Understanding the Problem and Its Requirements

When working with data that contains mixed case strings, it can be challenging to determine how to handle these values. In this article, we will explore a problem where you want to split a column based on whether the string is in uppercase or lowercase. This involves understanding regular expressions, how to use them in SQL queries, and how to process the results.

Introduction to Regular Expressions

Regular expressions (regex) are a powerful tool for matching patterns in strings. They allow us to search for specific characters, combinations of characters, and even entire phrases within a string. In this context, we will be using regex to identify whether a string is entirely uppercase or contains a mix of upper and lowercase letters.

Understanding Regex Basics

Before diving into the specifics of our problem, let’s take a look at some basic concepts in regex:

• Characters: Regular expressions can match any single character. For example, [a-z] would match any letter from a to z.
• Character Classes: These are used to match entire sets of characters. For instance, \w matches any word character (letters, numbers, or underscores).
• Patterns: Regex patterns can include repetition (*, +, {n,m}), character classes ([a-z], [A-Z]), and anchors (^, $).

Identifying Upper and Lower Case Strings

To split a column based on whether the string is in uppercase or lowercase, we need to identify strings that contain at least one upper case letter.

For this purpose, we will use two regex patterns:

• One pattern to match any single character that is both an upper case letter ([A-Z]) and a lower case letter ([a-z]). This combination indicates the presence of at least one mixed case character.
• Another pattern specifically to match uppercase letters without any mix of upper and lower case characters.

These patterns can be combined using | (the pipe) or \| in some regex flavors, which is equivalent. We will use [[:upper:]]+[[:lower:]]+ for matching mixed cases and [[:upper:]][[:lower:]]? would match an upper case letter followed by zero or one lower case letters.

Using REGEXP_SUBSTR in SQL

Now that we have identified the patterns, let’s see how to use them in a SQL query. The REGEXP_SUBSTR function is used to extract substrings from a string that match a given pattern.

Select
  REGEXP_SUBSTR(A, '[[:upper:]]+[[:lower:]]+') as B,
  REGEXP_SUBSTR(A, '[[:upper:]][[:lower:]]?') as C,
From MY_TABLE;

How It Works

1. The REGEXP_SUBSTR function takes three arguments:
   • The first argument is the string from which to extract substrings (in this case, column A).
   • The second and third arguments specify the pattern to match.
2. When the query executes, it will find all occurrences of the specified patterns in column A and return them as separate columns.

Processing the Results

After executing the REGEXP_SUBSTR function, we need to process the results. Since we’re splitting a single column into two, we can use the values returned by B for one column and the values returned by C for another column.

Handling Mixed Case Strings

For columns that contain mixed case strings, we simply include them in both columns.

However, when dealing with pure upper or lower case strings, the values returned will be different. For our purpose of separating these strings into distinct groups, we can use a combination of IF and conditional logic to assign the appropriate values to each column.

Assigning Values

We’ll create two new columns: Group A for the string if it contains any mix of upper case and lower case letters, and Group B for strings that are entirely in either upper or lower case. We will use a combination of SQL logic and conditional statements to achieve this:

Select
  B as Group_A,
  C as Group_B,
From (
  Select
    REGEXP_SUBSTR(A, '[[:upper:]]+[[:lower:]]+') as B,
    REGEXP_SUBSTR(A, '[[:upper:]][[:lower:]]?') as C
  From MY_TABLE
) as subquery;

In the above code:

• The subquery uses REGEXP_SUBSTR to extract substrings from column A that match the specified patterns.
• For values returned by B, we want to include those in both columns, so they will appear in Group_A.
• However, for the value of C, we only care if it’s not empty. This means any string with a mix of upper and lower case letters won’t have an entry here because we’re assigning them based on non-empty entries.

Handling Empty Results

If some strings do not contain any mixed-case characters but are still entirely in either upper or lower case, this code will return NULL for the second column. This might be acceptable behavior depending on your specific use case and requirements.

Alternative Approach: Using Case Statements

An alternative to handling empty results directly is using a CASE statement. However, since SQL (especially PostgreSQL) does not support regular expressions with conditional logic within them, we can only use it in subqueries:

SELECT REGEXP_SUBSTR(A, '[[:upper:]]+[[:lower:]]+') AS Group_A,
       CASE WHEN REGEXP_SUBSTR(A, '[[:upper:]][[:lower:]]?') != '' THEN REGEXP_SUBSTR(A, '[[:upper:]][[:lower:]]?') ELSE NULL END AS Group_B
FROM MY_TABLE;

This version of the code checks if C contains any value and assigns it accordingly. If no non-empty values are found for C, it results in NULL.

Final Considerations

Handling mixed case strings, especially in data that may contain both uppercase and lowercase letters without clear rules about how to interpret these strings, can be challenging.

• Flexibility: Depending on your specific requirements, you might want to adjust the logic or use different regex patterns. However, keep in mind that using more complex logic could impact performance.
• Performance: The use of regular expressions with string functions (like REGEXP_SUBSTR) can have a significant performance impact. It’s recommended to test various approaches and consider rewriting complex queries for better optimization.
• Data Integrity: Before executing any data manipulation, ensure that your approach aligns with the expected format of your input data and will not introduce errors.

Handling mixed case strings effectively requires a combination of understanding regular expressions, SQL syntax, and potentially conditional logic to assign values to different columns. The methods outlined above can be adapted based on specific requirements or further refined as needed for optimal results in your project.

Last modified on 2024-09-28