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.
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.
Is it possible to study for the college entrance exam and still learn Python? Of course! And that is one of the goals of the Brazilian Exam Problems category on this site. In it, we use computational tools to analyze and expand problems from the ENEM, Brazil’s national high school exam. In today’s article, we analyze a problem from the 2019 exam. And, along the way, we learn to make 3D and 4D(?!) plots with Matplotlib. All because of a killer brick.
Colorless, made of invisible constituents, and odorless (we hope!). We often forget that we are surrounded by the gases that make up our atmosphere. In this article, we will see how to easily get a list of the main constituents of air with Python and web scraping. We will also do some calculations, such as density and mean molar mass, and, of course, present everything in spectacular plots.
