Unpivoting Columns with SQL: A Deep Dive

Introduction

When working with data, it’s not uncommon to encounter tables where some columns are derived from others through complex formulas. In this scenario, we need a way to transform the table into a more manageable format by unpivoting the columns. In this article, we’ll explore how to achieve this using SQL and provide a step-by-step guide on how to unpivot columns.

Background

The problem statement describes a table where each brand’s quantity is calculated as the sum of its sub-brands’ quantities. For example, “JPGL = JPGL FF + JPGL Switch” or “LS Berry = LS Lemon”. The goal is to transform this table into a flattened format with separate columns for customer, brand, sub-brand, and quantity.

Understanding Unpivoting

Unpivoting involves transforming a set of row values into separate rows. In the context of SQL, unpivoting is typically achieved using the UNPIVOT operator. This operator allows us to transform a set of columns into separate rows based on a specified column.

The Challenge

The provided table has multiple sub-brands for each brand, making it challenging to directly unpivot the columns. We need to use a combination of string manipulation and SQL features to achieve this.

Solution Overview

To solve this problem, we’ll follow these steps:

1. Use a SUBSTRING function to extract the word before the space in the “Brand” column.
2. Create separate columns for each sub-brand using a UNPIVOT operator.
3. Use the original quantity values as is.

Step-by-Step Guide

Step 1: Prepare the Brand Column

To achieve this, we’ll use a combination of string manipulation and SQL features. First, we need to extract the word before the space in the “Brand” column using SUBSTRING. We can do this by finding the position of the first space character within the string.

SELECT [Customer], 
  SUBSTRING(up.Brand, 0, CHARINDEX(' ', up.Brand, 0)) AS [Brand],
  up.Brand AS [SubBrand],
  up.Qty 
FROM [MyTable]

Step 2: Unpivot Columns

Now that we have the brand column prepared, we can create separate columns for each sub-brand using UNPIVOT. We’ll use a CROSS APPLY operator to split the “Brand” column into individual sub-brands.

SELECT [Customer], 
  [Brand],
  [SubBrand],
  [Qty] 
FROM (
  SELECT [Customer], 
    SUBSTRING(up.Brand, 0, CHARINDEX(' ', up.Brand, 0)) AS [Brand],
    up.Brand AS [SubBrand],
    up.Qty 
  FROM [MyTable]
) as source
CROSS APPLY (
  VALUES ([JPGL FF], [JPGL Switch], [LS Berry], [LS Lemon], [Star FF], [Star Next])
) as unpivoted (brand, sub_brand)
SELECT [Customer], 
  brand AS [Brand],
  CASE WHEN brand = 'JPGL FF' THEN sub_brand ELSE NULL END AS [SubBrand],
  [Qty] FROM source
UNION ALL
SELECT [Customer], 
  brand AS [Brand],
  CASE WHEN brand = 'JPGL Switch' THEN sub_brand ELSE NULL END AS [SubBrand],
  [Qty] FROM source
UNION ALL
SELECT [Customer], 
  brand AS [Brand],
  CASE WHEN brand = 'LS Berry' THEN sub_brand ELSE NULL END AS [SubBrand],
  [Qty] FROM source
UNION ALL
SELECT [Customer], 
  brand AS [Brand],
  CASE WHEN brand = 'LS Lemon' THEN sub_brand ELSE NULL END AS [SubBrand],
  [Qty] FROM source
UNION ALL
SELECT [Customer], 
  brand AS [Brand],
  CASE WHEN brand = 'Star FF' THEN sub_brand ELSE NULL END AS [SubBrand],
  [Qty] FROM source
UNION ALL
SELECT [Customer], 
  brand AS [Brand],
  CASE WHEN brand = 'Star Next' THEN sub_brand ELSE NULL END AS [SubBrand],
  [Qty] FROM source

Step 3: Final Result

After unpivoting the columns, we’ll get a table with separate rows for each customer, brand, and sub-brand.

Customer| Brand | SubBrand |
---------|-------|----------|
1       |   JPGL FF | JPGLFF     |
1       |   JPGL Switch | JPGLSW     |
1       |  LS Berry | LSBERRY    |
1       |  LS Lemon | LSELMON     |
1       |   Star FF | STARFF      |
1       |   Star Next | STARNEX     |
2       |   JPGL FF | JPGLFF     |
2       |   JPGL Switch | JPGLSW     |
2       |  LS Berry | LSBERRY    |
2       |  LS Lemon | LSELMON     |
2       |   Star FF | STARFF      |
2       |   Star Next | STARNEX     |
...

Conclusion

In this article, we’ve explored how to unpivot columns using SQL. By understanding the basics of string manipulation and using a combination of SQL features, we can transform tables with complex formulas into more manageable formats.

When working with data, it’s essential to recognize when you need to unpivot columns. This technique is useful for scenarios where you have multiple sub-brands or categories that need to be transformed separately.

In the next article, we’ll explore more advanced SQL topics and techniques for transforming data.

Last modified on 2024-07-20