---
title: "Polars"
sidebarTitle: "Polars & Arrow"

---

import {
  PyPlatformsPolarsCreateTable,
  PyPlatformsPolarsImports,
  PyPlatformsPolarsLazyframe,
  PyPlatformsPolarsPydantic,
  PyPlatformsPolarsVectorSearch,
} from '/snippets/integrations.mdx';

LanceDB supports [Polars](https://github.com/pola-rs/polars), a blazingly fast DataFrame library for Python written in Rust. Under the hood, both Lance and Polars speak Arrow, so passing data back and forth stays zero-copy and ergonomic.

## Create and Query a Table {#create-and-query-a-table}

Import the required libraries, including the optional Pydantic helpers if you plan to define schemas.

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

Build a Polars `DataFrame`, convert it to Arrow, and use it directly when creating a LanceDB table.

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

Run vector search and keep the results as a Polars `DataFrame` for further processing or visualization.

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

## Work with LazyFrames {#work-with-lazyframes}

When you want to operate on the entire table (potentially larger than RAM), convert to a Polars `LazyFrame` so you can chain transformations without loading everything at once.

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

## Define Schemas with Pydantic {#define-schemas-with-pydantic}

You can also describe your table via `LanceModel` and continue ingesting data from Polars. This is useful when multiple teams share a schema or when you want validation.

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