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Filtering Rows Using Join and Subquery

In this post, we demonstrate how to combine two datasets and filter results using SQL techniques like JOIN and subqueries. This approach is useful when you need to cross-reference and extract filtered information from multiple tables. For example, let’s find Nobel Prize winners from South America by combining two datasets: a list of Nobel Laureates and a list of countries with their respective continents.

Step 1: Setup

Assume we have imported the following two tables into a database named Nobel_Laureates:

Table Name Columns
Countries Country, Continent
Nobel_Laureates Winner, Country_of_Birth

We want to filter Nobel Laureates who were born in countries located in South America. The first step is to identify the countries in South America.

Step 2: Filter Countries Using Subquery

To get the list of countries in South America, we use the Countries table with the following query:

SELECT Country
FROM Countries
WHERE Continent = 'South America';

This query returns all countries in South America. Now, we can use this result to filter Nobel Laureates born in these countries by applying a subquery:

SELECT Winner
FROM Nobel_Laureates
WHERE Country_of_Birth IN (
    SELECT Country
    FROM Countries
    WHERE Continent = 'South America'
);

This query filters the Nobel_Laureates table using the subquery, which dynamically retrieves the list of South American countries.

Step 3: Filter Countries Using JOIN

Alternatively, we can achieve the same result using a JOIN. By joining the Nobel_Laureates table with the Countries table, we directly match Nobel Laureates to their respective continents:

SELECT Winner
FROM Nobel_Laureates
INNER JOIN Countries
ON Nobel_Laureates.Country_of_Birth = Countries.Country
WHERE Countries.Continent = 'South America';

In this query, the INNER JOIN creates a connection between the two tables based on the shared Country column. The WHERE clause then filters the rows to include only those belonging to South America.

Result Comparison

Both the subquery and the JOIN approach produce the same result. However, the choice between these methods depends on your specific use case. JOIN is generally faster when working with large datasets because it avoids creating a temporary result set like a subquery does.

Approach Advantages Use Case
Subquery Simpler and easier to read for small datasets Filtering data based on dynamic criteria
JOIN More efficient for large datasets Cross-referencing data between tables

Conclusion

Both subqueries and joins are powerful tools for combining and filtering data in SQL. Understanding their strengths and when to use them will help you write more efficient and maintainable queries. Practice with these techniques to master filtering rows across multiple tables.

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