Скачать или смотреть How to Modify Your SQL Query to Count Monthly Values with Zero for No Data

Learn how to enhance your SQL queries to include zero values for months with no data in Oracle 12C.
---
This video is based on the question https://stackoverflow.com/q/62821359/ asked by the user 'user1941319' ( https://stackoverflow.com/u/1941319/ ) and on the answer https://stackoverflow.com/a/62821687/ provided by the user 'Sayan Malakshinov' ( https://stackoverflow.com/u/429100/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Count(*) Query based on a time period by month need to add zero value instead of no data

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Enhancing Your SQL Queries: Including Zero Values for Missing Data

If you're working with databases, you might often face situations where you want to count values over a specific period but find that some months have no data. In this guide, we'll tackle the common problem of counting values by month, specifically focusing on how to display zero instead of no data when a month has no entries. This is particularly useful for clear reporting and analysis. We will be using Oracle 12C as our database platform.

Understanding the Problem

Here's a scenario: you have a database that logs transactions or events with date fields. When querying this data by month, you might get results like this:

[[See Video to Reveal this Text or Code Snippet]]

As you can see, March has no data, and hence it does not appear in the results at all. However, if you want your result set to include all months within your specified range and show a count of 0 for any month with no data, you'll need to modify your SQL query.

Solution: Using a Date Generator

To achieve this, you can utilize a date generator that creates a list of months in your specified range, then perform a left join with your original query. This way, even if there are no records for a month, it will still appear in the final output with a count of zero.

Step-by-Step Solution

Create a Date Generator: This generates a list of months between your desired start and end dates.

Perform a Left Join: Join the date generator with your original query to display counts, ensuring to show 0 for months with no data.

Here’s How You Can Implement This:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Code:

Date Generation: The inner query creates a list of months from January 2020 up to May 2020.

DATE '2020-01-01': This marks the starting point.

LEVEL: This is a special Oracle keyword that helps to generate the desired number of rows dynamically.

MONTHS_BETWEEN: This function calculates the number of months between two dates, allowing the generator to create enough months.

Left Join: The join merges the generated month list with your original count query. This ensures that every month appears even if there are no associated records.

Ordering: Finally, you order the results by date to ensure clarity.

Desired Output

By following this method, your output will look something like this, including the missing March month with a count of zero:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Incorporating zeros for months that lack data is essential for maintaining complete visibility in your reports. By using a date generator and employing a left join, you can easily adjust your SQL queries to achieve this. This approach is not only simple but also effective, making your data analysis clearer and more comprehensive.

Feel free to experiment with the provided code and see how it works with your dataset!