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. The copyright of the book belongs to Elsevier.

This chapter gets you started with Python, using it as a calculator, managing Python packages, getting familiar with the Jupyter Notebook. As you will see, Python has a great community with packages that could potentially do anything.

We saw many built-in Python functions already, such as type, len, and so on. Also we saw the functions from some packages, for example, math.sin , np.array and so on. Do you still remember how could we call and use these functions.

The easiest way to run Python code is through the Python shell or Ipython Shell (which stands for Interactive Python). The Ipython shell is richer than Python shell, such as Tab autocompletion, color-highlighted error messages, basic UNIX shell integration and so on.

There are three families of linear multistep methods are commonly used: Adams–Bashforth methods, Adams–Moulton methods, and the backward differentiation formulas (BDFs).

This chapter describes several methods of numerically integrating functions. By the end of this chapter, you should understand these methods, how they are derived, their geometric interpretation, and their accuracy.

Teach Python programming to science and engineering students who do not have prior exposure to programming. Introduce a variety of numerical analysis tools that are useful for solving science and engineering problems.

Now we see how we can train a model to do the classification in Python, there are also many other models that you can use in scikit-learn, we leave this for you to explore. For example, you can use an artificial neural network (ANN) to do the …

In the 2nd part of this book, we will study the numerical methods by using Python. We will use array/matrix a lot later in the book. Therefore, here we are going to introduce the most common way to handle arrays in Python using the Numpy module .

The Riemann Integral, Trapezoid Rule, and Simpson’s Rule are common methods of approximating integrals. Each method has an order of accuracy that depends on the approximation of the area below the function.