Python Dunder Methods for Cleaner Code

Managing Python environments shouldn’t be a nightmare. But for many — it still is. Here’s how to keep things clean, fast, and production-ready in 2026: 🛠️ Use uv or poetry to manage environments & dependencies. Faster, safer, and simpler than old-school pip+venv. ⚡ Speed matters – use polars, numpy, and numba to vectorize heavy loops. Even small tweaks can give 10× performance wins. 🧼 Lint + Format + Type-check = non-negotiable Ruff for linting Black for formatting Pyright or Pyrefly for fast type-checks 💡 Bonus tip: Use Typer to build CLIs in minutes. So clean, it feels like magic. 💬 What’s one Python setup rule you wish you knew earlier? #Python #CodeQuality #Productivity #DevTools #DataEngineering

Python Challenge – Can you solve this? Today was all about deep-diving into Lists vs. Sets and I came across a common mistake that we can sometimes overlook. Let’s test your Python understanding👇 numbers = [1, 2, 3] numbers.append([4, 5]) print(len(numbers)) A) 3 B) 4 C) 5 D) Error It’s a classic interview question that tests if you truly understand how Python handles memory and lists. Day 15/30 #30DaysOfCode #DataStructures #Day15 #PythonQuiz

Here's a Python dictionary merge challenge from @dontmisstmr — can you get it right without running the code? dict1 = {"name": "Alice", "age": 25} dict2 = {"city": "Noida", "age": 26} merged = dict1 | dict2 print(merged) The | operator was introduced in Python 3.9. When both dicts share a key, which value wins? Drop your answer in the comments! #Python #SoftwareDevelopment #CodingChallenge #ProgrammingTips #TechCommunity

Most implementations of the State pattern in Python look very “clean”. Lots of small classes. A base interface. One class per state. But if you’ve ever worked with one in a real project, you know the downside: transitions are scattered, behaviour is hard to see in one place, and adding new states often means touching multiple files. In today’s video, I rebuild the State pattern in a very different way. Instead of relying on inheritance, I make the state machine explicit as data and use decorators to define transitions. The result is a small, reusable engine where the entire flow becomes visible at a glance. If you’re interested in writing Python that’s easier to reason about and extend, this is a pattern worth understanding. 👉 Watch here: https://lnkd.in/eg22yEHR. #python #softwaredesign #designpatterns #statemachine #cleancode

𝗗𝗮𝘆 𝟵/𝟯𝟬 Instead of forcing my old coding habits into Python, I’m leaning into how the language handles data natively. Why write four lines of code when you can write one? 1. 𝗧𝗵𝗲 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗪𝗮𝘆 (Looping & Appending): nums = [1, 2, 3, 4] squared = [] for n in nums: squared.append(n * n) 2. 𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗿𝗻 Approach 🚀: squared = [n * n for n in nums] 👉🏻It’s cleaner, faster, and much more intuitive. These are the small details that make the Python journey so satisfying🤌🏻 At what point do you find List Comprehensions become too complex? Do you stick to them for simple one-liners, or use them for nested logic too? #Python #30daysofcode #CodingJourney #Day9 #SoftwareDevelopment

At this point, Python is starting to feel less like a language… and more like a toolkit. Today’s Python MahaRevision 🧠 Chapter 13: Advanced Python (Part 2) This chapter introduced some really powerful and practical concepts: → Virtual environments → pip freeze (managing dependencies) → Lambda functions → bin() method → format() function → map, filter, reduce It’s interesting how these tools make code shorter, cleaner, and more efficient—once you understand how to use them properly. Practice set done: Worked on applying lambda functions, transforming data using map/filter, experimenting with reduce, and managing environments and dependencies. Some concepts felt a bit abstract at first (especially map/filter/reduce)… but with practice, they started making more sense. Biggest takeaway: Better tools don’t just make coding easier—they change how you think about solving problems. Still exploring, still improving. #Python #LearningInPublic #CodingJourney #Programming #AdvancedPython

Most implementations of the State pattern in Python look very “clean”. Lots of small classes. A base interface. One class per state. But if you’ve ever worked with one in a real project, you know the downside: transitions are scattered, behaviour is hard to see in one place, and adding new states often means touching multiple files. In today’s video, I rebuild the State pattern in a very different way. Instead of relying on inheritance, I make the state machine explicit as data and use decorators to define transitions. The result is a small, reusable engine where the entire flow becomes visible at a glance. If you’re interested in writing Python that’s easier to reason about and extend, this is a pattern worth understanding. 👉 Watch here: https://lnkd.in/e9Y3xGNF. #python #softwaredesign #designpatterns #statemachine #cleancode

Python Clarity Series – Episode 25 Topic: Mutable vs Immutable Function Behavior 📌 Why did my list change after function call? def modify(lst): lst.append(100) a = [1, 2] modify(a) print(a) Output: [1, 2, 100] 👉 Lists are mutable → changes reflect outside Now: def modify(x): x = x + 10 a = 5 modify(a) print(a) Output: 5 👉 Integers are immutable → no change outside 💡 Rule: Mutable → changes persist Immutable → changes don’t This confusion causes logic errors. #PythonBasics #FunctionConcepts #StudentClarity #python #clarity

Type check your Python codebase 15x faster with Pyrefly ⚡ Tools like MyPy and Pyright process files sequentially, so larger codebases lead to longer wait times. Pyrefly, Meta's Rust-based type checker, runs checks in parallel, keeping performance nearly constant as your codebase grows. Key features: • Re-checks only changed modules for faster incremental runs • Automatically infers types for variables and return values On the PyTorch codebase, Pyrefly completes a full check in 2.4 seconds, about 15x faster than Pyright and 20x faster than MyPy. --- 📬 I share 2 practical tips on practical tools for data and AI twice a week on Substack. Subscribe here: https://bit.ly/46fdOPl #Python #TypeChecking #Rust

C++26 Reflection & Python Bindings Writing bindings manually is tedious: * Extra code you need to read, maintain(,and write). * Extra dependencies in the project. * Extra bugs. I’ve been exploring C++26 reflection and built a small prototype: automatic Python bindings without writing bindings. Here’s how it works 👇 Post: https://lnkd.in/gwJYhnnF Code: https://lnkd.in/gbQqPVNr #cpp #reflection #c++26