Combining Data from Multiple Tables

Relational databases often store data across multiple tables. This modular approach facilitates efficient storage and organization but can pose challenges when a comprehensive view of the data is required. This chapter explores essential techniques for combining data from multiple tables, a crucial skill for data scientists.

You'll begin by understanding joins, SQL constructs that link tables based on related columns. We will cover various join types, such as INNER, LEFT, RIGHT, and FULL OUTER joins, each serving a specific purpose in data retrieval. Through practical examples, you will learn how to choose the appropriate join type based on your data requirements.

Additionally, this chapter will introduce you to the concepts of UNION, INTERSECT, and EXCEPT. These set operations enable you to combine query results in versatile ways, expanding your ability to manipulate and analyze data.

By the end of this chapter, you will be equipped with the skills to seamlessly integrate data from multiple sources, providing you with a more holistic view of the datasets you work with. This ability to synthesize information from different tables is invaluable in drawing meaningful insights and making data-driven decisions.