When to Use It
- Clean up datasets with too many unnecessary columns
- Remove sensitive data before sharing reports
- Prepare data for systems with field limitations
- Reduce data size for better performance
- Focus on specific metrics for analysis
Inputs
| Field | Type | Required | Description |
|---|---|---|---|
| Data | Data | Yes | The dataset containing fields you want to remove |
| Removal Mode | Select | Yes | Choose “Remove Specific Fields” or “Keep Only Specific Fields” |
| Fields to Remove | List | Yes* | Names of fields to remove (*For Remove mode only) |
| Fields to Keep | List | Yes* | Names of fields to keep (*For Keep mode only) |
Outputs
| Output | Description |
|---|---|
| Filtered Data | Dataset with specified fields removed |
Credit Cost
Free to use - no credits required.How It Works
Remove Specific Fields Mode: Removes only the fields you specify, keeps everything else. Keep Only Specific Fields Mode: Keeps only the fields you specify, removes everything else. Example: Original Data:| campaign_name | campaign_id | cost | impressions |
|---|---|---|---|
| Summer Sale | 12345 | $150 | 5,000 |
| Black Friday | 67890 | $300 | 8,500 |
| campaign_name | cost | impressions |
|---|---|---|
| Summer Sale | $150 | 5,000 |
| Black Friday | $300 | 8,500 |
Choosing the Right Mode
Use “Remove Specific Fields” when:- You have mostly useful data with a few unwanted fields
- You want to remove sensitive or technical fields
- Most of your columns should stay in the final dataset
- You have lots of data but only need a few specific fields
- You want to create a focused, minimal dataset
- You’re dealing with very wide datasets with many unnecessary columns
Tips
Field Identification:- Run a small test first to see what fields are available
- Check field names carefully - they’re case-sensitive
- Use data preview to understand your dataset structure
- If you need most fields, use “Remove Specific Fields”
- If you need only a few fields, use “Keep Only Specific Fields”
- Keep mode is often faster for very wide datasets
- Remove fields with mostly empty or null values
- Eliminate duplicate or redundant information
- Keep fields that provide unique, valuable insights
FAQ
What happens if I try to remove a field that doesn't exist?
What happens if I try to remove a field that doesn't exist?
The node will ignore non-existent field names and continue processing. Your data won’t be affected, but you won’t see any change for those field names.
Can I remove all fields from my data?
Can I remove all fields from my data?
Technically yes, but this would leave you with empty data rows. Always keep at least the fields you need for your intended use case.
Which mode is better for performance?
Which mode is better for performance?
“Keep Only Specific Fields” is often faster when you need just a few fields from a very wide dataset. “Remove Specific Fields” is better when removing just a few unwanted fields.
Can I remove fields with certain patterns or prefixes?
Can I remove fields with certain patterns or prefixes?
Not directly - you need to specify exact field names. If you have many similar field names, you’ll need to list each one individually.
How do I know what fields are in my data?
How do I know what fields are in my data?
Connect your data source first, then check the preview or run a small test. You can also use tools like AI Analyze Data to get a summary of your dataset structure.
Does removing fields affect data relationships?
Does removing fields affect data relationships?
No, removing fields only eliminates columns. The relationships between remaining fields and the row structure stay intact.
Should I remove fields before or after other processing?
Should I remove fields before or after other processing?
Generally remove fields early in your workflow to improve performance of subsequent nodes. However, keep fields you might need for calculations or analysis until after those steps.