> ## Documentation Index
> Fetch the complete documentation index at: https://docs.markifact.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Remove Fields

> Remove unwanted fields from your datasets to clean data and focus on relevant information.

**Remove Fields** eliminates unnecessary columns/fields from your data to reduce clutter, improve performance, and focus on relevant information. Choose specific fields to remove or keep only the fields you need.

***

## 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       |

**After Removing "campaign\_id":**

| 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

**Use "Keep Only Specific Fields" when:**

* 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

**Mode Selection:**

* 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

**Data Quality:**

* Remove fields with mostly empty or null values
* Eliminate duplicate or redundant information
* Keep fields that provide unique, valuable insights

***

## FAQ

<Accordion title="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.
</Accordion>

<Accordion title="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.
</Accordion>

<Accordion title="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.
</Accordion>

<Accordion title="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.
</Accordion>

<Accordion title="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.
</Accordion>

<Accordion title="Does removing fields affect data relationships?">
  No, removing fields only eliminates columns. The relationships between remaining fields and the row structure stay intact.
</Accordion>

<Accordion title="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.
</Accordion>