#### 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

The scikits-learn project comes with a number of datasets and sample images with which we can experiment. In this recipe, we will load an example dataset, that is included with the scikits-learn distribution. The datasets hold data as a NumPy, two-dimensional array and metadata linked to the data.

### How to do it...

We will load a sample data set of the Boston house prices. It is a tiny dataset, so if you are looking for a house in Boston, don't get too excited. There are more datasets as described in http://scikit-learn.org/dev/modules/classes.html#module-sklearn.datasets.

We will look at the shape of the raw data, and its maximum and minimum value. The shape is a tuple , representing the dimensions of the NumPy array. We will do the same for the target array, which contains values that are the learning objectives. The following code accomplishes our goals:

```from sklearn import datasets