Python Data Structures

#python #beginners

Python has four built-in structures for holding collections of data, and each one exists because it makes a different trade-off between order, mutability, and uniqueness.

Lists

A list is an ordered, mutable (changeable) collection, written with square brackets. Items can be added, removed, or changed after creation, and duplicates are allowed.

fruits = ["apple", "banana", "apple", "mango"]
fruits.append("orange")
fruits[0] = "pineapple"
print(fruits)   # ['pineapple', 'banana', 'apple', 'mango', 'orange']

Tuples

A tuple is an ordered, immutable (unchangeable) collection, written with parentheses. Once created, its contents can't be modified - no adding, removing, or changing items.

coordinates = (36.8219, -1.2921)
# coordinates[0] = 40   # this would raise an error - tuples can't be modified

Dictionaries

A dictionary stores data as key-value pairs, written with curly braces. Instead of accessing items by position (like a list), you access them by a unique key - which makes lookups fast and the data self-describing.

customer = {
    "name": "Emilio ochieng",
    "city": "Nairobi",
    "loyalty_points": 120
}

print(customer["name"])       # Emilio ochieng
customer["loyalty_points"] += 10

Sets

A set is an unordered collection of unique items - duplicates are automatically removed, and there's no guaranteed order to how items are stored.

cities = {"Nairobi", "Nakuru", "Nairobi", "Mombasa"}
print(cities)   # {'Nairobi', 'Nakuru', 'Mombasa'} - the duplicate is gone

Sets are particularly useful for membership checks ("Nairobi" in cities) and for operations like finding what two collections have in common:

supported_cities = {"Nairobi", "Nakuru", "Mombasa"}
customer_cities = {"Nakuru", "Eldoret"}

print(supported_cities & customer_cities)   # {'Nakuru'} - intersection

When to use each

Structure	Ordered?	Mutable?	Duplicates?	Use it when...
List	Yes	Yes	Allowed	You need a sequence you'll modify — adding, removing, reordering items
Tuple	Yes	No	Allowed	The data shouldn't change — fixed coordinates, a date (year, month, day)
Dictionary	Insertion order (3.7+)	Yes	Keys must be unique	You need to look things up by a meaningful label rather than position
Set	No	Yes	Not allowed	You need uniqueness enforced, or fast membership checks

Practical examples

Modeling something like a single row from the Sunrise Supermarket products table as a dictionary - it maps naturally onto named fields the way a database row does:

product = {
    "product_id": 1,
    "product_name": "Maize Flour 2kg",
    "category": "Groceries",
    "unit_price": 180.00
}

print(f"{product['product_name']} costs KES {product['unit_price']}")

Using a list of those dictionaries to represent an entire table, and a set to quickly answer "what categories exist?":

products = [
    {"product_name": "Maize Flour 2kg", "category": "Groceries"},
    {"product_name": "Cooking Oil 1L", "category": "Groceries"},
    {"product_name": "Bathing Soap", "category": "Toiletries"},
]

categories = {p["category"] for p in products}
print(categories)   # {'Groceries', 'Toiletries'}

What I understood from this

Choosing between these stopped being confusing once I stopped thinking about syntax (brackets vs. braces vs. parentheses) and started thinking about the rule each one enforces. A list says "order matters, and you're allowed to change your mind." A tuple says "order matters, but this is locked." A dictionary says "forget position - find things by name." A set says "I don't care about order, but I will not tolerate duplicates." Once the question became "which rule does my data actually need," the right structure usually picked itself - a database row, with its named fields, is obviously a dictionary; a fixed set of coordinates that should never change is obviously a tuple.

DEV Community