Excellent.
DAY 27 is about real performance engineering — not folklore or micro-benchmarks, but measuring, understanding, and fixing real bottlenecks.

After today, you’ll optimize with confidence, not guesswork.

🧠 DAY 27 — Performance & Optimization

(Profiling, Time/Space Complexity & Practical Tuning)

🔑 CORE IDEA OF DAY 27

Never optimize blindly.
Measure first, optimize the real bottleneck, then re-measure.

Python performance problems are usually:

• Algorithmic (O(n²) hiding in plain sight)
• Excess object creation
• Wrong data structures
• Unnecessary Python-level loops

1️⃣ Performance Pyramid (What Matters Most)

Priority order:

1. Algorithm & data structures (biggest impact)
2. I/O & waiting (often overlooked)
3. Python vs C boundary
4. Micro-optimizations (smallest impact)

🧠 90% of gains come from level 1 & 2.

2️⃣ Algorithmic Complexity Beats Everything

Example:

# O(n²)
for i in data:
    for j in data:
        ...

Replacing with:

# O(n)
seen = set(data)

Will outperform any micro-optimization.

Interview mantra:

“Choose the right algorithm first.”

3️⃣ Measure Time Correctly (timeit)

Never use time.time() for micro benchmarks.

import timeit

timeit.timeit("x*x", globals={"x": 10}, number=1_000_000)

Why timeit:

• Disables GC
• Runs multiple times
• Reduces noise

4️⃣ Find Real Bottlenecks (Profiling)

CPU profiling

import cProfile
cProfile.run("main()")

Shows:

• Function call counts
• Total time
• Cumulative time

Look for:

• High cumulative time
• Functions called too often

5️⃣ Line-Level Profiling (Precision Tool)

Use when you know which function is slow.

Conceptually:

• Which line dominates runtime?
• Is it Python code or C call?

🧠 Don’t optimize until you see the hotspot.

6️⃣ Memory Profiling (Silent Killer)

Problems:

• Large temporary lists
• Hidden copies
• Reference leaks

Mental checklist:

• Am I materializing large iterables?
• Can this be streamed?
• Is caching unbounded?

Generators often solve memory issues without changing algorithms.

7️⃣ Python vs C — Cross the Boundary Wisely

Python is slow at:

• Tight loops
• Numeric heavy work
• Repeated attribute access

Fast paths:

• Built-ins (sum, max, any)
• Comprehensions
• C-extensions (NumPy, Pandas)

Example:

# slower
total = 0
for x in data:
    total += x

# faster
total = sum(data)

8️⃣ Attribute & Local Variable Optimization

def f(obj):
    return obj.value + obj.value

Better:

def f(obj):
    v = obj.value
    return v + v

Why:

• Local variable lookup is faster than attribute lookup

This matters inside hot loops.

9️⃣ __slots__ — Memory & Speed Optimization

class Point:
    __slots__ = ("x", "y")

Benefits:

• No __dict__
• Less memory
• Faster attribute access

Tradeoffs:

• Less flexible
• Harder debugging
• No dynamic attributes

Use for:

• Millions of small objects
• Performance-critical models

🔟 Avoid Accidental O(n²) Patterns

Classic trap:

s = ""
for x in data:
    s += x

Fix:

"".join(data)

Another:

lst = []
for x in data:
    if x not in lst:   # O(n)
        lst.append(x)

Fix:

seen = set()

11️⃣ Caching (When It Helps)

from functools import lru_cache

@lru_cache(maxsize=None)
def fib(n):
    ...

Use caching when:

• Function is pure
• Inputs repeat
• Computation expensive

Avoid caching when:

• High cardinality inputs
• Memory growth unbounded

12️⃣ When NOT to Optimize 🚨

Avoid premature optimization when:

• Code runs once
• I/O dominates runtime
• Requirements may change
• Clarity matters more

Interview-grade quote:

“First make it correct, then make it fast, then make it clean.”

🔥 INTERVIEW TRAPS (DAY 27)

Q1

Why sum() is faster than a loop?

✔ Implemented in C
✔ Fewer bytecode ops

Q2

When does __slots__ help?

✔ Many small objects
✔ Attribute access hot path

Q3

Why generators improve performance?

✔ Less memory
✔ Better cache behavior
✔ Lower GC pressure

🧠 DAY 27 MENTAL CHECKLIST

Before optimizing:

1. Did I measure?
2. Is algorithm optimal?
3. Is data structure correct?
4. Is this Python or C cost?
5. Is readability still acceptable?

📝 DAY 27 ASSIGNMENT (MANDATORY)

1️⃣ Predict performance (NO RUNNING):

Which is faster and why?

[x*x for x in data]

vs

list(map(lambda x: x*x, data))

2️⃣ Explain clearly:

• Why algorithmic improvements dominate micro-optimizations
• Why profiling must precede optimization

3️⃣ Design question:

How would you optimize a Python ETL job processing 1TB of data daily?

(Hint: streaming, batching, C-extensions, architecture)

🔜 DAY 28 PREVIEW

DAY 28 — Writing Pythonic Code (Idioms, Anti-Patterns & Clean Design)

You’ll learn:

• What “Pythonic” really means
• Idioms senior engineers expect
• Common anti-patterns
• Writing code that reads like English

When ready, say 👉 “START DAY 28”