Learn Matplotlib with Estrella Popoca

𝗽𝗮𝗻𝗱𝗮𝘀 𝟯.𝟬: 𝗧𝗵𝗲 𝗘𝗻𝗱 𝗼𝗳 𝗦𝗲𝘁𝘁𝗶𝗻𝗴𝗪𝗶𝘁𝗵𝗖𝗼𝗽𝘆𝗪𝗮𝗿𝗻𝗶𝗻𝗴 New Feature: new default string dtype 🤖Problem When you filter a DataFrame and modify the result, you expect the original to stay unchanged. But sometimes pandas modified your original data anyway, triggering the SettingWithCopyWarning. 🌝Solution pandas 3.0 fixes this. Filtering now always creates a separate copy, so modifying the result never affects your original data. Upgrade to pandas 3.0 with “pip install -U pandas”. #data #dataanalysis #Pandas3 #datascience #tech #python

📅 Day 21/30 – Matplotlib (Data Visualization) Today I learned Matplotlib, a powerful Python library used for data visualization. What I covered: • Introduction to Matplotlib • Line plots • Bar charts • Pie charts • Labels, titles, and legends • Customizing graphs It was exciting to turn raw data into meaningful visual insights 📊 📚 Learning resource: HackerBytez – https://lnkd.in/gzKTANVt Visualization makes data easier to understand and analyze 🚀 #Day21 #PythonChallenge #30DaysOfPython #Matplotlib #DataVisualization #Python #LearningInPublic #CodingJourney

Pandas Advanced – Part 7 🐼📊 This video focuses on how analysts think, not just syntax. Instead of jumping into code, we learn how to: Clean data correctly Avoid misleading insights Ask better analytical questions If you’re learning Pandas for real-world data analysis, this part is important. ▶️ Watch: https://lnkd.in/gT2xC4EE 📁 GitHub: https://lnkd.in/gdzNcMaT #Pandas #DataAnalysis #Python #Analytics #LearningInPublic #PyAIHub

Pandas 3.0 is here! 🎉https://lnkd.in/dfAUP2bH - Copy-on-Write (CoW) fully implemented: SettingWithCopyWarning is gone ✅. No more debugging mysterious copies - chained assignments just work - pd.col() syntax: Clean column references in assign() and loc() without messy lambdas. E.g., df.assign(c=pd.col('a') + pd.col('b')) - Faster UDFs 🚀: No more "slow as molasses" user-defined functions - major perf boosts via better optimization (full Arrow backend didn't land, but it's solid) I made a Kaggle notebook to try https://lnkd.in/d-SsfryV #Pandas #DataScience #Python #DataAnalysis #MachineLearning

📊 If you know Python but can’t visualize data… you’re missing HALF the story! Most beginners stop at print() Professionals use Matplotlib to tell stories with data 🐍📈 From simple line charts ➝ powerful business insights This single library can turn raw numbers into decisions. 🚀 Save this post 💬 Comment “MATPLOTLIB” for practice examples 🔁 Repost to help your data friends #Python #Matplotlib #DataVisualization #DataAnalytics #DataScience #LearningInPublic #CareerGrowth #mdluqmanali

📊 New Video: Pandas Advanced – Part 5 Advanced Indexing & Query Thinking is one of the most misunderstood areas in Pandas — and also one of the most important in real-world analysis. In this video, I cover: • .loc vs .iloc with clear examples • Label-based vs position-based indexing • How to think like an analyst when querying data • Common mistakes that silently break results 🎥 Watch here: https://lnkd.in/gTaT9s5p 📂 GitHub (code & notebooks): https://lnkd.in/gNFk2iPa Sharing this for anyone learning Pandas beyond the basics. #pyaihub #DataAnalysis #Python #PandasAdvanced

𝐅𝐢𝐫𝐬𝐭 𝐓𝐢𝐦𝐞 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐚 𝐌𝐞𝐬𝐬𝐲 𝐃𝐚𝐭𝐚𝐬𝐞𝐭📊 Today I worked on a raw dataset in Python using pandas — and honestly, cleaning the data took more effort than analyzing it. Dealt with missing values, inconsistent formats, duplicate entries, and weird column names. It made me realize that real-world data is rarely clean, and most of the work actually happens before any “analysis” begins. Still learning, but this felt like a real step forward from just theory. #Python #Pandas #DataAnalytics #RealLearning

Day 89 – Data Analytics Project (Continuation) 📊 Continued my project by analyzing screen time distribution across age groups using a box plot in Jupyter Notebook. 🔎 Key insight: Screen time varies significantly between age groups, and the distribution (median, quartiles, outliers) tells a deeper story than just averages. Learning to move from plotting graphs → to extracting meaningful insights. #Day89 #DataAnalytics #Python #DataVisualization #LearningJourney

Leveling up my Pandas game 📊🐼 This cheat sheet is a lifesaver for anyone working with data in Python—from loading datasets and filtering rows to groupby, aggregation, and exporting results. Simple, clean, and super practical for daily data analysis tasks. Whether you’re just starting with data science or polishing your data analytics skills, mastering Pandas is a must. Consistency + practice = progress 🚀 #Pandas #Python #DataScience #DataAnalytics #MachineLearning #LearningJourney #DataSkills #CheatSheet #KeepLearning

🐼 pandas 3.0 is LIVE! The Python data library just got a MAJOR upgrade. Here's what you need to know: ✨ Native String Type - PyArrow-backed by default for faster ops 🔒 Copy-on-Write - Now the only mode (goodbye warnings!) 💻 pd.col() Syntax - Cleaner, more readable code ⚡ Performance Boost - Faster across the board ⚠️ Breaking changes ahead! Upgrade to 2.3 first, fix warnings, then move to 3.0. Swipe to see what's new 👉 Have you tried pandas 3.0? Drop your thoughts below! 💭 #Python #Pandas #DataScience #MachineLearning #DataAnalysis #Python3 #DataEngineering #Analytics #OpenSource #TechNews