Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

Lumen

Illuminate your data

Build Status Linux/MacOS/Windows Build Status
Coverage codecov
Latest dev release Github tag dev-site
Latest release Github release PyPI version lumen version conda-forge version defaults version
Docs gh-pages site
Support Discourse

Why Lumen?

The Lumen project provides a framework for visual analytics, which allows users to build data-driven dashboards from a simple yaml specification. The power of Lumen comes from the ability to leverage the powerful data intake, data processing and data visualization libraries available in the PyData ecosystem.

  • Data Intake: A flexible system for declaring data sources with strong integration with Intake, allows Lumen to query data from a wide range of sources including many file formats such as CSV or Parquet but also SQL and many others.
  • Data Proccessing: Internally Lumen stores data as DataFrame objects, allowing users to leverage familiar APIs for filtering and transforming data using Pandas while also providing the ability to scale these transformations out to a cluster thanks to Dask.
  • Data Visualization: Since Lumen is built on Panel all the most popular plotting libraries and many other components such as powerful datagrids and BI indicators are supported.

The core strengths of Lumen include:

  • Flexibility: The design of Lumen allows flexibly combining data intake, data processing and data visualization into a simple declarative pipeline.
  • Extensibility: Every part of Lumen is designed to be extended letting you define custom Source, Filter, Transform and View components.
  • Scalability: Lumen is designed with performance in mind and supports scalable Dask DataFrames out of the box, letting you scale to datasets larger than memory or even scale out to a cluster.
  • Security: Lumen ships with a wide range of OAuth providers out of the box, making it a breeze to add authentication to your applications.

Examples

London Bike Points
NYC Taxi
Palmer Penguins
USGS Earthquakes
Seattle Weather
Windturbines

Getting started

Lumen works with Python 3 and above on Linux, Windows, or Mac. The recommended way to install Lumen is using the conda command provided by Anaconda or Miniconda:

conda install -c pyviz lumen

or using PyPI:

pip install lumen

Once installed you will be able to start a Lumen server by running:

lumen serve dashboard.yaml --show

This will open a browser serving the application or dashboard declared by your yaml file in a browser window. During development it is very helpful to use the --autoreload flag, which will automatically refresh and update the application in your browser window, whenever you make an edit to the dashboard yaml specification. In this way you can quickly iterate on your dashboard.

Try it out! Click on one of the examples below, copy the yaml specification and launch your first Lumen application.

Metadata

Release files for lumen 0.7.0a20

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for lumen 0.7.0a20
File Size Uploaded
lumen-0.7.0a20.tar.gz 439.7 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for lumen 0.7.0a20
File Interpreter ABI Platform
lumen-0.7.0a20-py3-none-any.whl Python 3 none any Details

Total release size: 929.6 kB

Release files / lumen-0.7.0a20.tar.gz

Download URL lumen-0.7.0a20.tar.gz
Size 439.7 kB
Tags Source
SHA-256 checksum
How to use checksums
5ec26bb86723c3df5af065c9086d27c0a64e527737be9090763d4b36c0b21905
BLAKE2b-256 checksum
How to use checksums
8d459aa225d6a4c77df9ecaa201cfdb1e35d0f79a303a21d77dd49b43133aae7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.0 CPython/3.12.5

Release files / lumen-0.7.0a20-py3-none-any.whl

Download URL lumen-0.7.0a20-py3-none-any.whl
Size 489.9 kB
Tags Python 3
SHA-256 checksum
How to use checksums
87c18be70bde7add2cd9cef8504c2e2a5d2f8c3a3f97f6a62192dfd977e69821
BLAKE2b-256 checksum
How to use checksums
be83c56b97900e9b7e16ba34ee54aa5dbd2f47fd3639e711aff5cd9af88d838a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.0 CPython/3.12.5

Release history Release notifications | RSS feed

1.3.0

2 release files

1.2.1

2 release files

1.2.0

2 release files

1.1.0

2 release files

1.0.0

2 release files

0.10.0

2 release files

0.9.0

2 release files

0.8.0

2 release files

0.7.0

2 release files

This release

0.7.0a20 This release

2 release files

0.6.3

2 release files

0.6.2

2 release files

0.6.1

2 release files

0.6.0

2 release files

0.5.1

2 release files

0.5.0

2 release files

0.4.1

2 release files

0.4.0

2 release files

0.3.2

2 release files

0.3.1

2 release files

0.3.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page