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  • Book Overview & Buying NumPy Essentials
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NumPy Essentials

NumPy Essentials

By : Jaidev Deshpande, Chin, Tanmay Dutta, Shane Holloway
3.3 (3)
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NumPy Essentials

NumPy Essentials

3.3 (3)
By: Jaidev Deshpande, Chin, Tanmay Dutta, Shane Holloway

Overview of this book

In today’s world of science and technology, it’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.
Table of Contents (11 chapters)
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Creating an array squared function using Python C-API


The Python function passes a reference to itself as the first argument, followed by real arguments given to the function. The PyArg_ParseTuple function is used to parse values from the Python function to local variables in the C function. In this function, we cast a value to a double, and hence we use d as the second argument. You can see a full list of strings that are accepted by this function at  https://docs.python.org/2/c-api/arg.html .

The final result of the computations is returned using Py_Buildvalue, which takes a similar type of format string to create a Python value from your answer. We use f here, which stands for float, to demonstrate that double and float are treated similarly:

/* 
Implementation of the actual C funtions 
*/ 
 
static PyObject* square_func(PyObject* self, PyObject* args) 
{ 
double value; 
double answer; 
 
/*  parse the input, from python float to c double */...
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NumPy Essentials
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