My Anaconda installation was 28GB. I had 12 conda environments I'd forgotten about — one for every tutorial I'd followed, every Kaggle competition I'd started and abandoned, every "let me just try this library real quick" session. Each one had its own copy of NumPy, pandas, and scikit-learn. Three of them had PyTorch installed. That's 2-3GB per copy, sitting in folders I hadn't touched in months. If you do any data science or ML work on a Mac, you've been there. Python's environment model is designed to keep things isolated — and it does, by duplicating everything. That isolation comes at a cost: disk space, and lots of it. This guide covers every place Python stores things on macOS, how big each category typically gets, and how to clean them safely — whether you do it manually or let a tool handle it. Quick Reference Category Path Typical Size Safe to Delete?…