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Brendan Clement
758f738b8c
This PR is part of the documentation revamp. Once the full set of PRs is merged, we’ll publish the combined site to staging for review before updating docs.lancedb.com. Ingestion, search and integration examples are incomplete or inconsistent across languages, and some guidance misstates SDK behavior. This brings the guides and their example sources together. It includes complete Python, TypeScript and Rust workflows, isolated provider and reranker examples, the Superlinked corrections, and guidance on retries that can duplicate writes. Stacked on #4491. Each MDX file has one PR owner. Rebase onto main after #4491 is squash-merged. Local assembly, Mint validation and link checks pass. Example code is unchanged from the previously validated package. Publication stays held through integration.
53 lines
1.6 KiB
Plaintext
53 lines
1.6 KiB
Plaintext
---
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title: "Pandas and PyArrow"
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sidebarTitle: "Pandas & PyArrow"
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availability:
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oss: available
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enterprise: available
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summary: >-
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DataFrames go into `create_table` and come back from query results the same
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way for local and deployment tables.
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---
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import {
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PyPlatformsPandasAsyncExample,
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PyPlatformsPandasCreateTable,
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PyPlatformsPandasImports,
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PyPlatformsPandasVectorSearch,
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} from '/snippets/integrations.mdx';
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Because Lance is built on top of [Apache Arrow](https://arrow.apache.org/),
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LanceDB fits naturally into Pandas-first workflows. You can ingest a `DataFrame`,
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query it with LanceDB's vector operators, and keep working in Pandas without any glue code.
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## Create a dataset {#create-a-dataset}
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Start by importing LanceDB alongside your usual Pandas utilities and connect to a temporary database.
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyPlatformsPandasImports}
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</CodeBlock>
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Use the familiar `pd.DataFrame` API to prepare your rows, then pass the entire frame to `db.create_table`.
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyPlatformsPandasCreateTable}
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</CodeBlock>
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## Vector search {#vector-search}
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Queries can return Pandas frames as well, so you can immediately inspect the results or pipe them into downstream analytics.
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyPlatformsPandasVectorSearch}
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</CodeBlock>
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## Async API {#async-api}
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For web services or background jobs that already rely on `asyncio`, use the asynchronous helpers to keep everything non-blocking.
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyPlatformsPandasAsyncExample}
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</CodeBlock>
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