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🎙️ PyCast S4EP10 | PySkill: Dive into itertools ⚒️ Still using messy math hacks? It's time for some itertools magic —a must-have for every Pythonista. 🧙♂️ This episode introduces Python's built-in itertools library, perfect for arithmetic and permutations.Ready to level up? Take notes and learn new spells with us! 📌 **Episode Highlights**:   - **`count`**: Infinite counting, like our endless passion for Python ❤️   - **`cycle`**: Effortlessly repeat sequences—no more redundant loops!   - **`accumulate`**: Simplify running totals (goodbye, manual calculations!).   - **`batched`**: Split your data into manageable chunks in no time.   - **`compress`**: Filter what matters most with precision.  🎧 Tune in now: [PyCast S4EP10 | A Bite of PySkill](https://lnkd.in/gTjSycGP) 

Open source research and trading strategy ideas. From the team at QuantConnect. All free: A collection of research notebooks and tutorials using the QuantConnect LEAN platform. Grab it on GitHub: https://lnkd.in/eCWxnjuf ~~~ Looking to start using Python for systematic trading? Here's a free Crash Course with everything you need to get started. Join the 1,000s of people who finally started with Python after reading it: https://lnkd.in/eaayJEfj

🏃♂️⏪ 🏃♀️ 𝐀𝐧 𝐈𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 𝐟𝐨𝐫 𝐏𝐫𝐞𝐬𝐬𝐢𝐧𝐠 𝐢𝐧 𝐒𝐨𝐜𝐜𝐞𝐫 It's been over a year now since I was invited to present my 𝐏𝐫𝐞𝐬𝐬𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐧𝐬𝐢𝐭𝐲 metric to a small group of people at a SkillCorner event in Dallas, TX. I've finally put some effort into writing a blog on it! ⚽ 𝐏𝐫𝐞𝐬𝐬𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐧𝐬𝐢𝐭𝐲 uses the time to intercept aspect of William Spearman's pitch control model. But, instead of computing the time it takes a player to reach a specific location on the pitch, we compute the time it takes for a player to reach the location other players are moving towards. 🐍 The blog contains a snippet of Python code on how to replicate the computation(s) in a vectorized manner. 🔗 https://lnkd.in/dzgWg4Ag

🚀 Excited to Share My First EDA Project on Food Waste Analysis! 🍎📊 On 30th November 2024, I completed my very first Exploratory Data Analysis (EDA) project, diving into food waste trends using Python. From analyzing food categories to understanding seasonal and event-based waste patterns, this project was an incredible learning journey! 📚 Alongside the analysis, I also created a Python Lab Manual, included in the repository, to document the key Python techniques I used throughout the project. 🔗 Check out my project on GitHub: https://lnkd.in/gdxfcgpw 💡 Key insights are captured in the attached image. I’d love to hear your thoughts and feedback! #DataScience #EDA #Python #FoodWasteAnalysis #DataVisualization #LearningJourney

We're always on the lookout for meaningful partnerships at Blacksmith 👀 Celery went from hearing about us, to trying us out and instantly seeing the upside of running their GitHub Actions on Blacksmith, within the span of a day. Today, we power all CI for the most popular task queuing library for Python! Hear more about this from Tomer Nosrati in the link below

🌟 Happy Thursday, everyone! 🌟 🧠 Excited to share a fascinating problem I recently tackled on LeetCode: "Count Triplets That Can Form Two Arrays of Equal XOR" 🤓 💡 Intuition: The task involves finding triplets (i, j, k) such that the XOR of the subarray arr[i] is 0. Leveraging XOR properties, we can exploit prefix XOR and a hashmap to efficiently identify such triplets.🚀 Approach: 1️⃣ Iterate through the array arr. 2️⃣ Maintain a running XOR value (prefix_xor) and track the frequency of each prefix XOR value encountered so far. 3️⃣ Also, keep tabs on the total count of indices where each prefix XOR value occurred. 4️⃣ Whenever we encounter a prefix XOR value seen before, update the count of triplets accordingly. 📈 Complexity Analysis: Time complexity: O(n) - where n is the length of the input array arr. We iterate through the array once. Space complexity: O(n) - in the worst case, as we use two dictionaries (freq and total) to store prefix XOR values' information. 🔗 Check out the problem on LeetCode: https://lnkd.in/gB4fZSJN Can't wait to hear your thoughts and solutions! Let's keep coding and learning together! 💻💡 #LeetCode #Coding #Algorithms #Python #HappyThursday 🎉✨

⭐ Day 19 #50daysofcode : Solved the Zigzag Conversion problem! ⭐ Today's challenge was about converting a string into a zigzag pattern across a specified number of rows and then reading the pattern line by line. ✅Problem Breakdown: Given a string and a number of rows, the task was to arrange the string in a zigzag pattern. The characters are placed in rows, and the direction alternates between going down and up as we traverse across the rows. ✅Approach: 1. If the number of rows is 1, there's no zigzag, so the string remains unchanged. 2 . I used an array to represent the rows and iterated through the string, placing each character in the correct row while alternating the direction when hitting the top or bottom row. 3.Finally, I joined all the rows to form the final result. ✅Time Complexity: The time complexity of the solution is O(n), where n is the length of the string, as the algorithm only makes one pass through the string. #100DaysOfCode #LeetCode #ZigzagConversion #Python #Algorithms #DataStructures #CodingChallenges #ProblemSolving #google

Excited to announce a personal milestone! Recently, I was working on a contribution to Albumentations, an open-source high performance Python image augmentation library. My contribution got merged into the library today. I have added a new transform, Constrained Coarse Dropout to the library. Coarse Dropout randomly drops out rectangular regions from the image. Constrained Coarse Dropout extends the functionality of Coarse Dropout by dropping out rectangular images from the foreground objects belonging to a set of classes (specified by the user). This transform can be used to make a model robust to occlusions for a particular class. Contributing to the library was a great learning experience. Thanks a lot Vladimir Iglovikov for the insightful discussions and support. You can checkout the source code for Constrained Coarse Dropout here - https://lnkd.in/dsXZZ69K #albumentations #deeplearning #opensource #machinelearning #computervision

DAY 10: Diving Deep into NumPy Today, I explored the foundational concepts of NumPy, a core library for scientific computing in Python. The journey was both enlightening and rewarding as I tackled some critical aspects that make NumPy a powerhouse for data manipulation. Here’s what I covered: 🔹 Data Types: Understanding NumPy's efficient, fixed-type arrays and how they optimize memory usage. 🔹 Shape & Reshape: Learned to analyze and transform array dimensions, enabling seamless data preparation. 🔹 Copy vs. View: Delved into the subtleties of memory management—knowing when data is linked or independently duplicated is a game-changer.

Excited to share that I've completed the 'Data Visualization in Python' course by GUVI Geek Networks, IITM Research Park! Ready to turn insights into impactful visuals and drive data-driven decision.