jupyter

# Jupyter Notebook Skill ## Description Create and execute Jupyter notebooks for interactive data analysis and visualization. ## Tools Used - `jupyter_execute` - Execute Python code in Jupyter kernel (auto-switches to Jupyter) - `jupyter_notebook` - Create, read, update, delete, and list notebooks - `update_notebook` - Add or update cells in the notebook without executing - `update_gallery` - Display generated plots and visualizations in gallery view - `update_data_grid` - Display structured tabular data (DataFrames, query results) in AG Grid - `update_code` - Show code examples and scripts in the Code Playground - `save_artifact` - Save generated artifacts (plots, data files) to workspace collection ## Capabilities - Create new notebooks with proper structure - Add and execute code cells - Add markdown documentation cells - Display inline visualizations - Display tabular data in interactive grid view - Show code examples with syntax highlighting - Export to various formats (HTML, PDF) ## Usage Patterns ### Create Analysis Notebook When user says: "Create a notebook for [analysis]" 1. Create notebook with title and imports 2. Add data loading cell 3. Add exploration cells 4. Structure with markdown headers 5. Execute cells sequentially ### Execute and Debug When user says: "Run this code" 1. Execute cell 2. Capture output and errors 3. If error, diagnose and fix 4. Show results or visualizations ### Document Workflow When user says: "Add explanation for this step" 1. Add markdown cell before code 2. Explain methodology 3. Note assumptions and limitations ## Tool Examples ### Execute Python code ``` jupyter_execute code="import pandas as pd\ndf = pd.read_csv('/workspace/data/results.csv')\nprint(df.describe())" ``` ### Create a notebook with cells ``` update_notebook cells=[{"type": "markdown", "source": "# Analysis"}, {"type": "code", "source": "import pandas as pd\nimport matplotlib.pyplot as plt"}] execute=false ``` ### Display generated plots ``` update_gallery images=[{"url": "/workspace/data/plot.png", "title": "Analysis Results"}] ``` ## Best Practices 1. **Cell Independence**: Each cell should run independently when possible 2. **Import First**: All imports at notebook start 3. **Check output before proceeding**: Verify execution output is correct before running dependent cells 4. **Markdown Structure**: Use headers for navigation 5. **Save Often**: Checkpoint regularly