Now it's time to say goodbye. But before that, let's sum up what we learned in Scientific Computing with Python, the basics.

We learned the basic environment for scientific computing.

What is it? The best way to have a good environment for scientific computing is to use Anaconda distribution of Python, with Spyder development of environment, with a console and an editor.

NumPy package allows scientific computing with the mathematical functions and the vectors.

matplotlib.pyplot allows scientific graphics, the plotting, together with NumPy.

We learned to use Python as a calculator.

The basic Python allows arithmetic operations, the addition, the subtraction, the multiplication, the division and the power.

The NumPy package allows mathematical functions, for instance, the cosine, the sinus, the exponential, the logarithms, etc.

We learned also two useful programming topics, the control flows.

The conditionals if, elif, else, with elif optional and else optional.

And the sequential loops for with the range lists and the arange vectors of NumPy.

And finally, we learned to make graphics with NumPy and matplotlib.pyplot.

We learned to define a sequence as a vector of NumPy and to draw a sequence with plot of u of matplotlib.pyplot.

And to define a function with vectors of NumPy x and y, with y a function of x, and to draw that function with plot of x and y of matplotlib.pyplot.

So, goodbye!