jupyter
Read, modify, execute, and convert Jupyter notebooks programmatically. Use when working with .ipynb files for data science workflows, including editing cells, clearing outputs, or converting to other formats.
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- Dépôt
- OpenHands/extensions
- Dernière activité de la source
- 1 juillet 2026 à 22:49
- Langue détectée de SKILL.md
- anglais
- Étoiles
- 157
- Forks
- 102
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SKILL.md
Instructions source · Aperçu en lecture seule- name
- jupyter
- description
- Read, modify, execute, and convert Jupyter notebooks programmatically. Use when working with .ipynb files for data science workflows, including editing cells, clearing outputs, or converting to other formats.
- triggers
- ["ipynb","jupyter"]
# Jupyter Notebook Guide
Notebooks are JSON files. Cells are in `nb['cells']`, each has `source` (list of strings) and `cell_type` ('code', 'markdown', or 'raw').
## Modifying Notebooks
```python
import json
with open('notebook.ipynb') as f:
nb = json.load(f)
# Modify nb['cells'][i]['source'], then:
with open('notebook.ipynb', 'w') as f:
json.dump(nb, f, indent=1)
```
## Executing & Converting
```bash
jupyter nbconvert --to notebook --execute --inplace notebook.ipynb # Execute in place
jupyter nbconvert --to html notebook.ipynb # Convert to HTML
jupyter nbconvert --to script notebook.ipynb # Convert to Python
jupyter nbconvert --to markdown notebook.ipynb # Convert to Markdown
```
## Finding Code
```bash
grep -n "search_term" notebook.ipynb
```
PowerShell equivalent:
```powershell
Select-String -Path notebook.ipynb -Pattern "search_term"
```
## Cell Structure
```python
# Code cell
{"cell_type": "code", "execution_count": None, "metadata": {}, "outputs": [], "source": ["code\n"]}
# Markdown cell
{"cell_type": "markdown", "metadata": {}, "source": ["# Title\n"]}
```
## Clear Outputs
```python
for cell in nb['cells']:
if cell['cell_type'] == 'code':
cell['outputs'] = []
cell['execution_count'] = None
```
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