Merge Columns | Create Composite Fields with No Code
Creating structured, readable, and reusable fields from fragmented data often requires scripting or post-processing. Whether you're generating full names, unified addresses, or composite identifiers, Edilitics makes this seamless with a no-code, schema-aware Merge Columns operation.
It enables you to combine multiple fields into a single column—complete with delimiter selection, naming enforcement, and support for multiple merges in a single step.
Why Merge Columns Matters
Scattered values across multiple columns often result in:
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❌ Fragmented identifiers that hinder joins or exports
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❌ Hard-to-read outputs across reports and dashboards
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❌ Inconsistent formats when merging across systems
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❌ Complex ETL logic to concatenate fields correctly
Edilitics simplifies all of this with:
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✅ Visual selection of columns
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✅ Predefined and custom delimiters
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✅ Support for multiple merge steps in a single workflow
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✅ Schema validation and naming enforcement
How to Merge Columns in Edilitics
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Select columns to merge
Choose two or more columns from your table. You can merge as many as required—there’s no upper limit.
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Choose a delimiter
Pick from standard delimiters or define your own:
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Space – For names and phrases
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Tab (
\t) – For structured file exports -
Pipe (
|) – For clean field separation in pipelines -
Hyphen (
-) – For compound IDs -
Underscore (
_) – For readable keys -
Custom – Enter any character(s) for specific needs
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Name your new column
Assign a clear, valid name to the merged field. Edilitics enforces naming rules (alphanumeric + underscores, no starting digits or underscores).
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Add more merge configurations (optional)
Create multiple merged fields within the same operation—for example, one for full name, another for address.
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Submit the operation
Execute the transformation. Edilitics adds the merged columns to your dataset while preserving originals.
Common Use Cases for Column Merging
| Industry | Merged Column | Source Columns | Delimiter | Purpose |
|---|---|---|---|---|
| Retail | FullAddress | StreetAddress, City, State, Zip | Space | Create a CRM-ready, unified address string |
| Healthcare | PatientRecordID | PatientID, FirstName, LastName | Underscore | Standardize identifiers across medical systems |
| Finance | TransactionDetail | Type, AccountNumber, Date | Pipe | Generate readable transaction logs for audits |
| Manufacturing | BatchID | BatchNumber, Date, FactoryCode | Hyphen | Create traceable batch keys for quality tracking |
| Education | StudentProfileKey | StudentID, Name, Year | Tab | Build unique keys for student records across systems |
Manual Equivalent – SQL & Pandas Examples
SQL Example – Redshift
SELECT CONCAT_WS(' ', StreetAddress, City, State, ZipCode) AS FullAddressFROM customers;
Pandas Example
df['FullAddress'] = df[['StreetAddress', 'City', 'State', 'ZipCode']].agg(' '.join, axis=1)
In Edilitics, this is done in a dropdown—no syntax, no debugging, no intermediate staging.
Reliable, Flexible, and Built for Scale
The Merge Columns operation in Edilitics is:
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✅ Schema-aware – Only text-compatible columns shown
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✅ Naming safe – Prevents malformed or duplicate column names
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✅ Flexible – Supports multiple delimiter types and merges in one step
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✅ Reversible – All changes are logged and versioned within the flow
Whether you're building unified keys, standardizing output formats, or simplifying exports, Edilitics turns fragmented columns into clean, structured, and reusable fields—without a single line of code.
Next: Refine or Export Your Unified Columns
After merging, continue shaping your dataset with:
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