Comparing Top Two Rows and Identifying Columns with Different Values in the Same Table

Introduction

In this article, we will explore a common problem in data analysis: comparing top two rows of a table and identifying columns whose values are different. We will use SQL Server 2019 as our database management system and demonstrate how to solve this problem using techniques such as unpivoting and aggregation.

Table Representation

Let’s start by representing the table with few columns and multiple rows, where some fields have the same value for a few rows.

+----+------------+---------------+-------------+-------+-----+
| Id | EmpName   | ProjectName  | ChargeDesc  | CostCenter | Jan | Feb |
+----+------------+---------------+-------------+-------+-----+
| 32 | 2487      | 1386          | 124030       | 017N     | 0   | 0   |
| 33 | 2487      | 1386          | 124030       | 017N     | 3   | 3   |
+----+------------+---------------+-------------+-------+-----+

As shown in the example, rows with Id = 32 and Id = 33 have same values except for the Jul column.

Problem Statement

Given a table with few columns and multiple rows, we want to get top 2 rows where condition as follows (empname, projname, chargedesc, costcenter) and created date as desc. We also want to compare these two rows and find differences of columns with values.

We can achieve this using SQL Server 2019 by unpivoting and aggregating the data.

Unpivoting and Aggregating Data

To solve this problem, we will use a combination of techniques such as unpivoting, grouping, and aggregation. We will first unpivot the Jan, Feb, Mar, Apr, May, Jun, and Jul columns into separate columns using the CROSS APPLY operator.

Here’s an example code snippet:

SELECT empname, projname, chargedesc, costcenter, mon,
       MIN(val) AS min_val, MAX(val) AS max_val
FROM t
CROSS APPLY (VALUES ('Jan', jan), ('Feb', feb), 
             ('Mar', mar), ('Apr', apr), 
             ('May', may), ('Jun', jun),
             ('Jul', jul)) v(mon, val)
GROUP BY empname, projname, chargedesc, costcenter, mon
HAVING MIN(val) <> MAX(val);

In this code snippet:

We use the CROSS APPLY operator to unpivot the Jan, Feb, Mar, Apr, May, Jun, and Jul columns into separate columns.
We create a new column called mon with values from v(mon, val).
We group by the empname, projname, chargedesc, costcenter, and mon columns to ensure that we get distinct pairs of values for each row.
We use the HAVING clause to filter out rows where the minimum value is equal to the maximum value.

Result

After running the above SQL query, we will get a result set that looks like this:

+------------+---------------+-------------+----------+-------+-------+
| empname    | projname     | chargedesc  | costcenter| min_val| max_val|
+------------+---------------+-------------+----------+-------+-------+
| 2487      | 1386          | 124030       | 017N     | 0     | 3     |
+------------+---------------+-------------+----------+-------+-------+

In this result set, we can see that the only column with different values is Jul, which has a value of 3 in the second row.

Conclusion

In this article, we explored how to compare top two rows of a table and identify columns whose values are different. We used SQL Server 2019 techniques such as unpivoting and aggregation to achieve this result. By following these steps, you can solve similar problems involving data analysis and comparison of column values.

Best Practices

Always use meaningful and descriptive table and column names.
Use aggregate functions like MIN, MAX, and SUM to analyze and summarize large datasets.
Use the CROSS APPLY operator to unpivot data when dealing with fixed-length records or tables.
Use the GROUP BY clause to group rows based on common values.

Common Mistakes

Failing to use meaningful table and column names can lead to confusion and incorrect results.
Not using aggregate functions can result in incomplete or inaccurate analysis of large datasets.
Incorrectly applying unpivoting techniques can lead to incorrect data transformations.

Last modified on 2023-11-11