How to identify strings that can be treated as numbers in PostgreSQL?

In PostgreSQL, you can use the isnumeric function to identify strings that can be treated as numbers. The isnumeric function returns TRUE if all characters in the string are numeric, and FALSE otherwise.

Here is an example of how to use the isnumeric function in PostgreSQL:

SELECT isnumeric(‘123’); — Returns TRUE
SELECT isnumeric(‘1.23’); — Returns FALSE
SELECT isnumeric(‘1,000’); — Returns FALSE

In the first example, the isnumeric function returns TRUE because the string ‘123’ contains only numeric characters. In the second example, the isnumeric function returns FALSE because the string ‘1.23’ contains a non-numeric character (.). In the third example, the isnumeric function returns FALSE because the string ‘1,000’ contains a non-numeric character (,`).

Real-life data examples of using the isnumeric function in PostgreSQL can be found in various applications that handle data analysis or data validation. For example, a financial application might use the isnumeric function to validate input data before processing it, to ensure that only valid numeric data is used in financial calculations. Similarly, a data analytics application might use the isnumeric function to filter out non-numeric data before performing statistical analysis, to ensure that only valid data is used in the analysis.

Here’s an example SQL query that demonstrates the use of the isnumeric function to filter out non-numeric data:

SELECT *
FROM my_table
WHERE isnumeric(my_column) = TRUE;

In this example, my_table is the name of the table to be queried, and my_column is the name of the column containing the data to be filtered. The WHERE clause filters out all rows where the value in my_column is not numeric.

By using the isnumeric function in this way, you can ensure that only valid numeric data is used in your data analysis or calculations.

About Shiv Iyer 422 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.