Python Data Structures

Python includes a rich set of built-in data structures. This cheatsheet covers the core types, their use cases, and examples.

Lists

Ordered, mutable sequences.

fruits = ["apple", "banana", "cherry"]
fruits.append("date")
print(fruits[1])  # banana

When to use: Ordered collections with frequent insertions, deletions, or updates.

Ordered, immutable sequences.

point = (3, 4)
print(point[0])  # 3

When to use: Fixed collections, function return values, or dictionary keys.

Unordered collections of unique elements.

colors = {"red", "green", "blue"}
colors.add("yellow")
print("red" in colors)

When to use: Membership tests, eliminating duplicates, set operations (union, intersection).

Key-value mappings.

person = {"name": "Alice", "age": 30}
print(person["name"])
person["city"] = "New York"

When to use: Fast lookups by unique keys, mapping relationships.

Immutable sequences of characters.

greeting = "Hello, World"
print(greeting.lower())

When to use: Text processing, immutable sequences.

stack = []
stack.append(1)
stack.append(2)
print(stack.pop())  # 2

When to use: Last-in, first-out (LIFO) data handling.

Use collections.deque for efficiency.

from collections import deque
queue = deque([1, 2, 3])
queue.append(4)
print(queue.popleft())  # 1

When to use: First-in, first-out (FIFO) tasks.

import heapq
heap = [3, 1, 4]
heapq.heapify(heap)
heapq.heappush(heap, 2)
print(heapq.heappop(heap))  # 1

When to use: Retrieve smallest/largest items efficiently.

For numeric data, use array or numpy for performance.

from array import array
nums = array('i', [1, 2, 3])

When to use: Memory-efficient storage of large numeric data.

Structure	Ordered	Mutable	Duplicates
list	Yes	Yes	Yes
tuple	Yes	No	Yes
set	No	Yes	No
dict	No*	Yes	Keys: No

*Insertion order preserved from Python 3.7+.

Knowing Python’s built-in data structures and their performance characteristics helps you write clean, efficient code.