Marimo Development
by aiskillstore
Expert guidance for creating and working with marimo notebooks - reactive Python notebooks that can be executed as scripts and deployed as apps. Use when the user asks to create marimo notebooks, convert Jupyter notebooks to marimo, build interactive dashboards or data apps with marimo, work with marimo's reactive programming model, debug marimo notebooks, or needs help with marimo-specific features (cells, UI elements, reactivity, SQL integration, deploying apps, etc.).
Skill Details
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name: marimo-development description: Expert guidance for creating and working with marimo notebooks - reactive Python notebooks that can be executed as scripts and deployed as apps. Use when the user asks to create marimo notebooks, convert Jupyter notebooks to marimo, build interactive dashboards or data apps with marimo, work with marimo's reactive programming model, debug marimo notebooks, or needs help with marimo-specific features (cells, UI elements, reactivity, SQL integration, deploying apps, etc.).
Marimo Development
Create reactive Python notebooks with marimo's interactive programming environment.
Core Workflow
- Start with fundamentals: Read
references/core-concepts.md- contains marimo's cell structure, reactivity model, UI elements, and essential examples - Use recipes for common tasks: Check
references/recipes.mdfor code snippets - Refer to API docs: Navigate
references/api/for specific function details - Troubleshoot issues: See
references/faq.mdandreferences/troubleshooting.md
Key Marimo Concepts
Cell Structure
Every marimo cell follows this structure:
@app.cell
def _():
# Your code here
return
When editing cells, only modify the code inside the function - marimo handles parameters and returns automatically.
Reactivity Rules
- Automatic execution: When a variable changes, cells using it automatically re-run
- No redeclaration: Variables cannot be redeclared across cells
- DAG structure: Cells form a directed acyclic graph (no circular dependencies)
- Last expression displays: The final expression in a cell is automatically shown
- UI reactivity: UI element values accessed via
.valuetrigger automatic updates - Local variables: Variables prefixed with
_(e.g.,_temp) are local to the cell
Import Pattern
Always import marimo in the first cell:
@app.cell
def _():
import marimo as mo
# other imports
return
Common Tasks
Creating Interactive UIs
# Create UI element in one cell
@app.cell
def _():
slider = mo.ui.slider(0, 100, value=50, label="Value")
slider
return
# Use its value in another cell
@app.cell
def _():
result = slider.value * 2
mo.md(f"Double the value: {result}")
return
Working with Data
# Load and display data
@app.cell
def _():
import polars as pl
df = pl.read_csv("data.csv")
df # Automatically displays as table
return
# Interactive data exploration
@app.cell
def _():
mo.ui.data_explorer(df)
return
SQL with DuckDB
@app.cell
def _():
# marimo has built-in DuckDB support
result = mo.sql(f"""
SELECT * FROM df WHERE column > 100
""")
return
Layouts
@app.cell
def _():
# Horizontal stack
mo.hstack([element1, element2, element3])
# Vertical stack
mo.vstack([top, middle, bottom])
# Tabs
mo.tabs({"Tab 1": content1, "Tab 2": content2})
return
Visualization Best Practices
- matplotlib: Use
plt.gca()as last expression (notplt.show()) - plotly: Return the figure object directly
- altair: Return the chart object; add tooltips; accepts polars dataframes directly
Reference Documentation
Use references/NAVIGATION.md to understand the complete documentation structure. Key references:
Essential Reading
- core-concepts.md - Start here for fundamentals and examples
- recipes.md - Code snippets for common tasks
Detailed Guides
- reactivity.md - Deep dive into reactive execution
- interactivity.md - Building interactive UIs
- best_practices.md - Coding standards for marimo
Working with Data
- working_with_data/sql.md - SQL and DuckDB integration
- working_with_data/dataframes.md - pandas, polars, etc.
- working_with_data/plotting.md - Visualization libraries
Deployment
- apps.md - Deploy as interactive web apps
- scripts.md - Run as Python scripts with CLI args
API Reference
- api/inputs/ - All UI elements (slider, dropdown, button, table, etc.)
- api/layouts/ - Layout components (tabs, accordion, sidebar, etc.)
- api/control_flow.md - Cell execution control
- api/state.md - State management
- api/caching.md - Performance optimization
Troubleshooting
- faq.md - Common questions and solutions
- troubleshooting.md - Error fixes
- debugging.md - Debugging techniques
Common Pitfalls
- Circular dependencies: Reorganize code to remove cycles
- UI value access: Can't access
.valuein the same cell where UI element is defined - Variable redeclaration: Each variable can only be defined once across all cells
- Visualization not showing: Ensure visualization object is the last expression
- Global keyword: Never use
global- violates marimo's execution model
After Creating a Notebook
Run marimo check --fix to automatically catch and fix common formatting issues and detect pitfalls.
Quick Reference: Most Used UI Elements
mo.ui.slider(start, stop, value=None, label=None)
mo.ui.dropdown(options, value=None, label=None)
mo.ui.text(value='', label=None)
mo.ui.button(value=None, kind='primary')
mo.ui.checkbox(label='', value=False)
mo.ui.table(data, sortable=True, filterable=True)
mo.ui.data_explorer(df) # Interactive dataframe explorer
mo.ui.dataframe(df) # Editable dataframe
mo.ui.form(element, label='') # Wrap elements in a form
mo.ui.array(elements) # Array of UI elements
See references/api/inputs/index.md for the complete list.
Quick Reference: Layout Functions
mo.md(text) # Display markdown
mo.hstack(elements) # Horizontal layout
mo.vstack(elements) # Vertical layout
mo.tabs(dict) # Tabbed interface
mo.stop(predicate, output=None) # Conditional execution
mo.output.append(value) # Append to output
mo.output.replace(value) # Replace output
See references/api/layouts/index.md for all layout options.
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