#### Overview of this book

Today's world of science and technology is all about speed and flexibility. When it comes to scientific computing, NumPy is on the top of the list. NumPy will give you both speed and high productivity. "NumPy Cookbook" will teach you all about NumPy, a leading scientific computing library. NumPy replaces a lot of the functionality of Matlab and Mathematica, but in contrast to those products, it is free and open source. "Numpy Cookbook" will teach you to write readable, efficient, and fast code that is as close to the language of Mathematics as much as possible with the cutting edge open source NumPy software library. You will learn about installing and using NumPy and related concepts. At the end of the book, we will explore related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project through examples. "NumPy Cookbook" will help you to be productive with NumPy and write clean and fast code.
NumPy Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Winding Along with IPython
Get to Grips with Commonly Used Functions
Connecting NumPy with the Rest of the World
Audio and Image Processing
Special Arrays and Universal Functions
Profiling and Debugging
Quality Assurance
Speed Up Code with Cython
Index

## Calling C functions

We can call C functions from Cython. For instance, in this example, we will call the C `log` function. This function works on a single number only. Remember that the NumPy `log` function can also work with arrays. We will compute the so-called log returns of stock prices.

### How to do it...

We will start by writing some Cython code:

1. Write the `.pyx` file.

First, we need to import the C `log` function from the `libc` namespace. Second, we will apply this function to numbers in a `for` loop. Finally, we will use the NumPy `diff` function to get the first order difference between the log values in the second step.

```from libc.math cimport log
import numpy

def logrets(numbers):
logs = [log(x) for x in numbers]
return numpy.diff(logs)```

Building has been covered in the previous recipes already. We only need to change some values in the `setup.py` file.

2. Plot the log returns.

Let's download stock price data with matplotlib, again. Apply the Cython `logrets` function that we just created on the...