🔁 Repetition and Loops

Repetition is one of the most powerful concepts in programming. Loops allow your programs to execute the same code multiple times without writing it repeatedly. Whether you're processing lists of data, asking for user input until you get a valid response, or performing calculations on large datasets, loops make your code efficient and maintainable.

Without loops, you would need to write the same code over and over again for each repetition. Loops eliminate this redundancy and make your programs capable of handling tasks of any size, from processing a few items to millions of records.

# Without loops - repetitive and limited
print("Count: 1")
print("Count: 2")
print("Count: 3")
print("Count: 4")
print("Count: 5")

# With loops - efficient and flexible
for i in range(1, 6):
    print(f"Count: {i}")

💡 Why Loops Matter

Loops transform static programs into dynamic, scalable applications. They enable your code to handle varying amounts of data, respond to user input repeatedly, and perform complex calculations efficiently. Every meaningful program uses loops to process data, validate input, or repeat operations.

Think of loops as the engine that powers data processing, user interaction, and automation in your programs. They're essential for creating responsive applications that can handle real-world scenarios.

🎯 Types of Loops in Python

Python provides several types of loops, each designed for specific scenarios and use cases.

Each type serves different purposes and helps you solve various programming challenges efficiently.

# Different types of loops
numbers = [1, 2, 3, 4, 5]

# While loop - condition-based repetition
count = 0
while count < 3:
    print(f"While loop: {count}")
    count += 1

# For loop - sequence iteration
for number in numbers:
    print(f"For loop: {number}")

# Range-based for loop
for i in range(3):
    print(f"Range loop: {i}")

📋 Common Loop Patterns

Programming involves many repetitive patterns that loops handle elegantly across different application domains.

Learning these patterns helps you recognize when and how to apply loops in your own programs when working with user input and data processing.

# Common loop patterns
print("=== DATA PROCESSING PATTERN ===")
numbers = [1, 2, 3, 4, 5]
squared = []
for num in numbers:
    squared.append(num ** 2)
print(f"Original: {numbers}")
print(f"Squared: {squared}")

print("\n=== USER VALIDATION PATTERN ===")
attempts = 0
max_attempts = 3
while attempts < max_attempts:
    user_input = "123" if attempts == 1 else "abc"  # Simulated input
    if user_input.isdigit():
        print(f"Valid number: {user_input}")
        break
    else:
        attempts += 1
        print(f"Invalid input. Try again. ({max_attempts - attempts} attempts left)")

print("\n=== MATHEMATICAL COMPUTATION PATTERN ===")
total = 0
for i in range(1, 6):
    total += i
print(f"Sum of 1-5: {total}")

🎛️ Loop Control and Flow

Loops provide sophisticated control mechanisms that let you modify their behavior during execution using decision-making concepts. You can exit loops early, skip iterations, or even execute code when loops complete normally.

These control mechanisms make loops flexible tools that can handle complex logic and edge cases gracefully.

# Loop control examples
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

print("=== BREAK EXAMPLE ===")
# Finding first even number
for num in numbers:
    if num % 2 == 0:
        print(f"First even number: {num}")
        break  # Exit loop early

print("\n=== CONTINUE EXAMPLE ===")
# Processing only odd numbers
print("Odd numbers:")
for num in numbers:
    if num % 2 == 0:
        continue  # Skip even numbers
    print(num)

print("\n=== ELSE EXAMPLE ===")
# Using else with for loop
target = 15
for num in numbers:
    if num == target:
        print(f"Found {target}!")
        break
else:
    print(f"{target} not found in list")

⚡ Performance and Efficiency

Well-designed loops can process large amounts of data efficiently, while poorly designed loops can slow down your programs significantly. Understanding loop efficiency helps you write programs that scale well with increasing data sizes.

📚 Key Takeaways

🚀 Explore Loop Types

Ready to start using loops in your Python programs? Each type of loop has its own detailed lesson with practical examples and exercises:

• While Loops - Learn condition-based repetition for unknown iteration counts
• For Loops - Master sequence iteration and collection processing
• Loop Control - Discover advanced flow control with break, continue, and else
• Nested Loops - Handle multi-dimensional data and complex iterations

Test Your Knowledge

Test your understanding of repetition and loop concepts in Python:

🚀 What's Next?

Now that you understand the fundamentals of repetition and loops, you're ready to learn about the first type of loop: the while loop. While loops are perfect for situations where you don't know exactly how many times you need to repeat an action.

In the next lesson, we'll explore While Loops, where you'll learn to create condition-based repetition that continues until specific criteria are met.

Understanding loops prepares you for working with data structures, function development, and file processing. These concepts are also essential for user interaction and error handling.