Split Columns | Break Down Data Structures Without Code

Concatenated or complex fields often obscure critical details—making datasets harder to analyze, join, or report. The Split Columns operation in Edilitics solves this cleanly: users can divide a single column into multiple structured fields using delimiters or regex patterns—with no scripts required.

Built for precision, Edilitics' split operations offer delimiter flexibility, regex support, and full schema validation—helping you normalize messy fields with governed, no-code transformations.

Why Splitting Matters

Poorly structured columns often cause:

  • ❌ Hard-to-analyze datasets

  • ❌ Inconsistent field references

  • ❌ Broken joins, filters, and aggregations

  • ❌ Complex manual parsing in external tools

Edilitics solves this with:

  • ✅ Visual column splitting using delimiters or regex

  • ✅ Support for multi-column outputs

  • ✅ Schema validation and safe naming enforcement

  • ✅ Governed, reversible transformations

How to Split Columns in Edilitics

  1. Select the Column to Split

    Pick any eligible column from your table. Only columns with text-compatible data types are listed.

📌 Columns already split or flattened will be excluded automatically.

  1. Choose Your Split Method

You have two options:

Delimiter-Based Split

Use predefined or custom delimiters to split text:

  • Space – Separate names, words, phrases

  • Tab (\t) – Handle structured exports

  • Pipe (|) – Split pipeline-separated fields

  • Hyphen (-) – Divide IDs, dates, or codes

  • Underscore (_) – Separate system-generated keys

  • Custom – Define any single-character delimiter

Regex-Based Split (Advanced)

For complex cases, define a regular expression (regex) to split based on text patterns, not just static characters.

Examples:

  • Split by multiple spaces: \s+
  • Split by dash OR underscore: [-_]
  • Extract account|type|date format: (\d+)\|([A-Z]+)\|(\d{4}-\d{2}-\d{2})

📌 Regex gives you flexible, pattern-driven control over splits.

  1. Define New Column Names

    Assign clear names to each resulting column. Edilitics enforces naming conventions (no special characters, no leading numbers or underscores).

  2. Submit the Operation

    After previewing the split results, submit the operation. The newly generated columns will appear alongside your original dataset.

Real-World Use Cases for Splitting

IndustryScenarioSplit MethodPurpose
RetailBreak down ProductCode like ELEC-LAPTOP-001HyphenEnable category/subcategory/item-level analytics
HealthcareSeparate DateOfBirth into Year, Month, DayHyphenPerform age group analysis or temporal reporting
FinanceParse TransactionID like `123456CREDIT2024-08-01`
ManufacturingSplit BatchDetails like B001_LINEA_SHIFT1UnderscoreStreamline batch tracing and quality checks
EducationDivide FullName like John Doe into FirstName, LastNameRegexImprove student record organization and reporting

Manual Equivalent – SQL & Pandas Examples

SQL Example – Delimiter-Based Split (Redshift)


SELECT
SPLIT_PART(ProductCode, '-', 1) AS Category,
SPLIT_PART(ProductCode, '-', 2) AS Subcategory,
SPLIT_PART(ProductCode, '-', 3) AS Item
FROM sales_data;

🔵 SQL regex splits are complex and database-specific (e.g., REGEXP_SUBSTR), so Edilitics focuses on no-code and safe delimiter splits for SQL users.

Pandas Example – Delimiter and Regex Splits


import pandas as pd
# Sample delimiter-based split
df[['Category', 'Subcategory', 'Item']] = df['ProductCode'].str.split('-', expand=True)
# Sample regex-based split
df[['AccountNumber', 'TransactionType', 'TransactionDate']] = df['TransactionID'].str.extract(r'(\d+)\|([A-Z]+)\|(\d{4}-\d{2}-\d{2})')

✅ Edilitics lets you configure both types with dropdowns or simple regex input—no scripting needed.

Governed, Safe, and Regex-Ready

Split Columns in Edilitics is:

  • ✅ Schema-validated – Only text-compatible columns are shown

  • ✅ Regex-capable – Pattern-driven parsing without manual coding

  • ✅ Naming enforced – Prevent invalid or duplicate columns

  • ✅ Non-destructive – Originals preserved until finalization

Whether you're preparing data for analytics, cleaning exports, or enhancing traceability, Split Columns makes normalization effortless and governed—helping teams move from messy fields to structured, usable datasets with no manual overhead.

Next: Continue Structuring Your Data

After splitting columns, strengthen your dataset with:

Enterprise Support & Technical Assistance

For technical inquiries, implementation support, or enterprise-level assistance, our dedicated technical support team is available to ensure optimal deployment and utilization of Edilitics solutions. Please contact our enterprise support desk at support@edilitics.com. Our team of specialists will respond promptly to address your requirements.

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