Automated data-driven modeling, the process of directly discovering the governing equations of a dynamical system from data, is increasingly being used across the scientific community. PySINDy (github.com/dyn...) is a Python package that provides tools for applying the sparse identification of nonlinear dynamics (SINDy) approach to data-driven model discovery. In a recent update to PySINDy (arxiv.org/pdf/..., we implement several advanced features that enable the discovery of more general differential equations from noisy and limited data. Here we provide a brief overview of the SINDy method, with followup tutorial videos for effectively using the PySINDy code.