Learning Matplotlib for Data Analytics and Visualization with Python

🚀 Day 22/100 – Python, Data Analytics & Machine Learning Journey 📊 Started Matplotlib – The foundation of Data Analytics and visualization Today I learned: 3. Plot Customization in Matplotlib 4. Working with Multiple Plots 5. Advanced Charts 📌 Code & notes :- https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic

📊 Starting my Data Analysis Journey with Python! Today I explored a dataset and used Pandas to display the first 10 rows using df.head(10) in Google Colab. 🔍 Observations from the dataset: • Columns like Survived, Age, Fare • Some missing values (NaN) in the Age column • Useful for practicing data cleaning and analysis 💡 Learning step by step: 🐍 Python 📊 Data Analysis 🧠 Problem Solving #Python #DataAnalysis #Pandas #MachineLearning #DataScience #LearningJourney 🚀

🚀 Day 23/100 – Python, Data Analytics & Machine Learning Journey 📊 Started Seaborn – Mastering Data Visualization for Data Analytics Today I learned: 1. Introduction to Seaborn 2. Basic Plot Types like (Line Plot, Bar Plot, Count Plot, Scatter Plot, Histogram, KDE Plot) 📌 Code & notes :- https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic

🚀 Day 24 – 100 Days of Python & Data Science Today I practiced data preprocessing and cleaning — an important step before building any Machine Learning model. Clean data leads to better analysis and better results. Learning and improving every day 💻✨ #100DaysOfPython #DataScience #Python #MachineLearning 💻GitHub:https://lnkd.in/dUG6qvk5

🚀 Day 34/100 – Python, Data Analytics & Machine Learning Journey 📊 Started Power BI – The Pillar of Data Visualization Today I learned: 7. Pie Chart 8. Donut Chart 9. Scatter Plot 10. Funnel Chart 📌 Code & notes :- https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic

One practical habit that improved my data analysis workflow Before starting any analysis, I create a quick data profiling summary In Python using pandas it takes less than a minute 🗯️ This instantly shows: • statistical distribution • missing data ratio • columns with low or high cardinality It helps me detect problems in the dataset before building any model or visualization #DataAnalysis #Python #DataScience

25 Python 🐍 libraries every data professional should know !!!!! I used to think I needed to learn all of these before I could call myself a Python developer. Turns out, the best way to learn a library is to have a problem that needs it. Start with NumPy + Pandas for data. Add Matplotlib when you need to see it. Reach for Scikit-learn when you want to predict something. The rest follow naturally. Save this for when you need it — and drop a comment with which library you're learning right now 👇 #Python #DataScience #Programming #MachineLearning #DataAnalytics #LearnPython #TechSkills #PythonLibraries #DataEngineering #ContinuousLearning #PythonDeveloper #AI #TechCommunity #UpSkill

🚀 Day 37/100 – Python, Data Analytics & Machine Learning Journey 📊 Started Power BI – The Pillar of Data Visualization Today I learned: 16. Selection Pane 17. Buttons 18. Bookmarks 19. Sync Slicers 20. Visual Interactivity 📌 Code & notes :- https://lnkd.in/dmFHqCrK #100DaysOfPython #MachineLearning #AIML #Python #LearningInPublic

🚀 Day 2 – Data Science Learning Journey Today’s session was all about Matplotlib, one of the most important libraries for data visualization in Python. I explored various functions used to create different types of graphs and plots. It was really interesting to see how raw data can be transformed into meaningful visual insights, making patterns and trends much easier to understand. Every step in this journey is helping me understand how data tells a story through visualization. 📊 #DataScience #Python #Matplotlib #DataVisualization #LearningJourney