Marcos Vinicius Thibes Kemer’s Post

I’ve put together a quick reference guide covering essential Python Dictionary and Set methods! 🐍 Whether you are just starting out with Python or need a quick refresher, this document walks through everything from basic dictionary operations like .get() and .update(), to mathematical set operations like .intersection() and .symmetric_difference(). It includes brief explanations and simple code snippets for each method to help you write cleaner, more efficient code. Check out the document below, and let me know your favorite or most-used method in the comments! 👇 #Python #Programming #Coding #DataStructures #PythonDeveloper #Cheatsheet

Today’s Python lesson was a quiet reminder that time is one of the most useful things code can help us handle. 🐍 Day 16 of my #30DaysOfPython journey was all about date and time. Python’s date and time module helps us work with: 1. current date and time 2. formatted date strings 3. converting strings into datetime objects 4. time objects 5. time differences and time spans A few things I explored today: 1. dir() and help() to check what a module offers 2. datetime.now() for current date, time, and timestamp 3. strftime() for formatting dates and time 4. strptime() for converting string dates into datetime objects 5. date() to get only day, month, and year 6. subtraction to find the difference between two time points 7. timedelta() to work with time intervals What stood out to me today was how Python does not just store time — it helps you shape it, compare it, and format it in ways that actually make sense for real projects. One more day, one more topic, one more layer of Python making everyday things easier to manage. Github Link - https://lnkd.in/gMy-QseU #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

Day 64 of the #three90challenge 📊 Today I learned about Functions in Python — a key concept for writing clean and reusable code. Instead of repeating the same logic multiple times, functions allow us to define it once and reuse it whenever needed. What I practiced today: • Creating functions using def • Passing inputs (parameters) • Returning outputs using return • Writing reusable and organized code Example thinking: Instead of writing the same code again and again, functions help turn it into a single reusable block. Example: def calculate_total(a, b): return a + b print(calculate_total(5, 10)) This makes code more efficient, readable, and scalable. From writing code → to structuring it better 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #DataAnalytics #LearningInPublic #Consistency #Upskilling #PythonBasics

🚀 Week 2 — DSA + System Design This week I focused on making my basics stronger. 📚 DSA (C++ & Python): • Practiced Arrays again • Worked more on Time & Space Complexity • Learned Binary Search and solved different types of problems • Solved around 30–35 questions 🏗️ System Design (LLD): • Revised OOP concepts • Practiced writing cleaner and better structured code ❌ Challenges: Binary Search was confusing in different problems Applying time complexity in real questions was not easy 💡 Realization: Now I’m starting to understand when to use which approach 🎯 Week 3 Goal: Start Linked List and Recursion Staying consistent. Chandan Kumar #DSA #SystemDesign #BuildInPublic #LearningInPublic

Just solved “Second Largest Digit in a String” on LeetCode — and here’s the simple approach I followed 👇 Instead of overcomplicating it, I focused on clean thinking + Python basics: 🔹 Converted the string into a set → removes duplicates instantly 🔹 Filtered only digits using isdigit() 🔹 Stored them as integers in a list 🔹 Sorted the list → easy access to largest & second largest 🔹 Edge case check: if less than 2 digits → return -1 💡 Key takeaway: Sometimes the most optimal solution isn’t about complex algorithms — it’s about using the right built-in tools smartly. 🚀 What I’m improving with each problem: • Writing cleaner logic • Thinking in steps instead of rushing • Handling edge cases early Consistency > Complexity. #LeetCode #DSA #Python #ProblemSolving #CodingJourney #100DaysOfCode

semantic winner ≠ policy winner Ran RISWIS Applied on a fresh machine (PowerShell, Python 3.12). Same system. Different query phrasing. → one returns pure semantic ranking → one triggers policy override RISWIS doesn’t force outcomes. It makes ranking decisions visible. can demo from GitHub https://lnkd.in/giw8HXCd

Today’s Python topic felt like the point where code stops being one-time work and starts becoming reusable. 🐍 Day 11 of my #30DaysOfPython journey was all about the basics of function, and this one was a big reminder that good code is not just about writing more — it is about writing smarter. A function is a reusable block of code designed to do a specific task, and in Python, we define it using the def keyword. Today I explored: 1. How functions are created and called 2. How return sends values back from a function and return None when nothing is returned 3. Passing parameters and arguments 4. Passing arguments using key-value style 5. Default parameters 6. Arbitrary arguments with *args 7. Arbitrary named arguments with **kwargs What stood out to me today was how functions make code feel organized, reusable, and much easier to scale. Instead of repeating the same logic again and again, you write it once and use it wherever needed. One more day, one more topic, one more step toward writing code that is cleaner, smarter, and actually built to last. Github Link - https://lnkd.in/gUhhaW_y #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

For years, we accepted the GIL as a tax on Python performance. But with the "No-GIL" movement officially maturing in Python 3.14 and 3.15, we are finally unlocking true multi-core parallelism. It is a massive shift in how we think about CPU-bound tasks. We no longer have to default to multiprocessing and the memory overhead that comes with it just to bypass the lock. Seeing a single Python process actually saturate multiple cores without the "ceremony" of older workarounds feels like a new era for the language. The performance gap with Go or Rust is narrowing where it matters most, making Python an even stronger contender for high-throughput backends. Are you already experimenting with free-threaded builds for your heavy processing, or are you waiting for library support to catch up? #Python315 #PerformanceEngineering #BackendDevelopment #NoGIL #ProgrammingTrends

Today’s Python topic felt less like syntax and more like learning how code makes decisions. 🐍 Day 09 of my #30DaysOfPython journey was all about conditionals, and this one felt important because it is where Python starts reacting to situations instead of just following instructions. Conditionals help a program choose what to do based on whether something is true or false. Today I explored: 1. if — runs a block when a condition is true 2. else — runs when the condition is false 3. elif — used when there is more than one condition to check 4. shorthand if-else → code if condition else code 5. nested conditions → condition inside a condition 6. logical operators like and (both conditions needs to be true) & or (any one condition needs to be true) What stood out to me today was how much control conditionals give you. They are basically the part of Python that makes logic feel alive. One more day, one more topic, one more step toward writing code that can actually think through a situation. When you first learned conditionals, what was the trickiest part: if-else, elif, or nested conditions? Github Link - https://lnkd.in/g4_tYUDG #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

Today’s Python lesson made the whole language feel more connected. 🐍 Day 12 of my #30DaysOfPython journey was all about modules, and this one felt like learning how Python organizes its tools behind the scenes. A module is basically a file that contains code, functions, or variables that you can reuse in another file. Instead of writing everything from scratch, you can create something once and bring it into your main program whenever needed. Today I explored: 1. What modules are and why they matter 2. Creating a separate file and importing it into another file 3. Importing only specific parts instead of the whole file 4. Renaming something while importing it 5. Built-in modules like os, statistics, math, string, and random What stood out to me today was how modules make Python feel less like a single script and more like a system of connected pieces. That shift matters because it is what makes code easier to reuse, organize, and scale. One more day, one more topic, one more step toward writing code that is cleaner, smarter, and more modular. Which felt more useful to you first: creating your own module or using built-in ones like math and os? Github Link - https://lnkd.in/gVPWQWiS #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney