Neat Python trick objects than implement buffor protocol are internally implemented as C arrays so we can abuse memoryview to cast multidimensional arrays into flat ones like that without making copies: “memoryview(matrix).cast('B').cast('d')” Thanks to Jerry Gamashe and Frieder Erdman from AWSF slack to help figure this! #python #vfx #techart
I like trains
Not every validation fits a single field. Some rules only make sense when you see the whole object. That’s where model validators shine. Clean rules. Centralized logic. No hacks. 📘 Learn real-world validation patterns in Practical Pydantic → https://lnkd.in/eGiB7ZxU #Python #Pydantic #CleanCode
Played around with FastAPI, Python threading, Streamlit, and Matplotlib to compare Sequential vs Threading. Same workload, same processing only the execution model changed. Put together a small Streamlit dashboard with Matplotlib charts to visualize the difference instead of relying on logs. Chect it out on Github , will extend this next with more advanced threading patterns. Github link : https://lnkd.in/gXZDsX3h #Python #FastAPI #Threading #Streamlit #Performance
LeetCode Problem 59 "Spiral Matrix II": Given a positive integer n, generate an n x n matrix filled with elements from 1 to n2 in spiral order. Time complexity is O(n*n), a square matrix has to be built #dsa #leetcode #python #competitiveprogramming #codingchallenge #matrix #optimalsolution #problemsolving
If you’re duplicating fields across models, you’re doing extra work. Use Pydantic model inheritance to share validation logic cleanly. One source of truth. Less duplication. Fewer bugs. 📘 Learn scalable modelling patterns in Practical Pydantic: https://lnkd.in/eGiB7ZxU #Python #Pydantic #CleanCode
Day 9/60 🚀 60 Days Python Series 🐍 🔥 Difference between assigning 0 and 1 to a variable In Python: ➡️ 0 is treated as False ➡️ 1 is treated as True Same data type, but different boolean behavior 💡 This concept is very important for conditions, loops, and logic building. 📌 Save this post for revision 💬 Comment “python” if you’re following the series ➡️ Follow for daily Python basics #60dayspython #pythonseries #learnpython #pythonbasics #codingreels #logicbuilding #pythonforbeginners #programminglife
✂️ Beginner-Friendly Guide 'Divide an Array Into Subarrays With Minimum Cost I' - Problem 3010 (C++, Python, JavaScript) By Om Shree Breaking down a large dataset into smaller, manageable chunks is a fundamental skill in algorithm design. In this problem, we explore how to minimize the "cost" of partitioning an array by identifying the most influential elements. This challenge provides a great introduction to greedy logic and ... https://lnkd.in/eE28XPax
🚀 New Article: Bringing Reactive State Management to Python. I just took a deep dive into the reaktiv library. If you are looking for a type-safe, declarative way to handle state updates (without the headache of the Observer pattern), this is for you. I break down the basics of Signals and Computed values with practical code examples. 🔗 Read it here: https://lnkd.in/gY99g62M #Python #Reaktiv #Developer #ReactiveStateManagement #Library
One small Python feature that really makes Airflow DAGs more readable and testable is the @dataclass decorator. It handles common boilerplate like __init__ and __repr__ automatically, which means less setup code and clearer logs when tasks run. In Airflow, where you’re often passing configs, parameters, or event data between tasks, @dataclass provides a clean and explicit way to model that data. I enjoy finding these small tidbits that help build reliable pipelines. #ApacheAirflow #Python #DataEngineering #ETL #CleanCode
I recently worked through a problem from LeetCode. I’d recommend first looking at the problem itself and trying to solve it on your own, and only then comparing it with my solution. Problem: https://lnkd.in/dJ5qxybS My solution: https://lnkd.in/da-dTDKB A good example of how the standard library enables concise and expressive code. #LeetCode #Python #Algorithms #ProblemSolving #SoftwareEngineering #CleanCode
LeetCode Problem 1572: "Matrix Diagonal Sum" : Given a square matrix mat, return the sum of the matrix diagonals. Only include the sum of all the elements on the primary diagonal and all the elements on the secondary diagonal that are not part of the primary diagonal. The below code in python resolves this problem successfully. #dsa #matrix #leetcode #python #competitiveprogramming #datastructures #algorithms #problemsolving #coddingchallenge #dailycoding #programming
Clever, though it depends on memory layout assumptions i would normally consider as unsafe.