Write C Code in Python with PythoC Library

In his latest tutorial, Tom Reid shows how you can leverage PythoC to compile native, standalone applications in C using the Python syntax you already know.

Python GIL explained: why it exists and how it affects multithreading Many developers hear about the Python GIL and assume one thing: “Python can't use multiple CPU cores.” But the reality is a bit more nuanced. What the GIL actually is The Global Interpreter Lock (GIL) is a mutex inside CPython that ensures only one thread executes Python bytecode at a time. Even if your program has multiple threads, only one can run Python code simultaneously. Example: Thread A → running Thread B → waiting Thread C → waiting This means Python supports threads, but they take turns executing code. Why Python has the GIL The main reason is memory management. CPython relies heavily on reference counting for garbage collection. Every Python object tracks how many references point to it. When the count reaches zero, the object can be safely removed from memory. If multiple threads updated these counters simultaneously, memory corruption could occur. The GIL prevents this by ensuring only one thread modifies Python objects at a time. Trade-off: simpler interpreter design safer memory management faster single-thread performance How the GIL affects multithreading The limitation mainly impacts CPU-bound workloads. Example: def compute(): for i in range(10_000_000): pass Running this across multiple threads does not give real parallelism because threads must wait for the GIL. Thread A → holds GIL Thread B → waiting Thread C → waiting The workload becomes effectively serialized. Why Python threads still work well for web servers Python threads still scale well for I/O-bound tasks. Examples: API requests database queries file operations While a thread waits for I/O, the interpreter releases the GIL, allowing another thread to run. That's why frameworks like FastAPI and Django can handle many concurrent requests. Common ways developers work around the GIL 1️⃣ Multiprocessing Each process has its own interpreter and its own GIL. 2️⃣ Native extensions Libraries like NumPy or PyTorch run heavy computations in C/C++ and release the GIL. 3️⃣ Async I/O Libraries like asyncio allow thousands of concurrent tasks in a single thread. ## Interesting development: Python without the GIL Simple visual model Threads → Queue → GIL → CPU Only one thread can pass through the GIL and execute Python bytecode. Question for the community: How do you handle CPU-bound workloads in Python? multiprocessing distributed workloads native extensions #Python #PythonProgramming #BackendDevelopment #SoftwareEngineering

Understanding How Python Code Runs: From Source Code to Execution When we write Python programs, it may appear that the code runs directly after we execute it. However, behind the scenes, Python follows a well-defined process before producing the final output. Here is a step-by-step overview of how Python code is executed: 1️⃣ Writing the Source Code The process begins when a developer writes Python code in a file with the ".py" extension (for example, "main.py"). This file contains the human-readable instructions written using Python syntax. 2️⃣ Python Interpreter Reads the Code When the program is executed (e.g., "python main.py"), the Python interpreter reads the source code. Unlike compiled languages such as C or C++, Python does not directly convert code into machine code. 3️⃣ Compilation to Bytecode The interpreter first compiles the source code into an intermediate format called bytecode. Bytecode is a low-level, platform-independent representation of the program instructions. 4️⃣ Storage in "__pycache__" The generated bytecode is often stored in the "__pycache__" directory as ".pyc" files. This allows Python to reuse the compiled bytecode in future executions, improving performance. 5️⃣ Execution by the Python Virtual Machine (PVM) Finally, the Python Virtual Machine (PVM) reads the bytecode and executes it instruction by instruction. The PVM acts as a runtime engine that translates bytecode into operations understandable by the underlying system. 📌 In Summary: Python Execution Flow → "Source Code (.py) → Bytecode (.pyc) → Python Virtual Machine → Output" #Python #Programming #SoftwareDevelopment #Coding #PythonInternals #Developers #LearningPython

Machine Learning Graph Data using python igraph #machinelearning #datascience #graphdata #pythonigraph igraph is a fast open source tool to manipulate and analyze graphs or networks. It is primarily written in C. python-igraph is igraph’s interface for the Python programming language. python-graph includes functionality for graph plotting and conversion from/to networkx. Python interface of igraph, a fast and open source C library to manipulate and analyze graphs (aka networks). It can be used to: Create, manipulate, and analyze networks. Convert graphs from/to networkx, graph-tool and many file formats. Plot networks using Cairo, matplotlib, and plotly. https://lnkd.in/gzzzK7eU

This is a short post about why plain Python logging is not enough when you move to real‑world distributed systems. It shows how we went from simple text logs to structured JSON and basic context passing so that debugging and observability actually work. #Python #Logging #DevOps #Observability #SoftwareEngineering #Backend #PythonDev #Kubernetes #ELK #Datadog https://lnkd.in/eRXbuSP9

Python Files & Data — Reflective Takeaway   Working with Python often exposes hidden errors—sometimes hours after a script runs. Recently, I guided a team through a file-handling bug that could have been prevented with simple upfront validation. The lesson: build checks early and often to save time, frustration, and keep projects on track.   https://lnkd.in/g5a758Wh   #Python #Automation #DataHandling #Workflow #Productivity

Unlock Python's full potential for automating file and data tasks in your homelab. Many scripts get stuck because they can't efficiently read or write files, costing hours in repetitive work. Learning proper file handling not only speeds up workflows but opens doors to more advanced automation.   https://lnkd.in/g5a758Wh   #Python #Automation #DataProcessing #Homelab #TechSkills

🚀 Python Practice – Updating List Elements Dynamically! Today I explored how to update list elements using user input in Python 🐍 🔹 Sample Code: alist = ["praveen","ajay","san","kiran","chandru","fun","joy","rrrr"] po = int(input("Enter the position: ")) newele = input("Enter the new element: ") alist[po] = newele print(alist) ✅ Key Learnings: 🔸 Taking user input using input() 🔸 Converting input to integer using int() 🔸 Updating list element using index → alist[po] = newele 🔸 Understanding how dynamic data works in Python 💡 Example: If user enters: Position → 2 New Element → cloud 👉 Updated list: ['praveen', 'ajay', 'cloud', 'kiran', 'chandru', 'fun', 'joy', 'rrrr'] ⚠️ Note: Make sure the index is within range, otherwise it will throw an error. 📌 Simple programs like this help in building strong logic for real-time applications and automation tasks. #Python #Learning #DevOps #Automation #Coding #Programming #Beginners

📢 Day 2 of my Python series is LIVE on MrCloudBook! 🐍 𝗣𝘆𝘁𝗵𝗼𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗼𝗿𝘀 & 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻𝘀 — the building blocks that make your variables actually DO something! In Day 1, we covered variables and data types — the nouns of Python. Day 2 is all about the verbs. ✅ Here's what's inside: 🔢 Arithmetic operators — including the 3 that surprise every beginner: //, %, ** 🔍 Comparison operators — and the classic = vs == trap 🧠 Logical operators — and, or, not (with short-circuit evaluation!) ✅ Truthiness — what Python considers True or False 📝 Assignment operators — +=, -=, *= and more 🔤 String operators — +, *, and in 🎯 Operator precedence — so your expressions mean what you think they mean 💼 A complete Invoice Calculator project using every concept from the article If you're starting your Python journey or know someone who is — this one's for you. 🙌 👇 Read it here: https://lnkd.in/gSqznx_T #Python #LearnPython #PythonForBeginners #MrCloudBook #DevOps #100DaysOfCode #Programming #TechCommunity

Ever wondered why FastAPI is so fast even though it's built with Python? Python is often considered slower than languages like Go or Node.js. Yet FastAPI can deliver performance that competes with them. So why is FastAPI so fast? The answer lies in the architecture underneath it. 1️⃣ Starlette (Lightweight ASGI framework) FastAPI is built on Starlette, which is an ASGI (Asynchronous Server Gateway Interface) framework. FastAPI uses Starlette for the core web functionality such as: • Routing • Middleware • WebSockets • Background tasks • Request/response handling Majorly performance is coming from the Starlette. 2️⃣ Pydantic for data validation FastAPI relies heavily on type hints in Python. These are used by Pydantic to automatically validate the incoming data. Pydantic v2 which is written in Rust, making validation extremely fast compared to traditional Python validation logic. 3️⃣ Uvicorn as the ASGI server FastAPI typically runs on Uvicorn, which is optimized for performance. Uvicorn uses: • uvloop — a high-performance event loop written in C • httptools — a fast HTTP parser also written in C FastAPI isn’t fast because Python suddenly became faster. It’s fast because of it underlying Architecture. Good architecture often matters more than the language itself.