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