Its important to remember that most of the python ecosystem, isn't written in python. The functions are often thin wrappers/objects around the real computation, which is often written in a faster language, C/C++/Fortran.
Julia excels in composability, performance, ease of development. You don't need to recode your algorithms in another language due to the performance of Julia, as is needed in Python's case.
Generally speaking, I see Julia's biggest fault, time to first plot, being addressed far sooner than python being redesigned to have Julia's capabilities. For the record, I use python daily in the day job. And I use Julia there for analytics there, often with jupyter notebooks. Easy for users to consume and interact with.