Handling common data issues using pandas

Your data may feel special, it is unique, you have created the world's best systems for collecting it, and you have done everything you can to ensure it is clean and accurate. Congratulations! But your data will almost certainly have some problems, and these problems are not special, or unique, and are probably a result of your systems or data entry. The e-scooter dataset is collected using GPS with little to no human input, yet there are end locations that are missing. How is it possible that a scooter was rented, ridden, and stopped, yet the data doesn't know where it stopped? Seems strange, yet here we are. In this section, you will learn how to deal with common data problems using the e-scooter dataset.

Drop rows and columns

Before you modify any fields in your data, you should first decide whether you are going to use all the fields. Looking at the e-scooter data, there is a field named region_id. This field is a code used...