* Pandas is wrapped by Pandas.jl, interfaces with Julia's DataFrame, generally excellent.
* Images.jl is a 60%-to-70% replacement for scikit-image.
* NumPy and SciPy are mostly covered by StatsBase and Base, can't think of anything off-hand that's missing.
Julia's still immature in the machine learning domain, but a lot of new work is being done. One interesting somewhat-new package is Madeleine Udell's LowRankModels.jl, which has a particularly Julian approach to low rank fitting.
This is totally fair. Even the Julia creators regularly mention "stay with existing tools for now, if you are happy." Julia is an increasingly attractive option for greenfield, high-performance development, but for folks who aren't doing a lot of that, a more stable language and mature ecosystem is obviously important. Julia and many libraries can still require some elbow-grease, and the maturity and coverage varies between fields. However, this is rapidly changing as folks do new research in Julia and build up everything else they need along the way. The current situation is very similar to SciPy in the mid-2000s, though with perhaps a somewhat different mix of backgrounds. Here are a few examples:
So, hopefully you'll have reason to take another look in a few years. The most important thing, to me, is that all of these communities want more open, reproducible, and accessible science. There is plenty of room for growth, because users of closed-source software still probably outnumber py/r/jl by 5:1 or more.
