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  1. NumPy

    Nearly every scientist working in Python draws on the power of NumPy. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to …

  2. NumPy documentation — NumPy v2.3 Manual

    The reference guide contains a detailed description of the functions, modules, and objects included in NumPy. The reference describes how the methods work and which parameters …

  3. NumPy - Installing NumPy

    The only prerequisite for installing NumPy is Python itself. If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes …

  4. NumPy - Learn

    Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community.

  5. NumPy: the absolute basics for beginners — NumPy v2.3 Manual

    The NumPy library contains multidimensional array data structures, such as the homogeneous, N-dimensional ndarray, and a large library of functions that operate efficiently on these data …

  6. NumPy quickstart — NumPy v2.3 Manual

    NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers.

  7. NumPy reference — NumPy v2.3 Manual

    Jun 9, 2025 · This reference manual details functions, modules, and objects included in NumPy, describing what they are and what they do. For learning how to use NumPy, see the complete …

  8. NumPy Documentation

    NumPy 1.20 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1.19 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1.18 Manual [HTML+zip] …

  9. What is NumPy? — NumPy v2.3 Manual

    What is NumPy? # NumPy is the fundamental package for scientific computing in Python.

  10. numpy.matmul — NumPy v2.3 Manual

    The matmul function implements the semantics of the @ operator defined in PEP 465. It uses an optimized BLAS library when possible (see numpy.linalg). Examples Try it in your browser! For …