Revisiting Python Fundamentals

🚀 Day 16 of #100DaysOfCoding Today I worked on pattern problems using Python, focusing on nested loops and condition logic. 🔹 Built a hollow square pattern using while loops 🔹 Strengthened understanding of loop control (i, j iterations) 🔹 Learned how to apply conditions for borders vs inner spaces 🔹 Practiced dry run techniques to debug and visualize code execution 💡 Key Learning: Breaking a problem into rows and columns makes pattern questions much easier to solve. The real trick is identifying where to print values and where to skip. Consistency is slowly turning confusion into clarity 💪 #Python #Coding #DSA #Programming #LearningJourney #Consistency n = int(input()) i = 1 while(i <= n):   j = 1   while(j <= n):     if(i == 1 or i == n):       print("*", end="")     elif(j == 1 or j == n):       print("*", end="")     else:         print(" ", end="")       j += 1   print("")     i += 1

Today's office discussion took an unexpected turn, diving deep into the nuances of loops. We began with a seemingly simple question: What’s the real difference between a for loop and a while loop? Initially, it seemed straightforward: - Use a for loop when you know how many times to iterate. - Use a while loop when you only know the condition, not the count. However, the conversation quickly evolved. We discovered that the difference goes beyond syntax; it’s about intent and control. Interestingly, despite their differences, Python allows us to make for and while loops behave similarly. For instance: - A for loop is driven by an iterator. - A while loop is driven by a condition check. You can even rewrite a for loop using a while loop by manually handling the iterator. Ultimately, it’s not about which loop is more powerful; it’s about how you approach the problem. Final insights: - For loop → cleaner and more readable when iteration is defined. - While loop → more flexible when termination is dynamic. - Under the hood, both are simply different methods of controlling flow. It's fascinating how a "beginner topic" can lead to a rich discussion about Python internals and abstraction layers. Sometimes, the simplest concepts reveal the deepest insights. #Python #Programming #SoftwareEngineering #LearningEveryday #CleanCode

Day 12/30 🔹 Problem: Print multiplication table of a number 🔹 What I focused on today: Using loops to repeat calculations efficiently 🔹 My Thinking Process: Take a number as input Use a loop from 1 to 10 Multiply the number with each value Print the result step by step 👉 Repetition becomes easy with loops 🔹 Inputs I used: A number 🔹 Code: num = int(input("Enter a number: ")) for i in range(1, 11): result = num * i print(num, "x", i, "=", result) 🔹 Example: Input: 5 Output: 5 x 1 = 5 5 x 2 = 10 5 x 3 = 15 ... 5 x 10 = 50 🔹 Key Takeaway: Loops help automate repetitive tasks, making code more efficient and scalable #Day12 #Python #30DaysOfCode #LearningInPublic #DataAnalytics #ProblemSolving

My thesis was stuck. A matrix had the wrong shape and I had no idea why. I could have printed the entire dataset to find the error. I did not. Instead I used Python's debugger. One breakpoint. One look at the intermediate state. Wrong dimensions. Found in seconds. That moment changed how I work. Not because debugging saved my thesis. But because it taught me something I still use every day: You do not need to see all the data to understand what is wrong. You just need to see the right data at the right moment. Since then, every time a pipeline breaks or a model behaves unexpectedly, I reach for the debugger first. Not print statements. Not guesswork. A breakpoint. An intermediate result. A clear answer. Debugging is not a last resort. It is the fastest way to understand what your code is actually doing. What is your go-to strategy when something breaks unexpectedly? #Python #Debugging #DataScience #MachineLearning #FreelanceDataScientist

Week 4 of #100DaysOfCode — done! 🎉 Last week I learned how to build classes. This week I learned how to make them talk to each other. Topics covered: 🧹 Inheritance → Superclass, subclass, base class — and what they actually mean → How Python finds attributes (spoiler: it walks up the chain) → Overriding methods — and extending them with super() → isinstance, issubclass, type — when to use which → Multiple inheritance + MRO (Method Resolution Order) 🏗️ Abstract Classes & Interfaces → ABC + @abstractmethod — enforcing a contract on subclasses → Duck typing — "if it has a speak() method, it speaks" → Subclassing built-ins (list, dict, iterator) → Mixins — plugging in behaviour without full inheritance I’ve structured my learning into notes and practical examples to better understand the concepts : https://lnkd.in/epaBymnJ #100DaysOfCode #Python #OOP #LearningInPublic #Programming

🐍 Day 8 of Learning Python — and things are getting real! Today's lab was all about writing code that doesn't break (or at least fails gracefully 😄). Here's what I worked through: ✅ Exception Handling — try / except / else / finally • Caught ZeroDivisionError, FileNotFoundError, ValueError, and TypeError • Used `raise` to throw custom error messages • Built my own exception class: TooSmallError 🎉 ✅ Standard library deep dive: • math — calculated circle areas, factorials, GCD, and compound interest • random — shuffled lists, simulated 1000 coin flips, generated reproducible sequences with seed() • datetime — parsed date strings, added time deltas, sorted ISO dates, and printed 5-day schedules ✅ Introspection with dir() and help() The biggest lesson today? Real-life programs don't always get perfect input. Learning to handle errors gracefully is just as important as writing the happy path. Day by day, the pieces are coming together. 💪 #Python #100DaysOfCode #LearningToCode #PythonProgramming #CodingJourney #Day8

From “it works” to “it won’t break” While writing a code, Getting it to work is one thing, 𝗠𝗮𝗸𝗶𝗻𝗴 𝘀𝘂𝗿𝗲 𝗶𝘁 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗯𝗿𝗲𝗮𝗸 is another. price = products["Laptop"] This works fine… until the 𝗸𝗲𝘆 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗲𝘅𝗶𝘀𝘁 . That’s when the program crashes. So instead of assuming every piece of data is present, Its better to start thinking about what happens when it isn’t. In college projects, we often focus on making things work. In real-world scenarios, 𝗲𝗱𝗴𝗲 𝗰𝗮𝘀𝗲𝘀 matter just as much. 𝗗𝗮𝘆 𝟭𝟮/𝟯𝟬 #Python #LearningInPublic #Day12 #30DaysOfCode #SoftwareEngineering

Today I focused on understanding one of the most important concepts in Python — Loops 🐍 I learned how loops help in repeating tasks efficiently and make code more powerful. 🔹 Topics Covered: ✔️ for loop and while loop ✔️ Using range() (start, stop, step) ✔️ Iterating over strings and lists ✔️ Nested loops ✔️ Control statements: break, continue, pass 📚 Practice I Did: Factorial without built-in functions Reverse a number using while loop Pattern printing Logical problems using loops 💡 My Learning: Loops are not just syntax — they help in building logic and problem-solving skills, which are essential in programming. Small steps every day → Strong foundation 🚀 👉 Which one do you prefer — for loop or while loop? Let’s connect if you're also learning Python 🤝 #Python #PythonProgramming #CodingPractice #LearnToCode #DeveloperJourney #100DaysOfCode #ComputerScience #ProgrammingBasics #LogicBuilding

🚀 Day 4/100: Randomization & Data Structures! 🎰⚔️ Continuing the #100DaysOfCode challenge! Today’s training was all about making programs unpredictable and managing organized data. I built the classic "Rock Paper Scissors" game, focusing on: ✅ Python Lists (Storing and accessing data) ✅ The Random Module (Generating unpredictable outcomes) ✅ Indexing & Nested Logic (Mapping user choices to game results) Mastering how to handle lists and random events is a huge power-up for simulation and data sampling! ⚡️ Check out my code here: 🔗 https://lnkd.in/gCkGcSg6 Progressing one day at a time. Day 5, I'm coming for you! 👊 #Python #100DaysOfCode #GameDev #LogicBuilding #PythonLists #Programming #CodingLife

Week 3 of #100DaysOfCode — done! 🎉 This week I started thinking in objects. Topics covered: 🧱 Classes & Objects → What OOP actually is (and why it matters) → Classes, instances, attributes, methods → public, _protected, and __private attributes → __init__, self, and how Python works under the hood 🔒 Properties → @property — the Pythonic way to write getters & setters → No more get_age() / set_age() — just person.age ✅ ⚙️ More Classes → __str__ vs __repr__ — and why both matter → Class attributes vs instance attributes → @classmethod and @staticmethod → __dict__, getattr, dynamic attributes I’ve structured my learning into notes and practical examples to better understand the concepts : https://lnkd.in/epaBymnJ 21 days down. 79 to go. 💻 #100DaysOfCode #Python #LearningInPublic #Programming