Split Columns

The Split Columns feature in Edilitics provides users with a powerful tool to divide a single column into multiple, more manageable columns. This operation is particularly valuable for separating concatenated data, such as full names or composite identifiers, into distinct components. By offering both delimiter-based and regex-based splitting options, Edilitics ensures flexibility and precision in data preparation.

How to Execute a Split Columns Operation in Edilitics#

1. Select a Column to Split#

• Selection Process: Choose the column you wish to split from the dropdown list provided. This column will be the source data for creating new, separate fields.

2. Choose a Delimiter or Regular Expression (Regex)#

• Delimiter Options: Edilitics offers several predefined delimiters to facilitate straightforward column splitting: _ Space: Ideal for splitting text strings where words are separated by spaces, such as full names. _ Tab: Suitable for text fields where values are tab-separated, often used in raw data exports. _ Pipe (|): Commonly used in data streams where values are separated by a vertical bar. _ Hyphen (-): Useful for splitting structured identifiers, such as dates or codes. * Underscore (_): Frequently used in programming and database fields for separating components.
• Custom Delimiters and Regex: Users can define a custom delimiter or enter a regular expression (regex) to split the column based on more complex patterns. Regex provides advanced pattern-matching capabilities, making it possible to handle even the most intricate data splitting requirements.

3. Input New Column Names#

• Naming Flexibility: Assign names to the new columns created from the split operation. There is no limit to the number of new columns that can be generated from a single split operation. Properly naming these columns ensures clarity and consistency within your dataset.

4. Repeat for Additional Columns#

• Batch Processing: You can configure multiple split operations within the same workflow, applying them to different columns. This capability streamlines the data preparation process, saving time and maintaining consistency across the dataset.

5. Finalize the Split Operation#

• Submission: Once all configurations are complete, submit the split operation. Edilitics will process the data, creating new columns as specified, thereby refining and organizing your dataset for better analysis and reporting.

Basics of Regular Expressions (Regex)#

Regular expressions (regex) are sequences of characters that define search patterns. They are powerful tools for performing complex searches and text manipulations. Here are some common regex components:

• Literal Characters: Match themselves exactly (e.g., cat matches the string "cat").
• Meta Characters: Special characters with specific meanings (e.g., . matches any single character except newline).
• Character Classes: Denote a set of characters to match (e.g., [abc] matches any one of 'a', 'b', or 'c').
• Quantifiers: Specify the number of occurrences to match (e.g., * matches zero or more occurrences of the preceding element).
• Anchors: Assert positions within the string (e.g., ^ matches the start of a string, and $ matches the end).

Common Regex Syntax:

• .: Matches any character except newline.
• *: Matches 0 or more repetitions of the preceding element.
• +: Matches 1 or more repetitions of the preceding element.
• ?: Matches 0 or 1 repetition of the preceding element.
• []: Matches any one of the enclosed characters.
• \d: Matches any digit (0-9).
• \s: Matches any whitespace character.
• \w: Matches any word character (alphanumeric and underscore).
• ^: Matches the start of the string.
• $: Matches the end of the string.
• |: Acts like a boolean OR.
• (): Groups together the enclosed pattern.

Examples of Default Regex Patterns:

• Find all digits: \d+
  • Use Case: Identifies and replaces all numeric values within a text.
• Find all whitespace characters: \s+
  • Use Case: Removes or replaces extra spaces in text.
• Find all word characters: \w+
  • Use Case: Matches any word character (alphanumeric and underscore).
• Find specific words: \bword\b
  • Use Case: Locates the exact word "word" in the text.
• Find email addresses: [a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}
  • Use Case: Identifies email addresses in the text for validation or replacement.

Real-World Applications of Split Columns#

1. Retail Industry#

• Objective: Separate product codes into their constituent parts for detailed analysis.
• Scenario: Splitting a ProductCode column that uses a hyphen to combine multiple identifiers, such as Category-Subcategory-Item.
• Delimiter: Hyphen (-)
• Sample Data:
  • Before Split:
    • ProductCode: "ELEC-LAPTOP-001"
  • After Split:
    • Category: "ELEC"
    • Subcategory: "LAPTOP"
    • Item: "001"
• Use Case: Enhancing product categorization by breaking down the ProductCode into separate, analyzable fields.

2. Healthcare Industry#

• Objective: Isolate date components for temporal analysis.
• Scenario: Splitting a DateOfBirth column into Year, Month, and Day components.
• Delimiter: Hyphen (-)
• Sample Data:
  • Before Split:
    • DateOfBirth: "1985-08-15"
  • After Split:
    • Year: "1985"
    • Month: "08"
    • Day: "15"
• Use Case: Facilitating demographic and age-based analyses by separating the date of birth into its components.

3. Finance Industry#

• Objective: Break down transaction identifiers for auditing purposes.
• Scenario: Splitting a TransactionID column that includes a combination of AccountNumber, TransactionType, and Date using a regex pattern.
• Regex Pattern: (\d+)\|([A-Z]+)\|(\d{4}-\d{2}-\d{2})
• Sample Data:
  • Before Split:
    • TransactionID: "123456|CREDIT|2024-08-01"
  • After Split:
    • AccountNumber: "123456"
    • TransactionType: "CREDIT"
    • TransactionDate: "2024-08-01"
• Use Case: Enhancing transparency in financial records by breaking down transaction identifiers into their individual components.

4. Manufacturing Industry#

• Objective: Extract batch information for quality control.
• Scenario: Splitting a BatchDetails column containing combined data on BatchNumber, ProductionLine, and Shift separated by underscores.
• Delimiter: Underscore (_)
• Sample Data:
  • Before Split:
    • BatchDetails: "B001_LINEA_SHIFT1"
  • After Split:
    • BatchNumber: "B001"
    • ProductionLine: "LINEA"
    • Shift: "SHIFT1"
• Use Case: Improving traceability and quality control by separating batch details into distinct, analyzable fields.

5. Education Industry#

• Objective: Separate student full names into first and last names using regex for record management.
• Scenario: Splitting a FullName column into FirstName and LastName using a regex pattern.
• Regex Pattern: (\w+)\s(\w+)
• Sample Data:
  • Before Split:
    • FullName: "John Doe"
  • After Split:
    • FirstName: "John"
    • LastName: "Doe"
• Use Case: Streamlining student data management by creating separate fields for first and last names, facilitating sorting and analysis.

The Split Columns feature in Edilitics offers a robust solution for data professionals seeking to refine and organize their datasets. By enabling the division of a single column into multiple distinct fields, this feature enhances the clarity and usability of your data, ensuring it is prepared for in-depth analysis and reporting. Whether working with simple delimiters or complex regex patterns, Edilitics provides the tools necessary to optimize your data structure and drive meaningful insights.