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Stop Writing Slow Python: 14 Mind-Blowing Speed Hacks You Need Right Now!

Yaswanth
5 min readFeb 5, 2025

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Python

Introduction: The Data Analysis Nightmare

Last month, I faced a crisis. A client needed a real-time analysis of 10GB of sensor data, but my Python script was crawling — processing just 1GB took 45 minutes. Desperate, I embarked on an optimization journey that transformed my code from sluggish to supersonic. Here’s the battle-tested playbook that achieved a 400% speed boost, using approaches you won’t find in most tutorials.

1. Profile Relentlessly (But Smarter)

Before optimization, measure — wisely.

The Mistake: I initially relied solely on cProfile, which showed process_data() as the culprit. But why?

The Hack: Combine timeit for micro-benchmarks and pyinstrument for macro-level insights:

# Micro-benchmark with timeit
import timeit

def test_function():
return sum([x**2 for x in range(10_000)])
print(timeit.timeit(test_function, number=1000))
# Macro-profiling with pyinstrument
from pyinstrument import Profiler
profiler = Profiler()
profiler.start()
# Your main code here
process_sensor_data()
profiler.stop()
print(profiler.output_text())

Result: pyinstrument revealed I was wasting 60% of time in CSV parsing loops I’d assumed were efficient.

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Yaswanth
Yaswanth

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