AgentSkillsCN

data-science-notebooks

数据科学的交互式笔记本：Jupyter、JupyterLab与marimo。可用于探索性分析、可重复研究、文档编写，以及与利益相关者分享见解。

SKILL.md
--- frontmatter
name: data-science-notebooks
description: "Interactive notebooks for data science: Jupyter, JupyterLab, and marimo. Use for exploratory analysis, reproducible research, documentation, and sharing insights with stakeholders."
dependsOn: ["@data-science-eda", "@data-engineering-core"]

Interactive Notebooks

Use this skill for creating reproducible, well-structured notebooks for data exploration, analysis, and communication.

When to use this skill

  • •Exploratory analysis — interactively investigate data
  • •Reproducible research — document methodology with code and results
  • •Teaching/demos — explain concepts with executable examples
  • •Stakeholder communication — share insights with narrative + visuals
  • •Prototyping — quickly iterate on data transformations or models

Tool selection

ToolBest ForKey Feature
JupyterLabTraditional data science, extensions ecosystemFull IDE experience
marimoReproducible notebooks, reactive executionPython-native, version-control friendly
VS Code + JupyterIDE-native notebook experienceIntellisense, debugging, git integration
Google ColabCloud GPUs, sharing, collaborationFree TPU/GPU, easy sharing

Core principles

1) Structure for readability

markdown
# Title: Clear project/question description

## Setup
Imports and configuration

## Data Loading
Load and validate data

## Analysis
- Subsection per question/hypothesis
- Clear markdown explanations
- Visualizations with interpretations

## Conclusions
Key findings and next steps

2) Ensure reproducibility

python
# Set random seeds
import numpy as np
import random

np.random.seed(42)
random.seed(42)

# Pin versions in requirements.txt or environment.yml
# requirements.txt example:
# pandas==2.1.0
# scikit-learn==1.3.0

3) Keep cells focused

  • •One concept per cell
  • •Avoid cells with >50 lines
  • •Refactor helper functions to .py files

4) Never hardcode secrets

python
# ✅ Use environment variables
import os

api_key = os.environ.get("OPENAI_API_KEY")

# ❌ Never do this
api_key = "sk-abc123..."

Jupyter best practices

Magic commands (Jupyter/IPython)

python
# In a Jupyter cell (these are IPython magics, not standard Python)
# Auto-reload modules during development
# %load_ext autoreload
# %autoreload 2

# Timing
# %timeit function_call()

# Debugging
# %debug

# Environment info (requires watermark package)
# %watermark -v -m -p numpy,pandas,sklearn

Clean outputs before git

bash
# Using nbstripout
pip install nbstripout
nbstripout --install

# Or pre-commit hook
pip install pre-commit
pre-commit install

marimo advantages

Reactive execution

python
# marimo notebook - cells auto-recompute when dependencies change
import marimo as mo

slider = mo.ui.slider(1, 100, value=50)
slider  # Display the slider

# This cell re-runs automatically when slider changes
df_filtered = df[df['value'] > slider.value]

Version control friendly

  • •Pure Python (.py files)
  • •No output blobs in git
  • •Readable diffs

Convert Jupyter to marimo

bash
marimo convert notebook.ipynb -o notebook.py

Common anti-patterns

  • •❌ Running cells out of order (Jupyter)
  • •❌ Giant cells with mixed concerns
  • •❌ Hardcoded file paths
  • •❌ No markdown explanations
  • •❌ Committing large output files
  • •❌ Inline data (use data/ folder)

Progressive disclosure

  • •../references/jupyter-advanced.md — Widgets, extensions, debugging
  • •../references/marimo-guide.md — Reactive patterns, UI components
  • •../references/notebook-testing.md — Unit tests for notebook code
  • •../references/sharing-publishing.md — nbconvert, Quarto, Voilà

Related skills

  • •@data-science-eda — Exploration patterns for notebooks
  • •@data-science-interactive-apps — Convert notebooks to apps
  • •@data-engineering-core — Production-ready code patterns

References