Indeed, Python suffered, and continues to suffer, many of same issues. But,
- Python started as a general purpose language, while Julia, at least as I understood it, was always targeted at scientific computing and related disciplines, with the intention of displacing MATLAB along with the SciPy suite.
- I agree that this all may clear up in time, but as you note, Python now has NumPy/SciPy. So all that core functionality that comes to mind when you think of MATLAB (arrays, integration, root finding, interpolation, etc.) is located inside of SciPy. More challenging things, like ML, which lack some of the unified theory of older numerical algorithms, are still being worked out. My gripe is that Julia doesn’t, yet, have the full complement to SciPy as part of, if not an official set of packages, at least a well recognized suite that tackles all of the classical MATLAB stuff out of, or nearly out of, the box, with common function call styles and consistent use of data structures.