lamindb 0.51.1

Open-source data platform for biology.

LaminDB - Open-source data platform for biology

Public beta: Close to having converged a stable API, but some breaking changes might still occur.

Here is an [intro video](https://www.youtube.com/watch?v=DtJ9KnqWA8Q) to guide beta testing.

LaminDB is a Python library to manage data & analyses related to biology:

• Query, validate & link data batches using biological registries & ontologies.
• Track & query data lineage across pipelines, notebooks & app uploads.
• Manage features & labels schema-less or schema-full.
• Collaborate across a mesh of LaminDB instances.

If you want a UI: LaminApp is built on LaminDB. If LaminDB ~ git, LaminApp ~ GitHub.

(Enterprise features for LaminApp, support, integration tests & schemas are available on a paid plan - in your or our infrastructure.)

Quickstart

Run pip install 'lamindb[jupyter]' and lamin signup <email> on the command line (more info).

Init a LaminDB instance with local or cloud default storage like you'd init a git repository:

$ lamin init --storage ./mydata   # or s3://my-bucket, gs://my-bucket

Validate & register a DataFrame that comes with basic metadata:

import lamindb as ln
import pandas as pd

ln.track()  # track run context in a notebook

# save target feature names in Feature registry
features = ln.Feature.from_values(["feature1", "feature2", "perturbation"])
ln.save(features)

# receive a batch of data
df = pd.DataFrame(
    {"feature1": [1, 2, 3], "feature2": [3, 4, 5], "perturbation": ["pert1", "pert2", "pert1"]}
)

# validate features & create a Dataset object
dataset = ln.Dataset.from_df(df, name="Dataset 1")
dataset.save()  # save/upload dataset

Search, query, and load a DataFrame:

ln.Dataset.search("dataset 1")  # run a search

# run a query (under the hood, you have the full power of SQL to query)
dataset = ln.Dataset.filter(name__contains="set 1").one()

df = dataset.load()

Documentation

Read the docs.