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# NumPy Essentials

 Learn Manipulate the key attributes and universal functions of NumPy Utilize matrix and mathematical computation using linear algebra modules Implement regression and curve fitting for models Perform time frequency / spectral density analysis using the Fourier Transform modules Collate with the distutils and setuptools modules used by other Python libraries Establish Cython with NumPy arrays Write extension modules for NumPy code using the C API Build sophisticated data structures using NumPy array with libraries such as Panda and Scikits In todayâ€™s world of science and technology, itâ€™s all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy gives you both the speed and high productivity you need. This book will walk you through NumPy using clear, step-by-step examples and just the right amount of theory. We will guide you through wider applications of NumPy in scientific computing and will then focus on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier Transform; solving linear systems of equations, interpolation, extrapolation, regression, and curve fitting; and evaluating integrals and derivatives. We will also introduce you to using Cython with NumPy arrays and writing extension modules for NumPy code using the C API. This book will give you exposure to the vast NumPy library and help you build efficient, high-speed programs using a wide range of mathematical features. Optimize your Python scripts with powerful NumPy modules Explore the vast opportunities to build outstanding scientific/ analytical modules by yourself Packed with rich examples to help you master NumPy arrays and universal functions 156 4 hours 40 minutes 9781784393670 27 Apr 2016
 Getting started with numpy.ndarray Array indexing and slicing Memory layout of ndarray Views and copies Creating arrays Array data types Summary
 Vectorized operations Universal functions (ufuncs) Broadcasting and shape manipulation A boolean mask Helper functions Summary
 Introducing strides Structured arrays Summary
 The matrix class Linear algebra in NumPy Decomposition Polynomial mathematics Application - regression and curve fitting Summary
 Before we start Signal processing Fourier analysis Fourier transform application Summary
 The first step toward optimizing code Setting up Cython Hello world in Cython Multithreaded code NumPy and Cython Summary
 pandas scikit-learn netCDF4 SciPy Summary