Data Analysis Structure Guide

German version: README_DE.md

Welcome to our Project Structure Guide! This guide is designed to help new students understand how to structure data analysis projects in Python effectively. By following these best practices, you'll create projects that are organized, maintainable, and reproducible.

Table of Contents

1. Project Structure
   • Separating Data and Code
   • Separating Figures and Code
2. Version Control with Git
   • Basic Git Commands
3. Best Practices for Data Analysis Projects
4. Additional Resources
5. Conclusion

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Project Structure

A well-organized project structure is crucial for collaboration and scalability. Here's a recommended directory layout:

project-name/
├── data/
│   ├── model_weights/          # Trained model weights
│   ├── raw/                    # Original, unmodified datasets
│   └── processed/              # Cleaned or transformed data
├── code/
│   └── my_python_program.py    # Python scripts
├── figures/
├── docs/
│   ├── my_fancy_latex_thesis/  # LaTeX files for thesis (advised)
│   ├── my_presentation.pptx    # Presentation slides
│   └── my_thesis.docx          # Word document (not recommended)
├── .gitignore                  # Files and directories for Git to ignore
├── README.md                   # Project overview and instructions
└── requirements.txt            # Python dependencies

Separating Data and Code

• Data Directory (data/): Store all your datasets here.
  • raw/: Original, unmodified datasets.
  • processed/: Data that's been cleaned or transformed.
• Source Code Directory (code/): Contains all the code scripts and modules.

Benefits:

• Organization: Keeps your data separate from code, making it easier to manage.
• Reproducibility: Clear separation ensures that data processing steps are documented and repeatable.
• Collaboration: We/you/collaborators can easily find and understand different components of the project.

Separating Figures and Code

• Figures Directory (figures/): Store all generated plots, images, and visualizations.

Benefits:

• Clarity: Separates output from code, reducing clutter.
• Version Control: Easier to track changes in code without large binary files like images.
• Presentation: Simplifies the process of creating reports or presentations by having all figures in one place.

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Version Control with Git

Git is a powerful version control system that helps you track changes, collaborate with others, and manage your project's history. But what is version control? Have you ever found yourself creating files like project_final_v2.py or project_final_final.py? Version control solves this problem by keeping track of changes and allowing you to revert to previous versions. As a bonus, you'll also have a backup of your project in case something goes wrong.

Basic Git Commands

• Initialize a Repository

  git init
  

• Add Remote Repository (GitHub, Gittea)

  git remote add origin <repository-url>
  

• Clone a Repository

  git clone <repository-url>
  

• Check Status

  git status
  

• Add Changes

  git add <file-name>
  # Or add all changes
  git add .
  

• Commit Changes

  git commit -m "Commit message"
  

• Push to Remote Repository

  git push origin main
  

• Pull from Remote Repository

  git pull origin main
  

  Advanced Git Commands

• Create a New Branch

  git branch <branch-name>
  

• Switch Branches

  git checkout <branch-name>
  

• Merge Branches

  git merge <branch-name>
  

• View Commit History

  git log
  

Tips:

• Commit Often: Regular commits make it easier to track changes.
• Meaningful Messages: Use descriptive commit messages for better understanding.
• Use .gitignore: Exclude files and directories that shouldn't be tracked (e.g., large data files, virtual environments).

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Best Practices for Data Analysis Projects

1. Use Virtual Environments

   • Utilize venv, conda or pyenv to manage project-specific dependencies.
   • Document dependencies in requirements.txt or use poetry for package management.

2. Document Your Work

   • Maintain a clear and informative README.md.
   • Use docstrings and comments in your code.
   • Keep a changelog for significant updates.

3. Write Modular Code

   • Break code into functions and classes.
   • Reuse code to avoid duplication.

4. Follow Coding Standards

   • Adhere to PEP 8 guidelines for Python code.
   • Use linters like flake8 or formatters like black or ruff to maintain code quality.

5. Automate Data Processing

   • Write scripts to automate data cleaning and preprocessing.
   • Ensure scripts can be run end-to-end to reproduce results.

6. Test Your Code

   • Implement unit tests using frameworks like unittest or pytest.
   • Keep tests in the tests/ directory.

7. Handle Data Carefully

   • Do not commit data to version control.

8. Version Your Data and Models

   • Save model versions with timestamps or unique identifiers.

9. Backup Regularly

   • Push changes to a remote repository frequently.
   • Consider additional backups for critical data.

10. Collaborate Effectively

    • Use branches for new features or experiments.
    • Merge changes with pull requests and code reviews.

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Additional Resources

• Git Documentation: git-scm.com/docs
• PEP 8 Style Guide: python.org/dev/peps/pep-0008
• Python Virtual Environments:
  • venv Module
  • Anaconda Distribution
  • pyenv Virtual Environments

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Conclusion

Structuring your data analysis projects effectively is the first step towards successful and reproducible research. By separating data, code, and figures, using version control, and following best practices, you set a strong foundation for your work and collaboration with others.

Happy coding!