Can Julia replace Python and R for Data Science?

As data-driven research has become more crucial now, the need of having more efficient and powerful scientific computing tools has increased. In order to have more speed, data scientists may need to switch over Julia as this newcomer has great potential in terms of flexibility and high-performance.

Recently the lead developers of Julia has announced the release of version 1.0 for their project. This release clearly indicated that it will not be considered as work in progress language from now. They also admitted that the code written in this version will be valid and run for future versions as well. For instance, code written in version 0.4 was not guaranteed to work under version 0.6.

Now, why to switch over Julia?

You must be thinking why the users of R and Python, who spent years of hard work in learning them will switch to Julia? Well, actually there are quite a few reasons behind it.

First, It’s completely free and open source computing language which is mainly designed to excel in numerical computing and data science. Second, it’s faster than these two. Third, It’s already widely used with over 2 million downloads.

Its low barrier regarding the entrance of scientists and mathematicians has clearly flattered everyone. Julia’s graphics and visualization and also the ability to handle large computational problems made it more preferable than others.

But like every other product, it also has some pros and cons related to it.

Pros:

  • Compiled: It is compiled but not interpreted. The reason behind this is speed. Its just in time compiler uses LLVM framework.
  • Straightforward syntax: Its syntax is as straightforward as python but yet very powerful at the same time.
  • Dynamic typing with static type benefits: You can specify types for variables, like “unsigned 32-bit integer.” But you can also create hierarchies of types to allow general cases for handling the specific type of variables.
  • Calling other language libraries: Julia is capable of calling libraries written in Python, C, and Fortran. It’s also possible to interface with Python code by PyCall library and even share data between Python and Julia.
  • Metaprogramming: Julia programs can very well generate other Julia programs and can also modify their own code. This quality is really admired by the users.

Cons:

  • Not properly Developed: Considering its recent entry, there is still room for improvements. Many R users, when shifted to Julia, realized that it doesn’t work as smooth as R. Julia’s tools didn’t seem to be as fluid and reliable as they are expected.
  • Unable to identify issues: Julia is far behind from Python and R in terms of identifying issues and debugging tools. But soon more tools were expected to be developed for users.
  • Safety issues regarding Interface: Unsafe interface to native APIs by default.
  • Poor text formatting: Poor text formatting facilities in the language and lack of good unit testing frameworks.
  • No powerful Tools: Developer at “R” developed terrific tools for data analysis tasks, which Julia still lacks.

Conclusion: Though Python and R are doing good till now but the chances of Julia overtaking them seems to be high over time.

At last Julia, developers are also constantly working on updates, So more stable updates are to be expected near future.

For more such articles, Stay tuned with us.

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