#### 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

## Ignoring negative and extreme values

Masked arrays are useful when we want to ignore negative values, for instance, when taking the logarithm of array values. Another use case for masked arrays is excluding extreme values. This works based on an upper and lower bound for extreme values.

In this tutorial, we will apply these techniques to stock price data. We will skip the steps for downloading data, as they are repeated in previous chapters.

### How to do it...

We will take the logarithm of an array that contains negative numbers.

1. Take the logarithm of negative numbers.

First, let's create an array containing numbers divisible by three:

```triples = numpy.arange(0, len(close), 3)
print "Triples", triples[:10], "..."```

Next, we will create an array with the ones that have the same size as the price data array:

```signs = numpy.ones(len(close))
print "Signs", signs[:10], "..."```

We will set each third number to be negative, with the help of indexing tricks we learned about in Chapter 2, Advanced Indexing and Array...