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mio - Cross-platform C++11 header-only library for memory mapped file IO

  •    C++

An easy to use header-only cross-platform C++11 memory mapping library with an MIT license. mio has been created with the goal to be easily includable (i.e. no dependencies) in any C++ project that needs memory mapped file IO without the need to pull in Boost.

vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀

  •    Python

Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted). HDF5 and Apache Arrow supported.

vaex - Lazy Out-of-Core DataFrames for Python, visualize and explore big tabular data at a billion rows per second

  •    Python

Vaex is a python library for Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (109) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).

bigstatsr - R package for statistical tools with big matrices stored on disk.

  •    HTML

R package {bigstatsr} provides functions for fast statistical analysis of large-scale data encoded as matrices. The package can handle matrices that are too large to fit in memory thanks to memory-mapping to binary files on disk. This is very similar to the format big.matrix provided by R package {bigmemory}, which is no longer used by this package (see the corresponding vignette). Note that most of the algorithms of this package don't handle missing values.









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