Day 45:Not profiling before optimizing

 

🐍 Python Mistakes Everyone Makes ❌

Day 45: Not Profiling Before Optimizing

One of the biggest performance mistakes is trying to optimize code without knowing where the real problem is.

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❌ The Mistake

Optimizing code based on guesses.

# Premature optimization
data = []
for i in range(100000):

    data.append(i * 2)

You might refactor this endlessly — but it may not even be the slow part.

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❌ Why This Fails

• You optimize the wrong code

• Waste time on non-critical paths

• Increase code complexity unnecessarily

• Miss the actual performance bottleneck

• Can even make performance worse

Guessing is not optimization.

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✅ The Correct Way

Profile first. Then optimize only what matters.

import cProfile

def work():
    data = []
   for i in range(100000):
      data.append(i * 2)

cProfile.run("work()")

This shows:

• Which functions are slow

• How often they’re called

• Where time is really spent

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🧠 Common Profiling Tools

• cProfile — built-in, reliable

• timeit — for small code snippets

• line_profiler — line-by-line analysis

• perf / py-spy — production profiling

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🧠 Simple Rule to Remember

🐍 Measure first, optimize later
🐍 Fix bottlenecks, not guesses

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🚀 Final Takeaway

Fast code isn’t about clever tricks — it’s about informed decisions.

Before rewriting anything, ask one question:

👉 Do I know what’s actually slow?

Profile. Then optimize.