NumPy Cookbook

NumPy Cookbook

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

About the Author

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Winding Along with IPython

Introduction

Installing IPython

Using IPython as a shell

Reading manual pages

Installing Matplotlib

Running a web notebook

Exporting a web notebook

Importing a web notebook

Configuring a notebook server

Exploring the SymPy profile

Advanced Indexing and Array Concepts

Creating views and copies

Flipping Lena

Fancy indexing

Indexing with a list of locations

Indexing with booleans

Stride tricks for Sudoku

Broadcasting arrays

Get to Grips with Commonly Used Functions

Introduction

Summing Fibonacci numbers

Finding prime factors

Finding palindromic numbers

The steady state vector determination

Discovering a power law

Trading periodically on dips

Simulating trading at random

Sieving integers with the Sieve of Erasthothenes

Connecting NumPy with the Rest of the World

Introduction

Using the buffer protocol

Using the array interface

Exchanging data with MATLAB and Octave

Installing RPy2

Interfacing with R

Installing JPype

Sending a NumPy array to JPype

Installing Google App Engine

Deploying NumPy code in the Google cloud

Running NumPy code in a Python Anywhere web console

Setting up PiCloud

Audio and Image Processing

Introduction

Loading images into memory map

Combining images

Blurring images

Repeating audio fragments

Generating sounds

Designing an audio filter

Edge detection with the Sobel filter

Special Arrays and Universal Functions

Introduction

Creating a universal function

Finding Pythagorean triples

Performing string operations with chararray

Creating a masked array

Ignoring negative and extreme values

Creating a scores table with recarray

Profiling and Debugging

Introduction

Profiling with timeit

Profiling with IPython

Installing line_profiler

Profiling code with line_profiler

Profiling code with the cProfile extension

Debugging with IPython

Debugging with pudb

Quality Assurance

Introduction

Installing Pyflakes

Performing static analysis with Pyflakes

Analyzing code with Pylint

Speed Up Code with Cython

Introduction

Installing Cython

Building a Hello World program

Using Cython with NumPy

Calling C functions

Profiling Cython code

Approximating factorials with Cython

Fun with Scikits

Introduction

Installing scikits-learn

Loading an example dataset

Clustering Dow Jones stocks with scikits-learn

Installing scikits-statsmodels

Performing a normality test with scikits-statsmodels

Installing scikits-image

Detecting corners

Detecting edges

Installing Pandas

Estimating stock returns correlation with Pandas

Loading data as pandas objects from statsmodels

Resampling time series data

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

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.

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)
```
Operating on the data frame...

NumPy Cookbook

NumPy Cookbook

Overview of this book

Related Content you might be interested in

Current Title:

NumPy Cookbook

Estimating stock returns correlation with Pandas

How to do it...