Is Julia better than Python?

Python is a well-established language and is efficient in machine learning and data science. Over time, Python has evolved to handle various tasks, offering compelling advantages over other programming languages. Contrarily, Julia’s programming language is purpose-built to handle data science – scientific and numerical computation. The real reason behind the creation of Julia was to overcome the problems in Python, notably speed issues. 

Reasons why Julia is better:

• Julia is fast: Julia’s just-in-time (JIT) compilation and type declarations give it an edge “pure,” unoptimized Python in terms of magnitude. To make Python faster, you’ll need a third-party JIT compiler (PyPy), external libraries, and optimizations with tools such as Python. 
• Math-friendly syntax: Julia targets users of scientific computing languages and environments such as Mathematica, MATLAB, R, and Octave. Its maths operation syntax is more like math formulas outside the computing world; thus, non-programmers can easily pick it up.
• Automatic memory management: the user doesn’t allocate and free memory. Besides, it partly allows manual garbage collection. 
• Superior parallelism: while Julia and Python can run programs in parallel, Julia’s parallelization is more refined, unlike Python’s which requires data serialization and deserialization between threads/nodes. Furthermore, Julia’s parallelization syntax is less top-heavy.

Also, Python has some compelling features that make it a better alternative over Julia:

• Zero-based array indexing: the first element is accessed with a 0, while Julia uses one like Mathematica. However, 1-indexing might make it hard to adopt by a more general-use audience with ingrained programming habits.
• Less start-up overhead: Python run time is more lightweight, but Python programs may be slower in Julia. It takes less time for Python programs to start and deliver results. So, Julia has a low start-up. 
• Mature: Python has been around 30 years, while Julia is only ten years old. 
• More third-party packages: Python’s wide-ranging third-party tools and packages make it more appealing to users. Julia has few libraries of its own. 

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Will Julia take over Python?

By now, you know that Python is a popular language among data scientists and deep learning professionals. Besides, Python is the world’s most used programming language. Besides machine learning, its applications are wide-ranging – enterprise-level applications, artificial intelligence models, etc.; Python is extensively used in almost every industry and field.

Currently, there are 8+ million Python developers worldwide – they use Python religiously for various functions. Most developers prefer Python for its ease of scalability and dynamic nature. These properties also gave Python an edge over Java, which was the developer’s favorite language for a long period of time. 

Modern programming languages like Julia are developed to solve modern problems. Note that languages developed long ago solved challenges at that time. Thus, it might be a bit tricky for them to solve modern problems – losing relevance. However, Python has remained relevant, as shown by the large and supportive community. The many inbuilt functions and libraries make it a popular choice for organizations, enterprises, developers, and data scientists.

Will Julia disrupt Python’s fame? 

New languages pose threats to older languages – Julia is threatening Python. Unlike before, modern languages are tailored for particular challenges. For instance, R/Python is for analytics, Julia for scientific calculations, Go for enterprise development, and Rust for system programming. 

So, Python can be termed as an all-around language. However, Julia is fast catching up. A critical difference between Julia and Python is how they approach a challenge. Julia is built purposefully to mitigate challenges arising from high-performance computing, while Python evolved to embrace this challenge. Though Python has been up to the task in this field, it was designed for such tasks. 

Julia s attracting most data scientists and researchers because of the purposeful creation and speed. Besides, Julia offers each code conversion capability. Developers can easily convert a Python or C code to Julia. The opposite isn’t an accessible route. 

Though a powerful language, Julia is in its infancy stages. So it might take longer to take over Python.  

Is Julia similar to Python?

While both Julia and Python are both open source languages with community support, they differ in some aspects. 

Julia is a relatively new language with the following features. 

• It uses the low-level virtual machine (LLVM) compiler, making JIT compilation possible. 
• It has an interactive command-line interface just like Python.
• Allows direct interface capabilities with libraries written in other languages, e.g., C and Fortran. Besides, Julia can interface with Python code. 
• It supports metaprogramming, i.e., one program can generate another program and Lisp Macros. Also, it can modify its code. 
• Combines static and dynamic typing
• Supports functional and object-oriented programming
• Version 1.1, Julia supports a full-featured debugger
• Supports Parallel Computing
• A powerful type of system

Beyond these features, Julia is compiled, interpreted, and can call C, Fortran, and Python libraries. 

The common similarities between Julia and Python are: 

Both have a similar syntax and interactive command-line interface.

High-Level Language – both Python and Julia have been developed into high-level programming languages. Developers don’t need to know code architecture, structure, and memory management. 

Portability: Python is highly portable across various platforms – it won’t change the code. While this feature is unique to Python, Julia has developed a portable version for the Windows platform. 

Data science training: Julia was purposefully developed to handle scientific computing, machine learning, and artificial intelligence. By contrast, Python has evolved to handle these challenges, making it a favorite language among data scientists and researchers. 

Interoperability with other languages: Julia supports between 10 to 20 different languages. The Julia call keyword can be C-exported or Fortran shared library functions and packages to enable calling other languages such as Python, R, MATLAB, Java, or Scala. Similarly, Python is coded in C; by default, it supports the execution of code written in other programming languages such as Java, C, and C#.  

Julia vs. Python code comparison

Python is an interpreted programming language. Besides, it is a high level, dynamically typed, general-purpose, and garbage-collected programming language. On the other hand, Julia is a high-performance, high-level, and dynamic programming language but can support static typing. Being a general-purpose language, Julia can write any application, but most of its features are perfectly suited for computational science and numerical analysis. 

The type system in Julia is distinct as it has a parametric polymorphism in a dynamic programming language alongside multiple dispatches as its core programming paradigm. Besides, Julia supports concurrent, parallel, and distributed computing or the built-in corresponding to “OpenMP-style” threads. 

The JIT compiler also called the “just-ahead-of-time” (JAOT) in the community, compiles all to machine code before running it.

Python code – interpreted 

Python is an interpreted language for most of its part and not a complied one. However, the compilation is just a step. Python codes in files(.pyc) id first compiled into bytecode and stored as either .pyc or .pyo format. 

The Python bytecode is a low-level set of instructions that an interpreter can execute. The installation of the Python compiler in most PCs is at /usr/local/bin/python3.8. Rather than executing the instructions on the CPU, the bytecode instructions execution is on a Virtual Machine.

Julia code – compiled. 

Julia compiles languages that need a compilation of the code, i.e., translating it from an understandable human code into a machine-understandable code/machine code. A machine code is the base level instruction form that a CPU can directly execute. If the code is successfully compiled, it generates an executable file, which runs your code’s operation stepwise.

Julia supports various development tools, e.g., Microsoft’s Visual Studio Code and linting support. Julia 1.5 has changed behavior in the REPL but fully compatible with non-REPL code. Most importantly, Julia is efficient at generating specialized and efficient codes for different argument types.

Julia syntax vs. Python

Since Julia is a compiled language, programs can be directly executed on the computer processor, unlike in Python. One of Julia’s powerful features is that it combines the syntax and interactivity of scripting languages such as Python and R. what’s more, it features the speed of compiled languages such as C and Fortran. 

While Fortran was the adopted language for data science due to its speed, most modern and young developers do not use it because it doesn’t accommodate the latest software and hardware design trends. Thus, adopting a modern language like Julia can handle the modern challenges. 

The Julia syntax is user-friendly, just like Python’s syntax. While programmers like prototyping algorithms in a user-friendly language like Python, they prefer rewriting the algorithms in a much faster language, especially when dealing with computationally intensive problems.  

Typically, Julia offers flexible performance and portability. Julia, being a computational language alongside R and Python, is an ideally compiled language with scripting potential. Remember, Julia offers higher speed because it’s a compiled language – its code is compiled into machine-readable instructions before execution. 

The Julia syntax is math-friendly, unlike Python. Its creation put in mind the needs of the scientific community. Thus, all mathematical expression in Julia resembles natural math expressions. That makes it easy to grasp for developers of all levels. 

Keywords. Python features 35 reserved words, which can’t be used as identifiers, while Julia has 29 keywords/reserved words which can be used as a variable. 

Indentation. In Python, whitespace delimits the control flow block. However, Julia lacks indentation because the developers wanted them to feel familiar with MATLAB. 

Data structures. Values and not variables carry information since it is a dynamically typed language. This also applies to Julia. 

Strings. Python has different types of strings – standard, multi-line, and raw strings, representing a particular value with a source code. On the other hand, in Julia, strings are finite character sequences. Double quotes or triple-double quotes delimit all string literals. 

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Julia vs. Python speed

The speed of the Julia programming language can only be compared to C. note that both are compiled languages. Data shows that C is 450 million loops in a second. This is about 45 times faster than Python. 

The unique thing about Julia is that it can be compiled in an optimized mode to offer better performance. When you run a Python script, the interpreter interprets the script line by line before generating an output, unlike in Julia. The compiler compiles the script fast before generating an optimized output as per the hardware. 

Julia was designed for speed – a problem that is prominent in Python. Typically, the Julia developers wanted a faster program by making it compiled rather than interpreted. Note the Julia is compiled at Just-In-Time/run time using the LLVM framework. Thus, the language will give great speed without needing optimization as well as handcrafted profiling techniques. Hence, this design offers a solution to all performance problems.

Julia is a superb choice for numerical computing – the language takes less time for more complex and extensive codes, an area where Python has failed. It’s clear; Julia beats Python when it comes to speed. The brilliant Julia code speed comes with exemplary performance. However, you can also improve the speed of Python. 

Users might find Psyco very helpful to make Python a bit speedier, especially when dealing with complex algorithms. Psyco works like a just-in-time (JIT) compiler to emit machine code on the fly rather than interpreting the Python program in use stepwise. The idea here is that Psyco writes several versions of the same block, which are specialized into particular variables – optimization. You’ll have a Python program that runs faster at the end of it, and it is still unmodified. 

Julia uses type declarations to execute codes; thus, it can quickly execute code functions. Further, it has multiple dispatches that quickly define data types like arrays and numbers. 

Julia vs. Python for machine learning

Julia and Python are both great languages for machine learning. However, Python was created for a different function other than machine learning. But with time, the programming language evolved to support machine learning, and it has become so popular for that role. 

By contrast, Julia was primarily developed to perform machine learning and statistical tasks. Since Julia’s programming language was developed specifically for high-level statistical work, it has the upper hand over Python. For instance, in linear algebra, “vanilla” Julia performs better than “vanilla” Python. 

The reason here is that Python doesn’t support all expression – matrices and equations needed in machine learning. Python is great for machine learning when using NumPy; however. Julia is much better when it comes to non-package experience – Julia caters to machine learning calculations more. Besides, its operand system is comparable only to that of R, and Python is much weaker when it comes to performance. 

With Julia, developers get a better execution speed only comparable to C/C++. Beyond that, Julia supports parallelism – Julia has many inbuilt functionalities for multi-process operations and composable multi-threading. The creation of the general task parallelism in Julia, which was inspired by programming systems such as Cilk and Go, developers can mark program pieces for execution in parallel; thus, starting a task automatically to run that code on an available thread. Here, a dynamic schedular will handle all the decisions and details.

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The majority of deep learning frameworks use C++ at the backend for performance and Python at the front-end for ease of use. Julia has qualities of the two programming languages; therefore, Julia plays a significant role in writing non-trivial deep learning code. Julia leads to the creation of syntax similar to MATLAB, which most developers can easily transition. 

Deep learning libraries in Julia are Flux.jl. PyTorch or TensorFlow, which are written in C++ or Cuda for excellent CPU performance. However, these libraries aren’t great for writing custom codes – nonetheless, Julia has a custom loss function for better custom codes. 

Python libraries include Pandas, Keras, Matplotlib, and StatsModels. 

Julia vs. Python vs. Go

There are many programming languages globally, but only a few get the attention of the developers. In essence, each acceptable programming language for general use must show a business case for its adoption. While the aesthetics of the language might be an exciting feature for academicians, mainstream use requires that someone write an actual code in that program. 

Julia and Python are dynamically typed languages, making them scripting languages while Go is statically typed. However, Julia has some properties of statically typed languages, giving it some versatility. Go, and Julia are compiled, while Python is an interpreted language. That makes Julia and Go much faster languages. Julia and Go compile quickly into an optimized machine code. Thus, they can outperform interpreted languages like Python. Did you know that compiled languages feel like dynamically typed languages? 

Go, Python and Julia are open source languages. So, you can use them freely. 

Julia’s programming language was created to handle scientific computations and artificial intelligence. This language has a rich ecosystem that allows developers to create versatile programs. Its static and dynamic interface allows incredible data visualization, at times in real-time. Besides, with lots of data frames, e.g., DataFrames.jl, you can efficiently work with datasets and perform common data manipulations. Amazingly, machine learning is scalable, with the MLJ.jl package providing a unified interface.

On the other hand, Python allows developers to work much faster and allows effective integration of the user systems. Ideally, Python runs on all platforms; it’s friendly, easy to learn, and open source. Python’s strongest point is its thousands of third-party modules, which allow endless possibilities. 

If you are looking to build simple, reliable, and efficient codes, the Go programming language might be the right choice for you. Developed by Google employs, Go is small and simple. The program was built for concurrency – so you can run many goroutines concurrently.

Conclusion

Python is an established programming language – it has been around for 30 years. The python architecture presents it as a simple to use, easy-to-learn high-level language. Many beginner programmers can use the language without any issues. 

Over the years, Python has developed a vast, dedicated community that contributes to the development of tools and solving any emerging issues. This makes Python a more mature language. Python was created to solve the problems that were there in the 1990s. However, the amazing thing with Python developers is the continued expansion into the modern space. Essentially, Python is ever-evolving to meet the arising needs. For instance, the current Python versions can support artificial intelligence and data science – an emerging field, although it wasn’t created for such issues. 

Python’s maturity, ease of use, portability, and rich library of tools and functions makes it a popular option. 

Julia is about 10 years old, although it’s a massive competitor to Python – reason, it handles data and scientific computations much better than Python. This property has made many researchers and scientists shift towards Julia. 

Compared to Python, Julia has a much higher speed, just like C, but offering the convenience and ease of use of Python. Indeed, Python supports data science, but Julia was created for data science – this implies that Julia has an architecture that features all necessary tools to handle anything machine learning, data visualization, scientific computing, and parallel computing. 

Still, you can use Julia to handle ordinary general-purpose coding like building, deploying, and embedding your code. 

So, which is better? Each programming language has an advantage over the other. If you are a data scientist, analyze the benefits and shortcomings that these languages offer before settling on any of them. Both are easy to learn, and many other factors are similar. So, your choice boils down to what you prefer, your objectives, and the amount of community support you need.