Python Tutorial: Joining data: a real-world necessity

Want to learn more? Take the full course at https://learn.datacamp.com/courses/pandas-joins-for-spreadsheet-users at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome! Collecting and organizing data is an important part of preparing data for analysis. In this course, you’ll learn how to use the Python package Pandas to join data from two or more sources. Joining data with pandas is similar in many ways to joining data in spreadsheets. Pandas stores data in data frames with rows and columns, just as spreadsheets do. Now, you will need to learn new formulas, or expressions as they're called in pandas since pandas uses the python language. Learning a new language can be challenging. But you'll see how the power and flexibility of pandas makes it worth the effort. You'll be working with data from the National Football League collected to explore factors associated with player concussions. Who knows, you might find some useful insights! To analyze data and gain insight, we often need to combine data from various sources in a helpful way. There are two common situations that pretty much guarantee the need for joining. The first is when similar data is split by time, location or other factor and stored separately. The second situation is when two or more datasets may have different but related factors. Let's talk about these two situations in more detail. Split data is quite common in a spreadsheet environment. People will often enter data, produce a report, and save the result. Then they repeat the cycle for the next time period, starting with a blank template. Time-based splits are commonly used for accounting data. Data from each month might be saved in a separate worksheet with each file holding a year's worth of data. There are other ways of splitting data to mirror reporting practices. Splitting by geography or business unit are both common options. Split data is best combined row-