Polars vs Pandas: Efficient Data Manipulation in Python

🚀 Python Practice – NumPy Continuing my Python learning journey by stepping into data analysis tools 📊🐍 In this session, I explored NumPy: ✔️ Creating arrays (1D & 2D) ✔️ Array operations and indexing ✔️ Mathematical operations on arrays ✔️ Reshaping and slicing arrays Practiced using NumPy for efficient numerical computations and handling large datasets compared to regular Python lists. Understanding NumPy is helping me work with data faster and perform calculations more efficiently 💡 A big thanks to Krish Naik for his amazing teaching and guidance 🙌 Documented my practice in a Jupyter Notebook and shared it as a PDF to track my progress. Excited to move closer to real-world data analysis 🚀 Next: Pandas and working with datasets 📈 #Python #NumPy #DataAnalytics #LearningJourney #Coding #KrishNaik

Day 1 — Revising Data Science fundamentals Today I revisited Python fundamentals from the very beginning: * Variables & data types * Input/Output * Type casting * Operators (arithmetic, logical, comparison) Applied these concepts by building a basic calculator program Revisiting the basics gave me more clarity than rushing ahead ever could. GitHub: https://lnkd.in/gqJkKJ36 Looking forward to staying consistent and improving every day. #DataScience #Python #LearningInPublic #Consistency

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

I opened a Python EDA cheatsheet this morning and realized something: 𝗜 𝗱𝗶𝗱𝗻'𝘁 𝗻𝗲𝗲𝗱 𝗶𝘁 𝗮𝗻𝘆𝗺𝗼𝗿𝗲. I already knew most of it. Not perfectly. Not without the occasional syntax error. But the 𝗹𝗼𝗴𝗶𝗰 was there. It’s so easy to obsess over the "What’s Missing": • SQL is still sitting on my to-do list. • Machine Learning feels like a challenge some days. • GitHub still gives me the occasional nightmare. 😅 But I forgot to look at the 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀. 𝗗𝗲𝗰𝗲𝗺𝗯𝗲𝗿 𝗠𝗲 couldn’t clean a messy dataset. I couldn't write a simple function. I definitely couldn't run a Random Forest model and actually explain what was happening under the hood. 𝗠𝗮𝗿𝗰𝗵 𝗠𝗲 can. Mostly. The distance between where you are and where you want to be is real. But the distance between where you started and where you are 𝗻𝗼𝘄 is just as significant. Both deserve your attention. It’s okay to be a work in progress. 𝗪𝗵𝗮𝘁’𝘀 𝗼𝗻𝗲 𝘀𝗸𝗶𝗹𝗹 𝘆𝗼𝘂’𝘃𝗲 𝗽𝗶𝗰𝗸𝗲𝗱 𝘂𝗽 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝘁𝗵𝗮𝘁 "𝗣𝗮𝘀𝘁 𝗬𝗼𝘂" 𝘄𝗼𝘂𝗹𝗱 𝗯𝗲 𝗶𝗺𝗽𝗿𝗲𝘀𝘀𝗲𝗱 𝗯𝘆? #DataScience #LearningInPublic #CareerChange #Python #WomenInTech #AcademiaToIndustry

🔢 NumPy Explained (Core of Data Science) NumPy is used for numerical operations. 🔹 Key Functions: ✔ array() → Create arrays ✔ zeros() → Create array of zeros ✔ ones() → Create array of ones ✔ arange() → Range of numbers ✔ reshape() → Change shape of array 💡 NumPy is faster than Python lists and used in almost every Data Science project. #NumPy #Python #DataScience

Part 6: Python Programming in One Page --> statistics for data science. statistics is base for data science. https://lnkd.in/g9aqe-Qp This is Part 6 of the One Page Learning Series. Next: data science fundamentlas in one page Follow Scooplist for more #python #programming #statistics #datascience

Data analysis often goes wrong before the analysis even begins. The ingestion step: where data is pulled from databases, web sources, and APIs: is where silent errors go undetected. Duplicates, nulls, schema mismatches. Episode 3 of the Practical Learning Series covers the patterns, the validation checklist, and the mistakes to avoid. Because reliable analysis starts with trustworthy data. Swipe through → #DataScience #Python #PracticalLearning #Analytics #DataManagement #DataScienceInstitute

🚀 Day-74 of #100DaysOfCode 📊 NumPy Practice – Replacing Negative Values Today I worked on replacing negative values with zero using NumPy. 🔹 Concepts Practiced ✔ Boolean indexing ✔ Array filtering ✔ Data cleaning techniques 🔹 Key Learning NumPy makes it easy to modify data efficiently without loops, which is very useful in real-world data preprocessing tasks. Step by step improving my data handling and NumPy skills 🚀 #Python #NumPy #DataScience #MachineLearning #100DaysOfCode #PythonProgramming