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What It Does

  • Dynamically creates new columns in your DataFrame based on static or dynamic values, including placeholder variables.
  • Automatically resolves column name conflicts by adding numeric suffixes (e.g., deal_stage → deal_stage_1).
  • Supports creating multiple columns in a single operation to streamline your workflow.
  • Includes a test mode, limiting the output to the first 5 rows for validation purposes.

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🏁 Getting Started

Create Column Node config screenshot
1

Add the Create Column Node

Drag and drop the Create Column Node into your workflow.
2

Define Columns to Create

Specify the columns to be created with either static or dynamic values (e.g., lead_source or deal_size).
3

Run the Workflow

Execute the workflow to create the new columns in the output DataFrame.
4

Monitor the Output

The output DataFrame will contain the original columns plus the newly created ones.

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Inputs

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🛠️ Required Fields

  • Column Name (✅)
    The name of the column to be created.
    Why it matters: Specifies the name of the new column that will be added to the DataFrame.
  • Value (✅)
    The value to be assigned to the column. This can be a static value or a dynamic placeholder.
    Why it matters: The value that will fill the new column, allowing for custom data calculations or classifications.

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Output

You’ll get back your input data — plus the columns you chose to add.
  • Row count and column order are preserved.
Create Column Node output screenshot
✨ If your table already has any of those column names, the new ones will be renamed automatically (e.g., summary_1, summary_2, etc.).

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How It Works

  1. Loads the input DataFrame from the previous node.
  2. Resolves column names to avoid conflicts with existing columns.
  3. Processes each row to replace placeholder variables with actual values.
  4. Adds the new columns to the output DataFrame.
  5. If in test mode, limits the output to the first 5 rows.
  6. Saves the output DataFrame to S3 and returns the output metadata.

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🚀 Example Use Cases & Prompts

Use CaseSetup or Prompt Example
Track lead source"lead_source": "LinkedIn"
Calculate deal size"deal_value": quantity*price_per_unit“
Classify lead status"lead_status": "MQL" (based on lead score threshold)
Calculate deal urgency"deal_urgency": "High" (based on close_date proximity)
Add account region"account_region": country“

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✨ Pro Tips

Use this node to create a lead score classification column by combining behavior data with firmographics (e.g., "lead_classification": ”High” if engagement_score > 75“).
Use this node to add targeted campaign tags based on dynamic conditions like product_interest or last_contacted_date.

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⚠️ Important Considerations

At least one column must be selected for the node to run.

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🛠 Troubleshooting & Gotchas

SymptomLikely CauseQuick Fix
Column contains different informationRenamed due to conflictLook for suffix like lead_status_1

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📝 FAQ

No. Use other nodes like Filter Data to achieve the same.
No, use the Insert Input feature to easily select and insert columns from the previous node.
The node automatically adds numeric suffixes (e.g., lead_status_1) to avoid overwriting.

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💰 Pricing

The Create Column Node incurs no additional cost for column creation.
ActionCredit Cost
Column creation (static/dynamic)0 credits

Drop this node into your flow to dynamically create new columns in your DataFrame. 🚀