#### 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
Advanced Indexing and Array Concepts
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

## Estimating stock returns correlation with Pandas

A Pandas `DataFrame` is a matrix and dictionary-like data structure similar to the functionality available in R. In fact, it is the central data structure in Pandas and you can apply all kinds of operations on it. It is quite common to have a look, for instance, at the correlation matrix of a portfolio. So let's do that.

### How to do it...

First, we will create the `DataFrame` with Pandas for each symbol's daily log returns. Then we will join these on the date. At the end, the correlation will be printed, and plot will be shown.

1. Creating the data frame.

To create the data frame, we will create a dictionary containing stock symbols as keys, and the corresponding log returns as values. The data frame itself has the date as index and the stock symbols as column labels:

```data = {}

for i in xrange(len(symbols)):
data[symbols[i]] = numpy.diff(numpy.log(close[i]))

df = pandas.DataFrame(data, index=dates[0][:-1], columns=symbols)```
2. Operating on the data frame...