When I first started using Pandas, I wrote code the same way I wrote normal Python. Lots of loops. Lots of step-by-step logic. And it worked… at first. But then datasets got bigger. And things slowed down quickly. That’s when I learned something important: 👉 Pandas works best when you think in vectorized operations. Instead of: looping through rows You start thinking in columns. Example mindset shift: Instead of processing each row individually, you transform entire columns at once. This small change made my code: ✔ faster ✔ simpler ✔ easier to read Still learning, but it's one of those small mental shifts that really changes how you work with data. #DataEngineering #Python #Pandas

#Day2 of learning Python 🐍 Today was more of a recall and reinforcement day. I revisited concepts like recursive functions and higher-order functions, which I had learned before. It was a great refresher to understand how recursive functions allow a function to call itself to solve complex problems step by step, and how higher-order functions can take other functions as arguments or return them, making code more flexible and powerful. Revisiting known concepts helps strengthen fundamentals and build more confidence while moving forward. Day 2 complete — 98 days to go! 🚀 #Day2 #PythonLearning #Recursion #HigherOrderFunctions #100DaysOfLearning #CodingJourney #SkillShikshya

🚀 Day 6 of My Python Learning Journey Today I worked with core data structures in Python: ✅ Strings ✅ Lists ✅ Tuples ✅ Sets ✅ Dictionaries 💡 What I practiced: Finding unique characters in a string Merging and sorting lists Detecting duplicate elements Working with tuples and sets Building a menu-driven dictionary program 📌 Key Learning: Understanding how data structures behave is crucial. The same operation can behave very differently depending on the data type (string vs list vs set). I’m improving my problem-solving skills step by step and documenting everything on GitHub. #Python #CodingJourney #LearningInPublic #AI #MachineLearning

While learning LangGraph, one small Python concept suddenly became much more important to me: TypedDict. At first, I thought it was just “type annotations for dictionaries.” Useful, sure—but nothing special. Then I started thinking about state. When multiple nodes in a workflow keep reading and updating shared data, an unstructured dict becomes chaos very quickly. - Missing keys. - Unexpected values. - Confusing debugging. TypedDict solves that by forcing structure into state. That was my takeaway: - Sometimes tools that look “optional” become essential once systems start growing. #Python #BackendDevelopment #LangGraph #AIEngineering #BuildInPublic

𝗗𝗮𝘆 𝟮𝟬/𝟯𝟬  This week wasn't about learning new syntax. It was about noticing how Python actually behaves 𝗕𝘂𝗴𝘀 are rarely confusing because they are complex. They are confusing because they don't show up where they actually start 📍 𝗔 𝗹𝗶𝘀𝘁 changes… but the issue was three functions ago 📍 𝗔 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻 runs fine… but your data is already different 📍 𝗧𝘄𝗼 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 look the same… but behave differently That’s the tricky part. it might feel simple until you have to 𝗱𝗲𝗯𝘂𝗴  it. Still figuring it out, but now I know what to pay attention to. #Python #30DaysOfCode #LearningInPublic #Day20 #JECRC

🚀 Starting my Python Learning Series – Part 1 I’ve begun my journey into Python and created the first presentation covering the fundamentals in a clear and structured way. 📌 In this part, I’ve covered: • What Python is and how it works • Why Python is widely used • Key features of Python • Importance in technical roles • Real-life use cases This is just the beginning — I’ll be sharing more parts as I continue learning and building my skills step by step. 💡 Follow along if you’re also learning Python or interested in tech! I’d appreciate your feedback and suggestions 🙌 #Python #LearningJourney #Programming #DataAnalytics #TechSkills #CareerGrowth

Built Linear Regression from scratch using Python (no libraries) Wanted to understand what’s happening under the hood before moving to sklearn. So I implemented a simple model to predict marks based on hours studied using Gradient Descent. 🔹 What I did: Implemented the prediction function (y = wx + b) Calculated Mean Squared Error (MSE) manually Computed gradients and updated parameters over 1000 epochs 🔹 What I learned: How gradient descent updates weights step by step Why learning rate plays a critical role How loss decreases as the model learns 🔹 Result: The model successfully learned the relationship. Example: If a student studies 9 hours → predicted marks ≈ 89.3 🔗 Code: https://lnkd.in/gPHCenhB Next step: implementing this using NumPy and then sklearn. #MachineLearning #Python #LearningInPublic

🚀 Day 10/30 – Python Challenge Learning about tuples in Python today! 🐍 🔹 Key Concepts Covered: * Creating tuples * Accessing elements using index * Iterating through tuple elements using loops * Understanding immutability (tuples cannot be changed) 💻 Mini Task: Created a tuple of numbers, accessed the first element, and used a loop to display all the values. 🎯 Learning Outcome: Understood how tuples are used to store fixed collections of data and how they differ from lists. They are especially useful when data should not be modified. Building a strong foundation in data structures step by step 💪 #Python #CodingChallenge #LearningJourney #DataStructures #StudentDeveloper #Day10

💻 Day 18 of #100DaysOfCode Today I explored some deeper concepts in Python 🐍 What I covered: - Learned about recursive functions - Understood local and global variables - Practiced examples to see how scope works I realized that recursion requires clear thinking and proper base conditions, otherwise it can easily lead to infinite loops. Also, understanding variable scope is important to avoid unexpected behavior in programs. Focused on practicing with examples to build clarity. See you all tomorrow with new learnings and more progress 🚀 #Python #100DaysOfCode #CodingJourney #Learning #Consistency

Day 5 Consistency is key! 🚀 I’ve been dedicating time to strengthening my Python fundamentals, specifically diving deep into how to work with data sequences. From understanding immutability to mastering indexing and slicing techniques, I’m building a solid foundation to handle data manipulation more effectively. It’s rewarding to see how these concepts translate into cleaner, more efficient. Today I’ve been practicing advanced sequence manipulation in Python. Key takeaways from my study session: Immutability: Understanding why certain data types (like strings) cannot be changed in place. Slicing Syntax: Mastering [start:stop] and how to omit indices for cleaner, faster code. Negative Indexing: Leveraging indexing from the end to make my code more dynamic. There is always something new to learn when it comes to optimizing data extraction! 💡 #PythonProgramming #SoftwareDevelopment #LearningToCode #DataManipulation #CodingTips #Python #CodingJourney #ContinuousLearning #DataHandling #SelfDevelopment #TechSkills