#### 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

## Interfacing with R

RPy2 can only be used to call R from Python, and not the other way around. We will import some sample R datasets, and plot the data of one of them.

Install RPy2 if necessary. See the previous recipe.

### How to do it...

1. Load a data set into an array.

Load the datasets with the RPy2 `importr` function. This function can import R packages. In this example, we will import the datasets R package. Create a NumPy array from the `mtcars` dataset:

```datasets = importr('datasets')
mtcars = numpy.array(datasets.mtcars)```
2. Plot the dataset.

Plot the dataset with Matplotlib:

```matplotlib.pyplot.plot(mtcars)
matplotlib.pyplot.show()```

The following image shows the data, which is a two dimensional array:

The complete code for this recipe is as follows:

```from rpy2.robjects.packages import importr
import numpy
import matplotlib.pyplot

datasets = importr('datasets')
mtcars = numpy.array(datasets.mtcars)

matplotlib.pyplot.plot(mtcars)
matplotlib.pyplot.show...```