Python Alternatives
Python is the primary programming language for various application development processes, including web-based applications, data analytical applications, and machine learning implementation. Some alternate options for Python are NodeJS, Ruby, PHP, Golang, Java, and Scala. Most of these Python alternate frameworks or platforms offer all of the functions of Python, and a few have partial features of Python combined with their improved qualities, which makes them either equal to or finer than Python.
Web development, programming languages, Software testing & others
List of Python Alternatives
Below are the different Python Alternatives which are as follows:
1. Java
- It is an object-oriented language like Python, which handles bulky applications carrying very involved business logic.
- Many products have been developed using Java, and Oracle Corporation is currently handling all the upcoming versions.
- Java offers multiple features, from basic OOPS concepts to multithreading, streams, lambdas, functional interfaces, etc.
- You can follow design patterns implementation using concepts of abstract classes, inheritance, interfaces, etc.
- Rest Webservices, SOAP web service can be developed with Java (web services are used for inter-application communication).
- Java-based projects are generally chosen with a stack containing Spring (for dependency resolutions, security, and JDBC template, etc.), Hibernate (for persisting with database), and Java (8 or above preferred). These all make the backend of the application.
- Android-based applications, video-streaming-based applications, applets, etc., can be developed with Java.
- Java is platform-independent too.
2. NodeJS
- This is one of the most used technologies for server-side development these days, as it offers higher flexibility by just putting Javascript into the picture, so you need not learn different languages for the backend and frontend.
- Node is a single-threaded asynchronous (non-blocking) communication-based technology; hence, it is lightweight and doesn’t initiate a thread for every request. It’s fast, built on Google Chrome’s V8 Javascript Engine.
- Multiple middlewares can be added to perform the task.
- People have published their packages using a node package manager.
- You can incorporate new services like Kafka, Twilio, LogDNA, Auth0, etc., with great ease here.
- Small-scale companies find it easy to continue with this technology for application development.
- A very flexible and in-demand stack called MEAN stack is used, M-> MongoDB, E-> Express.js, A-> AngularJS, and N -> AngularJS
- Applications like Paypal and Linkedin use this technology.
- They are also highly compatible with NoSQL databases, which deal with JSON or BSON formats.
3. PHP
- Developers use this language extensively to create a wide range of small-scale and large-scale applications, making it one of the most flexible languages ever.
- Good products based on PHP, like WordPress, have come up in the market, where it’s easy for people to host and maintain their websites and manage content efficiently.
- PHP and MySQL are used in conjunction with solution building.
- It has a limited scope as it is restricted to the web development arena only, while Python is a full-stack programming language that can be used in data analytics and machine learning also.
- PHP is a scripting language, and Python is a programming language.
- So only a subset of features offered by PHP match up with Python; if just web application development is the focus, then you can go by PHP only.
4. Ruby
- It has served as the foundation for developing noteworthy products such as Github and Slideshare.
- Ruby and Rails structure a stack when looking for web application development.
- These two languages are powerful enough to accomplish CRUD operations with just a few steps and minimal code, whereas Python often requires more extensive coding for the same tasks.
- Ruby and Rails provide ample advantages as they generate a healthy skeleton for web development.
- Again the significant difference is that Python holds a strong hand in domains other than web development, like data science and machine learning, while Ruby is just for web development’s sake.
- It keeps certain things abstract to keep the life of developers accessible, while Python throws up everything to the programmer.
- Like Python, it is an interpreted language compatible across multiple platforms.
- Ruby, being a dynamically typed language, allows modification of a program while it’s running. Metaprogramming is the term for the ability to treat data as executable code.
5. Golang
- Google developed this language, so they built certain products, like Kubernetes and Docker, exclusively on Golang.
- Go is a fast, statically typed, compiled programming language. Go is similar in syntactic to C language but has memory safety and garbage collection-like features.
- The concurrency mechanisms are suitable, making them write programs for multicore and networked machines.
- Go has support for runtime reflection.
- It is ideal for developing the backend for Single Page Applications and mobile applications, but traditional applications need not be developed using it.
- Go offers simple syntax, fast compilation, and object-oriented programming based on “structural typing.”
- Go has support for socket programming via TCP and UDP protocols.
- Building REST APIs is more powerful and reliable with Go. Probably the developers are fine to migrate from Node and C# to Golang for this.
- While Python is a dynamically typed language, Go is statically typed only.
6. Scala
- Scala is both a functional programming language and an object-oriented language; primitives are not present here, and hence everything is an object; static keyword also is not significant here. Scala is a contraction of the words “scalable” and “language.”
- Developers have built tools like Apache Kafka and Apache Spark on Scala.
- Scala’s static types help avoid bugs in complex applications.
- Designed to interoperate with Java Runtime Environment, Scala also allows the nesting of functions and supports currying.
- Singleton objects provide a convenient way to group functions without class members.
- Using Scala means we need to put less code into a picture, and it will be efficient.
- Scala also offers features like immutability, pure functions, recursion, high-order functions, currying, and partially applied functions.
Conclusion
Python is an all-around functional language as it has to offer a lot in web development, data science, and machine learning. Now it all depends on what perfectly fits your application and satisfies your business logic, taking care of scalability and complexity-like constraints.
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Facts about Cython Programming Language
Cython is a programming language. It can run on Windows, macOS, and Linux operating systems. It had a version ranging from 2.6 to 3.8. Cython 3.0.0 is under development. In Cython, the Code written in Python is converted to C language. High traffic websites such as Quora use Cython Programming language.
History
Cython is actually derived from the Pyrex language. It is more advanced and has more features and optimizations than the Pyrex language. Cython was separated from the Pyrex development in the year 2007 because its developers envisioned a wider scope of the language than Pyrex. It was a part of a project called Sage. Cython programming language has a .pyx extension. Scientific users of Python use the Cython programming language a lot. It was created by Guido van Rossum and developed by Robert Bradshaw and Stefan Behnel. It was initially released on the 28th of July, 2007. It had its stable release on 24th March 2020.
How to use Cython Programming Language
Cython is aimed at being the superset of the Python programming language. It is so designed that it gives C like performance along with codes mostly written in the Python language allowing extra syntax that is inspired by C. When Cython is compiled it gives CPython extension modules. It provides lesser computational overhead than Python at run-time. C and C++ codes can be wrapped into the Cython modules. The Cython is dependent on the Python interpreter and standard library. Cython employs optimistic optimizations, optional type inference, low control structures overheads, and low function call overhead. Its performance is dependent on the generation and implementation of the C codes. The Cython programming Language is much like Python with very little difference. To understand this, let us take, for example, Python code and its relevant Cython code.
Python code:
Python3
Cython code:
Python3
In the two codes, it can be seen that very little has been changed. Only the variables have been explicitly declared and it affects the performance thereby improving its speed.
Advantages of the Cython Programming Language
The Cython programming language is used to speed the written codes. Cython language allows easy working with the C libraries. Cython also supports C++. Cython allows easy interaction with the Python Libraries without Python in the way. Cython Libraries have the same garbage collection as that of Python. It is also possible to manage the C-level Structures using malloc/free. Cython automatically checks for runtime problems that arise in C. The C-code generated by Cython is very safe. If error checks are not required at runtime, they can even be disabled. Cython also uses the Global Interpreter Lock of Python. It is used for countering the problem of resource contention. Cython can be used in Python application and software modules that need extra protection from attacks such as snooping.
Limitations of Cython
When Cython encounters the Python codes its complete conversion to C language is not possible which results in several calls to the Python interpreter. This might give a little speedup (15-20%) or in some cases either have no effect or degradation of performance. Cython code is best in its performance when it is written only in the C language. Cython provides a source code report that illustrates which parts of it are written in Python to avoid performance bottlenecks.
Cython Numpy
Cython has the ability to improve the usage of the third-party number-crunching libraries like NumPy that are based on C. It uses NumPy to counter Python bottleneck problems by taking them outside the loop. Fast access to arrays of Numpy is provided by Cython. The syntax in the Cython written for Numpy is similar to the syntax that is used in Python. For faster bindings of the Cython and Numpy, the custom of Cython is needed. This includes the use of the statement, “cimport”. This statement is used by the Cython programming language to see the C- level constructs at the time of program compilation.