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__Numerical Python__by **Robert Johansson** shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using this book, you'll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment alternatives), and even games. Up until very recently, Python was mostly regarded as just a web scripting language. Well, computational scientists and engineers have recently discovered the flexibility and power of Python to do more. Big data analytics and cloud computing programmers are seeing Python's immense use. Financial engineers are also now employing Python in their work. Python seems to be evolving as a language that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical computations. Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving. Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work.^℗l ℗l ℗l ℗l ℗l ℗l℗l After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computat ional methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning.^Specific topics that are covered include: ℗l How to work with vectors and matrices using NumPy How to work with symbolic computing using SymPy How to plot and visualize data with Matplotlib How to solve linear and nonlinear equations with SymPy and SciPy How to solve solve optimization, interpolation, and integration problems using SciPy How to solve ordinary and partial differential equations with SciPy and FEniCS How to perform data analysis tasks and solve statistical problems with Pandas and SciPy How to work with statistical modeling and machine learning with statsmodels and scikit-learn How to handle file I/O using HDF5 and other common file formats for numerical data How to optimize Python code using Numba and Cython Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving. Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work. After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computat ional methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Specific topics that are covered include: How to work with vectors and matrices using NumPy How to work with symbolic computing using SymPy How to plot and visualize data with Matplotlib How to solve linear and nonlinear equations with SymPy and SciPy How to solve solve optimization, interpolation, and integration problems using SciPy How to solve ordinary and partial differential equations with SciPy and FEniCS How to perform data analysis tasks and solve statistical problems with Pandas and SciPy How to work with statistical modeling and machine learning with statsmodels and scikit-learn How to handle file I/O using HDF5 and other common file formats for numerical data How to optimize Python code using Numba and Cython Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using this book, you'll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment alternatives), and even games. Up until very recently, Python was mostly regarded as just a web scripting language. Well, computational scientists and engineers have recently discovered the flexibility and power of Python to do more. Big data analytics and cloud computing programmers are seeing Python's immense use. Financial engineers are also now employing Python in their work. Python seems to be evolving as a language that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical computations. Front Matter....Pages i-xxii Introduction to Computing with Python....Pages 1-24 Vectors, Matrices, and Multidimensional Arrays....Pages 25-62 Symbolic Computing....Pages 63-88 Plotting and Visualization....Pages 89-123 Equation Solving....Pages 125-145 Optimization....Pages 147-168 Interpolation....Pages 169-186 Integration....Pages 187-206 Ordinary Differential Equations....Pages 207-234 Sparse Matrices and Graphs....Pages 235-254 Partial Differential Equations....Pages 255-284 Data Processing and Analysis....Pages 285-311 Statistics....Pages 313-332 Statistical Modeling....Pages 333-362 Machine Learning....Pages 363-382 Bayesian Statistics....Pages 383-404 Signal Processing....Pages 405-424 Data Input and Output....Pages 425-451 Code Optimization....Pages 453-470 Installation....Pages 471-479 Back Matter....Pages 481-487