CPython Internals: Python 3 Interpreter Guide

Day 37 – Understanding How Python Stores Data Today, I am continuing on hash tables in Python, which is what powers dictionaries (dict). In simple terms: Python uses a smart system to store and find data almost instantly, instead of searching line by line. That’s why dictionaries are fast and used everywhere — from logins to APIs to caching. Today, I didn’t just read about how Python dictionaries work — I built a simple hash table from scratch in VS Code. What I did: Created a basic HashTable class Used Python’s hash() function to decide where data should live Stored values in buckets (lists) to safely handle collisions Retrieved values using keys, just like a real Python dict Even tested collisions by inserting keys that land in the same bucket I learned: Why dictionary keys must not change What a hash is (Python’s way of knowing where to store data) Why this concept matters for building fast and scalable systems This might look small, but it’s one of the ideas behind efficient backend and full-stack development. Slow progress is still progress. Understanding beats rushing. Which of the terms or concepts used here sounds too scary and unusual for you? Let me know, let's learn together 😊 #Day37 #LearningInPublic #Python #DataStructures #BackendBasics #Consistency

In C, an integer takes 4 bytes. In Python, the number 1 takes 28 bytes. Why? As python is a "Dynamically Typed" language, it needs to store more than just the value 1. It needs to store "metadata" about that value so the interpreter knows what to do with it. How? In the CPython source code, every single thing is a PyObject. So when we create x = 1, following structure is created with it: 1. ob_refcnt (8 bytes): The Reference Counter. It tracks how many variables point to this object. 2. ob_type (8 bytes): A pointer to the "Type Object" (telling Python the datatype). 3. ob_size (8 bytes): For variable-sized objects (like lists) 4. The Actual Value (4-8 bytes): The actual number 1 Understanding this overhead explains why Python is memory-intensive being a dynamically typed language. I am trying to learn Python Internals in detail and will share my learnings. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

There is a one line trick to save about 50% RAM usage in Python. By default, Python objects are flexible but heavy. To have flexibility to store any attribute inside objects, every Python object carries a dictionary called __dict__. Each attribute of the object is mapped inside this underlying dictionary. This is why we can add new attributes to an object on the fly: user.new_attribute = "surprise!" That means building a backend system that creates millions of objects (users, transactions, events) will create millions of dictionaries too. But dictionaries are memory consuming because they use hash tables to stay fast. Solution: __slots__ If you know exactly which attributes your object will have, you can tell Python: reserve space just for these fields only. Impact - Memory: SlotsUser uses significantly less RAM (40% to 60%) Speed: Attribute access is slightly faster Strictness: You can’t add random attributes anymore Takeaway - When you don’t need that flexibility, __slots__ helps you keep things lean. I am trying to learn Python Internals in detail and will share my learnings. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

More on the Python tools I’ve been building lately: I recently finished two backend utilities that I’m really proud of. PyError Repo: https://lnkd.in/ekpQhw6i A structured error‑handling tool for Python. It intercepts exceptions, logs them to a text file, and prints a clean, readable error message to the console. You get the error type, file path, method, exact line, and even the timestamp — all formatted so you can actually understand what went wrong. PyCommand Repo: https://lnkd.in/eFurREpX A lightweight command‑routing system. You register your functions once, and then invoke them by index with parameters. Paired with PyError, it gives you fully automated error catching and logging for any command you run. Route your commands and boom — instant structure. And there’s more coming. A lot more. This whole experience made something very clear to me: I love Python, and I have a real affinity for it. I’ve only been learning Python since Monday, and I built all of this without using any AI assistance. If you’re curious, I’ve even posted timelapses of some of my earlier tools on YouTube: https://lnkd.in/ev_5FPvA

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

What are the 33 words in Python? I thought Python was simple—until I learned this A lot of beginners ask: “Are there really only 33 words in Python?” Yes — Python has a small set of reserved keywords you can’t use as variable names. Here’s the simple way to remember them 👇 💡 Logic & flow: if, else, elif, for, while, break, continue 💡 Functions & classes: def, return, class, lambda 💡 Truth & logic: True, False, and, or, not, is 💡 Exceptions & context: try, except, finally, raise, with 💡 Misc essentials: import, from, as, pass, None, global, nonlocal, assert, del, yield That’s it. Master these—and Python suddenly feels way less scary. 🐍 Comment “Python” and I’ll DM you a beginner cheat sheet. #Python #LearnToCode #TechCareers #ProgrammingBasics #LinkedInLearning

🚀 Python for Beginners – Post 8/∞ 🧠 Python Memory Secrets: How Variables Really Work Many beginners think variables store values. But in Python… that’s not the full truth 👀 👉 Variables don’t store values — they store references. What this means: ✔ Multiple variables can point to the same object ✔ Small integers & short strings may share memory (interning) ✔ Reassigning a variable doesn’t change the object — it changes the reference ✔ Python automatically cleans unused objects (Garbage Collection) 💡 Understanding this concept helps you: • Avoid confusing bugs • Write memory-efficient code • Think like a real Python developer If this concept feels tricky now, that’s okay — clarity comes with practice 🔁 📌 Save this post for revision 💬 Comment “MEMORY” if you want a simple hands-on example next 🔄 Share if this helped your learning journey #PythonForBeginners #LearnPython #PythonConcepts #PythonDeveloper #ProgrammingBasics #CodingJourney #SoftwareEngineering #TechLearning #PythonTips

Most Python developers know @property. Very few know what comes after that. There’s a part of the language that lets attributes become active objects: they know their own name, intercept reads and writes, and inject behavior at class creation time. No decorators. No hacks. Just Python — used at a deeper level. This is the kind of feature that changes how you model systems: less boilerplate, more intention, more declarative code. If you’ve been writing Python for years and never saw this… you’ve just found a new layer of the language. 👇 Curious to go deeper?

Why 60 * 60 * 24 costs the same as 86400 in Python? TL;DR: Python evaluates it once at compile time! You might think writing out the math makes the code slower because Python has to do the multiplication every time. When Python compiles source code into Bytecode, it uses an optimiser (Peephole) that looks for constants. This is called Constant Folding. It’s not just for numbers! Python also folds small strings. "Deep" + "Tech" becomes "DeepTech" in the bytecode. Takeaway: -> Never sacrifice readability for a "assumed" performance gain in constants. -> Python’s compiler is not just a translator, it’s an optimiser. -> Compiler handles the math smartly, so you can focus on writing code that humans can actually read! I’m deep-diving into Python internals and performance. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

⚠️ Unpopular Opinion: Python 2 didn’t just die. It made Python 3 necessary. 🧵 Python 2 vs Python 3 Python 2: print "Hello" Works… until it doesn’t 😬 Python 3: print("Hello") Clear. Explicit. Future-proof This wasn’t about syntax. It was about discipline. Python 3 forced developers to: • Stop relying on magic • Handle errors properly • Think about Unicode, data, and scale • Write code for teams — not just machines Python 2 taught us how to start. Python 3 taught us how to build for production. If you’re still writing code that “just works”… Python 3 asks: will it still work tomorrow? 👇 Drop a 🐍 if Python 3 made you a better developer #Python #Python3 #DeveloperMindset #FullStackDeveloper #CleanCode #Programming #TechTalk #LinkedInTech