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# How to solve coding interview questions: Linked lists, part 2

## In case you missed part 1…

Before you continue reading, be sure to check out part 1! It’s where I explain what a linked list is, and provide a couple of basic classes that we’ll build on in this article.

## A quick recap

A linked list is a data structure that holds data as a list of items in a specific order. Each item in the list is represented by a node, which “knows” two things:

• Its data
• Where the next node is

Here’s a picture of a node’s structure:

Linked lists are created by connecting nodes together, as shown in the diagram below for a list containing the characters `a`, `b`, `c`, and `d` in that order:

The diagram above features the following:

• Four nodes, each one for a letter in the list.
• A variable called `head`, which is a pointer or reference to the first item in the list. It’s the entry point to the list, and it’s a necessary part of the list — you can’t access a linked list if you can’t access its head.
• An optional variable called `tail`, which is a pointer or reference to the last item in the list. It’s not absolutely necessary, but it’s convenient if you use the list like a queue, where the last element in the list is also the last item that was added to the list.

## The JavaScript version of the previous article’s code

In the previous article, I provided code for the `Node` and `LinkedList` classes in Python. As promised, here are those classes implemented in JavaScript.

``````// JavaScript

class Node {

constructor(data) {
this.data = data
this.next = null
}

toString() {
return this.data.toString()
}

}

constructor() {
}

toString() {
let output = ""

while (currentNode) {
output += `\${currentNode.data.toString()}\n`
currentNode = currentNode.next
}

toString() {
return('Empty list.')
}

let output = ""

while (currentNode) {
output += `\${currentNode.data.toString()}\n`
currentNode = currentNode.next
}

return output.trim()
}

let newNode = new Node(data)

// If the list is empty,
// and exit.
return
}

// If the list isn’t empty,
// traverse the list by going to each node’s
// `next` node until there isn’t a `next` node...
while (currentNode.next) {
currentNode = currentNode.next
}

// If you’re here, `currentNode` is
// the last node in the list.
// Point `currentNode` at
currentNode.next = newNode
}

}``````

…where each item in a list is represented by the node. A node knows only about the data it contains and where the next node in the list is.

The JavaScript version of `Node` and `LinkedList` uses the same algorithms as the Python version. The Python version follows the `snake_case` naming convention, while the JavaScript version follows the `camelCase` convention.

The JavaScript LinkedList class has the following members:

• `head`: A property containing a pointer or reference to the first item in the list, or `null` if the list is empty. This property has the same name in the Python version.
• `constructor()`: The class constructor, which initializes `head` to `null`, meaning that any newly created `LinkedList` instance is an empty list. In the Python version, the `__init__()` method has this role.
• `toString()`: Returns a string representation of the contents of the linked list for debugging purposes. If the list contains at least one item, it returns the contents of the list. If the list is empty, it returns the string `Empty list`. In the Python version, the `__str__()` method has this role.
• `addLast()`: Given a value, it creates a new `Node` containing that value and adds that `Node` to the end of the list. In the Python version, the `add_last()` method has this role.

## Adding more functionality to the list

• Reporting how many elements are in the list
• Getting the value of the nth item in the list
• Setting the value of the nth item in the list

I’ll provide both Python and JavaScript implementations.

### How many elements are in the list?

To find out how many elements are in a linked list, you have to start at its head and “step through” every item in the list by following each node’s `next` property, keeping a count of each node you visit. When you reach the last node — the one whose `next` property is set to `None` in Python or `null` in JavaScript — report the number of nodes you counted.

The Python version uses one of its built-in “dunder” (Pythonese for double-underscore) methods, `__len__()`, to report the number of items in the list. Defining `__len__()` for LinkedList means that any `LinkedList` instance will report the number of items it contains when passed as an argument to the built-in `len()` function. For example, if a `LinkedList` instance named `my_list` contains 5 items, `len(my_list)` returns the value `5`.

Here’s the Python version, which you should add to the Python version of `LinkedList`:

``````# Python

def __len__(self):
count = 0

# Traverse the list, keeping count of
# the nodes that you visit,
# until you’ve gone past the last node.
while current_node:
current_node = current_node.next
count += 1

return count    ``````

The JavaScript version is `getCount()`. If a `LinkedList` instance named `myList` contains 5 items, `len(myList)` returns the value `5`.

Here’s the JavaScript version, which you should add to the JavaScript version of `LinkedList`:

``````// JavaScript

getCount() {
let count = 0

// Traverse the list, keeping count of
// the nodes that you visit,
// until you’ve gone past the last node.
while (currentNode) {
currentNode = currentNode.next
count++
}

return count
}``````

### How do I get the data at the nth node in the list?

To get the data at the nth node of the list, you can use an approach that’s similar to the one for counting the list’s elements. You have to start at the head and “step through” every item in the list by following each node’s `next` property, keeping a count of each node you visit and comparing it against n, where n is the index of the node whose data you want. When the count of nodes you’ve visited is equal to n, report the data contained within the current node.

Here’s the Python version, `get_element_at()`, which you should add to the Python version of `LinkedList`:

``````# Python

def get_element_at(self, index):
current_index = 0

# Traverse the list, keeping count of
# the nodes that you visit,
# until you’ve reached the specified node.
while current_node:
if current_index == index:
# We’re at the node at the given index!
return current_node.data

# We’re not there yet...
current_node = current_node.next
current_index += 1

# If you’re here, the given index is larger
# than the number of elements in the list.
return None``````

Here’s the JavaScript version, `getElementAt()`, which you should add to the JavaScript version of `LinkedList`:

``````// JavaScript

getElementAt(index) {
let currentIndex = 0

// Traverse the list, keeping count of
// the nodes that you visit,
// until you’ve reached the specified node.
while (currentNode) {
if (currentIndex === index) {
// We’re at the node at the given index!
return currentNode.data
}

// We’re not there yet...
currentNode = currentNode.next
currentIndex++
}

// If you’re here, the given index is larger
// than the number of elements in the list.
return null
}``````

### How do I set the value of the nth node in the list?

To set the data at the nth node of the list, you can use an approach that’s similar to the one for getting the data at the nth node. You have to start at the head and “step through” every item in the list by following each node’s `next` property, keeping a count of each node you visit and comparing it against n, where n is the index of the node whose data you want. When the count of nodes you’ve visited is equal to n, set the current node’s `data` property to the new value.

Here’s the Python version, `set_element_at()`, which you should add to the Python version of `LinkedList`:

``````# Python

def set_element_at(self, index, value):
current_index = 0

# Traverse the list, keeping count of
# the nodes that you visit,
# until you’ve reached the specified node.
while current_node:
if current_index == index:
# We’re at the node at the given index!
current_node.data = value
return True

# We’re not there yet...
current_node = current_node.next
current_index += 1

# If you’re here, the given index is larger
# than the number of elements in the list.
return False``````

Here’s the JavaScript version, `setElementAt()`, which you should add to the JavaScript version of `LinkedList`:

``````// JavaScript

setElementAt(index, value) {
let currentIndex = 0

// Traverse the list, keeping count of
// the nodes that you visit,
// until you’ve reached the specified node.
while (currentNode) {
if (currentIndex === index) {
// We’re at the node at the given index!
currentNode.data = value
return true
}

// We’re not there yet...
currentNode = currentNode.next
currentIndex++
}

// If you’re here, the given index is larger
// than the number of elements in the list.
return false
}``````

## The classes so far

Let’s take a look at the complete `Node` and `LinkedList` classes so far, in both Python and JavaScript.

### Python

``````# Python

class Node:

def __init__(self, data):
self.data = data
self.next = None

def __str__(self):
return f"{self.data}"

def __init__(self):

def __str__(self):
return('Empty list.')

result = ""
while current_node:
result += f'{current_node}\n'
current_node = current_node.next

return result.strip()

new_node = Node(value)

# If the list is empty,
# and exit.
return

# If the list isn’t empty,
# traverse the list by going to each node’s
# `next` node until there isn’t a `next` node...
while current_node.next:
current_node = current_node.next

# If you’re here, `current_node` is
# the last node in the list.
# Point `current_node` at
current_node.next = new_node

def __len__(self):
count = 0

# Traverse the list, keeping count of
# the nodes that you visit,
# until you’ve gone past the last node.
while current_node:
current_node = current_node.next
count += 1

return count

def get_element_at(self, index):
current_index = 0

# Traverse the list, keeping count of
# the nodes that you visit,
# until you’ve reached the specified node.
while current_node:
if current_index == index:
# We’re at the node at the given index!
return current_node.data

# We’re not there yet...
current_node = current_node.next
current_index += 1

# If you’re here, the given index is larger
# than the number of elements in the list.
return None

def set_element_at(self, index, value):
current_index = 0

# Traverse the list, keeping count of
# the nodes that you visit,
# until you’ve reached the specified node.
while current_node:
if current_index == index:
# We’re at the node at the given index!
current_node.data = value
return True

# We’re not there yet...
current_node = current_node.next
current_index += 1

# If you’re here, the given index is larger
# than the number of elements in the list.
return False``````

### JavaScript

``````// JavaScript

class Node {

constructor(data) {
this.data = data
this.next = null
}

toString() {
return this.data.toString()
}

}

constructor() {
}

toString() {
return('Empty list.')
}

let output = ""

while (currentNode) {
output += `\${currentNode.data.toString()}\n`
currentNode = currentNode.next
}

return output.trim()
}

let newNode = new Node(value)

// If the list is empty,
// and exit.
return
}

// If the list isn’t empty,
// traverse the list by going to each node’s
// `next` node until there isn’t a `next` node...
while (currentNode.next) {
currentNode = currentNode.next
}

// If you’re here, `currentNode` is
// the last node in the list.
// Point `currentNode` at
currentNode.next = newNode
}

getCount() {
let count = 0

// Traverse the list, keeping count of
// the nodes that you visit,
// until you’ve gone past the last node.
while (currentNode) {
currentNode = currentNode.next
count++
}

return count
}

getElementAt(index) {
let currentIndex = 0

// Traverse the list, keeping count of
// the nodes that you visit,
// until you’ve reached the specified node.
while (currentNode) {
if (currentIndex === index) {
// We’re at the node at the given index!
return currentNode.data
}

// We’re not there yet...
currentNode = currentNode.next
currentIndex++
}

// If you’re here, the given index is larger
// than the number of elements in the list.
return null
}

setElementAt(index, value) {
let currentIndex = 0

// Traverse the list, keeping count of
// the nodes that you visit,
// until you’ve reached the specified node.
while (currentNode) {
if (currentIndex === index) {
// We’re at the node at the given index!
currentNode.data = value
return true
}

// We’re not there yet...
currentNode = currentNode.next
currentIndex++
}

// If you’re here, the given index is larger
// than the number of elements in the list.
return false
}

}``````

## Wait…this “linked list” thing is beginning to look like a Python or JavaScript array, but not as fully-featured. What’s going on here?

Here’s the truth: there’s a better-than-even chance that you’ll never use them…directly.

If you program in most languages from the 1990s or later (for example, Python was first released in 1991, PHP’s from 1994, Java and JavaScript came out in 1995), you’re working with arrays — or in Python’s case, lists, which are almost the same thing — that you can you can add elements to, remove elements from, perform searches on, and do all sorts of things with.

When you use one of these data types in a modern language…

• Python lists or a JavaScript arrays
• Dictionaries, also known as objects in JavaScript, hashes in Ruby, or associative arrays
• Stacks and queues
• React components

…there’s a linked list behind the scenes, moving data around by traversing, adding, and deleting nodes. There’s a pretty good chance that you’re indirectly using linked lists in your day-to-day programming; you’re just insulated from the details by layers of abstractions.

### So why do they still pose linked list challenges in coding interviews?

History plays a major factor.

In older languages like FORTRAN (1957), Pascal (1970), and C (1972), arrays weren’t dynamic, but fixed in size. If you declared an array with 20 elements, it remained a 20-element array. You couldn’t add or remove elements at run-time.

But Pascal and C supported pointers from the beginning, and pointers make it possible to create dynamic data structures — the kind where you can add or delete elements at run-time. In those early pre-web days, when programming languages’ standard libraries weren’t as rich and complete as today’s, and you often had to “roll your own” dynamic data structures, such as trees, queues, stacks, and…linked lists.

If you majored in computer science in the ’70s, ’80s, and even the ’90s, you were expected to know how to build your own dynamic data structures from scratch, and they most definitely appeared on the exam. These computer science majors ended up in charge at tech companies, and they expected the people they hired to know this stuff as well. Over time, it became tradition, and to this day, you’ll still get the occasional linked list question in a coding interview.

### What’s the point in learning about linked lists when we’re mostly insulated from having to work with them directly?

For starters, there’s just plain old pragmatism. As long as they’re still asking linked list questions in coding interviews, it’s a good idea to get comfortable with them and be prepared to answer all manner of questions about algorithms and data structures.

Secondly, it’s a good way to get better at the kind of problem solving that we need in day-to-day programming. Working with linked lists requires us to think about pointers/references, space and time efficiency, tradeoffs, and even writing readable, maintainable code. And of course, there’s always a chance that you might have to implement a linked list yourself, whether because you’re working on an IoT or embedded programming project with a low-level language like C or implementing something lower-level such as the reconciler in React.