Strengthening Python Fundamentals with Dictionaries and Sets

I have been spending more time working with pandas in Python, and honestly, I didn’t realize how powerful it actually is. What started as basic data cleaning slowly turned into understanding how easily large datasets can be transformed, filtered, and structured with just a few lines of code. I’ve been exploring things like: → handling messy data → grouping and aggregations → preparing datasets before analysis And it’s starting to change how I look at data — not just from a reporting side, but how it’s actually processed behind the scenes. Still learning, but definitely enjoying the process of uncovering what pandas can really do. #Python #Pandas #DataAnalytics #Learning #DataEngineering

🚀 My Data Science Journey Today, I focused on strengthening my Python fundamentals 🐍 — the backbone of every Data Science & ML workflow. 📚 Here’s what I covered: 🔹 Python Basics & Syntax 🔹 Operators, If-Else Conditions & Loops 🔹 String Handling in Python 🔹 Practicing real-world string-based problems 💡 Key Learnings: - Writing clean and readable code is more important than just making it work - Loops + conditions = powerful logic building - Strings are everywhere — mastering them is a must! ⏳ Consistency is the real game changer. Small steps every day lead to big results. #Python #DataScience #MachineLearning #CodingJourney #CampusX #100DaysOfCode #LearnInPublic

Many people learn Python and Pandas as tools. But the real transformation happens when you learn Pandas as a way of thinking. Because data isn’t just “numbers in a table”—it’s evidence. And evidence has shape, structure, friction, and sometimes silence (missing values, messy formats, inconsistent categories). When you master core Pandas operations, you stop merely processing datasets… and you start understanding systems. #Python #Pandas #LakkiData #LearningSteps

Week 1 – Learning Progress in Generative AI 🚀 This week I focused on: Python fundamentals for data handling Working with libraries like pandas, numpy and matplotlib Setting up the development environment in VS Code Key takeaway: Understanding the environment setup and libraries is just as important as writing code. Small setup issues can slow you down, but solving them builds confidence. Looking forward to diving deeper into real-world data problems next. #GenerativeAI #Python #LearningJourney #CareerTransition

🚫 𝗦𝘁𝗼𝗽 𝗺𝗮𝗻𝘂𝗮𝗹 𝘀𝗰𝗿𝗮𝗽𝗶𝗻𝗴. 𝗨𝘀𝗲 𝘁𝗵𝗲 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗔𝗣𝗜. I just published a simple guide on Medium about fetching and visualizing YouTube data using Python. 𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: - Getting your API key. - Fetching channel stats. - Visualizing data with Python. - Exporting to Excel. Read the full guide here: https://lnkd.in/gkRijvnS #Python #YouTubeAPI #DataScience #Automation LinkedIn YouTube

🚀 Day 6 of My Python Learning Journey Today I worked with core data structures in Python: ✅ Strings ✅ Lists ✅ Tuples ✅ Sets ✅ Dictionaries 💡 What I practiced: Finding unique characters in a string Merging and sorting lists Detecting duplicate elements Working with tuples and sets Building a menu-driven dictionary program 📌 Key Learning: Understanding how data structures behave is crucial. The same operation can behave very differently depending on the data type (string vs list vs set). I’m improving my problem-solving skills step by step and documenting everything on GitHub. #Python #CodingJourney #LearningInPublic #AI #MachineLearning

Nobody talks about the 80% of time spent cleaning data. So I made the cheat sheet I wish I had. Pretty models don't fix ugly data. Clean it first, thank yourself later. Here's what actually matters before you even think about building a model👇🏼 • Inspect data in seconds • Handle missing values smartly • Clean & transform efficiently • Filter exactly what you need • Aggregate insights fast • Merge datasets seamlessly Day 14/30 #DataScience #Python #DataCleaning #DataAnalytics #MachineLearning #Pandas #100DaysOfCode #LearningInPublic

Today, I started diving into the basics of Python, the programming language at the heart of AI and Machine Learning. I explored different data types like integers, floats, booleans, complex numbers, and strings, and learned the rules for using parentheses and other syntax essentials. My Key Takeaways: Choosing the right data type is critical for correct operations Understanding Python syntax ensures your code runs smoothly These foundational concepts make everything else in AI/ML easier to learn Python may seem simple at first glance, but mastering the basics is the first step to building complex AI solutions. #Python #AI #MachineLearning #DataScience #30DayChallenge #M4ACE

Python is more than just code; it’s a powerful calculator! 🧮 Today, while diving deeper into my Data Science journey, I spent some time mastering Python's mathematical operators. It’s not just about simple math; it's about understanding how the machine processes different operations to build solid business logic. From basic addition to Floor Division and Exponentiation, understanding these basics is crucial for building accurate data models later on at Data Hub. 📊 In this snippet: Handled different types of operations. Explored how Python handles float results vs integers. Question for the experts: What’s the most common mathematical error you faced when you first started coding? 🧐 #DataHub #Python #Coding #DataAnalysis #LearningJourney #TechCommunity

Just wrapped an energizing session teaching data loaders for local LLMs in Python! We mapped out Text Loaders pulling PDFs, JSONs, CSVs, and TXTs into strings or key-value trees via #LangChain, Image Loaders converting to binary formats, plus #OCR magic on pure-image #PPTX files using Python’s pptx loader—and Pandas crushing CSV/XLS flows. Total game-changer for building rock-solid AI pipelines! Tell me how do you think about texts on whiteboard? #Python #AI #LocalLLM #whiteboardKnowledge