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[3] the predecessor of numpy, numeric, was originally created by jim hugunin with contributions from several other developers [1] by analogy with the mathematical concepts vector and matrix, array types with one and two indices are. This comparison of programming languages (array) compares the features of array data structures or matrix processing for various computer programming languages.

In computer science, array programming refers to solutions that allow the application of operations to an entire set of values at once Such a collection is usually called an array variable or array value Such solutions are commonly used in scientific and engineering settings.

In computer science, an array is a data structure consisting of a collection of elements (values or variables), of same memory size, each identified by at least one array index or key, a collection of which may be a tuple, known as an index tuple

In general, an array is a mutable and linear collection of elements with the same data type. In the python library numpy, the outer product can be computed with function np.outer() [8] in contrast, np.kron results in a flat array The outer product of multidimensional arrays can be computed using np.multiply.outer.

The difference between the orders lies in which elements of an array are contiguous in memory. The logical size and capacity of the final array are shown Other software implementations include the hpcsharp library [11] for the c# language and the standard library summation [12] in d. Numpy serves as the backbone for a number of other numerical libraries, notably scipy

De facto standard for matrix/tensor operations in python.

In computer science, array is a data type that represents a collection of elements (values or variables), each selected by one or more indices (identifying keys) that can be computed at run time during program execution

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