Python OOP: Message Passing ✉️ 🤔What is message passing in python OOP? In Python message passing means an object calling a method on another object or in other words basically, sending a “message” to ask it to do something 👉 We can simply understand from the dog and cat examples below 👉 I used dog.bark and cat.meouw to indicate that a message must be passed to dog and cat object 👉 When we call p.call_dog(my_dog), the Person object “sends a message” to the Dog object, asking it to bark() 👉 dog.bark() executes inside the Dog object and same with the cat example ‼️Message Passing is a way for objects to communicate in OOP ------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists & Tuples: https://lnkd.in/eZ8KiQNs 📁Python Dictionaries & Sets: https://lnkd.in/eDmgj7pc 📁Python OOP: https://lnkd.in/eJFupCiK 📁Python DSAs: https://lnkd.in/ebR3rjkt ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗 GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #messagepassing #objectorientedprogramming #pythonoop #OOP #oopfundamentals #OOPinpython

Day 7 of My 30-Day Python Learning Challenge 👉 Python Modules & Packages – Organizing Code Like a Real Project** Today’s learning took Python from basic scripts to real-world project structure. Modules and packages are what turn simple Python code into scalable, reusable, and organized applications — exactly how professional developers build software. 🔧 1️⃣ What Are Modules in Python? A module is simply a Python file (.py) that contains reusable code — functions, classes, or variables. Instead of writing long scripts, you divide your logic into separate files and import them when needed. ✔ Helps keep code clean ✔ Makes debugging easier ✔ Encourages reusability ✔ Ideal for team projects Example: utilities.py def add(a, b): return a + b You can use this in any other file: import utilities print(utilities.add(5, 10)) 📝 2️⃣ How to Create Your Own Module 1. Create a Python file (example: math_operations.py) 2. Add functions inside it 3. Import it into your main script math_operations.py def multiply(x, y): return x * y main.py from math_operations import multiply print(multiply(4, 5)) This is how real applications stay structured and maintainable. 📦 3️⃣ Built-in Python Modules Python provides hundreds of built-in modules that save time and effort. Today, I explored some essential ones: ✔ math Used for mathematical calculations import math print(math.sqrt(25)) ✔ datetime Used to handle dates and time from datetime import datetime print(datetime.now()) ✔ random Used for generating random numbers import random print(random.randint(1, 10)) Instead of writing logic from scratch, these modules allow you to perform complex operations with one line of code. 📥 4️⃣ How to Import Functions Different ways to import: 1. Import the whole module import math 2. Import specific functions from math import sqrt 3. Import with alias (short name) import datetime as dt 4. Import everything (not recommended) from math import * Imports make your code readable and avoid repetition across multiple files. 📦 5️⃣ What Are Packages? A package is a collection of modules stored in a folder. Inside the folder, an __init__.py file tells Python “this folder is a package.” Packages help you group similar modules together for large projects. Example folder structure: analytics/ __init__.py data_cleaning.py data_visualization.py calculations.py This structure is used in real-world applications like Django, Flask, Pandas, NumPy, etc. 🏗️ 6️⃣ Structuring Large Projects Using Modules & Folders A scalable Python project usually follows this format: project/ │ ├── main.py ├── config/ │ └── settings.py ├── utils/ │ └── helpers.py ├── services/ │ └── api.py └── modules/ └── salary.py This helps in: ✔ Organizing code logically ✔ Working better with teams ✔ Making the project easier to maintain ✔ Promoting code reuse across multiple files

Day 32 — Python: Modules & User-Defined Modules 🐍 Introduction to Modules What is a Module? A module is a Python file (.py) containing functions, classes, and variables. Modules help organize code into logical sections. Modules let you reuse code across multiple programs. Modules make code more readable and maintainable. Modules support collaborative development. Types of Modules in Python User-Defined Modules — created by you. Built-in Modules — provided by Python (e.g., math, os). (Covered separately.) External Modules — third-party packages installed via pip. This post focuses on user-defined modules and how to use them. Importing Modules in Python — common ways a. Import entire module: import my_module print(my_module.function_name()) b. Import specific items: from my_module import function_name print(function_name()) c. Import with an alias: import my_module as mm print(mm.function_name()) d. Avoid: from my_module import * # can cause name conflicts User-Defined Modules — step-by-step Step 1 — Create the module file my_module.py: def greet(name): return f"Hello, {name}!" pi = 3.14159 Step 2 — Use the module in another file (main.py): import my_module print(my_module.greet("Alice")) print("Value of Pi:", my_module.pi) Import specific function: from my_module import greet print(greet("Bob")) Using an alias: import my_module as m print(m.greet("Charlie")) Python module search path When importing, Python searches: Current directory Directories in sys.path To view or modify: import sys print(sys.path) sys.path.append("C:/path/to/your/modules") # add custom path Using if name == "main" Use this to prevent code from running on import. inside my_module.py def greet(name): return f"Hello, {name}!" if name == "main": print(greet("Alice")) # runs only when file executed directly Run: python my_module.py → executes the block. If imported, the block is ignored. 7) Installing & using external modules Install with pip: pip install requests Example usage: import requests response = requests.get("https://www.example.com") print(response.status_code) ✨ Tip: Start organizing larger projects into packages (folders with init.py) for better structure. 💬 What module will you package into a module next? Share below! ⬇️ #Python #DaysOfCode #PythonModules #CodingJourney

Python: Awesome language, lots of use-cases. I have used python for 5-6 years, and I think it’s a good language to use and misuse. Yesterday I revising my datastructures and algorithms, and wanted to make a red-black-tree. I did it in C, thought to myself "I don‘t care about the intricacies I need to understand it at a high level first" So i used Python Python isn‘t a language that fits my definition of recreational programming. Python has all these fancy features, and it enables you to not take care when you program (but you _should_ care. Here you can afford to not care though, unlike in C), and furthermore with metaclasses using __new__ (a constructor for classes, not instances as far as i know) and dynamic classes (type taking 3 arguments, the name __class__, the parents of which it inherits, and the dictionary of methods and properties), you get this… mess…, you can overwrite and override everything, and forcibly one-line-ify literally the entire source code, you can make recursive lists for crying out loud My goal isnt to learn the craft, my goal is to learn the *art* of the craft, and for that i need recreational use languages. My definition of recreational language is "is the representation of the entity relatable at a lower level". For Python, Java, JavaScript, TypeScript the answer is a clear No. Another addition to my arbitrary ruleset is "How much nonsense does it add?" Not lack, add. Not having strings is nonsense, bounds checks, allowing buffer overflows etc, that’s nonsense, but that isn‘t _added_. Convenience fixes are added to fix these things, which is awesome, but C doesn‘t add its problems. It just doesn‘t add solutions. Another "paradigm" of my recreational language checklist is "How much abstraction are you willing to take?" C is high level in that regard, but the code has a clear correspondence to the generated assembly Summary: - Is the representation of the entity relatable at a lower level (if you were to take everything apart layer by layer, when would things stop existing) - How much "extra" does it add? (What features are there that are useful but don‘t match the 99% usecase Python: metaclasses, name mangling with match, hacking the import resolver, etc for example. I am not good enough in Java, I just artificially don‘t like the language. JSFuck, and TypeScript types are turing complete.) - How much abstractions are you willing to take? (In my case very little. The job of the compiler is to compile, not make me dinner and sing me to sleep. If I make a mistake, this is my fault. Compile the code. This is what I installed you for. You are here to compile code, not tell me that I am bad at programming. I know that) I went back to C and it felt good again. Maybe I just am too far gone in the C rabbit hole, or maybe I am just saying 3000 characters worth of cope and skill issue. If this post offends you because I dunked on your favourite language, make 100 C compilers out of spite Have a great day everyone

"𝗕𝘂𝗱𝗴𝗲𝘁: 37500.0 INR - 75000.0 INR 𝗧𝗶𝘁𝗹𝗲: asPy - Assamese Python Transpiler Project Name: asPy — Assamese → Python Transpiler & VS Code Extension Objective: Build a functional VS Code extension enabling Assamese programming by transpiling Assamese code into Python and executing it seamlessly within VS Code. Project Overview The asPy project aims to make programming accessible in Indian regional languages—starting with Assamese. The solution must allow users to: Write code in Assamese in .aspy files inside VS Code. Click “Run” to: Transpile Assamese → Python. Execute the Python code in a controlled environment. Display both transpiled Python and program output inside VS Code. The extension should work offline, bundle the Python transpiler engine, and provide cross-platform support (Windows primary, Linux/macOS secondary). Scope of Work Core Components ComponentDescriptionTranspiler Engine (Python)Converts Assamese keywords & identifiers to Python equivalents using a JSON mapping. Executes code in a sandbox. (Already designed in MVP)VS Code Extension (TypeScript)Provides UI, command registration, syntax highlighting, and communication with the transpiler backend.Backend BridgeThe extension launches a bundled Python process (runner_cli.py) that handles transpilation and execution.Syntax HighlightingTextMate grammar to highlight Assamese keywords and built-ins.Output Panel / WebviewDisplays transpiled Python and program output side by side. Functional Requirements a. Language Handling File extension: .aspy Syntax highlighting for Assamese keywords (from mapping.json). Supports Assamese digits ০–৯ and transliterated functions. b. Commands & Features CommandActionaspy.runRuns current file; shows transpiled Python + output.aspy.showPythonShows transpiled Python only.aspy.newSampleInserts a “Hello World” Assamese template. c. Settings KeyTypeDefaultDescriptionaspy.pythonPathstringautoCustom Python interpreter path.aspy.mappingPathstringinternalPath to keyword mapping file.aspy.maxExecMillisnumber3000Max runtime per execution (ms). d. Output Opens a new VS Code panel or webview showing: ### Transpiled Python <code> ### Output <result> Handles runtime errors gracefully (show traceback in red). e. Security Executes in restricted sandbox (safe builtins only). Enforces timeout and memory safety (limit execution time to avoid infinite loops). Technical Stack LayerTechnologyBackendPython 3.10+FrontendTypeScript (VS Code Extension API)Editor FrameworkVS CodePackagingvsce (Visual Studio Code Extension Manager)JSON MappingAssamese → Python keyword dictionary Deliverables VS Code Extension Package (aspy-x.y.vsix) Installable extension with bundled backend. Includes syntax highlighting and run commands. ..." #VSCodeExtension #Transpiler #Assamese #Python #ProgrammingLanguage #Localization #DeveloperTools #CodeEditor #IndianLanguages #Accessibility  → https://lnkd.in/dBTg_2y9

📊 Day 8: Python — OOP, Error Handling & Files Today I learned three concepts that separate "learning Python" from "building real ML systems." Covered: OOP, Exception Handling, and File I/O — sounds dry, but these are everywhere in actual ML code. Key realizations: 1. Classes = create model templates (like model.fit(), model.predict()) 2. Error handling = code doesn't crash when files are missing 3. File operations = load data, save results Every ML library uses these three concepts. It clicked — every time you use model.fit(), you're using a class. When your code doesn't crash on missing files, that's error handling. When you load CSV datasets, that's file operations. What I practiced: ✅ Classes & Objects (creating model blueprints) ⭐⭐⭐ ✅ Inheritance (building on existing code) ✅ Try-Except blocks (handling errors) ⭐⭐⭐ ✅ Custom exceptions (ML-specific errors) ✅ Reading/Writing files (txt, CSV, JSON) ⭐⭐⭐ ✅ Safe file handling (with error checking) Built some practical examples: Example 1 - OOP for ML Models: class MLModel:   def __init__(self, name):     self.name = name     self.accuracy = 0.0     self.is_trained = False       def train(self, data):     print(f"Training {self.name}...")     self.is_trained = True     self.accuracy = 0.85       def predict(self, x):     if not self.is_trained:       return "Train me first!"     return f"Prediction for {x}" # Create and use model model = MLModel("CNN_Classifier") model.train([1, 2, 3, 4, 5]) print(model.predict("cat.jpg")) Example 2 - Complete ML Pipeline: class SimpleMLPipeline: def __init__(self, model_name): self.model_name = model_name self.trained = False def load_data(self, filepath): try: with open(filepath, 'r') as file: return file.readlines() except FileNotFoundError: print(f"File not found: {filepath}") return None def train(self, data): if not data: raise ValueError("No data!") print(f"Training {self.model_name}...") self.trained = True def save_results(self): import json results = {"model": self.model_name, "trained": self.trained} with open("results.json", "w") as file: json.dump(results, file, indent=2) # Run pipeline pipeline = SimpleMLPipeline("CNN_Model") data = pipeline.load_data("train.txt") if data: pipeline.train(data) pipeline.save_results() This combines OOP (classes), exception handling (try-except), and file I/O (reading/writing files) — exactly how production ML code works. These aren't "advanced" concepts — they're fundamental to writing ML code that actually works in the real world. Follow along and learn with me! Code below 👇 #MachineLearning #AI #Python #DataScience #DeepLearning #LearningInPublic #AspiringAIEngineer

🐍 Pythonic Idioms: A Core Driver of Python’s Popularity Python’s rise to dominance in the programming world isn’t just about its versatility—it’s about its philosophy. At the heart of this philosophy lies the concept of Pythonic idioms: elegant, readable, and concise coding patterns that make Python code feel natural and intuitive. 📜 A Brief History Python was created by Guido van Rossum in the late 1980s and released in 1991. From the beginning, it emphasized simplicity and readability. This ethos was later captured in the Zen of Python by Tim Peters—a collection of 19 guiding principles that define what it means to write “Pythonic” code. Some favorites: Beautiful is better than ugly Simple is better than complex Readability counts These principles laid the foundation for Pythonic idioms, which evolved as best practices within the community. 🎯 Why Pythonic Idioms Matter Pythonic idioms were introduced to: Promote clarity and readability Encourage consistency across codebases Improve performance and efficiency Reduce errors and complexity They serve as a shared language among Python developers, making collaboration smoother and code more maintainable. ✅ Benefits of Pythonic Idioms Readable & Maintainable: Easier to understand and debug. Concise: Express logic in fewer lines. Performant: Often faster than verbose alternatives. Community-Driven: Reinforced by PEP 8 and Python’s culture. 🧰 Examples of Pythonic Idioms Here are some idioms that every Python developer should know: # List Comprehension squares = [x**2 for x in range(10)] # Dictionary Comprehension expensive = {k: v for k, v in prices.items() if v > 0.4} # Enumerate for i, item in enumerate(['a', 'b', 'c']): print(i, item) # Zip for name, age in zip(names, ages): print(f"{name} is {age} years old") # Truthiness if items: print("List is not empty") # EAFP try: value = my_dict['key'] except KeyError: value = None # Context Manager with open('file.txt') as f: content = f.read() # Throwaway Variable for _, value in enumerate(data): process(value) # Safe Dictionary Access value = my_dict.get('key', default_value) # Conditional Expression status = "active" if is_enabled else "inactive" 📈 Impact on Python’s Popularity Research shows that Pythonic idioms: Boost developer productivity Improve code comprehension Drive adoption across industries Serve as a hallmark of expert-level Python They’re not just stylistic—they’re strategic. 🏁 Final Thoughts Pythonic idioms are a big reason why Python is loved by beginners and experts alike. They make code elegant, maintainable, and powerful. Whether you're just starting out or refining your craft, embracing Pythonic idioms will elevate your coding style and help you think more clearly about your solutions. 💬 What’s your favorite Pythonic idiom? 👇 Share it in the comments and let’s celebrate clean code! #Python #CleanCode #SoftwareEngineering #Pythonic #CodingTips #DeveloperExperience #ZenOfPython #ProgrammingPhilosophy

🔁 Loops in Python — Automate Repetition Like a Pro! In programming, loops are the secret sauce behind automation. They help you run a block of code multiple times — without typing it again and again! 🚀 Python makes looping simple, elegant, and powerful. Let’s understand how 👇 🔹 What is a Loop? A loop allows you to repeat tasks efficiently until a certain condition is met. In Python, we mainly use two types of loops: 1️⃣ for loop 2️⃣ while loop 🌀 1️⃣ for Loop — Iterate Over a Sequence A for loop is used to iterate through items like lists, strings, or ranges. 🧩 Example: fruits = ["apple", "banana", "cherry"] for fruit in fruits: print(fruit) 💬 Output: apple banana cherry You can also loop through a range of numbers: for i in range(1, 6): print(i) ✅ Prints numbers 1 to 5 🔁 2️⃣ while Loop — Repeat Until Condition Becomes False A while loop runs as long as its condition is True. 🧠 Example: count = 1 while count <= 5: print("Count:", count) count += 1 💡 Use while when you don’t know beforehand how many times the loop should run. ⚙️ Loop Control Statements Python offers special keywords to control how loops behave: 🔸 break Stops the loop completely. for i in range(10): if i == 5: break print(i) 🔸 continue Skips the current iteration and continues with the next. for i in range(5): if i == 2: continue print(i) 🔸 else Yes, Python loops can have an else! 😮 It runs after the loop finishes, unless you break out early. for i in range(3): print(i) else: print("Loop completed!") 🔄 Nested Loops You can put a loop inside another loop for complex tasks. for i in range(1, 4): for j in range(1, 3): print(i, j) Useful for working with matrices, patterns, or multi-level data. ⚡ Practical Uses of Loops ✅ Automating repetitive tasks ✅ Traversing lists, strings, or files ✅ Generating patterns or tables ✅ Performing data transformations ✅ Iterating through APIs or databases 💡 Pro Tips 🔹 Use for loop when iterating over known sequences 🔹 Use while loop when the end condition is uncertain 🔹 Avoid infinite loops — always ensure your condition changes! 🔹 Combine with if statements for powerful logic 🧩 Example: Find Even Numbers for num in range(1, 11): if num % 2 == 0: print(num) ✅ Output: 2, 4, 6, 8, 10 🚀 Quick Recap Loop Type Used For Condition Based? Mutable? for Iterating over sequence No Yes while Repetition until condition false Yes Yes #Python #Programming #Coding #DataScience #MachineLearning #LinkedInLearning #Tech #CupuleChicago #analyticssolution #cupulegwalior #cupuleeducation

Python Tuple Packing and Unpacking 🐍 In Python, tuples are more than just immutable lists. They are powerful tools that make your code cleaner, more readable, and incredibly Pythonic. And the concepts of tuple packing and unpacking are at the heart of writing elegant Python code. 🔹 Tuple Packing: Packing means grouping multiple values into a single tuple variable. Python makes this seamless: my_tuple = 1, 2, 3, 4, 5 👉Values are packed into a tuple 👉This allows you to store multiple values in a single variable, return multiple values from a function, or pass collections around without extra boilerplate. 🔹 Tuple Unpacking: 👉Unpacking is the reverse: extracting tuple elements into individual variables in a single, readable line. a, b, c = (10, 20, 30) print(a, b, c) 👉Output: 10 20 30 💡 Why Tuple Packing & Unpacking Matters? 1. Makes code more readable than indexing elements manually. 2. Enables returning multiple values from functions effortlessly. 3. Works beautifully with loops, function arguments, and nested data structures. ✨ Pro Tip: Tuple unpacking is especially powerful when swapping variables without a temporary placeholder: x, y = y, x No extra line, no temp variable—just clean, Pythonic magic. -------------------------- 🤓 Check Out More About Tuple Packing and Unpacking in my Python Lists Repo down below! -------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists: https://lnkd.in/eZ8KiQNs ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #pythontuples #tuplepackingandunpacking #pythonforbeginners #pythonlanguage #pythonfordatascience