Understanding Python Metaclasses and Their Applications

This blog covers everything from creating your first dictionary to advanced patterns like nested dicts, defaultdict, Counter, and dictionary comprehensions — with output tables after every example so you can see exactly what each operation produces. #Python #DataEngineering https://lnkd.in/g23zDgfm

Python functions with fixed signatures break the moment you need to forward arguments across abstraction boundaries. *args and **kwargs solve that — and this python tutorial goes well past the syntax. — How CPython actually handles variadic calls at the C level (PyTupleObject, PyDictObject, and why there's a real allocation cost) — Why a defaulted parameter before *args is effectively unreachable via positional calling — and the idiomatic fix — The difference between *args isolating the mapping vs sharing mutable values inside it — ParamSpec (PEP 612) for preserving decorator signatures through the type system — TypedDict + Unpack (PEP 692, Python 3.12) for per-key precision on **kwargs — inspect.Parameter.kind for reading variadic signatures at runtime — the foundation of FastAPI and pytest's dispatch logic — Lambda variadic syntax, functools.wraps, kwargs.setdefault patterns, and common SyntaxErrors caught at parse time Includes interactive quizzes, spot-the-bug challenges, a design decision review, and a 15-question final exam with a downloadable certificate of completion. Full guide: https://lnkd.in/gHkdvCn5 #Python #PythonProgramming #SoftwareDevelopment

Comparing Python packages for A/B test analysis A/B testing in Python sounds simple until you try to standardize results across experiments. This post compares SciPy, statsmodels, Pingouin, and tea-tasting through a practical lens and shows how much work it actually takes to get clean, repeatable outputs. https://lnkd.in/dvQ9nzwG

Comparing Python packages for A/B test analysis: tea-tasting, Pingouin, statsmodels, and SciPy This article compares four Python packages that are relevant to A/B test analysis: tea-tasting, Pingouin, statsmodels, and SciPy. It does not try to pick a universal winner. Instead, it clarifies what each package does well for common experimentation tasks and how much manual work is needed to produce production-style A/B test outputs… https://lnkd.in/druNaMft

Ever screamed at your screen because Python changed a variable you never touched?   Or a function suddenly "remembered" values from previous calls?   Or a SyntaxError pointed to a line that looked perfect? These aren't random bugs — they're Python's design decisions in action. And they trip up beginners and experienced devs alike. I wrote the guide I wish existed when I started:   "Getting Started with Python: Overview & Real-World Applications"   Not another "Python is readable" list — but a practitioner's breakdown of the **8 core surprises** that explain most "why does this behave that way?" moments in your first year. The 8 problems covered: - Terminal says Python doesn't exist (PATH hell) - Error on a line that looks fine (parser vs runtime) - Changing one variable changes another (name binding) - Function modifies input it should only read - Mutable defaults trap — function remembers across calls - "1992" isn't a number (input() strings) - Code runs but nobody understands it (naming/docstrings) - Windows paths break silently (escape sequences/raw strings) Plus: how these same concepts power real-world Python in data science (Pandas views/copies), web (Django/FastAPI), and automation. If you've ever wasted hours debugging a "perfectly logical" Python script — this post gives you the mental model to stop it. Read it here: https://lnkd.in/gcsHx66Q What's the #1 Python surprise that cost you the most time early on? Drop it below — let's commiserate and learn from each other. #Python #LearnPython #PythonBeginners #ProgrammingTips #DataScience #Coding (Full Python Fundamentals series linked inside — 13 articles building from install to production concepts)

🚀 Understanding if __name__ == "__main__": in Python (Once and For All!) 👀 You’ve definitely seen this line before: python code: if __name__ == "__main__": But… do you really know why it exists and when to use it? Let’s break it down in a simple, practical way 👇 🧠 The Core Idea When Python imports a file (module), it doesn’t just import functions… 👉 It executes the entire file. Yes — including: - print() statements - input() prompts - Any top-level logic Even if all you wanted was a single function 😅 ⚠️ The Problem Imagine this scenario: You have a file calculator.py with functions and some executable code. Then you import it into another file: python code: import calculator 💥 Suddenly: - It prints messages - It asks for user input - It runs calculations All before your main program continues 👉 Not because you did anything wrong… 👉 But because that’s how Python imports work ✅ The Solution This is where the magic comes in: python code: if__name__=="__main__": ✨ This line gives you control over execution 🔍 How It Works - When you run a file directly → __name__ == "__main__" - When you import the file → __name__ == "module_name" So: 👉 Code inside this block only runs when the file is executed directly 👉 It does NOT run when the file is imported elsewhere 💡 Best Practice Example python codes: def add(a, b): return a+b def subtract(a, b): return a-b if__name__=="__main__": print("This is a simple calculator") x=int(input("Enter a number: ")) y=int(input("Enter another number: ")) print(add(x, y)) print(subtract(x, y)) 🎯 Why This Matters ✔ Keeps your code clean and reusable ✔ Separates logic from execution ✔ Prevents unwanted side effects during imports ✔ Makes your code interview-ready 💼 🧩 Simple Rule to Remember 👉 Write functions at the top 👉 Put execution/testing code inside if __name__ == "__main__": 🏁 Final Thought If your Python file is meant to be: - 🔁 Reusable (imported elsewhere) - ▶️ Executable (run directly) Then this pattern isn’t optional — it’s essential. 💬 Have you ever run into this issue while importing modules? Let’s discuss! #Python #Programming #SoftwareDevelopment #CodingTips #PythonTips #LearnToCode #TechEducation #Developers

Python raises no error and produces no warning when an instance attribute shadows a @classmethod. The method is still on the class — it's just hidden from that specific instance. This happens because @classmethod is a non-data descriptor. It defines __get__ but not __set__, which puts it in tier 3 of Python's three-tier attribute lookup. An instance attribute with the same name sits in tier 2 (the instance __dict__) and wins every time. The result: c.create() raises TypeError with a message that never mentions shadowing. The bug can sit undetected for a long time. A new article on PythonCodeCrack covers how the descriptor protocol makes this possible, how to detect an active shadow using vars() and an MRO walk, and six prevention strategies — from naming conventions and __slots__ to ProtectedClassMethod data descriptors and a ProtectedMeta metaclass for hierarchy-wide coverage. There's also an interactive step-through visualizer, a Spot the Bug challenge, and a decision flowchart that routes to the right prevention strategy based on your codebase constraints. https://lnkd.in/ghRPQF9U #Python #PythonProgramming #SoftwareEngineering #DescriptorProtocol #PythonTips

Slow python ? Meet pybind11 ! It started with a simple curiosity while exploring autograd in Deep Learning frameworks. To better understand how gradients work behind the scenes, we implemented #micrograd by Andrej Karpathy a tiny educational autograd engine written in Python. After building it, a natural question emerged: If Python is slow, how are performance-heavy libraries still so fast? Digging deeper, we discovered that many high-performance Python libraries rely on C++ under the hood, with Python acting as a clean interface. This led us to explore pybind11, a lightweight way to bridge Python and C++ ! We then, - Implemented micrograd in C++ - Exposed it to Python using pybind11 - Generated .so modules using setup.py - Recreated a simplified Python plus C++ architecture This small experiment helped us understand how Python can leverage C++ for performance while maintaining developer productivity.. Also, pybind11 is one of the several alternatives like SWIG, Boost.Python, and Cython that can also be used for language bindings. This exploration was done in collaboration with Kavin Kumar, where we jointly worked on both the medium article and the C++ micrograd implementation. To check our implementation, Github: https://lnkd.in/gz6GBuNV For a deep dive, Explore our article: https://lnkd.in/g2y8KRta PS: Not AI Generated :) #Python #CPP #CPlusPlus #Pybind11 #MachineLearning #DeepLearning #Autograd #Micrograd #PythonPerformance

🚀 **DSA with Python – Recursion Practice** Continuing my **Data Structures & Algorithms with Python** journey, today I practiced more **recursion-based problems** to strengthen my understanding of how recursive calls break problems into smaller subproblems. 📚 **Problems I Practiced** 🔹 **Count Number of Digits (Recursion)** Logic: Repeatedly divide the number by 10 until it reaches 0. Each recursive call counts one digit. 🔹 **Sum of Digits of a Number** Example: 5251 → 5 + 2 + 5 + 1 = 13 Using recursion: `sum(n) = (n % 10) + sum(n // 10)` 🔹 **Reverse a String using Recursion** Example: `"abcd"` → `"dcba"` Recursively reduce the string and build the reversed result step by step. 🔹 Palindrome Check using Recursion Compared characters from the start and end of the string recursively until the middle is reached. Works for both odd and even length strings. 🔹 Reverse a String using Recursion Example: "abcd" → "dcba" Recursively append characters from the end to build the reversed string. 🔹 Sum of All Elements in an Array using Recursion Logic: sum(arr, i) = arr[i] + sum(arr, i+1) Continue until the index reaches the length of the array. 💡 **Key Learnings** ✔ Importance of **base conditions** in recursion ✔ How recursive calls create a **call stack** ✔ Breaking complex problems into **smaller subproblems** ✔ Strengthening **algorithmic thinking** Step by step building stronger foundations in **DSA and problem solving**. #DSA #Python #Recursion #Algorithms #DataStructures #CodingPractice #ProblemSolving #CodingInterview #PythonDeveloper #SoftwareEngineering #BackendDevelopment #LearnInPublic #DeveloperJourney #ContinuousLearning #Programming #TechLearning #100DaysOfCode #CodingJourney

Modern Python tooling like ruff, pytest, mypy, black, py-spy, and pre-commit can help streamline your Python workflow, improve code quality, and catch bugs before deployment. My latest article on the Towards Data Science platform talks about all these tools and covers how to build a cleaner, faster feedback loop so you can spend less time fixing avoidable issues later and more time actually shipping. If you’re working in Python and want a more reliable development setup, this should be useful. Read it here for free: https://lnkd.in/ewuXn6NF