Memory management in Python is mostly automatic - but not invisible. From reference cycles and unexpected leaks to costly object growth and GC pauses, memory issues still affect real-world Python applications. Understanding how Python allocates, tracks, and reclaims memory is key to writing efficient and predictable programs - especially for long-running services, data-heavy scripts, or performance-sensitive tools.

This talk starts from first principles - how reference counting and garbage collection work - then builds up to real-world issues like circular references, hidden object retention, and finalizer behavior. We’ll explore modern tools like tracemalloc and pympler for memory diagnostics, and cover practical techniques like using slots, generators, and object reuse patterns. A live demo will walk through detecting and fixing a subtle memory leak. The goal is to make memory behavior in Python understandable, measurable, and improvable.