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

# Importing from File

> Upload CSV or JSON to turn existing spreadsheets into datasets: headers become columns and each row is ready for prompt-driven runs.

<img src="https://mintcdn.com/quashbugs/8CKl0mBxr-B3bXzO/images/image-11.png?fit=max&auto=format&n=8CKl0mBxr-B3bXzO&q=85&s=8940c31e521641565e5da16e697763e1" alt="" width="1730" height="1009" data-path="images/image-11.png" />

## Supported formats

Quash supports two file formats for import:

**CSV** A plain text file with a header row followed by data rows. The header row becomes the column names. Each subsequent row becomes one dataset row.

```csv theme={null}
email,password,role
valid@example.com,Correct123!,standard
admin@example.com,Admin456!,admin
guest@example.com,Guest789!,guest
```

**JSON** An array of flat objects. Each object becomes one row. The keys of the objects become the column names. All objects should have the same keys.

```json theme={null}
[
  { "email": "valid@example.com", "password": "Correct123!", "role": "standard" },
  { "email": "admin@example.com", "password": "Admin456!", "role": "admin" },
  { "email": "guest@example.com", "password": "Guest789!", "role": "guest" }
]
```

## How to import

1. Click **Test Data** in the left navigation panel.
2. Click **Upload** in the top right of the library view (next to **+ New**).
3. Drag and drop your file into the upload modal, or click to browse and select it.
4. Quash reads the file structure and creates the dataset automatically.
5. The dataset opens in the editor. Check that column names and values are correct before saving.
6. Edit the **title** and **slug** — the import uses the filename as the default title, which is usually not descriptive enough.

## Before you import

A few things to check in your source file before importing:

**Header row is present and correct.** CSV files must have a header row as the first line. JSON files must use consistent keys across all objects. Missing or inconsistent headers cause import errors or misaligned columns.

**No empty columns.** Empty columns import as unnamed columns and clutter the dataset. Remove any blank columns from your source file before importing.

**Values are plain text.** Quash stores text only. Formulas, rich formatting, merged cells, and special characters in Excel or Google Sheets do not transfer. Export to plain CSV first — not `.xlsx`.

**File size is reasonable.** Files with more than 200 rows will be truncated at import. Split large files into multiple datasets with descriptive slugs before importing.

## Updating an existing dataset from a file

Importing always creates a **new** dataset — it does not update an existing one. If your source data changes and you need to refresh a dataset:

1. Import the updated file as a new dataset.
2. Update the slug of the new dataset to match the old one (after deleting or renaming the old dataset).
3. Or: open the existing dataset and manually update the changed values.

For frequently-changing data — test accounts refreshed weekly, product inventories updated daily — consider keeping a canonical source file and re-importing on a cadence rather than manually maintaining the dataset in Quash.