Combine advanced codecs and filters for efficient lossless and lossy compression to reduce storage space while maintaining high performance.

NDArray objects enable efficient storage and manipulation of arbitrarily large N-dimensional datasets, following the Array API standard, with an additional C API.

Combines compression with high-speed computation of complex mathematical expressions and reductions, while maintaining compatibility with NumPy.

Efficiently store data hierarchically with the TreeStore class for convenience and optimized performance.

Access data from anywhere: read/write in memory or disk, stream from the network, or use memory-mapped files for high-performance I/O.

Blosc2’s format is simple and accessible, with specifications under 4000 words that make it easy to read and integrate.

New to Python-Blosc2? Start here for installation instructions and a high-level overview of core concepts.

Step-by-step interactive tutorials walking you through NDArrays, lazy expressions, reductions, SChunk, CTable, and more.

Task-oriented recipes and in-depth guides on performance optimization, remote arrays, IPC sharing, and CLI tools.

The reference guide provides a comprehensive description of the Python-Blosc2 API, detailing functions and parameters.

Found a typo in the documentation or want to improve functionality? The contributing guidelines will walk you through the process.

Want to see what’s new in the latest release? Explore the release notes to discover new features, improvements, and bug fixes.