Working with dates in SQL
It is often the case that when working with a SQL database, one typically has to work with tables that contain a date column showing the date for each relevant record.
However, the ability of SQL to work with dates and yield valuable insights from such data types is often not well understood.
Weather Data Example
Let us consider the following example. Suppose there exists a weather database with recorded dates and relevant weather information in a table. Here is a snippet of the data:
Also, let us suppose that a month variable has been defined in the table and the relevant values have been extracted from the table as follows:
update weatherdata set month=extract(month from date);
Now, for the purposes of ensuring that we have sufficient temperature records for each month and do not have too long a lag between records — let us assume that we would like to calculate the average duration between each consecutive record in the table, and group them by month.
This task will be accomplished by:
- Calculating the difference between each consecutive date using the LAG() function
- Using a subquery to calculate the average duration between each record that has been calculated in step 1, and then grouping them by month
Calculate duration between dates
By using the LAG function, we can calculate the duration between each consecutive date. However, we would also like to display the date and month columns in the new table as well — we will need to use the month column when it comes to subsequently grouping the average duration by month.
To accomplish this, we must:
- Calculate the duration between dates by subtracting the difference between each consecutive date using the LAG function
- Inner join the relevant table to itself through the use of an INNER JOIN function
This is done as follows:
select t1.date, t1.date - lag(t1.date) over (order by t1.date) as date_difference, t1.month from weatherdata as t1 inner join weatherdata as t2 on t1.date=t2.date;
Here is the generated table from the above query:
We can see that for the weather data recorded last month — there is less than a day of duration for most entries — meaning that weather patterns are being recorded with regularity and we are likely obtaining a representative sample for that month!
Using subquery with a GROUP BY function
Having calculated the above table, we now wish to calculate the average duration between recorded dates by month.
In order to do this using the data we have just generated above — we must now use a subquery. That is to say, we will be incorporating the above query into a broader aggregate query that can use the GROUP BY function.
To group the duration by month, the following query is run:
select month, avg(date_difference) from (select t1.date as date, t1.date - lag(t1.date) over (order by t1.date) as date_difference, t1.month as month from weatherdata as t1 inner join weatherdata as t2 on t1.date=t2.date) as subquery group by month order by month;
Here is the generated data:
From the above, we can see that across months 1–12 (January to December) — we have calculated the average duration between each recorded date for each month.
Conclusion
In this article, you have seen:
- How to extract month values from dates
- How to use the LAG function to calculate differences in duration between consecutive dates
- Use of subqueries to allow for use of aggregate functions such as GROUP BY
Many thanks for reading, and any questions or feedback are greatly appreciated!
Disclaimer: This article is written on an “as is” basis and without warranty. It was written with the intention of providing an overview of data science concepts, and should not be interpreted as professional advice. The findings and interpretations in this article are those of the author and are not endorsed by or affiliated with any third-party mentioned in this article. The author has no relationship with any third parties mentioned in this article.
