lance
Enrichment pendingOpen Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and
GraphCanon updated today · GitHub synced today
Trust & integrity
Full report- Maintenance
- Very active (0d since push)
- As of today · Source: github_public_v1
- Provenance
- Not a fork · Organization account
- As of today · Source: github_public_v1
- Security (OSV)
- No lockfile
- As of today · Source: none
Public GitHub metadata and optional OSV dependency scans. Signals, not a guarantee. Trust methodology.
Overview
Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..
Capability facts
- Deploy
- Self-host
Source: dockerfile:docker-compose.yml · Jul 11, 2026
- Docker
- Dockerfile present
Source: dockerfile:docker-compose.yml · Jul 11, 2026
- Languages
- rust
Source: github.language · Jul 11, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 11, 2026)
```python import lanceSource link
Tags
README
Quick Start
Installation
pip install pylance
To install a preview release:
pip install --pre --extra-index-url https://pypi.fury.io/lance-format pylance
[!TIP] Preview releases are released more often than full releases and contain the latest features and bug fixes. They receive the same level of testing as full releases. We guarantee they will remain published and available for download for at least 6 months. When you want to pin to a specific version, prefer a stable release.
Converting to Lance
import lance
import pandas as pd
import pyarrow as pa
import pyarrow.dataset
df = pd.DataFrame({"a": [5], "b": [10]})
uri = "/tmp/test.parquet"
tbl = pa.Table.from_pandas(df)
pa.dataset.write_dataset(tbl, uri, format='parquet')
parquet = pa.dataset.dataset(uri, format='parquet')
lance.write_dataset(parquet, "/tmp/test.lance")
Reading Lance data
dataset = lance.dataset("/tmp/test.lance")
assert isinstance(dataset, pa.dataset.Dataset)
Pandas
df = dataset.to_table().to_pandas()
df
DuckDB
import duckdb