >= len(self.fruits):
            raise StopIteration
            current_fruit = self.fruits[self.index]
            self.index += 1
            return current_fruit

# Define a list of fruits
fruits = ["apple", "banana", "cherry", "date", "elderberry"]

# Using the custom iterable for fruits
fruit_iterator = CustomFruitIterable(fruits)
for fruit in fruit_iterator:



In this code:

  • We create a class called CustomFruitIterable that takes a list of fruits as its input when initialized.
  • The '__init__' method initializes the fruits list and an index variable to keep track of the current position in the list.
  • The '__iter__' method resets the index to '0' and 'returns' the iterable object itself (self).
  • The '__next__' method returns the next fruit in the list if there are more fruits to iterate through. When the end of the list is reached, it raises a StopIteration exception.
  • We define a list of fruits and create an instance of the CustomFruitIterable class with this list.
  • We use a 'for loop' to iterate through the custom iterable and print each fruit.

Using 'iter()' and 'next()' Function:

'iter()': The iter() function is used to create an iterator from an iterable. An iterator is an object that keeps track of the current position in the iterable and provides a way to retrieve the next element. It's used in conjunction with the next() function.

'next()': The next() function is used to retrieve the next item from an iterator. It raises a StopIteration exception when there are no more items to retrieve. You can catch this exception to handle the end of the iteration gracefully.

fruits = ['apple', 'orange', 'grapes']
fruits_iter = iter(fruits)

fruits = next(fruits_iter)

fruits = next(fruits_iter)

fruits = next(fruits_iter)



Handling the StopIteration Exception

When you iterate over an iterable using a for loop, Python handles the StopIteration exception for you. However, if you manually create an iterator using 'iter()' and retrieve items with 'next()', it's your responsibility to catch this exception to avoid program termination.

Here's an example of how to handle the StopIteration exception:

my_list = [1, 2, 3, 4, 5]
my_iter = iter(my_list)

while True:
        item = next(my_iter)
    except StopIteration:



One more simple python iterable example using a list of fruits. We will deliberately cause an error and then handle it gracefully. Here's the code:

fruits = ["apple", "banana", "cherry", "date"]

    fruit_iter = iter(fruits)
    while True:
        fruit = next(fruit_iter)
except StopIteration:
    print("No more fruits to iterate.")
except Exception as e:
    print(f"An error occurred: {e}")

No more fruits to iterate.

In this code, we attempt to iterate through more items than there are in the 'fruits' list. This will eventually result in a 'StopIteration' error when there are no more items to iterate. We use a 'try...except' block to handle the error gracefully. If a 'StopIteration' error occurs, we print a message stating that there are no more fruits to iterate. If any other exception occurs, we catch it and print an error message indicating that something went wrong.


Understanding iterables is a fundamental aspect of Python programming. Whether you're working with built-in data structures or creating your custom iterables, these concepts will serve you well in handling and processing data effectively. So, keep practicing and exploring the power of iterables in Python to become a more proficient programmer. Happy coding!

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