Introduction
In this chapter, we will learn about the secret sauce behind creating a successful data wrangling pipeline. In the previous chapters, we were introduced to the basic data structures and building blocks of Data Wrangling, such as pandas and NumPy. In this chapter, we will look at the data handling section of data wrangling.
Imagine that you have a database of patients who have heart diseases, and like any survey, the data is either missing, incorrect, or has outliers. Outliers are values that are abnormal and tend to be far away from the central tendency, and thus including it into your fancy machine learning model may introduce a terrible bias that we need to avoid. Often, these problems can cause a huge difference in terms of money, man-hours, and other organizational resources. It is undeniable that someone with the skills to solve these problems will prove to be an asset to an organization.
Additional Software Required for This Section
The code for this exercise depends on...
