Excellent.
DAY 23 is where Python’s scalability and elegance really show up. Iterators and generators are not conveniences — they are state machines + lazy execution.

After today, you’ll:

• Think in streams, not lists
• Avoid memory blowups
• Understand how for actually works at runtime
• Ace iterator/generator interview questions

🧠 DAY 23 — Iterators & Generators

(Lazy Evaluation, Protocols & State Machines)

🔑 CORE IDEA OF DAY 23

Iteration in Python is a protocol.
Generators are resumable functions that implement this protocol automatically.

1️⃣ The Iterator Protocol (The Contract)

An iterator must implement:

• __iter__() → returns iterator object
• __next__() → returns next value or raises StopIteration

That’s it.

class CountUp:
    def __init__(self, n):
        self.n = n
        self.i = 0

    def __iter__(self):
        return self

    def __next__(self):
        if self.i >= self.n:
            raise StopIteration
        self.i += 1
        return self.i

Now:

for x in CountUp(3):
    print(x)

2️⃣ How for REALLY Works (Recap with Precision)

for x in iterable:
    body

Is equivalent to:

it = iter(iterable)
while True:
    try:
        x = next(it)
    except StopIteration:
        break
    body

🧠 Every for loop is try/except-driven.

3️⃣ Iterables vs Iterators (INTERVIEW FAVORITE)

Examples:

iter([1, 2, 3])   # iterator
[1, 2, 3]         # iterable

Key rule:

An iterator is exhaustible.
An iterable can create new iterators.

4️⃣ Why Lists Are NOT Iterators

lst = [1, 2, 3]
it = iter(lst)

list(it)   # [1, 2, 3]
list(it)   # []   (exhausted)

But:

list(lst)  # [1, 2, 3] every time

Because:

• lst → iterable
• iter(lst) → iterator

5️⃣ Generators — The Shortcut to Iterators

A generator function:

• Contains yield
• Returns a generator object
• Automatically implements iterator protocol

def count_up(n):
    i = 0
    while i < n:
        i += 1
        yield i

Usage:

for x in count_up(3):
    print(x)

No class needed.
No __next__ written manually.

6️⃣ What yield REALLY Does (Critical Insight)

yield:

• Produces a value
• Pauses function execution
• Saves local state
• Resumes on next next()

This turns a function into a state machine.

7️⃣ Generator State Machine (Visual)

States:

CREATED → RUNNING → SUSPENDED → RUNNING → DONE

Once DONE → StopIteration forever.

8️⃣ Generator Memory Advantage (WHY THEY MATTER)

Compare:

lst = [x*x for x in range(10_000_000)]

vs

gen = (x*x for x in range(10_000_000))

• List → allocates all memory upfront
• Generator → computes on demand

🧠 This is why generators are essential for:

• Big data
• Streaming
• Pipelines
• ETL logic

9️⃣ Generator Expressions vs List Comprehensions

(x*x for x in range(5))   # generator
[x*x for x in range(5)]  # list

Differences:

• Generator is lazy
• List is eager
• Generator uses constant memory

Rule:

Use generator unless you need random access or reuse.

🔟 yield from (Advanced but Powerful)

def chain(a, b):
    yield from a
    yield from b

Equivalent to:

for x in a:
    yield x
for x in b:
    yield x

Benefits:

• Cleaner code
• Faster delegation
• Proper exception forwarding

11️⃣ Generator Exhaustion Trap 🔥

g = (x for x in range(3))
print(list(g))
print(list(g))

Output:

[0, 1, 2]
[]

Once consumed, it’s gone.

12️⃣ Exceptions Inside Generators

def gen():
    yield 1
    raise ValueError("boom")
    yield 2

for x in gen():
    print(x)

• Exception propagates normally
• Generator terminates

Generators are exception-aware.

🔥 INTERVIEW TRAPS (DAY 23)

Q1

g = (i for i in range(3))
print(next(g))
print(list(g))

✔ Output:

0
[1, 2]

Q2

Why generators are faster for pipelines?

✔ No intermediate lists
✔ Lazy evaluation
✔ Less memory pressure

Q3

Can a generator be restarted?

❌ No
✔ Create a new generator instead

🧠 DAY 23 MENTAL CHECKLIST

When handling large data:

1. Can this be streamed?
2. Do I really need a list?
3. Is one-pass enough?
4. Will exhaustion cause bugs?
5. Should I document generator behavior?

📝 DAY 23 ASSIGNMENT (MANDATORY)

1️⃣ Predict output (NO RUNNING):

def g():
    yield 1
    yield 2

x = g()
print(next(x))
print(list(x))
print(list(x))

2️⃣ Explain clearly:

• Difference between iterable and iterator
• How yield preserves state

3️⃣ Design question:

How would you design a memory-efficient log processing pipeline in Python?

(Hint: generators + composition)

🔜 DAY 24 PREVIEW

DAY 24 — Comprehensions & Lazy Evaluation (Performance & Readability Tradeoffs)

You’ll learn:

• List vs set vs dict comprehensions
• Nested comprehensions (and when NOT to use them)
• Generator pipelines
• Readability vs cleverness

When ready, say 👉 “START DAY 24”