Best Practices

Best Practices

Working Efficiently

1. Start small: Test your code on subsets of data before processing entire datasets
2. Save frequently: Use Ctrl+S (Cmd+S on Mac) to save your work
3. Document your work: Use markdown cells to explain your analysis

Performance Considerations

• Large files may take time to load
• Consider sampling data for initial exploration
• Use efficient libraries like pandas for data manipulation
• Monitor memory usage for large datasets

Collaboration

While notebooks are personal workspaces, you can share your work by:

• Exporting notebooks as HTML or PDF
• Copying code snippets to share with colleagues
• Creating reproducible analysis workflows