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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 NumPy C-API


In this section, we will create a function to square all the values of the NumPy Array. The aim here is to demonstrate how to get a NumPy Array in C and then iterate over it. In a real-world scenario, this can be done in an easier way using a map or by vectorizing a square function. We are using the same PyArg_ParseTuple function with the O! format string. This format string has a (object) [typeobject, PyObject *] signature and takes the Python type object as the first argument. Users should go through the official API doc to take a look at what other format strings are permissible and which one suits their needs:

Note

If the passed value does not have the same type, then a TypeError is raised.

The following code snippet explain how to parse the argument using PyArg_ParseTuple.

// Implementation of square of numpy array 
 
static PyObject* square_nparray_func(PyObject* self, PyObject* args) 
{ 
 
// variable declarations...
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NumPy Essentials
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