Understanding the Problem Statement

The problem statement presents a table with multiple rows, each representing a single data point. The task is to pivot this table into a new form where multiple rows are merged into a single row and multiple columns are created based on specific conditions.

The input table has three columns: NAME, Unit, and Date. Each row represents a data point with a unique combination of these values. The problem requires pivoting the table so that for each unique date and time, the corresponding rows are merged into a single row, creating new columns based on the type of measurement (CU, CH, or TA).

Analyzing the Desired Output

The desired output is a pivot table where each row corresponds to a specific date and time. The TypeCULoad, TypeCHLoad, and TypeTALoad columns are created by aggregating the values in the original Load column based on the type of measurement.

For example, for the date and time “2020-01-02 10:30”, there should be a single row with:

Understanding the Provided Solution

The provided solution uses a Common Table Expression (CTE) to transform the original table and then applies a pivot operation to create the desired output.

Common Table Expression (CTE)

A CTE is a temporary result set that can be used within a SELECT, INSERT, UPDATE, or DELETE statement. In this case, the CTE is used to transform the original table by adding a new column xxx and a ranking column dd.

The CTE has two main columns:

• name: The original name value.
• unit: The original unit value.
• date: The original date value.
• time: The original time value.
• 'Type' + Type + 'Load': A new column created by concatenating the type of measurement with “Load”.
• Load: The original load value.
• Type: The original type of measurement.

The CTE also includes a ranking column dd that partitions the rows based on the left two characters of the name (i.e., the unit). This is done to ensure that rows with the same unit are grouped together and ranked accordingly.

Pivot Operation

After transforming the data using the CTE, a pivot operation is applied to create the desired output. The pivot operation takes the transformed table as input and creates new columns based on the specified columns in the PIVOT clause.

In this case, the pivot operation has three columns:

• [TypeCULoad]
• [TypeCHLoad]
• [TypeTALoad]

The pivot operation aggregates the values in these columns by taking the maximum value for each combination of date and time.

Code Explanation

Let’s break down the code step by step to understand how it works:

Common Table Expression (CTE)

with cte as (
  SELECT 
    name, 
    unit, 
    date, 
    time, 
    'Type' + Type + 'Load' as Col, 
    Load as Val, 
    Type, 
    LEFT(name, 2) xxx,
    RANK() OVER(PARTITION BY LEFT(name, 2) ORDER BY CASE type WHEN 'CU' THEN 1 WHEN 'CH' THEN 2 ELSE 3 END) dd
  FROM TESTAIR 
)

This CTE transforms the original table by adding a new column xxx and a ranking column dd. The xxx column is created by taking the left two characters of the name (i.e., the unit). The dd column partitions the rows based on the unit, ensuring that rows with the same unit are grouped together.

Pivot Operation

select b.name, b.unit, a.date, a.time, a.col, a.val
from cte a
join cte b on a.xxx = b.xxx and b.dd = 1

This pivot operation takes the transformed data from the CTE as input. It joins two instances of the CTE together based on the xxx column, ensuring that rows with the same unit are paired together.

Final Pivot Operation

PIVOT (max(val) for Col in ([TypeCULoad], [TypeCHLoad],[TypeTALoad]) ) AS pvt

This final pivot operation aggregates the values in the val column based on the specified columns in the PIVOT clause. It creates new columns called [TypeCULoad], [TypeCHLoad], and [TypeTALoad].

Conclusion

In this article, we have discussed how to pivot a table with multiple rows into a single row and multiple columns based on specific conditions. We have analyzed the desired output and understood the provided solution using a Common Table Expression (CTE) and pivot operation.

We have also broken down the code step by step to understand how it works. The CTE transforms the original table, while the pivot operation aggregates the values in the transformed data to create the desired output.

This technique can be applied in various scenarios where data needs to be aggregated based on specific conditions. It provides a flexible and efficient way to transform data into different formats.

Last modified on 2024-03-12