#run new code without compiling - python programming language - python interpreter

[filmroman]

Python Programming Language - Run Code In Interpreter To Change Code Or Adjust Parameters Without Recompile

Electromagnetic Cavitation

Electromagnetic Cavitation

[guzsdaily]

Good Code is Boring

Daily Blogs 358 - Oct 28th, 12.024

Something I started to notice and think about, is how much most good code is kinda boring.

Clever Code

Go (or "Golang" for SEO friendliness) is my third or fourth programming language that I learned, and it is somewhat a new paradigm for me.

My first language was Java, famous for its Object-Oriented Programming (OOP) paradigms and features. I learned it for game development, which is somewhat okay with Java, and to be honest, I hardly remember how it was. However, I learned from others how much OOP can get out of control and be a nightmare with inheritance inside inheritance inside inheritance.

And then I learned JavaScript after some years... fucking god. But being honest, in the start JS was a blast, and I still think it is a good language... for the browser. If you start to go outside from the standard vanilla JavaScript, things start to be clever. In an engineering view, the ecosystem is really powerful, things such as JSX and all the frameworks that use it, the compilers for Vue and Svelte, and the whole bundling, and splitting, and transpiling of Rollup, ESBuild, Vite and using TypeScript, to compile a language to another, that will have a build process, all of this, for an interpreted language... it is a marvel of engineering, but it is just too much.

Finally, I learned Rust... which I kinda like it. I didn't really make a big project with it, just a small CLI for manipulating markdown, which was nice and when I found a good solution for converting Markdown AST to NPF it was a big hit of dopamine because it was really elegant. However, nowadays, I do feel like it is having the same problems of JavaScript. Macros are a good feature, but end up being the go-to solution when you simply can't make the code "look pretty"; or having to use a library to anything a little more complex; or having to deal with lifetimes. And if you want to do anything a little more complex "the Rust way", you will easily do head to head with a wall of skill-issues. I still love it and its complexity, and for things like compiler and transpilers it feels like a good shot.

Going Go

This year I started to learn Go (or "Golang" for SEO friendliness), and it has being kinda awesome.

Go is kinda like Python in its learning curve, and it is somewhat like C but without all the needing of handling memory and needing to create complex data structured from scratch. And I have never really loved it, but never really hated it, since it is mostly just boring and simple.

There are no macros or magic syntax. No pattern matching on types, since you can just use a switch statement. You don't have to worry a lot about packages, since the standard library will cover you up to 80% of features. If you need a package, you don't need to worry about a centralized registry to upload and the security vulnerability of a single failure point, all packages are just Git repositories that you import and that's it. And no file management, since it just uses the file system for packages and imports.

And it feels like Go pretty much made all the obvious decisions that make sense, and you mostly never question or care about them, because they don't annoy you. The syntax doesn't get into your way. And in the end you just end up comparing to other languages' features, saying to yourself "man... we could save some lines here" knowing damn well it's not worth it. It's boring.

You write code, make your feature be completed in some hours, and compile it with go build. And run the binary, and it's fast.

Going Simple

And writing Go kinda opened a new passion in programming for me.

Coming from JavaScript and Rust really made me be costumed with complexity, and going now to Go really is making me value simplicity and having the less moving parts are possible.

I am becoming more aware from installing dependencies, checking to see their dependencies, to be sure that I'm not putting 100 projects under my own. And when I need something more complex but specific, just copy-and-paste it and put the proper license and notice of it, no need to install a whole project. All other necessities I just write my own version, since most of the time it can be simpler, a learning opportunity, and a better solution for your specific problem. With Go I just need go build to build my project, and when I need JavaScript, I just fucking write it and that's it, no TypeScript (JSDoc covers 99% of the use cases for TS), just write JS for the browser, check if what you're using is supported by modern browsers, and serve them as-is.

Doing this is really opening some opportunities to learn how to implement solutions, instead of just using libraries or cumbersome language features to implement it, since I mostly read from source-code of said libraries and implement the concept myself. Not only this, but this is really making me appreciate more standards and tooling, both from languages and from ecosystem (such as web standards), since I can just follow them and have things work easily with the outside world.

The evolution

And I kinda already feel like this is making me a better developer overhaul. I knew that with an interesting experiment I made.

One of my first actual projects was, of course, a to-do app. I wrote it in Vue using Nuxt, and it was great not-gonna-lie, Nuxt and Vue are awesome frameworks and still one of my favorites, but damn well it was overkill for a to-do app. Looking back... more than 30k lines of code for this app is just too much.

And that's what I thought around the start of this year, which is why I made an experiment, creating a to-do app in just one HTML file, using AlpineJS and PicoCSS.

The file ended up having just 350 files.

Today's artists & creative things Music: Torna a casa - by Måneskin

© 2024 Gustavo "Guz" L. de Mello. Licensed under CC BY-SA 4.0

[mitwanair]

Python Vs Java – Which one to choose

Java

Oracle created Java in 1995 as a high-level, class-based, object-oriented programming language. One of the main goals of the Java designers was to have as few implementation dependencies as possible. It is a general-purpose programming language that is frequently used in desktop computing, mobile computing, games, and numerical computing.

Python

Guido Van Rossum created Python in 1989 as a high-level, general-purpose, interactive, and interpreted programming language. It is open source, and its source code is available under the GNU General Public License (GPL), allowing anyone to modify it. It has simple syntax, which makes it ideal for beginners. Python can be extended to C and C++ and has interfaces to various libraries and operating system calls.

Trends in Python vs Java

Although Java is no longer as popular as it once was, it remains one of the most widely used programming languages. Python, on the other hand, is new and still evolving, and its elegant coding style is gaining popularity among developers. Python is predicted to overtake Java in popularity in the future. Python's rapid growth can be attributed to its productivity, libraries, ease of learning, and language flexibility.

Java is still in the race. The developers are constantly adding new features to increase its power, speed, and flexibility. After all, it comes with the powerful Java Virtual Machine, which facilitates cross-platform application development.

Let's see some of the major differences between Python and Java:

Python

Python is an interpretable programming language that allows programmes to be run using a Python interpreter.

Python is compatible with imperative, object-oriented, functional, and procedural programming paradigms.

Python code is concise, clean, and readable.

Python is a dynamically typed language that eliminates the need for the developer to declare variables. It enables the interpreter to detect and change the data type of the variable without requiring the variables to be declared.

Python is slower because it is an interpreter language, and the types are assumed at runtime.

Java

Java is a compiled programming language. The compiled code is converted to bytecode and can be executed on any platform that supports the Java Virtual Machine (JVM).

Java was created to support the class-based and object-oriented paradigms.

To keep Java code readable, developers must expend more effort and time.

Java is a strongly typed programming language. It prevents the compiler from changing the data type of a variable unless it is type-casted.

Because all types are assigned during compilation, Java is faster than Python.

Python vs Java -  Portability

Both programming languages are highly portable, but Java has a slight advantage over Python due to the JVM. Python runs everywhere; simply change the paths and settings in the respective operating systems (MAC, WINDOWS, LINUX). To run a Python programme, a compiler must be installed to convert the Python code into a format that the operating system understands.

Java does not require a compiler to convert code into understandable code for the operating system; all that is required is a Java Virtual Machine. Because of technological advancements, JVM is now pre-installed on almost all mobile and computer devices. As a result, the developer can be confident that their application will be used by nearly all users.

Python vs Java  Security

Both Python and Java are considered secure languages, but Java is more secure than Python. Java provides advanced authentication and access control features that keep the web application secure. Because Java uses bytecode, when the compiler compiles the code, a class file is created along with the bytecode, which is then tested for malware and viruses by the JVM.

Python is a simple and easy-to-debug programming language. It is easier to debug and avoid future risks of code complexity with minimal code. Python has advanced security measures as well, but it falls short of Java.

Python's Future vs. Java's

Both languages have a promising future because they have advanced libraries and frameworks that can keep up with modern technologies. It all depends on the applications being developed. Java is preferred for developing infrastructure software (such as operating systems), cross-platform software, and security. Developers, on the other hand, prefer Python for developing new algorithms for Data Science, analytics, and machine learning. Both languages are still evolving and improving on a daily basis.

Conclusion

Both programming languages have advantages and disadvantages. For example, Python is easy to code, simpler to learn, and has a large library with a high level of code readability, whereas Java is more compatible and best for developing mobile applications and games. Both are widely used and capable programming languages that can evolve in tandem with new technologies. Python is better for people who are new to programming because it is easy to learn and has an English-like syntax, whereas Java is better for building enterprise level applications.

If you want to learn Python and become a Python expert, this is the place to be. Then look into our Python training in kochi.

[web-scraping-tutorial-blog]

Replace C language! Many Python developers are joining the Rust team

In the future, more and more libraries will use Python as the front end (improving programming efficiency) and Rust as the back end (improving performance).

python

Rust is replacing C as the “backend” for high-performance Python packages. What is the reason behind this?

First, let’s consider motivation. Python is easy to write, but has the problem of slow execution speed. I especially can’t write data processing libraries because Python is very slow and it’s difficult to write high-performance libraries in pure Python. However, Python is the primary language for machine learning and data engineering. So when you try to write a library for data engineers or machine learning engineers, you run into the following problems:

Although we need to write APIs in Python, high-performance data processing tasks cannot be done solely in Python. This means that you have the following options for writing a library:

Either you learn and use C, or someone else learns C, writes a library, and you rely on that library to be able to perform low-level operations. Someone familiar with the C language might ask, “Is there anything wrong with this? Many library authors may outsource their numerical computation to NumPy or SciPy. You might think, “I can learn.”

However, the situation is not so ideal. It’s convenient to outsource some tasks to libraries like NumPy, SciPy, etc., but it requires all functions to be vectorized and you can’t write your code in a for loop. You also have to worry about certain operations being blocked by the Global Interpreter Lock (GIL), and there are various other issues. Not everything you want to do can be easily found in a library that already exists.

Therefore, there is another method. What about writing a library from scratch in C and adding the Python bindings afterwards? However, if you have a Python background, writing low-level code using C feels very low-level, and learning the language takes effort. Null pointer dereferences, buffer overflows, memory leaks… these are traps you can encounter when using the C language, and are unfamiliar to programmers learning Python for the first time.

Rust

Rust is fast and has efficient memory management. Therefore, concurrent and parallel programming becomes easier. Rust has great tools, a friendly compiler, and a large and active developer community. Rust makes your programs faster and allows you to make more friends while learning.

Most importantly, Rust is easier to learn than C for Python developers.

It improves the “first level” experience and makes it easier for beginners to write “safe” code. The learning curve is smoother, allowing you to gradually master more advanced language features over time.

Therefore, over the past few years, more and more high-performance libraries are choosing Python as their front end and Rust as their back end. example:

Lance: A high-performance, low-cost vector database. Founders Chang She and Lei Xu originally wrote code libraries in C++, but decided to switch to Rust when they no longer needed to work with CMake. Here’s why Chang made this decision.

“The decision to switch from C++ to Rust was because I could work more efficiently, without losing performance, and didn’t have to deal with CMake.I basically started learning Rust from scratch, and while I was learning it, Lei and I We rewrote about 4 months worth of C++ code in Rust, and each time we write and release a new feature in Rust, we become more confident that we won’t get a segfault every time we run every other command. I don’t have to worry about it happening.”

Not only is Rust well-suited for data processing, it can also serve as a backend for many other Python packages with high performance requirements.

Pydantic: A Python verification library for developers. The Pydantic team rewrote the second version in Rust and saw a 20x performance improvement for even simple models. Besides performance improvements, Rust has several other benefits. Pydantic founder Samuel Colvin cites several advantages.

“Another thing about Rust is that in addition to its speed, code written in Rust is usually easier to use and maintain. In particular, Rust catches and handles all possible errors. , and the Python (and TypeScript) type system tends to ignore these errors, so I’m not sure which exceptions can be raised in which situations when calling ‘foobar()’ I have no idea. Basically I have to try iteratively to find possible failures.”

Combining Python and Rust

In the future, more and more libraries will use Python as the front end and Rust as the back end. Overall, Python developers today have a better and smoother approach to building high-performance libraries.

[knowledgdenext]

How Does C++ Compare to Other Programming Languages?

C++ has been a cornerstone of modern software development for decades, serving as the backbone for applications ranging from video games to operating systems. But how does C++ stack up against other popular programming languages? In this article, we’ll explore the strengths and weaknesses of C++, compare it with languages like Python, Java, and C#, and delve into the key features that make it both powerful and challenging. If you're considering diving into C++ programming, you can join the best C++ course at CADL in Zirakpur to master this complex yet rewarding language.

Table of Contents:

Overview of C++

Key Features of C++

Comparison with Other Languages

C++ vs. Python

C++ vs. Java

C++ vs. C#

C++ vs. C

When to Choose C++

Conclusion: Why C++ Still Matters

Learn C++ at CADL Zirakpur

1. Overview of C++

C++ was developed by Bjarne Stroustrup in the early 1980s as an extension of the C programming language. It was designed to bring object-oriented programming (OOP) into the world of C, allowing for more structured and reusable code. Over the years, C++ has evolved significantly, gaining new features, performance improvements, and becoming a favorite language for system-level programming, game development, and large-scale software solutions.

While C++ is known for its power and efficiency, it also has a reputation for being complex due to its flexibility and feature-rich syntax. The language allows programmers to work at both high and low levels, offering control over system resources while supporting advanced programming concepts like polymorphism, inheritance, and templates.

2. Key Features of C++

Before comparing C++ to other programming languages, it’s essential to understand its key features:

Object-Oriented Programming: C++ introduced classes, inheritance, polymorphism, and encapsulation to the C language, giving developers the ability to model complex systems more intuitively.

Low-Level Memory Manipulation: Unlike many high-level languages, C++ allows programmers to directly manipulate memory through pointers. This provides fine-grained control over system resources, which is essential for applications requiring high performance.

Portability: C++ is portable across many platforms, making it a good choice for applications that need to run on different operating systems with minimal modifications.

Standard Template Library (STL): C++ has a powerful Standard Template Library that includes commonly used data structures (like vectors and lists) and algorithms (like sorting and searching), which speeds up development.

Multi-Paradigm Support: C++ is a multi-paradigm language, meaning it supports procedural programming (like C), object-oriented programming, and even generic programming with templates.

These features make C++ highly versatile, but also more difficult to learn and master than many newer languages.

3. Comparison with Other Languages

C++ vs. Python

Syntax and Ease of Use: Python is often praised for its simplicity and readability, making it a preferred language for beginners. Its dynamically-typed nature allows for rapid development without worrying about data types or memory management. C++, on the other hand, has a more rigid and complex syntax that demands greater attention to detail. Developers need to manually manage memory and declare data types.

Performance: C++ is significantly faster than Python because it’s a compiled language, whereas Python is interpreted. This makes C++ a better choice for performance-critical applications, like game engines or real-time simulations. Python excels in web development, data analysis, and artificial intelligence, where speed is not the top priority.

Use Cases: Python is widely used in data science, machine learning, automation, and web development. C++, with its superior performance, is more commonly used in game development, embedded systems, and applications requiring direct hardware interaction.

Verdict: If you need speed and control over system resources, C++ is the better option. For easier syntax and faster development cycles, Python wins.

C++ vs. Java

Object-Oriented Focus: Both C++ and Java are object-oriented, but C++ also supports procedural programming, giving it more flexibility. Java, on the other hand, was designed to be purely object-oriented from the ground up. This makes Java easier to learn for those specifically looking to focus on OOP concepts, whereas C++ can be overcomplicated with its multi-paradigm nature.

Memory Management: C++ requires manual memory management through pointers and dynamic allocation, which can be error-prone. Java simplifies memory management by using automatic garbage collection, making it easier to avoid memory leaks and segmentation faults. This is one of the reasons Java is preferred for large-scale enterprise applications.

Performance: While both languages are compiled, C++ typically offers better performance because Java runs on the Java Virtual Machine (JVM), introducing some overhead. However, the difference in speed has diminished with modern optimizations in Java.

Use Cases: Java is widely used in enterprise-level applications, Android app development, and backend systems. C++ is used for system-level programming, high-performance games, and real-time simulations.

Verdict: If you need fine-grained control over resources or real-time performance, C++ is the better choice. For ease of use and strong object-oriented design, Java is preferable.

C++ vs. C#

Platform Dependence: C++ is platform-independent, meaning that C++ programs can run on any operating system with minimal changes. C# was designed by Microsoft and is deeply integrated into the .NET framework, making it the best choice for Windows-based applications, but less portable across other platforms.

Memory Management: Like Java, C# provides automatic memory management through garbage collection, which can make it easier to write robust applications compared to C++’s manual memory management.

Performance: C++ still offers better performance than C# in most cases, especially in real-time applications. However, for most business and desktop applications, C#'s performance is more than sufficient.

Use Cases: C# is primarily used for Windows application development, game development with Unity, and enterprise solutions. C++ remains the go-to for system-level programming, high-performance applications, and cross-platform projects.

Verdict: C++ is better suited for cross-platform, high-performance software, while C# is ideal for Windows-specific or business applications.

C++ vs. C

Language Features: C++ is essentially an extension of C, meaning that it includes all the features of C while adding object-oriented programming, templates, and more. C is procedural and doesn’t support classes or objects, making it less suitable for modeling complex systems.

Use Cases: C is still widely used for embedded systems, firmware development, and system-level programming where minimal overhead is essential. C++ is used in many of the same areas but excels in situations where object-oriented programming or reusable components are beneficial.

Verdict: Choose C for low-level system programming or embedded systems, and C++ when you need object-oriented features and performance.

4. When to Choose C++

C++ is a powerful language, but it’s not always the right tool for every job. Here are some situations where C++ is the best choice:

High-Performance Applications: Games, real-time simulations, and financial systems requiring low-latency performance benefit from C++.

System-Level Programming: C++’s ability to interact directly with hardware makes it ideal for operating systems, drivers, and embedded systems.

Cross-Platform Development: If you need to develop applications that run on multiple operating systems with minimal changes, C++ is a good choice.

Legacy Systems: Many older systems and libraries are written in C or C++, so if you're working with legacy code, C++ is the natural language to use.

5. Conclusion: Why C++ Still Matters

Despite the rise of newer, easier-to-learn languages, C++ continues to play a crucial role in modern software development. Its balance between high-level programming concepts and low-level hardware control makes it indispensable for performance-critical and system-level programming. While the language has a steep learning curve, its power and flexibility are unmatched.

If you're ready to dive into the world of C++ and unlock its full potential, consider joining the best C++ programming course at CADL in Zirakpur. You’ll learn from experts who can guide you from the basics to advanced concepts, ensuring that you gain the skills needed to succeed in the ever-evolving tech landscape.

6. Learn C++ at CADL Zirakpur

At CADL in Zirakpur, we offer a comprehensive C++ programming course that covers everything from basic syntax to advanced memory management, data structures, and algorithms. Whether you're a beginner or an experienced developer looking to sharpen your skills, our course is designed to provide hands-on learning and real-world application.

Expert Instructors: Learn from industry professionals with years of experience in C++ programming.

Hands-On Projects: Work on real-world projects that simulate actual industry challenges.

Flexible Schedule: Our course offers flexible timings to accommodate working professionals and students.

Join the best C++ programming course at CADL in Zirakpur and take the first step toward mastering one of the most powerful programming languages in the world.

[shalu620]

Exploring Python: Key Features that Set It Apart

Python stands out in the programming world for its intuitive design and wide-ranging applications. If you're considering diving into Python or want to deepen your understanding, knowing its core features can help you see why it’s such a powerful and popular language. Here’s a fresh perspective on what makes Python a top choice for developers of all levels.

Considering the kind support of Learn Python Course in Pune, Whatever your level of experience or reason for switching from another programming language, learning Python gets much more fun.

1. Easy-to-Read and Understand Syntax

Python's syntax is one of its most compelling features. It’s designed to be easy to read and write, resembling plain English more closely than many other programming languages. This user-friendly syntax makes it easier for new developers to grasp programming concepts and allows experienced programmers to write and maintain code more effectively.

2. Interpreted Nature for Flexibility

As an interpreted language, Python executes code line-by-line, which allows for rapid testing and debugging. This means you can immediately see the results of your code and make adjustments on the fly, streamlining the development process and reducing the time spent waiting for compilation.

3. Dynamic Typing for Flexibility

In Python, variable types are determined at runtime rather than being explicitly declared. This dynamic typing feature enhances the flexibility of coding, enabling you to write more adaptable and concise code. It also simplifies the development process by removing the need for type declarations.

4. Embracing Object-Oriented Programming

Python’s support for object-oriented programming (OOP) allows you to create and manage code using classes and objects. This paradigm supports principles such as encapsulation, inheritance, and polymorphism, which can help you build well-structured and reusable code. OOP in Python promotes better organization and easier maintenance of complex programs.

Enrolling in the Best Python Certification Online can help people realise Python's full potential and gain a deeper understanding of its complexities.

5. Rich Standard Library

Python's standard library is a treasure trove of built-in modules and functions that cover a wide array of tasks. From handling files and directories to network communication and data manipulation, Python’s extensive library provides ready-to-use tools that can accelerate your development process and reduce the need for additional dependencies.

6. Platform Independence

One of Python’s strengths is its ability to run on various operating systems like Windows, macOS, and Linux without requiring modifications. This cross-platform compatibility means that Python programs can be developed and executed in different environments, making it a versatile choice for applications that need to work across multiple platforms.

7. Versatile Programming Paradigms

Python supports several programming paradigms, including procedural, object-oriented, and functional programming. This versatility allows you to choose the best approach for your specific needs, whether you're creating simple scripts or complex software solutions. The ability to mix and match paradigms adds to Python's adaptability and power.

8. Thriving Community and Ecosystem

Python benefits from a large, active community of developers who contribute to a rich ecosystem of libraries, frameworks, and tools. This extensive network means you have access to a wide range of resources and support, from troubleshooting advice to advanced libraries that extend Python's capabilities for specialized tasks.

Conclusion

Python's unique blend of simplicity, flexibility, and functionality makes it a standout language for both newcomers and experienced developers. Its clear syntax, dynamic typing, support for multiple programming paradigms, and extensive standard library contribute to its widespread adoption and success. With its broad community support and platform compatibility, Python is well-equipped to handle a variety of programming challenges, making it an excellent choice for a wide range of applications.

[technicalmanoj]

Python: A Beginner's Best Friend

Python, often praised as the "programming language for everyone," has established itself as a welcoming entry point for beginners venturing into the world of coding. This article delves into why Python is considered easy for beginners and outlines the steps to learn Python development effectively.

Python: A Beginner's Best Friend

Readable and Simple Syntax: Python's primary allure for beginners is its clean and readable syntax. Unlike some programming languages with complex and cryptic code, Python's structure resembles the English language, making it easy for new programmers to understand and write code. The simple syntax eliminates the need for excessive punctuation and curly braces, resulting in concise and easy-to-follow scripts.

Comprehensive Documentation: Python offers a wealth of official documentation and tutorials. The Python community is renowned for its friendliness and willingness to help newcomers, and this extensive documentation serves as a valuable resource for learning and troubleshooting. Whether you're a beginner or an experienced developer, Python's extensive resources are a significant advantage.

Large and Supportive Community: Python's global community is a treasure trove of support for beginners. Online forums, social media groups, and local meetups provide ample opportunities to connect with fellow Python enthusiasts, share knowledge, and seek help. This vibrant community ensures that beginners never feel alone in their coding journey.

Versatile and Cross-Platform: Python's versatility is a significant asset for beginners. It can be used for a wide range of applications, from web development and data analysis to artificial intelligence and scientific computing. The ability to explore different domains allows beginners to find their niche in the programming world.

Immediate Gratification: Python's interpreted nature allows beginners to see the immediate results of their code without the need for complex compilation steps. This instant feedback helps learners understand their mistakes and progress quickly.

How to Learn Python Development

If you're a beginner eager to start your Python journey, here are some steps to effectively learn Python development:

Choose Your Learning Path: Decide on your primary motivation for learning Python. Do you want to become a web developer, data scientist, or automate repetitive tasks? Understanding your goals will guide your learning path.

Set Up Your Development Environment: Install Python on your computer. You can use Python's official website (python.org) or popular Python distributions like Anaconda. You'll also need a code editor or integrated development environment (IDE) to write and run Python code. Editors like Visual Studio Code and PyCharm are excellent choices.

Start with the Basics: Begin with the fundamentals, such as variables, data types, loops, and conditional statements. Online tutorials, courses, and textbooks are valuable resources for learning these basics.

Work on Projects: Hands-on practice is essential. Start small by working on simple projects that interest you, such as building a to-do list app, a basic website, or a data analysis task. Projects not only reinforce your knowledge but also provide you with a portfolio to showcase your skills.

Learn from Others: Join coding communities and online forums to learn from others. Participate in open-source projects or collaborate with peers on coding challenges.

Seek Specialized Knowledge: As you progress, explore specialized areas of Python development, such as web development (using frameworks like Django or Flask), data analysis (utilizing libraries like NumPy and Pandas), or machine learning (using libraries like TensorFlow or PyTorch).

Stay Consistent: Consistency is key. Allocate dedicated time to practice and learn Python regularly. Set achievable goals and milestones to track your progress.

[this-week-in-rust]

This Week in Rust 466

Hello and welcome to another issue of This Week in Rust! Rust is a programming language empowering everyone to build reliable and efficient software. This is a weekly summary of its progress and community. Want something mentioned? Tweet us at @ThisWeekInRust or send us a pull request. Want to get involved? We love contributions.

This Week in Rust is openly developed on GitHub. If you find any errors in this week's issue, please submit a PR.

Updates from Rust Community

Foundation

Implementing the Network Time Protocol (NTP) in Rust

Project/Tooling Updates

rust-analyzer changelog #152

IntelliJ Rust Changelog #181

mirrord 3.0 is out!

gix credential and diffing with similar

New release – gtk-rs

Zellij 0.32.0: YAML => KDL, actions through CLI, command panes and a new layout system

RPITs, RPITITs and AFITs and their relationship

Evaluating Build Scripts in the IntelliJ Rust Plugin

Observations/Thoughts

Adding Ada to Rust

Adding a JavaScript interpreter to your Rust project

Rust in the Linux Kernel: Just the Beginning

A deeper look into the GCC Rust front-end

The HTTP crash course nobody asked for

Making Rust attractive for writing GTK applications

Adventures In Cross Compilation

Compiling Brainfuck code - Part 1: A Optimized Interpreter

Rust Embedded Graphics with the MAX7219

Buffers on the edge: Python and Rust · Alex Gaynor

Writing Better Integration Tests with RAII

Contention on multi-threaded regex matching

Rust Walkthroughs

Serde by Example 2: OpenStreetMap

Enums and Pattern Matching in Rust

Creating a minimal RESTful song request API using Rocket

Compiling Rust libraries for Android apps: a deep dive

Inline Crates

Writing a HashMap in Rust without unsafe

A Rust web app with HTML templates

Nine Rules for Creating Procedural Macros in Rust: Practical Lessons from anyinput, a New Macro for Easily Accepting String/Path/Iterator/Array-Like Inputs

[series] Sqlite File Parser Pt 4

MacroKata: Rustlings style exercises for learning macros

Miscellaneous

[video] Web-native Rust apps (what will YOU build?)

[video] Rapid Prototyping in Rust: Write fast like Python; Run fast like C

[video] Let our rusty crab explore the depths of the C by Yvan Sraka

[video] Case Study: Rust in axle OS by Philip Tennen

[video] Aya: Extending the Linux Kernel with eBPF and Rust by Michal Rostecki

[video] Building a Lightweight IR and Backend for YJIT / Maxime Chevalier-Boisvert

[video] RustcContributor::explore: @compiler-errors session - RPITIT deep dive

Step-by-step guide to building a web-crawler

SQLx in 12 minutes - Rust + Actix Web + PostgreSQL

Bevy Basics Scenes(re-upload)

Crate of the Week

This week's crate is humantime, a parser and formatter for std::time::{Duration, SystemTime}.

Thanks to Aleksey Kladov for the suggestion!

Please submit your suggestions and votes for next week!

Call for Participation

Always wanted to contribute to open-source projects but didn't know where to start? Every week we highlight some tasks from the Rust community for you to pick and get started!

Some of these tasks may also have mentors available, visit the task page for more information.

zerocopy - Optimize caching in CI

boa - hacktoberfest issues

boa - bugs that cause boa to panic

Ockam - Show "help" output when no args passed on subscription show clap command

Ockam - Add argument to node create clap command to terminate on EOF on STDIN

Ockam - Extract duplicated code into a shared helper function

If you are a Rust project owner and are looking for contributors, please submit tasks here.

Updates from the Rust Project

398 pull requests were merged in the last week

linker: fix weak lang item linking with combination windows-gnu + LLD + LTO

recover when unclosed char literal is parsed as a lifetime in some positions

allow #[unstable] impls for fn() with unstable abi

allow Vec::leak when using no_global_oom_handling

allow semicolon after closure within parentheses in macros

change unknown_lint applicability to MaybeIncorrect

require Drop impls to have the same constness on its bounds as the bounds on the struct have

require lifetime bounds for opaque types in order to allow hidden types to capture said lifetimes

add default trait implementations for "c-unwind" ABI function pointers

filtering spans when emitting json

suggest let for assignment, and some code refactor

do not suggest trivially false const predicates

standardize "use parentheses to call" suggestions between typeck and trait selection

escape string literals when fixing overlong char literal

handle return-position impl Trait in traits properly in register_hidden_type

improve "~const is not allowed here" message

add diagnostic for calling a function with the same name with unresolved Macro

chalk: consider ADT's generic parameters

miri: fix ICE when trying to GC a Stack with an unknown bottom

miri: add scalar-abi-only field retagging option

erase regions before checking for Default in uninitialized binding error

introduce deduced parameter attributes, and use them for deducing readonly on indirect immutable freeze by-value function parameters

let expressions on RHS shouldn't be terminating scopes

make diagnostic for unsatisfied Termination bounds more precise

make order_dependent_trait_objects show up in future-breakage reports

reduce false positives in msys2 detection

enable LTO for rustc_driver.so

remove byte swap of valtree hash on big endian

remove more attributes from metadata

use Set instead of Vec in transitive_relation

sort tests at compile time, not at startup

use already checked RHS ty for LHS deref suggestions

stabilize proc_macro::Span::source_text

stabilize duration_checked_float

stabilize asm_sym

make transpose const and inline

mark std::os::wasi::io::AsFd etc. as stable

eliminate 280-byte memset from ReadDir iterator

optimize slice_iter.copied().next_chunk()

implement String::leak

adjust argument type for mutable with_metadata_of

hashbrown: add support for 16-bit targets

futures: do not store items field in ReadyChunks

cargo: fix publishing with a dependency on a sparse registry

cargo: improve the error message if publish is false or empty list

cargo: publish: check remote git registry more than once post-publish

rustdoc: eliminate uses of EarlyDocLinkResolver::all_traits

rustdoc: do not filter out cross-crate Self: Sized bounds

crates.io: introduce daily limit of published versions per crate

docs.rs: perf: change the link in the topbar to avoid a redirect

bindgen: avoid suppressing panic messages

bindgen: use panic hooks instead of using catch_unwind

clippy: add missing_trait_methods lint

clippy: add lint to tell about let else pattern

clippy: enable test no_std_main_recursion

clippy: fix allow_attributes_without_reason applying to external crate macros

clippy: fix ICE due to out-of-bounds array access

clippy: improvement for equatable_if_let

clippy: collapsible_match specify field name when destructuring structs

clippy: unwrap_used, expect_used do not lint in test cfg

clippy: ref_option_ref do not lint when inner reference is mutable

clippy: add from_raw_with_void_ptr lint

clippy: fix box-default ignoring trait objects' types

clippy: support map_or for or_fun_call lint

rust-analyzer: support const generics for builtin derive macro

rust-analyzer: workaround the python vscode extension's polyfill

rust-analyzer: add multiple getters mode in generate_getter

rust-analyzer: don't catch the server activation error

rust-analyzer: don't respond with an error when requesting a shutdown while starting

rust-analyzer: fix DidSaveDocument requests blocking the server on startup

rust-analyzer: fix standard flycheck command not being executed in the workspace it is being invoked for

rust-analyzer: handle multiple projects sharing dependency correctly in once strategy

Rust Compiler Performance Triage

An amazing week. We saw more wins than losses; I want to call out specifically the wins from removing attributes from metadata (up to 8.2% faster builds for 18 benchmarks) and from enabling LTO for rustc_driver.so (up to 9.6% faster builds for an epic 230 benchmarks, with zero regressions).

Triage done by @pnkfelix. Revision range: e0f8e60d..629a414d

2 Regressions, 6 Improvements, 2 Mixed; 2 of them in rollups 53 artifact comparisons made in total

See full report for details.

Call for Testing

An important step for RFC implementation is for people to experiment with the implementation and give feedback, especially before stabilization. The following RFCs would benefit from user testing before moving forward:

No RFCs issued a call for testing this week.

If you are a feature implementer and would like your RFC to appear on the above list, add the new call-for-testing label to your RFC along with a comment providing testing instructions and/or guidance on which aspect(s) of the feature need testing.

Approved RFCs

Changes to Rust follow the Rust RFC (request for comments) process. These are the RFCs that were approved for implementation this week:

No RFCs were approved this week.

Final Comment Period

Every week, the team announces the 'final comment period' for RFCs and key PRs which are reaching a decision. Express your opinions now.

RFCs

No RFCs entered Final Comment Period this week.

Tracking Issues & PRs

[disposition: merge] Make PROC_MACRO_DERIVE_RESOLUTION_FALLBACK a hard error

[disposition: merge] Elaborate supertrait obligations when deducing closure signatures

[disposition: merge] Tracking Issue for Integer::{ilog,ilog2,ilog10}

[disposition: close] Propagate deref coercion into block

[disposition: merge] Derive Eq and Hash for ControlFlow

New and Updated RFCs

[new] Warning on unintended implicit drops

[new] Niches

[new] Deprecate PhantomData dropck

Upcoming Events

Rusty Events between 2022-10-26 - 2022-11-23 🦀

Virtual

2022-10-26 | Virtual (Redmond, WA, US / New York, NY, US / Toronto, CA / Stockholm, SE / London, UK) | Microsoft Reactor Redmond

Your First Rust Project: Rust Basics | New York Mirror | Toronto Mirror | Stockholm Mirror | London Mirror

2022-10-27 | Virtual (Charlottesville, VA, US) | Charlottesville Rust Meetup

Using Applicative Functors to parse command line options

2022-10-27 | Virtual (Karlsruhe, DE) | The Karlsruhe Functional Programmers Meetup Group

Stammtisch (gemeinsam mit der C++ UG KA) (various topics, from C++ to Rust...)

2022-10-27 | Virtual (Linz, AT) | Rust Linz

Rust Meetup Linz - 26th Edition

2022-10-29 | Virtual (Ludwigslust, DE) | Ludwigslust Rust Meetup

Von Nullen und Einsen | Rust Meetup Ludwigslust #1

2022-11-01 | Virtual (Beijing, CN) | WebAssembly and Rust Meetup (Rustlang)

Monthly WasmEdge Community Meeting, a CNCF sandbox WebAssembly runtime

2022-11-01 | Virtual (Buffalo, NY, US) | Buffalo Rust Meetup

Buffalo Rust User Group, First Tuesdays

2022-11-02 | Virtual (Cardiff, UK) | Rust and C++ Cardiff

Rust and C++ Cardiff Virtual Meet

2022-11-02 | Virtual (Indianapolis, IN, US) | Indy Rust

Indy.rs - with Social Distancing

2022-11-02 | Virtual (Redmond, WA, US / San Francisco, SF, US / New York, NY, US / Toronto, CA / London, UK) | Microsoft Reactor Redmond

Getting Started with Rust: From Java Dev to Rust Developer | San Francisco Mirror | New York Mirror | Toronto Mirror | London Mirror

2022-11-02 | Virtual (Stuttgart, DE) | Rust Community Stuttgart

Rust-Meetup

2022-11-08 | Virtual (Berlin, DE) | OpenTechSchool Berlin

Rust Hack and Learn

2022-11-08 | Virtual (Dallas, TX, US) | Dallas Rust

Second Tuesday

2022-11-08 | Virtual (Rostock, DE) | Altow Academy

Rust Meetup Rostock

2022-11-08 | Virtual (Stockholm, SE) | Func Prog Sweden

Tenth Func Prog Sweden MeetUp 2022 – Online (with "Ready for Rust" by Erik Dörnenburg)

2022-11-09 | Virtual (Malaysia, MY) | Rust Malaysia

Rust Meetup November 2022 - a couple of lightning talks

2022-11-10 | Virtual (Budapest, HU) | HWSW free!

RUST! RUST! RUST! meetup (online formában!)

2022-11-12 | Virtual | Rust GameDev

Rust GameDev Monthly Meetup

2022-11-15 | Virtual (Washington, DC, US) | Rust DC

Mid-month Rustful

2022-11-15 | Virtual (Nairobi, KE / New York, NY, US)| Data Umbrella Africa

Online: Introduction to Rust Programming | New York Mirror

2022-11-16 | Virtual (Vancouver, BC, CA) | Vancouver Rust

Rust Study/Hack/Hang-out

2022-11-17 | Virtual (Amsterdam, NL) | ITGilde Tech-Talks

Introduction “Rust” an ITGilde Tech Talk delivered by Pascal van Dam

2022-11-21 | Virtual (Paris, FR) | Meetup Paris - École Supérieure de Génie Informatique (ESGI)

Découverte de WebAssembly

Asia

2022-11-08 | Bangkok, TH | Tech@Agoda

Rustacean Bangkok 5.0.0

Europe

2022-10-26 | London, UK | Rust London User Group

LDN Talks October 2022: Host by Amazon Prime Video

2022-10-26 | Bristol, UK | Rust and C++ Cardiff/Rust Bristol

Programming Veloren & Rust for a living

2022-10-27 | København, DK | Copenhagen Rust Group

Hack Night #30

2022-11-23 | Bratislava, SK | Bratislava Rust Meetup Group

Initial Meet and Greet Rust meetup

North America

2022-10-27 | Lehi, UT, US | Utah Rust

Bevy Crash Course with Nathan and Food!

2022-11-10 | Columbus, OH, US | Columbus Rust Society

Monthly Meeting

Oceania

2022-11-09 | Sydney, NSW, AU | Rust Sydney

RustAU Sydney - Last physical for 2022 !

2022-11-22 | Canberra, ACT, AU | Canberra Rust User Group

November Meetup

South America

2022-11-05 | São Paulo, SP, BR | Rust São Paulo Meetup

Rust-SP meetup Outubro 2022

If you are running a Rust event please add it to the calendar to get it mentioned here. Please remember to add a link to the event too. Email the Rust Community Team for access.

Jobs

Please see the latest Who's Hiring thread on r/rust

Quote of the Week

Also, I don't know how much of this is because Rust is special or because BurntSushi is a national treasure and his CSV library is impeccably constructed and documented.

– Gabe Durazo on github

Thanks to scottmcm for the suggestion!

Please submit quotes and vote for next week!

This Week in Rust is edited by: nellshamrell, llogiq, cdmistman, ericseppanen, extrawurst, andrewpollack, U007D, kolharsam, joelmarcey, mariannegoldin, bennyvasquez.

Email list hosting is sponsored by The Rust Foundation

Discuss on r/rust

[educationtech]

C++ Introduction - Read Full Blog - Arya College

What is C++, brief about its components?

Every C++ programmer has probably written a linked list or set, searching and sorting routines. Probably the programmer of best engineering colleges in Jaipur has re-invented the wheel for every new user-defined data type. Design changes are not that difficult to implement in such cases. Maintaining such code is very complex. If the common programming components were important part of the C++ language, programmers would not need to re-invent the wheel. Finally, the C++ language offers you with general purpose components for common programming tasks through the Standard C++ Library. The Standard C++ Library provides powerful and flexible containers, programmable algorithms, and other efficient and extensible components. Several Standard C++ library components can be explained via C++ templates.

C++ is a general-purpose and object-oriented programming language. It is very similar to C and will probably compile over 99% of C programs without changing a line of source code. Some computer languages are written for a particular purpose. Java was initially devised to control toasters and other electronics. Pascal was the popular concept to teach programming techniques. 

Popular programming languages in use

Popular languages that are mainly in use by the students of top engineering colleges in Jaipur are Java, Python, C++, and C and the lower-level languages are Assembly Language, C, C++. These languages force the programmer to think more about the problem in terms of computer programming and its implementations, instead of the business logic. Today, C++ is relatively popular which is a feat in and its own. 

Is C++ best programming language?

C++ depends on perspective and requirements. Other languages like Visual Basic, Python have GUI design elements that are built into them. Therefore, they are perfectly suitable for GUI type of task. Some of the scripting languages provide extra programmability to applications including MS Word and even photoshop that tend to be variants of Basic. C++ is widely used and the most famous software have their backbone in C++.

Who uses C++?

Today, some of the most visible used systems have their critical parts written in C++. Many programming languages are based on C++’s performance and reliability in their implementation. For instance, Java Virtual Machines, JavaScript interpreters, Browsers, Application and Web frameworks, etc.

Applications that involve local and wide area networks, user interaction, graphics, numeric, and database access highly depend on C++ language for the students of private engineering colleges in Jaipur.

Basic Concepts of C++

There are some basic C++ concepts including the following: 

C++ Variables

Variables are the backbone of programming language.

a.                A variable helps you store some information for later use. You can retrieve this value or data by referring to a “word” that will describe this information.

b.               While declaring and defining, they may be used many times within the scope in which they were declared.

2.     C++ Control Structures

While running a program, the code is read by the compiler line by line. This is popularly known as “code flow”. When the code is being read from top to bottom, it may find a point where it needs to make a decision. Based on the decision, the program may jump to a varied part of the code. It may make the compiler re-run a particular piece again, or just skip a bunch of code.

For instance, if students of private engineering colleges in Jaipur choose from different courses. Once you decide, click a link and skip a few pages. Similarly, a computer program has a set of strict rules to decide the flow of program execution.

3.               C++ Data Structures

With a list of courses in front of you, there are lot of courses, and different users may register for different courses.

4.               C++ Syntax

The syntax is a layout of expression, words, and symbols. This is because an email address has its well-defined syntax. You need some combination of numbers, letters, potentially with underscores (_) or periods (.) in between, followed by an at the rate (@) symbol, followed by some website domain (company.com).

Thus, the syntax in a programming language is much the same. They are well-defined set of rules that allow you to create some piece of well-functioning software.

5.               C++ Tools

In the real world, tools help you to get a certain job done promptly. This holds true with the programming world too. A tool in programming is a part of software which when used with the code allows students of best BTech colleges in Jaipur to program faster. There are probably tens of thousands. Most crucial tool, considered by many, is an IDE, an Integrated Development Environment. An IDE is a software which will make your coding life easier. IDEs allow your files and folders are organized and give you a nice and clean way to view them.

Use of C++ Programming Language

There are some prime uses of C++ Programming Language:

1.         Operating Systems

Wheater it is Microsoft Windows or Mac OSX or Linux, all of the operating systems have various parts which are programmed in C++. It is the backbone of all the well-known OSs as C++ is a strongly typed and quick programming language, that makes it an ideal choice to develop an operating system.

2.               Games

Nobody denies the fact that it is one of the fastest programming languages, C++ is widely used in programming of game development engines. C++ lets you manipulate hardware resources and it can also provide procedural programming for CPU intensive functions.

3.               Banking Applications

One of the most popularly used core-banking systems includes Infosys Finacle, uses C++ as the backend programming language. Banking applications must process millions of transactions on a daily basis and require high concurrency and low latency support.

4.               Cloud/Distributed Systems

Cloud storage systems use scalable file-systems that work according to the hardware. That is why, C++ becomes an important choice for Cloud systems.

[oliviachows]

Technologies in Mobile App Development.

Tools, frameworks, components, libraries, and other software that enable the creation of apps for a variety of devices are all considered technologies in mobile app development. These incorporate cell phones, PCs, tablets, and smartwatches.

Early on in the process, teams must make an important decision about the appropriate technology stack. It has an impact on distribution, device types, the number of codebases and languages to maintain, and other factors.

Need for any Mobile app development services? Reach here, https://rentindiancoders.com/what-we-do/

Apache Cordova.

Apache Cordova is a cross-platform mobile app development framework that makes use of common web technologies like HTML5, CSS3, and JavaScript to create applications that can run on a variety of platforms. It executes web application code inside a native container tailored to the platform.

It also comes with pre-made plugins that let you access the camera, GPS, file system, and other hardware features of the device.

However, some Cordova applications with comparable functionality may run slower than native ones. This could be a problem if you're making big apps that need to work with a lot of devices or if a user wants the app to feel like it's native.

React Native.

React Native is a framework that lets web developers use their knowledge of JavaScript to create mobile applications. Without sacrificing application quality, this technology enables faster mobile development and code sharing across iOS, Android, and the web.

Facebook, Walmart, Uber Eats, Bloomberg, Instagram, and a lot of other businesses use React Native. It also works with new platforms and is open-source.

Python.

Python is a well-known programming language that can be utilized to develop a wide range of mobile applications. It is an excellent choice for a wide range of applications, such as data analysis, voice and face recognition systems, image processing software, neural networks, and machine learning systems, due to its adaptability and ease of use.

Since Python is an interpreted language, it does not need to be compiled before it can run, which makes it easier and faster to develop than native programs. It likewise allows designers to test their progress without trusting that the code will arrange.

Java.

Java is a generally utilized PC programming language that is likewise exceptionally helpful for portable applications. Because it is platform-independent, you can write your code once and run it anywhere.

Java is extremely secure to use due to its security features. Additionally, it is one of the world's most widely used languages and is supported by billions of devices.

Artificial Intelligence (AI) & Machine Learning (ML).

These are emerging technologies that are altering our relationship with technology. They can look at data, find patterns, and create models that can predict how users will act.

The app's performance, usability, and user satisfaction can all be enhanced with the help of these technologies. They can also be used to improve search results, find anomalies, and make it easier to process natural language.

Blockchain.

Blockchain technology is a novel approach to data sharing and security. Additionally, it prevents data breaches and reduces transaction costs by eliminating intermediaries.

The technology enables low-cost mobile app development with top-notch features and is simple to implement.

Additionally, it is a dependable platform that reduces the likelihood of data breaches caused by apps and fraudulent transactions.

From recording and verifying digital assets to transferring ownership of real-world property, blockchain can be used in a variety of ways.

Low-Code App Development.

Low-Code App Development is a visual app-building method that lets anyone make apps without having to hire developers. Drag-and-drop functionality and visual icons to represent coding functions make application creation easier.

Businesses can create applications that function at digital speed and are compatible with a variety of devices using this method. Additionally, it aids businesses in automating business procedures.

[estelledarcy]

PYTHON

PYTHON

Let's give this language a good introduction even if it scarcely calls for one in order to learn what Python is mostly used for. Since its introduction in 1990, Python has developed into a crucial all-purpose tool for successive generations of programmers. This high-level language offers a number of other advantageous properties in addition to the support for many paradigms and its inherent extensibility, which are detailed in more detail below.

As of May 2019, Python is the fourth most popular programming language, according to the TIOBE index. Python was ranked eighth in popularity by Stack Overflow's poll in 2018 and was named the top programming language with the highest growth

Despite its ongoing progress, you will probably agree that Python is a rather old technology by industry standards. You could be wondering, given its age, "why use Python programming instead of newer alternatives" or even "why it has not become outdated, but instead still occupies its place among the most popular programming languages." The solution may be found in the many advantageous qualities that make it valuable for software development overall. Let's look at a short summary of the Python programming language's most illustrative benefits.

The language's simplicity is considered by many programmers to be its most distinctive characteristic. In compared to C++, Java, and other languages, the code is significantly clearer and shorter because to the concise and understandable command line inputs. Furthermore, even those who have no prior experience with coding find it simple to learn and comprehend. Achieving a certain level of proficiency in Python may also serve as a useful foundation for learning additional programming languages. Additionally, the short and simple code makes it simple to develop software prototypes quickly.

The built-in modules in the standard library make up a large portion of its comprehensiveness. It may be used in many situations "out of the box" without the need for additional software from a third party. Nevertheless, new frameworks and modules let Python establish itself as a general-purpose language that can be used to accomplish almost any objective or need in software development.

Programming paradigms including functional, reflection, and object-oriented coding are all supported by Python. Because of this, regardless of the market it is intended for, Python is a solid choice for practically any software projects, as seen by the many and incredibly varied web apps that have been created using it. Giants like Youtube, Instagram, Dropbox, Spotify, Pinterest, Uber, Reddit, Netflix, and other well-known services and businesses are among those who utilise Python.

Python is an interpreted language, thus its commands may be run without needing to be first compiled. Python software can operate on Windows, Linux, or any other operating system with the necessary interpreter, giving it a cross-platform advantage. In addition, most Linux distributions and Unix-like operating systems incorporate Python.

Over the past 30 years, Python has continued to advance mostly due to the enormous number of active supporters. The community's responsibility includes not just preserving and improving the language but also exchanging knowledge and looking for workable solutions. This is especially helpful for new Python programmers as they can quickly access several manuals and instructions as well as specialised guidance on writing applications using Python.

There are stringent security requirements for medical software development since the healthcare sector deals with very sensitive proprietary data. Python includes methods for addressing security issues, even if none of the computer languages used in healthcare are completely secure.

Python's flaws are swiftly identified and rectified by a team of experts because to its widespread use and loyal community. Additionally, there are Python-based frameworks like Django that come with pre-built modules for authentication, administration, and other security-related features in addition to providing built-in defence against assaults.

For software developers working in the sector of financial technology, fintech sets a variety of objectives and obstacles. For example, they manage huge volumes of data for trading platforms, credit bureaus, and other financial service providers. This area also entails extensive use of big data, with an emphasis on its safe storage, quick retrieval, and practical management, all of which could be achieved, for instance, using blockchain technology. Python can satisfy every specific requirement for a banking programming language and offer straightforward yet efficient solutions for data science thanks to the benefits mentioned above.

IPCS

[thewertsearch]

@morganwick asked: My interpretation of TA's code is that the color-coding does have syntactic value, like whitespace in Python, but so does how the lines are arranged linearly, otherwise there's no reason not to have both loops written completely separately one after the other. So taking into account the rest of your interpretation, I read the code like this:  1: The red loop begins and begins checking whether universe 1 exists. 2: While the red loop is running, the blue loop begins checking whether universe 2 exists. It's possible a new instance of the blue loop begins every moment universe 1 exists, allowing the "curse" to stack up.  3: Once universe 1 dies, the red loop fires the code checking whether the blue loop is running, which may be in a separate file containing the "curse" code. Either way, that loop is separate from the red loop itself, which can close without waiting for the blue loop to close, or alternately the last line we see closes both loops simultaneously. This part can only close while the blue loop is running; if both universes exist simultaneously, you have a paradox or hung code that can't abort. And finally, once universe 2 exists the blue loop fires the code checking whether the red loop is running and ends, similarly to the previous line.

This is an interesting interpretation of the code. My answer gets super long and super nerdy, so I’m sticking this one under the cut. 

[...] the color-coding does have syntactic value, like whitespace in Python, but so does how the lines are arranged linearly, otherwise there's no reason not to have both loops written completely separately one after the other.

It’s difficult to say. I mean - you’re right, if the red and blue loops are different code blocks, running on different threads, then they could have been written one after the other, and that would certainly be less confusing for us to read. That’s definitely what I’d do. 

But interweaving them like this might make more intuitive sense to TA, a Gemini troll with an obvious duality theme. He might genuinely have an easier time comprehending these interweaved code blocks than he would with ‘ordinary’ code. 

In other words, bifurcation might just be his syntactic style. I think the linear arrangement of the red and blue lines might genuinely be meaningless to someone who views code in multiple dimensions. To him, ~ATH(!U2) isn’t below ~ATH(U1), it’s behind it. 

However, to completely ignore the linear arrangement of the lines is to ignore some very interesting things this code could be doing, so for the sake of the discussion, let’s take another look.

2: While the red loop is running, the blue loop begins checking whether universe 2 exists. It's possible a new instance of the blue loop begins every moment universe 1 exists, allowing the "curse" to stack up.

If we read the red loop in a linear fashion, it seems to run the beginning of the blue loop every frame. This is extremely hard to analyze, because you can’t run part of a code block like this in any conventional programming language. This wouldn't even compile in most languages - it doesn’t mean anything.

If we throw convention out the window, there are ways to interpret this that almost make sense. Maybe the death loop created by ~ATH(!U2) is an object, and you’re allowed to instantiate a new ‘loop object’ without giving it a body. That’s possible, but super weird, and definitely ‘cursed’ in the memetic sense. 

3: Once universe 1 dies, the red loop fires the code checking whether the blue loop is running [...]

When the red loop dies with U1, it calls another loop, which waits for the death of the blue ‘THIS’. 

Now, here’s the million-dollar question.

Does the blue THIS refer to the blue loop, which ends when U2 comes to life, or the blue thread, which ends with DIE()? There’s no definitive answer, but I think it’s the latter.

[...] that loop is separate from the red loop itself, which can close without waiting for the blue loop to close[...]

I agree. When we’re in the red ‘EXECUTE()’, the red loop is considered closed, but the red thread is still running. And I think the red THIS is the thread, because its bifrucate[] call is before the loop - ie. it starts on a line which is out of the loop’s scope. 

If this is the case, then both red and blue are able to complete their loops just fine, but can’t finish their threads, and they’re back to being deadlocked. Neither of them can get to DIE(), because they’re both waiting for the other thread to get to DIE() first. 

or alternately the last line we see closes both loops simultaneously.

I think bifurcate[THIS, THIS] splits the threads, and then [THIS,THIS].DIE() kills them both. There’s a duality there, and it definitely deadlocks the code, no matter how you look at it. 

This part can only close while the blue loop is running; if both universes exist simultaneously, you have a paradox or hung code that can't abort.

I actually don’t think it’s possible for this code not to hang. But on the other hand, this is a code snippet which interweaves two loops in an impossible way, so really, you could probably get any interpretation you want out of it by changing your interpretation of how code like this ‘should’ execute. There’s no right answer, just a lot of fun conjecture. 

This is the kind of confusion that led me to see the two blocks in two different planes. I really do think it’s the only way to make sense of the code.

which may be in a separate file containing the "curse" code.

The idea that we’re not seeing the curse because it’s in a separate file is an intriguing one. I think that if that’s the case, the most likely culprits are the two import directives from the beginning.

The import directive generally tells the compiler or interpreter to include data external to your program, so maybe one or both of these ‘universe’ objects contains the curse.

Wow, that got long. Anyway, thank you for giving me the opportunity to talk about ~ATH even more. 

[blubberquark]

Why PyGame is Slow

This has come up often enough on the Discord to justify a comprehensive post on the matter.

People like to say that PyGame is slow, and that you should use a fast programming language like C++, and a “real” engine like Unity3D or UE4. People tell that to beginners too, but beginners can write slow code in any language, in any game engine, yet they quickly start to parrot "PyGame is slow" without understanding why that is and when performance matters.

Something like “Python is slow“ or “PyGame is slow” only helps if you understand what makes other game engines fast.

If your game is running fast enough, if you get a stable 60 FPS with some CPU cycles to spare, then you shouldn't waste your time optimising. PyGame might be slower than UE4, but it is fast enough often enough, and I happily trade some speed for the convenience of writing Python. Especially in web development, where correctness and development time are more important than speed, people happily make the same trade-off for business-critical software.

If you want to write a clone of Pong, or Tetris, or Flappy Bird, then PyGame is fast enough. If you are making a point-and-click adventure, or a top-down RPG, or a Sokoban-like game, or a narrative game, then PyGame is also fast enough, but you may want to consider using AGS, RPG Maker, PuzzleScript, or Twine.

Interpretation

Python is a dynamic, interpreted language. That has many advantages for rapid development. You can get the type of a value at run time, you can always add a new instance variable to an object, you can use dir() to list the variable names in a module, and you can type an expression into the Python shell and evaluate it interactively.

The CPython interpreter (the default Python implementation you can download from python.org) achieves this level of dynamism in a rather straighforward way, but at the cost of run-time performance. When a simple line like x +=1 is executed in Python, the interpreter needs to look up x in some dictionary of variables and values, increment the refcount of the value of x, see if x is a number or if it needs to invoke the __add__(self, other) method, add the numbers, increment the refcount of the result, decrement the refcount of the old value of X twice, and assign the new value to x in the dictionary of local variables.

In C, if x is an int, code like x++; would in all likelihood compile into a single instruction, and if x is already loaded into a register, and the pipelining of the CPU works, it might be executed within a single CPU cycle.

Of course, the compiled binary has no idea that the variable is called x and that its type is int.

All this makes pure Python between 10 and 100 times slower than pure C, but in practice, this difference is not quite as big. Many Python functions like list.sort() or re.find() are implemented in C, so even if the function call overhead is 10 times slower, the time spent in the body of the function would be roughly the same.

PyGame is implemented in C, and uses the cross-platform C library SDL2 (which achieves its cross-platformness by implementing platform-specific functionality again and again for each supported processor architecture, operating system or game console). The performance-critical party of PyGame are not implemented in Python. This makes PyGame reasonably fast.

Software Rendering

PyGame doesn't use the GPU for drawing. Instead of textures, vertices, and polygons on the GPU, PyGame uses 2D pixel buffers (called “surfaces” in SDL jargon), which are stored in RAM (main memory). You can either draw the contents of a surface into another surface or onto the screen (which is called called “blitting”), rotate and scale surfaces with the pygame.transform module, or draw shapes into surfaces with the pygame.draw module.

Blitting and drawing operations in PyGame are reasonably fast, because they are implemented in C, but they use the CPU and manipulate RAM. That means that every drawing and blitting operation has some Python function call overhead, but takes an amount of time that is roughly proportional to the number of pixels drawn.

The higher your resolution, the more pixels are drawn. Theoretically you can blit 100 8x8 surfaces, which makes for 6400 pixels, faster than you can draw 10 32x32 surfaces, which would amount to 10240 pixels. In practice, the overhead of Python will take its toll when you blit hundreds of small surfaces, and when you set individual pixels of a surface, the overhead of Python interpretation will make this operation 100x slower than the equivalent C code.

When rendering using a graphics card, it's not just faster because the graphics card is optimised for pushing pixels around and has a higher fill rate, but because your code running on the CPU can do other things while the GPU is doing the rendering. As long as your GPU is not maxed out, using it is "infinitely" faster than the CPU - at least in terms of CPU time used. Some GPU operations are as slow as or slower than software rendering. Loading a surface from RAM into a texture in graphics memory (VRAM) is limited by both the speed of the RAM and the system bus (your graphics card is probably connected via PCIe), but it's probably still faster than loading an image file from a hard disk (not so sure about loading a file from NVMe SSD). As long as all the textures have been loaded into the GPU (not just RAM) when you loaded the level, drawing them on the GPU every frame will be much, much faster than the equivalent software-based rendering.

Unfortunately, you can't just use a graphics card as a drop-in replacement for software rendering in PyGame for two connected reasons: First, there is some overhead involved in talking to the graphics card, so replacing every blit with a draw call to the graphics card might be slower, and second, you can blit surfaces not just onto the screen, but onto other surfaces, and you can draw onto every surface, and read back the pixels. To achieve 100% backward compatibility for rendering all existing PyGame games with hardware acceleration, you would have to copy pixels back and forth between main memory and VRAM, and have the CPU wait for the GPU to finsih drawing before reading back pixels. That kind of overhead would be way worse than Python function call overhead on small drawing operations, comparable to the overhead of making queries to your SQL database in a for loop instead of using a JOIN statement. Not all games in PyGame take advantage of all features, so it's possible to rewrite most but not all PyGame games to take advantage of the GPU easily.

On the bright side, many PyGame games are using only simple graphics with a low resolution, so rendering is usually not the bottleneck in the first place.

One benefit of software rendering is that drawing works similar to the way it did back in BASIC, and that makes PyGame a great teaching tool. Rendering is conceptually simple, and fully exposed to the user. With hardware-accelerated graphics, either a lof of complexity would be exposed to the user, or a lot of complexity would be hidden away behind a game engine. This brings us to the next point:

Not A Game Engine

PyGame is a library, not an engine. As a pythonic wrapper around SDL, it occupies a similar niche to Löve2D, libGDX, XNA/FNA/MonoGame, SFML, and raylib.

In PyGame, you are free to design your game loop however you like, and there is no concept of "game objects", levels, or components. You are free to draw whatever you want to the screen, and use your own algorithms and data structures.

A game engine usually controls the entry point of the program, and loads level data and game-objects into its own data structures. The gameplay code written by the game designer is invoked during loading, when game objects are updated, or during collisions, but the game loop of the engine cannot be changed. Furthermore, the engine is structured so gameplay code doesn't need to do a lot of complex computation: Rendering, physics, and collision detection are already handled by the engine, and gameplay code only needs to set parameters on game objects, create or destroy them, which is usually quite fast - or rather, if the game engine is fast enough, the game will probably run fast enough.

In contrast to this, libraries like PyGame require the game designer to implement functionality like culling and collision detection (if desired), and it is easy enough to get them slightly wrong, or accidentally implement them with quadratic run-time. There are more ways to shoot yourself in the foot. You could make too many draw calls, read a file from disk every frame, or stop updating the game while you wait for network input. All of these problems can be avoided when you know what you are doing, or they can become problems when "rolling your own engine" in a fast language like C++. The next one is unavoidable when using Python and PyGame, but not as impactful as choosing algorithms and data structures with good worst-case complexity.

In a game engine, the core functionality is usually implemented in C or C++, with only the game logic written scripting language like lua or a managed language like C#. This way, the "hot" code paths of rendering and collision detection do not only use algorithms with good worst-case behaviour, they also have very little overhead. Many modern engines even lay out the game objects in a contiguous block of memory to avoid following pointers, speed up memory access, and allow fast iteration over all game objects.

If this code was written in pure Python, even if the Python-based main loop called fast blitting routines implemented in assembly language, or if rendering was based on OpenGL, iterating over all game objects would involve Python lists of Python objects, following pointers to each object, and virtual method dispatch. There would be a much greater overhead per object.

It is possible to implement the core of an engine in efficient C, with game objects laid out in memory for fast access, and to expose this data structure to Python scripts through some kind of façade or proxy that translates Python method calls into manipulations of these flat data structures. This is of course a performance trade-off, trading slightly worse performance of custom scripts and a fixed main loop for much better performance of the engine core, but it might be worth it, especially if scripts leave the heavy lifting to the engine core and just change values of game objects. Fast native-code game engines that can be scripted with Python already exist.

More On Python Performance

PyGame has some fast functions implemented in C, but unfortunately, performance is like dieting: It only adds up. Just like you can't "even out" the calories of a meal if you eat three salads after you had three bars of chocolate, you can't really improve performance by calling more PyGame functions. Sometimes, the slight overhead of the Python/C API has a higher impact on overall performance than the time spent actually doing the thing.

The global interpreter lock or GIL is another annoying implementation detail of CPython. It ensures that C extension modules are running single-threaded by default, and Python code is always running single-threaded. You can execute Python code while a C extension waits for input or performs a long-running computation in another thread, but only if the C extension is programmed to allow that. And it better not access any Python objects while another thread is running Python code, or your code will crash.

If you use an engine that can be scripted with Python, only one script can be executed at a time, but if your engine core is all C, you can at least make the performance-critical parts multi-threaded.

PyGame can perform some costly operations like blitting or pygame.transform.scale() while another thread is running Python code, but in general, multithreading does not improve the performance of PyGame all that much.

Python 3 has introduced the asyncio module to make it slightly easier to write network servers that concurrently perform I/O with many clients (which was something you could already do with threads) and the numpy module can use a multi-threaded algorithm to compute the eigenvalues of large matrices. Neither can be used to make PyGame utilise another CPU core.

There is another Python implementation, called PyPy, consisting of a Python VM and compiler written in Python itself. PyPy code runs much faster then ordinary CPython code. It's not quite as fast as C, but close. PyPy also does not have a GIL, which means that you can see real performance gains from running multi-threaded PyPy code on multi-core CPUs.

Unfortunately, Python modules that are implemented in C assume the GIL is in place and use the old CPython API, and so it happens that PyGame talks to PyPy via some kind of proxy or facade that translates between the old Python API and the fast compiled PyPy code. As a result, the overhead of writing your main loop on PyPy is much lower, but the function call overhead when calling PyGame is much, much higher, so that many games will actually run slower on PyPy than on CPython. Smart people are already working on a new API to make C extension modules run fast on different Python implementations, so this might change in the future.

[shalu620]

Python Unveiled: A Comprehensive Look at Its Appeal and Advantages

In the ever-evolving landscape of programming languages, Python has carved out a distinctive niche for itself, appealing to both novice coders and seasoned professionals alike. What sets Python apart and makes it such a popular choice among developers? Let's delve into the unique aspects that define Python and highlight its strengths in the realm of software development.

Considering the kind support of Learn Python Course in Pune, Whatever your level of experience or reason for switching from another programming language, learning Python gets much more fun.

1. Intuitive and Elegant Syntax

Python's allure begins with its intuitive and elegant syntax. Unlike some programming languages that can be daunting for beginners, Python adopts a clean and readable format that resembles pseudo-code. This simplicity not only accelerates the learning curve but also promotes good coding practices. Developers find it easier to write and maintain Python code, fostering collaboration and enhancing productivity across diverse projects.

2. Versatility Across Various Paradigms

Python's versatility is another hallmark feature. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming styles. This adaptability allows developers to choose the most suitable approach based on project requirements and coding preferences. Whether you're building web applications, analyzing data, or developing artificial intelligence models, Python offers the flexibility needed to tackle a wide array of tasks effectively.

3. Extensive Library Ecosystem and Frameworks

One of Python's greatest strengths lies in its expansive library ecosystem and robust frameworks. From Django and Flask for web development to NumPy and Pandas for data manipulation and TensorFlow and PyTorch for machine learning, Python provides a wealth of pre-built libraries and tools that streamline development processes. These libraries not only simplify complex tasks but also empower developers to focus on innovating rather than reinventing the wheel.

4. Supportive Community and Resources

Python thrives on its vibrant and supportive community of developers. Whether you're seeking advice, troubleshooting a problem, or exploring new techniques, the Python community offers a wealth of resources. Forums, online tutorials, and collaborative projects abound, providing ample opportunities for learning and growth. Furthermore, Python's official documentation is comprehensive and accessible, serving as a reliable guide for developers at all skill levels. Enrolling in the Best Python Certification Online can help people realise Python's full potential and gain a deeper understanding of its complexities.

5. Cross-Platform Compatibility and Accessibility

Python's cross-platform compatibility is another standout feature. Code written in Python runs seamlessly on major operating systems, including Windows, macOS, and Linux, without requiring platform-specific adjustments. This portability simplifies deployment and maintenance, ensuring consistent performance across different environments and facilitating widespread adoption in diverse technological landscapes.

6. Performance Optimization and Scalability

While Python is interpreted and typically slower than compiled languages, it offers tools and techniques for optimizing performance when necessary. Developers can utilize tools like Cython to compile Python code into efficient C or C++ code, achieving significant performance enhancements without sacrificing Python's inherent productivity benefits. This scalability makes Python suitable for both small-scale scripts and large-scale applications demanding high-performance capabilities.

7. Seamless Integration and Interoperability

Python's compatibility with other languages and technologies enhances its versatility. Through APIs, wrappers, and extensions, Python seamlessly integrates with existing software systems, enabling developers to leverage complementary technologies within a unified framework. This interoperability fosters innovation and facilitates the creation of integrated solutions that meet complex business requirements across various domains.

Embracing Python for Modern Development Challenges

In essence, Python's appeal lies in its intuitive syntax, versatility across programming paradigms, extensive library ecosystem, supportive community, cross-platform compatibility, and scalability. Whether you're embarking on your programming journey or leading sophisticated projects, Python equips you with the tools and resources to innovate, collaborate effectively, and solve real-world problems with confidence.

Are you ready to harness the power of Python for your next project? Join the global community of Python enthusiasts, explore its vast ecosystem of libraries, and unlock endless possibilities in software development and beyond.

[a-coda]

Advent of Code

As a child, advent calendars always added to the sense of anticipation in the lead up to Christmas. In my day you would be lucky to get a small picture behind each of the doors. These days, children expect chocolates or sweets. My wife has once even had a "Ginvent Calendar", with gin behind each door.

This year I marked Advent by having a go at the "Advent of Code" which has Christmas-themed programming puzzles posted each day. Most days are in two parts, with an easier puzzle followed by a harder one. Traditionally, I've posted a (mostly ignored) programming puzzle to our development team each Christmas. Last year I just recycled one of the Advent of Code puzzles, but this year I suggested we attempt the whole thing. The puzzles are so well thought out, in comparison to my efforts, that it seemed pointless to compete.

In the end, several of the team had a go. Some of the puzzles were harder than others, but I managed to solve them all by Boxing Day. What follows are some personal anecdotes from the various days with some general thoughts at the end. Note that there are some spoilers and the notes won't mean much if you've not done the puzzles. So in this case just skip to the end.

a sum-finder. I implemented the search tree via recursive calls. I drifted into using Python right from the start. It just felt like the easiest way to hack the puzzles quickly. In the past I had thought about using the puzzles to learn a new language. A colleague had done that with Rust in a previous year. Despite these good intentions, expediency took a firm hold. That said, in several puzzles I would have liked immutable collections or at least Lisp-style lists.

a pattern counter. Not that interesting except patterns were emerging in the programs themselves. Regular expressions got used a lot to read in the puzzle data. I learnt about things like match.group(1,2,3) which returns a tuple of the first three match groups, so you don't have to write (m.group(1), m.group(2), m.group(3)).

a grid tracer. The first interesting one because it was unfamiliar. Some other patterns started emerging: problem parameters got promoted to command line arguments, and data structure printers got hacked to help debugging. These two were often added between part 1 and part 2 of each problem.

a data validator. This felt like a bit of a slog. It was mostly about capturing the validation rules as code. Even though I made a point of reminding myself at the start that re.search doesn't match the whole string I still forgot it later. Duh.

an indexing problem. I patted myself on the back for realizing that the index was a binary number (or pair of binary numbers as I did it). At this point the solutions were still neat and I would do a little code golfing after the solution to tidy them up a bit and make them more concise.

another pattern counter. Pre-calculating some things during data reading kept the later code simple.

a recursive calculator. This was one of those puzzles where I had to reread the description several times to try and understand what it was asking for. It entailed a slightly tricky recursive sum and product, which was again made easier by creating more supporting data structures while reading the input data.

an interpreter. Probably my favourite individual puzzle because it was so sweet, especially after a bit of refactoring to make the language more data-driven.

another sum-finder. I found I didn't particularly like these.

an order-finder. This was the first one that made me pause for thought. An overly naive search algorithm from part 1 hit a computational complexity wall in part 2. I beat the problem by realizing that the search only had to be done on small islands of the data, but a colleague pointed out there was a better linear solution. The code was starting to get a bit ragged, with commented out debugging statements.

the game of life. The classic simulation but with some out-of-bounds spaces and some line-of-sight rules. It helped to print the board.

a map navigator. I liked this one even though I forgot to convert degrees to radians and that rotation matrices go anti-clockwise. I even introduced an abstract data type (ADT) to see if it would simplify the code (I'm not sure it ever did - I mostly used lists, tuples, strings, and numbers). The second parts of the puzzles were starting to get their own files now (usually bootstrapped by copying and pasting the first part's file).

a prime number theorem. I actually got stalled on this one for a bit. It eventually turned out I had a bug in the code and was missing a modulus. In effect I wasn't accounting for small primes far to the right. I left the puzzle and went on to complete a couple of others before coming back to this one. I checked what I was doing by Googling for hints, but in the end I had to take a long hard look at the data and find my own bug.

some bit twiddling. Part 1 felt like I found the expected bitwise operations, but part 2 felt like I was bashing square pegs into round holes.

a number sequence problem. Another pat on the back, this time for keeping a dictionary of recent occurrences and not searching back down the list of numbers each time. Another recurring pattern is evident: running a sequence of steps over the data. I liked to code the step as its own function.

a constraint solver. A nice one about labelling fields that satisfy the known constraints. Half the code was parsing the textual rules into data.

another game of life simulation. This time it was in more dimensions. I generalized from 3 dimensions to N instead of just doing 4. This made it more of a drag. I started naming auxiliary functions with placeholder names (social services should have been called). Also, I tacked on extra space along each dimension to make room at each step. This felt very ugly. I should have used a sparser representation like I did for day 24.

an expression evaluator. I used another actual ADT and wrote a simple but horrible tokenizer. The evaluator was okay but I hacked the precedence by inserting parentheses into the token stream. Don't try this at home kids.

another pattern matcher. Probably my biggest hack. My code compiled the pattern rules into a single regular expression. This was cute but meant the recursive rules in part 2 needed special treatment. One rule just compiled into a repeated pattern with +. Unfortunately, the other rule entailed matching balanced sub-patterns, which every schoolchild knows regular languages can't do. Perhaps some recursive pattern extensions might have worked, but I assumed there would be no more than 10 elements of the sub-patterns and compiled the rule into a large alternative of the possible symmetrical matchers. Yuck.

a map assembler. I did this one the most methodically. It had proper comments and unit tests. Overall it took the most code but perhaps it was just dealing with all the edge cases (ba dum tss). But seriously, it seemed to take a lot of code for rotating and flipping the tiles even after knowing how they must be connected. So probably there was a better approach. It was still satisfying the see the answer come out after all that work. Curiously, this one involved little debugging. I wonder if perhaps there is some connection between preparation and outcome?

a constraint solver. I tried a dumb approach first based on searching all the possible bindings. That didn't look like it was terminating any time soon. So I reverted to a previously successful technique of intersecting the associations and then then refining them based on the already unique ones.

a recursive card game. This card game playing puzzle seemed to be going okay, but the real data didn't converge for part 2. Had a quick Google for a hint after battling with it for a while, and the first hit was from someone who said they'd misread the question. Sure enough I had too. My recursive games were on the whole deck instead of the part dictated by the cards played. Duh. The description was clear enough and included a whole worked game. I just hadn't read it properly. It still seemed to need some game state memoization to run tolerably fast.

a circular sequence. Took three attempts. A brute force approach using an array was good enough for part 1, but no way was it going to work on part 2. Even optimizing it to use ranges was still 'non-terminating' for the array-based solution. So I Googled for a little inspiration and found the phrase "linked lists" and slapped my forehead hard. I switched to a dictionary of labels to labels and the solution popped out very easily, without any further optimization. Embarrassing. Was it time to ceremonially hand in my Lisp symbol and fall on a sharpened parenthesis?

another game of life. This one sounded neat because it was about a hex grid, but I didn't know how hex grids are usually indexed. So for the first time I did a little bit of general research at the start. Turns out there are a bunch of ways to index a hex grid. I opted for using 3-axes as that seemed natural despite the redundancy. The map itself was just a dictionary of locations. I should have looked up how to have structured dictionary keys in Python (implement __hash__) but I couldn't be bothered so I (look away now) serialized and deserialized the locations to and from strings. I still had a bug which I couldn't find until I hacked a crude hex board printer and realized I wasn't carrying the unchanged cells over from one iteration to the next.

a cryptographic puzzle. Came out quite short but only after some faffing around. Main trick seemed to be to keep the transformation ticking along instead of recalculating it from scratch each time. There was slight disappointment (tinged with relief) that there was no part 2.

Some general lessons I felt I (re)learned:

Read the questions very carefully, then reread them.

Try and use terms from the questions. Don't invent your own terminology and then have to map back and forth.

Make the trace output exactly like the examples to help comparison.

Next time I'd consider using BDD to turn their examples directly into tests. Next time.

Try the problem for a while by yourself, then think about it offline, and only then Google for hints.

Next time I'd consider using some form of source control from the start, or just a better set of file naming conventions.

Regular expressions go a long way, but can then they can get in the way.

Next time I'll consider doing it using a language I'm learning.

Sometimes when you get stuck you have to start again.

During some low moments it all felt like make-work that I'd inflicted on myself, but in the end it was a nice set of training exercises. I'd encourage others to have a go at their leisure.

"Practice is the best of all instructors." -- Publilius Syrus

[theapoorvevermaproject]

What is coding? What is coding used for, how coding works, and benefits of learning coding #1

If you don’t know anything about coding, then you’ve come to the right place. We will guide you how you can learn to code. Today, we are going to talk about coding. We will answer the question: What is coding? How coding is used How coding works and benefits of learning.

What is coding?

Computers are amazing, but they can’t think for themselves (yet). Coding is a method of communicating with a computer. Coding is used for writing systematic lines of instructions that will perform certain actions. Coding uses computer programming languages ​​to give computers and machines a set of instructions to perform tasks. Coding allows us to create things like computer software, websites, apps, and video games, operating system, etc…

What is computer programming languages?

A programming language is terminology and a set of syntax rules to instruct computer devices to perform specific tasks. Programmers who use code writing are called “high-level languages”. And after compilation, the code changes to a “low-level language” that the computer can understand. Almost all programming languages work in the same way, but each programming language has a different syntax.

There are some example of programming languages.

Python, JavaScript, Java, Ruby, C Language, C++, C#, Go, etc…

Here is an example of printing Hello World using the Python programming language.

print(“Hello, world This is Apoorve Verma”)

How coding works?

Simply, code is what tells your computer what to do. Let’s take a deeper dive, computers don’t understand words. They only understand the concepts of on and off. The capabilities of a computer are switched on and off by switches and transistors. The binary code represents these on and off transistors as 1 and 0 digits. An infinite number of combinations of these codes make your computer work. To make binary code manageable, computer programming languages ​​were formed. These languages ​​serve various purposes, but they all allow programmers to translate important commands into binary code.

The Process of compiled programming languages:

A compiled language is a programming language whose implementations are typically compilers and not interpreters.

Example of Compiled Programming languages.

Example: C, C++, Go, COBOL, etc…

The Process of Interpreted programming languages:

An interpreted language is a programming language whose implementations execute instructions directly and freely, without previously compiling a program into machine-language instructions.

Example of Interpreted Programming languages.

Example: JavaScript, Perl, Python, BASIC, etc…

JIT Compilation or dynamic translation or run-time compilations:

Just-in-time (JIT) compilation is a method of executing computer code that involves compilation during the execution of a program – at run time – rather than before execution. It is a hybrid between normal compilation also known as ahead-of-time compilation and interpretation.

Example of JIT Compilation language.

Example: JavaScript, C#, Java, etc…

There are a few main characteristics:

Benefits of learning coding:

·         You can make your own Website.

·         Coding ability gives a new perspective to problem-solving.

·         Learning to code offers career opportunities.

·         You can start your own Business.

·         You can do Freelancing.

·         etc…

Terminology:

Source code is a list of human-readable instructions that a computer programmer writes while he is developing a program.

Compiler is a special program that processes statements written in a particular programming language (the source code) to a machine language module (called an object file).

Interpreter is a computer program that directly executes instructions written in a programming or scripting language, without requiring them previously to have been compiled into a machine language program.

Object file is a computer file containing the object code, which is the machine code output of the assembler or compiler. Object code is usually moveable, and is usually not directly executable.

Runtime library is a collection of software programs used at program run time to provide one or more basic program functions or services.

Linker is a computer system program that takes one or more object files and combines them into a single executable file, “library file”, or another “object file”.

Executable File: A program file that can be run by a computer or device.

Thank You😀!

Source of Article: What is coding?