#### 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

## Performing string operations with chararray

NumPy has a specialized chararray object, which can hold strings. It is a subclass of ndarray, and has special string methods. We will download a text from the Python website and use those methods. The advantages of chararray over a normal array of strings are as follows:

• Whitespace of array elements is automatically trimmed on indexing

• Whitespace at the ends of strings is also trimmed by comparison operators

• Vectorized string operations are available, so loops are not needed

### How to do it...

Let's create the character array.

1. Create the character array.

We can create the character array as a view:

carray = numpy.array(html).view(numpy.chararray)
2. Expand tabs to spaces.

Expand tabs to spaces with the expandtabs function. This function accepts the tab size as argument. The value is 8, if not specified:

carray = carray.expandtabs(1)
3. Split lines.

The splitlines function can split a string into separate lines:

carray = carray.splitlines()

The following is the complete...