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:
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❌ Hard-to-analyze datasets
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❌ Inconsistent field references
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❌ Broken joins, filters, and aggregations
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❌ Complex manual parsing in external tools
Edilitics solves this with:
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✅ Visual column splitting using delimiters or regex
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✅ Support for multi-column outputs
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✅ Schema validation and safe naming enforcement
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✅ Governed, reversible transformations
How to Split Columns in Edilitics
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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.
- 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.
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Define New Column Names
Assign clear names to each resulting column. Edilitics enforces naming conventions (no special characters, no leading numbers or underscores).
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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
Industry | Scenario | Split Method | Purpose |
---|---|---|---|
Retail | Break down ProductCode like ELEC-LAPTOP-001 | Hyphen | Enable category/subcategory/item-level analytics |
Healthcare | Separate DateOfBirth into Year, Month, Day | Hyphen | Perform age group analysis or temporal reporting |
Finance | Parse TransactionID like `123456 | CREDIT | 2024-08-01` |
Manufacturing | Split BatchDetails like B001_LINEA_SHIFT1 | Underscore | Streamline batch tracing and quality checks |
Education | Divide FullName like John Doe into FirstName , LastName | Regex | Improve 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 ItemFROM 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 splitdf[['Category', 'Subcategory', 'Item']] = df['ProductCode'].str.split('-', expand=True)# Sample regex-based splitdf[['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:
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✅ Schema-validated – Only text-compatible columns are shown
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✅ Regex-capable – Pattern-driven parsing without manual coding
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✅ Naming enforced – Prevent invalid or duplicate columns
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✅ 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