This article explores how Python allocates memory for integers, revealing that every integer is represented as a heap-allocated object in CPython. The author conducts experiments to measure allocation frequency during arithmetic operations, discovering optimizations that reduce unnecessary allocations. Despite these efficiencies, the article highlights performance overhead and suggests potential improvements.

This article presents key performance numbers every Python programmer should know, including operation latencies and memory usage for various data types. It features detailed tables and graphs to help developers understand performance implications in their code.