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

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

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

grep -n "search_term" notebook.ipynb

Cell Structure

# Code cell
{"cell_type": "code", "execution_count": None, "metadata": {}, "outputs": [], "source": ["code\n"]}
# Markdown cell
{"cell_type": "markdown", "metadata": {}, "source": ["# Title\n"]}

Clear Outputs

for cell in nb['cells']:
    if cell['cell_type'] == 'code':
        cell['outputs'] = []
        cell['execution_count'] = None

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.

Triggers

This skill is activated by the following keywords:

• ipynb
• jupyter

Details

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

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

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

grep -n "search_term" notebook.ipynb

Cell Structure

# Code cell
{"cell_type": "code", "execution_count": None, "metadata": {}, "outputs": [], "source": ["code\n"]}
# Markdown cell
{"cell_type": "markdown", "metadata": {}, "source": ["# Title\n"]}

Clear Outputs

for cell in nb['cells']:
    if cell['cell_type'] == 'code':
        cell['outputs'] = []
        cell['execution_count'] = None