Files
lancedb/python
Pranav Achar ff81428a9c fix(python): flatten_columns raises when flatten=False (#3629)
### Summary

`flatten_columns` raises `ValueError` when called with `flatten=False`,
even though `False` should mean "do not flatten". This is reachable from
the public API — `Query.to_pandas(flatten=...)` and
`to_batches(flatten=...)` type their `flatten` param as
`Optional[Union[int, bool]]` and pass it straight to `flatten_columns`.

### Cause

`bool` is a subclass of `int`, so `isinstance(False, int)` is `True`.
`flatten=False` skips the `flatten is True` check, falls into the
integer branch, and `False <= 0` evaluates to `True`, raising:

```
ValueError: Please specify a positive integer for flatten or the boolean value `True`
```

### Reproduction

```python
import lancedb
db = lancedb.connect("/tmp/db")
t = db.create_table("t", data=[{"id": 1, "vector": [0.1, 0.2]}])
t.search([0.1, 0.2]).to_pandas(flatten=False)   # -> ValueError
```

### Fix

Guard the integer branch with `not isinstance(flatten, bool)` so that
`flatten=False` (and `None`) mean "do not flatten". Behavior is
otherwise unchanged:

- `flatten=True` → flatten all nested levels
- positive `int` → flatten to that depth
- non-positive `int` (e.g. `0`) → still rejected with `ValueError`

Added a regression test in `tests/test_util.py` covering `None`,
`False`, `True`, a positive depth, and `0`.
2026-07-09 11:07:26 -07:00
..
2025-01-29 08:27:07 -08:00
2024-04-05 16:22:59 -07:00

LanceDB Python SDK

A Python library for LanceDB.

Installation

pip install lancedb

Preview Releases

Stable releases are created about every 2 weeks. For the latest features and bug fixes, you can install the preview release. These releases receive the same level of testing as stable releases, but are not guaranteed to be available for more than 6 months after they are released. Once your application is stable, we recommend switching to stable releases.

pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb

Usage

Basic Example

import lancedb
db = lancedb.connect('<PATH_TO_LANCEDB_DATASET>')
table = db.open_table('my_table')
results = table.search([0.1, 0.3]).limit(20).to_list()
print(results)

Development

See CONTRIBUTING.md for information on how to contribute to LanceDB.