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docs: clarify bad-vector handling for null components

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Gatefixer
2026-10-22 20:56:42 +00:00
parent f9d6589a93
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2 changed files with 15 additions and 7 deletions
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docs/web/ingestion/create-table.mdx
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@@ -148,9 +148,11 @@ non-nullable column, or provide actual nulls for it, ingestion fails; nullable c
omitted or written with null values. Without an explicit schema, Python infers list-like vector
values as fixed-size `float32` vector fields from the observed dimension.
For Python ingest, malformed vector values fail by default. If you expect occasional wrong-length,
null, or NaN vectors, choose an `on_bad_vectors` policy: `"drop"` removes those rows, `"fill"` writes
`fill_value`, and `"null"` writes nulls.
For Python ingestion, malformed vectors fail by default (`on_bad_vectors="error"`). This includes
vectors with incorrect sizes, NaNs, or null components, including pandas NaNs converted to Arrow
nulls. Use `"drop"` to remove affected rows, or `"null"` to replace affected vectors with nulls.
Use `"fill"` to replace NaN or null components with `fill_value` while preserving valid components.
This also pads short vectors with `fill_value` and truncates long vectors to the expected dimension.
### From an Arrow Table {#from-an-arrow-table}
You can also create LanceDB tables directly from Arrow tables.