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NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
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.
A modern, abstract point of view contrasts large function spaces, which are infinite-dimensional and within which most functions are 'anonymous', with special functions picked out by properties such as symmetry, or relationship to harmonic analysis and group representations. See also List of types of functions
In calculus, the integral of a function is a generalization of area, mass, volume, sum, and total. The following pages list the integrals of many different functions. Lists of integrals; List of integrals of exponential functions; List of integrals of hyperbolic functions; List of integrals of inverse hyperbolic functions
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 same set of APIs defined in the NumPy package (numpy.*) are available under cupy.* package. Multi-dimensional array (cupy.ndarray) for boolean, integer, float, and complex data types; Module-level functions; Linear algebra functions; Fast Fourier transform; Random number generator
The library NumPy can be used for manipulating arrays, SciPy for scientific and mathematical analysis, Pandas for analyzing table data, Scikit-learn for various machine learning tasks, NLTK and spaCy for natural language processing, OpenCV for computer vision, and Matplotlib for data visualization. [3]
Generates a ranked list of several plots & visualizations based on an analysis of the data provided, allowing the user to choose their favorite graphic, share it, and export it as an image. DataGraph: GUI, command line: Proprietary: No 2006: February 17, 2020 / 4.5.1: macOS: 2D graphing, animations, data analysis, linear and non-linear curve ...