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🎉 NumPy 2.0 is here! 🎉 We're thrilled to celebrate this monumental milestone in scientific programming. NumPy , the cornerstone of open-source software, just released its second version, thanks to 212 contributors and 1078 pull requests! 💪 🔍 Learn more: NumPy 2.0 Release https://lnkd.in/dphbqVip At Dribia, we've relied on NumPy since day one. Huge thanks and congrats to the team for their dedication. Open-source is thriving, ensuring technology that is understandable and constantly improving—just like Dribia! 🌟 👏 Let's celebrate the open-source community! Thank you, NumPy team! 🙌 #NumPy #OpenSource #Python #TechInnovation #Dribia (Feel free to reshare ♻️ and follow us for more updates on tech and innovation!)

🐍𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗦𝗸𝗶𝗹𝗹𝘀: 𝗨𝗻𝗶𝗾𝘂𝗲 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗧𝗿𝗶𝗰𝗸𝘆 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀! 🚀 Let's explore some cool tricks to solve those uncommon problems that often leave us scratching our heads. 💡 ➡𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗖𝗶𝗿𝗰𝘂𝗹𝗮𝗿 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗶𝗲𝘀: Ever got stuck in a loop with circular dependencies? Don't worry! You can use Importlib's import_module trick to untangle the mess and keep your code neat and clean. ➡𝗦𝗽𝗲𝗲𝗱𝘆 𝗝𝗦𝗢𝗡 𝗣𝗮𝗿𝘀𝗶𝗻𝗴: Parsing big JSON files slowing you down? Try RapidJSON! It's like a turbocharger for your JSON parsing needs. With a little help from pybind11, you'll breeze through those big files in no time. ➡𝗦𝗺𝗼𝗼𝘁𝗵𝗲𝗿 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝗗𝗕++: Debugging doesn't have to be boring! PDB++ adds some pizzazz to your debugging sessions with fancy features like colors and autocomplete. Say goodbye to dull debugging and hello to a more colorful coding experience! ➡𝗙𝗮𝘀𝘁𝗲𝗿 𝗖𝗼𝗱𝗲 𝘄𝗶𝘁𝗵 𝗖𝘆𝘁𝗵𝗼𝗻:Want to speed up your Python code? Cython's got your back! Just sprinkle in some type hints, compile with Cython, and watch your code zoom past the competition. ➡𝗘𝗮𝘀𝘆 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝘄𝗶𝘁𝗵 𝗧𝗿𝗶𝗼: Concurrency sounds scary, but Trio makes it friendly! With its simple API, you'll be juggling tasks like a pro in no time. Say goodbye to tangled threads and hello to smoother, more efficient code. With these tips up your sleeve, you'll be a Python pro in no time, tackling even the trickiest of challenges with ease. 💪 Keep learning, keep exploring, and let's unlock the full potential of Python together! 🔓 #Python #Programming #TechTips #DeveloperCommunity #syncclouds

🚀 𝗗𝗮𝘆 𝟵 𝗼𝗳 #𝟭𝟬𝟬𝗗𝗮𝘆𝘀𝗢𝗳𝗖𝗼𝗱𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲! Today, I tackled the 650. 2 𝙆𝙚𝙮𝙨 𝙆𝙚𝙮𝙗𝙤𝙖𝙧𝙙 𝙥𝙧𝙤𝙗𝙡𝙚𝙢 on LeetCode. This problem dives into dynamic programming and efficient operations to minimize keystrokes needed to reach a desired number of 'A's on the screen. 🔍 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄: Given an initial single 'A' on the screen and two available operations (Copy All and Paste), the goal is to determine the minimum number of operations required to achieve exactly n 'A's on the screen. 🧠 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: 🔹Factorization Strategy: 🔹Focused on breaking down the problem by using prime factorization, as each prime factor represents a necessary series of operations. 🔹Used dynamic programming to store the minimum operations for each factor and accumulated the total. 📊 𝗥𝗲𝘀𝘂𝗹𝘁𝘀:  🔹Runtime: 33 ms (Beats 91.27% of Python submissions)  🔹Memory: 16.48 MB (Efficient memory usage) 💡 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: 🔹Prime Factorization: Breaking the problem down into smaller sub-problems using prime factors helped in minimizing the number of operations. 🔹Efficiency: This approach ensured that each operation was optimized to achieve the goal in the least possible steps. 🤝 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗧𝗵𝗮𝗻𝗸𝘀 𝘁𝗼: My coding buddies: Muhammad Ahad,Muhammad Qasim, Shahzil Imran, abida Shoukat, Ifrah Tariq, Syed Najam U Saqib Mariam Ashraf, Muhammad Hassan Raza Ahmad Hassan, and Osama Ghaffar. Your support keeps me motivated throughout this journey! #Python #LeetCode #DynamicProgramming #CodingChallenge #ProblemSolving #Algorithms #100DaysOfCode

👉 If you work with Spectral Indices for Remote Sensing applications and Python, check: https://lnkd.in/dyjvw7-i There you will find a huge collection of indices for vegetation, water, urban, and more. 𝐂𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐞 𝐰𝐢𝐭𝐡 𝐢𝐧𝐝𝐢𝐜𝐞𝐬! Currently, I added: OSI - Oil Spill Index, by Rajendran et al., 2021 PI - Plastic Index, by Themistocleous et al., 2020 FAI - Floating Algae Index, by Hu, 2009. #remotesensing #spectral #python #ocean #contribute

Hey Seattle people, Puget sound Programming Python (PuPPy) is hosting talk nights again! Next week we'll be at the beautiful GitHub office! Talk 1: Simulating the 3-Polarizer Experiment on a Quantum Computer Speaker: Brandon Warren, SW Dev Engineer at Zonar Systems Description: This talk will allow the audience to see the 3-polarizer experiment in action and simulate it using an understandable quantum circuit. Lightening talk: Why I love planning and you should too! Speaker: Hanna Landrus, Senior Data Scientist at Bevy Description: Do you feel your work life is always encroaching on your personal life? Do you wish you could work through the pile of tech debt? In this talk, I'll step through some practical advice on how to make the project planning process work for you. Talk 2: The Rising Sea Speaker: Matt Drury, Principle Machine Learning Engineer at Remitly Description: We discuss the pleasure of problem solving by dissolution. Alexander Grothendieck gave the analogy of a rising sea as a metaphor for his approach to problem solving. While it is sometimes possible to solve a problem through sheer force, to Grothendieck, more satisfying and fruitful is a slow process that builds careful theory, where each step is as simple and trivial as possible, until the problem simply dissipates. We discuss what this strategy looks like in the context of programming, giving examples from Advent of Code. we're also looking for folks to talk at future meetups, if you're interested reach out, or submit it! https://lnkd.in/gBMX_HAn

🤓 Do you know all the abilities of Python? Their number is huge! ✅ Ease of Learning. ✅ Versatility. ✅ Rich Standard Library. ✅ Community Support. ✅ Interpreted Language. ✅ Dynamically Typed. ✅ Object-Oriented Programming (OOP). ✅ Extensibility. ✅ Data Science and Machine Learning. ✅ Web Development. ✅ Automation and Scripting. ✅ Cross-Platform Compatibility. ✅ Community-driven Development. 🤔 Did we miss something? Add it from yourself in the comments below the post!) ---------------------------- 🚀 Your child may start his way in programming right now! Contact us now: 📞 +352 (661) 333-559 ✍🏻 or write "+" below this post/ in the chat ---------------------------- #algorithmicsluxembourg #algorithmicsschool

Hi LinkedIn. It's a new year, so I decided to throw out one of my old projects.👨💻 Meet Voicebox. An open-source Python tool for peer-to-peer voice calls and messaging over LAN, running on a custom protocol built on the TCP protocol. The project was built in October 2022. I got the inspiration from the frustration I felt when my phone stopped working all of a sudden. My roommate Alfred Onuada said (greatly paraphrased) "How are you going to keep in touch while you wait for a new one? Not like you can build yourself a new phone". Turns out I took our conversation a bit too seriously. 😂 I didn't build a phone, but something I intended to run on the school's network. Unfortunately, while the program works on every other network, our school's network seemed to have some sort of firewall to take care of suspicious-looking traffic (the audio packets were intercepted and a generic response was returned to the receiver). It's something I'm going to keep maintaining this year by adding new features and improving the codebase. A bunch of things I'd like to improve: Implementing a custom decentralized naming system so people can have funky usernames rather than using their IP addresses, Adding support for call hang-ups without having to stop the program, Implementing end-to-end encryption for packets in the network, Implement audio compression to make it a lot faster, and lots more. But first, looks like I'm going to be doing some major refactoring 😂. I'm more experienced now and know better ways to write some of the code. 🚀 Feel free to check out the code, run it on your local computer, and contribute at https://lnkd.in/dvsihbDs. #opensource #python #networking #programming #data #softwareengineering #computerscience #hacker

Day 66 of LeetCode Challenge Problem Solved: Combination Sum Intuition: We are given a list of candidates and a target sum. The goal is to find all unique combinations of candidates that sum up to the target. We use a recursive approach (backtracking) to explore all possible combinations. We maintain an array ds to store the current combination and an array ans to store all valid combinations. At each step, we choose a candidate, add it to the current combination, and recursively explore further. If the current combination sums up to the target, we add it to the result. After exploring with a candidate, we backtrack by removing it from the current combination to try other possibilities. Time Complexity: O(2^n) In the worst case, we explore all possible combinations, where n is the number of candidates. Space Complexity: O(n) The space complexity is determined by the depth of the recursive call stack, which is at most the number of candidates. LinkedIn Post: "Just solved the Combination Sum problem on LeetCode! 🚀 This problem tests your understanding of backtracking and recursion. The goal is to find all unique combinations of candidates that sum up to a given target. The recursive approach explores all possibilities, and by backtracking, we efficiently find the solutions. Here's the Python solution with a time complexity of O(2^n) and space complexity of O(n). #LeetCode #CodingChallenge #Python

Ever encountered a NameError in Python that left you scratching your head? 🤔 You're not alone! In our latest article, we delve into the fascinating world of coding, specifically tackling the notorious NameError while building a unique Diary Application. Through this, we unravel the complexities of coding, providing you with a comprehensive guide to constructing your own digital diary. 📖✨ This application serves as a personal diary with impressive functionalities, including adding, viewing, searching, and deleting entries, all backed by an SQLite database, 'diary.db'. Imagine typing your thoughts into your own custom-built diary application at the end of the day. Makes you feel like Tony Stark, doesn't it? 😉 Through a deep dive into the application's key functions and the pivotal role of variables, this guide will empower you to debug and fix errors, ensuring the smooth functioning of your digital diary. This isn't just about becoming a coder. It's about becoming a master problem solver. It's about understanding the intricacies of coding, the power of modules, and the art of debugging. So embrace the journey and let the coding begin! Explore our guide now to kick-start your digital diary. Because the world needs more problem solvers and innovators, just like you. 💡 #Coding #Python #ProblemSolving #DigitalDiary #Debugging #Innovation Note: This model is capable of creating content that meets the constraints, however, the instruction itself exceeds the character limit. Read more on our blog: https://lnkd.in/eXx_8674

Enhancing My Code: Switching from 'math.exp' to 'np.exp' I wanted to share a quick tip from my recent work. When I first wrote a function, I used 'math.exp' to handle the exponential calculation. It worked great for single numbers, but when I tried to make the function handle both single values and arrays, it just didn’t work. The reason? 'math.exp' can only handle scalar values, not arrays. So, I made a switch to 'np.exp' from the NumPy library. This change made my function flexible enough to handle both single values and arrays without any hiccups. Here’s the difference: 'math.exp': - Library: Built-in Python math library. - Best For: Single values. - Functionality: Calculates the exponential of one number at a time. 'np.exp': - Library: Part of NumPy, great for scientific computing. - Best For: Single values, vectors, and matrices. - Functionality: Calculates the exponential for each element in an array or a single number. Why This Matters: - Flexibility: 'np.exp' handles both single numbers and arrays, making your code more versatile. - Efficiency: 'np.exp is optimized for handling large datasets quickly, thanks to NumPy’s powerful performance. Switching to 'np.exp' made my function more robust and reusable, saving me time and effort in the long run. This is especially useful when creating functions that handle exponential values, like the sigmoid function. Always happy to learn and share these little nuggets of wisdom. Keep coding smart! 🚀 #Python #DataScience #MachineLearning #NumPy #Programming