Python, an illustrious titan in the pantheon of programming languages, has secured its eminence through a mélange of remarkable features and versatile applications. Born from the fertile imagination of Guido van Rossum in the late 1980s, Python’s elegant simplicity and robust capability have catapulted it into the forefront of modern computing.

Elegant Simplicity and Versatility

At the heart of Python’s allure is its elegant syntax—a feature that emphasizes readability and conciseness. Python eschews the complexity that often encumbers other languages, instead favoring a syntax that reads like prose. This design philosophy not only enhances comprehension but also accelerates development, making Python an accessible language for both neophytes and seasoned developers.

Another hallmark of Python is its versatile standard library. With a comprehensive collection of modules and packages, Python furnishes developers with the tools needed to tackle a plethora of tasks—from file I/O operations to complex data manipulations. The vast array of third-party libraries further amplifies Python’s versatility, enabling it to seamlessly integrate with various technologies and platforms.

Web Development: Crafting Digital Realms

Python’s utility extends profoundly into the realm of web development. Frameworks such as Django and Flask empower developers to construct dynamic, scalable web applications with remarkable efficiency. Django, renowned for its “batteries-included” philosophy, provides a rich suite of built-in features, fostering rapid development and robust security. Flask, in contrast, offers a minimalist approach, granting developers the flexibility to tailor their applications with precision.

These frameworks encapsulate Python’s ability to streamline the web development process, facilitating the creation of everything from intricate e-commerce platforms to streamlined content management systems.

Data Science and Analytics: Unveiling Insights

In the domain of data science, Python reigns supreme. Its prowess is evident in libraries such as Pandas, NumPy, and Matplotlib. Pandas, with its data structures and operations for manipulating numerical tables and time series, has become a cornerstone for data wrangling and analysis. NumPy underpins numerical computations, offering a suite of mathematical functions that operate on arrays. Matplotlib, a plotting library, allows for the visualization of complex datasets, transforming raw data into comprehensible graphs and charts.

These tools collectively empower data scientists to uncover insights, develop predictive models, and make data-driven decisions with unprecedented ease and accuracy.

Machine Learning and Artificial Intelligence: Forging Intelligent Systems

Python’s ascendancy in the field of machine learning and artificial intelligence (AI) is nothing short of transformative. Libraries such as TensorFlow, Keras, and Scikit-learn epitomize Python’s capacity to facilitate sophisticated machine learning algorithms and models.

TensorFlow, an open-source library developed by Google, offers a comprehensive ecosystem for building and training machine learning models. Keras, a high-level neural networks API, simplifies the process of designing and experimenting with deep learning models. Scikit-learn provides a plethora of tools for implementing classical machine learning algorithms, from regression to clustering.

Together, these libraries enable the development of intelligent systems capable of tasks ranging from natural language processing to image recognition, underpinning advancements in AI across various sectors.

Scientific Computing and Automation: Streamlining Complex Tasks

Python’s capabilities extend into scientific computing and automation, areas where precision and efficiency are paramount. Libraries such as SciPy and SymPy are instrumental in performing complex numerical computations and symbolic mathematics. SciPy offers algorithms for optimization, integration, and interpolation, while SymPy provides tools for algebraic manipulation and equation solving.

In the realm of automation, Python’s Selenium and BeautifulSoup libraries facilitate web scraping and browser automation, streamlining repetitive tasks and enabling the extraction of valuable information from the web.

Conclusion

Python’s omnipresence in contemporary technology is a testament to its multifaceted features and applications. Its intuitive syntax, extensive libraries, and adaptability across diverse domains—from web development to machine learning—underscore its significance in shaping the digital landscape. As technology continues to evolve, Python’s role as a pivotal tool in innovation and development remains unwavering.

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What is Open Source Platform Development?

Open source platform development is a software development approach that involves creating and maintaining software applications and websites using open source technology. Unlike traditional software development, open source software is developed collaboratively by a community of developers who share the code and make modifications to it. It is also typically free to use and anyone can contribute to it, making it more accessible and adaptable to a wide range of users.

The popularity of open source platform development has grown in recent years, as companies recognize the benefits of using open source technologies. One of the key advantages of open source development is the ability to leverage the collective knowledge and expertise of the community, resulting in faster innovation and development. Additionally, open source software is often more secure, reliable, and customizable than proprietary software.

When planning an open source platform development project, it is important to define the project scope and objectives, as well as identify the tools, resources, and expertise required for successful implementation. Some of the best open source platform development tools, courses, and services include GitHub, GitLab, Apache Maven, and Udacity’s Open Source courses, among others.

Open Source Platform Development Strategy

When developing an open source platform, having a well-defined strategy is crucial. Firstly, creating a roadmap of the project that outlines the goals, timeline, and tasks required to complete it is essential. Additionally, a list of the technologies and tools that will be used in the project should be created.

It is also important to have a plan for project management. This should include the roles and responsibilities of each team member, how tasks will be assigned and tracked, and how communication will be handled. Furthermore, a plan for testing and deploying the code should also be created.

Open Source Platform Development Tips

When developing an open source platform, there are a few tips that can help to ensure a successful project. Firstly, you should ensure that the project is well-documented. This includes the goals of the project, the timeline, the team members, and the tasks that need to be completed. Additionally, you should create a plan for how the project will be managed and how communication will be handled.

You should also create a plan for how the code will be tested and deployed. Additionally, you should ensure that the project is well-structured, with clearly defined roles and responsibilities for each team member. Finally, you should ensure that the project is well-tested, to ensure that any potential issues are identified and addressed quickly.

The Best Open Source Platform Development Tools

When developing an open source platform, there are several tools that can be used to streamline the process. Here are some of the best open source platform development tools:

GitHub: GitHub is a widely used platform for hosting and managing open source projects. It offers a wide range of tools for version control, collaboration, and project management.

Bitbucket: Bitbucket is a cloud-based source code repository and version control system. It provides tools for collaboration, code review, and project management.

Jenkins: Jenkins is an open source automation server that can be used to automate building, testing, and deployment of software applications.

Docker: Docker is an open source container platform that can be used to package, deploy, and run applications.

Puppet: Puppet is an open source configuration management tool that can be used to automate system administration tasks.

Courses for Open Source Platform Development

The open source platform development process can be complex and challenging. To help make the process easier, there are a number of courses available for those interested in learning more about open source platform development. Here are some of the best courses for open source platform development:

Open Source Platform Development Services

If you don’t have the necessary time or resources to develop an open source platform on your own, there are several companies offering open source platform development services. These companies can assist in creating an open source platform from start to finish. Here are some of the best open source platform development services available:

DevOps: DevOps is a cloud-based open source platform development service that offers tools for version control, collaboration, and project management.

CloudBees: CloudBees is an open source platform development service that provides tools for collaboration, code review, and project management.

Zendesk: Zendesk is a cloud-based open source platform development service that provides tools for automated testing, deployment, and system administration tasks.

GitHub Actions: GitHub Actions is an open source platform development service that provides tools for automating the building, testing, and deployment of software applications.

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conciseness of the programming language;
compatibility with Java;
is supported by Google;
a growing community.
It is possible that soon applications for Android devices will be written exclusively in Kotlin, so those wishing to develop in the direction of mobile development are strongly encouraged to take a closer look at this language.

10. Rust
    In 2016, Rust took first place in the “what developers love most” poll on Stack Overflow. Rust was the programming language that developers really appreciate (79.1% of the votes). The open-source development language, developed by the Mozilla Foundation, works as a low-level language.

What’s most interesting here is that Rust emphasizes secure code (i.e., objects are managed by the programming language from start to finish). While it has no inheritance, it has traits and structures. It is universal and rests on three pillars: ergonomics, speed, and safety.

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A program in a programming language is first of all text. Text is human-readable and relatively easy to be processed by a computer because letters and other text characters in the computer are represented by integers, they are also called character codes. The program, which processes the text in a programming language and creates a sequence of instructions for the microprocessor is called a compiler. That is, the compiler translates numbers that humans perceive as text into other numbers that the computer perceives as microprocessor commands.

Languages that require a compiler are called compilers. To run such a program, it is not enough just to write it. It is necessary to run it through a compiler, to get an executable module, for example, in the Windows operating system is a file with the extension .exe, and only then run it.

This scheme, of course, did not suit everyone, and programmers invented languages that do not require a compiler. For these languages, text to microprocessor commands are translated inconspicuously right after you run a text program. However, to do this, the text program must be run under another ready-made program, called the Interpreter. The Interpreter does this invisible compilation. Languages that require an interpreter are called Interpreted languages.

The main difference between compiled languages and interpreted languages is the speed of execution of programs. It is said that programs written in compiled languages run faster than in interpreted ones. But the process of writing and testing the interpreted program is easier, because there is no need for the intermediate compilation step.

For some languages, such as C#, compilation is done in a special way in two steps. The point is that in the .Net environment, a C# program doesn’t become a microprocessor instruction set after compilation, but is converted into another intermediate language, CIL – Common Intermediate Language, (formerly called MSIL – Microsoft Intermediate Language) which is given to the .Net compiler for execution. This sequence of conversions from one language to another makes it possible to not worry about the type of microprocessor installed in the computer and gives more flexibility to the programs.

In a similar way, a TypeScript program first compiled into a text program, or as they say, into JavaScript code, which can then be executed by the JavaScript interpreter. This complication allows us to take advantage of the strict data typing and compile-time error catching that are available in TypeScript.

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The fact is that the computer was created only after the titanic efforts of such geniuses as Alan Turing, who developed his theory, the Turing machine, on the basis of which all numerical computers work today. The principle of the Turing machine, in short, is that the RAM stores a sequence of microprocessor instructions, including conditional or unconditional jumps to other instructions. In assembly language these transitions are called JMP (jump) and in high-level languages they are called GOTO (go to).

The language was originally Assembler, which is almost exactly the same as the microprocessor. Theoretically any program can be written in Assembler, but in practice it is not so easy to port application tasks abstractions to it.

For programming applications, from about the beginning of the 70’s of the 20th century came structure programming, which required the efforts of other geniuses, such as Niklaus Wirth, creator of the Pascal language, and Edsger Dijkstra, who was the first to write about the need to get rid of the GOTO operator in high-level languages, and proposed the solution to do this with three types of operators and functions.

In practice, this resulted in programming languages such as Basic, C, Pascal, Algol, Cobol, Fortran, and PL1. The development of programs by the method “from top to bottom” in structural programming became a sheer pleasure. It consisted in writing a set of functions containing subfunctions, which could be called by putting necessary data at the input and getting the corresponding result.

Thus, in structured programming languages, algorithms based on functions are as if in the first place, and data for them can be taken from anywhere. The idea of cybernetics author Norbert Wiener about a function as a black box to which you can feed any data and observe the resulting output played a role in this.

For small problems, such as sorting data or finding the shortest path, structured programming suited it perfectly. The solutions for most complex algorithmic problems were found. Fundamental works appeared, such as Donald Knuth’s multi-volume The Art of Programming, which is still considered a handbook for programmers.

However, the resulting increase in the complexity of programs also increased the chance of introducing errors into programs, as the ability to substitute any data for procedures and functions had side effects. For example, in 1999, NASA’s Mars Climate Orbiter crashed due to a program error, where the wrong data was substituted.

As a result, a new concept of object-oriented programming emerged, which focuses on what I call the principle of data relevance, and functions become a kind of appendage to the data they have to handle. An object is, first of all, a set of data with its functions. The OOP introduces restrictions on the access of functions to “alien” data, which reduces the possibility of unintentional data changes and dramatically increases the reliability of programs.

After the appearance of object-oriented programming languages such as C++, Object Pascal, Java, C#, as well as new hardware capabilities of computers, the volume of programs and data for them have increased many times, if not by orders of magnitude, which is easy to assess at least by the volume of software distributions, which no longer fit on floppy disks first, and then on CDs. And programming has once again sort of risen from its head to its feet.

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This type of classification, although slightly losing relevance since the vast majority of languages can now be classified as high-level, is still in place since low-level languages exist.

This classification was relevant in the early days of computers, when the number of available compilers could be counted on the fingers, and to write, for example, a keyboard driver in Assembler could be used as a pastime in your spare time.

Recall that Assembler is a language whose commands correspond as much as possible to the commands of the microprocessor itself, and that allows to process data with size one, two or four bytes, so that it is very difficult to represent complex data types in it. But the Assembler language has no equal in speed of program execution.

The CIL language, already mentioned above, is in a sense an assembly language, but for the .Net system. CIL commands are converted by the .Net JIT compiler into microprocessor instruction sets, depending on the type of microprocessor itself on which .Net is installed. As a rule, a CIL program is the result of compiling a program written in one of the .Net high-level languages.

Moreover, CIL is a special language. Writing a program directly in it may be too long. Programs in this language are generated by compilers and used by the .Net platform software. That is why it is considered a programming language for machines rather than humans.

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Examples: the .Net platform or framework. Programming languages such as C#, F# work only on that platform and understanding the features of the platform is important to understand how programs written in those languages work.

To use Microsoft’s C++ language for the first 32-bit versions of Windows, programmers also had to learn the MFC library to create windowed applications.

To develop web applications using Microsoft’s MVC technology, programmers would also need to learn the Razor web page markup language.

Creating modern applications on Microsoft’s universal platform may require the XAML markup language.

Other examples:
Ruby on Rails, a server-side web application development platform.
For computer games, such as Unity, Cocos, Unreal Engine.
For 3D graphics: OpenGL, DirectX.

Probably other ways to classify programming languages are possible, such as with and without strict typing. But they are interesting for those who already know programming, this overview is more for beginners.

In principle, the more languages a programmer knows, the more confident he feels as a professional. But in our fast-paced times, it is possible that the version of the language can lose its relevance literally a year and a half or two years. For example, in the TypeScript language, from 2015 to 2019, that is, approximately 5 years, there were more than 20 updates.

If a person has already decided on the application area, the operating system, and the company in which he will work, the choice of programming language is simplified. However, companies may have quite exotic requirements, which can make beginners’ eyes glaze over. For example, this stack of languages and technologies in the job description of one of the major companies: .Net Core 3.1, Java 14, Kotlin, ElasticSearch, Lucene, Kafka, Redis, PostgreSQL, MSSQL, Airflow, Spark, Kubernetes, GitLab CI, Prometheus, Graylog, Jaeger, Grafana. This set somehow touches at least four programming languages: Java, Kotlin, SQL and Lua.

If you are still undecided about the company, you can start with one of the universal programming languages. Because of the ubiquity of the Internet, it is desirable for a programmer to have at least a general idea of what the HTML language is, as well as related languages of data description such as XML and JSON. It is also desirable to have an idea about the SQL database management language.

Gone are the days when you could work with the same version of a programming language for decades. Nowadays, the peculiarity of a programmer’s job is the constant learning of new languages and technologies. Courses on programming can be a good springboard, but programmers get the main experience while working, as if learning and working at the same time.

And finally, just a few years ago, as mushrooms after the rain have appeared visual programming systems without programmers, for example to create Web sites such as WordPress, Wix, Bitrix24. For ordinary users, they allow you to choose a design, connect to the data, and program the business processes yourself.

Looking at these systems, one might get the impression that programmers will soon be unnecessary. But guess who creates all these programming systems without programmers? The same programmers with the same common programming languages.

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Another situation with the language Delphi, which was an extension of Turbo Pascal, and which was very popular at the time of the first 32-bit Windows, but could not compete with other programming languages, including from Microsoft, which developed more actively.

It could also be a victory in the competition of two similar languages, such as Microsoft’s JScript for web browsers and JavaScript, originally introduced by Netscape. JavaScript has become popular due to greater openness and support from a large number of developer companies.

C and C++ languages have long been popular due to the opinion of the high efficiency of programs written in them. In general, this is the case. However, gradually other programming languages began to gain popularity not only for the efficiency of execution, but also for the ease of learning, writing and maintaining programs, which cannot be said with certainty about C++.

For its great capabilities and flexibility, C++ requires programmer discipline and programming culture, otherwise, as programmers joke, it can turn from a language for writing programs into a language for writing bugs.

Despite the proclaimed standard of C and C++ languages, programs written for compilers of different companies are rarely completely compatible in source code. This feature must also be considered when studying it.

One of the competitors of C++, the language C# (read ‘C sharp’) appeared and became popular with the advent of the new technology from Microsoft – .Net.

The TypeScript language gained popularity as an answer to the problem of difficult to support programs written in JavaScript, a language which itself is quite popular.

Of today’s popular languages Python is worth mentioning because of the comparative ease of learning, openness, and the possibility of using it in various subject areas such as the web, artificial intelligence, computer games.

Almost every programming language has its own group of fans, although popularity today does not mean popularity in the near future, or that a popular language will necessarily be useful to you. In general, the choice is always up to you.

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