Data Structures
Lists
Ordered, mutable sequences:
fruits = ["apple", "banana", "cherry"]
fruits.append("date") # ["apple", "banana", "cherry", "date"]
fruits[0] # "apple"
fruits[-1] # "date"
fruits[1:3] # ["banana", "cherry"]
# Common methods
fruits.insert(1, "blueberry")
fruits.remove("banana")
popped = fruits.pop() # removes and returns last item
fruits.sort()
len(fruits)Tuples
Ordered, immutable sequences:
point = (3, 4)
x, y = point # unpacking
# point[0] = 5 # Error! Tuples are immutable
# Useful for returning multiple values
def get_location():
return (40.7128, -74.0060)Sets
Unordered collections of unique elements:
colors = {"red", "green", "blue"}
colors.add("yellow")
colors.add("red") # no effect — already present
# Set operations
a = {1, 2, 3, 4}
b = {3, 4, 5, 6}
a | b # union: {1, 2, 3, 4, 5, 6}
a & b # intersection: {3, 4}
a - b # difference: {1, 2}Dictionaries
Key-value mappings:
student = {
"name": "Alice",
"age": 22,
"courses": ["Math", "CS"],
}
student["name"] # "Alice"
student.get("grade", "N/A") # "N/A" — safe access
student["grade"] = "A" # adding a new key
# Iteration
for key in student:
print(key)
for key, value in student.items():
print(f"{key}: {value}")List Comprehensions
A concise way to build lists:
squares = [x ** 2 for x in range(10)]
# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
evens = [x for x in range(20) if x % 2 == 0]
# Dictionary comprehension
square_map = {x: x ** 2 for x in range(5)}
# {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}
# Set comprehension
unique_lengths = {len(word) for word in ["hi", "hello", "hey"]}
# {2, 3, 5}Choosing the Right Structure
| Use case | Structure |
|---|---|
| Ordered collection of items | List |
| Fixed collection, shouldn’t change | Tuple |
| Unique items, membership tests | Set |
| Key-value lookups | Dictionary |
| Need fast append/pop from both ends | collections.deque |