Data Structures

Python's Built-in Data Structures

Choosing the right data structure is one of the most important decisions in programming. Using a list when you should use a set can make your program 1000x slower. Using a dict when you need a list makes your code confusing. Let's understand each one deeply so you can make the right choice every time.

Lists

Lists are Python's workhorse data structure. Think of a list as a numbered shelf — each item has a position (starting from 0), you can add or remove items freely, and items stay in the order you put them. Lists can hold any type of data, and you can even mix types in a single list (though it's usually better not to).

Lists are the most commonly used data structure in Python. If you're unsure which collection to use, a list is usually a safe default. You'll use them for storing search results, collecting user inputs, building sequences, and much more.

Creating and Accessing

Create a list with square brackets. Access elements by index (position number). Remember: indexing starts at 0, not 1. You can also use negative indices — -1 is the last element, -2 is second-to-last:

fruits = ["apple", "banana", "cherry", "date"]
numbers = [1, 2, 3, 4, 5]
mixed = [42, "hello", True, 3.14]

print(fruits[0])     # apple (first)
print(fruits[-1])    # date (last)
print(fruits[1:3])   # ['banana', 'cherry']
print(len(fruits))   # 4

apple
date
['banana', 'cherry']
4

Modifying Lists

fruits = ["apple", "banana", "cherry"]
fruits.append("date")           # Add to end
fruits.insert(1, "avocado")     # Insert at index
fruits.extend(["fig", "grape"]) # Add multiple
print(fruits)

fruits.remove("banana")  # Remove by value
popped = fruits.pop()    # Remove last
print(f"Popped: {popped}")

nums = [3, 1, 4, 1, 5, 9]
nums.sort()
print(nums)

['apple', 'avocado', 'banana', 'cherry', 'date', 'fig', 'grape']
Popped: grape
[1, 1, 3, 4, 5, 9]

Tuples

point = (3, 4)
x, y = point  # Unpacking
print(f"x={x}, y={y}")

# Swap!
a, b = 1, 2
a, b = b, a
print(a, b)

from collections import namedtuple
Point = namedtuple("Point", ["x", "y"])
p = Point(3, 4)
print(p.x, p.y)

x=3, y=4
2 1
3 4

Sets

nums = set([1, 2, 2, 3, 3, 3])  # Duplicates removed
print(nums)  # {1, 2, 3}

a = {1, 2, 3, 4}
b = {3, 4, 5, 6}
print(a | b)    # Union: {1,2,3,4,5,6}
print(a & b)    # Intersection: {3,4}
print(a - b)    # Difference: {1,2}
print(3 in a)   # True — O(1) lookup!

{1, 2, 3}
{1, 2, 3, 4, 5, 6}
{3, 4}
{1, 2}
True

# O(n) — scans every element
if 999_999 in list(range(1_000_000)):  # SLOW
    print("Found!")

# O(1) — hash lookup
large_set = set(range(1_000_000))
if 999_999 in large_set:  # FAST
    print("Found!")

Dictionaries

Creating and Accessing

person = {
    "name": "Alice",
    "age": 30,
    "hobbies": ["reading", "coding"]
}

print(person["name"])                # Alice
print(person.get("phone", "N/A"))    # N/A (safe)

person["email"] = "[email protected]"  # Add

for key, value in person.items():
    print(f"  {key}: {value}")

Alice
N/A
  name: Alice
  age: 30
  hobbies: ['reading', 'coding']
  email: [email protected]

Useful Methods

scores = {"Alice": 95, "Bob": 87}

scores.setdefault("Charlie", 0)  # Add if missing
print(scores)

# Merge (Python 3.9+)
defaults = {"theme": "light", "lang": "en"}
user = {"theme": "dark"}
final = defaults | user
print(final)

{'Alice': 95, 'Bob': 87, 'Charlie': 0}
{'theme': 'dark', 'lang': 'en'}

Comprehensions

# List comprehension
squares = [x**2 for x in range(6)]
print(squares)

# With filter
evens = [x for x in range(10) if x % 2 == 0]
print(evens)

# Dict comprehension
word_len = {w: len(w) for w in ["hi", "hello", "hey"]}
print(word_len)

# Set comprehension
unique = {len(w) for w in ["hi", "hello", "hey", "ha"]}
print(unique)

[0, 1, 4, 9, 16, 25]
[0, 2, 4, 6, 8]
{'hi': 2, 'hello': 5, 'hey': 3}
{2, 3, 5}

Choosing the Right Structure