Arguing with Data in Backend Engineering

Excited to announce that #Python is now part of #Psicle’s ecosystem of industrialisation and automation! This unlocks the ability for businesses to harness Python scripts in a governed, auditable, and scalable way - bringing confidence and control to advanced modelling.

Worked on core development fundamentals today. Backend • Implemented a centralized error handling mechanism for consistent API responses Data Fundamentals • Practiced MCQs on hypothesis testing • Covered t-test, z-test, chi-square test, ANOVA, and related statistical concepts Staying consistent with backend reliability and data analysis basics. #BackendDevelopment #ErrorHandling #APIDevelopment #Python #Statistics #HypothesisTesting #LearningInPublic

🐍 Day 72 – NumPy Indexing, Slicing & Boolean Masking Code can be correct. Logic can be sound. And performance can still suffer — if you think one element at a time. Today, I focused on shifting how I work with data in NumPy — moving from loop-based thinking to true array-based computation. What I explored today: ✅ NumPy indexing for fast, direct access to data ✅ Array slicing that scales effortlessly across large datasets ✅ Boolean masking to filter data without explicit loops ✅ Vectorized operations outperform traditional Python patterns ✅ Thinking in arrays simplifies both code and logic Why this matters: ✅ Cleaner code with fewer loops and conditionals ✅ Massive performance gains on large datasets ✅ More expressive data transformations with less effort Key takeaway: NumPy isn’t just faster Python — it’s a different way of thinking. Stop processing values one by one. Start operating on the entire dataset at once. Python journey continues… onward and upward! #MyPythonJourney #NumPy #Python #DataAnalytics #LearningInPublic #AnalyticsJourney

Are you using R, Python, SAS — all in one workflow? Then you need the Exponential Experimentation guide! This guide will walk you through on how to break down multilingual modeling actually works in SAS Viya Workbench, with cross-language code examples and architecture that you can use immediately. #Developers #ModelDevelopment #MLOps #Python #RStats

Choosing the wrong data structure is a common source of inefficiency in Python codebases. It's not just about making the code run; it's about performance, memory usage, and communicating intent to other developers. I designed this infographic to visualize the core "Python Data Ecosystem" and the trade-offs between the four fundamental structures. Quick Breakdown: 🔹 LISTS: Your go-to for ordered sequences where items need to change or grow. 🔹 TUPLES: Crucial for ensuring data integrity. If it shouldn't change during execution, lock it in a tuple. 🔹 SETS: Highly efficient for mathematical operations (unions, intersections) and guaranteeing uniqueness. 🔹 DICTIONARIES: The backbone of fast data retrieval using key-value pairs. Mastering the distinction between mutable (changeable) and immutable (fixed) types is the first step toward writing robust Pythonic code. What’s the most interesting use case you’ve found for Python Sets in a production environment? Share your thoughts below. #Python #SoftwareEngineering #DataScience #CodingBestPractices #TechnicalSkills #DataStructures #ProgrammingData #codeayan

I wrote an article about Python packages. Yes — probably the most boring Python feature imaginable 🤣 But while working on my data quality engine, I realized how critical this “boring” topic is: • clean project structure • testable classes and functions • data pipelines that don’t collapse as they grow This is the next part of my series on data pipelines & quality, and it’s all about building solid foundations. I’m surprisingly proud of this one — even if it’s not the sexiest topic on the internet. If you care about long-term maintainability, here it is: 🔗 https://lnkd.in/ea3idttQ #Python #data #pipelines #Pydantic #protocols #SoftwareArchitecture #QA #packages #DataScience #AI

I open-sourced a Python implementation of Google’s DS-STAR - a multi-agent data science pipeline that answers natural-language questions over your data. No need to clean or script anything yourself; point at a data directory and ask. Works with Gemini or OpenAI, includes examples (including DABstep), and you get the generated code and logs so answers are auditable. 🔗 Repo: https://lnkd.in/eVSGvd_X 📖 Read more (blog): https://lnkd.in/ekdhjmm5 📄 Paper (arXiv): https://lnkd.in/e7AbFcmU

Python is not that bad performer , but .... I was delivering a session on #Rust for an #analytics team (not mentioning the company name , dont want to disclouse their stack). I said Rust can be 20x–200x faster than #Python for many workloads. Someone challenged that with a real example: 15M records Python: 44s (Using #pandas) Rust: 39s (using #polars) Fair question: why such a small difference? Because in analytics, Python often isn’t doing the work. Libraries like #NumPy / Pandas execute in #C/#C++ and vectorized code. If the workload is already optimized or memory-bound, Rust has limited headroom. Rust shines when: Tight loops Custom algorithms CPU-bound, low-latency pipelines Lesson learned: Changing the #technology stack can yield great results, but it’s never automatic. #Performance depends on many factors, not just the language. What do you say?

As developers, our most valuable asset is Time. Why write 100 lines of code when a library can do it in 1 line? I curated 10 Python libraries that I use to build AI apps faster and better. Here are my top picks from the slides: Streamlit: Turned my Python scripts into web apps instantly. No HTML needed. Rich: Because staring at black-and-white terminal logs is boring. Icecream: The smartest way to debug (Goodbye print("test")) Typer: Building CLIs has never been this beautiful. (Swipe through the carousel to see all 10 gems 👉) 💡 Pro Tip: If you are building data apps, Streamlit is a non-negotiable tool in 2026. #Python #SoftwareEngineering #Productivity #AI #DataScience #CodingBestPractices #MuthukumarPl #CodeWithMK

It was 3am when the pager blared: Stripe needed real-time analytics over trillions of events with sub-100ms latency. From that 3am moment to a sub-100ms dash, a tiny Python trick changed the game. Dashboards and alerts had to process multi-petabyte streams in near real-time. Python 3.13 with free-threading and a JIT opened new horizons, but sub-100ms latency remained elusive. Here's what we learned: 🔍 Data locality matters more than brute force ⚡ JIT + threading unlock micro-latency when I/O is overlapped 🎯 Reuse buffers and minimize serialization overhead Takeaway: Small, well-targeted Python tweaks can deliver sub-100ms dashboards at scale. ───────────────────────── 🔗 Read the full article: https://lnkd.in/gjzrdKW4 🎯 Practice interview questions: https://lnkd.in/gmy5drNw #python