Reproducible Analysis

What You'll Learn

Why reproducibility matters
Project structure
Requirements files
Documentation (Docstrings & Markdown)
Version control basics (Git)

Why Reproducibility?

"It works on my machine" is not good enough.

Collaboration: Others need to run your code.
Future You: You will forget what you did in 6 months.
Trust: Science requires verification.

Project Structure

A standard structure helps everyone navigate.

my_project/
├── data/
│   ├── raw/          # Immutable original data
│   └── processed/    # Cleaned data
├── notebooks/        # Jupyter notebooks for exploration
├── src/              # Reusable Python scripts
│   ├── __init__.py
│   ├── data_cleaning.py
│   └── modeling.py
├── requirements.txt  # Dependencies
├── README.md         # Project overview
└── .gitignore        # Files to ignore

Managing Dependencies

Always list your libraries.

Creating requirements.txt:

terminal

pip freeze > requirements.txt

Installing from requirements:

terminal

pip install -r requirements.txt

Documentation

Code Comments: Explain why, not what. Docstrings: Explain functions.

code.py

def calculate_metrics(y_true, y_pred):
    """
    Calculates MSE and R2 score.

    Args:
        y_true (array): Actual values
        y_pred (array): Predicted values

    Returns:
        dict: Dictionary containing MSE and R2
    """
    pass

README.md:

Project Title
Description
Installation instructions
Usage examples
Credits

Version Control (Git)

git init: Start tracking.
git add .: Stage changes.
git commit -m "message": Save snapshot.
git push: Upload to GitHub/GitLab.

Important: Add data/ and .env to your .gitignore file! Never commit large data or passwords.

Next Steps

Let's make our insights pop with advanced visualizations!