Data Structures Basics: Arrays, Lists and Stacks

Why organisation of data matters

Imagine a library with books thrown in a pile versus a library with labelled shelves. Finding a book is the same job — wildly different effort. A data structure is a way of organising data so operations like search, insert and delete are efficient. Interviews and real systems both revolve around choosing the right one.

Arrays — the numbered row of boxes

An array stores items side by side, each with an index starting at 0:

marks = [80, 75, 90, 60]
print(marks[2])   # 90 — instant access by index

Strength: reaching any position instantly. Weakness: inserting in the middle means shifting everything after it, and fixed-size arrays cannot grow.

Lists — the flexible chain

Python’s list behaves like a smarter, growable array:

tasks = ["study", "gym"]
tasks.append("project")   # grows easily
tasks.remove("gym")       # removes an item

For most beginner programs, lists are the default choice: ordered, changeable, and packed with useful methods.

Stacks — last in, first out

A stack is a pile of plates: you add and remove from the top only.

stack = []
stack.append("page1")   # push
stack.append("page2")
top = stack.pop()       # removes page2 first

Your browser’s Back button is a stack of pages; the Undo feature in editors is a stack of actions. Wherever “go back to the most recent thing” appears, a stack is hiding.

Queues — first in, first out

A queue is a line at a ticket counter: the first to arrive is the first served. Printers use queues for documents; apps use them for tasks waiting their turn. Stack = pile, queue = line — that picture alone answers many exam questions.

Practice task (45 minutes)

Simulate a browser: keep a stack list, “visit” five pages by appending their names, then press Back three times using pop(), printing the current page after each step. Then write, in one sentence each, whether a printer, an undo button, and a playlist should use a stack or a queue — and why.