Introduction
We begin with the fourth and final article of our saga of training with Pandas. In this article, we are going to make a summary of the different functions that are used in Pandas to perform missing data treatment. Dealing with missing data is key and a standard challenge of day-by-day data science work, and it has a direct impact on algorithmic performance.
Missing Data
Before we start, we are going to visualize the example dataset that we are going to follow to explain the functions. It is a dataset created by us that includes several cases of use to be able to clearly deal with all the examples that we will call `uncompleted_data`.