Python’s Built-in Data Structures
# Creating and manipulating lists
fruits = ["apple", "banana", "cherry"]
numbers = [1, 2, 3, 4, 5]
# Adding elements
fruits.append("orange")
fruits.insert(1, "grape")
# Accessing elements
first_fruit = fruits[0] # "apple"
last_fruit = fruits[-1] # "orange"
# Slicing
first_three = fruits[:3]Key Features: Ordered, mutable, allow duplicates
numbers = [3, 1, 4, 1, 5, 9, 2, 6]
# Common operations
print(len(numbers)) # Length: 8
print(sum(numbers)) # Sum: 31
print(max(numbers)) # Maximum: 9
# Sorting and reversing
numbers.sort() # [1, 1, 2, 3, 4, 5, 6, 9]
numbers.reverse() # [9, 6, 5, 4, 3, 2, 1, 1]
# List comprehensions
squares = [x**2 for x in range(1, 6)]
# Result: [1, 4, 9, 16, 25]# Creating tuples
point = (10, 20)
rgb_color = (255, 128, 0)
person = ("Alice", 25, "Engineer")
# Tuple unpacking
name, age, job = person
print(f"{name} is {age} years old")
# Tuples are immutable
# point[0] = 15 # This would cause an error!
# But you can create new tuples
new_point = (point[0] + 5, point[1] + 3)Perfect for: Coordinates, RGB values, database records
# Creating dictionaries
student = {
"name": "Alice",
"age": 20,
"major": "Computer Science",
"gpa": 3.8
}
# Accessing and modifying
print(student["name"]) # "Alice"
student["graduation_year"] = 2025 # Add new key
student["age"] = 21 # Update existing
# Safe access with get()
email = student.get("email", "Not provided")
# Iterating
for key, value in student.items():
print(f"{key}: {value}")Dictionaries have powerful built-in methods for data manipulation:
inventory = {
"apples": 50,
"bananas": 30,
"oranges": 25
}
# .keys() gets all the dictionary keys (item names)
print(inventory.keys()) # dict_keys(['apples', 'bananas', 'oranges'])
# .values() gets all the dictionary values (quantities)
print(inventory.values()) # dict_values([50, 30, 25])
# Use 'in' to check if a key exists (very fast!)
if "apples" in inventory:
print(f"We have {inventory['apples']} apples")
# Dictionary comprehension: transform all key-value pairs
# This reads as: "for each key-value pair, create new pair with doubled value"
doubled = {k: v*2 for k, v in inventory.items()}
# Result: {"apples": 100, "bananas": 60, "oranges": 50}Key insight: Dictionary operations are very fast because they use hash tables for lookups!
Sets are collections that automatically eliminate duplicates and provide fast membership testing:
# Creating sets - notice curly braces like dictionaries, but no key:value pairs
fruits = {"apple", "banana", "cherry"}
numbers = {1, 2, 3, 4, 5}
# Adding elements - only unique items are stored
fruits.add("orange")
# Sets automatically handle duplicates - perfect for removing duplicates!
mixed = {1, 2, 2, 3, 3, 3} # We added duplicates here
print(mixed) # {1, 2, 3} - duplicates automatically removed!
# Real-world example: Get unique items from a list
email_list = ["alice@test.com", "bob@test.com", "alice@test.com", "charlie@test.com"]
unique_emails = set(email_list) # Converts list to set, removing duplicates
print(f"Unique emails: {unique_emails}")When to use sets: Fast membership testing (item in my_set) and removing duplicates!
set_a = {1, 2, 3, 4, 5} set_b = {4, 5, 6, 7, 8}
union = set_a | set_b # {1, 2, 3, 4, 5, 6, 7, 8} intersection = set_a & set_b # {4, 5} difference = set_a - set_b # {1, 2, 3}
## Choosing the Right Data Structure
| Structure | When to Use | Example |
|-----------|-------------|---------|
| **List** | Ordered, mutable collection | Shopping list, scores |
| **Tuple** | Ordered, immutable data | Coordinates, RGB |
| **Dictionary** | Key-value lookup | User profiles, settings |
| **Set** | Unique items, fast lookup | Tags, unique IDs |
## Real-World Example: Student System {.smaller}
```python
# Student management using different data structures
students = [] # List of student records
def add_student(name, age, grades):
student = {
"name": name, # Dictionary for structured data
"age": age,
"grades": tuple(grades), # Tuple for immutable grade history
"subjects": set(), # Set for unique subjects
"average": sum(grades) / len(grades)
}
students.append(student)
# Usage
add_student("Alice", 20, [95, 87, 92])
add_student("Bob", 19, [78, 84, 88])
# Find top student
top_student = max(students, key=lambda s: s["average"])
print(f"Top student: {top_student['name']}")
# Traditional approach
squares = []
for x in range(1, 6):
squares.append(x**2)
# List comprehension (Pythonic!)
squares = [x**2 for x in range(1, 6)]
# With conditions
even_squares = [x**2 for x in range(1, 11) if x % 2 == 0]
# Result: [4, 16, 36, 64, 100]
# Working with strings
words = ["hello", "world", "python"]
uppercase = [word.upper() for word in words]
lengths = [len(word) for word in words]
# Dictionary comprehension
squares_dict = {x: x**2 for x in range(1, 6)}
# Result: {1: 1, 2: 4, 3: 9, 4: 16, 5: 25}
# Filter existing dictionary
prices = {"apple": 1.2, "banana": 0.8, "orange": 1.5}
expensive = {k: v for k, v in prices.items() if v > 1.0}
# Set comprehension
vowels_in_words = {char for word in ["hello", "world"]
for char in word if char in "aeiou"}
# Result: {'e', 'o'}Lists - Append: O(1) - Insert: O(n) - Search: O(n) - Good for: Sequential access
Sets & Dicts - Add/Remove: O(1) - Lookup: O(1) - Good for: Fast membership testing
Create a word frequency analyzer:
def word_frequency(text):
words = text.lower().split()
frequency = {}
for word in words:
frequency[word] = frequency.get(word, 0) + 1
return frequency
# Test it
text = "python is great python is powerful"
result = word_frequency(text)
print(result) # {'python': 2, 'is': 2, 'great': 1, 'powerful': 1}
# Find most common word
most_common = max(result.items(), key=lambda x: x[1])Ready to continue with Control Flow?
Practice more: - Build a contact book with dictionaries - Create a shopping cart with lists - Implement a tag system with sets
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