10 Python Dev Mistakes to Avoid in 2026

𝗧𝗵𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗦𝗸𝗶𝗹𝗹 𝗧𝗿𝗲𝗲 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝗶𝗻 𝟮𝟬𝟮𝟲 🌳🐍 Python in 2026 is no longer “just a programming language.” It’s an ecosystem that spans 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗱𝗮𝘁𝗮, 𝗔𝗜, 𝗰𝗹𝗼𝘂𝗱, 𝗮𝗻𝗱 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. This skill tree captures what industry-ready Python expertise really looks like today. Here’s how experienced Python professionals think about it 👇 🌱 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 (𝗥𝗼𝗼𝘁𝘀 𝗠𝗮𝘁𝘁𝗲𝗿) Strong Python careers are built on fundamentals: • Core Python (data types, OOP, error handling) • Algorithms & problem-solving • Math & data fundamentals (statistics, linear algebra) • Development tools (Git, Jupyter, IDEs) Without strong roots, advanced skills won’t scale. 🌿 𝗖𝗼𝗿𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 (𝗧𝗵𝗲 𝗧𝗿𝘂𝗻𝗸) This is where Python becomes production-grade: • 𝐃𝐚𝐭𝐚 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: Pandas, NumPy, data pipelines • 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 & 𝐌𝐋: scikit-learn, model evaluation, experimentation • 𝐀𝐏𝐈𝐬 & 𝐁𝐚𝐜𝐤𝐞𝐧𝐝𝐬: FastAPI, Flask, async Python • 𝐂𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬: Docker, CI/CD, cloud-native Python This layer separates coders from engineers. 🌳 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗚𝗿𝗼𝘄𝘁𝗵 (𝗕𝗿𝗮𝗻𝗰𝗵𝗲𝘀) As systems grow, specialization matters: • Deployment & scaling (containers, orchestration) • Performance optimization • Backend data processing • Domain-driven design & architecture This is where Python powers real-world platforms. 🤖 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 & 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 The fastest-growing branch: • LLM integration & orchestration • Prompt engineering & evaluation • Vector databases & embeddings • AI agents and workflow orchestration Python is the default language for GenAI systems. 🎯 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 You don’t need to learn everything at once. You need to 𝗴𝗿𝗼𝘄 𝗹𝗮𝘆𝗲𝗿 𝗯𝘆 𝗹𝗮𝘆𝗲𝗿: 𝟭 Strong foundations 𝟮 Solid core techniques 𝟯 One or two deep specializations 𝟰 Awareness of GenAI & agentic systems That’s how Python careers stay relevant in 2026 and beyond. If you’re building or mentoring Python talent, this tree is a great roadmap. What branch are you currently growing into? 🌱➡️🌳 📘 𝙇𝙚𝙖𝙧𝙣 𝙋𝙮𝙩𝙝𝙤𝙣 𝙩𝙝𝙚 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙙 𝙒𝙖𝙮 🔗 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀:-https://lnkd.in/drnrg2uQ 💬 𝙅𝙤𝙞𝙣 𝙩𝙝𝙚 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝘾𝙤𝙢𝙢𝙪𝙣𝙞𝙩𝙮 📲 𝗪𝗵𝗮𝘁𝘀𝗔𝗽𝗽 𝗖𝗵𝗮𝗻𝗻𝗲𝗹:-https://lnkd.in/dTy7S9AS 👉𝗧𝗲𝗹𝗲𝗴𝗿𝗮𝗺:- https://t.me/pythonpundit

🚀 Mastering Arrays (Lists) in Python – Complete Guide Arrays (Lists) are one of the most important and powerful data structures in Python. Whether you're preparing for coding interviews, improving your problem-solving skills, or building real-world applications, strong knowledge of lists is essential. 🔹 Creating Lists Lists can be created in multiple ways — directly with values, using repetition, generating sequences with range, using list comprehension, creating 2D lists (matrices), or even converting strings into lists. Python gives flexible and simple ways to initialize data. 🔹 Accessing Elements You can access elements using positive indexing (from the start) or negative indexing (from the end). You can also determine the size of the list using length functions. Understanding indexing is the foundation of list operations. 🔹 Modifying Elements Lists are mutable, meaning you can change their values after creation. You can update a single element or multiple elements at once using slicing techniques. 🔹 Slicing Techniques Slicing allows you to extract portions of a list. You can define start, stop, and step values. It also enables advanced operations like skipping elements or reversing a list efficiently. 🔹 Adding Elements You can add elements at the end, at specific positions, or merge multiple lists together. Python provides built-in methods that make list expansion simple and efficient. 🔹 Removing Elements Elements can be removed by value, by index, or completely clearing the list. Understanding the difference between these removal methods is important for avoiding errors. 🔹 Searching Elements Lists allow you to find the index of an element, count occurrences, or simply check whether an element exists. These operations are widely used in problem-solving scenarios. 🔹 Linear Search Concept Linear search scans each element one by one until the target is found. Its time complexity is O(n), which means performance depends on the size of the list. This concept builds the base for understanding more advanced search algorithms. 🔹 Sorting & Reversing Lists can be sorted in ascending or descending order. Python also allows custom sorting based on conditions like length or absolute value. Reversing a list is another fundamental operation often used in algorithms. 🔹 Traversal Techniques Lists can be traversed using for loops, while loops, backward iteration, or enumeration with index tracking. Choosing the right traversal method improves readability and efficiency. 🎯 Why Learning Lists is Important? Lists are the backbone of data handling in Python. Most advanced topics like stacks, queues, dynamic programming, and even frameworks rely on strong list fundamentals. Master the basics. Practice consistently. Strong foundations create strong programmers. #Python #DataStructures #Programming #InterviewPreparation #CodingJourney

Is your career progress stuck at learning Python? Well, Python feels tough only when it’s taught the wrong way. Python was never meant to feel intimidating. It was designed to be readable. The same code that looks like Morse code in other languages often reads like plain English in Python — which is exactly why it’s so powerful for finance and risk. So why do so many people still struggle to use it confidently? 👉 Because you don’t learn a language from a dictionary. You learn a language by speaking it, by making mistakes, and by being part of a community that already uses it regularly. This was the core idea behind how I designed our Python for Finance training — as a language you learn by using it in real applications, not memorizing syntax. Over time, I’ve trained 100+ quants and risk professionals with this mindset, and the program is only gaining momentum. Here’s what the curriculum looks like — practical, step-by-step, and finance-focused: 📑 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 𝗖𝘂𝗿𝗿𝗶𝗰𝘂𝗹𝘂𝗺 ➪ 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 & 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗦𝗲𝘁𝘂𝗽 ↳ Jupyter Notebooks hosted on Google Collab ↳ No local setup required to get started ↳ Jump directly into action ➪ 𝗗𝗮𝘁𝗮 𝗧𝘆𝗽𝗲𝘀 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 ↳ Data Types: numeric, boolean, string, None ↳ Date Structures: list, tuple, dict, set ↳ Practical Examples ➪ 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 𝗮𝗻𝗱 𝗖𝗼𝗺𝗺𝗲𝗻𝘁𝗶𝗻𝗴 ↳ Variables naming conventions & best practices ↳ Code annotation & best practices ↳ Practical Examples ➪ 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁𝘀 ↳ if-elif-else ↳ nested conditional statements ↳ A simple scorecard application ➪ 𝗟𝗼𝗼𝗽𝘀 ↳ for & while ↳ nested loops ↳ 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Pricing a Corporate bond using for loop ➪ 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗠𝗼𝗱𝘂𝗹𝗲𝘀 ↳ NumPy ↳ Pandas ↳ Matplotlib ➪ 𝗩𝗮𝗹𝘂𝗲 𝗮𝘁 𝗥𝗶𝘀𝗸 (𝗩𝗮𝗥) 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 ↳ Historical Simulation VaR (HS VaR) ↳ Monte Carlo Simulation VaR (HS VaR) ↳ Parametric VaR (HS VaR) ➪ 𝗢𝗽𝘁𝗶𝗼𝗻𝘀 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 & 𝗜𝗺𝗽𝗹𝗶𝗲𝗱 𝗩𝗼𝗹𝗮𝘁𝗶𝗹𝗶𝘁𝘆 ↳ Blacks-Scholes-Merton (BSM) Model for European Options ↳ Binomial Tree Model for American Options ↳ Implied Vol Calibration This isn’t just theory — everything is tied to actual quant and risk workflows. 💼 Professionals with strong Python skills earn handsomely: ✧ 𝗣𝘆𝘁𝗵𝗼𝗻 𝗤𝘂𝗮𝗻𝘁𝘀: Earn USD 100,000–150,000+ (varies by region & experience) ✧ 𝗥𝗶𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀/𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀: Start USD 80,000–120,000+ for early-career roles If you’ve been wanting to learn Python but never felt confident enough to start, you’re not alone. Here’s what made the difference for our learners: ✓ Learning by doing! ✓ Real finance applications (VaR, options, volatility)! ✓ Community support and practice! 💬 Comment “Python” below and I’ll share the registration details. #PythonForFinance #QuantFinance #RiskManagement #Python #QuantJobs #RiskAnalytics #Upskilling #CareerTransition

Here are important Python interview questions and answers. 1. What is Python? Answer: Python is a high-level, interpreted, object-oriented programming language known for its simple syntax and readability. It is used in web development, data science, automation, AI, and scripting. 2. What are Python’s key features? Answer: Easy to learn and read Interpreted language Dynamically typed Object-oriented Large standard library Platform independent 3. What is the difference between List and Tuple? Answer: List Tuple Mutable (can change) Immutable (cannot change) Uses [] Uses () Slower Faster Example: [1,2,3] Example: (1,2,3) 4. What is the difference between == and is? Answer: == compares values. is compares memory location (object identity). Example: a = [1,2] b = [1,2] print(a == b) # True print(a is b) # False 5. What is a Dictionary in Python? Answer: A dictionary stores data in key-value pairs. Example: student = {"name": "Keerthi", "age": 24} 6. What are Python data types? Answer: int float str list tuple set dict bool 7. What is a function in Python? Answer: A function is a block of code that performs a specific task. Example: def add(a, b): return a + b 8. What is OOP in Python? Answer: Object-Oriented Programming is a programming style based on objects and classes. Main concepts: Encapsulation Inheritance Polymorphism Abstraction 9. What is the difference between append() and extend()? Answer: append() adds one element. extend() adds multiple elements. Example: a = [1,2] a.append([3,4]) # [1,2,[3,4]] a.extend([3,4]) # [1,2,3,4 10. What is Exception Handling? Answer: It is used to handle runtime errors using try, except, finally. Example: try: print(10/0) except ZeroDivisionError: print("Error occurred") 11. What is Lambda Function? Answer: A small anonymous function written in one line. Example: square = lambda x: x*x 12. What is PEP 8? Answer: PEP 8 is Python’s style guide that explains how to write clean and readable Python code.

As I go deeper into Python, I’m realizing that learning to code isn’t just about writing lines of syntax - it’s about learning how to organize information, ask better questions, and think in structures. This phase of my journey introduced me to Data Structures & Functions, and one concept stood out immediately: 𝐁𝐞𝐲𝐨𝐧𝐝 𝐕𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬: 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐋𝐢𝐬𝐭𝐬. I like to think of a variable as a single grocery bag - it can only hold one item at a time. A list, on the other hand, is a shopping trolley. It holds multiple items, keeps them organized, and lets you access any item whenever you need it. In Python, lists are written using 𝒔𝒒𝒖𝒂𝒓𝒆 𝒃𝒓𝒂𝒄𝒌𝒆𝒕𝒔 [], with items separated by commas. Simple syntax, powerful idea. Suddenly, I wasn’t just working with single values - I was managing collections of data. 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠: 𝐓𝐡𝐞 “𝐙𝐞𝐫𝐨 𝐑𝐮𝐥𝐞” (𝐓𝐡𝐞 𝐏𝐚𝐫𝐭 𝐓𝐡𝐚𝐭 𝐓𝐫𝐢𝐩𝐬 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐔𝐩) One of the first mindset shifts was understanding indexing. In Python, counting doesn’t start at 1 - it starts at 0. That means: The first item in a list is at index 0 The second item is at index 1 The third is at index 2, and so on It feels strange at first, but it’s non-negotiable in most programming languages. To access any item, you simply place the index inside square brackets after the list name. Once this clicks, lists suddenly feel predictable and logical. 𝐒𝐥𝐢𝐜𝐢𝐧𝐠: 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐚 𝐂𝐡𝐮𝐧𝐤 𝐨𝐟 𝐃𝐚𝐭𝐚. Indexing lets you grab one item, but slicing is where things get really interesting. Slicing allows you to extract a range of items from a list. I imagine it like cutting a sandwich — you decide where to start cutting and where to stop, and Python hands you that section neatly. Even better, Python offers slicing shortcuts. These shorthand patterns make your code cleaner, faster to write, and easier to read. It’s one of those moments where you realize programmers value efficiency just as much as correctness. 𝐌𝐨𝐝𝐢𝐟𝐲𝐢𝐧𝐠 𝐋𝐢𝐬𝐭𝐬: 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐃𝐚𝐭𝐚 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 Unlike some data structures, lists are mutable — meaning they can change after creation. You can add items, remove them, rearrange them, or update values entirely. Python makes this easy with built-in list methods and functions that handle common operations. Instead of reinventing the wheel, you focus on the logic and let Python do the heavy lifting. 𝐌𝐨𝐝𝐢𝐟𝐲𝐢𝐧𝐠 𝐋𝐢𝐬𝐭𝐬: 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐃𝐚𝐭𝐚 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 This was a big “𝒂𝒉𝒂” moment. List comprehension provides a concise and elegant way to create new lists from existing ones. Instead of writing multiple lines with loops and conditionals, you can express your intent in one clean, readable line. It shifts your thinking from “how do I do this step by step?” to “what do I want to create?”. What I’m learning is that Python isn’t just teaching me syntax - it’s teaching me how to structure data, reason logically, and write code that scales.

Typed Python is a very strong choice as the “main teaching language” for the Lindy-AI Manifesto, as long as you enforce it as truly typed Python (not “Python with vibes”). Python fits your manifesto unusually well because it supports the entire loop: Bash-first orchestration (Python is excellent for Unix-style CLIs) Markdown-based specs (natural pairing with Python tooling + docs culture) SQL-centric data work (SQLite/Postgres integrations are first-class) Tests-first verification (pytest ecosystem is world-class) AI-as-implementer (Python is the language where AI codegen is typically strongest) Why it aligns with “Lindy-AI” Python is “Lindy enough” now. It’s mature, stable, ubiquitous, and has multi-decade institutional adoption. More importantly, it has durable interfaces: file IO, subprocesses, HTTP clients, SQL connectors, and testing frameworks have been stable for years. That stability matters more than any single framework trend. The condition: you must teach Typed Python as a constraint system Your manifesto depends on compiler/type-checker guardrails to contain hallucinations and sloppy assumptions. Python’s type system is optional, so you need to make it mandatory via tooling + rules. If you choose Typed Python as the main language, I recommend this as non-negotiable: Required guardrails (the “Python becomes strict” package) Pyright in --strict mode (primary type checker) pytest for verification-first (TDG loop) ruff for lint + formatting (fast, consistent feedback) Pydantic (or similar) for runtime validation at boundaries (CLI args, JSON, API responses) Rule for students: If it doesn’t pass tests + pyright strict, it’s not done. This turns Python into what you actually want: a spec-driven, verification-centric toolchain. What you gain by teaching Typed Python first 1) Fast iteration without sacrificing rigor You get rapid feedback loops (pytest + typecheck) with minimal boilerplate, which is perfect for “tests are the prompt.” 2) Best-in-class teaching ergonomics Students can focus on problem decomposition, interfaces, and tests, without getting bogged down by ceremony. 3) Great Unix-style tooling Python excels at writing small composable utilities: read stdin transform data write stdout use exit codes properly This matches your “composition over monoliths” principle. 4) SQL integration is natural Python is excellent as the glue language around SQL (SQLite in early chapters, Postgres later). The main risk: Python can “leak” into untyped chaos If you don’t enforce discipline, students will drift into: Any everywhere dynamic dicts with no schema weak interfaces tests that only check happy paths That becomes the opposite of Lindy-AI: AI-generated code + unclear contracts + false confidence. Mitigation: bake strictness into templates + CI from day one.

Prompt Engineering - Stop Thinking You Need to "Learn Python." Start Learning to Speak to It. The biggest barrier to automation isn't syntax; it's logic. Most people think they can’t write a Python script because they don’t know where the brackets go. The Truth: In 2026, the LLM is your Senior Developer. You are the Product Manager. Your job isn't to code; it's to define the "What" and the "How." If you can describe a sandwich, you can prompt a Python script. Here is the framework I use to get perfect, error-free scripts on the first try—even if you’ve never opened a terminal. 1. The "Context First" Rule Don’t just say "Write a script to scrape a website." The AI will guess, and it will guess wrong. Try this: "You are an expert Python developer specializing in BeautifulSoup. I am a marketer who needs to extract product names and prices from [URL] into a CSV file." 2. The Pseudo-Code Method Break your request into "Human Steps." If you can’t explain the logic to a 10-year-old, the AI will struggle. Step A: Open the website. Step B: Look for the HTML tag div class="price". Step C: If the price is over $50, save it. Otherwise, ignore it. 3. Demand "The Safety Net" Non-coders often fail because they don't know how to fix errors. Always add this to your prompt: "Include detailed comments for every line of code and add 'Error Handling' so the script doesn't crash if a webpage fails to load." The "Golden Prompt" Template ----------------------------------- Copy and paste this for your next task: Role: You are a Python automation expert. Goal: Create a script that [Action: e.g., merges 10 Excel files into one]. Requirements: 1. Use the [Library: e.g., Pandas] library. 2. Assume I have no coding environment set up; tell me exactly which libraries to install. 3. Output the result as a [File type: e.g., .xlsx]. Constraint: Keep the code modular and include a 'ReadMe' style comment at the top explaining how to run it. The takeaway: We are moving from the era of "Writing Code" to the era of "Architecting Intent." The most valuable skill today isn't knowing Python; it's knowing how to decompose a problem into instructions. #Leadership #GenerativeAI #AgenticAI #PromptEngineering #LLMOps #MLOps #DevOps #PlatformEngineering #CloudSecurity #FinOps #AgileLeadership #DigitalTransformation #Innovation #SoftwareSupplyChain #TechStrategy #FutureOfWork #Terraform #Docker #Kubernetes #Git #CICD #RAG #Python #Vibecoding #Automation

Python in 2026 feels different. Here’s why. State of Python in 2026, from the perspective of a senior engineer who’s watched this language grow up. Python in 2026 feels very different from the Python many of us started with. What stood out to me most from the latest Python Developers Survey is not just where Python is used, but who is using it and why. Exactly half of Python developers now have less than two years of professional experience. That explains a lot. Python continues to win because it stays approachable. As engineers with more experience, that puts a responsibility on us to design systems that stay simple, readable, and maintainable, even as scale and complexity grow. For years, Python felt neatly split between web development, data, and everything else. That balance is gone. Data processing and AI have clearly pulled Python’s center of gravity toward them. Yet something interesting happened this year. Web development came back strong. Not with the old frameworks dominating, but with FastAPI leading the charge. That makes sense. When data engineers suddenly need APIs, they reach for what feels modern, fast, and production-ready. One uncomfortable truth stood out. Most teams are still running old Python versions. Not because they don’t know better, but because upgrading feels like “something we’ll do later.” The reality is that Python upgrades now translate directly into performance gains. Faster code without changing logic is not a nice-to-have anymore. At scale, it’s real money, real infrastructure cost, and real efficiency. Another quiet shift is how much Rust is now part of Python’s ecosystem. Python hasn’t become slower or weaker. It has become smarter about where performance matters. Rust-backed libraries are increasingly the secret weapon behind high-performance Python systems. Looking ahead, three things feel inevitable. AI coding assistants will become standard, not optional. Free-threaded Python will force many of us to finally take concurrency seriously. And Python stepping closer to native mobile platforms will blur boundaries that once felt permanent. Python in 2025 isn’t just a beginner-friendly language anymore. It’s a serious platform being shaped by data, AI, performance demands, and a massive new generation of developers. The question for senior engineers isn’t whether Python is ready for the future. It’s whether we are ready to use it responsibly at scale. Explore more : https://lnkd.in/eAgz_cNM #Python #SoftwareEngineering #Java #AWS #C2C #Azure #GCP #BackendEngineering #AI #WebDevelopment #FastAPI #CloudEngineering #Developer Korn Ferry Michael Page The Judge Group TEKsystems AVM Consulting Inc Beacon Hill Robert Half KellyOCG

Today I went through my Python basics notes. Sharing some key takeaways that helped me understand things better. First thing I noted was why Python is preferred for AI work. It is one of the easiest programming languages and AI models understand Python more accurately compared to other languages. The interesting part is, before 2022, learning Python meant memorizing syntax. But now with AI tools, you just need to know what you want to do and how to ask. AI helps you write the actual code. I also revised Code vs No Code approach. Code gives you more control over what you build. No code tools let you use drag and drop interfaces with prebuilt templates. Knowing both is useful because sometimes you need flexibility, sometimes you need speed. One concept that stuck with me is the 5 Step Rule for problem solving. Before writing any code, break down the task into 5 simple steps in plain language. For example, to send an email: From, To, Subject, Content, When. Once this is clear, converting it to code becomes much easier with or without AI help. I also revised Python virtual environments. When working on multiple projects, each project uses different package versions. If you install everything globally, packages will conflict and throw errors. Virtual environment keeps each project isolated with its own packages. Simple command to create one is python -m venv yourname and then activate it. Covered the basic building blocks too. Variables store values. Operators do calculations and comparisons. Data types like List, Tuple, Set and Dictionary each have their own use. Lists are changeable and ordered. Tuples cannot be changed once created. Sets remove duplicates automatically. Dictionaries store data in key value pairs which is very useful for handling structured data in AI and app development. Control flow using if, elif, else helps the program make decisions. Loops like for and while help repeat tasks. Functions let you write reusable code blocks instead of repeating same code multiple times. Error handling using try, except, finally is important. It prevents your program from crashing when something goes wrong. Instead of stopping, it can show a friendly message or do something else. File handling lets you read, write and modify files using Python. Useful for automation tasks. Small tip from my notes: Use Google Colab for learning and testing line by line. Use VS Code for actual project work where you write bigger code and run. Human brain is still superior to AI. AI is a tool to increase our creativity and productivity, not replace our thinking. What Python concept took you the longest to understand? . . . #Python #PythonProgramming #LearnPython #PythonBasics #CodingJourney #Programming #VirtualEnvironment #VSCode #GoogleColab #DataTypes #PythonFunctions #ErrorHandling #CodeVsNoCode #AITools #TechLearning #LearningInPublic #PythonForAI #Automation #ProblemSolving #Developer

Python in 2025: Beyond the Hype, the Era of Efficiency and Agents. In 2025, it became clear that “knowing Python” isn’t just about scripts and APIs anymore — it’s about connecting LLMs to the real world, saving tokens, orchestrating agents, and turning documents into AI-ready data. Here’s a LinkedIn-ready recap of the Python libraries that stood out: 🚀 1) MCP Python SDK + FastMCP 2.0 They standardize how apps expose data, tools, and prompts to LLMs (think “REST for AI”). The SDK covers the protocol fundamentals; FastMCP 2.0 adds production-grade features (enterprise auth, deployment tooling, composition, testing utilities). 🧩 2) TOON (Token-Oriented Object Notation) A JSON alternative optimized for LLMs, cutting token usage (especially for arrays) without losing structure. Great for RAG, structured prompts, and large-scale pipelines. 🤖 3) Deep Agents (LangChain/LangGraph) A framework for long-running, multi-step agents with planning (to-dos), filesystem tools, and specialized sub-agents — built for reliability at scale. 🛠️ 4) smolagents (Hugging Face) Agents that “act as code”: the LLM writes actions in Python instead of JSON. More transparency, often fewer steps, and easier to understand what’s happening. 🔁 5) LlamaIndex Workflows An event-driven way to build complex AI flows (loops, parallel runs, conditional branching) using async steps and clean state management. 💸 6) Batchata Unified batch processing across OpenAI / Anthropic / Gemini, with budget limits, dry-runs, persistence, and structured output validation via Pydantic. 📄 7) MarkItDown (Microsoft) Converts PDF, DOCX, PPTX, XLSX, images (OCR), audio (transcription) and more into clean Markdown — perfect for LLM ingestion and RAG pipelines. 📊 8) Data Formulator (Microsoft Research) AI-assisted data exploration + visualization: you design the chart, and it generates the transformations (pandas/SQL) to produce the fields you intended. 🧾 9) LangExtract (Google) Structured extraction with traceability: every extracted entity maps back to the exact span in the source text. Great for healthcare, legal, and audit-heavy use cases. 🌍 10) GeoAI An end-to-end pipeline for geo + AI: fetch imagery, prep datasets, train models (segmentation/classification), run inference, and visualize results (Leafmap), with strong PyTorch/Transformers integration. 2025 was the year Python became the glue between LLMs, data, agents, documents, and production systems. 👉 Which Python library did you use the most in 2025 — or which one surprised you the most? And what did you build with it? Source / Reference: https://lnkd.in/dmpq_W4z #Python #AI #LLM #GenAI #DataEngineering #MachineLearning #RAG #LangChain #LlamaIndex #HuggingFace #MCP