Module 6: Laziness

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Learning outcomes

• explain eager and lazy behavior in Python;
• write and read a generator;
• use yield to produce values on demand;
• connect iteration with memory-friendly code.

Prerequisites

Complete Module 5 first. Make sure you are comfortable with higher-order functions and transformations.

The idea in one sentence

Iteration lets Python move through values repeatedly, while lazy behavior delays work until a value is actually needed.

Everyday analogy

Think of a water tap. You do not fill every bucket in advance. You open the tap and take water only when needed. A generator behaves in a similar on-demand way.

Syntax

def count_up_to(n):
    i = 1
    while i <= n:
        yield i
        i += 1

Worked example

nums = count_up_to(3)
first = next(nums)
second = next(nums)

Step by step:

1. count_up_to(3) creates a generator.
2. next(nums) gives 1.
3. The generator pauses.
4. The next next(nums) gives 2.
5. Another call gives 3.

Why this matters in practice

• In college notes, generators show how Python can delay work.
• In real code, they are useful for large files, streams, and data pipelines.
• They help save memory because values are produced one at a time.

Important concepts

Eager evaluation

Python usually computes values right away.

result = 2 + 3

Lazy behavior

Lazy behavior waits until the value is needed.

def numbers():
    yield 1
    yield 2

Generator

A generator is an iterator that produces values one by one.

def gen():
    yield "A"
    yield "B"

yield

yield returns a value and pauses the function so it can continue later.

Technical meaning

Lazy evaluation means computation is delayed until the result is required. Python is not fully lazy, but generators and iterators give a lazy-style way to handle sequences efficiently.

Memory rule

• eager = compute now
• lazy = compute when needed
• yield = hand out one value and pause

Quick check

• Why does next() work on a generator?
• What does yield do?
• Why are generators useful for large data?
• How is a generator different from a list?

Short exam answer

Python is usually eager, but generators provide lazy-style evaluation. A generator uses yield to produce values one at a time, which helps save memory and makes it easier to work with large or endless sequences.

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