Building Python Backend with FastAPI and SQLAlchemy

I recently contributed to keon/algorithms, one of the most popular algorithm repositories on GitHub that helps developers worldwide learn data structures and algorithms through clean Python implementations. 🔍 My Contribution I implemented a Generalized Binary Search algorithm that works over a numeric range using a monotonic boolean predicate, instead of searching for a fixed value in an array. 💡 Why this enhancement matters: • Flexible Input – Operates on a range [low, high] with a predicate function f(x) • Smart Output – Finds the smallest value x where f(x) becomes True • Optimal Performance – O(log N) time, O(1) space • Broader Applications – Ideal for optimization problems, boundary detection, and non-array search spaces • Backward Compatible – Existing implementations remain untouched This abstraction makes binary search far more powerful and reusable, enabling real-world problem solving beyond traditional array searches. 🔗 Repository: https://lnkd.in/daS_m-dM #OpenSource #GitHub #Algorithms #BinarySearch #Python #DataStructures #SoftwareEngineering #Coding #TechCommunity #OpenSourceContribution

If you love Python but need systems-level performance, this one’s for you. I’ve just published a deep dive on Creating Python Extensions Using Rust (PyO3), covering how to bridge Python’s productivity with Rust’s memory safety and speed. From setup with maturin to exposing Rust structs as Python classes and releasing the GIL for real parallelism, this guide walks through building production-ready extensions the right way. If performance is your bottleneck, this approach can be transformational. #Python #Rust #PyO3 #PerformanceEngineering #SoftwareArchitecture #BackendDevelopment #DataEngineering #SystemsProgramming

Most Python tutorials show you how to use tools. Nobody shows you how to build something that chooses its own tools. There is a difference. A script executes. An agent decides. I gave mine one instruction. It chose its own path. I gave it a different instruction. It chose a completely different path. Same agent. Zero code change. That ability to think and decide - that is where data science is heading. Full breakdown in first comment 👇.

⚡ Just released FastIter, parallel iterators for Python 3.14+ For years, Python's Global Interpreter Lock (GIL) prevented threads from running Python code truly in parallel. Python 3.14 changes that. FastIter takes advantage of free-threaded mode to split work across CPU cores automatically, with 2 to 5.6x measured speedups on CPU-bound workloads. And it is written entirely in Python, no C extensions, no compiled code. Simple API, real parallelism, pure Python. If you work with large datasets, give it a try: pip install fastiter GitHub: https://lnkd.in/eKtJVsfH Feedback and contributions welcome 🙌 #Python #OpenSource #Performance #Parallelism #Python314

🚀 Day 4 of Python for Data Science – Functions & Reusability Today’s focus was simple but mission-critical: stop writing repetitive code and start thinking in reusable logic. 🔹 What I worked on Creating custom functions for clean, modular code Using parameters & return values to make logic flexible Applying default and keyword arguments for scalability Understanding local vs global scope (no more hidden bugs) 🔧 Hands-on practice Built reusable data cleaning functions Created metric calculators (mean, percentage change) Designed functions that can plug directly into real-world data workflows 💡 Key takeaway Functions aren’t just syntax—they’re a productivity multiplier. They improve readability, reduce errors, and make your code enterprise-ready. Onward to more structured, scalable Python. 📈 #Python #DataScience #LearningByDoing #CleanCode #AnalyticsJourney #Upskilling Kishan Timbadiya Digbijoy Sarkar

In Part 1, we saw packets flow through the kernel into socket buffers. Now let's see what Python does with them. Published Part 2, covering how asyncio and ASGI actually work under the hood. How the event loop uses epoll to monitor thousands of sockets, what happens when you await, how Uvicorn bridges bytes to FastAPI, and why async scales so well. The best part? Seeing how it all connects. The kernel work sets up everything for asyncio to handle massive concurrency with minimal overhead. https://lnkd.in/gcTm9fyB #Python #FastAPI #asyncio #HTTP #ASGI

🐍 Day 2/60 – Variables & Data Types Today I learned how Python stores information. Everything starts with variables. Small steps. Big systems loading. This is the moment you stop “reading about Python” and actually using it. A variable is just a name that stores a value. Main Data Types You Must Know str → Text int → Whole numbers float → Decimal numbers bool → True / False Mini Challenge Create 5 variables about yourself: Your name Your age Your dream job Your current skill Are you consistent? (True/False) What You Should Understand Deeply Today Python is dynamically typed (no need to declare type) Variable names must not start with numbers Use meaningful names (not x, y, z like it’s math class) #CodeEveryday#LevelUp#TechEra#DigitalGrowth#StayHungry

Built a Simple CLI Calculator with Python Today I practiced core Python fundamentals by building a small command-line calculator that performs basic operations like addition, subtraction, and multiplication based on user input. ♦️Concepts I reinforced: • User input handling • Type conversion (int) • Conditional logic (if statements) • Clean output formatting Even simple projects like this help strengthen problem-solving skills and build confidence in writing structured, readable code. Next step: expanding this into a more robust version with error handling and division support. #Python #LearningByDoing #DataAnalytics #CodingJourney #Azure #dataengineering #data

📘 Python Data Types – Strengthening the Basics Today, I revised Python Data Types, which are the foundation for writing clean, efficient, and error-free code. 🔹 What are Data Types? Data types define the kind of data a variable can store and the operations that can be performed on it. Python is dynamically typed, meaning the data type is determined at runtime. 📌 Key Data Types Covered Numeric: int, float, complex Boolean: bool Sequence: str, list, tuple Set: set Mapping: dict NoneType: None 📌 Important Concepts Mutable vs Immutable data types Type checking using type() and isinstance() Type conversion (int, float, str) Real-time usage of lists, dictionaries, and sets 💡 Understanding data types helps in: Writing optimized code Avoiding runtime errors Handling real-world data efficiently Building strong fundamentals, one concept at a time 🚀 #Python #DataTypes #PythonLearning #ProgrammingBasics #DataAnalytics #CodingJourney #TechSkills

Python’s core data structures is essential for writing efficient code. Here’s a quick refresher: 🔹 Set – Unordered and unindexed collection that stores unique values. 🔹 List – Ordered, changeable collection that allows duplicates. 🔹 Tuple – Ordered but unchangeable collection for fixed data. 🔹 Dictionary – Key–value pair collection that is ordered and mutable. Mastering these fundamentals builds a strong Python foundation. 💡 🔗 Stay connected for more such content. w3schools.com GeeksforGeeks Codewars HackerRank LeetCode #python #methods #code #pythonmethods #list #tuple #set #dictionary