Titlebook: Numerical Python; Scientific Computing Robert Johansson Book 20192nd edition Robert Johansson 2019 Python.numerical.NumPy.SciPy.computation

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le I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning978-1-4842-4246-9

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Integration,grals (double integrals) and higher-order integrals can be numerically computed with repeated single-dimension integration or using methods that are multidimensional generalizations of the techniques used to solve single-dimensional integrals. However, the computational complexity grows quickly with

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Data Processing and Analysis,ch, the . library has become a de facto standard library for high-level data processing in Python, especially for statistics applications. The . library itself contains only limited support for statistical modeling (namely, linear regression). For more involved statistical analysis and modeling, the

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Statistics, of statistics, albeit not all, while also providing the unique advantages of the Python programming language and its environment. The pandas library that we discussed in Chapter . is an example of a development within the Python community that was strongly influenced by statistical software, with t

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Book 20192nd editionng techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning

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