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Long-tailed distributions are common in natural and engineered systems; as a result, we encounter extreme values more often than we would expect from a short-tailed distribution. If we are not prepared for these "black swans", they can be disastrous. But we have statistical tools for identifying long-tailed distributions, estimating their parameters, and making better predictions about rare events. In this talk, I present evidence of long-tailed distributions in a variety of datasets -- including earthquakes, asteroids, and stock market crashes -- discuss statistical methods for dealing with them, and show implementations using scientific Python libraries. ---- SciPy is a community dedicated to the advancement of scientific computing through open source Python software for mathematics, science, and engineering. The annual SciPy Conference allows participants from all types of organizations to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. SciPy is organized by NumFOCUS, a 501(c)(3) nonprofit organization that promotes open source scientific computing.