Explore a reproducible and scalable Python data analysis pipeline for revealing tumor heterogeneity in this 28-minute EuroPython Conference talk. Dive into the world of single-cell DNA sequencing and learn how to infer the evolutionary history of copy number alterations in tumors. Discover the pipeline's components, including Python, Conda environment management, and Snakemake workflow management system. Understand how this approach addresses reproducibility issues in computationally expensive cancer research and aids in personalized cancer therapies. Follow the process from raw sequencing files to the generation of reports and figures that inform treatment decisions for cancer patients.