Another day.... Day 7 of building my skills in IT Automation with Python. Today I focused on something a little simple(or looks that way-HaHa) on the surface, but is actually at the core of real automation work. Working with files. Up until now, most of what I’ve been doing has lived inside the code itself. Variables, functions, logic. But today shifted that perspective. Now the code is interacting with data. Real files. Real workflows. Here’s what I worked on: 🔹 Reading files Understanding how to open and read text files line by line. This is where data actually comes into your program. [this reminded me of the long lines I saw the other time in Java:( I was like Yay] 🔹 Writing files Creating and writing content into files. Not just consuming data, but producing it. 🔹 Copying and moving files Using Python to manage files across directories. This is where automation starts to feel practical. 🔹 Deleting files Learning how to safely remove files using code. Simple, but powerful when used correctly. 🔹 Practicing and exploring Putting everything together through hands-on exercises to reinforce the concepts. What stood out to me today is this: Files may seem basic, but they are at the center of most automation tasks. Logs, reports, configurations, datasets. Almost everything in IT lives in files. And now I can start building tools that interact with them. Grateful to Mentor Me Collective and Chanel Power 💡🌍 for providing the structure and access that makes this journey possible. Still learning. Still building. One step closer. #Python #ITAutomation #BuildInPublic #LearningInPublic #MentorMeCollective #TechJourney #BuildInPublic

🚀 Day 8 of #111DaysOfLearningForChange – Code for Change Built a Student Record Manager (CLI App) using Python 📚💻 📌 What I learned today: • Managing structured data using JSON • Implementing CRUD operations (Create, Read, Update, Delete) • Searching and updating records efficiently • Handling user input and validation 🛠️ What I built: A CLI-based Student Record Manager with features: • Add student records • Search student by ID • Update student marks • Delete student records ✨ Key takeaway: Building CRUD-based applications helped me understand how real-world systems manage and update data ⚡ Challenge faced: Handling edge cases like duplicate entries and correctly updating/deleting records #111DaysOfLearningForChange #CodeForChange #Python #CLI #Projects #LearningInPublic https://lnkd.in/gRNGRSzd

🚀 Day 11 of #111DaysOfLearningForChange – Code for Change Built a GitHub Trending CLI Tool to discover popular repositories 🌐💻 📌 What I learned today: • Advanced API usage with query parameters • Building flexible CLI tools using argparse • Filtering data based on time (day, week, month, year) • Handling API responses and errors effectively 🛠️ What I built: A CLI tool that: • Fetches trending GitHub repositories 📈 • Filters results by duration (day/week/month/year) • Displays repo details (name, stars, language, link) ✨ Example usage: python trending.py --duration week --limit 5 ✨ Key takeaway: Combining APIs with CLI tools can create powerful and practical developer utilities ⚡ Challenge faced: Constructing correct API queries and handling different response cases #111DaysOfLearningForChange #CodeForChange #Python #CLI #API #GitHub #LearningInPublic https://lnkd.in/gNBy3eiN

I built my first real Python project today. 🐍 A fully functional Contact Book — runs in the terminal, no frameworks, no tutorials to copy from. What it does: → ➕ Add multiple contacts at once → 📋 View all saved contacts → ❌ Delete contacts by number → 🔁 Keeps running until you choose to exit What I used to build it: → Dictionary — to store name + phone pairs → While loop — to keep the app running → Functions — to avoid repeating code → Enumerate — to number contacts cleanly 3 weeks ago I didn't know what a dictionary was. Today I built an app with one. That's what consistent learning looks like. 💪 #Python #LearningInPublic #DataAnalytics #PythonProject #BBA #buildinpublic

Day 9 of #100DaysOfCode – Mastering Lists in Python 🐍 Today’s focus was completely on one powerful concept: 👉 Lists – the backbone of data handling in Python Instead of jumping between topics, I went deep into list operations and logic building 💻🧠 ✨ What I practiced today (Programs 101–115): 🔹 Core list operations ✔️ Sum, product, count of elements ✔️ Finding largest & smallest (without built-ins) ✔️ Second largest & second smallest 🔹 Logical problem solving ✔️ Count even & odd numbers ✔️ Separate positive & negative values ✔️ Find indices of elements 🔹 Real-world list handling ✔️ Remove duplicates (without set) ✔️ Reverse list using loop ✔️ Copy list manually ✔️ Rotate list 💡 Key Learning: Lists are not just collections… They are the foundation for solving real-world problems Today helped me understand: 👉 How to think without built-in shortcuts 👉 How logic works behind the scenes 🔥 The more I practice, the more confident I feel in problem solving 🙏 Special thanks to Global Quest Technologies (GQT) for continuous support and guidance throughout this journey 💬 One step closer to becoming a better developer every day Global Quest Technologies ✨ #100DaysOfCode #Day9 #Python #PythonProgramming #CodingJourney #LearnPython #DataStructures #ListsInPython #ProblemSolving #DeveloperMindset #TechSkills #SoftwareDevelopment #Consistency #GlobalQuestTechnologies #GQT

Week 1 ✅ — Python fundamentals to production mindset in 7 days. Here's what I built and learned: 🔹 Day 1 — Core syntax, data types, loops, functions, list comprehensions 🔹 Day 2 — Lists, dicts, sets, tuples — deep practice with 15+ problems 🔹 Day 3 — OOP with classes, dunder methods, generators, decorators 🔹 Day 4 — Error handling, JSON file I/O, production-style logging 🔹 Day 5 — datetime, pathlib, async/await, asyncio 🔹 Day 6 — CLI Personal Finance Tracker — all concepts in one project 🔹 Day 7 — Full revision and Few Interview Questions The Finance Tracker project (Day 6) uses: ✅ @dataclass with dunder methods ✅ Custom exception hierarchy ✅ JSON persistence with logging ✅ asyncio.gather() for parallel tasks — same pattern as real LLM API calls ✅ Generators for memory-efficient iteration Zero third-party libraries. Zero tutorials. Pure Python. Week 2 starts tomorrow — NumPy + Pandas. 83 days left. The goal hasn't changed. GitHub in Link 👇 https://lnkd.in/gbFxTYVv #Python #WeeklyUpdate #GenAI #100DaysOfCode #OpenToWork #BuildInPublic

Mutable default arguments — the bug that's been in your code for years Most Python developers have shipped this bug. They just don't know it yet. def add_item(item, items=[]):    items.append(item)    return items Looks innocent. Isn't. Most people think the empty list is created fresh on every call. It's not. The default value is evaluated exactly once — when the function is defined. The same list object is reused on every call where you don't pass items explicitly. Call it three times without arguments and you don't get three lists with one item each. You get one list with three items, growing across every call you forget to make. In production this shows up as: a function that caches results between requests when you didn't ask it to. State leaking across users. Tests that pass alone and fail in a suite. The fix is one line: def add_item(item, items=None):    if items is None:        items = []    items.append(item) Mutable defaults are not a feature. They're a sharp edge. Sentinel-and-rebuild is the only safe pattern. #PythonInternals #Python #DataEngineering #SoftwareEngineering #Developer #PythonDeveloper #Backend #CodingInterview #Developers #Programming #Learning #PythonTips

#7 Days of Advanced Python — Learning Beyond Basics I’ve been working with Python for quite some time now — building projects, solving problems, and exploring different concepts. But recently, I realized something. Knowing Python is one thing. Using Python efficiently in real-world workflows is something else. There are so many small things that we often ignore — tools, setup, debugging, project structure — but those are exactly the things that make a big difference when you start building seriously. So I decided to start a small 7-day challenge for myself. Every day, I’ll share one thing I’m learning that is helping me move from just “writing code” to actually “building better systems”. Not theory. Just practical improvements. #𝗗𝗮𝘆 𝟭 — 𝗨𝗽𝗴𝗿𝗮𝗱𝗶𝗻𝗴 𝗺𝘆 𝗣𝘆𝘁𝗵𝗼𝗻 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 Today I explored a tool called 𝘂𝘃 — 𝗮 𝗺𝗼𝗱𝗲𝗿𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗽𝗮𝗰𝗸𝗮𝗴𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗿. Until now, I was mostly using pip with virtual environments. It worked, but it often felt a bit fragmented — multiple steps, dependency issues, and sometimes inconsistent setups. Using 𝘂𝘃 felt different. It’s not just about installing packages faster, it’s about simplifying the entire workflow. What stood out to me: • Faster dependency installation • Lockfiles for reproducible environments • Simpler project setup • Cleaner and more predictable workflow What I liked most is how it removes small frictions that we usually ignore — like broken environments or “it works on my machine” problems. This made me realize something important: Improvement in development is not always about learning new concepts. Sometimes, it’s about upgrading the way you work. If you want to explore it, the official documentation is a great place to start: https://docs.astral.sh/uv/ Curious — are you still using pip for everything, or have you explored tools like uv? #Python #AdvancedPython #LearningInPublic #DevTools #SoftwareDevelopment

As I prepare for my upcoming lab performance on FastAPI with Python, I wanted to break down something simple but important: how it actually works and why we even need it. How FastAPI works: FastAPI is a modern Python framework used to build APIs (Application Programming Interfaces). In simple terms, it acts as a bridge between the frontend and the database. - You define routes (URLs) where users or systems send requests - FastAPI processes those requests using Python functions - It validates data automatically (which saves a lot of time) - Then it sends back a response (like data, status, or results) It’s built on top of powerful standards like async programming, which makes it super fast and efficient. Why we need FastAPI: Here’s the real point— - Modern apps (web, mobile) need fast and reliable backends - APIs are the core way different systems communicate - FastAPI helps build these APIs quickly with less code and fewer errors - It’s perfect for scalable systems, AI apps, and real-time services What this really means is: learning FastAPI is not just about passing a lab—it’s about understanding how real-world applications handle data and communication behind the scenes. Still learning, still improving—but getting closer to building something real. #FastAPI #Python #BackendDevelopment #APIs #CSE #LearningJourney

Day 58 of my #100DaysOfCode challenge 🚀 Today I implemented a Python program to generate prime numbers within a given range. This is a practical extension of prime checking and useful in many DSA and real-world problems. What the program does: • Takes a range (start, end) as input • Checks each number in the range • Identifies whether it is prime or not • Returns a list of all prime numbers in that range Example Output: Prime numbers between 1 and 50: [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47] How the logic works: Start from max(2, start) For each number: • Assume it is prime • Check divisibility from 2 → √n If divisible → not prime If not divisible → add to result list 👉 Uses square root optimization for better performance Why this is important: – Builds on prime number fundamentals – Useful in: Competitive programming Number theory problems Range-based queries – Helps understand optimization using √n Time Complexity: O(n√n) Space Complexity: O(k) (number of primes) Key Takeaways: – Applying optimized prime checking – Working with ranges and loops – Improving efficiency using √n – Writing clean and scalable code #100DaysOfCode #Day58 #Python #Programming #DSA #Algorithms #PrimeNumbers #NumberTheory #CodingPractice #ProblemSolving #InterviewPrep #Optimization #DeveloperJourney #Consistency #BTech #CSE #AIandML #VITBhopal #TechJourney