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's interesting. How do you think R fares in this respect?
(The problem that I'm having with Julia isn't the math/computational aspect, it's Julia's use as a more general purpose programming language in additional to math.)
