A hands-on interactive learning platform for Data Scientists to learn, write, run, and understand Matplotlib, Seaborn, Plotly & Pandas Plotting — directly in your browser. No installation. No server. No cost.
import matplotlib.pyplot as plt import seaborn as sns import pandas as pd # Load sample data df = sns.load_dataset('iris') # Create scatter plot sns.scatterplot( data=df, x='sepal_length', y='petal_length', hue='species', palette='Set2' ) plt.title('Iris Dataset') plt.show()
Step-by-step lessons organized by library and difficulty. Each lesson contains the concept, annotated code, explanation of every parameter, and a "Why it matters" section linking to real data science workflows.
Browse Lessons →Write and run Python visualization code directly in your browser using Pyodide (WebAssembly). Zero setup required. See your charts instantly.
Open Playground →Quick-reference syntax cards for every major chart type across all four libraries. Bookmark, search, and copy snippets in one tap.
View Cheat Sheet →Test what you've learned with code-reading quizzes. Identify chart types, fix broken code, predict outputs — all based on real examples.
Take a Quiz →Browse 50+ ready-to-use chart examples with full code. Filter by chart type, library, or dataset. Click any chart to open it in the playground.
View Gallery →Paste any matplotlib/seaborn code into the annotator and get a line-by-line breakdown explaining what each parameter does, why it's used, and what happens if you change it. Perfect for learning from existing code in tutorials, Kaggle notebooks, or Stack Overflow.
Try Annotator →A guided sequence that starts with chart recognition and gradually moves into parameter control, debugging, storytelling, and capstone work. Learners tick milestones and track completion locally in their browser.
Open Roadmap →Embedded synthetic datasets with analysis questions, recommended chart types, CSV downloads, and one-click starter code for the Playground.
Practice with Data →Six portfolio-style projects from beginner score reports to advanced operations diagnostics. Each includes a problem brief, required outputs, rubric, and starter code.
Build Projects →A transparent rule-based assistant that recommends chart types and helps learners produce insight, evidence, action, and limitation notes — without using any AI API.
Tell the Data Story →Upload a small CSV privately in the browser, inspect inferred column types, receive rule-based chart suggestions, and generate Python code that opens in the Playground.
Upload CSV →Generate classroom-ready lesson plans, activity timing, assessment prompts, and rubrics using transparent templates — useful for HMG Academy, bootcamps, YouTube classes, and school workshops.
Plan a Class →Search the full platform, save personal notes, review flashcards, check visualization quality, and generate printable learning certificates — all locally in the browser with no account and no backend.
Open Learning Tools →A complete beginner-to-expert visualization curriculum with modules, outcomes, concepts, activities, assessments, projects, study plan generator, local progress tracking, and syllabus export for cohorts or self-paced learning.
Open Curriculum →Generate training proposals, weekly schemes of work, facilitator checklists, attendance CSV, completion register, and assessment score templates using only free browser tools.
Generate Training Pack →A local-first administration layer for schools, academies, NGOs and bootcamps: organisation profile, cohort manager, learner CSV import/export, aggregate progress analytics, assignment builder, issue register, audit log and governance guidance.
Open Enterprise Console →A service worker and web manifest make the static pages installable and cacheable on supported browsers. The platform remains free because it uses GitHub Pages/Cloudflare Pages, localStorage, and browser execution.
Track & Export Progress →The foundation of Python visualization. Learn figures, axes, subplots, plot types, styling, and full customization control.
Statistical visualization made elegant. Understand distributions, relationships, and categorical data with beautiful defaults.
Interactive charts for the modern data scientist. Hover, zoom, filter — all in the browser with expressive Python code.
Quick exploratory visualization built right into Pandas DataFrames. Fast EDA without switching contexts.
Each lesson explains a chart type with the concept, when to use it in data science, common pitfalls, and full annotated code with every parameter explained.
Every lesson has a "Run in Playground" button. Execute the code in your browser and see the actual chart output — no Jupyter, no Colab, no installation needed.
Change parameters, swap datasets, try different palettes. The playground gives instant feedback so you build intuition through experimentation.
After each lesson, a short quiz challenges you to read code, identify chart types, spot errors, and explain outputs — just like you would in a real project.