# Python {.unnumbered}

## Motivation

In BioE 10, you will use Python notebooks to run calculations, visualize data, and document reasoning.

## Learning Objectives

- Build reproducible notebook workflows with explicit execution order.
- Use numerical Python tools for vectorized computation and plotting.
- Translate engineering models into inspectable code pipelines.

**Jupyter notebooks** combine code, explanatory text, and output in one place. In this book, notebooks primarily use the [Python](https://www.python.org/) stack **numpy**, **pandas**, and **matplotlib**.

For Berkeley students in this course, the default onboarding path is **Google Colab** because it minimizes setup friction and works immediately in a browser. A local Jupyter workflow is also supported for students who want offline use and tighter environment control.

Credit: Material adapted from UC Berkeley Data 8 ([Computational and Inferential Thinking](https://inferentialthinking.com/chapters/intro.html)).