It's somewhat the other direction. In the area that I work, scientific machine learning and differential equation modeling, Python does not really have a well-developed ecosystem while Julia has all of the tools. High performance methods with stiffness handling, automatic detection of Jacobian sparsity form a Julia program, methods for stochastic/delay/differential-algebraic equations, and the ability to embed neural networks into arbitrary differential equations and train them in a scientific context. Python is very far behind even MATLAB or Mathematica in this domain.

That wasn't its main promise and that doesn't much affect the developer experience if the existing libraries are of high quality.

No. But the quality of the libraries is often much higher, and can do things that are impossible in Python and in most other languages (owing to the high degree of Julia's polymorphism and homoiconicity).