Aggregating DataFrames with Pandas

I’ve started my journey into the world of Data Science and AI, and I’m excited to share my notes from Session 1. It’s amazing how a few simple shortcuts and understanding the "Kernel" can make coding so much smoother A special thanks to my mentor Bhavya Rohilla for the insightful session! Looking forward to learning more. #Python #DataScience #ArtificialIntelligence #LearningJourney #JupyterNotebook #CodingBasics

Day 27/30 to learn python for data analysis Understanding your data is the first step in Data Science 📊 Today, I explored the Titanic dataset and checked for missing values using Pandas. 🔍 Key Insights: age has 177 missing values deck has 688 missing values (major data gap) Few missing values in embarked and embark_town #Python #DataScience #DataAnalysis #Pandas #DataCleaning #MachineLearning #Analytics #LearnPython #CodingJourney #100DaysOfCode #BeginnerDataScientist #TitanicDataset #AI #TechLearning #DataPreprocessing Handling missing data is crucial before building any model. Learning step by step and improving every day 🚀

Pandas is not just a library, it’s a superpower for anyone working with data. 🐼 From loading files to cleaning, transforming, and analyzing — a few lines of code can do what used to take hours. Mastering functions like groupby(), merge(), and pivot_table() can seriously level up your data game. Small functions. Big impact. 🚀 #DataAnalytics #Python #Pandas #DataScience #LearningEveryday

DAY 30/30 TO LEARN PYTHON FOR DATA ANALYSIS Understanding data using GroupBy in Pandas 📊 Analyzed the Titanic dataset to see how passengers are distributed across different classes using: 👉 groupby() + count() 💡 Insight: Most passengers were in 3rd class Fewer passengers in 1st and 2nd class Also learned: ✔️ count() ignores missing values ✔️ GroupBy helps in summarizing data quickly Small insights like these help build strong analytical thinking 🚀 #Python #DataScience #Pandas #DataAnalysis #MachineLearning #AI #DataAnalytics #LearnPython #CodingJourney #100DaysOfCode #BeginnerDataScientist #GroupBy #DataPreprocessing #TechLearning #Analytics

🚀 Day 2 of My AI/ML Engineer Journey Today, I explored one of the most powerful Python libraries — NumPy. 🔍 What I learned: NumPy stands for Numerical Python Designed for fast operations on large datasets 💡 Why NumPy over Python lists? ⚡ Faster (contiguous memory) 💾 Memory efficient 🧩 Easy to work with 📊 Supports multi-dimensional arrays 📈 Rich mathematical & statistical functions This is where data handling starts getting serious. Excited to go deeper into data analysis next! 📌 Consistency is key. Learning step by step. Building daily. 🔖 Hashtags: #Day2 #AIJourney #MachineLearning #NumPy #Python #DataScience #LearningInPublic #DeveloperJourney #100DaysOfCode #AIEngineer #CodingLife #TechGrowth #SoftwareDeveloper #DataAnalysis #AbishekSathiyan

Day 70 of the #three90challenge 📊 Today I started learning Pandas — one of the most powerful libraries for data analysis in Python. After working with NumPy arrays, Pandas takes things further by making data easier to organize, analyze, and manipulate. What I explored today: • Introduction to Series and DataFrames • Loading data into Pandas • Viewing and understanding dataset structure • Basic operations on tabular data Example thinking: NumPy works with arrays. Pandas works with real-world datasets. Example: import pandas as pd data = {"Name": ["A", "B", "C"], "Age": [25, 30, 22]} df = pd.DataFrame(data) print(df) This is where data starts to feel structured and analysis-ready. From numerical operations → to real data analysis 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #Pandas #DataAnalytics #LearningInPublic #Consistency #Upskilling

📊 My First Machine Learning Project — CGPA vs Salary Prediction! I built a Linear Regression model in Python that predicts student salary packages based on CGPA. 🔍 What I did: ✅ Exploratory Data Analysis ✅ Trained a Linear Regression model ✅ Evaluated predictions with % error ✅ Visualized the regression line 🔧 Tools: Python | Pandas | Scikit-learn | Matplotlib 🔗 Full project on GitHub: https://lnkd.in/dEtZaUdm #MachineLearning #Python #DataScience #LinearRegression #FirstProject

If you want to improve your Data Science and Python skills, this course is for you. You'll use popular Python libraries like Pandas, scikit-learn, and NumPy to extract and clean data, then analyze it. You'll also learn about grouping & aggregation functions, merging datasets, and using regex, plus some Machine Learning techniques, too. https://lnkd.in/gK3gfthg

📘 Day 2 of My Data Science Journey Yesterday, I learned the basics of NumPy and Pandas — two very powerful libraries in Python for data handling and analysis. Key takeaways: • NumPy helps in working with arrays and performing fast mathematical operations • Pandas makes it easy to handle datasets (like CSV files) • Learned how to read data, explore it, and perform basic operations It feels great to start understanding how real-world data is handled. Excited to keep learning and building! #DataScience #Python #NumPy #Pandas #LearningJourney

𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗠𝗟 𝗠𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵 𝗬𝗲𝗹𝗹𝗼𝘄𝗯𝗿𝗶𝗰𝗸! 📊 Yellowbrick is a Python library that provides useful visualizations for machine learning models. For example, regression models can be visualized with a prediction error plot or Cook's distance, whereas ROC/AUC curves and the confusion matrix are suitable for classification models. Furthermore, Yellowbrick can be installed by itself, or alternatively used with the PyCaret library that integrates its functionality. Have you ever utilized Yellowbrick to visualize machine learning models? Visit the links below for more information, and make sure to follow me for regular data science content! 𝗬𝗲𝗹𝗹𝗼𝘄𝗯𝗿𝗶𝗰𝗸 𝘄𝗲𝗯𝘀𝗶𝘁𝗲: https://lnkd.in/enK2fQ2D 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #deeplearning #machinelearning