Python SQLite Integration Basics

Day 2 of my Python Full Stack journey. ✅ Today I covered the very first building block of Python: → Variables → Data Types (int, float, str, bool) → f-strings to print dynamic output Looks simple. But this is the foundation everything else is built on. Here's what I actually typed today: name = "Punith" # str age = 24. # int score = 9.5 # float is_dev = True # bool print("Hi, I'm {Punith}, age {24}") and many. One thing that clicked today: Python figures out the data type automatically. No need to declare it like in Java or C. This is called dynamic typing — and it makes Python so much cleaner to write. 45 minutes. Committed to GitHub. Showing up again tomorrow. If you're learning to code right now — what was the first concept that actually made sense to you? #PythonFullStack #Day2 #BuildingInPublic #100DaysOfCode #Bangalore

🚀 Master Python: From Zero to Expert Want to learn Python but don’t know where to start? This definitive visual guide breaks down everything you need to know into a vibrant, organized mind map. From the basics and Object-Oriented Programming (OOP) to powerful data science libraries and web development frameworks. This guide is designed to keep you on track as you grow as a programmer. Save this post to refer back to whenever you need to recall a key concept or essential tool. The Python ecosystem is vast, but with the right roadmap, the sky’s the limit! 🐍💻 #PythonProgramming #DataScience #CodeNewbie #Programming #SoftwareDevelopment

🐍 Documenting my Python learnings for Data Engineering use cases I’ve been working on strengthening my Python understanding specifically from a Data Engineering perspective. To keep things structured, I’ve been documenting my learnings in a GitHub repository — focusing only on concepts and use-cases that are relevant for working with data. The idea was to build something that: • Organizes key Python concepts in one place • Connects them to practical data-related use cases • Serves as a quick reference while revising If you already have some familiarity with Python and want a concise, organized way to revisit important concepts in a data-focused context, feel free to check it out 👇 🔗https://lnkd.in/gYdG3R7V Open to feedback and suggestions 🙂 #Python #DataEngineering #LearningInPublic

🚀 Learning Python the Practical Way: Understanding Virtual Environments Over the past few days, I started learning Python and decided to focus on building instead of just reading syntax. Today, I explored one of the most important concepts for any developer: Virtual Environments (venv) Here’s what I understood: 🔹 A virtual environment is an isolated Python setup for a specific project 🔹 It prevents version conflicts between different projects 🔹 Each project can have its own dependencies without affecting others 💡 Why it matters: While working on multiple projects, different versions of the same library can break things. Virtual environments solve this by keeping everything separate and controlled. 🛠️ What I practiced: Creating a virtual environment Activating and deactivating it Installing packages inside it Understanding how Python uses project-specific paths This concept is very similar to how we manage dependencies in Node.js projects, but implemented differently in Python. Next step: Building a simple backend server using FastAPI to apply this knowledge in real projects. #Python #BackendDevelopment #FastAPI #WebDevelopment #LearningInPublic

Tips for larger Python programs! (I am not referring to small scripts, but program that are over 20,000) lines of code! 1 - modularize your design, this means split it into different python files for the different functional parts of your overall program! SUPER Important! 2 - if you cram everything into a single python file - maintenance will be a nightmare! How are you going to find anything ? the Human brain has limitation in very large program files no matter how smart you are! It doesn't matter if you have 50 years of experience or even if you invented the language! 3 - compiling a single large python file into a C code to protect you IP will take a very long time to run as the GCC compiler has limitations! even if you have a powerful computer - the rule is 1 python file to 1 core when compiling, you can not split that into Parallel, which will take forever to compile and you will waste a lot of time!!! 4 - after implementing modules, implement a Cache system / example you have 20 modules, but only made change to 1 Python file, why would you recompile the other 19 if there was zero change ? "BIG waste of time" and Unproductive! 5 - I written this, not used AI #python #tips #large #programs

Python Learning Journey Today I explored some core fundamentals that build a strong foundation in Python development: ◆ Installed VS Code and set up the Python environment ◆ Learned about different Python flavors: CPython (default implementation) Jython (Java integration) IronPython (.NET framework) PyPy (fast execution with JIT) Anaconda Python (data science ecosystem) RubyPython (experimental) ◆ Understood Python versions and compatibility ◆ Compared Java vs Python with real examples ◆ Practiced basic syntax like printing messages using print() Key concepts covered: Identifiers in Python Data Types & their types (int, float, list, tuple, dict, etc.) Typecasting Operators in Python eval() function Conditional statements (if, else, elif) Range data type and its variants Every day is a step closer to mastering Python. Consistency is the key! #Globalquesttechnologies #G R Narendra Reddy #Python #Coding Journey #Learning Python #VSCode #Programming #Developer #100DaysOfCode #TechSkills

Starting my journey into databases with Python 🐍 One of the first things I’m learning is how to connect Python to a database and begin interacting with data using SQL. To make this easier, I’m using SQLite a simple and lightweight database alongside SQLAlchemy, which helps Python communicate with different types of databases. Here’s what I’ve learned so far: Import create engine from SQLAlchemy Create a database engine by specifying the database type and name. Use the engine to connect and interact with the database. Explore the database by retrieving table names using engine.table_names() It’s a small step, but an important foundation for querying and analyzing data. Small steps, big growth 🚀 #Python #SQL #DataEngineering #LearningJourney #TechGrowth

🧠 How Python Works Internally (Big Picture Explained) Python is one of the most popular programming languages today, used in web development, data science, automation, and artificial intelligence. But most developers only scratch the surface. If you truly want to master Python, you need to understand what happens behind the scenes when your code runs. The Big Picture When you run a Python program, it goes through this pipeline: Source Code → Bytecode → Python Virtual Machine → Output Unlike languages such as C or C++, Python does not directly execute your code or compile it into machine code. When you run: x = 5 print(x) Internally: 1. Code is tokenized 2. Converted to AST 3. Compiled to bytecode 4. Executed by PVM 5. Memory managed automatically Have you ever explored Python internals before? Which concept surprised you the most? Let’s discuss in the comments 👇

🚀 Day 24 of My Python Learning Journey – Constructors & Access Specifiers Today I learned about Constructors in Python (__init__) and how objects are initialized. 🔹 Key Learnings: __init__() is a special method automatically called when an object is created Used to initialize attributes inside a class 🔐 Access Types in Python: Public → Accessible anywhere Protected (_var) → Convention-based usage Private (__var) → Name mangling for restricted access 💡 Practiced multiple examples to understand how objects store and manage data in real-world scenarios. 📂 GitHub Repository (My Daily Practice Code): 👉 https://lnkd.in/giyEbqTH 📌 Consistency is helping me build a strong foundation in OOP. #Python #OOP #100DaysOfCode #GitHub #CodingJourney #Learning #codegnan