## Related terms

- data\-transformation
- workflow
- automation\-module

See the [glossary](/glossary.md) for definitions.

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# Remove Columns


This transformation step allows you to remove columns that match the rule you define in the configuration of the step. As a result, if you use a transformation that contains this step, the system removes the matching columns as a part of data transformation in the workflow.  

## Step configuration
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<figure><img src="/api/docs/image/5a887ed9817467324004bf8ca7fff4fc34631e0a/docs/automation/_gfx/dts-remove-columns.png" class="medium" alt="Configuration of the Remove Columns step with two rules, each with three conditions joined with or"><figcaption>Configuration of the Remove Columns step</figcaption></figure>

1. In the Data Transformation Studio, [add](/docs/automation/data-transformation-and-imports/creating-data-transformation#add-transformation-steps) the **Remove Columns** step.
2. In the header of the rule, click **Remove** and select one of the following options:  
    - **Remove** - This option deletes columns that match the conditions you define in the further steps.
    - **Keep** - This option keeps only the columns that match the conditions you define in the further steps. The columns that don't match the conditions are removed.
3. From the dropdown list, select one of the logical operators that the column name must meet:  
    - **Equal**
    - **Not equal**
    - **Starts with**  
    - **Ends with**  
    - **Contain**  
    - **Not contain**
    - **Regex**
4. In the text field, enter the value.
5. Optionally, to add another condition to the rule, click **Add condition** and repeat steps 3-4.  
    The conditions in a rule are joined with the **or** operator: a column matches the rule if its name meets at least one of the conditions.
6. To add more rules, click **Add rule** and repeat steps 2-5.  
    The rules are performed one after another. Each rule works on the columns that are left after the previous one.
7. To define what happens if rows contain errors, from the dropdown list next to **Add rule**, select one of the [error handling](#handle-errors) options.
8. Confirm the settings by clicking **Apply**.
9. You can check the result in the **Output** tab.

### Handle errors

You can define what happens with rows in which the step fails (for example, as a result of Jinja rendering). From the dropdown list next to **Add rule**, select one of the following options:
- **Skip row if error occurred** - It omits the row when an exception occurs.
- **Skip if result is null or empty string** - It omits rows with empty strings and `null` (which may be the result of rendering Jinja).
- **Stop further transformation** - The transformation stops at the row with an error. The data before the line that caused the error is transformed (and imported if the transformation is used in the Data Transformation Studio node).
- **Insert null if error occurred** - Wrong values are replaced with `null`. If you use such a transformation for the import of profiles, the existing value of an attribute is cleared and replaced with the `null` value.
- **Insert empty string if error occurred** - Wrong value is removed and the cell remains empty.

## Example of use
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While importing a list of transactions, you can edit out unnecessary columns. In this example, these are any columns that include an underscore in their name (`import_type`, `payment_info`) and columns whose name is exactly `customer`


<details class="accordion"><summary>Click to see example file</summary><div class="accordion-content"><pre><code class="language-plaintext">price,customer,orderId,payment_info,date,import_type 10.99,John Doe,123345,cash,12.03.2022,toSynerise 4.99,Dwight Addams,122345,cash,12.03.2022,toSynerise 8.99,Andrew Malone,112345,cash,12.03.2022,toSynerise</code></pre></div></details>
  

1. In the Data Transformation Studio, [add sample data](/docs/automation/data-transformation-and-imports/creating-data-transformation#add-sample-data).
2. Click **Add step** and select **Remove Columns**.  
3. In the configuration of the step:  
    1. Leave the **Remove** option selected.  
    2. Select **Contain** and enter `_`
    3. Click **Add rule**.
    4. In the new rule, leave the **Remove** option selected.
    5. Select **Equal** and enter `customer`
4. Confirm by clicking **Apply**.  
    **Result**: In the **Output** tab, the data contains only the `price`, `orderId`, and `date` columns. You can use this transformation in [the workflow](/docs/automation/operation/data-transformation-node) to import or export files with the structure presented in this example.