Both NumPy and pandas use the concept of an axis, but with subtle differences. In NumPy, it refers to the dimension along which an operation is performed, while in pandas it indicates the direction of an operation. This article presents examples of using axis in each library.
We learn in math that there are rules for adding or multiplying matrices. But in computational math problems we sometimes have matrices or vectors that we would like to add or multiply and that do not meet the requirements. In this article we will see how the NumPy broadcasting concept is useful in these contexts and how it relates to vector quantization.
If you are venturing into data science, you have most likely seen a reshape in some code. Maybe with a mysterious value of -1 in one of the positions, as in reshape(-1, 1). But what does that mean? Why do we sometimes need to transform our data to use model methods in Scikit-Learn? That is what we will see in this article.
The Olympics are coming to an end… and, besides nostalgia and stories of perseverance, every Olympics leaves behind an enormous amount of data! In this article, we will see how to use Pandas to analyze part of that data, looking at how athletes’ times have evolved in track and field events.
Usain Bolt is considered by many to be the greatest sprinter of all time. In this article, we will see how to use Python to find the speed and acceleration the Jamaican reached in his races. We will also take the chance to discuss mathematical models and the care needed in choosing them and interpreting their results.
