Interactive Python Learning with marimo

Explore our collection of interactive notebooks for Python, data science, and machine learning.

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Why Learn with Marimo?

Interactive Learning

Learn by doing with interactive notebooks that run directly in your browser.

Practical Examples

Real-world examples and applications to reinforce your understanding.

Comprehensive Curriculum

From Python basics to advanced machine learning concepts.

Explore Our Courses

Learn Altair

Learn the basics of Altair, a high-performance visualization library, using lessons developed at the University of Washington.

  1. Introduction to Altair
  2. Data Types, Graphical Marks, and Visual Encoding Channels
  3. Data Transformation
  4. Scales, Axes, and Legends
  5. Multi-View Composition
  6. Interaction
  7. Cartographic Visualization
  8. Altair Debugging Guide

Learn DuckDB

These notebooks teach you the basics of DuckDB, a fast in-memory database engine that can interoperate with dataframes, and show how marimo gives DuckDB superpowers.

  1. What is DuckDB?
  2. Loading Parquet files with DuckDB
  3. Loading JSON
  4. Working with Apache Arrow

Learn Optimization

Learn the basics of convex optimization using Python, and see how to apply these ideas to vehicle control, portfolio allocation in finance, and other areas.

  1. Least Squares
  2. Linear Program
  3. Minimal Fuel Optimal Control
  4. Quadratic Program
  5. Portfolio Optimization
  6. Convex Optimization
  7. Semidefinite Program

Learn Polars

Learn the basics of data wrangling with a high-performance Python library called Polars.

  1. An introduction to Polars
  2. DataFrames
  3. Loading Data
  4. Basic operations on data
  5. Reactive Plots
  6. Polars with Marimo's Dataframe Transformer
  7. SQL Features in Marimo and Polars
  8. Working with Columns
  9. Data Types
  10. Strings
  11. Dealing with Missing Data
  12. Aggregations
  13. Window Functions
  14. User-Defined Functions
  15. Lazy Execution

Learn Probability

These marimo notebooks teach the fundamental of probability with an emphasis on interactive learning and computation in Python.

  1. Sets
  2. Axioms of Probability
  3. Probability of Or
  4. Conditional Probability
  5. Independence in Probability Theory
  6. Probability of And
  7. Law of Total Probability
  8. Bayes' Theorem
  9. Random Variables
  10. Probability Mass Functions
  11. Expectation
  12. Variance
  13. Bernoulli Distribution
  14. Binomial Distribution
  15. Poisson Distribution
  16. Continuous Distributions
  17. Normal Distribution
  18. Central Limit Theorem
  19. Maximum Likelihood Estimation
  20. Naive Bayes Classification
  21. Logistic Regression

Learn Python

These notebooks will help you learn the basics of Python programming in an easy, interactive way.

  1. Numbers
  2. Strings
  3. Collections
  4. Conditional logic
  5. Loops
  6. Dictionaries
  7. Advanced Collections
  8. Functions
  9. Using Modules
  10. Handling Errors

Learn Queueing Theory

Why is your line always slower than the other one? Why do traffic jams happen without any apparent cause? These lessons use a mixture of queueing theory and simulation to explain these scenarios and others.

  1. Basic Ideas in Queueing Theory
  2. Queue Formation
  3. Little's Law
  4. Sojourn Time
  5. M/M/1 Queue Nonlinearity
  6. Pooled vs. Separate Queues
  7. Late Merge
  8. The Inspector's Paradox
  9. The Convoy Effect
  10. Priority Starvation
  11. Tandem Queue Blocking
  12. Rush Hour Displacement
  13. Braess's Paradox

Learn SQL

Learn the basics of SQL, the industry standard for interacting with relational databases. These notebooks also show how easy it is to work with relational data in marimo.

  1. Basic Selection
  2. Filtering
  3. Aggregating and Grouping
  4. Missing Data
  5. Combining Tables
  6. Primary and Foreign Keys

Want to Contribute?

Help us improve these learning materials by contributing to the GitHub repository. We welcome new content, bug fixes, and improvements!

Contribute on GitHub