Python Performance Benchmarks: Essential Metrics for Developers
By
WoodenChair
Hand-rolled, kettle-boiled, baked to perfection. Worth every minute at the bakery.
Summary
This article provides a comprehensive cheat sheet of performance metrics and memory usage data for Python developers, covering practical benchmarks for common operations like list operations, file I/O, data structure performance, memory consumption of different data types, and framework comparisons. It serves as a reference guide for making performance-sensitive decisions in Python programming.
Key quotes
· 5 pulledThere are numbers every Python programmer should know.
I wanted to take a moment and write down performance numbers specifically focused on Python developers.
A cheat sheet of real-world timing and memory numbers to guide performance-sensitive decisions.
How fast or slow is it to add an item to a list in Python? What about opening a file?
If you have a performance sensitive algorithm, which data structure should you use?
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