Python Tutorial: Stanford Open Policing Project dataset

Want to learn more? Take the full course at https://learn.datacamp.com/courses/analyzing-police-activity-with-pandas at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, my name is Kevin Markham and I'll be your instructor for this course. I'm a data scientist and the founder of Data School. In this course, you'll be practicing a lot of what you've learned about pandas already to answer interesting questions about a real dataset. You'll gain valuable experience analyzing a dataset from start to finish, which will help to prepare you for your data science career. Let's start by introducing the data. You'll be working with a dataset of traffic stops by police officers that was collected by the Stanford Open Policing Project. They've collected data from 31 US states, but in this course you'll be focusing on data from the state of Rhode Island. For size reasons, some of the columns and rows have been removed, but you can download the full dataset for any of the 31 states from the project's website. This first chapter is about preparing the data for analysis. Before beginning an analysis, it's critical that you first examine the data to make sure that you understand it, and then clean the data, to make working with it a more efficient process. As always, we'll start by importing pandas as pd. We'll use the read_csv() function to read in the dataset from a file, and then store it in a DataFrame called ri, which stands for Rhode Island. We'll use the head() method in order to take a quick glance at the DataFrame, though there are many more columns than can fit on this screen. Each row represents a single traffic stop. You'll notice that the county_name column contains NaN values, which indicate missing values. These are often values that were not collected during the data gathering process, or are irrelevant for that particular row. It's important that you locate missing values so that you can proacti