Boost Python Performance with Smart Tweaks

Why range(1,000,000) is cheap, but list(range(1,000,000)) is costly in Python? TL;DR: Iteration Protocol in Python needs to know only next item and not full list. The "Next Page" Rule Iteration in Python isn't about having a collection of items; it’s about knowing how to get the next item. Two special methods make this possible: 1. __iter__() → tells Python “I can be looped over” 2. __next__() → returns the next value, one at a time When there’s nothing left, StopIteration tells Python to stop the loop. Why this matters? When we use a list, we pay for all the memory upfront. When we use the Iteration Protocol, we only pay for one item at a time. This is called Lazy Evaluation. Takeaway - If the object represents a collection or a stream of data, implement __iter__ and __next__. It makes the code more memory-efficient and much more "Pythonic." I’m deep-diving into the Python protocols this week and will share my learnings. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

How does len() function knows how to handle a List, a String, and a Dictionary all the same way in Python? TL;DR: Python Protocols A Protocol is just the set of rules (special methods) an object follows. "Duck Typing" Contract In Python does not care about what the object is, it cares only about what it does. These are represented as dunders (__method__) in Python. 3 Protocols we use everyday in Python: -> Sized (__len__): Tells Python that object has a size. (Powers len()) -> Container (__contains__): Tells Python how to check if something is "inside" the object. (Powers the in keyword) -> Iterable (__iter__): Tells Python that object can be looped over. (Powers for x in obj) Why is this useful? As a developer, we want our code to be intuitive. By following these protocols, we make our custom objects compatible with all of Python’s built-in tools. Protocols are the API of the Python language itself. Using them make custom objects start feeling like native Python parts. I’m deep-diving into the Python protocols this week and will share my learnings. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

Did you know in Python, 𝗹𝗶𝘁𝗲𝗿𝗮𝗹𝗹𝘆 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 is a 𝗣𝘆𝗢𝗯𝗷𝗲𝗰𝘁? Integers, strings, lists, functions… even `None`… even 𝘁𝗵𝗲 𝘁𝘆𝗽𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗶𝘁𝘀𝗲𝗹𝗳. Yep—Python is basically a cult where everyone wears the same robe and answers to the same ancient C structure. Each PyObject has a 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗰𝗼𝘂𝗻𝘁, so Python is constantly watching who loves you and who doesn’t. If nobody loves you anymore… 𝗽𝗼𝗼𝗳, garbage collector deletes you. 𝗕𝗮𝘀𝗶𝗰𝗮𝗹𝗹𝘆, 𝗹𝗶𝗳𝗲… 𝗯𝘂𝘁 𝗶𝗻 𝗖 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 😎 Your “simple” 5 + 5 ? It goes through layers of PyObject machinery like it’s filing government paperwork. Python isn’t slow—it’s just extremely dramatic. So next time your code runs, remember: beneath that friendly syntax is a C-powered entity counting friendships, controlling lifespans, and keeping the universe stable with pure chaos. 𝗦𝗼 𝘆𝗲𝗮𝗵: 𝗣𝘆𝗢𝗯𝗷𝗲𝗰𝘁 𝗺𝗮𝗸𝗲𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝘀𝘂𝗽𝗲𝗿 𝗳𝗹𝗲𝘅𝗶𝗯𝗹𝗲… 𝗮𝗻𝗱 𝗮 𝗹𝗶𝘁𝘁𝗹𝗲 𝗰𝗵𝗮𝗼𝘁𝗶𝗰. But honestly, that PyObject design is what lets Python stay so flexible, expressive, and ridiculously easy to work with. Python devs: “Everything is an object.” PyObject: “𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗶𝘀… 𝗠𝗘.” 𝗗𝗲𝗲𝗽 𝗗𝗼𝗰𝘀: https://lnkd.in/dkidEkUe 𝗣𝗘𝗣 683: An interesting read: https://lnkd.in/dhxGahzK #Python #ProgrammingHumor #PyObject #DevLife #CodeNerd #SoftwareEngineering #PythonInternals

🚀 Full Stack Journey Day 48: Python Error Mastery - Syntax vs. Runtime Exceptions! 🛠️🐍 Day 48 of my #FullStackDevelopment series is all about understanding the "why" behind broken code! I’ve been diving into Exception Handling in Python, specifically learning how to distinguish between Syntax Errors and Runtime Errors (Exceptions). 🧠 Understanding these two categories is the first step toward building resilient, "unbreakable" applications: Syntax Errors (The Grammar Mistake): These occur when Python doesn't understand your code because it violates the language rules—like a missing colon :, mismatched parentheses (), or incorrect indentation. Python won't even start running your code if it finds a syntax error. It's like trying to drive a car with no engine! ❌ Runtime Errors / Exceptions (The Execution Problem): Your code is grammatically perfect, but something goes wrong while it's running—like dividing by zero, trying to open a file that doesn't exist, or using a variable that hasn't been defined. Unlike syntax errors, these can be caught and handled gracefully using try and except blocks. ✅ Knowing the difference allows me to fix my own typos faster and write code that can handle real-world "surprises" from users or external systems without crashing. 📂 Access my detailed notes here: 👉 GitHub: https://lnkd.in/g_WzXQKf #Python #AdvancedPython #ExceptionHandling #SyntaxError #RuntimeError #CodingFundamentals #CleanCode #FullStackDeveloper #LearningToCode #Programming #TechJourney #SoftwareDevelopment #DailyLearning #CodingChallenge #Day48 LinkedIn Samruddhi P.

https://lnkd.in/e73QC5-9 FST fst Version 0.2.5 Overview This module exists in order to facilitate quick and easy high level editing of Python source in the form of an AST tree while preserving formatting. It is meant to allow you to change Python code functionality while not having to deal with the details of: Operator precedence and parentheses Indentation and line continuations Commas, semicolons, and tuple edge cases Comments and docstrings Various Python version-specific syntax quirks Lots more...

Is Python Interpreted or Compiled? 🐍 The answer is... actually, it's both. We often hear that Python is an "Interpreted Language." Technically true, but there is some hidden magic happening every time you hit the Run button. Python is a hybrid. Here is what happens under the hood: 🔹 Step 1: Compilation (The Secret Step) First, Python takes your code and translates it into Bytecode. This isn't machine code (0s and 1s) yet. It’s an intermediate language that only Python understands. (Ever seen those pycache folders? That’s where the bytecode lives!). 🔹 Step 2: Interpretation (The Performance) Then, the PVM (Python Virtual Machine) steps in. It takes that bytecode and executes it, instruction by instruction. ⚙️ Meet the Boss: CPython The version of Python most of us use is called CPython. It’s written in C, and it acts as the "engine" that does both jobs: it compiles your code to bytecode and then interprets it. So, Python is a language that is compiled to bytecode, then interpreted by a virtual machine. Best of both worlds! 🚀 Did you know about the bytecode step, or did you think it was pure magic? ✨ #PythonDeveloper #UnderTheHood #CPython #CodingFacts #TechEducation

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

🐍 A Python mistake that looks innocent… but bites HARD 👇 Common misconception: «“This creates a 3×3 grid.”» grid = [[0] * 3] * 3 Looks fine, right? Most beginners (and many intermediates) think this makes 3 separate rows. ❌ It doesn’t. What actually happens (easy explanation): Python doesn’t copy the inner list. It copies the reference. So all rows point to the same list. Watch this 👀 grid[0][0] = 1 print(grid) Output: [[1, 0, 0], [1, 0, 0], [1, 0, 0]] 😱 Why did all rows change? Because Python didn’t create 3 rows — it created 1 row, reused 3 times. ✅ The correct way: grid = [[0] * 3 for _ in range(3)] Now each row is truly independent. This single concept explains a ton of “Python is acting weird” moments. If you like clear, bite-sized Python lessons that explain why things behave this way — 👉 Get one daily in your inbox: https://lnkd.in/dVNDF-xw One idea. One email. Zero overwhelm 🐍

Using the Python zip() Function for Parallel Iteration Written by $DiligentTECH 💀⚔️ Imagine you are juggling three balls: one is a Name, one is an Age, and one is a Favorite Color. If you throw them one by one, you’re just doing extra work. But what if you could fuse them into a single, synchronized arc through the air? Are you tired of managing multiple list indexes like a stressed-out air traffic controller? Do you wish your data structures would just... shake hands and walk together? Let's quickly discuss simple tips on how Using the Python zip() Function for Parallel Iteration can help less the stress The Zipper Think of the zip() function exactly like the zipper on your favorite hoodie. You have two separate sides (iterables like lists or tuples), and as the slider moves up, it pairs the teeth from both sides together into a single, unified track. In Python, zip() takes multiple containers and aggregates them into a single object. https://lnkd.in/d7wPgSp5