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This notebook contains an excerpt from the Python Programming and Numerical Methods - A Guide for Engineers and Scientists, the content is also available at Berkeley Python Numerical Methods.

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< 19.6 Summary and Problems | Contents | 20.1 Numerical Differentiation Problem Statement >

Chapter 20. Numerical Differentiation


Motivation

Many engineering and science systems change over time, space, and many other dimensions of interest. In mathematics, function derivatives are often used to model these changes. However, in practice the function may not be explicitly known, or the function may be implicitly represented by a set of data points. In these cases and others, it may be desirable to compute derivatives numerically rather than analytically.

The focus of this chapter is numerical differentiation. By the end of this chapter you should be able to derive some basic numerical differentiation schemes and their accuracy.