PSF withdraws $1.5M funding proposal for Python security

🚀 Understanding Python Garbage Collection Made Simple! 🐍 Memory management can be tricky in any programming language, and Python is no exception. I recently put together a comprehensive guide on Python’s garbage collection, explaining: - Reference counting: how Python tracks how many references point to an object - Circular references: why some objects can get “stuck” in memory - Garbage collector (gc module): Python’s built-in mechanism to clean unreachable cycles - Generational collection: how Python efficiently manages objects of different ages This PDF is perfect for beginners who want to understand Python memory management, or anyone looking to debug memory leaks and optimize their code. 💡 Fun fact: Even simple operations like passing an object to "sys.getrefcount()" temporarily increases its reference count! 📂 I’ve also uploaded all the sample codes used in the guide on my GitHub repo for you to try out and experiment with: https://lnkd.in/gUMSpECz 📄 Check out the PDF and code samples to learn more! #Python #Programming #MemoryManagement #GarbageCollection #SoftwareDevelopment #PythonTips #GitHub

Every software supply chain relies on archives like ZIP and tar. This paper from Seth Michael Larson helps you understand where unseen risks exist and how the Python ecosystem is advancing stronger safeguards. Learn practical steps that help protect your software at scale. 📄 Read Slippery Zips and Sticky Tar Pits: Security and Archives: https://lnkd.in/eYEuiZ_a By Seth Michael Larson, Python Software Foundation Sponsored by Alpha-Omega #SupplyChainSecurity #Python #OpenSource

Archive formats like ZIP and tar can be abused to undermine the integrity of Python package users 📦 Learn how PSF Developer-in-Residence Seth Larson is strengthening Python's security with the #Python community in the new white paper "Slippery ZIPs and Sticky tar-pits" with Alpha-Omega. https://lnkd.in/gU9t5bgf

I spoke at EuroPython 2025 in Prague on "ORMs: A Bridge to Efficient Database Interactions with Python". I walked through the evolution from raw SQL to ORMs and when each approach makes sense. I covered: - Why raw SQL can become a bottleneck (boilerplate, maintenance overhead, db-specific syntax) - How ORMs bridge object-oriented Python with relational databases - Practical comparison of SQLAlchemy, Django ORM, Peewee, and Tortoise-ORM - When to stick with raw SQL vs when ORMs actually help The goal was to help developers make informed decisions based on their specific use case rather than following blanket recommendations. Thanks to everyone who came and engaged with questions. Prague was awesome! The recording is now available on YouTube here: https://lnkd.in/dv4_x6cP

Why are so many new Python libraries secretly written in Rust? Because Rust gives developers speed without sacrificing safety. For years, high performance Python libraries were written in C or C++. They were fast but hard to maintain and easy to break. One memory leak or pointer bug could crash everything. Rust changed that. It offers: • Memory safety without a garbage collector • C level performance (or even better in many cases) • Seamless integration with Python through tools like PyO3 and maturin • Safer concurrency and fewer runtime crashes That is why libraries like Polars, orjson, and Ruff are built on Rust. They are faster, safer, and easier to maintain while keeping Python’s friendly interface. Is Rust quietly becoming the new C for Python? If you have used any Rust powered Python library, I would love to hear your experience.

💻 Day 30 of #100DaysLearningChallenge by Saurabh Shukla Sir 📚 Learning Topic: Integrating Python with C for Speed 🧠 What I Learned Today: Today, I explored how to call fast, compiled C code from Python — combining Python simplicity with C’s performance. I practiced two practical methods: subprocess → Running compiled C executables and capturing their output in Python 💡 Concepts Covered: 👉 Why and when to mix Python + C (heavy computation, performance-critical tasks) 👉 Writing & compiling a C function 👉 Calling C functions in Python using ctypes 👉 Running C executables using subprocess and parsing results ⚙️ Key Takeaways: ✅ Keep C functions simple — use basic types (int, double, pointers) ✅ subprocess.run() works great for integrating existing C tools ✅ Make sure Python & C compiler architectures match (32-bit/64-bit) ✅ Moving performance-heavy logic to C can significantly boost speed 💡 Insight: Bridging Python with C lets you prototype fast and run faster — achieving the best balance between developer productivity and execution performance. GitHub: https://lnkd.in/gykf7b3F 🔥 Hashtags: #python #clang #cprogramming #pythonwithc #ctypes #subprocess #highperformancecomputing #codeoptimization #backenddevelopment #systemsprogramming #learninpublic #developerscommunity #webdevelopment #codingjourney #techskills #100DaysLearningChallenge #Day30Done

I think my initial hypothesis about the most valuable languages for GitHits was wrong. The data now points to C, C++, and Rust (among others) being in more demand than Python, TS, or JS. It makes sense in hindsight. LLMs are biased toward scripting languages because those dominate their training data. I’ll be rolling out support for some of the less-represented languages soon, after gathering feedback from users. Sometimes the data simply proves your assumptions wrong.

Understanding np.select(): A clean way to handle multiple conditions The numpy.select() function in Python's NumPy library provides a way to create a new array by selecting elements from a choicelist based on a list of conditions. It is particularly useful for implementing complex conditional logic on arrays in a vectorized manner, avoiding explicit loops. How it works: np.select() iterates through the condlist and choicelist simultaneously. For each element position in the array, it checks the conditions in condlist in order. The first condition that evaluates to True for that element position determines which value from choicelist will be placed in the output array at that position. If none of the conditions are True for an element, the default value is used. Why it matters: Helps apply many conditional rules at once Keeps your data transformations organized and readable Works faster than writing manual loops or nested conditions

Day 7 of Learning Python — Revisiting Closures (But This Time in Python 😴) I had already learned closures earlier in JavaScript, so I thought I fully understood them. But today, when I explored closures in Python, it felt like a fresh concept all over again. Same idea… different flavor. A closure in Python means: A function remembers the values from its outer function—even after the outer function has finished running. Here’s the example that made it click 👇 Even though outer() has completed, the inner() function still carries the value of msg. That’s the power of a closure. It reminded me that: 🔹 Python treats functions as first-class citizens 🔹 Functions can hold state without classes 🔹 Many advanced concepts (like decorators) are built on closures Some concepts feel universal across languages — but still worth re-learning in every new ecosystem. #Python #LearningInPublic #Closures #DeveloperJourney #ProgrammingConcepts #100DaysOfCode

Curious about what went on between the Python Software Foundation & the National Science Foundation (NSF)? Me too, so I asked PSF executive director Deb Nicholson onto RedMonk's MonkCast to discuss the situation and share her perspective https://lnkd.in/eQFceHNv