Data Science & Numerical Computing Packages
Numerical computing, dataframes, and scientific-analysis libraries used across research and production data pipelines (NumPy, pandas, SciPy).
Packages in Data Science & Numerical Computing
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scikit-learn
Simple and efficient tools for machine learning and data analysis in Python.
networkx
Python library for creating, manipulating, and analyzing complex networks and graphs.
mathjs
Extensive math library for JavaScript and Node.js with big numbers, complex numbers, units, and matrices.
d3-array
Array manipulation, ordering, searching, and statistical summarization for JavaScript, built to power D3's data-visualization pipeline.
SymPy
Full-featured computer algebra system (CAS) written entirely in pure Python
statsmodels
Statistical modeling and econometrics in Python, with rigorous estimation and inference.
glam
A simple, fast SIMD-accelerated 3D linear algebra library for Rust games and graphics, with no generics and no trait-fragmented API.
approx
Approximate floating point equality comparisons and assertions for Rust
HdrHistogram (Rust)
A native Rust port of HdrHistogram for fast, high-dynamic-range recording and analysis of latency and value distributions.
nalgebra
General-purpose linear algebra library for the Rust ecosystem
Brick\Math
Arbitrary-precision arithmetic for PHP with immutable big integer, decimal, and rational numbers.
python-igraph
Fast graph and complex-network analysis for Python.
linearmodels
Panel, instrumental-variable, system, and asset-pricing regression models that extend statsmodels for econometrics in Python.
libm
A pure-Rust, no_std implementation of the C math library for float functions.
ndarray-stats
Statistical routines for ndarray's ArrayBase — quantiles, correlation, entropy, and histograms as native extension traits.
percentile
Calculate one or many percentiles from an array of numbers, TypedArrays, or objects in a single pass.
