Publishing Meme-Errors on PyPI: Python Package for Dramatic Exceptions

A tricky Python concept 🔍 ✅ Why does this work: t = (1, 2, [3, 4]) t[2].append(5) ➡️ After this operation, the tuple becomes: t = (1, 2, [3, 4, 5]) ❌ But this fails: t[0] = 10 🔹 Explanation In Python, tuples are immutable, meaning their structure cannot be changed. However, they can contain mutable objects. In this example: • The tuple itself is fixed • But the list inside it is mutable So: ✔ You can modify the list ❌ But you cannot reassign elements of the tuple 📌 Core Idea A tuple stores references, not the actual values. That means: • You can’t change what the tuple points to • But you can modify the object if it’s mutable 💡 The “immutability” of a tuple means that the references it holds cannot be changed after creation. You cannot change which object a specific index in the tuple points to, nor can you add or remove elements from the tuple. 📌 Key Point A tuple is immutable, but it can contain mutable objects. #Python #PythonProgramming #CodingTips #DataStructures #LearnPython

The interview traces Python's core evolution. It starts with adding augmented assignment (+=) and the PEP 203 debates. Arguments followed. Nested scopes landed via future imports. Maintainers repackaged elementtree/xmlplus using path. asyncio rose and supplanted Twisted. Python moved to yearly releases. https://lnkd.in/eznNt2vK --- Enjoyed this? Sign up 👉 https://faun.dev/join

What Happens When You Call a Function in Python? Most of us use functions every day, but have you ever looked at the checklist Python follows before it actually runs your code? It is a 5-step journey that happens in milliseconds: 1. Identity Check: Python scans its internal data. If it doesn't recognize the name you used, the code stops right there. 2. The Math Check: It counts your arguments. If the function asks for two values and you only give one, Python will abort the process. 3. The Hand-off: Python pauses your main code. It "jumps" into the function, carrying your arguments along with it. 4. The Work: The function executes its logic, creates an effect, or calculates a result. 5. The Return: Once finished, Python returns to your main code—exactly where it left off—and resumes the rest of your script. Understanding this flow helps you debug faster. When you see an error, you can quickly tell if it was a naming mistake Step 1 or an argument mismatch Step 2. Keep coding and keep learning... #Python #SoftwareDevelopment #CodingTips #TechLearning #CleanCode

Python type annotations let you attach type information directly to variables, function parameters, and return values. They do not change how Python runs your code at runtime—but they transform what your editor and your teammates can understand about it before a single line executes. https://lnkd.in/g4JXgCgi #python

Day 33 of my #100DaysOfCode challenge 🚀 Today I worked on a Python program to implement a custom version of the len() function. Instead of using Python’s built-in len() function, I created my own function to count the number of elements in an iterable. What the program does: • Takes any iterable as input (list, string, tuple, etc.) • Iterates through each element • Counts elements one by one • Returns the total length How the logic works: 1)A function manual_len(iterable) is defined 2)A variable count is initialized to 0 3)The program loops through each element in the iterable 4)For every element, the counter is incremented by 1 5)After the loop ends, the final count is returned Example: List:[1, 2, 3, 4, 5] Output: Length = 5 String: "Hello, Colab!" Output: Length = 13 Tuple: (10, 20, 30) Output: Length = 3 Why this is useful: – Helps understand how built-in functions work internally – Demonstrates iteration over different data types – Strengthens understanding of iterables in Python Key learnings from Day 33: – Implementing built-in functionality manually – Working with iterables in Python – Understanding loops and counters – Strengthening Python fundamentals #100DaysOfCode #Day33 #Python #PythonProgramming #Algorithms #ProblemSolving #CodingPractice #LearnByDoing #ProgrammingJourney #DeveloperGrowth #ComputerScience #InterviewPrep #BTech #CSE #AIandML #VITBhopal #TechJourney

Built a Python package this week. It's called sklog — colored terminal logging, no dependencies, no setup. Nothing fancy. Just something I kept rewriting across projects and finally packaged it. PyPI → https://lnkd.in/gYPsjCvn GitHub → https://lnkd.in/gx7B2APn #Python #OpenSource #foss

🚀 Day 31 of My Python Full-Stack Journey 🐍 Today I learned about Types of Functions in Python based on Parameters and Return Values. Functions help us organize code and avoid repetition. Based on parameters and return values, functions can be classified into different types. 🔹 Function without Parameters and without Return Value This type of function does not take any input and does not return any value. It simply performs a task. Python Copy code def greet(): print("Hello, welcome to Python!") greet() 🔹 Function with Parameters and without Return Value This function takes input values but does not return anything. Python Copy code def greet(name): print("Hello", name) greet("Balaji") 🔹 Function with Parameters and Return Value This type takes input and returns a result using the return keyword. Python Copy code def add(a, b): return a + b result = add(5, 3) print(result) 🔹 Function without Parameters but with Return Value Python Copy code def get_number(): return 10 num = get_number() print(num) 💡 Key Takeaway: Understanding these function types helps in writing clean, reusable, and structured Python programs. Learning step by step and improving every day on my Python Full-Stack Journey 🚀 #Python #FullStack #CodingJourney #100DaysOfCode #PythonFunctions #LearningInPublic

🚀 Python Practice – Using extend() to Merge Lists! Today I explored another way to combine lists in Python using the extend() method 🐍 🔹 Sample Code: alist = ['praveen','ajay','san','kiran','chandru','fun','joy','rrrr'] blist = ['run','jun','jam'] alist.extend(blist) print(alist) ✅ Key Learnings: 🔸 extend() adds elements of one list to another 🔸 It modifies the original list (no new list is created) 🔸 Faster and more memory-efficient compared to creating a new list 💡 Output: ['praveen', 'ajay', 'san', 'kiran', 'chandru', 'fun', 'joy', 'rrrr', 'run', 'jun', 'jam'] 📌 Difference from + operator: + → creates a new list extend() → updates existing list ⚡ Useful in real-world scenarios like: Merging logs Combining datasets Automation scripts in DevOps #Python #DevOps #Learning #Automation #CodingJourney #Programming #Beginners

One reason Python feels so smooth to use? Smart memory decisions happening behind the scenes. Most of the time when we write Python, we don’t think about memory at all. We just focus on solving the problem. But under the hood, Python is constantly managing things for us. Objects are created in the heap. Variables don’t actually store the data they store references to those objects. And when objects are no longer needed, Python quietly cleans them up using its Garbage Collector. That’s why we rarely worry about freeing memory manually. But here’s the interesting part. The more you work with Python, the more you realize that understanding these internals changes the way you write code. You start thinking about... • unnecessary object creation   • memory-heavy operations   • how references behave  Suddenly Python is no longer just a scripting language. You start seeing it as a system that carefully balances simplicity with smart memory management. What was the first Python internal concept that surprised you the most? #Python

Python Tip: Use pathlib for File Operations pathlib is a modern, clean, and cross-platform way to handle file paths in Python. 1) Path("example_folder/data.txt") → defines the file path. 2) mkdir(parents=True, exist_ok=True) → creates folder(s) if missing. 3) write_text() / read_text() → write and read files easily. 4) exists() → check if the file exists. No more os.path.join or os.makedirs. Small change → more readable and professional code. 😊 😊 😊