Python has had a much longer head start, so it's no surprise it's still widely popular. You also bring up a good point regarding Julia's changing syntax and beh

Python has had a much longer head start, so it's no surprise it's still widely popular. You also bring up a good point regarding Julia's changing syntax and behavior—I've encountered the same issue trying copypasted code from various tutorials online (they didn't work.) Julia's package ecosystem is rapidly catching up with Python's, at least for the packages you mentioned. I.e. Julia offers DataFrames.jl (its Pandas competitor), Plots and Gadfly (its Matplotlib competitors), and a few different ML libraries (its sklearn competitor). As for Numpy, its functionality is already offered through the base library in Julia, so there's no need for a separate package. One especially nice feature of Julia is its interoperability with other languages. For example, if you need to use Python-specific functionality, you can do so from Julia through the PyCall library. Likewise for R, MATLAB, Java, etc. (C and Fortran FFIs are already offered through the base library.)