Adventures in Machine Learning

Mastering Data Formatting with CASE WHEN and GROUP BY in SQL

SQL is a powerful language that is widely used for manipulating and analyzing data. One of the key tasks in working with data is formatting it to meet specific requirements for analysis, visualization, or reporting.

In this article, we will explore how to use CASE WHEN and GROUP BY statements in SQL to format data effectively and efficiently.Have you ever worked with data that was hard to analyze or visualize because it was not formatted properly? If so, you know the importance of formatting data correctly, and SQL can help you do just that.

In this article, we will explore how to use CASE WHEN and GROUP BY statements to format data in SQL in a variety of ways.

A Brief Review of CASE WHEN

Before we dive into the examples, let’s do a quick review of CASE WHEN statements. A CASE WHEN statement is a conditional statement that allows you to perform different actions depending on the conditions met.

It works like an “if-then” statement in programming languages. Here’s what a basic CASE WHEN statement looks like:

“`

SELECT column1,

column2,

CASE WHEN column3 > 10 THEN ‘High’

WHEN column3 >= 5 AND column3 <= 10 THEN 'Medium'

ELSE ‘Low’

END AS Category

FROM table_name;

“`

In this example, we’re selecting three columns from a table and creating a new column called “Category” using a CASE WHEN statement.

Depending on the values in column3, we’ll categorize the data into “High,” “Medium,” or “Low.”

Example 1: Aggregations of Custom Categories

Let’s say you have a dataset that includes sale transactions of items from different categories. You want to group the items into custom categories and aggregate the sales by those categories.

Here’s an example query:

“`

SELECT CASE WHEN category_name LIKE ‘%fruit%’ THEN ‘Fruits’

WHEN category_name LIKE ‘%vegetable%’ THEN ‘Vegetables’

ELSE ‘Others’

END AS CustomCategory,

SUM(sales) AS TotalSales

FROM sales_table

GROUP BY CustomCategory;

“`

In this example, we’re grouping the data by a custom category we create using a CASE WHEN statement. The LIKE operator is used to match category names with specific keywords.

We then aggregate the sales by custom categories using the SUM function. Example 2: CASE WHEN With ELSE in GROUP BY

Let’s say you have a dataset of cities and population numbers.

You want to group the cities into two buckets, “high population” and “low population,” using a CASE WHEN statement. However, you also want to include a count of the number of cities in each bucket, including those not belonging to either bucket.

Here’s an example query:

“`

SELECT CASE WHEN population >= 1000000 THEN ‘High Population’

ELSE ‘Low Population’

END AS PopulationBucket,

COUNT(*) AS CityCount

FROM cities_table

GROUP BY PopulationBucket WITH ROLLUP;

“`

In this example, we’re grouping the cities by population buckets using a CASE WHEN statement. We also include a count of the number of cities in each bucket, including those not belonging to either bucket, using the COUNT(*) function.

The WITH ROLLUP clause adds a row that summarizes the population and city count for all cities. Example 3: CASE WHEN Statements Inside Aggregations

Let’s say you have a dataset that includes sales data for different categories of products, and you want to count the number of products sold in each category in the last quarter.

You want to use a CASE WHEN statement to classify the sales data into two categories, “High Sales” and “Low Sales.” Here’s an example query:

“`

SELECT product_category,

COUNT(CASE WHEN sales > 50000 THEN 1 ELSE NULL END) AS HighSalesCount,

COUNT(CASE WHEN sales <= 50000 THEN 1 ELSE NULL END) AS LowSalesCount

FROM sales_table

WHERE date >= ‘2021-01-01’ AND date <= '2021-03-31'

GROUP BY product_category;

“`

In this example, we’re using a CASE WHEN statement inside the COUNT function to classify the sales data into “High Sales” and “Low Sales” categories. We then count the number of products in each category sold during the last quarter and group the data by the product category.

Adding a New Column with CASE WHEN and GROUP BY

So far, we’ve used CASE WHEN and GROUP BY to format data for analysis. In addition to formatting data, we can also use them to create new columns that we can use for further analysis.

Let’s see an example:

Creating a Product Category Column

Suppose you have a dataset of sales transactions that includes product names but no product categories. You want to create a new column called “Product Category” that classifies the products into categories based on the keywords in their names.

Here’s an example query:

“`

SELECT product_name,

CASE WHEN product_name LIKE ‘%shirt%’ THEN ‘Clothing’

WHEN product_name LIKE ‘%book%’ THEN ‘Books’

WHEN product_name LIKE ‘%phone%’ THEN ‘Electronics’

ELSE ‘Other’

END AS ProductCategory

FROM sales_table;

“`

In this example, we’re creating a new column called “Product Category” using a CASE WHEN statement. Depending on the keywords in the product name, we’ll classify the product into one of four categories: “Clothing,” “Books,” “Electronics,” or “Other.”

Example: Counting Number of Products per Category

Now that we have the product categories, let’s see an example of how we can use them for further analysis.

Suppose you want to count the number of products sold per category. Here’s an example query:

“`

SELECT ProductCategory,

COUNT(DISTINCT product_name) AS NumProducts

FROM sales_table

GROUP BY ProductCategory;

“`

In this example, we’re counting the number of distinct product names in each category using the COUNT and DISTINCT functions. We then group the data by the product category.

Conclusion

In this article, we’ve seen how to format data using CASE WHEN and GROUP BY in SQL. We’ve also seen how to create new columns with CASE WHEN and use them for further analysis.

By mastering these techniques, you can organize, categorize, and analyze your data more effectively and efficiently.

Recap of Using CASE WHEN and GROUP BY

In the previous sections of this article, we’ve explored how to use CASE WHEN and GROUP BY statements in SQL to format data effectively and efficiently. We’ve looked at different examples and use cases where SQL can help you summarize data.

Let’s recap what we’ve learned so far.

Formatting Data with CASE WHEN and GROUP BY

CASE WHEN statements are conditional statements that allow you to perform different actions based on conditions met. You can use them in several ways, such as creating custom categories, adding new columns, and classifying data into different buckets.

In Example 1, we learned how to group items into custom categories using the LIKE operator and aggregate sales by those categories using the SUM function. In Example 2, we learned how to group cities into two population buckets using a CASE WHEN statement, count the number of cities in each bucket, including those not belonging to either bucket, and summarize the data using the WITH ROLLUP clause.

We also saw that you can use CASE WHEN statements inside aggregation functions to count rows that satisfy certain conditions. In Example 3, we counted the number of products sold in each category by classifying sales data into two categories, “High Sales” and “Low Sales,” using CASE WHEN statements inside the COUNT function.

Creating New Columns with CASE WHEN and GROUP BY

In addition to formatting data, we explored how to create new columns using CASE WHEN and GROUP BY statements. In the “

Creating a Product Category Column” example, we learned how to create a new column called “Product Category” that classifies products into categories based on the keywords in their names.

Once you have created a new column, you can use it for further analysis. In the “Counting Number of Products per Category” example, we used the newly created “Product Category” column to count the number of products sold per category by grouping the data by the product category.

Why Use CASE WHEN and GROUP BY in SQL? CASE WHEN and GROUP BY statements are powerful tools for summarizing data in SQL.

They help you organize and categorize your data to perform various analyses such as counting, summing, and averaging. By using these statements, you can make sense of large datasets and extract insights efficiently.

Moreover, using CASE WHEN and GROUP BY statements can simplify your queries, making your code more readable and easy to understand. This can save you a lot of time and effort when you need to revisit your code in the future.

Tips for Using CASE WHEN and GROUP BY in SQL

1. Plan your categories before starting to write your query.

This will save you time and keep you on track with your analysis. 2.

Use the CASE WHEN statement to categorize data before aggregating it. This will help you avoid errors and inaccuracies in your analysis.

3. Be mindful of the performance impact of your queries.

GROUP BY statements can be expensive to compute, especially with large datasets. Test your queries on smaller subsets of data before running them on the entire dataset.

4. Don’t forget to document your queries.

This will help you and your colleagues understand the purpose of the query and how to use it effectively in the future.

Conclusion

In conclusion, CASE WHEN and GROUP BY statements are powerful tools for summarizing data in SQL. They can help you organize, categorize, and analyze large datasets to extract meaningful insights.

By using them, you can simplify your queries, make your code more readable, and perform your analysis efficiently. Remember to plan your categories, be mindful of query performance, and document your queries to make your work more effective and enjoyable.

In this article, we’ve explored how to use the CASE WHEN and GROUP BY statements in SQL to format data and extract insights efficiently. We’ve learned how to categorize items into custom categories, create new columns, and classify data into different buckets.

Additionally, we’ve seen how to use CASE WHEN statements inside aggregation functions to count the number of rows that satisfy certain conditions. Using these techniques can simplify queries, make your code more readable, and help you make sense of large datasets.

Takeaways include planning your categories beforehand, being mindful of query performance, and documenting your queries to facilitate future use. With these tools, you can improve your data analysis and achieve better results in your work.

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