# 𝑫𝒂𝒚 - 4 𝑷𝒚𝒕𝒉𝒐𝒏 𝑲𝒂 𝑫𝒂𝒊𝒍𝒚 𝑫𝒐𝒔𝒆 In Python 3.12 (the current stable standard), there are 35 reserved keywords. These are "reserved" because the Python interpreter uses them to recognize the structure of your code. Because of this, you cannot use these words as variable names, function names, or any other identifier. 𝐏𝐫𝐨-𝐓𝐢𝐩: 𝐒𝐨𝐟𝐭 𝐊𝐞𝐲𝐰𝐨𝐫𝐝𝐬 Python also has a few "soft keywords" like match, case, and _ (used in pattern matching). These act like keywords in specific contexts but can still be used as variable names elsewhere—though it's usually better to avoid doing that to keep your code clean! 😊 Run this in your terminal or IDE: 𝐢𝐦𝐩𝐨𝐫𝐭 𝐤𝐞𝐲𝐰𝐨𝐫𝐝 # To see the list of all keywords: 𝐩𝐫𝐢𝐧𝐭(𝐤𝐞𝐲𝐰𝐨𝐫𝐝.𝐤𝐰𝐥𝐢𝐬𝐭) # To see how many there are: 𝐩𝐫𝐢𝐧𝐭(𝐥𝐞𝐧(𝐤𝐞𝐲𝐰𝐨𝐫𝐝.𝐤𝐰𝐥𝐢𝐬𝐭)) #PythonProgramming #CodingTips #SoftwareDevelopment #PythonTips #CleanCode #ProgrammingLife #DataScience #BackendDeveloper

Python 𝘭𝘰𝘰𝘱𝘴 almost made me wait an hour. 𝘕𝘶𝘮𝘗𝘺 laughed and did it in 90 seconds. While working on an assignment, I built two versions of the same function. The first used a 𝘗𝘺𝘵𝘩𝘰𝘯 𝘭𝘰𝘰𝘱 to run 60 model evaluations one by one. The second used 𝘕𝘶𝘮𝘗𝘺 to compute all 3,600 pairwise distances in a single operation. Both gave the exact same accuracy of 50%, proving the winner's curse on a fully random dataset. But the loop version took nearly an hour to finish, while the NumPy version was done in 1 to 1.5 minutes. Why is 𝐍𝐮𝐦𝐏𝐲 so much faster? Python loops handle one task at a time and carry overhead with every single step. NumPy does the whole job at once using fast, low level C code that runs directly on your processor. So instead of comparing 60 samples one by one, NumPy compares all 60 against all 60 in one shot. When you run thousands of these operations inside a nested loop, the time savings are massive. 𝘈𝘭𝘸𝘢𝘺𝘴 𝘷𝘦𝘤𝘵𝘰𝘳𝘪𝘻𝘦 𝘺𝘰𝘶𝘳 𝘤𝘰𝘥𝘦 𝘸𝘩𝘦𝘳𝘦 𝘺𝘰𝘶 𝘤𝘢𝘯. Your future self will thank you. Read more: https://lnkd.in/dkxgWqvr #𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 #𝐏𝐲𝐭𝐡𝐨𝐧 #𝐍𝐮𝐦𝐏𝐲 #𝐃𝐚𝐭𝐚𝐒𝐜𝐢𝐞𝐧𝐜𝐞 #𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 #𝐂𝐫𝐨𝐬𝐬𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧

# 𝑫𝒂𝒚 - 5 𝑷𝒚𝒕𝒉𝒐𝒏 𝑲𝒂 𝑫𝒂𝒊𝒍𝒚 𝑫𝒐𝒔𝒆 👇 𝐒𝐭𝐨𝐩 𝐮𝐬𝐢𝐧𝐠 𝐚𝐬𝐲𝐧𝐜𝐢𝐨.𝐠𝐚𝐭𝐡𝐞𝐫() 𝐟𝐨𝐫 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐭𝐚𝐬𝐤𝐬. If you are still managing multiple concurrent tasks in Python using old patterns, you might be leaving your applications vulnerable to "zombie tasks" and silent failures. 𝐄𝐧𝐭𝐞𝐫 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐂𝐨𝐧𝐜𝐮𝐫𝐫𝐞𝐧𝐜𝐲 𝐰𝐢𝐭𝐡 𝐚𝐬𝐲𝐧𝐜𝐢𝐨.𝐓𝐚𝐬𝐤𝐆𝐫𝐨𝐮𝐩. Introduced in Python 3.11, TaskGroup is the modern way to manage groups of tasks. Unlike asyncio.gather(), if one task in a group fails, TaskGroup ensures all other tasks in that group are cancelled automatically. 𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐚 𝐠𝐚𝐦𝐞-𝐜𝐡𝐚𝐧𝐠𝐞𝐫? ✅ 𝐒𝐚𝐟𝐞𝐭𝐲: No more "leaked" tasks running in the background after an error. ✅ 𝐂𝐥𝐞𝐚𝐧 𝐄𝐱𝐜𝐞𝐩𝐭𝐢𝐨𝐧 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠: Uses ExceptionGroup to catch multiple errors at once. ✅ 𝐁𝐞𝐭𝐭𝐞𝐫 𝐅𝐥𝐨𝐰: The async with syntax makes it clear exactly where a group of tasks starts and ends. As we move through 2026, writing "Pythonic" code means writing robust code. Structured concurrency is no longer optional for senior devs—it's the standard. #Python #SoftwareEngineering #BackendDevelopment #AsyncIO #CleanCode #Python311 #ProgrammingTips #WebDev

#PythonCoding 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁: 𝗥𝗲𝗺𝗼𝘃𝗲 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀 𝗳𝗿𝗼𝗺 𝗮 𝗟𝗶𝘀𝘁 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗨𝘀𝗶𝗻𝗴 𝗣𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 Write a Python function that takes a list as input and returns a new list with duplicate elements removed, while maintaining the original order of elements. 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀: The function should work for both numbers and strings. The function should not use built-in functions like set(), dict.fromkeys(), or collections.OrderedDict. The function should preserve the original order of elements. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 1.Create an empty list called unique_list to store only unique elements. 2.Iterate through the original list using a loop. 3.Check if the element is already in unique_list: -->If not, add it to unique_list. -->If it already exists, skip it. 4.Return the unique_list, which now contains elements without duplicates while preserving the original order. #Python #PythonProgramming #LearnPython #PythonDeveloper #CodingLife #PySpark #MachineLearning #Automation

Tuples might look simple… but they’re powerful! 👀✨ If you’re working with fixed data that shouldn’t change, tuples are your best friend 💡 ✔️ Ordered ✔️ Immutable ✔️ Supports heterogeneous data Understanding CRUD on tuples helps you clearly see why immutability matters in Python 🐍 Save this post for quick revision & follow for more Python concepts explained visually 🚀 #PythonProgramming #LearnPython #CodingJourney #SoftwareDevelopment #TechCareer

🐍 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 — 𝗗𝗮𝘆 𝟭𝟭 🚀 🚨𝗘𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 When writing Python programs, errors are inevitable. Exception handling helps us manage these errors gracefully without crashing the program. 🔹𝗪𝗵𝗮𝘁 𝗶𝘀 𝗘𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴? It is a mechanism used to handle runtime errors (exceptions) so the program can continue execution smoothly. 🔹𝗕𝗮𝘀𝗶𝗰 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 try:   # code that may cause an error except ExceptionType:   # runs if an error occurs else:   # runs if no error occurs finally:   # always executes 🔹𝗞𝗲𝘆 𝗕𝗹𝗼𝗰𝗸𝘀 ✅ `try` → Contains risky code ✅ `except` → Handles the error ✅ `else` → Executes when no exception occurs ✅ `finally` → Runs no matter what (cleanup tasks) 🔹𝗘𝘅𝗮𝗺𝗽𝗹𝗲 try:   num = int(input("Enter a number: "))   print(10 / num) except ZeroDivisionError:   print("Cannot divide by zero!") except ValueError:   print("Invalid input!") finally:   print("Execution completed.") 💡𝗪𝗵𝘆 𝗨𝘀𝗲 𝗘𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴? ✔ Prevents program crashes ✔ Improves user experience ✔ Helps debug efficiently ✔ Ensures resource cleanup 👉 Good developers don’t avoid errors — they handle them smartly! #Python #Programming #LearningInPublic #DeveloperJourney #30DaysChallenge #PythonTips

𝗤. 𝗜𝗳 𝗣𝘆𝘁𝗵𝗼𝗻 𝗛𝗮𝘀 𝗧𝗵𝗿𝗲𝗮𝗱𝘀… 𝗪𝗵𝘆 𝗗𝗼𝗲𝘀𝗻’𝘁 𝗜𝘁 𝗦𝗰𝗮𝗹𝗲 𝗼𝗻 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗖𝗼𝗿𝗲𝘀? When I first learned about multithreading in Python, I assumed it would use all CPU cores. It doesn’t (for CPU-heavy tasks). The reason is something called the 𝐆𝐥𝐨𝐛𝐚𝐥 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐞𝐫 𝐋𝐨𝐜𝐤 (𝐆𝐈𝐋). 👉 The GIL ensures that only one thread executes Python bytecode at a time. So even if you create multiple threads: CPU-bound tasks won’t run in parallel They take almost the same time as a single thread But here’s the important nuance:- The GIL mainly affects 𝐂𝐏𝐔-𝐛𝐨𝐮𝐧𝐝 𝐭𝐚𝐬𝐤𝐬. For: - I/O-bound work (API calls, file reading, DB queries) - Network operations - Threading still improves performance. So how do we use multiple cores in Python? -> multiprocessing -> ProcessPoolExecutor -> Libraries like NumPy (which release GIL internally) My takeaway: 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐆𝐈𝐋 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐡𝐨𝐰 𝐲𝐨𝐮 𝐝𝐞𝐬𝐢𝐠𝐧 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧. #Python #TechInterview #SoftwareEngineering #LearningInPublic #SystemDesign

💡 Python Insight from Production Code One mistake I often see—even in mature codebases—is confusing raise with return. They may look similar, but they serve very different purposes. 🔸 raise Used to stop execution immediately and signal that something went wrong. It enforces correctness and makes failures explicit. 🔹 return Used to exit a function gracefully and pass a result back to the caller. It keeps control flow predictable. 📌 Real-world rule: Use raise when continuing execution would hide a bug Use return when the outcome is expected and handled Clear error handling is not about writing more code — it’s about writing honest code. 👉 Save this if you write Python professionally 👉 Share with someone who’s still mixing these up #Python #PythonProgramming #BackendDevelopment #CleanCode #SoftwareEngineering #ProgrammingTips #CodeQuality #DeveloperCommunity #TechLeadership #LearnPython #CodingBestPractices #EngineeringMindset #100DaysOfCode

𝗠𝗔𝗖𝗛𝗜𝗡𝗘 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚 𝗙𝗢𝗥 𝗕𝗘𝗚𝗜𝗡𝗡𝗘𝗥𝗦 𝗣𝘆𝘁𝗵𝗼𝗻 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀: 𝗗𝗶𝗰𝘁𝗶𝗼𝗻𝗮𝗿𝗶𝗲𝘀, 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀, 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝗼𝗻𝘀 & 𝗟𝗮𝗺𝗯𝗱𝗮 Good Python code is rarely about knowing more features — it’s about choosing the right tools. In this notebook, I dive into concepts that directly impact performance, clarity, and design: • How dictionaries power fast lookups • Why selecting the right data structure matters • Writing cleaner loops with list comprehensions • Simplifying logic with lambda functions #Python #PythonProgramming #LearnPython #Coding #DataStructures #SoftwareDevelopment #ProgrammingLife

CPython Unveils Chained Assignment Mechanics in Python Bytecode Execution 📌 Chained assignments in Python don’t create separate objects-they share the same underlying instance, a bytecode-level quirk that can silently break your code. A deep dive into CPython’s execution reveals how a = b = [] uses COPY and STORE_FAST to assign the same list to multiple variables, exposing a common pitfall for developers working with mutable data. 🔗 Read more: https://lnkd.in/dUfiAJ44 #Cpython #Pythonbytecode #Chainedassignment #Interpreterexecution #Variablebinding