However, Julia also makes the experience of growing from a package user to a package developer basically seamless. A big part of what makes this seamless is that almost all julia packages are written in pure julia and thus relatively easy to read and understand for a julia programmer. This coupled with the strong introspection capabilities makes it so that users who would never be writing their own packages in Python end up contributing to the Julia ecosystem.

Coupled to this is the fact that julia is a highly composable language meaning that it's very easy to combine functionality together from two separate packages that weren't designed to work together. This composability makes julia's fewer packages have far greater leverage and applicability than Python packages and also makes it so that when you do need to make your own package, you're less likely to be reinventing the wheel every time.

Consider the fact that all the big machine learning libraries in Python all have basically implemented their own entire language with their own special compiler, all the requisite datastructures, etc. In Julia, that's rarely necessary. Flux.jl is just built using native julia datastructures and julia's own (very hackable) compiler.