#Python Object Oriented Paradigm

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Metaclasses – Python’s Object-Oriented Paradigm and Its Metaprogramming

Welcome to Python Metaclasses! To truly grasp the concept of metaclasses, we need to start by understanding a fundamental principle of Python: everything is an object. Yes, that includes classes themselves. We’ll explore the nature of objects, the functionality of the type() function, how classes instantiate objects, and the intriguing process of instantiating classes, which are objects in their own right. We’ll also discover how to tap into this mechanism to achieve remarkable results in our code.

Once we’ve covered the fundamental concepts of metaclasses, we’ll dive into a real-world example: the Enum class and its companion, the EnumType. This case study will showcase how metaclasses can be used effectively in practice.

Exploring Types and Unveiling Objects: Demystifying the type() Function

Python considers everything to be an object, and each object has a type that describes its nature. Numbers, for example, are of the type “int”, text is of the type “str”, and instances of a custom Person object are of the type “Person” class.

The type() method is a useful tool for determining the type of an object. Let’s experiment with it in the Python REPL. For example, when we create a list and use type() on it, we discover that it is an object instance of the “list” class. We can also ascertain the type of a string, such as “cat,” which is, predictably, “str”. In Python, even individual letters are considered strings, unlike some other programming languages that distinguish between characters and strings.

In fact, the “type” function is at the top of the class hierarchy. Just as calling “str()” on an object returns its string representation, calling “type()” on an object returns its type equivalent. It’s worth reiterating that “type” is the highest point in this hierarchy. We can confirm this by observing that the type of “type” is also “type”.

>>> print(type(1)) <type 'int'> >>> print(type("1")) <type 'str'> >>> print(type(ObjectCreator)) <type 'type'> >>> print(type(ObjectCreator())) <class '__main__.ObjectCreator'>

Well, type has also a completely different ability: it can create classes on the fly. type can take the description of a class as parameters, and return a class.

type works this way:

type(name, bases, attrs)

Where:

name: name of the class

bases: tuple of the parent class (for inheritance, can be empty)

attrs: dictionary containing attributes names and values

type accepts a dictionary to define the attributes of the class. So:

>>> class Foo(object): ... bar = True

Can be translated to:

>>> Foo = type('Foo', (), {'bar':True})

And used as a normal class:

>>> print(Foo) <class '__main__.Foo'> >>> print(Foo.bar) True >>> f = Foo() >>> print(f) <__main__.Foo object at 0x8a9b84c> >>> print(f.bar) True

You see where we are going: in Python, classes are objects, and you can create a class on the fly, dynamically.

This is what Python does when you use the keyword class, and it does so by using a metaclass.

Discovering Python’s Class Instantiation

Dynamic class creation is a great Python feature that allows us to construct classes on the fly, giving our code flexibility and extensibility. In this section, we will look at how to construct classes dynamically using metaprogramming techniques.

But it’s not so dynamic, since you still have to write the whole class yourself.

Since classes are objects, they must be generated by something.

When you use the class keyword, Python creates this object automatically. But as with most things in Python, it gives you a way to do it manually.

Dynamic class creation is the foundation of metaclasses, which are classes that define the behavior of other classes. Metaclasses allow us to intercept class creation and modify attributes, methods, and behavior before the class is fully formed. However, exploring metaclasses goes beyond the scope of this section, as it requires a deeper understanding of Python’s metaprogramming capabilities.

What are metaclasses (finally)

Metaclasses are the ‘stuff’ that creates classes.

You define classes in order to create objects, right?

But we learned that Python classes are objects.

Why would you use metaclasses instead of function?

The main use case for a metaclass is creating an API. A typical example of this is the Django ORM. It allows you to define something like this:

class Person(models.Model): name = models.CharField(max_length=30) age = models.IntegerField()

But if you do this:

person = Person(name='bob', age='35') print(person.age)

It won’t return an IntegerField object. It will return an int, and can even take it directly from the database.

This is possible because models.Model defines __metaclass__ and it uses some magic that will turn the Person you just defined with simple statements into a complex hook to a database field.

Django makes something complex look simple by exposing a simple API and using metaclasses, recreating code from this API to do the real job behind the scenes.

Conclusion

Although magicians are not meant to share their secrets, understanding metaclasses allows you to solve the puzzle for yourself. You’ve learned the key behind several of Python’s finest techniques, including class instantiation and object-relational mapping (ORM) models, as well as Enum.

It’s worth mentioning that creating bespoke metaclasses isn’t always necessary. If you can address the problem in a more straightforward manner, you should probably do so. Still, understanding metaclasses will help you understand Python classes in general and recognise when a metaclass is the best tool to utilize.

Originally published by: Metaclasses – Python’s Object-Oriented Paradigm and Its Metaprogramming

[python-programming-language]

Post #90: The pillars of object-oriented programming: Object, Class, Inheritance, Polymorphism, Abstraction and Encapsulation, 2023.

The concept of object oriented programming explained

Object-oriented programming is a fundamental concept present in numerous programming languages such as C++, Java, JavaScript, and more. It becomes straightforward once you truly grasp it, and that's precisely what this post aims to help you achieve. So, stop your scrolling for a second and delve into this post for a thorough and clear explanation.

Understanding the Term "Object-Oriented"

To grasp OOP, let's begin by explaining the name itself: "Object-Oriented." This term signifies that OOP revolves around entities known as "objects."

What Exactly Is an Object?

An object in OOP is any entity that possesses both state and behavior. Consider a dog as an example: it has states such as color, name, and breed, while its behaviors encompass actions like wagging the tail, barking, and eating.

The reason we introduce the concept of objects in programming is to effectively represent real-world entities, a task that cannot be accomplished with conventional variables or arrays.

Classes: Abstract Forms of Objects

Now, what about classes? A class is essentially the abstract form of an object. If we take the example of a "dog," the object "Mydog" is a concrete instance, while the class "dog" represents dogs in a more general sense. Think of a class as a blueprint or template from which you can create individual objects.

Four Pillars of Object-Oriented Programming

Now that we've established the fundamentals of objects and classes. OOP is built upon four key principles:

Inheritance: Inheritance occurs when one object inherits all the properties and behaviors of a parent object. It promotes code reusability and facilitates runtime polymorphism.

Polymorphism: Polymorphism entails performing a single task in multiple ways. For instance, it can involve presenting information differently to customers or implementing different shapes like triangles or rectangles.

Abstraction: Abstraction is about concealing internal details while exposing functionality. Consider a phone call; we don't need to understand the intricate inner workings.

Encapsulation: Encapsulation involves bundling code and data into a single unit. Just like a capsule contains various medicines . In a fully encapsulated class (e.g., a Java bean), all data members are private, ensuring data integrity and controlled access.

I remember finding these images that explained these concepts using the 'Squid Game' series, and they are just perfect. So, I'm sharing them here and giving all credit to their owner :

Polymorphism , Inheritance , Encapsulation

[sgwebapptech]

Python Programming Language: A Comprehensive Guide and Use Cases

Table of Contents

Introduction to Python

Key Features of Python

Python Syntax and Readability

Python vs. Other Programming Languages

Setting Up Python Development Environment

Python Data Structures and Control Flow

Object-Oriented Programming (OOP) in Python

Python Libraries and Frameworks

Python for Web Development

Python for Data Science and Machine Learning

Python for Automation and Scripting

Python for Game Development

Python for Cybersecurity

Python in Finance and Trading

Python for IoT and Embedded Systems

Python in Scientific Computing

Companies Using Python

Pros and Cons of Python

Future of Python

Conclusion

1. Introduction to Python

Python is a high-level, interpreted, and general-purpose programming language created by Guido van Rossum in 1991. Known for its simplicity, readability, and versatility, Python has become one of the most popular programming languages in the world.

Python supports multiple programming paradigms, including:

Procedural programming

Object-Oriented Programming (OOP)

Functional programming

[digitaldetoxworld]

Python Programming Language: A Comprehensive Guide

 Python is one of the maximum widely used and hastily growing programming languages within the world. Known for its simplicity, versatility, and great ecosystem, Python has become the cross-to desire for beginners, professionals, and organizations across industries.

What is Python used for

🐍 What is Python?

Python is a excessive-stage, interpreted, fashionable-purpose programming language.  The language emphasizes clarity, concise syntax, and code simplicity, making it an excellent device for the whole lot from web development to synthetic intelligence.

Its syntax is designed to be readable and easy, regularly described as being near the English language. This ease of information has led Python to be adopted no longer simplest through programmers but also by way of scientists, mathematicians, and analysts who may not have a formal heritage in software engineering.

📜 Brief History of Python

Late Nineteen Eighties: Guido van Rossum starts work on Python as a hobby task.

1991: Python zero.9.0 is released, presenting classes, functions, and exception managing.

2000: Python 2.Zero is launched, introducing capabilities like list comprehensions and rubbish collection.

2008: Python 3.Zero is launched with considerable upgrades but breaks backward compatibility.

2024: Python three.12 is the modern day strong model, enhancing performance and typing support.

⭐ Key Features of Python

Easy to Learn and Use:

Python's syntax is simple and similar to English, making it a high-quality first programming language.

Interpreted Language:

Python isn't always compiled into device code; it's far done line by using line the usage of an interpreter, which makes debugging less complicated.

Cross-Platform:

Python code runs on Windows, macOS, Linux, and even cell devices and embedded structures.

Dynamic Typing:

Variables don’t require explicit type declarations; types are decided at runtime.

Object-Oriented and Functional:

Python helps each item-orientated programming (OOP) and practical programming paradigms.

Extensive Standard Library:

Python includes a rich set of built-in modules for string operations, report I/O, databases, networking, and more.

Huge Ecosystem of Libraries:

From data technological know-how to net development, Python's atmosphere consists of thousands of programs like NumPy, pandas, TensorFlow, Flask, Django, and many greater.

📌 Basic Python Syntax

Here's an instance of a easy Python program:

python

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def greet(call):

    print(f"Hello, call!")

greet("Alice")

Output:

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Hello, Alice!

Key Syntax Elements:

Indentation is used to define blocks (no curly braces  like in different languages).

Variables are declared via task: x = 5

Comments use #:

# This is a remark

Print Function:

print("Hello")

📊 Python Data Types

Python has several built-in data kinds:

Numeric: int, go with the flow, complicated

Text: str

Boolean: bool (True, False)

Sequence: listing, tuple, range

Mapping: dict

Set Types: set, frozenset

Example:

python

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age = 25             # int

name = "John"        # str

top = 5.Nine         # drift

is_student = True    # bool

colors = ["red", "green", "blue"]  # listing

🔁 Control Structures

Conditional Statements:

python

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if age > 18:

    print("Adult")

elif age == 18:

    print("Just became an person")

else:

    print("Minor")

Loops:

python

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for color in hues:

    print(coloration)

while age < 30:

    age += 1

🔧 Functions and Modules

Defining a Function:

python

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def upload(a, b):

    return a + b

Importing a Module:

python

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import math

print(math.Sqrt(sixteen))  # Output: four.0

🗂️ Object-Oriented Programming (OOP)

Python supports OOP functions such as lessons, inheritance, and encapsulation.

Python

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elegance Animal:

    def __init__(self, call):

        self.Call = name

def communicate(self):

        print(f"self.Call makes a valid")

dog = Animal("Dog")

dog.Speak()  # Output: Dog makes a legitimate

🧠 Applications of Python

Python is used in nearly each area of era:

1. Web Development

Frameworks like Django, Flask, and FastAPI make Python fantastic for building scalable web programs.

2. Data Science & Analytics

Libraries like pandas, NumPy, and Matplotlib permit for data manipulation, evaluation, and visualization.

Three. Machine Learning & AI

Python is the dominant language for AI, way to TensorFlow, PyTorch, scikit-research, and Keras.

4. Automation & Scripting

Python is extensively used for automating tasks like file managing, device tracking, and data scraping.

Five. Game Development

Frameworks like Pygame allow builders to build simple 2D games.

6. Desktop Applications

With libraries like Tkinter and PyQt, Python may be used to create cross-platform computing device apps.

7. Cybersecurity

Python is often used to write security equipment, penetration trying out scripts, and make the most development.

📚 Popular Python Libraries

NumPy: Numerical computing

pandas: Data analysis

Matplotlib / Seaborn: Visualization

scikit-study: Machine mastering

BeautifulSoup / Scrapy: Web scraping

Flask / Django: Web frameworks

OpenCV: Image processing

PyTorch / TensorFlow: Deep mastering

SQLAlchemy: Database ORM

💻 Python Tools and IDEs

Popular environments and tools for writing Python code encompass:

PyCharm: Full-featured Python IDE.

VS Code: Lightweight and extensible editor.

Jupyter Notebook: Interactive environment for statistics technological know-how and studies.

IDLE: Python’s default editor.

🔐 Strengths of Python

Easy to study and write

Large community and wealthy documentation

Extensive 0.33-birthday celebration libraries

Strong support for clinical computing and AI

Cross-platform compatibility

⚠️ Limitations of Python

Slower than compiled languages like C/C++

Not perfect for mobile app improvement

High memory usage in massive-scale packages

GIL (Global Interpreter Lock) restricts genuine multithreading in CPython

🧭 Learning Path for Python Beginners

Learn variables, facts types, and control glide.

Practice features and loops.

Understand modules and report coping with.

Explore OOP concepts.

Work on small initiatives (e.G., calculator, to-do app).

Dive into unique areas like statistics technological know-how, automation, or web development.

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Object-Oriented Programming (OOP) Explaine

Object-Oriented Programming (OOP) is a programming paradigm based on the concept of "objects," which represent real-world entities. Objects combine data (attributes) and functions (methods) into a single unit. OOP promotes code reusability, modularity, and scalability, making it a popular approach in modern software development.

Core Concepts of Object-Oriented Programming

Classes and Objects

Class: A blueprint or template for creating objects. It defines properties (attributes) and behaviors (methods).

Object: An instance of a class. Each object has unique data but follows the structure defined by its

Encapsulations

Encapsulation means bundling data (attributes) and methods that operate on that data within a class. It protects object properties by restricting direct access.

Access to attributes is controlled through getter and setter methods.Example: pythonCopyEditclass Person: def __init__(self, name): self.__name = name # Private attribute def get_name(self): return self.__name person = Person("Alice") print(person.get_name()) # Output: Alice

Inheritance

Inheritance allows a class (child) to inherit properties and methods from another class (parent). It promotes code reuse and hierarchical relationships.Example: pythonCopyEditclass Animal: def speak(self): print("Animal speaks") class Dog(Animal): def speak(self): print("Dog barks") dog = Dog() dog.speak() # Output: Dog barks

Polymorphism

Polymorphism allows methods to have multiple forms. It enables the same function to work with different object types.

Two common types:

Method Overriding (child class redefines parent method).

Method Overloading (same method name, different parameters – not natively supported in Python).Example: pythonCopyEditclass Bird: def sound(self): print("Bird chirps") class Cat: def sound(self): print("Cat meows") def make_sound(animal): animal.sound() make_sound(Bird()) # Output: Bird chirps make_sound(Cat()) # Output: Cat meows

Abstraction

Abstraction hides complex implementation details and shows only the essential features.

In Python, this is achieved using abstract classes and methods (via the abc module).Example: pythonCopyEditfrom abc import ABC, abstractmethod class Shape(ABC): @abstractmethod def area(self): pass class Circle(Shape): def __init__(self, radius): self.radius = radius def area(self): return 3.14 * self.radius * self.radius circle = Circle(5) print(circle.area()) # Output: 78.5

Advantages of Object-Oriented Programming

Code Reusability: Use inheritance to reduce code duplication.

Modularity: Organize code into separate classes, improving readability and maintenance.

Scalability: Easily extend and modify programs as they grow.

Data Security: Protect sensitive data using encapsulation.

Flexibility: Use polymorphism for adaptable and reusable methods.

Real-World Applications of OOP

Software Development: Used in large-scale applications like operating systems, web frameworks, and databases.

Game Development: Objects represent game entities like characters and environments.

Banking Systems: Manage customer accounts, transactions, and security.

E-commerce Platforms: Handle products, users, and payment processing.

Machine Learning: Implement models as objects for efficient training and prediction.

Conclusion

Object-Oriented Programming is a powerful paradigm that enhances software design by using objects, encapsulation, inheritance, polymorphism, and abstraction. It is widely used in various industries to build scalable, maintainable, and efficient applications. Understanding and applying OOP principles is essential for modern software development.

: pythonCopyEdit

class Car: def __init__(self, brand, model): self.brand = brand self.model = model def display_info(self): print(f"Car: {self.brand} {self.model}") my_car = Car("Toyota", "Camry") my_car.display_info() # Output: Car: Toyota Camry

Encapsulation

[newcodesociety]

ByteByteGo | Newsletter/Blog

From the newsletter:

Imperative Programming Imperative programming describes a sequence of steps that change the program’s state. Languages like C, C++, Java, Python (to an extent), and many others support imperative programming styles.

Declarative Programming Declarative programming emphasizes expressing logic and functionalities without describing the control flow explicitly. Functional programming is a popular form of declarative programming.

Object-Oriented Programming (OOP) Object-oriented programming (OOP) revolves around the concept of objects, which encapsulate data (attributes) and behavior (methods or functions). Common object-oriented programming languages include Java, C++, Python, Ruby, and C#.

Aspect-Oriented Programming (AOP) Aspect-oriented programming (AOP) aims to modularize concerns that cut across multiple parts of a software system. AspectJ is one of the most well-known AOP frameworks that extends Java with AOP capabilities.

Functional Programming Functional Programming (FP) treats computation as the evaluation of mathematical functions and emphasizes the use of immutable data and declarative expressions. Languages like Haskell, Lisp, Erlang, and some features in languages like JavaScript, Python, and Scala support functional programming paradigms.

Reactive Programming Reactive Programming deals with asynchronous data streams and the propagation of changes. Event-driven applications, and streaming data processing applications benefit from reactive programming.

Generic Programming Generic Programming aims at creating reusable, flexible, and type-independent code by allowing algorithms and data structures to be written without specifying the types they will operate on. Generic programming is extensively used in libraries and frameworks to create data structures like lists, stacks, queues, and algorithms like sorting, searching.

Concurrent Programming Concurrent Programming deals with the execution of multiple tasks or processes simultaneously, improving performance and resource utilization. Concurrent programming is utilized in various applications, including multi-threaded servers, parallel processing, concurrent web servers, and high-performance computing.

[watchmorecinema]

Normally I just post about movies but I'm a software engineer by trade so I've got opinions on programming too.

Apparently it's a month of code or something because my dash is filled with people trying to learn Python. And that's great, because Python is a good language with a lot of support and job opportunities. I've just got some scattered thoughts that I thought I'd write down.

Python abstracts a number of useful concepts. It makes it easier to use, but it also means that if you don't understand the concepts then things might go wrong in ways you didn't expect. Memory management and pointer logic is so damn annoying, but you need to understand them. I learned these concepts by learning C++, hopefully there's an easier way these days.

Data structures and algorithms are the bread and butter of any real work (and they're pretty much all that come up in interviews) and they're language agnostic. If you don't know how to traverse a linked list, how to use recursion, what a hash map is for, etc. then you don't really know how to program. You'll pretty much never need to implement any of them from scratch, but you should know when to use them; think of them like building blocks in a Lego set.

Learning a new language is a hell of a lot easier after your first one. Going from Python to Java is mostly just syntax differences. Even "harder" languages like C++ mostly just mean more boilerplate while doing the same things. Learning a new spoken language in is hard, but learning a new programming language is generally closer to learning some new slang or a new accent. Lists in Python are called Vectors in C++, just like how french fries are called chips in London. If you know all the underlying concepts that are common to most programming languages then it's not a huge jump to a new one, at least if you're only doing all the most common stuff. (You will get tripped up by some of the minor differences though. Popping an item off of a stack in Python returns the element, but in Java it returns nothing. You have to read it with Top first. Definitely had a program fail due to that issue).

The above is not true for new paradigms. Python, C++ and Java are all iterative languages. You move to something functional like Haskell and you need a completely different way of thinking. Javascript (not in any way related to Java) has callbacks and I still don't quite have a good handle on them. Hardware languages like VHDL are all synchronous; every line of code in a program runs at the same time! That's a new way of thinking.

Python is stereotyped as a scripting language good only for glue programming or prototypes. It's excellent at those, but I've worked at a number of (successful) startups that all were Python on the backend. Python is robust enough and fast enough to be used for basically anything at this point, except maybe for embedded programming. If you do need the fastest speed possible then you can still drop in some raw C++ for the places you need it (one place I worked at had one very important piece of code in C++ because even milliseconds mattered there, but everything else was Python). The speed differences between Python and C++ are so much smaller these days that you only need them at the scale of the really big companies. It makes sense for Google to use C++ (and they use their own version of it to boot), but any company with less than 100 engineers is probably better off with Python in almost all cases. Honestly thought the best programming language is the one you like, and the one that you're good at.

Design patterns mostly don't matter. They really were only created to make up for language failures of C++; in the original design patterns book 17 of the 23 patterns were just core features of other contemporary languages like LISP. C++ was just really popular while also being kinda bad, so they were necessary. I don't think I've ever once thought about consciously using a design pattern since even before I graduated. Object oriented design is mostly in the same place. You'll use classes because it's a useful way to structure things but multiple inheritance and polymorphism and all the other terms you've learned really don't come into play too often and when they do you use the simplest possible form of them. Code should be simple and easy to understand so make it as simple as possible. As far as inheritance the most I'm willing to do is to have a class with abstract functions (i.e. classes where some functions are empty but are expected to be filled out by the child class) but even then there are usually good alternatives to this.

Related to the above: simple is best. Simple is elegant. If you solve a problem with 4000 lines of code using a bunch of esoteric data structures and language quirks, but someone else did it in 10 then I'll pick the 10. On the other hand a one liner function that requires a lot of unpacking, like a Python function with a bunch of nested lambdas, might be easier to read if you split it up a bit more. Time to read and understand the code is the most important metric, more important than runtime or memory use. You can optimize for the other two later if you have to, but simple has to prevail for the first pass otherwise it's going to be hard for other people to understand. In fact, it'll be hard for you to understand too when you come back to it 3 months later without any context.

Note that I've cut a few things for simplicity. For example: VHDL doesn't quite require every line to run at the same time, but it's still a major paradigm of the language that isn't present in most other languages.

Ok that was a lot to read. I guess I have more to say about programming than I thought. But the core ideas are: Python is pretty good, other languages don't need to be scary, learn your data structures and algorithms and above all keep your code simple and clean.

[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

[krnetwork]

Unlock the Power of Python Programming: A Complete Guide

Python programming has become one of the most sought-after skills in the world of technology. Its simplicity, flexibility, and vast ecosystem of libraries make it a top choice for both beginners and experienced developers. In this guide, we will explore various aspects of Python programming, from basic concepts to advanced applications like machine learning and web development.

Python Programming: A Beginner-Friendly Language

Python programming is renowned for its readability and straightforward syntax, making it ideal for beginners. Whether you are just starting to code or transitioning from another language, Python offers a smooth learning curve. Key Python programming concepts include variables, data types, and control structures, which are essential for writing functional code.

Python Data Structures: Organizing Data Efficiently

One of the core strengths of Python programming is its rich set of data structures. Lists, dictionaries, tuples, and sets help you store and manage data effectively. Understanding Python data structures allows you to create more efficient programs by organizing and manipulating data effortlessly.

Functions in Python Programming: Building Reusable Code

Functions are a fundamental part of Python programming. They allow you to break down complex problems into smaller, reusable chunks of code. Python functions not only promote code reusability but also make your programs more organized and easier to maintain.

Loops in Python Programming: Automating Repeated Tasks

Loops are an essential feature in Python programming, allowing you to perform repeated operations efficiently. With Python loops such as for and while, you can iterate over sequences or perform tasks until a specific condition is met. Mastering loops is a key part of becoming proficient in Python.

Object-Oriented Programming in Python: Structured Development

Python programming supports object-oriented programming (OOP), a paradigm that helps you build structured and scalable software. OOP in Python allows you to work with classes and objects, making it easier to model real-world scenarios and design complex systems in a manageable way.

Python Automation Scripts: Simplify Everyday Tasks

Python programming can be used to automate repetitive tasks, saving you time and effort. Python automation scripts can help with file management, web scraping, and even interacting with APIs. With Python libraries like os and shutil, automation becomes a breeze.

Python Web Development: Creating Dynamic Websites

Python programming is also a popular choice for web development. Frameworks like Django and Flask make it easy to build robust, scalable web applications. Whether you're developing a personal blog or an enterprise-level platform, Python web development empowers you to create dynamic and responsive websites.

APIs and Python Programming: Connecting Services

Python programming allows seamless integration with external services through APIs. Using libraries like requests, you can easily interact with third-party services, retrieve data, or send requests. This makes Python an excellent choice for building applications that rely on external data or services.

Error Handling in Python Programming: Writing Resilient Code

Python programming ensures that your code can handle unexpected issues using error handling mechanisms. With try-except blocks, you can manage errors gracefully and prevent your programs from crashing. Error handling is a critical aspect of writing robust and reliable Python code.

Python for Machine Learning: Leading the AI Revolution

Python programming plays a pivotal role in machine learning, thanks to powerful libraries like scikit-learn, TensorFlow, and PyTorch. With Python, you can build predictive models, analyze data, and develop intelligent systems. Machine learning with Python opens doors to exciting opportunities in artificial intelligence and data-driven decision-making.

Python Data Science: Turning Data Into Insights

Python programming is widely used in data science for tasks such as data analysis, visualization, and statistical modeling. Libraries like pandas, NumPy, and Matplotlib provide Python programmers with powerful tools to manipulate data and extract meaningful insights. Python data science skills are highly in demand across industries.

Python Libraries Overview: Tools for Every Task

One of the greatest advantages of Python programming is its extensive library support. Whether you're working on web development, automation, data science, or machine learning, Python has a library for almost every need. Exploring Python libraries like BeautifulSoup, NumPy, and Flask can significantly boost your productivity.

Python GUI Development: Building User Interfaces

Python programming isn't just limited to back-end or web development. With tools like Tkinter and PyQt, Python programmers can develop graphical user interfaces (GUIs) for desktop applications. Python GUI development allows you to create user-friendly software with visual elements like buttons, text fields, and images.

Conclusion: Python Programming for Every Developer

Python programming is a versatile and powerful language that can be applied in various domains, from web development and automation to machine learning and data science. Its simplicity, combined with its extensive libraries, makes it a must-learn language for developers at all levels. Whether you're new to programming or looking to advance your skills, Python offers endless possibilities.

At KR Network Cloud, we provide expert-led training to help you master Python programming and unlock your potential. Start your Python programming journey today and take the first step toward a successful career in tech!

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Python Development Course: Empowering the Future with Softs Solution Service

Python, a high-level programming language, has emerged as a favorite among developers worldwide due to its emphasis on readability and efficiency. Originating in the late 1980s, Python was conceived by Guido van Rossum as a successor to the ABC language. Its design philosophy, encapsulated by the phrase "Beautiful is better than ugly", reflects a commitment to aesthetic code and functionality. 

What sets Python apart is its versatile nature. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming. This flexibility allows developers to use Python for a wide range of applications, from web development and software engineering to scientific computing and artificial intelligence. 

Python’s standard library is another of its strengths, offering a rich set of modules and tools that enable developers to perform various tasks without the need for additional installations. This extensive library, combined with Python’s straightforward syntax, makes it an excellent language for rapid application development. 

One of Python's most significant contributions to the tech world is its role in data science and machine learning. Its easy-to-learn syntax and powerful libraries, like NumPy, Pandas, and Matplotlib, make it an ideal language for data analysis and visualization. Furthermore, frameworks like TensorFlow and PyTorch have solidified Python's position in the development of machine learning models. 

Education in Python programming has become crucial due to its growing demand in the industry. Recognizing this, institutions like Softs Solution Service, IT training institute in Ahmedabad, have stepped up to provide comprehensive Python Development Training. Their Online Python Development Course is tailored to meet the needs of both beginners and seasoned programmers. This course offers an in-depth exploration of Python's capabilities, covering everything from basic syntax to advanced programming concepts. 

The course structure usually begins with an introduction to Python's basic syntax and programming concepts. It then progressively moves into more complex topics, such as data structures, file operations, error and exception handling, and object-oriented programming principles. Participants also get to work on real-life projects, which is vital for understanding how Python can be applied in practical scenarios. 

A significant advantage of online courses like the one offered by Softs Solution Service is their accessibility. Students can learn at their own pace, with access to a wealth of resources and support from experienced instructors. Additionally, these courses often provide community support, where learners can interact with peers, share knowledge, and collaborate on projects. 

Python's future seems bright as it continues to evolve with new features and enhancements. Its growing popularity in various fields, including web development, data analytics, artificial intelligence, and scientific research, ensures that Python developers will remain in high demand. 

In summary, Python is not just a programming language; it's a tool that opens a world of possibilities for developers, data scientists, and tech enthusiasts. With resources like the Online Python Development Course from Softs Solution Service, mastering Python has become more accessible than ever, promising exciting opportunities in the ever-evolving world of technology.

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Introduction: As a high school student in the 21st century, there's no denying the importance of computer science in today's world. Whether you're a seasoned programmer or just dipping your toes into the world of coding, the power of computer science is undeniable. In this blog, I'll share my journey as a 12th-grader venturing into the fascinating realms of C, C++, and Python, and how this journey has not only improved my computer science profile but also shaped my outlook on technology and problem-solving.

Chapter 1: The Foundations - Learning C

Learning C:

C, often referred to as the "mother of all programming languages," is where my journey began. Its simplicity and efficiency make it an excellent choice for beginners. As a high school student with limited programming experience, I decided to start with the basics.

Challenges and Triumphs:

Learning C came with its fair share of challenges, but it was incredibly rewarding. I tackled problems like understanding pointers and memory management, and I quickly realized that the core concepts of C would lay a strong foundation for my future endeavors in computer science.

Chapter 2: Building on the Basics - C++

Transition to C++:

With C under my belt, I transitioned to C++. C++ builds upon the concepts of C while introducing the object-oriented programming paradigm. It was a natural progression, and I found myself enjoying the flexibility and power it offered.

Projects and Applications:

I started working on small projects and applications in C++. From simple text-based games to data structures and algorithms implementations, C++ opened up a world of possibilities. It was during this phase that I began to see how the knowledge of programming languages could translate into tangible solutions.

Chapter 3: Python - The Versatile Language

Exploring Python:

Python is often praised for its simplicity and readability. As I delved into Python, I realized why it's a favorite among developers for a wide range of applications, from web development to machine learning.

Python in Real-Life Projects:

Python allowed me to take on real-life projects with ease. I built web applications using frameworks like Flask and Django, and I even dabbled in data analysis and machine learning. The versatility of Python broadened my horizons and showed me the real-world applications of computer science.

Chapter 4: A Glimpse into the Future

Continual Learning:

As I prepare to graduate high school and venture into higher education, my journey with C, C++, and Python has instilled in me the importance of continual learning. The field of computer science is dynamic, and staying up-to-date with the latest technologies and trends is crucial.

Networking and Collaboration:

I've also come to appreciate the significance of networking and collaboration in the computer science community. Joining online forums, participating in coding challenges, and collaborating on open-source projects have enriched my learning experience.

Conclusion: Embracing the World of Computer Science

My journey as a 12th-grader exploring C, C++, and Python has been an enlightening experience. These languages have not only improved my computer science profile but have also given me a broader perspective on problem-solving and technology. As I step into the future, I'm excited to see where this journey will take me, and I'm ready to embrace the ever-evolving world of computer science.

If you're a fellow student or someone curious about programming, I encourage you to take the plunge and start your own journey. With determination and a willingness to learn, the world of computer science is yours to explore and conquer.

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python training london

python training london

what is python programmingWelcome to the captivating world of Python programming! If you've ever been curious about coding or are looking to enhance your skills, then you're in the right place. Whether you're a beginner eager to dip your toes into the vast ocean of programming or an experienced developer seeking to expand your repertoire, Python has something incredible in store for everyone.

In this blog post, we'll explore everything there is to know about Python - from its origins and benefits, to what makes it such a popular language among programmers worldwide. So grab your favorite beverage and get ready to embark on an exhilarating journey into the realm of Python programming. Let's dive in!

What is Python?Python is a high-level, interpreted programming language that was created by Guido van Rossum and first released in 1991. Known for its simplicity and readability, Python has gained immense popularity among programmers of all levels. It supports multiple programming paradigms, including object-oriented, procedural, and functional programming.

One of the standout features of Python is its clean and elegant syntax. With minimalistic code structure, developers can write concise programs that are easy to understand and maintain. The language also boasts a vast standard library that provides ready-to-use modules for various tasks such as file handling, networking operations, database access, and more.

Python's versatility extends beyond traditional software development. It finds application in areas like web development using frameworks like Django or Flask, data analysis with libraries like pandas or NumPy, machine learning through scikit-learn or TensorFlow - just to scratch the surface.

Furthermore, Python's cross-platform compatibility allows you to run your code seamlessly on different operating systems such as Windows, macOS, Linux without any modifications. This flexibility makes it an ideal choice for building applications across diverse environments.

Whether you're creating simple scripts or complex applications/systems from scratch – Python offers an extensive range of tools and resources to make your coding experience smooth sailing. Its vast community support ensures that you'll never be short on help when facing challenges along the way.

In summary (not conclusive), Python is a dynamic programming language loved by beginners and professionals alike due to its simplicity yet powerful capabilities across various domains – making it an essential tool in every programmer's arsenal.

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Unlock the World of Data Analysis: Programming Languages for Success!

💡 When it comes to data analysis, choosing the right programming language can make all the difference. Here are some popular languages that empower professionals in this exciting field

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🐍 Python: Known for its versatility, Python offers a robust ecosystem of libraries like Pandas, NumPy, and Matplotlib. It's beginner-friendly and widely used for data manipulation, visualization, and machine learning.

📈 R: Built specifically for statistical analysis, R provides an extensive collection of packages like dplyr, ggplot2, and caret. It excels in data exploration, visualization, and advanced statistical modeling.

🔢 SQL: Structured Query Language (SQL) is essential for working with databases. It allows you to extract, manipulate, and analyze large datasets efficiently, making it a go-to language for data retrieval and management.

💻 Java: Widely used in enterprise-level applications, Java offers powerful libraries like Apache Hadoop and Apache Spark for big data processing. It provides scalability and performance for complex data analysis tasks.

📊 MATLAB: Renowned for its mathematical and numerical computing capabilities, MATLAB is favored in academic and research settings. It excels in data visualization, signal processing, and algorithm development.

🔬 Julia: Known for its speed and ease of use, Julia is gaining popularity in scientific computing and data analysis. Its syntax resembles mathematical notation, making it intuitive for scientists and statisticians.

🌐 Scala: Scala, with its seamless integration with Apache Spark, is a valuable language for distributed data processing and big data analytics. It combines object-oriented and functional programming paradigms.

💪 The key is to choose a language that aligns with your specific goals and preferences. Embrace the power of programming and unleash your potential in the dynamic field of data analysis! 💻📈

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How to Learn Python for Beginners: Tips and Resources

Python has become one of the most popular programming languages in the world, known for its simplicity and versatility. Whether you are a complete novice or someone looking to expand your programming skills, learning Python can open up a world of opportunities in fields such as web development, data analysis, artificial intelligence, and more. This guide, "How to Learn Python for Beginners: Tips and Resources," aims to provide you with practical advice and valuable resources to kickstart your Python learning journey.

Understanding Python

Before diving into the learning process, it’s essential to understand what Python is and why it is so widely used. Python is an interpreted, high-level programming language that emphasizes code readability and simplicity. Its syntax is designed to be intuitive, making it an excellent choice for beginners. Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming, which allows developers to choose the best approach for their projects.

Setting Up Your Environment

The first step in learning Python is to set up your development environment. Here’s how to get started:

Install Python: Download the latest version of Python from the official website (https://www.python.org/downloads/). The installation process is straightforward, and you can choose to install additional tools like pip, which is a package manager for Python.

Choose an Integrated Development Environment (IDE): An IDE is a software application that provides comprehensive facilities to programmers for software development. Popular IDEs for Python include:PyCharm: A powerful IDE specifically designed for Python development. Visual Studio Code: A lightweight and versatile code editor with excellent Python support. Jupyter Notebook: Ideal for data analysis and visualization, allowing you to create and share documents that contain live code.

Set Up a Virtual Environment: It’s a good practice to create a virtual environment for your Python projects. This allows you to manage dependencies and avoid conflicts between different projects. You can create a virtual environment using the following command:bashRunCopy code1python -m venv myenvActivate it with:On Windows: myenv\Scripts\activate On macOS/Linux: source myenv/bin/activate

Learning Resources

With your environment set up, it’s time to explore various resources to learn Python effectively. Here are some recommended resources:

Online Courses:Coursera: Offers courses like "Python for Everybody" by the University of Michigan, which is perfect for beginners. edX: Provides a range of Python courses from institutions like MIT and Harvard. Udemy: Features numerous Python courses, often at discounted prices, covering everything from basics to advanced topics.

Books:"Automate the Boring Stuff with Python" by Al Sweigart: A great book for beginners that focuses on practical applications of Python. "Python Crash Course" by Eric Matthes: A hands-on introduction to programming with Python, ideal for beginners. "Learn Python the Hard Way" by Zed A. Shaw: A popular book that emphasizes practice and repetition.

Interactive Platforms:Codecademy: Offers an interactive Python course that allows you to write code directly in your browser. LeetCode: A platform for practicing coding problems, which can help you improve your problem-solving skills in Python. HackerRank: Provides coding challenges and competitions to test your skills and learn from others.

YouTube Channels:Corey Schafer: Offers a series of Python tutorials that cover various topics in depth. Programming with Mosh: Provides beginner-friendly Python tutorials that are easy to follow. freeCodeCamp.org: Features comprehensive Python courses and tutorials for beginners.

Tips for Effective Learning

As you embark on your Python learning journey, consider the following tips to enhance your experience:

Practice Regularly: Consistency is key when learning a new programming language. Set aside time each day or week to practice coding. The more you code, the more comfortable you will become with Python.

Work on Projects: Apply what you learn by working on small projects. This could be anything from a simple calculator to a web scraper or a personal website. Projects help reinforce your knowledge and provide practical experience.

Join a Community: Engage with other learners and experienced developers by joining online communities such as Stack Overflow, Reddit (r/learnpython), or Python Discord servers. These platforms allow you to ask questions, share your progress, and learn from others.

Utilize Documentation: Familiarize yourself with the official Python documentation (https://docs.python.org/3/). It is a valuable resource that provides detailed information about Python’s features, libraries, and best practices.

Learn by Teaching: One of the best ways to solidify your understanding of a topic is to teach it to someone else. Consider writing blog posts, creating tutorials, or explaining concepts to friends or peers.

Stay Curious: Python is a vast language with numerous libraries and frameworks. Explore different areas such as web development (Django, Flask), data analysis (Pandas, NumPy), and machine learning (TensorFlow, scikit-learn) to find what interests you the most.

Conclusion

Learning Python can be an exciting and rewarding journey, especially for beginners. By setting up your environment, utilizing the right resources, and following effective learning strategies, you can build a strong foundation in Python programming. Remember that persistence and practice are essential to mastering any skill, so stay motivated and keep coding!

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Top Python Interview Questions and Answers to Crack Your Next Tech Interview

Python is one of the most in-demand programming languages for developers, data scientists, automation engineers, and AI specialists. If you're preparing for a Python-based role, reviewing commonly asked Python interview questions and answers is a smart move.

This blog covers essential questions and sample answers to help you prepare for technical interviews at both beginner and advanced levels.

📘 Looking for the full list of expert-level Q&A? 👉 Visit: Python Interview Questions and Answers – Freshy Blog

🔹 Basic Python Interview Questions and Answers

1. What is Python?

Python is an interpreted, high-level programming language known for its simplicity and readability. It supports multiple programming paradigms including OOP, functional, and procedural styles.

2. What are Python's key features?

Easy-to-read syntax

Large standard library

Open-source and community-driven

Supports object-oriented and functional programming

Platform-independent

3. What are Python lists and tuples?

List: Mutable, allows changes

Tuple: Immutable, used for fixed collections

🔸 Intermediate Python Interview Questions and Answers

4. What is a dictionary in Python?

A dictionary is an unordered collection of key-value pairs. It allows fast lookups.

my_dict = {"name": "Alice", "age": 30}

5. What is a Python decorator?

A decorator is a function that takes another function and extends its behavior without explicitly modifying it.

def decorator(func):

    def wrapper():

        print("Before")

        func()

        print("After")

    return wrapper

🔹 Advanced Python Interview Questions and Answers

6. What is the difference between deep copy and shallow copy?

Shallow Copy: Copies the outer object; inner objects are still referenced.

Deep Copy: Copies all nested objects recursively.

7. Explain Python's Global Interpreter Lock (GIL).

GIL is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecode simultaneously in CPython.

🔍 More Covered in the Full Guide:

Exception handling and custom exceptions

Lambda functions and map/filter/reduce

File handling in Python

List comprehension vs generator expressions

Python 3.x updates and syntax changes

📘 Read them all in this full-length guide: 👉 https://www.freshyblog.com/python-interview-questions-and-answers/

✅ Tips to Crack Python Interviews

Practice writing code daily

Review OOP, exception handling, file I/O

Solve Python problems on LeetCode or HackerRank

Be prepared to explain your logic step-by-step

Final Thoughts

Whether you're a beginner or aiming for a senior developer position, reviewing these Python interview questions and answers will boost your confidence and interview performance.

🔗 Explore the full list with real-world examples and pro tips: 👉 https://www.freshyblog.com/python-interview-questions-and-answers/

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What Is Object-Oriented Programming in Python?

Object-oriented programming in Python is a powerful programming paradigm that focuses on creating reusable and modular code by defining classes and objects. It allows developers to structure programs using real-world entities like inheritance, encapsulation, polymorphism, and abstraction. Python supports OOP principles to promote cleaner syntax, easier debugging, and scalable development, especially in modern software engineering, machine learning projects, and web development frameworks like Django and Flask. By leveraging object-oriented design, Python developers can build efficient applications with better code maintainability and high performance, making it a top choice in today’s trending coding practices.