README.md

Generators

Functions that yield values lazily — one at a time, without building the full result in memory.

Files

File	Description
example.py	Generator functions, expressions, and .send()

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Descriptive Example

Scenario

Generate Fibonacci numbers below 100 without storing the entire sequence in a list.

def fibonacci(limit):
    a, b = 0, 1
    while a < limit:
        yield a
        a, b = b, a + b

for n in fibonacci(100):
    print(n, end=" ")
# 0 1 1 2 3 5 8 13 21 34 55 89

Each yield pauses the function and returns a value. The next call resumes right after the yield.

Reading a huge file

def read_lines(path):
    with open(path) as f:
        for line in f:
            yield line.rstrip("\n")   # one line in memory at a time

Generator expression vs list comprehension

sum(x * x for x in range(1_000_000))   # lazy — no million-item list
sum([x * x for x in range(1_000_000)]) # eager — builds full list first

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Interview Q&A

Q1: What is the difference between a generator and a regular function?
A: A generator uses yield instead of return. It pauses and resumes, maintaining local state between calls. Returns a generator iterator object.

Q2: What is StopIteration?
A: Raised when a generator is exhausted (no more values). for loops catch it automatically.

Q3: Why use a generator to read a 10 GB file?
A: Constant memory usage — only one line/chunk in memory at a time instead of loading the entire file.

Q4: What does .send(value) do?
A: Sends a value into the generator at the current yield point. Enables two-way communication between caller and generator.

Q5: What is yield from?
A: Delegates to another generator or iterable, flattening nested generators. Used in async and recursive yield patterns.

Q6: Generator vs iterator class?
A: Generators are simpler — Python auto-implements the iterator protocol. Iterator classes give more control over state and methods.

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Run

python3 example.py