Managing Your Data: Edit Rows, Rename Columns, and Export

Beyond upload: managing your data

csv-api isn't just a one-way street. After uploading your CSV, you can edit individual rows, rename columns, and export your modified data back to CSV. This tutorial covers all three.

Viewing your data

From your Dashboard, click on any dataset to view its detail page. You'll see:

• The dataset name and public ID
• Column definitions with their detected types
• A link to browse all rows
• Your API endpoint URL ready to copy

Editing rows

Click the View Data link on your dataset page to browse rows. On Pro and Enterprise plans, you can edit individual cell values directly:

1. 1 Navigate to the row listing for your dataset.
2. 2 Click the Edit button on the row you want to modify.
3. 3 Update the field values in the form.
4. 4 Click Save to persist your changes.

Changes take effect immediately — the next API request will return the updated values.

Adding new rows

You can also add new rows to your dataset directly from the web interface. From the row listing page, click Add Row and fill in the values for each column. The new row will be appended to your dataset and immediately available through the API.

Deleting rows

To remove a row, click the Delete button next to the row in the data listing. You'll be asked to confirm before the row is permanently removed.

Renaming columns

Noticed a typo in a column name, or want to clean up the headers from your original CSV? You can rename columns after upload:

1. 1 Go to your dataset's detail page.
2. 2 Find the column you want to rename in the columns list.
3. 3 Edit the name and save your changes.

The rename updates both the web interface and the API. After renaming, use the new column name in your filter, sort, and fields parameters.

Exporting to CSV

Need to download your data — including any edits you've made? Click the Export button on your dataset's detail page. This generates a fresh CSV file with all current data and column names.

This is useful for:

• Creating backups before making major changes
• Sharing cleaned-up data with colleagues
• Importing into other tools like Excel, Google Sheets, or databases

Deleting a dataset

If you no longer need a dataset, you can delete it from the dataset detail page. This permanently removes:

• The dataset record and metadata
• The underlying database table with all rows
• The API endpoint (any requests will return 404)