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- NumPy documentation — NumPy v2. 4 Manual
NumPy documentation # Version: 2 4 Download documentation: Historical versions of documentation Useful links: Home | Installation | Source Repository | Issue Tracker | Q A Support | Mailing List NumPy is the fundamental package for scientific computing in Python
- NumPy - Learn
Scientific Python Lectures Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem NumPy: the absolute basics for beginners NumPy tutorial by Nicolas Rougier Stanford CS231 by Justin Johnson NumPy User Guide Books Guide to NumPy by Travis E Oliphant This is the first and free edition of the book
- 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 Python, NumPy, and many other commonly used packages for scientific computing and data science
- NumPy Documentation
Web Latest (development) documentation NumPy Enhancement Proposals Versions: NumPy 2 4 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 2 3 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 2 2 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF] NumPy 2 1 Manual [HTML+zip] [Reference Guide PDF] [User Guide PDF
- NumPy user guide — NumPy v2. 4 Manual
NumPy user guide # This guide is an overview and explains the important features; details are found in NumPy reference
- NumPy quickstart — NumPy v2. 4 Manual
The basics # 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 In NumPy dimensions are called axes For example, the array for the coordinates of a point in 3D space, [1, 2, 1], has one axis
- NumPy
NumPyの高速で多機能なベクトル化計算、インデックス処理、ブロードキャストの考え方は、現在の配列計算におけるデファクト・スタ>ンダードです。
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