Learning Notebook Conversion

Learning Notebook Conversion

Objectives

• Convert .ipynb to .py while preserving structure and documentation
• Convert .py to .ipynb with proper cell organization
• Maintain code quality and academic formatting during conversion

Instructions

1. Conversion Methods

Method A: VS Code Built-in (Recommended for single files)

1. Open .ipynb file
2. Press Ctrl+Shift+P
3. Select "Notebook: Export to Python Script"
4. Choose save location

Method B: Jupyter nbconvert (Recommended for batch/automation)

# .ipynb to .py
jupyter nbconvert --to python notebook.ipynb

# .py to .ipynb
jupytext --to notebook script.py

Method C: Python script (For custom processing)

import nbformat
from nbconvert import PythonExporter

# Read notebook
with open('notebook.ipynb', 'r', encoding='utf-8') as f:
    nb = nbformat.read(f, as_version=4)

# Convert to Python
exporter = PythonExporter()
source, meta = exporter.from_notebook_node(nb)

# Write to file
with open('output.py', 'w', encoding='utf-8') as f:
    f.write(source)

2. .ipynb → .py Conversion

What gets converted:

• Code cells → Python code
• Markdown cells → Multi-line comments ("""...""")
• Cell separators → # %% markers (VS Code interactive cells)
• Outputs → Removed (code only)

Post-conversion cleanup:

1. Add file-level docstring (if missing):

   """
   Course Code Lab X: Title
   Author: [Student Name]
   Section: [Section Number]
   Date: [Date]
   """
   

2. Review cell markers:

   • Keep # %% for logical sections
   • Remove unnecessary markers
   • Add descriptive comments after markers

3. Convert markdown to docstrings:

   • Section headers → Comments
   • Important notes → Inline comments
   • Instructions → Function docstrings

4. Adjust outputs:

   • Change display() to print()
   • Ensure plt.show() for plots
   • Remove IPython-specific magic commands

Example conversion:

# Before (in .ipynb markdown cell):
# ## Step 1: Load Data
# Load the dataset and display first 5 rows

# After (in .py):
# %% Step 1: Load Data
# Load the dataset and display first 5 rows
import pandas as pd
df = pd.read_csv('data.csv')
print(df.head())

3. .py → .ipynb Conversion

Using jupytext:

# Install jupytext
pip install jupytext

# Convert with cell markers
jupytext --to notebook script.py

# Convert and execute
jupytext --to notebook --execute script.py

Cell organization rules:

• # %% markers → New code cells
• Multi-line strings at top → Markdown cells
• Section comments → Markdown headers
• Code blocks → Code cells

Post-conversion tasks:

1. Run all cells to generate outputs

2. Add markdown cells for better documentation:

   • Title and metadata at top
   • Section headers before major steps
   • Explanations for complex logic

3. Verify cell order:

   • Logical flow maintained
   • No circular dependencies
   • Outputs display correctly

4. Format markdown cells:

   ## Step 1: Import Libraries
   
   Import required packages for data analysis.
   

4. Batch Conversion

Convert multiple files:

# All .ipynb to .py in directory
jupyter nbconvert --to python *.ipynb

# All .py to .ipynb
for file in *.py; do jupytext --to notebook "$file"; done

Python script for batch processing:

import os
from pathlib import Path
import nbformat
from nbconvert import PythonExporter

def convert_notebooks_to_py(directory):
    """Convert all .ipynb files in directory to .py"""
    for ipynb_file in Path(directory).glob('*.ipynb'):
        with open(ipynb_file, 'r', encoding='utf-8') as f:
            nb = nbformat.read(f, as_version=4)

        exporter = PythonExporter()
        source, _ = exporter.from_notebook_node(nb)

        py_file = ipynb_file.with_suffix('.py')
        with open(py_file, 'w', encoding='utf-8') as f:
            f.write(source)

        print(f"Converted: {ipynb_file} → {py_file}")

# Usage
convert_notebooks_to_py('labs/')

5. Conversion Best Practices

Before conversion:

• Save and checkpoint current work
• Run all cells (for .ipynb) to verify execution
• Clear outputs if file size is large
• Commit to version control

After conversion:

• Test execution in target format
• Verify all imports work
• Check file paths are correct
• Validate outputs match original
• Update documentation if needed

Maintain consistency:

• Keep both formats if needed for different use cases
• Use version control to track changes
• Document which format is "source of truth"
• Sync changes between formats when updating

6. Format-Specific Considerations

When to use .ipynb:

• Interactive exploration and visualization
• Teaching and presentations
• Sharing with outputs visible
• Iterative development

When to use .py:

• Version control (cleaner diffs)
• Automated testing and CI/CD
• Production deployment
• Command-line execution
• Code review

Hybrid approach:

• Develop in .ipynb for interactivity
• Convert to .py for submission/version control
• Use jupytext to sync both formats automatically

Validation

After conversion, verify:

Code Execution

• Converted file runs without errors
• All imports resolve correctly
• File paths work in new format
• Outputs match original (if applicable)

Structure Preservation

• Logical sections maintained
• Comments and documentation intact
• Function definitions complete
• Variable scope correct

Academic Requirements

• File-level docstring present (for .py)
• Author and metadata included
• Step order follows assignment
• Submission format requirements met

Common Issues

Issue: Cell markers (# %%) not recognized

• Solution: Install Python extension in VS Code
• Or: Use Jupyter extension for interactive cells

Issue: Markdown cells lost in conversion

• Solution: Use jupytext instead of basic nbconvert
• Or: Manually convert important markdown to docstrings

Issue: Relative imports broken

• Solution: Check working directory and adjust paths
• Or: Use absolute imports with package structure

Issue: Magic commands cause errors in .py

• Solution: Remove or comment out IPython magic commands:
  • %matplotlib inline → Remove or comment
  • !pip install → Convert to regular shell command
  • %%time → Use time module instead

Issue: Outputs don't display in .py

• Solution: Replace display() with print()
• Add plt.show() for matplotlib plots
• Use explicit print statements for results

Installation Requirements

# Basic conversion
pip install jupyter nbconvert

# Advanced conversion with sync
pip install jupytext

# For Python API
pip install nbformat

Example Workflow

User request: "把这个 notebook 转成 .py 文件"

Your workflow:

1. Identify the .ipynb file (from context or ask user)
2. Choose conversion method based on needs:
   • Single file → VS Code export or nbconvert
   • Batch → Script or command-line loop
   • Custom processing → Python script
3. Execute conversion
4. Perform post-conversion cleanup:
   • Add/verify docstrings
   • Clean up cell markers
   • Test execution
5. Validate against checklist
6. Inform user of completion and any issues

User request: "生成这个 .py 的 notebook 版本"

Your workflow:

1. Identify the .py file
2. Use jupytext to convert
3. Open in Jupyter/VS Code
4. Run all cells to generate outputs
5. Add markdown cells for documentation
6. Verify cell organization
7. Save and inform user