Getting started

Turn a messy spreadsheet into a clean SQLite database.

In this tutorial, you’ll upload a spreadsheet, convert it into a structured sheet, and download it as a SQLite database and individual CSV files.

Prerequisites

Python 3.9+ or Node.js 20+
A DeepTable API key (get one at deeptable.com)

pip install deeptable

npm install @deeptable/deeptable

2. Set your API key

export DEEPTABLE_API_KEY="your-api-key"

3. Download an example spreadsheet

curl -O https://docs.deeptable.com/acme_subscriptions.xlsx

4. Run this script

Python
TypeScript

Create a file called main.py:

import asyncio
from pathlib import Path

from deeptable import AsyncDeepTable


async def main():
    client = AsyncDeepTable()

    # Upload your spreadsheet
    file = await client.files.upload(
        file=Path("acme_subscriptions.xlsx")
    )

    # Create a structured sheet (this triggers the conversion)
    structured_sheet = await client.structured_sheets.create(file_id=file.id)

    # Wait for processing to complete
    while structured_sheet.status in ("queued", "in_progress"):
        await asyncio.sleep(10)
        structured_sheet = await client.structured_sheets.retrieve(structured_sheet.id)

    # Download as a SQLite database
    sqlite = await client.structured_sheets.download(structured_sheet.id, format="sqlite")
    await sqlite.write_to_file("acme_subscriptions.sqlite")


if __name__ == "__main__":
    asyncio.run(main())

Run it:

python main.py

Create a file called main.ts:

import DeepTable from "@deeptable/deeptable";
import * as fs from "fs";

const client = new DeepTable();

async function main() {
  // Upload your spreadsheet
  const file = await client.files.upload({
    file: fs.createReadStream("acme_subscriptions.xlsx"),
  });

  // Create a structured sheet (this triggers the conversion)
  let structuredSheet = await client.structuredSheets.create({
    file_id: file.id,
  });

  // Wait for processing to complete
  while (structuredSheet.status === "queued" || structuredSheet.status === "in_progress") {
    await new Promise((resolve) => setTimeout(resolve, 10000));
    structuredSheet = await client.structuredSheets.retrieve(structuredSheet.id);
  }

  if (structuredSheet.status === "failed") {
    throw new Error("Processing failed");
  }

  // Download as a SQLite database
  const response = await client.structuredSheets.download(structuredSheet.id, {
    format: "sqlite",
  });
  const buffer = Buffer.from(await response.arrayBuffer());
  fs.writeFileSync("acme_subscriptions.sqlite", buffer);
}

main();

Run it:

npx tsx main.ts

What just happened?

You uploaded a spreadsheet, and DeepTable:

Analyzed the structure - finding tables, headers, and relationships
Converted the messy spreadsheet data into clean, normalized tables
Returned a SQLite database you or an LLM can query directly

Download as CSV or Parquet

Need individual tables instead of a database? You can download each table separately:

Python
TypeScript

async for table in client.structured_sheets.tables.list(structured_sheet.id):
    csv = await client.structured_sheets.tables.download(
        structured_sheet_id=structured_sheet.id,
        table_id=table.id,
        format="csv",  # or "parquet"
    )
    await csv.write_to_file(f"{table.name}.csv")

for await (const table of client.structuredSheets.tables.list(structuredSheet.id)) {
  const response = await client.structuredSheets.tables.download(
    structuredSheet.id,
    table.id,
    { format: "csv" }  // or "parquet"
  );
  const buffer = Buffer.from(await response.arrayBuffer());
  fs.writeFileSync(`${table.name}.csv`, buffer);
}

Next steps

Explore the API Reference to see all available endpoints