Repetition: Loops

In the previous chapter we learned how programs make decisions.

Another major need is repetition.

Many tasks are not one-time actions. They involve doing the same step many times.

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Why Loops Exist

Imagine processing 10,000 log entries.

Writing 10,000 manual statements is impossible.

Loops allow one block of logic to run repeatedly.

This is a core reason programming scales.

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Iteration in Practice

Common repetitive tasks include:

• counting from 1 to N
• scanning a list of files
• summing transaction amounts
• validating many user records

A loop turns these from manual work into automated workflows.

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Two Common Loop Styles

While Loop

Runs while a condition remains true.

count = 0
while count < 5:
    print(count)
    count = count + 1

For Loop

Iterates over items in a sequence.

numbers = [2, 4, 6]
for n in numbers:
    print(n)

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Loop Flow

graph TD
  A[Start Loop] --> B{Condition True?}
  B -->|Yes| C[Run Body]
  C --> D[Update State]
  D --> B
  B -->|No| E[Exit Loop]

The update step is critical. Without it, loops can run forever.

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Processing Collections

Loops are especially useful with lists of data.

Example:

total = 0
prices = [10, 25, 15]
for price in prices:
    total = total + price

This pattern appears in analytics, file processing, web backends, and system automation.

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Infinite Loops and Safety

A loop that never terminates is an infinite loop.

Sometimes infinite loops are intentional (for example, long-running servers).

But in beginner programs, they are usually bugs caused by missing state updates or incorrect conditions.

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Key Ideas to Remember

• Loops automate repeated work.
• while loops are condition-driven.
• for loops are collection-driven.
• Loop correctness depends on valid condition + state updates.
• Iteration is essential for working with large datasets.

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What Comes Next

Loops help us repeat logic.

Next we need a way to organize and reuse logic cleanly:

functions, the building blocks of structured programs.