============= Python-Blosc2

A fast & compressed ndarray library with a flexible compute engine

:Author: The Blosc development team :Contact: blosc@blosc.org :Github: https://github.com/Blosc/python-blosc2 :Actions: |actions| :PyPi: |version| :NumFOCUS: |numfocus| :Code of Conduct: |Contributor Covenant|

.. |version| image:: https://img.shields.io/pypi/v/blosc2.svg :target: https://pypi.python.org/pypi/blosc2 .. |Contributor Covenant| image:: https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg :target: https://github.com/Blosc/community/blob/master/code_of_conduct.md .. |numfocus| image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A :target: https://numfocus.org .. |actions| image:: https://github.com/Blosc/python-blosc2/actions/workflows/build.yml/badge.svg :target: https://github.com/Blosc/python-blosc2/actions/workflows/build.yml

What is Python-Blosc2?

Python-Blosc2 is a high-performance compressor, compute engine, and format for binary data containers that are portable and open-source. It comes with a lazy expression engine allowing for complex calculations on compressed data, whether stored in memory, on disk, or over the network (e.g., via Caterva2 <https://github.com/ironArray/Caterva2>_). It is especially optimized for storing and retrieving data from N-dimensional arrays (NDArray), columnar tables (CTable), and a query/indexing layer. The main use case is fast, compressed, out-of-core numerical data — especially when data is too large to fit comfortably in RAM.

C-Blosc2 <https://www.blosc.org/c-blosc2/c-blosc2.html>_ is used under the hood as its compression backend. Written in C, and building on its predecessor C-Blosc <https://github.com/Blosc/c-blosc>_, C-Blosc2 aims to be an extremely fast meta-compressor for binary data, supporting a diverse set of strategies, and with an extensible plugin architecture for a wide range of codecs and filters.

More info: https://www.blosc.org/python-blosc2/getting_started/overview.html

Installing

Binary packages are available for major OSes (Win, Mac, Linux) and platforms. Install from PyPi using pip:

.. code-block:: console

pip install blosc2 --upgrade

Conda users can install from conda-forge:

.. code-block:: console

conda install -c conda-forge python-blosc2

Command line tools

Two CLI tools are installed along with the package:

• b2view: an interactive terminal browser (TUI) for TreeStore bundles (.b2d directories or .b2z files), with paged views of NDArray and CTable data of any size (walkthrough <https://www.blosc.org/python-blosc2/getting_started/b2view.html>_).
• parquet-to-blosc2: converts Parquet files to Blosc2 columnar table stores, and back (walkthrough <https://www.blosc.org/python-blosc2/getting_started/parquet_to_blosc2.html>_; requires pip install "blosc2[parquet]").

Documentation

The documentation is available here:

https://blosc.org/python-blosc2/python-blosc2.html

You can find examples at:

https://github.com/Blosc/python-blosc2/tree/main/examples

A tutorial from PyData Global 2025 is available at:

https://github.com/Blosc/PyData-Global-2025-Tutorial

(Click here <https://www.youtube.com/watch?v=tUvSI3EpTBQ&list=PLGVZCDnMOq0qmerwB1eITnr5AfYRGm0DF&index=81>_ to watch the video recording of the tutorial)

It contains Jupyter notebooks explaining the main features of Python-Blosc2.

License

This software is licensed under a 3-Clause BSD license. A copy of the python-blosc2 license can be found in LICENSE.txt <https://github.com/Blosc/python-blosc2/tree/main/LICENSE.txt>_.

Discussion forum

Discussion about this package is welcome at:

https://github.com/Blosc/python-blosc2/discussions

Social feeds

Stay informed about the latest developments by following us in Mastodon <https://fosstodon.org/@Blosc2>, Bluesky <https://bsky.app/profile/blosc.org> or LinkedIn <https://www.linkedin.com/company/88381936/admin/dashboard/>_.

Thanks

Blosc2 is supported by the NumFOCUS foundation <https://numfocus.org>, the LEAPS-INNOV project <https://www.leaps-innov.eu> and ironArray SLU <https://ironarray.io>_, among many other donors. This allowed the following people to have contributed in an important way to the core development of the Blosc2 library:

• Francesc Alted
• Marta Iborra
• Luke Shaw
• Aleix Alcacer
• Oscar Guiñón
• Juan David Ibáñez
• Ivan Vilata i Balaguer
• Oumaima Ech.Chdig
• Ricardo Sales Piquer

In addition, other people have participated in the project in different aspects:

• Jan Sellner, contributed the mmap support for NDArray/SChunk objects.
• Dimitri Papadopoulos, contributed a large bunch of improvements to many aspects of the project. His attention to detail is remarkable.
• And many others that have contributed with bug reports, suggestions and improvements.

Developed using JetBrains IDEs.

.. image:: https://resources.jetbrains.com/storage/products/company/brand/logos/jetbrains.svg :target: https://jb.gg/OpenSource :alt: JetBrains logo.

Citing Blosc

You can cite our work on the various libraries under the Blosc umbrella as follows:

.. code-block:: console

@ONLINE{blosc, author = {{Blosc Development Team}}, title = "{A fast, compressed and persistent data store library}", year = {2009-2025}, note = {https://blosc.org} }

Support Blosc for a Sustainable Future

If you find Blosc useful and want to support its development, please consider making a donation or contract to the Blosc Development Team <https://www.blosc.org/pages/blosc-in-depth/#support-blosc>_. Thank you!

Compress Better, Compute Bigger