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
NumPy
The fundamental N-dimensional array library powering Python's scientific computing stack.
pandas
The Python DataFrame library for fast, flexible, and expressive data analysis and manipulation.
rand
The de facto standard random number generation library for Rust.
PyTorch
A Python-first tensor library with GPU acceleration and a dynamic, define-by-run autograd engine for building and training deep neural networks.
PyArrow
Python bindings for Apache Arrow's columnar in-memory format, giving pandas, NumPy, and data pipelines a fast, zero-copy way to move and process tabular data.
scikit-learn
Simple and efficient tools for machine learning and data analysis in Python.
Datasets
One-line loading and fast, Arrow-backed processing for thousands of ML datasets
SciPy
Fundamental algorithms for scientific computing, optimization, and statistics in Python.
Matplotlib
Python's foundational plotting library for static, animated, and interactive charts
mathjs
Extensive math library for JavaScript and Node.js with big numbers, complex numbers, units, and matrices.
duckdb
The official Python client for DuckDB, an in-process analytical SQL database
networkx
Python library for creating, manipulating, and analyzing complex networks and graphs.
num-traits
Numeric traits for generic mathematics in Rust, from Zero and One to Float and PrimInt
Arrow
The official Rust implementation of the Apache Arrow columnar in-memory data format.
Polars
Blazingly fast multi-threaded DataFrame library built in Rust with Python, Node.js, and R bindings
