Beyond Basics: Linked Lists and Python Best Practices

In this chapter, we're diving into a fundamental yet often misunderstood data structure — the linked list. If arrays are about contiguous memory and fast index access, linked lists are about flexibility, dynamic memory allocation, and pointer manipulation. While you may not use them every day in production code, understanding linked lists at a deeper level will seriously improve how you think about memory, performance, and algorithmic efficiency.

Types of Linked Lists

🔹 Singly Linked List Each node holds a value and a reference to the next node. It’s simple, space-efficient, and useful when you only need forward traversal — like implementing a basic stack or queue.

🔹 Doubly Linked List Nodes maintain references to both previous and next nodes. This bi-directional access makes insertions and deletions more flexible, especially in LRU cache implementations or text editors where navigation in both directions is key.

🔹 Circular Linked List Here, the tail of the list links back to the head. This is useful in scenarios requiring continuous iteration over elements, such as in round-robin schedulers or music playlists.

Key Interview Problems & Patterns

NeetCode , in my opinion, is the best resource for cracking coding interviews. I will be linking to his videos as a reference for video explanations of common interview problems.

✅ Reversing a Linked List This is a classic — you’ll be asked to reverse the nodes of a singly linked list in-place. Doing this well shows you understand pointer manipulation and in-place memory usage. Master both iterative and recursive solutions.

https://neetcode.io/problems/reverse-a-linked-list

✅ Cycle Detection (Floyd’s Algorithm) Also known as the tortoise and hare algorithm. You use two pointers moving at different speeds to detect if a loop exists in the list. This pattern is not only elegant but often pops up in other domains, such as graph traversal.

https://neetcode.io/problems/linked-list-cycle-detection

Why Linked Lists Still Matter

In high-level languages, we often rely on built-in data structures. But linked lists show up when systems need direct control over memory:

Memory Management - Allocators in OS kernels and custom memory pools often use free lists — a form of singly or doubly linked lists — to track memory blocks. Understanding how nodes are allocated and reclaimed is crucial in systems-level work.

LRU Caches - Least Recently Used caches are a practical use case. You maintain a doubly linked list for ordering elements by access recency, and a hashmap for O(1) lookup. When an item is accessed, you move it to the head of the list. When the cache exceeds capacity, you evict the tail — the least recently used.

Optimising Linked List Usage

In Python, linked lists aren’t built into the standard library in the same way as lists (which are actually dynamic arrays under the hood). Most engineers use Python’s list or deque for everyday tasks. But implementing your own linked list or using collections.deque for double-ended operations is still worth understanding — especially when thinking like a systems or backend engineer.

That said, raw linked lists have performance trade-offs that aren’t obvious at first glance. Because each node is a separate Python object, they’re stored non-contiguously in memory, and Python has no pointer arithmetic like C. This makes certain operations surprisingly slow — especially large-scale traversal.

Here’s how to write and use them more efficiently:

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

class LinkedList:
    def __init__(self, values=None):
        self.head = None
        if values:
            self.build_from_list(values)

    def build_from_list(self, values):
        """Batch build the linked list to minimise traversal overhead."""
        dummy = Node(0)
        current = dummy
        for val in values:
            current.next = Node(val)
            current = current.next
        self.head = dummy.next

    def traverse(self):
        """Efficient single-pass traversal."""
        current = self.head
        while current:
            print(current.value, end=" -> ")
            current = current.next
        print("None")

# Example usage
values = [1, 2, 3, 4, 5]
linked_list = LinkedList(values)
linked_list.traverse() -> this would return 1 -> 2 -> 3 -> 4 -> 5         

You’re probably thinking “why would I need to know this?”. You would be surprised how many high level firms have actually asked me ( and people i know) to implement a linked list from scratch - you won't necessarily do this in any real production code but understanding how to do it may be the difference between you getting an offer at your dream company or not.

Minimize Traversals

Python isn’t optimised for raw pointer-style data structures. Every access like node.next involves an attribute lookup on a Python object — which is much slower than array indexing.

To improve performance:

• Avoid repeated passes. If you need to find the length of a list, the midpoint, and reverse it — try doing that in a single traversal.
• Use early exits when searching or filtering. Don’t keep walking the list if you’ve already found what you need.
• Cache node references if you’ll be reusing them in later operations.

Reuse Nodes and Preallocate If Possible

Python manages memory for you, but creating thousands of tiny objects still costs time and memory. If you're working with a custom linked list implementation and plan to create many nodes (say, in a simulation or cache):

• Pre-allocate nodes in a list and reuse them instead of constantly instantiating new ones.
• Consider using object pooling techniques — reuse nodes by resetting their .value and .next fields instead of discarding and recreating.

This is especially helpful if you're implementing something like an LRU cache or task queue.

Batch Operations to Reduce Overhead

In Python, every function call, object access, or loop adds overhead. Batch operations help:

• Build the full list first, then link nodes in one go.
• For example, if you’re reading from a file or API, store items in a list, then construct the linked list once, rather than adding nodes one by one.
• Reverse in-place instead of creating a new list when possible.

Understand When NOT to Use Linked Lists in Python

Here’s the honest truth: in most real-world Python projects, linked lists aren’t the best default tool. Python’s built-in list and collections.deque are faster and simpler for almost every case.

Avoid linked lists when:

• You need fast indexing or slicing — Python lists are much faster.
• You’re dealing with short-lived data — a list or dictionary is easier.
• You want something production-ready — use deque for fast appends/pops on both ends.

However, knowing how linked lists work helps you level up your problem-solving — especially in interviews or when porting logic from lower-level languages. It's also a must-have for writing your own LRU cache, memory-efficient data structures, or even custom parsers.

In short: Python makes many things easier, but it doesn’t hide the need to think critically about structure and memory. As you move into mid/senior roles, being aware of what’s happening under the hood — even in high-level languages — will set you apart.

Final Thoughts

Linked lists are more than interview fodder — they offer a window into how memory and performance interact. If you want to level up from junior to mid or senior roles, mastering linked structures will sharpen your mental model of how programs work under the hood.

In the next chapter, we’ll explore Stacks & Queues — and why they’re the hidden backbone of recursion, parsing, and scheduling systems.

For practice on linked list questions, I would recommend using neetcode.io - i’m yet to see and use a better resource than it. If you’re getting ready for coding interviews, practice implementing a linked list from scratch as well as the standard algorithms for reversing and cycle detection.

See you in the next one!