11.4 Git Version Control Basics

Managing Code, Protecting Your Work

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Why Git?

• Track code changes
• Revert to previous versions
• Multi-person collaboration
• Backup code

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Basic Operations

Initialize Repository

# Initialize
git init

# Configure user information
git config --global user.name "Your Name"
git config --global user.email "your.email@example.com"

Basic Workflow

# 1. Check status
git status

# 2. Add files
git add analysis.py
git add .  # Add all files

# 3. Commit
git commit -m "Add data analysis script"

# 4. View history
git log
git log --oneline

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Common Commands

# View differences
git diff

# Revert changes
git checkout -- file.py  # Discard unstaged changes
git reset HEAD file.py   # Unstage

# View commit history
git log --oneline
git show <commit-id>

# Revert to specific version
git reset --hard <commit-id>

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GitHub Collaboration

# Clone repository
git clone https://github.com/username/repo.git

# Push to remote
git push origin main

# Pull updates
git pull origin main

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Best Practices

1. .gitignore

# .gitignore file
*.pyc
__pycache__/
.ipynb_checkpoints/
data/
.env
*.log

2. Commit Message Standards

# Good commit messages
git commit -m "Add income data cleaning function"
git commit -m "Fix age filtering bug"

# Poor commit messages
git commit -m "update"
git commit -m "fix"

3. Common Workflow

# Daily workflow
git status          # Check status
git add .           # Add all changes
git commit -m "Describe changes"  # Commit
git push            # Push to remote (if applicable)

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Social Science Research Project Example

research_project/
├── .git/
├── .gitignore
├── README.md
├── data/              # Don't upload (.gitignore)
│   ├── raw/
│   └── processed/
├── code/
│   ├── 01_data_cleaning.py
│   ├── 02_analysis.py
│   └── 03_visualization.py
├── notebooks/
│   └── exploratory_analysis.ipynb
├── outputs/
│   ├── figures/
│   └── tables/
└── requirements.txt

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Congratulations on Completing All 40 Lessons!

You Have Mastered:

• Python basic syntax
• Data structures and functions
• File operations and exception handling
• Pandas data analysis
• Data visualization
• Machine learning and LLM APIs
• Best practices

Next Steps:

1. Practice projects (analyze real data)
2. Read others' code (GitHub)
3. Contribute to open-source projects
4. Continuous learning

Begin your Python data analysis journey!

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Recommended Resources

• Python Official Documentation
• Pandas Documentation
• Scikit-learn Tutorial
• Real Python
• Stack Overflow

Happy Learning!