237 lines
6.5 KiB
Markdown
237 lines
6.5 KiB
Markdown
# Data Analysis Structure Guide
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**German version**: [README_DE.md](README_de.md)
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Welcome to our Project Structure Guide! This guide is designed to
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help new students understand how to structure data analysis projects in Python
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effectively. By following these best practices, you'll create projects that are
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organized, maintainable, and reproducible.
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## Table of Contents
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1. [Project Structure](#project-structure)
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- [Separating Data and Code](#separating-data-and-code)
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- [Separating Figures and Code](#separating-figures-and-code)
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2. [Version Control with Git](#version-control-with-git)
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- [Basic Git Commands](#basic-git-commands)
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3. [Best Practices for Data Analysis Projects](#best-practices-for-data-analysis-projects)
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4. [Additional Resources](#additional-resources)
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5. [Conclusion](#conclusion)
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---
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## Project Structure
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A well-organized project structure is crucial for collaboration and
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scalability. Here's a recommended directory layout:
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```
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project-name/
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├── data/
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│ ├── model_weights/ # Trained model weights
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│ ├── raw/ # Original, unmodified datasets
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│ └── processed/ # Cleaned or transformed data
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├── code/
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│ └── my_python_program.py # Python scripts
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├── figures/
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├── docs/
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│ ├── my_fancy_latex_thesis/ # LaTeX files for thesis (advised)
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│ ├── my_presentation.pptx # Presentation slides
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│ └── my_thesis.docx # Word document (not recommended)
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├── .gitignore # Files and directories for Git to ignore
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├── README.md # Project overview and instructions
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└── requirements.txt # Python dependencies
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```
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### Separating Data and Code
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- **Data Directory (`data/`)**: Store all your datasets here.
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- `raw/`: Original, unmodified datasets.
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- `processed/`: Data that's been cleaned or transformed.
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- **Source Code Directory (`code/`)**: Contains all the code scripts and modules.
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**Benefits:**
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- **Organization**: Keeps your data separate from code, making it easier to manage.
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- **Reproducibility**: Clear separation ensures that data processing steps are documented and repeatable.
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- **Collaboration**: We/you/collaborators can easily find and understand different components of the project.
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### Separating Figures and Code
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- **Figures Directory (`figures/`)**: Store all generated plots, images, and visualizations.
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**Benefits:**
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- **Clarity**: Separates output from code, reducing clutter.
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- **Version Control**: Easier to track changes in code without large binary files like images.
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- **Presentation**: Simplifies the process of creating reports or presentations by having all figures in one place.
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---
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## Version Control with Git
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Git is a powerful version control system that helps you track changes,
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collaborate with others, and manage your project's history. But what is version
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control? Have you ever found yourself creating files like `project_final_v2.py`
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or `project_final_final.py`? Version control solves this problem by keeping
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track of changes and allowing you to revert to previous versions.
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As a bonus, you'll also have a backup of your project in case something goes wrong.
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### Basic Git Commands
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- **Initialize a Repository**
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```bash
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git init
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```
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- **Add Remote Repository (GitHub, Gittea)**
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```bash
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git remote add origin <repository-url>
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```
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- **Clone a Repository**
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```bash
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git clone <repository-url>
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```
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- **Check Status**
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```bash
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git status
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```
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- **Add Changes**
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```bash
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git add <file-name>
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# Or add all changes
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git add .
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```
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- **Commit Changes**
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```bash
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git commit -m "Commit message"
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```
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- **Push to Remote Repository**
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```bash
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git push origin main
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```
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- **Pull from Remote Repository**
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```bash
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git pull origin main
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```
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#### Advanced Git Commands
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- **Create a New Branch**
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```bash
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git branch <branch-name>
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```
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- **Switch Branches**
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```bash
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git checkout <branch-name>
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```
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- **Merge Branches**
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```bash
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git merge <branch-name>
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```
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- **View Commit History**
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```bash
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git log
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```
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**Tips:**
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- **Commit Often**: Regular commits make it easier to track changes.
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- **Meaningful Messages**: Use descriptive commit messages for better understanding.
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- **Use `.gitignore`**: Exclude files and directories that shouldn't be tracked (e.g., large data files, virtual environments).
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---
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## Best Practices for Data Analysis Projects
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1. **Use Virtual Environments**
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- Utilize `venv`, `conda` or `pyenv` to manage project-specific dependencies.
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- Document dependencies in `requirements.txt` or use `poetry` for package management.
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2. **Document Your Work**
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- Maintain a clear and informative `README.md`.
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- Use docstrings and comments in your code.
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- Keep a changelog for significant updates.
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3. **Write Modular Code**
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- Break code into functions and classes.
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- Reuse code to avoid duplication.
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4. **Follow Coding Standards**
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- Adhere to PEP 8 guidelines for Python code.
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- Use linters like `flake8` or formatters like `black` or `ruff` to maintain code quality.
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5. **Automate Data Processing**
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- Write scripts to automate data cleaning and preprocessing.
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- Ensure scripts can be run end-to-end to reproduce results.
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6. **Test Your Code**
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- Implement unit tests using frameworks like `unittest` or `pytest`.
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- Keep tests in the `tests/` directory.
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7. **Handle Data Carefully**
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- Do not commit data to version control.
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8. **Version Your Data and Models**
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- Save model versions with timestamps or unique identifiers.
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9. **Backup Regularly**
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- Push changes to a remote repository frequently.
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- Consider additional backups for critical data.
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10. **Collaborate Effectively**
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- Use branches for new features or experiments.
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- Merge changes with pull requests and code reviews.
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---
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## Additional Resources
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- **Git Documentation**: [git-scm.com/docs](https://git-scm.com/docs)
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- **PEP 8 Style Guide**: [python.org/dev/peps/pep-0008](https://www.python.org/dev/peps/pep-0008/)
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- **Python Virtual Environments**:
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- [`venv` Module](https://docs.python.org/3/library/venv.html)
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- [Anaconda Distribution](https://www.anaconda.com/products/distribution)
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- [`pyenv` Virtual Environments](https://github.com/pyenv/pyenv)
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---
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## Conclusion
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Structuring your data analysis projects effectively is the first step towards
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successful and reproducible research. By separating data, code, and figures,
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using version control, and following best practices, you set a strong
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foundation for your work and collaboration with others.
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Happy coding!
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