Python Pioneers: A Beginner's Guide 🇪🇹

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...continued To access attributes or call methods on an object we use a dot-notation., the variable followed by dot and the attribute or the method name. Syntax: the dot-notation # Accessing an attribute object.attribute # Calling a method object.method() Example: simplified computer object class Computer: # __init__ is a special method (a constructor) def __init__(self, brand, model): # attributes self.brand = brand self.model = model # methods def powerOn(self): print(f"Powering on {self.brand} {self.model}") def powerOff(self): print(f"Powering off {self.brand} {self.model}") c1 = Computer("HP", "OMEN") c2 = Computer("Dell", "XPS") # Test code print(c2.brand + " " + c2.model) c1.powerOn() # Output Dell XPS Powering on HP OMEN #oop #PythonProgramming #DotNotation

In the tech world, things change fast. Adapting to these changes quickly is what helps us stand out as better programmers. Project changes are tracked using version numbers—and they aren't random. They convey crucial information about the current state of a project. The Python project uses a major.minor.micro versioning scheme for production releases (the versions we care about most!). Here is how it breaks down: 🔹 Major version (x.0.0): These are rarely incremented. They only occur when there are significant backward-incompatible changes or a complete, ground-up redesign of the project. These updates are planned years in advance. Python has only had four major versions in its history: 0.x.x, 1.x.x, 2.x.x, and 3.x.x (our current version). 🔹 Minor version (0.x.0): These are incremented for new features and library deprecations. They can sometimes introduce breaking changes too. In Python's release cycle, a new minor version comes out every year in October. 🔹 Micro version (0.0.x): These updates are reserved for bug fixes and security patches on an existing minor release. As of today, the current stable release of Python is Python 3.14.5. 🐍 #GettingToKnowPython #PythonProgramming #CodingTips

Good Morning Pioneers! 👋 I'm Kal, and I'll be taking over from here! Just wanted to let you know we'll be continuing our discussion on Python, focusing on language features and development tooling 🐍🛠. If you're new to Python, you should start with the topics covered in Python Basics 📚✨.

...continued To do OOP in python we use classes. Classes define the shape (attributes and methods) of an object. Think of classes like a blueprint for creating objects. Next we use classes to create objects. It's a simple two step process:- • Modeling:- defining a class (shape). • Instantiating:- creating objects (instances) using class. Syntax: to create a class we use the class keyword. # Step 1 - Modeling class ClassName: attr1 = 0 attr2 = True attr3 = "string" def method1(self): pass def method2(self): pass # Step 2 - Instantiating obj1 = ClassName() obj2 = ClassName() Note: • Attributes can be of any data type. • Methods are just functions attached to the object. • self is a reference to the object; obj1 or obj2 (we will talk more about them later)

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Lambda functions Another cool expressions or patterns in python are lambda functions a.k.a anonymous functions. We use them to define functions inline (in one line). # Syntax lambda arguments: expresion The subtle thing we need to understand about anonymous funtions/lambda functions is that they don't have a name unless we explicitly assign them to a variable. The return value of lambda function is the evaluated expression. The concept actually exists in many programming languages as anonymous functions or function expression, even-though the syntax could be different. # Examples # This is valid - With no name lambda x: x # With a name times2 = lambda x: x * 2 # With two argumetns prefix = lambda pre, post: pre + post; postfix = lambda post, pre: prefix(pre, post) print(times2(10)) print(prefix("main", ".py")) print(postfix(".py", "main")) # ----------Output---------- 20 main.py main.py #LambdaFunctions #PythonProgramming #PythonTips #GettingToKnowPython

List comprehensions List comprehensions are expressions that allow us to transform lists in a single line. The syntax: [ <inner_expression> for <item> in <list> ] It may look intimidating at first glance but what will happen is python loops through the list and executes the <inner_expression> for each <item> in <list> to create a new list. # Example nums = [1, 2, 3, 4, 5] squared = [n ** 2 for n in nums] print(squared) # output: [1, 4, 9, 16, 25] Above we have two expression the outer_expression which is the list comprehension itself and the inner_expression (n ** 2) which transforms the list. In this case the inner_expression squares each item from the list. #PythonTips #GettingToKnowPython #PythonPatterns

OOP in Python Object Oriented Programming (OOP) just means we organize code using objects that have Data and Actions. Data is something we know about the object, and Actions are things we can do to the object or with the object. For example, when looking at a computer, we may find ourselves asking the brand, model and specs of a computer, these are some of the data(information) we can have about a computer. Also we can do things on a computer; power it On & Off, Open & Close apps, adjust the volume - these are some of the action we can take on a computer. Note: In the context of programming we usually say Attribute or Property instead of Data and Method instead of Action.

We now have a new community group to discuss on different topics You can also dm additional topics to be created https://t.me/python_pioneers_com