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CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.
In Python, the ellipsis is a nullary expression that represents the Ellipsis singleton. It's used particularly in NumPy, where an ellipsis is used for slicing an arbitrary number of dimensions for a high-dimensional array: [10]
A = round (rand (3, 4, 5) * 10) % 3x4x5 three-dimensional or cubic array > A (:,:, 3) % 3x4 two-dimensional array along first and second dimensions ans = 8 3 5 7 8 9 1 4 4 4 2 5 > A (:, 2: 3, 3) % 3x2 two-dimensional array along first and second dimensions ans = 3 5 9 1 4 2 > A (2: end,:, 3) % 2x4 two-dimensional array using the 'end' keyword ...
Support for multi-dimensional arrays may also be provided by external libraries, which may even support arbitrary orderings, where each dimension has a stride value, and row-major or column-major are just two possible resulting interpretations. Row-major order is the default in NumPy [19] (for Python).
In Python, jagged arrays are not native but one can use list comprehensions to create a multi-dimensional list which supports any dimensional matrix: [8]
To avoid installing the large SciPy package just to get an array object, this new package was separated and called NumPy. Support for Python 3 was added in 2011 with NumPy version 1.5.0. [15] In 2011, PyPy started development on an implementation of the NumPy API for PyPy. [16] As of 2023, it is not yet fully compatible with NumPy. [17]
NumPy, a BSD-licensed library that adds support for the manipulation of large, multi-dimensional arrays and matrices; it also includes a large collection of high-level mathematical functions. NumPy serves as the backbone for a number of other numerical libraries, notably SciPy. De facto standard for matrix/tensor operations in Python.
The number of indices needed to specify an element is called the dimension, dimensionality, or rank of the array type. (This nomenclature conflicts with the concept of dimension in linear algebra, which expresses the shape of a matrix. Thus, an array of numbers with 5 rows and 4 columns, hence 20 elements, is said to have dimension 2 in ...