Mastering Bugs and Exceptions in Python

Today’s Python lesson felt less like learning syntax and more like learning how to stay calm when code gets messy. 🐍 Day 17 of my #30DaysOfPython journey was all about exception handling, and this one felt very real because errors are not rare — they are part of the process. Python gives us a way to handle errors without crashing the whole program. That makes code feel a lot more dependable. Today I explored: 1. try → run the risky code 2. except → handle the problem if something goes wrong 3. else → run only when no exception happens 4. finally → run no matter what I also learned about: 1. unpacking lists and tuples using *variable_name 2. unpacking dictionaries using **variable_name 3. packing values with *args and **kwargs 4. spreading values into function calls 5. enumerate() → when you need both index and value 6. zip() → when you want to loop through multiple lists together What stood out to me today was this: good code is not code that never fails — it is code that knows how to handle failure properly. One more day, one more topic, one more reminder that writing Python is also about writing with patience. Which one feels most useful in real code to you: try/except, enumerate(), or zip()? Github Link - #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

🐍 Why One Version Works… and the Other Doesn’t (Python Classes Explained) Ever faced this situation while learning Python? 👇 One version of your class throws an error ❌ Another version works perfectly ✅ Let’s understand why 👇 🔴 First Case (Error) class Dog: def __init__(self, name, breed): self.name = name self.breed = breed john = Dog(name="Husk") 💥 This gives an error because: The constructor __init__ expects 2 arguments → name and breed But you only provided 1 argument 👉 Python says: “Hey, I’m missing breed!” 🟢 Second Case (Works Fine) class Dog: def __init__(self, name, breed="None"): self.name = name self.breed = breed john = Dog(name="Husk") ✅ This works because: breed now has a default value If you don’t pass it, Python automatically uses "None" 💡 Key Concept: Default Parameters When you assign a default value: The argument becomes optional Your code becomes more flexible 🎯 Simple Rule to Remember No default value → argument is required Default value given → argument is optional 🚀 Small concepts like these build strong programming foundations. Keep experimenting and breaking things—that’s how you really learn! #Python #Coding #Programming #Beginners #LearnToCode #SoftwareDevelopment

🚀 Day 15 of Python Learning: Inheritance in Python Today I learned about Inheritance in Python — one of the most important concepts of Object-Oriented Programming (OOP). It helps reuse code and build relationships between classes. 🔹 What is Inheritance? Inheritance allows one class to use the properties and methods of another class. 🔸 Parent Class Example class Animal: def sound(self): print("Animal makes sound") 🔸 Child Class Example class Dog(Animal): pass dog1 = Dog() dog1.sound() 🔸 Overriding Method Example class Dog(Animal): def sound(self): print("Dog barks") dog1 = Dog() dog1.sound() 🔸 Benefits of Inheritance ✔ Code reusability ✔ Better structure ✔ Easy maintenance ✔ Supports hierarchy 💡 Key Learning: Child classes can inherit from parent classes and also customize behavior when needed. 🧪 Practice Task: ✔ Create a Vehicle parent class ✔ Create Car child class ✔ Add method start() ✔ Override one method in child class 🎯 Interview Question: What is the difference between inheritance and polymorphism in Python? Answer: Inheritance allows code reuse from another class, while polymorphism allows the same method name to behave differently in different classes. 📌 Day 15 completed — growing stronger in Python OOP! #Python #Learning #CodingJourney #Day15 #Programming #SDET #100DaysOfCode Masai #dailylearning #masaiverse

Most people learn Python by focusing on syntax. I’ve been trying to do the opposite. Instead of just writing code that works, I’ve been digging into a more fundamental idea: 👉 Everything in Python is an object — but more importantly, every object is defined by what it can do. That shift changed how I approach learning. Rather than memorizing how to use lists, strings, or functions, I’m trying to understand their roles: * Some objects hold data * Some objects execute behavior * Some objects create other objects * Some objects structure and organize information And the interesting part is: these roles overlap. A function is an object. A class is callable. A string has behavior. So instead of asking “what is this?”, I’ve started asking: 👉 “What capabilities does this object expose?” That way of thinking feels slower at first — but much more transferable. The goal isn’t to write code faster. It’s to understand systems well enough that you’re not guessing anymore. Curious — what concept forced you to rethink how programming actually works? #python #programming #learning #softwareengineering #mindset

Today was one of those Python lessons that felt less like learning code and more like learning how to read its warnings properly. 🐍 Day 15 of my #30DaysOfPython journey was all about errors, and honestly, this topic matters because every developer runs into them. When Python code fails, it gives feedback that tells us where the issue is and what kind of problem it is. Learning to understand those messages makes debugging a lot faster. Today I went through the common ones: 1. SyntaxError — when the code is written incorrectly 2. NameError — when a variable has not been defined 3. IndexError — when an index goes out of range 4. ModuleNotFoundError — when a module cannot be found 5. AttributeError — when an attribute does not exist 6. KeyError — when the wrong key is used in a dictionary 7. TypeError — when an operation is applied to the wrong data type 8. ImportError — when something is imported incorrectly 9. ValueError — when the value is valid in type, but not in meaning 10. ZeroDivisionError — when a number is divided by zero What stood out to me today was how errors are not just problems — they are clues. Once you stop panicking and start reading them properly, debugging becomes a lot less intimidating. One more day, one more topic, one more step toward writing code with less guessing and more understanding. Which error has annoyed you the most while coding so far? #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

🐍 Day 7 of Learning Python — and things are getting real! Today's session was all about making Python programs robust and practical. Here's what I covered: 🔴 Python Errors & Exception Handling → Understood common errors: ZeroDivisionError, ValueError, NameError, IndexError, FileNotFoundError → Learned try / except / else / finally to handle errors gracefully instead of crashing → Used Exception as e to catch and print meaningful error messages 📐 Math Module → sqrt(), pow(), ceil(), floor(), and math.pi — Python makes math clean and simple 🎲 Random Module → randint(), randrange(), choice(), choices(), and shuffle() — great for building games and simulations 📅 DateTime Module → Getting today's date, formatting with strftime(), and doing date arithmetic with timedelta 🗂️ OS Module → Navigating the file system with listdir(), getcwd(), mkdir(), and rmdir() — right from Python! The biggest mindset shift today? Errors aren't failures — they're information. Wrapping code in try/except means your program can fail gracefully and keep going. 💡 7 days in and I'm genuinely enjoying every session. The journey continues! 🚀 #Python #100DaysOfCode #LearningInPublic #PythonProgramming #CodingJourney #Day7 #LearnPython #PythonDeveloper #CodeNewbie #TechLearning #Programming #SoftwareDevelopment #Coding #DataScience #MachineLearning #AI #TechCommunity #Developer #OpenToWork #Upskilling #Growth #StudentLife #PythonBeginners #CodeEveryDay #ProgrammingLife #Tech #Innovation #LinkedInLearning #CareerGrowth #FutureOfTech

🚀 Today’s Learning: Python Conditional Statements (if, elif, nested if) 🧠🐍 Today in class, I learned how decision-making works in Python using if, elif, and nested if statements. 🔹 Key Takeaways: ✔️ if-else → Used when there are two possible outcomes (True / False) ✔️ if-elif-else → Used when checking multiple conditions (order matters ⚠️) ✔️ nested if → Used for multi-level decision making (filter → then decide) ✔️ Writing clean and optimized code is as important as writing working code 💡 One important lesson: 👉 “Code running ≠ Code correct” 👉 Logic and optimization make a real difference in programming 🔧 Also learned about common mistakes like: ❌ Wrong condition order ❌ Indentation errors ❌ Repeating unnecessary code 📌 Practiced real examples like: Marks grading system Discount calculation Finding largest number Even/Odd check with proper logic This session helped me understand how to think logically while coding, not just write code. #Python #LearningJourney #DataScience #Programming #CodingBasics #IfElse #LogicBuilding #StudentLife

Day 18 of my Python learning journey Today I tried another good problem based on the Two Pointer technique. Problem: Merge Sorted Array Given two sorted arrays, merge them into one sorted array. Example: arr1 = [1, 3, 5] arr2 = [2, 4, 6] Output: [1, 2, 3, 4, 5, 6] What I understood: Since both arrays are already sorted, we do not need to sort again. We can compare elements from both arrays and place the smaller one first. Better approach (Two Pointer): One pointer for arr1 One pointer for arr2 Compare both values Add the smaller value to the result Code I wrote: arr1 = [1, 3, 5] arr2 = [2, 4, 6] i = 0 j = 0 result = [] while i < len(arr1) and j < len(arr2): if arr1[i] < arr2[j]: result.append(arr1[i]) i += 1 else: result.append(arr2[j]) j += 1 while i < len(arr1): result.append(arr1[i]) i += 1 while j < len(arr2): result.append(arr2[j]) j += 1 print(result) Problems I faced while coding this: At first I thought I had to combine both arrays and then sort them. I was confused about why two pointers are enough. I also forgot to handle the remaining elements after one array finishes. What I finally understood: Because both arrays are sorted, we can merge them in one pass. This saves extra sorting work and makes the solution efficient. Time and Space Complexity: Time Complexity: O(n + m) Space Complexity: O(n + m) Question: What will be the output for: arr1 = [1, 2, 7] arr2 = [3, 4] Today’s realization: Sometimes the best solution is not creating something new, but combining what is already ordered. #Python #DSA #Coding #Programming #LearningInPublic #100DaysOfCode #PythonProgramming

Most people start learning Python… but quit halfway. Python isn’t difficult — the real problem is unstructured learning. Instead of jumping between random tutorials, I focused on building strong fundamentals like variables, loops, functions, and consistent practice. That’s when things finally clicked. Good notes are underrated. When you write and revise your own Python notes, concepts stay with you longer, and coding becomes much easier. From basic syntax to real-world use cases like web development, automation, and AI — Python opens doors everywhere. If you’re just starting, don’t rush. Focus on clarity, practice daily, and build small projects. Remember: consistency beats intensity. I’ve shared my Python notes to help you learn faster and avoid common mistakes. 📌 Connect with Himanshu Choure for more #Python #Coding #Programming #LearnToCode #PythonNotes #Developer #Tech #100DaysOfCode #CodingJourney #SoftwareDevelopment

Day 6 of my Python learning journey Today I tried solving a problem that looked easy at first, but understanding the logic took some time. Problem: Two Sum Given a list of numbers and a target value, find the indices of the two numbers that add up to the target. Example: nums = [4, 5, 1, 8] target = 9 Output: [0, 1] Because 4 + 5 = 9. Code I wrote: nums = [4, 5, 1, 8] target = 9 d = {} for i in range(len(nums)): num = nums[i] comp = target - num if comp in d: print("Indices:", d[comp], i) print("Values:", nums[d[comp]], nums[i]) break d[num] = i Problems I faced while coding this: At first I tried two nested loops, which worked but felt inefficient. I was confused about why we store numbers in a dictionary. The line d[num] = i also confused me because I didn’t understand why we save the index. It also took time to understand how comp = target - num helps find the pair. What I finally understood: Instead of checking every pair, we store numbers we have already seen in a dictionary. Then we check if the required number already exists. This reduces the time complexity from O(n²) to O(n). From tomorrow we will start something different. I’m planning to build a small Python project that will take about 1 week. Tomorrow I will share the project roadmap, and then we will start with Day 1 of the project. #Python #DSA #Coding #Programming #LearningInPublic #100DaysOfCode #PythonProgramming