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Stores NumPy arrays in a way that is self-documented and tool-independent.

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Darr is a Python science library that allows you to work efficiently with potentially very large, disk-based Numpy arrays that are self-documented. Documentation includes copy-paste ready code to read arrays in many popular data science languages such as R, Julia, Scilab, IDL, Matlab, Maple, and Mathematica, or in Python/Numpy without Darr. Without exporting them and with minimal effort.

Universal readability of data is a pillar of good scientific practice. It is also generally a good idea for anyone who wants to flexibly move between analysis environments, who wants to save data for the longer term, or who wants to share data with others without spending much time on figuring out and/or explaining how the receiver can read it. No idea how to read your 7-dimensional uint32 numpy array in Matlab to quickly try out an algorithm your colleague wrote? No worries, a quick copy-paste of code from the array documentation is all that is needed to read your data in, e.g. R or Matlab (see example). As you work with your array, its documentation is automatically kept up to date. No need to export anything, make notes, or to provide elaborate explanation. No looking up things. No dependence on complicated formats or specialized libraries for reading you data elsewhere later.

In essence, Darr makes it trivially easy to share your numerical arrays with others or with yourself when working in different computing environments, and stores them in a future-proof way.

More rationale for a tool-independent approach to numeric array storage is provided here.

Under the hood, Darr uses NumPy memory-mapped arrays, which is a widely established and trusted way of working with disk-based numerical data, and which makes Darr fully NumPy compatible. This enables efficient out-of-core read/write access to potentially very large arrays. In addition to automatic documentation, Darr adds other functionality to NumPy’s memmap, such as easy the appending and truncating of data, support for ragged arrays, the ability to create arrays from iterators, and easy use of metadata. Flat binary files and (JSON) text files are accompanied by a README text file that explains how the array and metadata are stored (see example arrays).

See this tutorial for a brief introduction, or the documentation for more info.

Darr is currently pre-1.0, still undergoing development. It is open source and freely available under the New BSD License terms.


  • Data is stored purely based on flat binary and text files, maximizing universal readability.

  • Automatic self-documention, including copy-paste ready code snippets for reading the array in a number of popular data analysis environments, such as Python (without Darr), R, Julia, Scilab, Octave/Matlab, GDL/IDL, and Mathematica (see example array).

  • Disk-persistent array data is directly accessible through NumPy indexing and may be larger than RAM

  • Easy and efficient appending of data (see example).

  • Supports ragged arrays.

  • Easy use of metadata, stored in a widely readable separate JSON text file (see example).

  • Many numeric types are supported: (u)int8-(u)int64, float16-float64, complex64, complex128.

  • Integrates easily with the Dask library for out-of-core computation on very large arrays.

  • Minimal dependencies, only NumPy.


  • No compression, although compression for archiving purposes is supported.

  • Array storages uses multiple files as binary data is separated from text documentation and metadata.

See the documentation for more information.

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