---
title: "Pandas and PyArrow"
sidebarTitle: "Pandas & PyArrow"

---

import {
  PyPlatformsPandasAsyncExample,
  PyPlatformsPandasCreateTable,
  PyPlatformsPandasImports,
  PyPlatformsPandasVectorSearch,
} from '/snippets/integrations.mdx';

Because Lance is built on top of [Apache Arrow](https://arrow.apache.org/),
LanceDB fits naturally into Pandas-first workflows. You can ingest a `DataFrame`,
query it with LanceDB's vector operators, and keep working in Pandas without any glue code.

## Create a dataset {#create-a-dataset}

Start by importing LanceDB alongside your usual Pandas utilities and connect to a temporary database.

<CodeBlock filename="Python" language="Python" icon="python">
  {PyPlatformsPandasImports}
</CodeBlock>

Use the familiar `pd.DataFrame` API to prepare your rows, then pass the entire frame to `db.create_table`.

<CodeBlock filename="Python" language="Python" icon="python">
  {PyPlatformsPandasCreateTable}
</CodeBlock>

## Vector search {#vector-search}

Queries can return Pandas frames as well, so you can immediately inspect the results or pipe them into downstream analytics.

<CodeBlock filename="Python" language="Python" icon="python">
  {PyPlatformsPandasVectorSearch}
</CodeBlock>

## Async API {#async-api}

For web services or background jobs that already rely on `asyncio`, use the asynchronous helpers to keep everything non-blocking.

<CodeBlock filename="Python" language="Python" icon="python">
  {PyPlatformsPandasAsyncExample}
</CodeBlock>