Data Structures

The everyday five

Structure	What it gives you	Typical use
List / array	Ordered items, access by position, duplicates allowed	Results of a query, rows to display, anything you loop over
Map / dictionary	Look up a value by a unique key	Find a user by id, count words, cache results
Set	Unique items, fast “is it in here?”	Remove duplicates, track visited ids, compare two groups
Stack	Last in, first out (LIFO)	Undo history, nested calls, back navigation
Queue	First in, first out (FIFO)	Jobs waiting for a worker, messages, breadth-first work

Names differ by language (dict, Map, HashMap, object), but the ideas carry over.

Choosing between them

Ask what you do most often with the data.

• Loop over everything in order: a list.
• Find one item by an identifier: a map. Searching a list for it means checking items one by one.
• Ask “have I seen this before?”: a set, or a map when you also need data attached.
• Process the oldest item first: a queue. Process the newest first: a stack.

A common beginner slowdown is calling a “contains” check on a list inside a loop. Copying the list into a set first often turns a slow job into a fast one. See Time and Space Complexity.

# Python: count how often each word appears
counts = {}
for word in words:
    counts[word] = counts.get(word, 0) + 1

Keys, order, sharing and queues

• Maps and sets need keys that can be hashed or compared. Mutable objects (a list in Python) usually cannot be keys.
• Do not rely on ordering unless the type promises it.
• Two variables can point to the same list, so changing one changes the other. Copy explicitly when you need independence. See State Management.
• Removing from the front of a plain list is slow in many languages. Use a proper queue or deque type.

Beyond the basics

Know that these exist, and look them up when a problem calls for them:

• Tree: hierarchical data such as folders, a page’s elements or a sorted index.
• Heap / priority queue: always gives the smallest or largest item next.
• Graph: things and the connections between them, such as routes or dependencies.

Databases use similar ideas to find rows fast; see Indexing Basics.

Try it

Write a small script that reads a text file and prints the five most common words. First use a list and nested loops, then a map. Time both on a large file and note the difference.