30DaysOfLeetCode Challenge: Roman to Integer Problem

Day 65 of the #three90challenge 📊 Today I learned about File Handling in Python — working with external data files. This is a big step because real-world data doesn’t live inside code — it comes from files like .txt, .csv, etc. What I practiced today: • Opening files using open() • Reading data (read(), readline()) • Writing data to files • Understanding file modes (r, w, a) • Closing files properly Example thinking: Instead of hardcoding data, I can now read data from files, process it, and even write results back. Example: with open("data.txt", "r") as file: content = file.read() print(content) This makes Python much more powerful for handling real datasets. From working with code → to working with real data 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #DataAnalytics #LearningInPublic #Consistency #Upskilling #PythonBasics

This week I spent 2 hours debugging a pipeline that broke because of a subtle mutable default argument. Last week I finished DataCamp's "Intermediate Python for Developers" - and guess what chapter was in there. Funny how that works sometimes. A few takeaways that'll stick with me: • Mutable defaults are a trap, even for people who "know Python" • Decorators aren't magic - they're just functions returning functions (but the mental model matters) • Comprehensions > loops, until they don't fit on one screen anymore Working with Python daily on dbt models, and data transformations, it's easy to get comfortable in a narrow slice of the language. Stepping back to revisit the fundamentals consistently makes my production code cleaner. What's your approach - do you block time for structured learning, or learn purely on the job? #Python #DataEngineering #LearningInPublic

Python is more than just code; it’s a powerful calculator! 🧮 Today, while diving deeper into my Data Science journey, I spent some time mastering Python's mathematical operators. It’s not just about simple math; it's about understanding how the machine processes different operations to build solid business logic. From basic addition to Floor Division and Exponentiation, understanding these basics is crucial for building accurate data models later on at Data Hub. 📊 In this snippet: Handled different types of operations. Explored how Python handles float results vs integers. Question for the experts: What’s the most common mathematical error you faced when you first started coding? 🧐 #DataHub #Python #Coding #DataAnalysis #LearningJourney #TechCommunity

🚀 ✨ 𝐃𝐀𝐘 12: 𝐔𝐍𝐃𝐄𝐑𝐒𝐓𝐀𝐍𝐃𝐈𝐍𝐆 𝐒𝐄𝐓𝐒 ✨ Today, I explored another important data structure in Python — 💻 𝐒𝐞𝐭𝐬. 🔹 📘 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐒𝐞𝐭𝐬? Sets are 𝐮𝐧𝐨𝐫𝐝𝐞𝐫𝐞𝐝 collections of unique elements, meaning they automatically remove duplicates. 🔹 ⚙️ 𝐖𝐡𝐚𝐭 𝐈 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 ✔️ Creating and using 𝐬𝐞𝐭𝐬 ✔️ Performing operations like 𝐮𝐧𝐢𝐨𝐧, 𝐢𝐧𝐭𝐞𝐫𝐬𝐞𝐜𝐭𝐢𝐨𝐧, 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 ✔️ Understanding how sets handle 𝐮𝐧𝐢𝐪𝐮𝐞 𝐯𝐚𝐥𝐮𝐞𝐬 🔹 🧠 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Sets are very useful for 𝐫𝐞𝐦𝐨𝐯𝐢𝐧𝐠 𝐝𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐬 and performing fast mathematical operations. 💡 𝐔𝐧𝐢𝐪𝐮𝐞 𝐝𝐚𝐭𝐚 = 𝐂𝐥𝐞𝐚𝐧 𝐚𝐧𝐝 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐜𝐨𝐝𝐞! 💪 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐢𝐧𝐠 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐚 𝐬𝐭𝐫𝐨𝐧𝐠 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧! 🚀 𝐎𝐧𝐞 𝐬𝐭𝐞𝐩 𝐜𝐥𝐨𝐬𝐞𝐫 𝐭𝐨 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚 𝐛𝐞𝐭𝐭𝐞𝐫 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫! #Python #Day12 #CodingJourney #Sets #DataStructures #LearningPython #Consistency 🚀

DATA ANALYSIS USING PYTHON - DAY 3 Suppose your manager hands you a dataset of 50,000 customers and says: "Find everyone who spent over $500 and lives in your city." Are you going to check them one by one? Definitely not. To do real Data Analysis, your code needs a "brain" to make decisions automatically. That’s exactly what we are covering in Day 3 of my Data Analysis Using Python course! 🚀 In this brand-new lesson on LogicStack, I’ll show you how to automate your analytical thinking. We cover: ✅ If/Else Statements: How to filter data based on specific rules. ✅ For & While Loops: How to process thousands of records in a matter of seconds. ✅ List Comprehensions: The ultimate 1-line shortcut used by professional analysts. The best part? You don't just read the theory. You get to write, test, and run the Python code right inside your browser using our interactive live editor! #Python #DataAnalysis #DataScience #LogicStack #Coding #PythonForBeginners #TechEducation #LearnToCode #Automation

A beginner mindset shift I’m learning in Python for data science: think in arrays, not loops. I used to believe that better performance meant writing more efficient 'for loops'. However, I’m starting to realize that in data science, the key question is: do I need the loop at all? When I loop through large data in Python, it processes values one by one. In contrast, using NumPy or Pandas operations allows the work to shift into optimized low-level code designed to handle arrays much more efficiently. This realization has transformed my approach to writing code for data work. It’s not solely about speed; it’s about adopting the right mental model for the problem. One beginner habit I’m working to break is reaching for a loop every time I want to transform data. Instead, I’m cultivating a better habit: if the data is array-shaped, I’ll try thinking in array operations first. #Python #DataScience #NumPy #Pandas #MachineLearning #CodingJourney

🚀 Day 12 & 13 – Consistency is the Key! Still going strong on my Python learning journey, and these two days were all about revision + real application 💻 🔁 Quick Revision: Revisited core concepts like loops, functions, and conditionals — because strong basics = strong foundation. 💡 Mini Project: Bill Generator Built a simple yet practical Python project using: ✔️ if-elif-else statements ✔️ Operators (arithmetic & logical) ✔️ User inputs for dynamic calculations 🔹 Features included: - Item selection & pricing - Quantity-based calculations - Discount logic - Final bill generation 🧠 What I Improved: - Better problem-solving approach - Writing cleaner, more readable code - Debugging with more confidence - Thinking in a more structured, logical way Every small project is making me more confident and bringing me one step closer to becoming a skilled data professional 📈 🙏 Special thanks to Anurag Srivastava and the Data Engineering Bootcamp for the constant guidance and support! #Python #LearningJourney #100DaysOfCode #DataEngineering #Coding #BeginnerToPro #Consistency

Attending SQLBits today? ⏰ 12:30 PM - don’t miss “Python in Microsoft Fabric: Execution Options and Scaling.” Matt Collins breaks down how to run and scale Python in Fabric - fast, practical, and straight to the point. If you’re working in data or analytics, this one’s worth your time. See you there. #SQLBits #MicrosoftFabric #Python #DataEngineering #Analytics

🚀 Unlocking the Power of Numerical Python with NumPy! I just finished a deep dive into NumPy, the foundational package for numerical computation in Python. It’s incredible how much complexity you can simplify with just a few lines of code! Here’s a quick recap of the core concepts I explored: Array Creation: Effortlessly generating data using np.zeros(), np.ones(), np.arange(), and np.linspace(). I also tapped into np.random.random() for statistical simulations. Indexing & Slicing: Mastering access to specific elements and rows. Boolean indexing (e.g., a[a > 2]) is a total game-changer for filtering data quickly. Mathematical Operations: Performing lightning-fast element-wise operations and using built-in functions like np.sqrt() for efficient transformations. Statistical Analysis: Calculating mean, median, and std across different axes. I especially appreciated learning about np.nanmean to handle missing values without breaking the code. Data Cleaning: Putting it all together to identify and remove extreme values (outliers) from a dataset to ensure cleaner, more accurate analysis. NumPy is an indispensable tool for Data Science, Machine Learning, and Scientific Computing. Its efficiency makes it a "must-have" in any Python developer's toolkit. #Python #NumPy #DataScience #MachineLearning #Coding #DataAnalysis #ProgrammingTips