Python Generators and Yield Explained

Frustrated with a Python script that feels sluggish? Instead of guessing where the slowdown is, you can measure it. Tom Reid shares a guide on using cProfile and snakeviz to find and fix the exact parts of your code that are causing delays.

Ever waste time optimizing a piece of Python code only to see no real speed improvement? Tom Reid's new article explains how to avoid this by profiling your code first.

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗲 𝗰𝗼𝘂𝗹𝗱 𝗿𝘂𝗻 𝟮𝟬× 𝗳𝗮𝘀𝘁𝗲𝗿 — 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗼𝘂𝗰𝗵𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗹𝗼𝗴𝗶𝗰? Sounds impossible, right? But it’s not. I just published an article that shows how you can dramatically boost Python performance using modern execution techniques like JIT compilation, smarter runtimes, and optimized libraries — all without rewriting your existing code. If you’re working with: ✅ Backend APIs ✅ Data processing ✅ Automation scripts ✅ Performance-critical Python apps …this is a must-read 👉 Read here: https://lnkd.in/gykbHu-z 💡 Faster code 💡 Same logic 💡 Zero pain refactoring #Python #PythonPerformance #JIT #SoftwareEngineering #BackendDevelopment #Developer #ProgrammingTips

This was a great read! 🔥 I liked how it highlights that Python performance bottlenecks are often caused by how we use the language rather than the language itself. Techniques like switching interpreters, relying on optimized C-backed libraries, and profiling hot paths instead of rewriting logic shows that scalability and maintainability don’t have to be trade-offs. Very relevant for real-world backend and automation systems. #Python #PythonProgramming #PythonPerformance #PythonTips #PythonOptimization #PyPy #Programming #Developers #CodingLife

Power of Generators in Python:- When dealing with large datasets, logs, or streams, loading everything into memory is expensive and slow. Generators solve this by producing values on demand, one at a time, as you iterate. 🔹 Real definition (Generators in Python):- A generator in Python is a function or expression that returns an iterator and yields values one-by-one using the yield keyword instead of return. The code inside a generator pauses at each yield, remembers its state, and resumes from there when the next value is requested (for example, in a for loop or with next()). >>Image explanation (for your graphic):- Design a simple, clear image that explains generators visually: >>Visual idea: >Show a water tap connected to a big water tank labeled “data”. >Water drops coming out one-by-one from the tap are labeled yield value, and the whole tap is labeled “generator function”. >>Concept on the image: >Tank = all possible data >Tap = generator (controls flow) >Drops = values produced lazily, only when needed >>Caption text on image: “Generators in Python: produce one value at a time using yield, saving memory and improving performance.” You can add a small code snippet on the side of the image: def gen(): for i in range(5): yield i >>Key advantages of generators:- >Memory efficient: No need to store the entire sequence in memory; values are generated on-the-fly, which is ideal for large files, logs, or big ranges. >Lazy evaluation: Values are computed only when requested, reducing unnecessary work and improving performance. >Easy to write iterators: Generators create iterators without writing __iter__() and __next__() manually, making code cleaner and more Pythonic. >Great for pipelines: Multiple generators can be chained to build efficient data-processing pipelines step by step (filtering, transforming, mapping, etc.). #Python #Generators #PythonTips #PythonDeveloper #Programming #Coding #SoftwareDevelopment #LearnPython #DataProcessing #CleanCode

Python Deep Secrets – Part 2 is live Even experienced Python developers get these wrong: is vs == shallow vs deep copy hidden reference cycles why closures behave “weirdly” I wrote Part 2 of my Python deep-dive blog, focusing on the things most tutorials never explain — but interviews and real bugs do test. 📖 Read here: https://lnkd.in/g8ujYuje

In the Metasploit Wrap-Up from last week, a new Python Site-Specific Hook Persistence module was released. [1] I wrote a detailed blog about this persistence, which I think is pretty cool. [2] If you have never heard of this technique, you might want to read up on it. [1] https://lnkd.in/ei_C5TgQ [2] https://lnkd.in/eYRmWrx8

Python Path Hijacking! Maybe you know DLL hijacking, but did you know you can also hijack Python methods? For example, the static method datetime.datetime.now() can be hijacked. A while back, we did some research into hijacking specific functions silently, without breaking the code that is calling the function: https://lnkd.in/ebxGiTXr Recently, we've discovered you can take this a step further by hijacking entire packages. You can read all about it here: https://lnkd.in/eyZFSKtm

Day 459: 6/1/2026 Why Python Objects Are Heavy? Python is loved for its simplicity and flexibility. But that flexibility comes with a cost — Python objects are memory-heavy by design. ⚙️ What Happens When You Create a Python Object? Let’s take a simple example: a string. When you create a string in Python, you are not just storing characters. Python allocates a full object structure around that value. Every Python object carries additional metadata. 🧱 1. Object Header (Core Overhead) Every Python object has an object header that stores: --> Type Pointer Points to the object’s type (e.g., str, int, list) Required because Python is dynamically typed Enables runtime checks like: which methods are valid whether operations are allowed This is why “a” + 1 raises a TypeError Unlike C/C++, Python must always know the object’s type at runtime. --> Reference Count Tracks how many variables reference the object Used for Python’s memory management When the count drops to zero, the object is immediately deallocated This bookkeeping happens for every object, all the time. 🔐 2. Hash Cache (For Immutable Objects) Immutable objects like strings store their hash value inside the object. Why? Hashing strings is expensive Dictionaries need fast lookups So Python caches the hash: Hash computed once Reused for dictionary and set operations Enables average O(1) lookups This improves speed — but adds more memory per object. 📏 3. Length Metadata Strings also store their length internally. This allows: len(s) to run in O(1) slicing and iteration without recomputing length efficient bounds checking Again: faster execution, but extra memory. Stay tuned for more AI insights! 😊 #Python #MemoryManagement #PerformanceOptimization

New Blog Published: A Python Concept Most People Miss In Python, variables don’t store values — they reference objects. This one idea explains many “weird” Python behaviors around lists, functions, and mutability. I wrote a short blog breaking this down in a simple way. Big thanks to Nitish Singh — his YouTube explanation inspired this post 🙌 📖 Read here: https://lnkd.in/gUbhhKPK