So, how big is X?

What this memory-size estimation question tests

This is a medium-difficulty Python question that probes your understanding of how the interpreter allocates and structures objects in memory. It's the kind of question quant firms ask to verify that candidates can reason about performance implications of their code choices — particularly relevant when working with large datasets or latency-sensitive systems.

To solve problems like this, you need to know how Python represents different types (integers, strings, containers) at the memory level, account for overhead from reference counting and type information, and understand the distinction between object size and the data it references. A 64-bit interpreter uses 8-byte pointers and different base object sizes than a 32-bit one.

• Python object headers and reference counting overhead
• Memory layout of built-in types (int, str, list, dict)
• Shallow vs. deep object sizes
• Pointer size on 64-bit systems