One of the most popular uses for Python, especially in recent years is data processing, analysis and visualisation. This then leads topics such as the analysis of ‘big data’ which has many applications in pretty much every type of business you can imagine, and a personal interest of mine; Machine Learning.
Python has a vast array of powerful tools available to help with this processing, analysis and visualisation of data and is one of the main reasons that Python has gained such momentum in the scientific world.
In this series of posts, we will take a look at the main libraries used in scientific Python and learn how to use them to bend data to our will. We won’t just be learning to churn out template code however, we will also learn a bit of the maths behind it so that we can understand what is going on a little better.
So let’s kick things off with a incredibly useful little number that we will be using throughout this series of posts; Matplotlib.