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BudiBadu
Online Python zfpy Compiler
zfpy is a Python library that provides bindings to the zfp compression library, enabling efficient compression and decompression of multidimensional floating-point and integer arrays. zfp (zfp compression) is a compressed format designed for scientific computing applications that need to balance storage efficiency with access performance. The library provides compressed-array classes that support high throughput read and write random access to individual array elements, making it ideal for applications handling large datasets where you need efficient storage without sacrificing the ability to access specific elements.
This compiler includes zfpy 1.0+ with full support for both lossless and lossy compression modes, powered by Pyodide WebAssembly technology. zfpy supports serial and parallel compression of whole arrays with configurable error tolerances and compression rates, making it perfect for scientific data storage and transmission. The library seamlessly integrates with NumPy arrays, allowing you to compress and decompress arrays with simple function calls. Whether you're working with large scientific datasets, image processing, numerical simulations, or data archiving, our zfpy playground offers instant execution with full NumPy integration. You can also upload and use files or folders directly in your code for data processing and compression workflows. This compiler is online and completely free to use.
Our example collection covers essential zfpy topics including basic compression and decompression, lossless and lossy compression modes with tolerance and rate control, multi-dimensional array compression, and performance comparisons. You'll learn how to compress NumPy arrays efficiently, control compression ratios and error tolerances, and work with compressed data structures for scientific computing and data storage applications.
Who Should Use This
- Scientific computing researchers working with large multidimensional datasets
- Data scientists needing efficient storage for numerical arrays and matrices
- Engineers developing applications that require random access to compressed array data
- Researchers archiving scientific data with configurable compression quality
- Developers building systems that balance storage efficiency with access performance
- Students learning array compression techniques and scientific computing workflows
Part of the BudiBadu Ecosystem
Specialized Online Python compiler powered by Pyodide WebAssembly. Run Python Library directly in your browser with zero setup.
Pyodide
WebAssembly
Monaco Editor
Python 3.13