Python is seen as the best alternative according to data scientists who use the Julia programming language for big data and analytics. If Julia did not exist, they would choose Python. Developer Julia Computing concludes this in its most recent annual study on the use, advantages and disadvantages of Julia.
Julia is an open-source programming language widely used by data scientists. The programming language is suitable for writing algorithms for high-performance technical computing and data science applications such as big data and analytics. Julia makes it possible to do large calculations simple, fast, scalable and accurate. The programming language also provides capacity and productivity for this purpose.
Python is popular
Every year Julia examines the satisfaction of its end-users and tests how the programming language compares to its closest competitors. The 2020 survey shows that data scientists would use Python programming language if Julia were unavailable. More than 75 percent of the respondents chose Pyhton as their favourite alternative.
The main reasons for users to choose Python is the fact that the programming language is most suitable for developing machine learning algorithms. There are also many Python modules available that can help data scientist with their algorithms. Besides Python, data scientists also choose the programming languages C++, MATLAB, R, C, Fortran, Bash/Shell/PowerShell and Mathematics as an alternative to Julia.
In the research, Julia Computing’s specialists also looked at what advantages end users have with the use of the programming language. End-users are especially happy with Julia because they find it faster than Python and R for big data analytics. Moreover, they are very pleased with the speed, performance, ease of use and open-source status.
End-users also mentioned a number of non-technical advantages. These include the free availability, the presence of an active community of end-users and that the programming language is available under an MIT license.
Disadvantages of Julia
Where there are advantages, there are of course also disadvantages. Julia Computing found in the research now released that end-users find that Julia generates the first plot too slowly and has slow compile times. In addition, end-users feel that the packages are not mature enough yet, unlike the Python ecosystem, and that developers cannot create self-contained binaries or libraries. Respondents also feel that the adoption of the programming language is still lacking. This is mainly due to the fact that colleagues often use other languages.
In short, Julia is in good shape as a programming language for data scientists, but it still has a lot to gain.