> Julia has to be way better than Python to give people an incentive to switch.
A language doesn't necessarily have to give all the old programmers an incentive to switch, if it can position itself as a good language for new programmers to learn.
For example: at our institute (computational biology), we had a PhD student who was an early Julia adopter and wrote his model in that. Several students have since joined the project he started, so obviously they're now writing Julia too. That project's experiences with the language were so good, it soon became obvious that for our use case, Julia was superior to any other language we'd used so far. So pretty much the whole research group has now shifted to Julia, and that's what we teach new students. Slowly, other groups in our institute became interested, and more and more people are adopting it, which in turn means that their new students will also end up learning it in future.
If you work with a lot of data, Julia is already a 10-100x improvement over Python.
Being able to iterate and mangle huge columns with real lambdas and without having to marshal arguments to/from C++ is a huge advantage.
Where I used to spend hours in aggregate searching through docs for pandas/numpy, for stupid shit like "how do I shift but also skip NaNs", now I just write a for-loop in a couple minutes and get on with my work.
There's a whole subclass of tasks in R/pandas to work around the interpreter that just aren't needed in Julia.
For me at least it's well worth the syntactical warts and slow interpreter.
As an experienced python/data science user, this (creating fast complex column-wise transforms) is rarely a problem for me.
The truly huge advantage for Julia is how it plays with parralelism. The GIL makes it an absolute pain to do parallelism in python. Always ends up in threading hacks with numba or joblib, or multiprocessing, which has its own unfixable flaws
Examples: Basic, C (both Basic and C, to a degree), Visual Basic, PHP, Javascript, Python. I'm probably missing some. These displaced older languages just by being adopted by newbies.
A language doesn't necessarily have to give all the old programmers an incentive to switch, if it can position itself as a good language for new programmers to learn.
For example: at our institute (computational biology), we had a PhD student who was an early Julia adopter and wrote his model in that. Several students have since joined the project he started, so obviously they're now writing Julia too. That project's experiences with the language were so good, it soon became obvious that for our use case, Julia was superior to any other language we'd used so far. So pretty much the whole research group has now shifted to Julia, and that's what we teach new students. Slowly, other groups in our institute became interested, and more and more people are adopting it, which in turn means that their new students will also end up learning it in future.