marimo-editor

What this skill does

# Marimo Notebook Assistant

This skill provides specialized guidance for creating data science notebooks using marimo's reactive programming model. Focus on creating clear, efficient, and reproducible data analysis workflows.

## Core Capabilities

- Data science and analytics using marimo notebooks
- Complete, runnable code that follows best practices
- Reproducibility and clear documentation
- Interactive data visualizations and analysis
- Understanding of marimo's reactive programming model

## Marimo Fundamentals

Marimo is a reactive notebook that differs from traditional notebooks in key ways:
- Cells execute automatically when their dependencies change
- Variables cannot be redeclared across cells
- The notebook forms a directed acyclic graph (DAG)
- The last expression in a cell is automatically displayed
- UI elements are reactive and update the notebook automatically

## Code Requirements

1. All code must be complete and runnable
2. Follow consistent coding style throughout
3. Include descriptive variable names and helpful comments
4. Import all modules in the first cell, always including `import marimo as mo`
5. Never redeclare variables across cells
6. Ensure no cycles in notebook dependency graph
7. The last expression in a cell is automatically displayed, just like in Jupyter notebooks
8. Don't include comments in markdown cells
9. Don't include comments in SQL cells

## Reactivity

Marimo's reactivity means:
- When a variable changes, all cells that use that variable automatically re-execute
- UI elements trigger updates when their values change without explicit callbacks
- UI element values are accessed through `.value` attribute
- Cannot access a UI element's value in the same cell where it's defined

## Best Practices

### Data Handling

- Use pandas for data manipulation
- Implement proper data validation
- Handle missing values appropriately
- Use efficient data structures
- A variable in the last expression of a cell is automatically displayed as a table

### Visualization

- For matplotlib: use `plt.gca()` as the last expression instead of `plt.show()`
- For plotly: return the figure object directly
- For altair: return the chart object directly
- Include proper labels, titles, and color schemes
- Make visualizations interactive where appropriate

### UI Elements

- Access UI element values with `.value` attribute (e.g., `slider.value`)
- Create UI elements in one cell and reference them in later cells
- Create intuitive layouts with `mo.hstack()`, `mo.vstack()`, and `mo.tabs()`
- Prefer reactive updates over callbacks (marimo handles reactivity automatically)
- Group related UI elements for better organization

### Data Sources

- Prefer GitHub-hosted datasets (e.g., raw.githubusercontent.com)
- Use CORS proxy for external URLs: https://corsproxy.marimo.app/<url>
- Implement proper error handling for data loading
- Consider using `vega_datasets` for common example datasets

### SQL

- When writing duckdb, prefer using marimo's SQL cells, which start with `_df = mo.sql(query)`
- See the SQL with duckdb example for an example on how to do this
- Don't add comments in cells that use `mo.sql()`
- Consider using `vega_datasets` for common example datasets

## Troubleshooting

Common issues and solutions:
- Circular dependencies: Reorganize code to remove cycles in the dependency graph
- UI element value access: Move access to a separate cell from definition
- Visualization not showing: Ensure the visualization object is the last expression

## Available UI Elements

* `mo.ui.altair_chart(altair_chart)`
* `mo.ui.button(value=None, kind='primary')`
* `mo.ui.run_button(label=None, tooltip=None, kind='primary')`
* `mo.ui.checkbox(label='', value=False)`
* `mo.ui.date(value=None, label=None, full_width=False)`
* `mo.ui.dropdown(options, value=None, label=None, full_width=False)`
* `mo.ui.file(label='', multiple=False, full_width=False)`
* `mo.ui.number(value=None, label=None, full_width=False)`
* `mo.ui.radio(options, value=None, label=None, full_width=False)`
* `mo.ui.refresh(options: List[str], default_interval: str)`
* `mo.ui.slider(start, stop, value=None, label=None, full_width=False, step=None)`
* `mo.ui.range_slider(start, stop, value=None, label=None, full_width=False, step=None)`
* `mo.ui.table(data, columns=None, on_select=None, sortable=True, filterable=True)`
* `mo.ui.text(value='', label=None, full_width=False)`
* `mo.ui.text_area(value='', label=None, full_width=False)`
* `mo.ui.data_explorer(df)`
* `mo.ui.dataframe(df)`
* `mo.ui.plotly(plotly_figure)`
* `mo.ui.tabs(elements: dict[str, mo.ui.Element])`
* `mo.ui.array(elements: list[mo.ui.Element])`
* `mo.ui.form(element: mo.ui.Element, label='', bordered=True)`

## Layout and Utility Functions

* `mo.md(text)` - display markdown
* `mo.stop(predicate, output=None)` - stop execution conditionally
* `mo.Html(html)` - display HTML
* `mo.image(image)` - display an image
* `mo.hstack(elements)` - stack elements horizontally
* `mo.vstack(elements)` - stack elements vertically
* `mo.tabs(elements)` - create a tabbed interface

## Examples

### Basic UI with Reactivity

```python
# Cell 1
import marimo as mo
import matplotlib.pyplot as plt
import numpy as np

# Cell 2
# Create a slider and display it
n_points = mo.ui.slider(10, 100, value=50, label="Number of points")
n_points  # Display the slider

# Cell 3
# Generate random data based on slider value
# This cell automatically re-executes when n_points.value changes
x = np.random.rand(n_points.value)
y = np.random.rand(n_points.value)

plt.figure(figsize=(8, 6))
plt.scatter(x, y, alpha=0.7)
plt.title(f"Scatter plot with {n_points.value} points")
plt.xlabel("X axis")
plt.ylabel("Y axis")
plt.gca()  # Return the current axes to display the plot
```

### Data Explorer

```python
# Cell 1
import marimo as mo
import pandas as pd
from vega_datasets import data

# Cell 2
# Load and display dataset with interactive explorer
cars_df = data.cars()
mo.ui.data_explorer(cars_df)
```

### Multiple UI Elements

```python
# Cell 1
import marimo as mo
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Cell 2
# Load dataset
iris = sns.load_dataset('iris')

# Cell 3
# Create UI elements
species_selector = mo.ui.dropdown(
    options=["All"] + iris["species"].unique().tolist(),
    value="All",
    label="Species"
)
x_feature = mo.ui.dropdown(
    options=iris.select_dtypes('number').columns.tolist(),
    value="sepal_length",
    label="X Feature"
)
y_feature = mo.ui.dropdown(
    options=iris.select_dtypes('number').columns.tolist(),
    value="sepal_width",
    label="Y Feature"
)

# Display UI elements in a horizontal stack
mo.hstack([species_selector, x_feature, y_feature])

# Cell 4
# Filter data based on selection
filtered_data = iris if species_selector.value == "All" else iris[iris["species"] == species_selector.value]

# Create visualization based on UI selections
plt.figure(figsize=(10, 6))
sns.scatterplot(
    data=filtered_data,
    x=x_feature.value,
    y=y_feature.value,
    hue="species"
)
plt.title(f"{y_feature.value} vs {x_feature.value}")
plt.gca()
```

### Interactive Chart with Altair

```python
# Cell 1
import marimo as mo
import altair as alt
import pandas as pd

# Cell 2
# Load dataset
cars_df = pd.read_csv('https://raw.githubusercontent.com/vega/vega-datasets/master/data/cars.json')
_chart = alt.Chart(cars_df).mark_point().encode(
    x='Horsepower',
    y='Miles_per_Gallon',
    color='Origin',
)

chart = mo.ui.altair_chart(_chart)
chart

# Cell 3
# Display the selection
chart.value
```

### Run Button Example

```python
# Cell 1
import marimo as mo

# Cell 2
first_button = mo.ui.run_button(label="Option 1")
second_button = mo.ui.run_button(label="Option 2")
[first_button, second_button]

# Cell 3
if first_button.value:
    print("You chose option 1!")
elif second_button.value:
    print("You chose option 2!")
else:
    print("Click a button!")
```

### SQL with DuckDB

```python
# Cell 1
impor