=============
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`) and columnar tables (`CTable`), complemented by 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/guides/b2view.html>`_;
  requires ``pip install "blosc2[tui]"``).
- ``parquet-to-blosc2``: converts Parquet files to Blosc2 columnar table
  stores, and back
  (`walkthrough <https://www.blosc.org/python-blosc2/guides/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/>`_.

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 = {2009-2026},
    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**