[ddragon]

Julia (and likely Dylan and CL which are all similar languages) are not nearly as dynamic as Python. Or more accurately, they are nearly as dynamic, but writing code like that is not idiomatic and it will lead to performance similar to Python.

The most important factor is that those languages were designed with JIT in mind, with a clear separation of compile time and runtime. Not only the macros, which are low cost abstractions, but also how all the dynamic parts of the language can actually be resolved during this period, like type inference and static/multiple dispatch, which are enabled by it's carefully crafted type system that bridges the dynamic world and the static world. Idiomatic Julia is not strictly a dynamic language but a superposition of many static programs, one of which will be selected for each runtime pass.

So changing a variable type in Python has basically no effect, but in Julia , while allowed, it causes what's called type instability and the compiler will be pessimist and create a dynamic box for the type that can't be optimized. Which is also why global variables are so damaging to performance in the language since they can't be inferred. Defining or redefining a function (not a lambda) or types or importing a library dynamically during runtime is another feature that is also allowed, but avoided in practice since they'll invalidate the JIT cache and force a recompilation. The culture of performance aware programming is the second key factor in their speed.