Fast Pandas Sanity Checks with mini-expectations

Functions are the building blocks of any serious Python application. They allow us to organize code into manageable, reusable chunks. If you are just starting out, the syntax can sometimes be confusing. Here is a quick cheat sheet on how to structure them properly. The Flow: Start with the def keyword. Give it a clear, descriptive name (like greet_person shown below). Pass data in using arguments. Send data back using return. Once you master this, you are ready to tackle modules and classes! #PythonLearning #CodingTips #DeveloperCommunity #DataScience #TechEducation

Know #Python #Pandas series methods? You already know data frame methods! s.mean() → returns a single value df.mean() → returns a series (one per column) s.fillna(0) → returns a series df.fillna(0) → returns a data frame Same methods, applied column-wise!

Today was all about going deeper into Python fundamentals 🐍💡 📌 What I covered today: 🔹 Scope (LEGB Rule) Understood how Python searches for variables and why scope matters for clean and predictable code. 🔹 Closures Learned how inner functions can remember variables from their enclosing scope even after the outer function has finished execution — powerful concept for state management and decorators. 🔹 OOPS – Class & Object Explored why classes are used over only functions: - Classes act as blueprints - Objects are real instances - Better structure, data protection, scalability, and real-world modeling - Also clarified how __init__ works and how each object maintains its own state. 👉 Revisiting fundamentals really changes how you think about writing better, cleaner code. Learning step by step, one concept at a time 🚀 #Python #LearningInPublic #PythonBasics #OOPS #Closures #Scope #Programming #DeveloperJourney

Built a Simple CLI Calculator with Python Today I practiced core Python fundamentals by building a small command-line calculator that performs basic operations like addition, subtraction, and multiplication based on user input. ♦️Concepts I reinforced: • User input handling • Type conversion (int) • Conditional logic (if statements) • Clean output formatting Even simple projects like this help strengthen problem-solving skills and build confidence in writing structured, readable code. Next step: expanding this into a more robust version with error handling and division support. #Python #LearningByDoing #DataAnalytics #CodingJourney #Azure #dataengineering #data

Excited to share my new open-source Python library: WBClean-XUM 🎉 If you’ve ever had to parse “creative” Excel sheets, you know the pain: shifted headers, blank columns, unpredictable layouts. WBClean-XUM is my attempt to make that workflow less painful with: ✅ Sheet-level transpose (entire used range — not just table transpose) ✅ Row/column cleanup helpers ✅ Regex-based column detection (ex: qty / quantity) ✅ XLS → XLSX conversion using Windows Excel automation ✅ Output to a clean pandas DataFrame 📦 Install: pip install WBClean-XUM PyPI: https://lnkd.in/d5mf742d Source: https://lnkd.in/dCztFGED If you try it and hit edge cases or have an idea or suggestion, drop feedback at https://lnkd.in/dWpnKVUA — I’m actively improving it. #python #opensource #excel #pandas #automation #datapipelines #github #trending #production

✓ Week 4 Complete From scripts to software. This week was about structure - modules, packages, virtual environments. The stuff that turns messy code into something reusable. Built llm-test-utils - a Python package that bundles everything from the past 4 weeks: → Prompt formatters (Week 1) → Test data organizers (Week 2) → Response validators (Week 3) → All wrapped in a clean package structure (Week 4) Phase 0 (Python Foundation) Still stacking bricks. Code's in the repo - link in comments. #GenAITesting #LearnInPublic #LLMTesting #Python #AITesting #QAEngineer #LearningInPublic #GenAI #SoftwareTesting #52WeekChallenge

Python Tip of the Day 🐍 sep() and end() in print() help control how output is formatted. sep changes the separator between values, and end controls how the line finishes. Small parameters — but very useful for clean output formatting. Day 5 of building Python basics. #PythonDaily #PythonBasics #DataAnalytics #LearningPython

Pandas is the easiest Python libraries to learn! After working with a lot of data, I noticed my EDA relies on just 12 core functions. This is what I use most of the time. P.S: Excel can do this too (pandas just makes it faster and more scalable).

Extracting a text's composition in terms of characters and the number of occurrences is quite straightforward using Python's collections module. Using SQL and without any fancy functions is another story. Swipe to see how I've done it before. Have you needed to do something similar? If so, how did you go about it? #SQL #DataEngineering