NumPy
Product information, use cases, and access for NumPy.
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What is NumPy?
NumPy is a foundational package for scientific computing with Python. Its central data model is the multidimensional array, supported by vectorized operations, indexing, and broadcasting that make array-oriented numerical work concise and practical. It is suited to Python programmers, researchers, and scientists who need a reusable computational foundation for working with numerical data.
The package includes mathematical functions, random number generators, linear algebra routines, and Fourier transforms for a broad range of numerical tasks. Its core is implemented in optimized C code while remaining accessible through Python syntax, and it interoperates with distributed, GPU, and sparse array libraries. NumPy can support workflows across data science, machine learning, visualization, image processing, and other scientific domains.
Key features of NumPy
N-dimensional arrays
Provides efficient multidimensional arrays for storing and manipulating numerical data through array-oriented computation.
Numerical toolkit
Includes mathematical functions, random number generation, linear algebra routines, and Fourier transforms for varied numerical workloads.
Array programming
Uses vectorization, indexing, and broadcasting to express computations over arrays and simplify numerical data processing.
Ecosystem interoperability
Works across a wide range of hardware and computing platforms and interoperates with distributed, GPU, and sparse array libraries.