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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 - 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 …

  3. 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.

  4. NumPy Documentation

    NumPy 1.19 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1.18 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 1.17 Manual [HTML+zip] …

  5. NumPy: the absolute basics for beginners — NumPy v2.5.dev0 …

    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 user guide — NumPy v2.3 Manual

    NumPy user guide # This guide is an overview and explains the important features; details are found in NumPy reference.

  7. numpy.where — NumPy v2.3 Manual

    numpy.where # numpy.where(condition, [x, y, ]/) # Return elements chosen from x or y depending on condition.

  8. NumPy - News

    Dec 8, 2024 · July 12, 2021 – At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings …

  9. 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 …

  10. Broadcasting — NumPy v2.3 Manual

    NumPy is smart enough to use the original scalar value without actually making copies so that broadcasting operations are as memory and computationally efficient as possible.