Joins

1. Joins

Lets dive into joins, a foundational operation in data handling.

2. Joins

Joins are a fundamental aspect of data manipulation that allow combination of tables based on related columns. Traditionally, data tables are structured using a relational strategy to minimize redundancy. Understanding joins is crucial for database work, as they enable complex analysis by merging data from multiple sources.

3. Mechanics of joins

Joins operate by associating tables through unique identifiers, commonly referred to as IDs or keys. A primary key in one table and a corresponding foreign key in the other to establish a relation. These keys serve as the crucial link, indicating that a row in one table is connected to one or more rows in another table.

4. Types of joins

Several types of joins exist for various data querying needs, each dictating how tables are merged. Joins can be visualized as intersections in a Venn diagram, providing a clear picture of what the final output is. There are four main types of joins. The inner join is perhaps the most utilized type of join, acting as the default in many query languages. An inner join only returns rows that match between the two tables.

5. Types of joins

Left joins return all rows from the left table and matched rows from the right. They're useful for identifying records without corresponding matches in another table.

6. Types of joins

Right joins focus on the right table, including all its records and those matched from the left. This join type ensures completeness of the dataset from the 'right' side.

7. Types of joins

Full joins merge left and right joins, including all records from both tables. They provide a comprehensive view of two datasets, invaluable for data completeness checks.

8. Example from Velocity Inc. dataset

Here's an example of a join using our data from Velocity Inc. Imagine we were tasked with putting together a list of all customers in California and their orders from 2018. We can achieve this by performing a left join between the customers_ca table, containing all California customers, and the orders_2018 table to include all California customers, even if they haven't placed an order.

9. Example from Velocity Inc. dataset

The output includes all fields from both tables. Additionally, the presence of customers with multiple orders in 2018 has led to an increase in the number of rows. Due to the use of a left join, customers without orders are also included in our output, resulting in empty values for all the orders_2018 fields for these customers.

10. Example from Velocity Inc. dataset

Now lets say we elected to instead do an inner join. We can see in our previous output that customer 569 did not have any orders but was included due to us electing to do a left join.

11. Example from Velocity Inc. dataset

Switching to an inner join would result in one fewer row, as it excludes any customers without orders. Now we only display records with a perfect match between the customers and orders tables.

12. Cross join

A less common type of join is the cross join, or the Cartesian join. A cross join produces a Cartesian product, pairing every row from one table with every row from another. They're suited for generating all possible combinations between two sets of data. We have worked with this type of join in the previous chapter as the Append Fields tool in Alteryx produces a cartesian join.

13. An example of a Cross join

Let's say we have two tables, Fruits and Colors.

14. An example of a Cross join

A Cartesian join of these tables would produce the following result. In this example, each fruit is paired with each color, resulting in nine combinations.

15. Common mistakes to avoid with joins

Using joins effectively requires an understanding of common pitfalls and how to avoid them. Some typical mistakes that can impact the performance and accuracy of your workflows are: Lacking precise detail in data representation, forgetting to specify join type, ignoring NULL values, and mismatched data types. Understanding these pitfalls is key to leveraging the full power of joins in data analysis.

16. Let's practice!

Now let's try an exercise to gauge your understanding of joins.

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