Python is a popular high-level, general-purpose programming language that can be used for a variety of purposes, from the scripting of web applications to artificial intelligence and machine learning tasks.

Julia is also a high-level and dynamic programming language that is used for technical computing. Indeed, just like Python, it can be used for data science, is open-source, and manages memory automatically.

1. Popularity And Maturity

In this regard, Python has the advantage over Julia. Since Python has been around longer, Python is more established and has a larger developer community which makes it easier for organizations to find talent. Julia has a small but growing community, and its popularity might increase as adoption goes beyond data science.

Python is hence the more mature language with more resources available at this time which can be a deciding factor when choosing between the two. Julia still has feature changes that may include changes or deletions as the syntax matures, although less than in the initial releases. Those changes can be a bummer to some.

2. Performance Comparaison

Python is an interpreted language, while Julia is compiled, which means it has the advantage of execution speed. Julia is hence faster than Python and can be as fast as C since it is directly executed on the processor as Julia code (just-in-time compiled).

3. Data Science Uses

Julia was created with statistics and machine learning in mind. The syntax of Julia is very similar to that of mathematical expressions. Python, on the other hand, needs libraries like NumPy for more advanced mathematics and algebra, which means that Julia can be an ideal language for the scientific community.

4. Librairies And Packages

Since Python has been around longer, there are more packages and libraries available for Python than Julia ready to be imported. Also, libraries are better maintained for Python at this point. This includes high-utility libraries for things like machine learning and neural networks, making data science easier in Python at the moment. Python also has more visualization libraries than Julia at the moment. Examples of Python-established libraries include SciPy, Pandas, Matplotlib, etc.

5. Versatility 

Python has more versatility as it can be used outside of scientific programming for things like scripting and web development. Hence, Python is more of a general-purpose language if that is what you are looking for.

6. Community And Tooling

Python has a wider community, and more tools are available for things like performance and debugging. Python is one of the most popular programming languages at this moment in time.

7. Parallel Operations And Shell

Julia performs better than Python for parallel operations, even though you can use Python for them. Julia can also be used to run bash commands in shell mode by typing; when starting a line. The Julia REPL acts like a Bash Shell at that moment. Julia has the upper hand on Python in this aspect.

Conclusion

If you need to do scientific computations quickly, then Julia might be worthy of your attention. Suppose you are more interested in a general-purpose language with a large community and many established libraries for things like visualization and ML. In that case, you can’t go wrong with Python, although we might see more programmers and scientists in the future training in both to leverage the best of both worlds.