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So I hope that now you understand better the concept of derivatives in Python.

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So we have learned about several different methods here.

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And for example, the Richardson method gives us a way higher accuracy compared to the forward differences

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methods, and we have even discussed why this is the case analytically.

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So I hope you like the example or the exercise in the middle of the section where you have calculated

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the velocity and the acceleration based on a data set position versus time.

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And I hope that you also got the results that the Richardson method is much better than to for what

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differences methods.

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So then at the end of the section, we discussed multi-dimensional derivatives, so the gradient, the

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curl and divergence.

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And before we get to an example with these concepts, let's first discuss integrals in the next section.

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So at the end of the next section, we will have an example where we will use both concepts integration

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and differentiation in multiple dimensions to calculate the magnetic fields of a charged wire.

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This may sound like a very simple problem because you know about the results already in high school,

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but it's actually very difficult to calculate this magnetic fields.

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But with the features and with the tools that we have just learned, we are able to do it.

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So I hope you're excited about the next section and let's get started.