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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NumPy
The fundamental N-dimensional array library powering Python's scientific computing stack.
PyTorch
A Python-first tensor library with GPU acceleration and a dynamic, define-by-run autograd engine for building and training deep neural networks.
Datasets
One-line loading and fast, Arrow-backed processing for thousands of ML datasets
einops
A readable, framework-independent notation for tensor rearrangement, reduction, and repetition.
TensorFlow Datasets
Ready-to-use, versioned public datasets exposed as tf.data.Dataset objects, with first-class support for TensorFlow, JAX, and NumPy pipelines.
SimSIMD
SIMD-accelerated distance, dot-product, and similarity kernels for high-dimensional vectors.
rust-numpy
PyO3-based Rust bindings to the NumPy C-API for sharing array data between Rust and Python.
tifffile
A comprehensive Python library for reading and writing TIFF, BigTIFF, OME-TIFF, and dozens of proprietary microscopy image formats as NumPy or Zarr arrays.
DataComPy
Compare Pandas, Polars, Spark, and Snowpark DataFrames with human-readable difference reports.
cython-blis
Fast BLAS-style matrix multiplication for Python, bundled as a self-contained C extension with no system BLAS dependency.
