Is list in Python same as linked list?

No, a Python list is not the same as a linked list. While both are data structures that can store collections of elements, they are implemented differently and have distinct characteristics.

Python List:

  • A Python list is implemented as a dynamic array, allowing efficient random access by index.
  • It automatically resizes itself as elements are added, ensuring that there’s enough space for new elements.
  • Appending elements to the end of a Python list has an average constant time complexity (amortized O(1)).
  • Random access to elements by index is also efficient, with a constant time complexity (O(1)).
  • Insertions and deletions within a Python list, especially in the middle, can be less efficient due to potential shifting of elements.
  • Python lists can store elements of different data types.

Here’s an example of a Python list:

my_list = [1, 2, 3, 4, 5]
print(my_list[2])  # Outputs: 3
print(my_list)     # Outputs: [1, 2, 3, 4, 5, 6]
Code language: Python (python)

Linked List:

  • A linked list is a data structure where elements (nodes) are connected through pointers or references.
  • Insertions and deletions in a linked list, especially in the middle, can be more efficient compared to a dynamic array.
  • Random access by index in a linked list is less efficient, with a time complexity of O(n), as you might need to traverse the list from the beginning to reach the desired index.
  • Linked lists can be more memory-efficient than dynamic arrays, as they only require memory for elements and pointers.

Here’s an example of a simple singly linked list:

class Node:
    def __init__(self, value):
        self.value = value = None

class LinkedList:
    def __init__(self):
        self.head = None

    def append(self, value):
        new_node = Node(value)
        if not self.head:
            self.head = new_node
            current = self.head
                current =
   = new_node

# Creating a linked list: 1 -> 2 -> 3 -> 4
linked_list = LinkedList()
Code language: Python (python)

Why does Python not have linked list?

Python does not have a built-in linked list data structure like some other programming languages (such as C++ or Java) do. There are a few reasons for this design choice:

  1. Trade-Offs: Python’s primary built-in list data structure is implemented as a dynamic array, which offers efficient random access and amortized constant-time appends. This design choice balances the needs of many common use cases by providing a versatile data structure. While linked lists are more efficient for certain operations, they can be less memory-efficient and have slower random access compared to dynamic arrays.
  2. Simplicity: Python strives to provide a clean and simple language syntax. Introducing linked lists as a built-in data type might increase the complexity of the language for relatively modest gains in certain scenarios.
  3. Use of Collections: Python’s standard library offers several alternative data structures that can be used as linked lists if needed. For instance, you can use the collections.deque class, which is implemented as a doubly-linked list. While it’s not exactly the same as a traditional linked list, it offers similar benefits for many use cases.
  4. Python’s Strengths: Python’s strengths lie in its ease of use, readability, and rapid development. For many tasks, the built-in list, along with other data structures like dictionaries and sets, suffices without requiring the added complexity of linked lists.
  5. Flexibility: Python allows users to define their own classes and data structures, so if someone needs a linked list for a specific project or application, they can easily implement one themselves.

While Python’s built-in list may not be a traditional linked list, it generally meets the needs of most developers. If you find yourself in a situation where a linked list is more appropriate for your problem, you can create your own custom linked list implementation using Python’s class capabilities.

What data structure is a Python list?

A Python list is implemented as a dynamic array data structure. Dynamic arrays combine the features of arrays and linked lists, offering efficient random access and amortized constant-time appends. Here’s a brief overview of how a dynamic array works:

  1. Dynamic Sizing: A dynamic array automatically resizes itself as elements are added. When the array becomes full, a new, larger chunk of memory is allocated, and the existing elements are copied over. This allows the array to grow without needing manual memory management.
  2. Random Access: Dynamic arrays provide efficient random access to elements using an index. Accessing an element by index has a constant time complexity (O(1)) because the array is implemented as a contiguous block of memory, enabling direct memory addressing.
  3. Amortized Constant-Time Appends: Appending an element to the end of a dynamic array has an average constant time complexity (amortized O(1)). This is achieved by occasionally allocating larger chunks of memory than necessary when resizing. This reduces the frequency of resizing operations.
  4. Insertions and Deletions: While dynamic arrays offer efficient random access and appends, insertions and deletions in the middle of the array are less efficient, as they may require shifting elements to make space.

In summary, Python lists provide a versatile data structure that balances the advantages of random access and efficient appends. While they are not exactly the same as traditional linked lists, Python’s dynamic arrays are optimized for common use cases and offer good performance for a wide range of scenarios.

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  • Muhammad Nabil

    I am a skilled and experienced Python developer with a huge passion for programming and a keen eye for details. I earned a Bachelor's degree in Computer Engineering in 2019 from the Modern Academy for Engineering and Technology. I am passionate about helping programmers write better Python code, and I am confident that I can make a significant contribution to any team. I am also a creative thinker who can come up with new and innovative ways to improve the efficiency and readability of code. My specialization includes Python, Django, SQL, Apache NiFi, Apache Hadoop, AWS, and Linux (CentOS and Ubuntu). Besides my passion for Python, I am a solo traveler who loves Pink Floyd, online video games, and Italian pizza.

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