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</div>

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```

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# SciPy

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# {index}`SciPy <single: SciPy>`

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```{index} single: Python; SciPy

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```

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In this lecture, we aim only to highlight some useful parts of the package.

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## SciPy versus NumPy

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## {index}`SciPy <single: SciPy>` versus {index}`NumPy <single: NumPy>`

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SciPy is a package that contains various tools that are built on top of NumPy, using its array data type and related functionality.

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Let's consider some numerical techniques for finding roots.

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### Bisection

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### {index}`Bisection <single: Bisection>`

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```{index} single: SciPy; Bisection

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```

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bisect(f, 0, 1)

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```

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### The Newton-Raphson Method

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### The {index}`Newton-Raphson Method <single: Newton-Raphson Method>`

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```{index} single: SciPy; Newton-Raphson Method

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```

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If you don't get good results, you can always switch back to the `brentq` root finder, since

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the fixed point of a function $f$ is the root of $g(x) := x - f(x)$.

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## Optimization

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## {index}`Optimization <single: Optimization>`

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```{index} single: SciPy; Optimization

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See the [documentation](http://docs.scipy.org/doc/scipy/reference/optimize.html) for details.

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## Integration

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## {index}`Integration <single: Integration>`

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```{index} single: SciPy; Integration

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```

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See the [documentation](http://docs.scipy.org/doc/scipy/reference/integrate.html) for more details.

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## Linear Algebra

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## {index}`Linear Algebra <single: Linear Algebra>`

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```{index} single: SciPy; Linear Algebra

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```

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